Faculty of Arts & Sciences

Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries. Built around three modules: prediction and elections, recommendation and business analytics, and sampling and social network analysis.

This course explores the Internet as a central component of modern scientific data processing and computation. We will examine the architecture of the Internet and the models of computation and data with which it is compatible. Through readings and exercises, students will explore the trade-offs of these various models and gain an appreciation for successful Internet-oriented system design for modern, federated, data- and compute-intensive scientific research. Students will complete a team project to develop an Internet-based system for scientific data processing. Python will be used for examples.

Systematic introduction to kinetic methods for studying fluids, based on the lattice Boltzmann equation. Emphasizes theory, including discrete dynamics and symmetry, as well as hands-on programming of basic algorithms for fluid flow simulations, paying attention to understanding of the theoretical basis and connection to real fluid physics. The course lays the foundation for further research on the method extensions, particularly in complex fluids and micro/nano-fluidics and presents specific applications in various science and engineering problems.

The course will describe major computational techniques for fluid flows, as described by the Navier-Stokes equations of continuum mechanics. In addition, the course will cover a new class of mesoscale techniques for complex flows and soft matter systems, which do not fit within the continuum Navier-Stokes description. The latter encompass a broad class of flowing materials of special interest to micro-nano engineering and biology.

This course will provide the background and an extensive set of examples showing how computational methods are applied to modern design of materials with desired functionality. The methods will span multiple length and time scales, including molecular dynamics simulations, first-principles approaches, stochastic methods for optimization and sampling, and continuum elasticity theory. Examples will include problems in electronic and photonic devices, materials for energy conversion, storage, and environmental protection, and those related to mechanical strength of materials.

The CSE capstone project is intended to integrate and apply the skills and ideas CSE students acquire in their core courses and electives. By requiring students to complete a substantial and challenging collaborative project, the capstone course will prepare students for the professional world and ensure that they are trained to conduct research. There will be no homework or lectures. Students will be dealing with real-world problems, messy data sets, and the chance to work on an end-to-end solution to a problem using computational methods.

This course, centered on the Institute for Applied Computation Science (IACS) seminar series, will provide broad exposure to cutting-edge topics, applications, and unifying concepts in Computational Science & Engineering. Students will read, present and discuss journal articles related to IACS talks, attend the seminars and meet with visiting speakers. Possible topics to be covered include scientific visualization, computational approaches to disease, mathematical neuroscience, computational archeology, and computational finance.

Supervision of experimental or theoretical research on acceptable applied computation problems and supervision of reading on topics not covered by regular courses of instruction.

Complex numbers. Multivariate calculus: partial differentiation, directional derivatives, techniques of integration and multiple integration. Vectors: dot and cross products, parameterized curves, line and surface integrals. Vector calculus: gradient, divergence and curl, Green's, Stokes' and Gauss' theorems, including orthogonal curvilinear coordinates. Applications in electrical and mechanical engineering.

Linear algebra: matrices, determinants, eigenvalues, eigenvectors, Markov processes. Optimization and least-squares analysis. Ordinary differential equations. Infinite series and Fourier series. Orthogonality and completeness. Introduction to partial differential equations. Applications in electrical and mechanical engineering.

Introduction to the problems and issues of applied mathematics. This will be accomplished both through the reading of papers that use mathematical arguments to have substantial impact on some field of human activity, as well as guest lecturers from around Harvard to discuss how mathematics is used in their field.

An individual project of guided reading and research culminating in a substantial paper or other piece of work which can be meaningfully evaluated to assign a letter grade; may not be taken on a PA/FL basis. Students engaged in preparation of a senior thesis ordinarily should take Applied Mathematics 99r instead.

Provides an opportunity for students to engage in preparatory research and the writing of a senior thesis. Graded on a SAT/UNS basis as recommended by the thesis supervisor. The thesis is evaluated by the supervisor and by two additional readers.

Introductory statistical methods for students in the applied sciences and engineering. Random variables and probability distributions; the concept of random sampling, including random samples, statistics, and sampling distributions; the Central Limit Theorem and its role in statistical inference; parameter estimation, including point estimation and maximum likelihood methods; confidence intervals; hypothesis testing; simple linear regression; and multiple linear regression. Introduction to more advanced techniques as time permits.

Introduces fundamental concepts for solving real-world problems and emphasizes their applications through examples from the physical and social sciences. Topics: series expansions and their convergence; complex functions, mappings, differentiation, integration, residues, Taylor and McLaurin expansions; wave (Fourier) and wavelet expansions and transformations, and their uses in signal and image analysis and solving differential equations.

Ordinary differential equations: power series solutions; special functions; eigenfunction expansions. Review of vector calculus. Elementary partial differential equations: separation of variables and series solutions; diffusion, wave and Laplace equations. Brief introduction to nonlinear dynamical systems and to numerical methods.

Introduction to abstract algebra and its applications. Sets, subsets, and partitions; mappings, operations, and equivalence relations; groups, rings, and fields, polynomials, encryption, computer coding, application of modular arithmetic, combinatorial designs, lattices, application of trellis representation of lattices, fast algorithms.

Topics in combinatorial mathematics that find frequent application in computer science, engineering, and general applied mathematics. Specific topics taken from graph theory, enumeration techniques, optimization theory, combinatorial algorithms, and discrete probability.

Many complex physical problems defy simple analytical solutions or even accurate analytical approximations. Scientific computing can address certain of these problems successfully, providing unique insight. This course introduces some of the widely used techniques in scientific computing through examples chosen from physics, chemistry, and biology. The purpose of the course is to introduce methods that are useful in applications and research and to give the students hands-on experience with these methods.

Abstracting the essential components and mechanisms from a natural system to produce a mathematical model, which can be analyzed with a variety of formal mathematical methods, is perhaps the most important, but least understood, task in applied mathematics. This course approaches a number of problems without the prejudice of trying to apply a particular method of solution. Topics drawn from biology, economics, engineering, physical and social sciences.

An algorithmic approach to topics in matrix theory which arise frequently in applied mathematics: linear equations, pseudoinverses, quadratic forms, eigenvalues and singular values, linear inequalities and optimization, linear differential and difference equations.

Introduction to basic mathematical ideas and computational methods for solving deterministic and stochastic optimization problems. Topics covered: linear programming, integer programming, branch-and-bound, branch-and-cut, Markov chains, Markov decision processes. Emphasis on modeling. Examples from business, society, engineering, sports, e-commerce. Exercises in AMPL, complemented by Maple or Matlab.

We often deal with incomplete information when going about our lives: recognizing a friend's face covered by a shadow, having a phone conversation where the reception is poor, reading a document with lots of spelling and grammatical errors. In such circumstances, we make good guesses to process and understand the data. How do we do this? What kind of mathematical framework do we need to interpret noisy and incomplete data? This course will develop a set of statistical tools that will help us solve such poorly posed problems. We will draw on examples from primary literature in biology to study optical illusions, text recognition, sequence alignment, decoding cryptographs, processing of chemo-attractive signals to find food, and survival strategies of bacteria in unpredictable environments to motivate the underlying mathematical framework.

An inquiry based and hands on exploration in computational geometry. Topics include: projective geometry (duality between points/lines, symmetry among spheric/planar/hyperbolic geometry), linear algebra (vectors, matrices, symmetry groups) and recursion. We will draw pretty pictures (fractals, tesselations, algebraic curves, etc.). We will write computer programs in Mathematica (and possibly Java, if time permits).

The mathematical investigation of cancer began in the 1950s, when Nordling, Armitage and Doll, and Fisher set out to explain the age-dependent incidence curves of human cancers. These seminal studies led to the idea that several probabilistic events are required for the somatic evolution of cancer. In the early 1970s, Knudson used a statistical analysis of the incidence of retinoblastoma in children to explain the role of tumor suppressor genes in sporadic and inherited cancers. This work was later extended to a two-stage stochastic model for the process of cancer initiation and progression, which inspired much subsequent work. Later on, considerable effort was devoted to the development of specific mathematical models for drug resistance, angiogenesis, immune responses against tumors, and genetic instabilities. This course will introduce the seminal mathematical models of cancer and will discuss both deterministic and probabilistic approaches. The course is focused on methodology; some limited theory will be covered. Computational techniques are now a standard research tool in mathematical modeling and as a result, there will also be discussion of computation. Students are expected to be familiar with a programming language such as Fortran, Matlab, C, C++, or equivalent.

An introduction to nonlinear dynamical phenomena, covering the behavior of systems described by ordinary differential equations. Topics include: stability; bifurcations; chaos; routes to chaos and universality; approximations by maps; strange attractors; fractals. Techniques for analyzing nonlinear systems are introduced with applications to physical, chemical, and biological systems such as forced oscillators, chaotic reactions, and population dynamics.

Introduction to methods for developing accurate approximate solutions for problems in the sciences that cannot be solved exactly, and integration with numerical methods and solutions. Topics include: approximate solution of integrals, algebraic equations, nonlinear ordinary differential equations and their stochastic counterparts, and partial differential equations. Introduction to "sophisticated" uses of MATLAB.

Theory and techniques for finding exact and approximate analytical solutions of partial differential equations: eigenfunction expansions, Green functions, variational calculus, transform techniques, perturbation methods, characteristics, integral equations, selected nonlinear PDEs including pattern formation and solitons, introduction to numerical methods.

The course will familiarize the students with various applications of probability theory, stochastic modeling and random processes, using examples from various disciplines, including physics, biology and economics.

An examination of the mathematical foundations of a range of well-established numerical algorithms, exploring their use through practical examples drawn from a range of scientific and engineering disciplines. Emphasizes theory and numerical analysis to elucidate the concepts that underpin each algorithm. There will be a significant programming component. Students will be expected to implement in Matlab a range of numerical methods through individual and group-based project work to get hands-on experience with modern scientific computing.

