ES 201/AM 231 is a course in statistical inference and estimation from a signal processing perspective. The course will emphasize the entire pipeline from writing a model, estimating its parameters and performing inference utilizing real data. The first part of the course will focus on linear and nonlinear probabilistic generative/regression models (e.g. linear, logistic, Poisson regression), and algorithms for optimization (ML/MAP estimation) and Bayesian inference in these models. We will play particular attention to sparsity-induced regression models, because of their relation to artificial neural networks, the topic of the second part of the course. The second part of the course will introduce students to the nascent and exciting research area of model-based deep learning. At present, we lack a principled way to design artificial neural networks, the workhorses of modern AI systems. Moreover, modern AI systems lack the ability to explain how they reach their decisions. In other words, we cannot yet call AI explainable or interpretable which, as a society, poses important questions as to the responsible use of such technology. Model-based deep learning provides a framework to develop and constrain neural-network architectures in a principled fashion. We will see, for instance, how neural-networks with ReLU nonlinearites arise from sparse probabilistic generative models introduced in the first part of the course. This will form the basis for a rigorous recipe we will teach you to build interpretable deep neural networks, from the ground up. We will invite an exciting line up of speakers. Time permitting, we will provide a model-based pespective of the building blocks of modern language and image generative models.

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.

This course introduces students to fundamental results and recently developed techniques in high-dimensional probability theory and statistical physics that have been successfully applied to the analysis of information processing and machine learning problems. Discussions will be focused on studying such problems in the high-dimensional limit, on analyzing the emergence of phase transitions, and on understanding the scaling limits of efficient algorithms. This course seeks to start from basics, assuming just a solid understanding of undergraduate probability theory. Students will take an active role by exploring and applying what they learn from the course to their own research problems.

This project-based course will introduce the physical concepts that can be applied to various human athletic endeavors. Students will focus on analyzing the dynamics of a specific sport/ physical activity through a project that they develop. This will allow students to construct physical models with an increasing level of realism that can used to analyze sporting events. Mathematics is the language of physics, and its use will be ever-present throughout the semester. However, we will focus more on the application of the laws of physics to understand the world of athletics. Students will learn the use of motion trackers and sensors to analyze motion in its dynamical and kinematic aspects.

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.

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.

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.

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.

Guided reading and research.

Guided reading and research.

Guided reading and research.

Entrepreneurship is increasingly transforming our society and economy. This course aims to provide for undergraduates an introduction to entrepreneurship and its implications for innovation. The class will primarily consist of case study discussions, but will include some traditional lecture sessions that build on academic papers to provide more frameworks. As such, it draws primarily on materials from the introductory MBA course at Harvard Business School, “The Entrepreneurial Manager” (TEM). Students will be expected to come to class prepared to discuss the cases.

Entrepreneurship is increasingly transforming our society and economy. This course aims to provide for undergraduates an introduction to entrepreneurship and its implications for innovation. The class will primarily consist of case study discussions, but will include some traditional lecture sessions that build on academic papers to provide more frameworks. As such, it draws primarily on materials from the introductory MBA course at Harvard Business School, “The Entrepreneurial Manager” (TEM). Students will be expected to come to class prepared to discuss the cases.

Entrepreneurship is increasingly transforming our society and economy. This course aims to provide for undergraduates an introduction to entrepreneurship and its implications for innovation. The class will primarily consist of case study discussions, but will include some traditional lecture sessions that build on academic papers to provide more frameworks. As such, it draws primarily on materials from the introductory MBA course at Harvard Business School, “The Entrepreneurial Manager” (TEM). Students will be expected to come to class prepared to discuss the cases.

Entrepreneurship is increasingly transforming our