Sets, subsets, and partitions; mappings, operations, and equivalence relations; groups, rings, and fields, polynomials, encryption, computer coding, application of modular arithmetic, combinatorial designs, lattices, application of trellis representation of lattices, fast algorithms; selected readings.

Develops skills for computational research with focus on stochastic approaches, emphasizing implementation and examples. Stochastic methods make it feasible to tackle very diverse problems when the solution space is too large to explore systematically, or when microscopic rules are known, but not the macroscopic behavior of a complex system. Methods will be illustrated with examples from a wide variety of fields, ranging from simulating the immune system to strategies for investing in financial markets.

The course will introduce Bayesian analysis, maximum entropy principles, hidden markov models and pattern theory. These concepts will be used to understand information processing in biology. The relevant biological background will be covered in depth.

This is a graduate level course on optimization which provides a foundation for applications such as statistical machine learning, signal processing, finance, and approximation algorithms. The course will cover fundamental concepts in optimization theory, modeling, and algorithmic techniques for solving large-scale optimization problems. Topics include elements of convex analysis, linear programming, Lagrangian duality, optimality conditions, and discrete and combinatorial optimization. Exercises and the class project will involve developing and implementing optimization algorithms.

The course covers the modeling, analysis, and control of stochastic systems. Topics include a review of probability fundamentals, Markov chains, Markov decision processes and dynamic programming, Poisson processes, queuing theory, and optimization under uncertainty. Applications will be presented in production planning, inventory management, service systems, and sports. The goal of the course is to introduce archetypical problems in each area as well as foundational theoretical results. Coming out of this course, students should be well-positioned to take further graduate courses on any of the areas covered.

Supervision of experimental or theoretical research on acceptable applied mathematics problems and supervision of reading on topics not covered by regular courses of instruction.

AP 50a is the first half of a one-year, team-based and project-based introduction to physics. This course teaches students to develop scientific reasoning and problem-solving skills. AP50a topics include: kinematics; linear and rotational motion; relativity; conservation of momentum and energy; forces; gravitation; and oscillations and waves. Multivariable and vector calculus is introduced and used extensively in the course. Students work in teams on three, month-long projects, each culminating in a project fair. The twice-weekly class periods are all inclusive: there are no separate labs or discussion sections.

AP 50b is the second half of a one-year, team-based and project-based introduction to physics. This course teaches students to develop scientific reasoning and problem-solving skills. AP50b topics include: electrostatics; electric currents; magnetostatics; electromagnetic induction; Maxwell's Equations; electromagnetic radiation; geometric optics; and, wave optics. Multivariable and vector calculus is introduced and used extensively in the course. Students work in teams on three, month-long projects, each culminating in a project fair. The twice-weekly class periods are all inclusive: there are no separate labs or discussion sections.

Fundamental physical properties of crystalline solids discussed in terms of the basic principles of quantum physics. Crystal structure, energy band structure of metals, semiconductors and insulators. Fermi gas, phonons, thermal properties, electronic transport, optical properties. Low dimensional solids including quantum nanostructures and graphene. Magnetism, superconductivity. Spintronic and photonic applications.

This course will focus on how electromagnetic fields and matter interact. Deterministic, statistical, classical, and quantum mechanical considerations will be covered. The course will be useful for experimental and applied physics students in atomic, solid state, optical, chemical, and biophysics.

Optical systems and lasers have recently revolutionized both technology and basic research. We cover simple models of light-matter interactions, Fourier optics and holography, light scattering, and optics in biology: single-molecule studies, optical coherence tomography, nonlinear imaging techniques.

Classical and quantum description of electrical, optical and magnetic properties, and their fundamental physical origins; experimental techniques. Properties of compositionally complex materials such as ceramics. Structure-property relations. Applications in semiconductor, information storage, and energy industries.

Introduction to the physics of soft matter, also called complex fluids or squishy physics, includes the study of capillarity, thin films, polymers, polymer solutions, surfactants, and colloids,. Emphasis is on physical principles which scale bulk behavior. Students will understand the concepts, experimental techniques, and, especially, the open questions. Lecture notes are supplied in place of a textbook.

Consider phenomena strongly influenced by surface tensions, high curvatures, thin films, diffusion, adsorption, wetting, which are variously mobile, dynamic, polymeric, transient, and fragile. Emphasis on the physics, thermodynamics, rheological, and scaling laws that govern bulk behavior.

Select topics in materials chemistry, focusing on chemical bonds, crystal chemistry, organic and polymeric materials, hybrid materials, surfaces and interfaces, self-assembly, electrochemistry, biomaterials, and bio-inspired materials synthesis.

The course will describe major computational techniques for fluid flows, as described by the Navier-Stokes equations of continuum mechanics. In addition, the course will cover a new class of mesoscale techniques for complex flows and soft matter systems, which do not fit within the continuum Navier-Stokes description. The latter encompass a broad class of flowing materials of special interest to micro-nano engineering and biology.

This course will provide the background and an extensive set of examples showing how computational methods are applied to modern design of materials with desired functionality. The methods will span multiple length and time scales, including molecular dynamics simulations, first-principles approaches, stochastic methods for optimization and sampling, and continuum elasticity theory. Examples will include problems in electronic and photonic devices, materials for energy conversion, storage, and environmental protection, and those related to mechanical strength of materials.

Bonding, crystallography, diffraction, phase diagrams, microstructure, point defects, dislocations, and grain boundaries.

Basic principles of statistical physics and thermodynamics, with applications including: the equilibrium properties of classical and quantum gases; phase diagrams, phase transitions and critical points, as illustrated by the gas-liquid transition and simple magnetic models; Bose-Einstein condensation.

Lectures and laboratory instruction on transmission electron microscopy (TEM) and Cs corrected, aberration-correction microscopy and microanalysis. Lab classes include; diffraction, dark field imaging, X-ray spectroscopy, electron energy-loss spectroscopy, atomic imaging, materials sample preparation, polymers, and biological samples.

Kinetic principles underlying atomic motions, transformations, and other atomic transport processes in condensed phases. Application to atomic diffusion, continuous phase transformations, nucleation, growth, coarsening and mechanisms of plastic deformation.

Dislocations are fundamental defects in crystalline solids affecting deformation and crystal growth. The use of dislocations to establish constitutive behavior for the deformation of materials over a wide variety of stresses and temperatures, as well as in modeling stress distributions and interfacial reactions will be included.

Special topics in materials science.

Electrical, optical, thermal, magnetic, and mechanical properties of solids will be treated based on an atomic scale picture and using the independent electron approximation. Metals, semiconductors, and insulators will be covered, with possible special topics such as superconductivity.

Theory of the electron liquid, Fermi liquid theory. Ferromagnetism of metals, BCS theory of superconductivity. Lattice models of correlated electrons: antiferromagnetism. Graphene: semi metals and quantum phase transitions. Non-fermi liquids in correlated metals.

Materials-related topics chosen from: Structure and Self-Assembly; Mechanical Properties; Surfaces and Interfaces; Biomaterials; Synthesis and Fabrication; Characterization Techniques; Soft Materials, and Complex Fluids.

Supervision of experimental or theoretical research on acceptable applied physics problems and supervision of reading on topics not covered by regular courses of instruction.

Guided reading and research.

A survey of systems theory with applications from bioengineering and physiology. Analysis: differential equations, linear and nonlinear systems, stability, the complementary nature of time and frequency domain methods, feedback, and biological oscillations. Applications: nerve function, muscle dynamics, cardiovascular regulation. Laboratory: neural models, feedback control systems, properties of muscle, cardiovascular function.

This is a combined introductory graduate/upper-level undergraduate course that focuses on examining modern techniques for manipulating cellular behavior and the application of these techniques to problems in the biomedical and biotechnological arenas. Topics will include expanding the genetic code, genetic circuits, rewiring signaling pathways, controlling behavior through cell-matrix interactions, and directed differentiation of stem cells. Lectures will review fundamental concepts in cell biology before delving into topical examples from current literature. Students will work individually and in teams to determine the boundaries of existing cellular engineering techniques using scientific literature and conduct original research in the laboratory.

Fundamental engineering and biological principles underlying field of tissue engineering, along with examples and strategies to engineer specific tissues for clinical use. Students will prepare a paper in the field of tissue engineering, and participate in a weekly laboratory in which they will learn and use methods to fabricate materials and perform 3-D cell culture.

Approaches from robotics, control theory, and neuroscience for understanding biological motor systems. Analytical and computational modeling of muscles, reflex arcs, and neural systems that contribute to motor control in the brain. Focus on understanding how the central nervous system plans and controls voluntary movement of the eyes and limbs. Learning and memory; effects of variability and noise on optimal motor planning and control in biological systems.

This course will introduce bioelectricity and bioelectromagnetics starting with Maxwell Equations, which will quickly be simplified to the quasi-static form typically applicable in physiology. We will introduce the basics of membrane electrical biophysics, which we will use to study action potentials and action potential propagation. Applications will include electro-cardiograms (ECGs), electro-myograms (EMGs), electro-oculograms (EOGs), and electro-encephalograms (EEGs). EEG investigations will include analyses of spatial resolution as well as dynamic properties. A course project will allow students to choose an area of specific interest for more in-depth investigation and analysis.

Introduction to chemical kinetics and reactor design with applications to bioengineering, chemical engineering, environmental sciences and other areas.

A biomaterial is any form of matter that is produced by or interacts with biological systems. One of the pillars of biomedical engineering is to use naturally derived and synthetic biomaterials to treat, augment, or replace human tissues. This course examines the structure, properties and processing of biomaterials.

An introduction to the most important discoveries and intellectual paradigms in computer science, designed for students with little or no previous background. Explores problem-solving using high and low-level programming languages; presents an integrated view of computer systems, from switching circuits up through compilers and GUI design. Examines theoretical and practical limitations related to unsolvable and intractable computational problems, and the social and ethical dilemmas presented by such issues as software unreliability and invasions of privacy.

The Internet operates in layers, and so does much of the technology that hooks up to it: PCs, mobile phones, tablets. Nearly two decades ago those platforms were conceptually simple: a "generative" base offered by one manufacturer, on which any third party could build. (Think: Windows and the programs that run on it.) Some efforts by platform makers to tip the scales in their favor in the layer above resulted in extended controversy and regulatory efforts, such as over Windows coming bundled with Internet Explorer. Today platforms are just as vital but far more complex. We have hybrids like the iOS and Android operating systems or the Facebook and Twitter platforms, where the platform makers offer their systems as services rather than products, influencing and sometimes outright limiting connection between users and independent developers for those platforms. How should we think about these new platforms? What counts as a "level playing field," and what responsibility, if any, is there for public authorities to enforce it? What lessons, if any, do the prior tangles offer for today?

Widely applicable mathematical tools for computer science, including topics from logic, set theory, combinatorics, number theory, probability theory, and graph theory. Practice in reasoning formally and proving theorems.

Introduction to the intellectual enterprises of computer science and the art of programming. This course teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, PHP, and JavaScript plus SQL, CSS, and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. Designed for concentrators and non-concentrators alike, with or without prior programming experience.

Introduction to the intellectual enterprises of computer science and the art of programming. This course teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, PHP, and JavaScript plus SQL, CSS, and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. Designed for concentrators and non-concentrators alike, with or without prior programming experience.

Abstraction and design in computation. Topics include functional and object-oriented styles of programming, software engineering in the small, and models of computation. Our main goal is to understand how to design large programs to make them readable, maintainable, elegant, and efficient. Exercises in OCaml.

Fundamentals of computer systems programming, machine organization, and performance tuning. This course provides a solid background in systems programming and a deep understanding of low-level machine organization and design. Topics include C and assembly language programming, program optimization, memory hierarchy and caching, virtual memory and dynamic memory management, concurrency, threads, and synchronization.

This seminar will examine the individuals and institutions that control the Internet, and how the Internet affects the distribution and operation of power, broadly conceived. We will examine technologies of control (such as surveillance, censorship, propaganda, and use control) and of evading control, the individuals and institutions that seek to regulate the Internet (such as governments, the IETF, and hackers), the relationship between cybersecurity, national security, and Internet governance, the economics of Internet communications, and more.

Supervised individual study of advanced topics in computer science. A student wishing to enroll in Computer Science 91r must be accepted by a faculty member who will supervise the course work. A form available from the Student Affairs Office, Pierce Hall 110, must be filled out and signed by the student and faculty supervisor. Students writing theses may enroll in this course while conducting thesis research and writing.

Cooperative design, development, and testing of a sizable and realistic computer system. Students work as a group with a client on a real-world open-ended problem, and gain experience in problem definition, software development, and system lifecycle issues, and in the area of application. Students work in groups; both student participation in the classroom and effective group cooperation outside the classroom are stressed.

What is privacy, and how is it affected by recent developments in technology? This course critically examines popular concepts of privacy and uses a rigorous analysis of technologies to understand the policy and ethical issues at play. Case studies: database anonymity, research ethics, wiretapping, surveillance, and others. Course relies on some technical material, but is open and accessible to all students, especially those with interest in economics, engineering, political science, computer science, sociology, biology, law, government, philosophy.

Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries. Built around three modules: prediction and elections, recommendation and business analytics, and sampling and social network analysis.

General introduction to the theory of computation, teaching how to reason precisely about computation and prove mathematical theorems about its capabilities and limitations. Finite automata, Turing machines, formal languages, computability, uncomputability, computational complexity, and the P vs. NP question.

Design and analysis of efficient algorithms and data structures. Algorithm design methods, graph algorithms, approximation algorithms, and randomized algorithms are covered.

An accelerated introduction to theoretical computer science for students with strong mathematical preparation, to be taken in place of both Computer Science 121 and 124. Algorithm design methods, including graph algorithms, approximation algorithms, and randomized algorithms. Models of computation, computability theory, and computational complexity, including the P vs. NP question.

Algorithms to guarantee privacy and authenticity of data during communication and computation. Proofs of security based on precise definitions and assumptions. Topics may include one-way functions, private-key and public-key encryption, digital signatures, pseudorandom generators, zero-knowledge proofs, fully homomorphic encryption, and the role of cryptography in network and systems security.

Introduction to the design, structure, and operation of digital computers; logic circuits and digital electronics; computer arithmetic; computer architecture; and machine language programming. Consideration of the design interactions between hardware and software systems.

Networking has enabled the emergence of mobile and cloud computing, creating the most important technological paradigm shift in computing of the past decade. Further advancements in networking are expected to similarly transform the technological landscape over the next decade through the emergence of the Internet of Things and gigabit wireless networks. In order to play a role in this era of new network-powered advancements, students must have a thorough understanding of emerging networking topics. Rather than teaching the basic networking protocols, which have become very mature and can be treated as a black box, in CS 143, we will teach the new issues and topics of interest which will power important emerging applications. This focus on upcoming applications is the motivation for CS 143 this semester. The class will be organized into the following nine modules: Basic Networking Concepts: Protocol Layering; Internet of Things: All-service Bluetooth Low Energy (BLE); Data Center Networking: Software Defined Networking; Web-scale Networking: Distributed Cloud Computing and Virtual Machine Migration; Content Networks: Video Streaming; Network Security: Defense Against Protocol Exploitation; Wireless Networking: Wireless Mesh, Geographic Routing; Machine Learning Assisted Networking: End-to-end Application Adaptive Protocols; Cyber-physical Networks: Vehicular Networking. Students will have the opportunity to implement the concepts learned in the course through programming assignments, read and discuss the latest networking literature, and design and implement a final project.

Cooperative design and development of advanced network-based systems with both technology and business considerations. Students will work in 2 person teams. Student work will include reading assignments, homework sets, a project proposal, and project reports and presentations. At the end of the class, all teams will defend their approaches and results in front of the class and invited guests.

Review of the fundamental structures in modern processor design. Topics include computer organization, memory system design, pipelining, and other techniques to exploit parallelism. Emphasis on a quantitative evaluation of design alternatives and an understanding of timing issues.

Presentation of concepts and techniques for the design and fabrication of VLSI systems and digital MOS integrated circuits. Topics include: basic semiconductor theory; MOS transistors and digital MOS circuits design; synchronous machines, clocking, and timing issues; high-level description and modeling of VLSI systems; synthesis and place and route design flows; and testing of VLSI circuits and systems. Various CAD tools for design, simulation, and verification are extensively used.

Comprehensive introduction to the principal features and overall design of both traditional and modern programming languages, including syntax, formal semantics, abstraction mechanisms, modularity, type systems, naming, polymorphism, closures, continuations, and concurrency. Provides the intellectual tools needed to design, evaluate, choose, and use programming languages.

Implementation of efficient interpreters and compilers for programming languages. Associated algorithms and pragmatic issues. Emphasizes practical applications including those outside of programming languages proper. Also shows relationships to programming-language theory and design. Participants build a working compiler including lexical analysis, parsing, type checking, code generation, and register allocation. Exposure to run-time issues and optimization.

The fundamental principles of resource management and abstraction in modern operating systems. Control abstractions: threads, processes, scheduling, synchronization. Storage abstractions: dynamic memory allocation, virtual memory, file system design. Communication abstractions: interprocess communication, networking. Case studies. Design and implementation of parts of a multiuser multitasking virtual-memory operating system.

Introduction to principles of software engineering and best practices, including code reviews, source control, and unit tests. Topics include Ajax, database schemas, event handling, HTTP, MVC, object-oriented design, and user experience. Projects include web apps with front-end UIs (mobile and desktop) and back-end APIs. Languages include JavaScript and PHP.

We are in the big data era and data systems sit in the critical path of everything we do, i.e., in businesses, in sciences, as well as in everyday life. This course will be a comprehensive introduction to modern data systems. The primary focus of the course will be on modern trends that are shaping the data management industry right now such as column-store and hybrid systems, shared nothing architectures, cache conscious algorithms, hardware/software codesign, main memory systems, adaptive indexing, stream processing, scientific data management, and key value stores. We will also study the history of data systems and traditional and seminal concepts and ideas such as the relational model, row-store database systems, optimization, indexing, concurrency control, recovery and SQL in order to understand both how data systems have evolved over the years and why, as well as how these concepts apply today and how data systems might evolve in the future.

An introduction to key design principles and techniques for visualizing data. Covers design practices, data and image models, visual perception, interaction principles, visualization tools, and applications. Introduces programming of web-based interactive visualizations.

This course covers the fundamentals of 3D computer graphics using a modern shader-based version of OpenGL. Main topics include: geometric coordinate systems and transformations, keyframe animation and interpolation, camera simulation, triangle rasterization, material simulation, texture mapping, image sampling and color theory. The course also touches on ray tracing, geometric modeling and simulation-based animation.

Usability and design as keys to successful technology. Covers user observation techniques, needs assessment, low and high fidelity prototyping, usability testing methods, as well as theory of human perception and performance, and design best practices. Focuses on understanding and applying the lessons of human interaction to the design of usable systems; will also look at lessons to be learned from less usable systems. The course includes several small and one large project.

Introduction to machine learning, providing a probabilistic view on artificial intelligence and reasoning under uncertainty. Topics include: supervised learning, ensemble methods and boosting, neural networks, support vector machines, kernel methods, clustering and unsupervised learning, maximum likelihood, graphical models, hidden Markov models, inference methods, and computational learning theory. Students should feel comfortable with multivariate calculus, linear algebra, probability theory, and complexity theory. Students will be required to produce non-trivial programs in Python.

Introduction to AI focused on problems in reasoning about action and rational decision making, covering search, knowledge representation and planning. Search: heuristics, informed search and optimization; constraint satisfaction; game playing. Knowledge representation: logics, efficient logical inference, reasoning about categories. Planning: action representations and planning algorithms, hierarchical task networks, sequential decision making. Applications to multi-agent systems, robotics and natural-language processing. Discussion of relevant work in philosophy, economics, and decision theory.

The interplay between economic thinking and computational thinking as it relates to electronic commerce, social networks, collective intelligence and networked systems. Topics covered include: game theory, peer production, reputation and recommender systems, prediction markets, crowd sourcing, network influence and dynamics, auctions and mechanisms, privacy and security, matching and allocation problems, computational social choice and behavioral game theory. Emphasis will be given to core methodologies, with students engaged in theoretical, computational and empirical exercises.

Watson is the world Jeopardy champion. Siri responds accurately to "Should I bring an umbrella tomorrow?". How do they work? This course provides an introduction to the field of computational linguistics, the study of human language using the tools and techniques of computer science, with applications to a variety of natural-language-processing problems such as those deployed in Watson and Siri, and covers pertinent ideas from linguistics, logic programming, and statistical modeling. The course will include an experimental practicum component covering skills in technical writing and editing that should be of general use as well.

Building autonomous robotic systems requires understanding how to make robots that observe, reason, and act. Each component uses many engineering principles: how to fuse, multiple, noisy sensors; how to balance short-term versus long-term goals; how to control one's actions and how to coordinate with others. This year, we will study these questions in the context of a project to develop autonomous robot soccer teams. The class format will mix seminar and lab formats.

An applications course highlighting the use of computers in solving scientific problems. Students will be exposed to fundamental computer science concepts such as computer architectures, data structures, algorithms, and parallel computing. Fundamentals of scientific computing including abstract thinking, algorithmic development, and assessment of computational approaches. Students will learn to use open source tools and libraries and apply them to data analysis, modeling, and visualization of real scientific problems. Emphasizes parallel programming and "parallel thinking."

This is a project-based course emphasizing designing, building, testing, maintaining and modifying software for scientific computing. Students will work in groups on a number of projects, ranging from small data-transformation utilities to large-scale systems. Students will learn to use a variety of tools and languages, as well as various techniques for organizing teams. Most important, students will learn to fit tools and approaches to the problem being solved.

A quantitative theory of the resources needed for computing and the impediments to efficient computation. The models of computation considered include ones that are finite or infinite, deterministic, randomized, quantum or nondeterministic, discrete or algebraic, sequential or parallel.

Covers topics related to algorithms for big data, especially related to networks. Themes include compression, cryptography, coding, and information retrieval related to the World Wide Web. Requires a major final project.

Probabilistic techniques and tools for the design and analysis of algorithms. Designed for all first-year graduate students in all areas.

Advanced algorithm design, including but not limited to amortization, randomization, online algorithms, graph algorithms, approximation algorithms, linear programming, and data structures.

Efficiently generating objects that "look random" despite being constructed using little or no randomness. Connections and applications to computational complexity, cryptography, and combinatorics. Pseudorandom generators, randomness extractors, expander graphs, error-correcting codes, hash functions.

Topics in cryptography and data privacy drawn from the theoretical computer science research literature. Focus for 2014-15: Differential Privacy -- a mathematical framework for privacy-preserving analysis of datasets, which enables aggregate computations while preventing the leakage of individual-level information.

Possibilities of and limitations to performing learning by computational agents. Topics include computational models, polynomial time learnability, learning from examples and learning from queries to oracles. Applications to Boolean functions, automata and geometric functions.

Biology abounds with step by step processes, whether in evolution, neural activity, development, or protein circuits. In many of these neither the actual steps taken nor the outcomes are well understood. Computer science is the study of step by step processes and offers an approach to understanding them as they occur in biology. Students will read, present, and critically evaluate research papers in this area.

The contents and course requirements are similar to those of Computer Science 144r, with the exception that students enrolled in Computer Science 244r are expected to do substantial system implementation and perform graduate-level work.

The contents and course requirements are similar to those of Computer Science 146, with the exception that students enrolled in Computer Science 246 are expected to undertake a substantial course project.

Seminar course exploring recent research in computer architecture. Topics vary from year to year and will include subjects such as multi-core architectures, energy-efficient computing, reliable computing, and the interactions of these issues with system software. Students read and present research papers, undertake a research project.

The contents and course requirements are similar to those of Computer Science 148, with the exception that students enrolled in Computer Science 248 are expected to do a substantial design project and paper discussions on advanced topics.

This course introduces concepts and techniques in the foundational study of programming languages, as well as their formal logical underpinnings. The central theme is the view of programs and languages as formal mathematical objects about which precise claims may be made and proved. Particular topics include operational techniques for formal definition of language features, type systems, and program logics. The models and proofs are formalized using mechanical theorem provers.

Seminar course exploring recent research in programming languages. Topics vary from year to year. Students read and present research papers, undertake a research project.

Modern software systems construction and analysis. Distributed systems; operating systems; networks; data centers; big data; emerging systems deployments. Close, careful reading of research papers and code, coupled with programming projects. Readability and programmability. Topic focus will change each offering. May be repeated for credit with instructor permission.

An introduction to operating systems research. Paper-based seminar course that introduces students to the state of the art in systems research through historical and quantitative lenses. Students will read and discuss research papers and complete a final research project.

An examination of the special problems associated with distributed computing such as partial failure, lack of global knowledge, asynchrony and coordination of time, and protocols that function in the face of these problems. Emphasis on both the theory that grounds thinking about these systems and in the ways to design and build such systems.

Big data is everywhere. A fundamental goal across numerous modern businesses and sciences is to be able to exploit as many machines as possible, to consume as much information as possible and as fast as possible. The big challenge is "how to turn data into useful knowledge". This is far from a simple task and a moving target as both the underlying hardware and our ability to collect data evolve. In this class, we will discuss how to design data systems and algorithms that can "scale up" and "scale out". Scale up refers to the ability to use a single machine to all its potential, i.e., to exploit properly the memory hierarchy and the multiple CPU and GPU cores. Scale out refers to the ability to use more than 1 machines (typically 100s or 1000s) effectively. This is a research oriented class. Every week we will read two modern research papers; one from the scale up area and one from the scale out area. We will use examples from several areas, including relational systems and distributed databases, graph processing systems (i.e., for social networks), key value stores, noSQL and newSQL systems as well as mobile computing. Each student will work on a semester long data systems research project (in groups of 2-4 students) which can be in any of the above areas and will be based on an open research problem.

Advanced seminar in computer graphics focusing on geometric representations and processing. Topics include: subdivision surfaces, surface parametrization, vector fields over surfaces, shape editing, shape matching and surface reconstruction.

Advanced course in computer graphics focusing on image rendering and processing. Topics include: light transport, efficient rendering, image based rendering, texture processing, interactive image processing.

The course covers major areas of inquiry and core research methods in Human-Computer Interaction including experimental design, statistical data analysis, and qualitative methods. Activities will include discussion of primary literature, a small number of lectures, assignments (design, execution and analysis of both lab-based and on-line experiments), and a research project. Special focus this year is on social computing and crowd-powered systems. Specifically, we will look at the design and analysis of systems, in which crowds of intrinsically motivated volunteers contribute to meaningful and non-trivial human computation tasks as a byproduct of doing something that they are motivated to do anyway.

Seminar course exploring research directions in artificial intelligence (AI), typically combining two or more of such areas as multi-agent systems, natural-language processing, machine learning, reasoning under uncertainty, representation systems. Topic for Spring 2015: Multi-agent systems teamwork and plan management.

Advanced statistical machine learning and probabilistic data analysis. Topics include: Markov chain Monte Carlo, variational inference, Bayesian nonparametrics, text topic modeling, unsupervised learning, dimensionality reduction and visualization. Requires a major final project.

The focus of the Spring 2015 course will be reinforcement learning, a framework for solving problems involving a sequence of decisions with uncertain outcomes. This course will cover the fundamental theory through readings of classic papers and build practical intuition through coding assignments. Topics will include Markov decision process and partially observable Markov decision processes, planning under uncertainty, model-free and model-based reinforcement learning, function approximation in reinforcement learning, and batch reinforcement learning.

Vision as an ill-posed inverse problem: image formation, two-dimensional signal processing; feature analysis; image segmentation; color, texture, and shading; multiple-view geometry; object and scene recognition; and applications.

Topics on the design and analysis of algorithms, processes, and systems related to crowds and social networks. Readings in AI, theoretical CS, machine learning, social science theory, economic theory, and operations research.

Algorithmic, game-theoretic and logical foundations of multi-agent systems, including distributed optimization and problem solving, non-cooperative game theory, learning and teaching, communication, social choice, mechanism design, auctions, negotiation, coalitional game theory, logics of knowledge and belief, collaborative plans and social systems.

Interplay between computation and economics. Topics in electronic commerce, computational social choice, computational mechanism design, peer production, prediction markets and reputation systems. Readings in AI, theoretical CS, multi-agent systems, economic theory, and operations research.

In-depth investigation of topics in computational linguistics and natural-language processing. Students discuss research papers and undertake a significant research project. This term, the course will focus on synchronous grammars and their use for formal modeling of the semantics of natural language, including background on Montague grammar, pertinent logic, lambda calculus, applications to machine translation and other language-processing problems.

Seminar course exploring recent research in computer vision. Topics vary from year to year, typically including object recognition; activity recognition; and visual surveillance. Students read and present research papers and undertake a research project.

Surveys biologically-inspired approaches to designing distributed systems. Focus is on algorithms, analysis, and programming paradigms. Topics: swarm intelligence, amorphous computing, immune-inspired systems, synthetic biology. Discussion of research papers and a research project required.

Supervision of experimental or theoretical research on acceptable computer science problems and supervision of reading on topics not covered by regular courses of instruction.

Gain effective skills for teaching applied sciences. Topics: presentation and communication, grading and giving feedback on assignments, cognition and learning, classroom practice and student interactions. Seminar style with an emphasis on observation, practice, feedback, discussion, and reflection.

An integrative introduction to engineering sciences. Combines classroom discussion with activity-based learning, and emphasizes concepts that span multiple disciplines. Covers topics having direct societal impact, and presents them in historical context. Involves qualitative and quantitative analysis, mathematical modeling, and design. Introduces common engineering software and hardware tools.

An introduction to the role of technology in the environmental sciences, with foci on energy and water topics. The basic scientific principles underlying human use and control of the environment are emphasized. The course includes several field trips.

This course teaches students to generate, develop and realize breakthrough ideas for social and cultural change. Students form groups of four to five around one of four seed ideas. Over the course of the semester students mold their idea, learn idea translation skills, and develop idea proposals they pitch at the end of the semester. Students have the opportunity to apply for Idea Translation Fellowships, funded by the Wyss Institute and the Harvard Global Health Institute. Winners are funded to develop their ideas further over the course of the summer starting with a week long workshop with students from around the world at Le Laboratoire Cambridge. Students brainstorm, give public presentations, and write final group reports. Students also engage in studio practice with artists, designers, technologists, and scientists. Case study and conceptual readings will complement student idea exploration.

Students gain direct experience overcoming under-represented challenges of becoming an innovator: identifying opportunities big enough to pursue, leading team projects under realistic entrepreneurial conditions, and building a coalition of support to scale their ideas. Students define their own interest areas and learn to: identify important unmet needs through behavioral fieldwork, work effectively with others to develop and prototype potential solutions, and lead innovative projects to implementation. They learn ethnographic and design processes (observing, interpreting, ideating, testing, refining, planning) for identifying needs, practice applying insights from industry cases and research to move their ideas through organizational and political systems (e.g., negotiate, strategize, motivate others, and build support). Students get substantial tailored individual feedback on research-based factors linked to effective multi-disciplinary innovation at multiple levels (individual, team, organization) designed to help prepare them for entrepreneurial work in the future. Features guest speakers from industry, academia, and involves collaborating with cutting-edge companies.

Multi-disciplinary course for students interested in designing products and services that are simple, irresistible, delightful, cool, covetable, viral, and, in today's competitive market, increasingly much more likely to be successful. Students study real world cases of exceptionally designed products and services (from Apple, Gucci, Zipcar and more) strategically design for desirability (via status, belonging, usability, etc.). In weekly design challenges, students use analogical transfer to apply these insights to diverse industries and target markets (e.g., health literacy campaigns, declining technologies, the future of luxury). Each week we prototype using different software and constraints, helping students to flexibly work across tools, with weekly prototyping workshops help supplement students' skills across prototyping areas. Weekly critiques enable students to develop their own design point of view and to finish with a diverse design portfolio.

Course description: Microorganisms produce a diverse array of specialized small molecules as part of their metabolic processes. In this course we will study the production, properties, and characterization of these molecules through the lens of food fermentation. In particular, we will focus on the small molecules that contribute taste and aroma in fermented foods. Students will experience the scientific inquiry process in a creative way by designing and implementing their own research project based on a fermented food of their choosing. Still a field with much potential for discovery, interested students are invited to continue their research project in the summer.

This Seminar/Workshop will look into the design and technical challenges involved in the development of web based interfaces for collaborative and participatory design scenarios where more than one agents are involved. The designer in a sense is given the chance to design and experiment with the communication architecture and temporality of the design process itself. Students will be introduced to web technologies for front end and back end development [javascript, webGL, nodeJS, MongoDB].

Digital design and fabrication methods available to composite material manufacturing have been employed by the automotive, marine, and aerospace industries for some time, allowing significant advancements in performance. Not yet a material system commonly employed by the construction industry, contemporary architecture is positioned to benefit from the adoption of this material system and the highly customizable opportunities it offers. Offered as a limited enrollment seminar/workshop, this course will focus on designing and prototyping envelope systems constructed of resin-infused fiber composite parts. Particular interest will be placed on controlling the direct correlation between geometry and material performance within the design solution. Workshops addressing the related material processes and necessary digital design tools will be offered as part of the weekly course content.

This is an introductory course to computational design and the prerequisite for a spring course that deals with more advanced topics in the field. This course is primarily intended for designers with little background in programming who are interested in developing their skills in order to be able to better understand, interface with and customize the digital tools they are using, or develop their own software and interactive applications. The course introduces students to fundamental concepts and techniques in computational design. By the term "computational design" we mean an ad hoc set of methods borrowed from computer science, computational geometry and other fields, and adapted to specific design problems such as design development, fabrication, analysis, interaction and communication.

The main course objectives are to introduce students to the exciting and powerful world of electrical engineering and to explain how gadgets that we use every day actually work. After taking ES 50, you will be able to leverage the power of electricity to build systems that sense, control and program the physical world around you. Examples include intelligent and autonomous systems (robots), audio amplifiers (e.g. guitar amp), interactive art installations, light-shows, mind-controlled machines, and so on.

A first course in the design and construction of mechanical and electromechanical devices. Engineering graphics and sketching; dimensions and tolerances. Introduction to materials selection and structural design. Machine elements and two-dimensional mechanisms; DC motors. Design methodology. Emphasis on laboratory work and design projects using professional solid modeling CAD software and numerically controlled machine tools.

Introduction to designing circuits to solve real problems. Two lecture and two lab sessions a week blend instruction with hands-on lab work to emphasize understanding, building and testing circuits. The course incorporates useful design experiences from day one. Covered topics include amplication, feedback, impedance, stability, filtering, switching, digital logic, microcontrollers, and more. The class ends with an open ended project that challenges students to build on core concepts.

A foundation in human organ systems physiology, including cardiac, respiratory, renal, gastrointestinal, and neural systems. Quantitative description of organ systems function and control in terms of physical principles and physiologic mechanisms. Simple mathematical models representing key aspects of organ systems function. Emphasis will be given to understanding the ways in which dysfunction in these systems gives rise to common human disease processes.

Guided reading and research.

Students do field-based work in entrepreneurship to develop their existing startup and explore new ideas and opportunities for startup design. The course is for students seeking innovation experience as a founder of a startup. Students may work individually; teams are preferred. Requires self-directed, independent work and active outreach to mentors, customers, and partners for guidance and feedback in addition to that provided by the instructor. Students will share their work regularly and engage in a peer-to-peer feedback forum. The coursework is customized to the needs of each student and their startup role and includes development of product, technology, market, business, organization and leadership.

Semester-long team project that provides engineering experience working with clients on real-world problems. Projects provide exposure to problem definition, performance measurement, quantitative analysis, modeling, generation of creative solutions, engineering design trade-offs, and documentation/communication skills. Ordinarily taken in the junior year.

Individual engineering design projects which demonstrate mastery of engineering knowledge and techniques. During the year, each student will pursue an appropriate capstone project which involves both engineering design and quantitative analysis and culminating in a final oral presentation and final report/thesis.

Individual engineering design projects which demonstrate mastery of engineering knowledge and techniques. During the year, each student will pursue an appropriate capstone project which involves both engineering design and quantitative analysis and culminating in a final oral presentation and final report/thesis.

Introduces the fundamental statistical and mapping tools needed for analysis of environmental systems. Topics will be linked by environmental and social themes and will include GIS concepts; data models; spatial statistics; density mapping; buffer zone analysis; surface estimation; map algebra; suitability modeling. Students will acquire technical skills in both mapping and spatial analysis. Software packages used will include ArcGis. There will be guest lectures by researchers and practitioners who use GIS for spatial analysis.

Activity-based course for beginning/intermediate science and engineering undergraduates. Combines readings and discussions on techniques for learning science and engineering design with implementation in an 8th grade science class in Cambridge. Students work directly with the 8th graders to guide how they learn. Students apply what they discover to improve their own understanding of college-level science and engineering.

Many complex physical problems defy simple analytical solutions or even accurate analytical approximations. Scientific computing can address certain of these problems successfully, providing unique insight. This course introduces some of the widely used techniques in scientific computing through examples chosen from physics, chemistry, and biology. The purpose of the course is to introduce methods that are useful in applications and research and to give the students hands-on experience with these methods.

The STC Center for Integrated Quantum Materials (Harvard University, Howard University, and MIT) builds electronics and photonics from Quantum Materials: Atomic Layers (graphene, boron nitride, transition-metal dichalcoginides) for atomic-scale devices, Topological Insulators for corruption-free data channels, and Nitrogen Vacancy Center Diamond for single-atom memory. Faculty from the Center will present tutorial lectures about their research.

Abstracting the essential components and mechanisms from a natural system to produce a mathematical model, which can be analyzed with a variety of formal mathematical methods, is perhaps the most important, but least understood, task in applied mathematics. This course approaches a number of problems without the prejudice of trying to apply a particular method of solution. Topics drawn from biology, economics, engineering, physical and social sciences.

A first course in the mechanical sciences which introduces elements of continuum mechanics and explains how materials and structures stretch, bend, twist, shake, buckle, and break. Stress-strain behavior of materials. Statically determinate and indeterminate structures. Stress and strain, equations of motion or equilibrium, strain-displacement relations. Torsion. Beam theory with applications to beam deflections, vibrations, and buckling. Three laboratory sessions required.

Introduction to basic mathematical ideas and computational methods for solving deterministic and stochastic optimization problems. Topics covered: linear programming, integer programming, branch-and-bound, branch-and-cut, Markov chains, Markov decision processes. Emphasis on modeling. Examples from business, society, engineering, sports, e-commerce. Exercises in AMPL, complemented by Maple or Matlab.

Dimensional analysis. Basic elements of steady and unsteady thermal conduction and mass diffusion. Statics and dynamics of fluids. Buoyancy-stability and hydrostatics. Laminar viscous flows, potential flows, origin of lift, and basic aspects of boundary layers. Navier-Stokes and continuity equations. Applications in aerodynamics, chemical, environmental, and mechanical engineering, and physics.

Modeling and analysis of mechanical and electromechanical systems. Topics include 3D rigid body dynamics, resonance, damping, frequency response, Laplace transform methods, Lagrange's equations, multiple degree-of-freedom systems and an introduction to nonlinear vibration, continuous systems, and control. Analytical modeling will be supplemented with numerical simulations and lab experiments. Laboratory exercises will explore vibration, stabilization, and nonlinear systems using data acquisition systems.

Introduction to finite element methods for analysis of steady-state and transient problems in solid, structural, fluid mechanics, and heat transfer. Implementation of simple MATLAB codes and use of existing general-purpose programs (ABAQUS and COMSOL).

Basic observations and theoretical understanding of ocean phenomena from local surface beach waves to the effects of the oceans on global climate. Observations and dynamics of ocean waves, currents, turbulence, temperature and salinity distributions; basic fluid dynamics equations; the ocean's role in climate: wind-driven circulation and the Gulf Stream, thermohaline circulation and the potential instability of Europe's climate, El Nino, the oceans and global warming.

Physical concepts necessary to understand atmospheric structure and motion. Phenomena studied include the formation of clouds and precipitation, solar and terrestrial radiation, dynamical balance of the large-scale wind, and the origin of cyclones. Concepts developed for understanding today's atmosphere are applied to understanding the record of past climate change and the prospects for climate change in the future.

Physical and chemical processes determining the composition of the atmosphere and its implications for climate, ecosystems, and human welfare. Construction of atmospheric composition models. Atmospheric transport. Nitrogen, oxygen, and carbon cycles. Climate forcing by greenhouse gases and aerosols. Stratospheric ozone. Oxidizing power of the atmosphere. Surface air pollution: aerosols and ozone. Deposition to ecosystems: acid rain, nitrogen, mercury.

A solution to the problems set by the intersection of global energy demand and climate feedbacks requires the teaching of physics and chemistry in that context. Core topics include thermodynamics, free energy, entropy, acid-base and oxidation-reduction reactions, electrochemistry, electromagnetic induction, circuit theory, AC and DC circuits, the nature of photons and of electromagnetic radiation, photochemistry, materials, catalysis, kinetics, molecular bonding, and biological processes for energy conversion and storage.

This course provides a systematic introduction to the energy system for students in engineering and applied sciences. Students should gain a working understanding of the some of the most important energy technologies, from prime movers--gas turbines, steam cycles, and reciprocating engines--to secondary energies including fuel production and refining technologies and the electricity transmission and distribution system. The course aims at a systematic understanding of the energy system's environmental footprint as a tool to help students who will work to reduce it. Energy is a commodity. One cannot hope to re-shape the energy system to meet environmental constrains without a rough working understanding of energy markets--costs, prices and elasticities of supply and demand. So the course will integrate engineering economics and other applied social sciences into the treatment of energy technologies to enable a system's view of energy.

Explores factors and conditions contributing to innovation in science and engineering; how important problems are found, defined, and solved; roles of teamwork and creativity; and applications of these methods to other endeavors. Students receive practical and professional training in techniques to define and solve problems, and in brainstorming and other individual and team approaches.

This course introduces students to probability theory and statistics, and their applications to physical, biological and information systems. Topics include: random variables, distributions and densities, conditional expectations, Bayes' rules, laws of large numbers, central limit theorems, Markov chains, Bayesian statistical inferences and parameter estimations. The goal of this course is to prepare students with adequate knowledge of probability theory and statistical methods, which will be useful in the study of several advanced undergraduate/graduate courses and in formulating and solving practical engineering problems.

Electromagnetism and its applications in science and technology. Topics: Maxwell's equations; electromagnetic waves (e.g., light, microwaves, etc.); wave propagation through media discontinuity; transmission lines, waveguides, and microwave circuits; radiation and antennae; interactions between electromagnetic fields and matters; optics of solids; optical devices; origin of colors; interference and diffraction; lasers and masers; nuclear magnetic resonance and MRI; radio astronomy; wireless networking; plasmonic wave (charge density wave).

A lab-intensive introduction to electronic circuit design. Develops circuit intuition and debugging skills through daily hands-on lab exercises, each preceded by class discussion, with minimal use of mathematics and physics. Moves quickly from passive circuits, to discrete transistors, then concentrates on operational amplifiers, used to make a variety of circuits including integrators, oscillators, regulators, and filters. The digital half of the course treats analog-digital interfacing, emphasizes the use of microcontrollers and programmable logic devices (PLDs).

Design of electronic circuits (including integrated circuits) using semiconductor transistors. Topics: the physics of electrical conduction; the physics of semiconductors; bipolar transistors; field effect transistors; single- and multi-stage amplifiers; operational amplifiers; frequency responses and stability; feedback circuits; the physics of noise; self-sustained oscillators; phase-locked loops.

General properties of common biosignals, Bioelectrical (electrophysiological), Biomechanical, Biomagnetic , and Biochemical signals, Bioelectrical acquisition process. Brief discussion of bio-signals obtained from tomography and inverse imaging. Brief introduction to underlying principles of MRI, Ultrasound, CT-Scan, PET, and SPECT, and their associated signals, inverse imaging, ill-posed problems and regularization. Non-transformed and transformed methods for biosignal processing. Structural and Graphical descriptions. Overview of Fourier transforms, Sine and cosine transform, Wavelet transform, Principle Component Analysis, dimension reduction techniques. Blind Source Separation, Representation models based on the statistical independence of the underlying sources, Independent component analysis (ICA), Dependent component analysis, Independent Subspace separation, Pattern Recognition, neural networks, clustering, and genetics algorithms. Applications to Biosignal Processing, and Human computer interaction.

Time and frequency domain representations and analysis of signals and systems. Convolution and linear input-output systems in continuous and discrete time. Fourier transforms and Fourier series for continuous- and discrete-time signals. Laplace and Z transforms. Analog and digital filtering. Modulation. Sampling. FFT. Applications in circuit analysis, communication, control, and computing.

This course provides an introduction to feedback and control in physical, biological, engineering, information, financial, and social sciences. The focus is on the basic principles of feedback and its use as a tool for inferring and/or altering the dynamics of systems under uncertainty. Key themes throughout the course will include linear system analysis, state/output feedback, frequency response, reference tracking, PID controller, dynamic programming, and limit of performance. This includes both the practical and theoretical aspects of the topic.

Introduction to computer-controlled robotic manipulators. Topics include coordinate frames and transformations, kinematic structure and solutions, statics and dynamics of serial and parallel chain manipulators, control and programming, introduction to path planning, introduction to teleoperation, robot design, and actuation and sensing devices. Laboratory exercises provide experience with industrial robot programming and robot simulation and control.

This course will examine the theory and practical application of environmental chemistry and toxicology for assessing the behavior, toxicity and human health risks of chemical contaminants in the environment. The goals of the course are to: (a) illustrate how various sub-disciplines in environmental toxicology are integrated to understand the behavior of pollutants; (b) demonstrate how scientific information is applied to inform environmental management decisions and public policy through several case studies; and (c) provide an introduction to the legislative framework in which environmental toxicology is conducted. This course will be directed toward undergraduate students with a basic understanding of chemistry and calculus and an interest in applied science and engineering to address environmental management problems.

Study of water as a critical resource and as a factor in Earth surface and near-surface processes. Focus on development of relevant mechanics and physics. Hydrologic cycle, surface and groundwater, evapotranspiration, soil physics. Flow in porous media, Darcy law, contaminant transport, remediation strategies. Poroelasticity, subsidence, well hydraulics. Seepage forces, landslides, dam failures, sediment liquefaction. Glacial processes. Stream flows, turbulence concepts. Gravity waves, flood control; tsunamis; erosion and sediment transport.

This course is focused on aspects of environmental engineering related to the fate, transport, and control of pollution in aquatic ecosystems. The course will cover human impacts to water resources; the sources and ecological impacts of environmental contaminants; quantitative models of the fate and transport of pollutants in natural aquatic ecosystems; best management practices for the prevention and control of pollution; and sustainable natural treatment systems for water quality improvement.

Basic concepts, principles, and applications of environmental chemistry for students in Earth and environmental sciences. We will investigate a variety of chemistry topics relevant for environmental systems, including water chemistry, acids and bases, redox reactions, precipitation/dissolution, sorption, gas solubility, and aqueous and atmospheric reaction rates and mechanisms. The principal goal is to explore and apply the fundamental principles of chemical thermodynamics and kinetics to understand Earth processes and solve complex environmental problems.

Introduces the fundamentals of water biology, chemistry, physics and transport processes needed to understand water quality and water purification technologies. Practical instruction in basic water analyses concluding with a final water treatment project in place of exam.

This course provides a cross-disciplinary overview of environmental science and how research contributes to public policy and human health risk assessment through a case study of a global pollution issue: lead biogeochemistry. The scientific foundations of environmental research methods are discussed (i.e., analytical chemistry, ecology, use of environmental archives, environmental modeling). Experience conducting multidisciplinary environmental research and data analysis will be provided. Course Activities: Lectures, discussions, case studies, field/lab visits.

Quantum mechanics provides not only an essential tool for engineers, material scientists and biologists to study and control objects in nano and atomic scales but also radical ways to do information processing, sensing, and fabrication. Advances in nanotechnology and quantum information processing raise growing interests in engineering students to learn applied quantum mechanics that is also essential for understanding modern devices and systems in electronics photonics. Topics of this course will cover topics ranging from solution of Schrodinger equation in quantum confined nano-structures and most recent topics such as quantum circuits and entanglement. Examples, problems and numerical simulation are designed to address the applications of the course contents to real problems in semiconductor electronics, optoelectronics, photonics, quantum information processing and superconducting electronics.

This course will focus on physical principles underlying semiconductor devices: electrons and holes in semiconductors , energies and bandgaps, transport properties of electrons and holes, p-n junctions, transistors, light emitting diodes, lasers, solar cells and thermoelectric devices.

The course will provide an overview of the solid state device physics and p-n junction operation necessary to understand the operation. Several different solar architectures will be discussed including crystalline and amorphous silicon, multijunction, CdTe, CIGS, organic, dye sensitized as well as additional related topics light management, building integrated devices, and policy and economic issues relating to adoption.

This course introduces student to the rapidly emerging, multi-disciplinary and exciting field of MicroElectroMechanical Systems (MEMS). It teaches fundamentals of micro machining and Micro fabrication techniques, including planar thin-film process technologies, photolithography and soft-lithography techniques, deposition and etching techniques, and surface, bulk, and electroplating micro machining technologies.

Introduction to micro- and nanofabrication processes used for photonic and electronic devices. Students use state-of-the-art cleanroom in Center for Nanoscale Systems to fabricate transistors and light-emitting diodes (LEDs). Lectures on fabrication processes, including lithography, deposition, etching, oxidation, implantation, diffusion and electrical characterization.

Introduction to classical engineering thermodynamics. Topics: Zeroth Law and temperature. Properties of single-component gases, liquids, and solids. Equations of state for ideal and simple nonideal substances. First Law, heat and heat transfer, work, internal energy, enthalpy. Second Law, Third Law, entropy, free energy, exergy. Heat engines and important engineering applications such as refrigerators, power cycles. Properties and simple models of solutions. Phase and chemical equilibrium in multicomponent systems; chemical potential. Electrochemistry, batteries, fuel cells. Laboratory included.

The macroscopic description of the fundamentals of heat transfer and applications to practical problems in energy conversion, electronics and living systems with an emphasis on developing a physical and analytical understanding of conductive, convective and radiative heat transfer. Emphasis will also be given to problem solving skills based on applying governing principles, mathematical models and physical intuition. Includes laboratory sessions and semester-long projects.

Introduction to the structure, properties, and applications of materials. Crystal structure and defects. Phase transformations: phase diagrams, diffusion, nucleation and growth. Mechanisms of deformation and fracture. Effect of microstructure on properties. Examples from a variety of engineering applications.

Introductory statistical methods for students in the applied sciences and engineering with a focus on social networks. Random variables and probability distributions; the concept of random sampling, including random samples, statistics, and sampling distributions; role of statistics in social network analysis; mathematical interpretation of social networks; connections and homophily, propinquity, mutuality/reciprocity, multiplexity, network closure; distributions and bridges, distance, centrality, density; segmentation and cliques, cohesion, clustering; graph theory and adjacency matrices; Erdos-Renyi model; Watts-Strogatz Small World model; Barabasi -Albert (BA) Preferential Attachment model; special topics in social network analysis.

Mathematical analysis of decision making. Bayesian inference and risk. Maximum likelihood and nonparametric methods. Algorithmic methods for decision rules: perceptrons, neural nets, and back propagation. Hidden Markov models, Blum-Welch, principal and independent components.

This graduate level course studies dynamic systems in time domain with inputs and outputs. Students will learn how to design estimator and controller for a system to ensure desirable properties (e.g., stability, performance, robustness) of the dynamical system. In particular, the course will focus on systems that can be modeled by linear ordinary differential equations (ODEs) and that satisfy time-invariance conditions. The course will introduces the fundamental mathematics of linear spaces, linear operator theory, and then proceeds with the analysis of the response of linear time-variant systems. Advanced topics such as robust control, model predictive control, linear quadratic games and distributed control will be presented based on allowable time and interest from the class. The material learned in this course will form a valuable foundation for further work in systems, control, estimation, identification, detection, signal processing, and communications.

Introduction to the theory of stochastic differential equations based on Wiener processes and Poisson counters, and an introduction to random fields. The formulation and solution of problems in nonlinear estimation theory. The Kalman-Bucy filter and nonlinear analogues. Identification theory. Adaptive systems. Applications.

Climate change, health insurance reform, space exploration, the teaching of science and a host of other issues - today Americans confront more and more important public debates in which the argument hangs on technical issues. On the whole, however, they have difficulty dealing with these issues, in large part because the scientists and engineers who could help them are missing from the debate. This course is designed for graduate students in engineering and the biological and physical sciences who are interested in learning how to engage with the public on these and other issues. It also offers useful guidance on how to explain their own work - writing, speaking and online - intelligently and intelligibly.

Study of nonlinear input-output systems including controllability, observability, uniqueness of models, stability, and qualitative behavior of nonlinear dynamical systems. Differential geometry and Lie theory methods developed to study control of classical and quantum mechanical systems.

A sophisticated perspective on the design, construction, and testing of model physiological systems recapitulated with tissue engineering and lab on a chip technologies. Topics include organ and multiorgan physiology and pathophysiology; in vitro disease models; and design tools and fabrication techniques for lab on a chip technologies.

Cell biology - from foundations to current research topics. Intended for students without cell/molecular biology training. Cell architecture, molecular and phenomenological aspects, signaling, organelle form/function, trafficking, quantitative experimental techniques, models of cellular organization and dynamics.

Continuum mechanics; conservation of mass and momentum, energy; stress, kinematics, and constitutive equations; vector and tensor calculus. Dimensional analysis and scaling. Navier-Stokes equations, Reynolds number. Solutions for simple flow states. Low Reynolds number flows; porous media flows; lubrication theory; gravity currents. Inviscid flows, Kelvin circulation theorem, Bernoulli integrals, Vortical flows. Waves in fluids; acoustics, shocks, water waves. Airfoil theory. Boundary layers. Flow instabilities. Mixing, and turbulence in unbounded and bounded flows.

Methods to deliver molecules to the human body. Physiological obstacles and engineering solutions. Characterization techniques for drug delivery synthesis and in vitro analysis. Case studies of current pharmaceutical products.

This is a combined introductory graduate/upper-level undergraduate course that focuses on examining modern techniques for manipulating cellular behavior and the application of these techniques to problems in the biomedical and biotechnological arenas. Topics will include expanding the genetic code, genetic circuits, rewiring signaling pathways, controlling behavior through cell-matrix interactions, and directed differentiation of stem cells. Lectures will review fundamental concepts in cell biology before delving into topical examples from current literature. Students will work individually and in teams to determine the boundaries of existing cellular engineering techniques using scientific literature and propose original research to address unmet technological needs.

Course will present classical findings and new research that give insight into mechanisms of learning and memory formation in neural systems. Learning and memory will be studied both as neurobiological phenomena and as computational challenges.

Project-based course on the design of medical devices to address needs identified by hospital-based clinicians. Students work in teams with physicians to develop a novel device. The design process includes: needs finding; problem identification; prior art searches; strategy and concept generation; estimation; sketching; sketch modeling; machine elements, ergonomics and prototyping.

Overview of materials for biomedical devices and therapies. Polysaccharide- and protein-based polymers as building blocks. Biological templating of inorganic structures. Emerging frontiers in protein and DNA self-assembly. Molecular scale origin of materials properties for naturally occurring biological materials and the use of this information to rationally design new biomaterials for specific applications.

Principles governing energy generation and interconversion. Current and projected world energy use. Selected important current and anticipated future technologies for energy generation, interconversion, storage, and end usage.

Fundamental engineering and biological principles underlying field of tissue engineering, along with examples and strategies to engineer specific tissues for clinical use. Student design teams prepare a research proposal and participate in a weekly laboratory.

Principles governing energy generation and interconversion. Current and projected world energy use. Selected important current and anticipated future technologies for energy generation, interconversion, storage, and end usage.

This course helps students to create successful entrepreneurial health care ventures by enabling them to: 1) Identify the alignment between an entrepreneurial health care venture and the six forces that shape health care - structure, financing, technology, consumers, accountability, and public policy; and 2) Create a product and business model that responds appropriately to any misalignments. The course covers four modules: The analytic framework, case studies of the six forces, case studies of firms responses to the forces, and student presentation of business plans.

This is a field study course in which students undertake significant external research in the in-depth development of a business plan for a health-care and technology business venture. Students will learn to develop such a business plan, evaluate and select appropriate technologies, define a new technology based product in the health-care space, and develop appropriate prototypes for presentation to customers and investors.

Atmospheric radiative transfer, including stellar properties, spectroscopy, gray and real gas calculations, Mie theory and scattering, satellite retrievals, and radiative-convective climate modelling. Climate feedbacks: the runaway greenhouse, volatile cycles on Mars and Titan, and atmospheric collapse around M-stars. Atmospheric evolution and escape (Jeans, diffusion-limited, hydrodynamic), and key processes in planetary atmospheric chemistry.

This course is designed for those who want to understand the role of start-ups and venture capital in the creation of new products and services in the for-profit and not-for-profit sectors. It is intended to help students identify areas in which changes in science and technology, consumer and social attitudes, or political and regulatory processes support the creation of new businesses and organizations in independent ventures or within established organizations. Margo Seltzer and Bill Anderson are supporting the course by supervising course projects in their respective areas of expertise.

Students are expected to meet all the requirements of Engineering Sciences 139 and in addition are required to prepare an individual term project with significant analytic emphasis in an area of scientific or technological innovation.

Foundations of continuum mechanics, development of elasticity theory, and introduction to plasticity and creep. Elastic waves. Basic elasticity solutions. Variational principles.

Finite deformation; instabilities; thermodynamics; thermoelasticity; poroelasticity; electroactive polymers, hydrogels, polyelectrolyte gels

Finite elements for analysis and design. The key goal of this class is the application of the finite element method to classical and state-of-the-art modeling and design problems. We introduce a commercial finite element program - ABAQUS - and demonstrate how to use it in modeling and analysing design problems. Topics include the implementations of user-defined subroutines (UMAT and VUMAT), instability analyses, analysis of waves propagation, fluid-structure interactions.

Phenomenological theories for strain hardening materials; flow and deformation theories. Variational principles and other general theorems. Mechanisms of plastic deformation, physical theories for strain hardening materials, and polycrystals. Ideal plasticity. Boundary value problems, plastic collapse, buckling of structures.

Fundamentals of fracture with applications in materials and structural mechanics. Micromechanics of fracture in ceramics, metals, and polymers. Fracture of composite materials. Interfacial fracture mechanics. Fatigue crack propagation.

Students expected to meet all of the requirements of Biomedical Engineering 130 (formerly Engineering Sciences 149) and in addition to submit a term project with significant analytic content.

Fundamental concepts of information theory, Entropy, Kullback-Leibler divergence, Mutual information; typical sequences and their applications, Loss-less data compression, Huffman codes, Elias Codes, Arithmetic Codes, Discrete Memory-less Channels, Channel Coding and Capacity, Differential Entropy, Gaussian Channels, rate distortion theory, Multi-user Information Theory, Connections between information theory and statistics.

A graduate seminar course on advanced topics in robotics research. Students read and present research papers and undertake a research project. Spring 2013 will focus on robot design and manipulation.

This course will introduce bioelectricity and bioelectromagnetics starting with Maxwell Equations, which will quickly be simplified to the quasi-static form typically applicable in physiology. We will introduce the basics of membrane electrical biophysics, which we will use to study action potentials and action potential propagation. Applications will include electro-cardiograms (ECGs), electro-myograms (EMGs), electro-oculograms (EOGs), and electro-encephalograms (EEGs). EEG investigations will include analyses of spatial resolution as well as dynamic properties. A course project will allow students to choose an area of specific interest for more in-depth investigation and analysis.

Statistical decision theory; hypothesis testing; linear and non-linear estimation; maximum likelihood and Bayes approaches; graphical models and message passing algorithms; large deviation analysis and asymptotic methods in statistics; stochastic processes and systems; Wiener and Kalman filtering; Markov chain Monte-Carlo methods; applications to physical, chemical, biological and information systems.

Today, robotic devices are being made from folded paper, carbon laminates or soft gels. Rather than assembled, they can be formed directly from 2D or 3D printer. These Informal robots are light, flexible, compliant, highly customized, and demonstrate programmable behavior that is closely coupled with material composition. Taught in collaboration with the Wyss Institute, the course will focus on techniques to create original robotic devices. Lectures will be organized along four primary topics: Kinematics, Fabrication, Controls and Applications. There will be assignments to produce test mechanisms and CAD models, followed by semester-long group projects.

Course requirements are similar to Engineering Sciences 159, with the exception that students enrolled in Engineering Sciences 259 are required to prepare a term project analyzing current research in a specific problem area within Robotics.

The course focuses on the engineering, infrastructure and institutions needed to manage water on a basin and city scale. The course does this using the case study method. All cases are presented by practitioners who have had deep engagement with the cases.

Advanced Water Treatment will give students detailed instruction in emerging technologies for municipal wastewater treatment, industrial wastewater treatment, wastewater reclamation and reuse, desalination, and groundwater remediation. The course will begin by introducing wastewater quality, effluent water quality endpoints, and conventional treatment methodologies. The theoretical focus of the course will be on the fundamental biology, chemistry, and physics of processes including nanofiltration, reverse osmosis, membrane bioreactors, denitrification and phosphate removal, ozonolysis, UV photolysis, photocatalysis, and sonolysis. We will also discuss wastewater-to-energy processes including microbial fuel cells, anaerobic digestion, and electrochemical waste-to-hydrogen.

Physics and chemistry of aerosol particles. Concepts: size, shape, and density; number size distributions; uniform, accelerated, and Brownian motion; electrical properties; measurement instrumentation; condensation/evaporation; coagulation; and optical properties. Taught by reference to topical problems.

Time rate of change of chemical species. Rate constants. Formulating a coupled chemical system. Numerical analysis of complex systems.

Introduces students to the environmental aspects of nanoscience and nanotechnology. We will study the fundamental physical chemical properties, characterization, environmental implications, and environmental applications of nanoparticles and nanomaterials. Case studies from recent publications on engineered carbon nanomaterials such as fullerenes, carbon nanotubes, and graphene will be discussed.

A seminar course that reviews research and development of various topics in integrated circuits and systems for low-power and/or high-performance computing.

Design of RF and high speed integrated communication circuits at both transistor and system levels.

The focus is on the foundations of optics/photonics and on some of its most important modern developments and applications. Powerful and widely used computational tools will be developed in the sections. Topics to be covered: Maxwell's equations, Free space optics. Reflection, refraction, polarization (Jones Calculus and Stokes parameters); interference and diffraction. Light-matter interaction, dispersion and absorption. Guided wave optics (including optical fibers). Perturbation and couple mode theory, transfer matrix methods; numerical methods. Optical resonators. Lasers and elements of nonlinear optics. Photonic crystals. Near-field optics. Metal optics and Plasmonics. Metamaterials.

Electronic structure of crystals. Semiconductor heterostructures: bandstructure engineering. Low-dimensional solids: quantum wells, wires and dots; superlattices; 2D electron gas; carbon nanotubes, nanowires, graphene. Tunneling and resonant tunneling, superlattice transport. Quantum point contacts. Interband and intersubband optical transistions. Quantum confined Stark effect. Device concepts (diodes, transistors, lasers). Quantum well lasers, modulators and detectors. Resonant tunneling devices. Quantum cascade lasers.

Recent developments in micro- and nano-photonic materials, devices and microscopy. Computational electromagnetics. Photonic crystals. Optical properties of metal nanostructures. Optical forces. Scanning near-field optical microscopy. Term-long research project.

This course introduces student to the rapidly emerging, multi-disciplinary and exciting field of MicroElectroMechanical Systems (MEMS). It teaches fundamentals of micro machining and Micro fabrication techniques, including planar thin-film process technologies, photolithography and soft-lithography techniques, deposition and etching techniques, and surface, bulk, and electroplating micro machining technologies.

Content and requirements are similar to ENG-SCI 177, with the exception that students enrolled in ENG-SCI 277 are assigned more demanding problem sets and are required to prepare a term project.

Unpacking the dynamics of getting transformational policy reforms over the line. An examination of case studies in the design and implementation of successful reforms in water, fishery, natural resource and environmental management.

This course will ask if the US defense industrial complex facilitates victory by technological innovation. We define defense technologies in three categories: 1) Convenience Technologies, 2) Sustainability Technologies, and 3) Decisive Technologies. If we define Decisive Technologies as those technologies that are strategically managed and tactically deployed, the last century may have seen only two: radar and the atomic bomb. We will debate money as a weapon system and whether or not it is a valid weapon technology. We will examine technologies introduced during the Global War on Terror (GWOT) and ask which category they fall in, if they were a true technological innovation, and if they facilitated victory in a battle vs war. We will discuss barriers to innovation and technological surprise on the battlefield.

This course explores research methods and techniques through the analyses of emerging energy-efficient materials and systems and their applications in buildings. It serves as an interdisciplinary platform for engineers, materials and computer scientists to interact with the design students and develop new products. The course introduces ideas-to-innovation processes in a hands-on, project/product focused manner that balance engineering concepts with promising, real-world opportunities. Switching back and forth between guided discovery and focused development, between bottom-up and top-down thinking, and market analyses, the course helps students establish generalizable frameworks as researchers and innovators with a focus on new and emerging technologies.

This course is designed for engineers who are interested to learn applied quantum mechanics to study quantum behavior of electron, photon and their interaction. The course content is a mix of topics usually covered in more conventional courses such as quantum electronics and quantum optics to invite a wide range of audiences who are working on areas such as optoelectronics, quantum photonics, nanoelectronics, nanophotonics, spintronics, and in general quantum devices and systems. The course emphasizes on the fundamental concepts and engineering applications without a need for previous exposure to quantum mechanics. Examples and problems are designed to address the applications of the course contents to real problems.

Supervision of experimental or theoretical research on acceptable engineering and applied science problems and supervision of reading on topics not covered by regular courses of instruction.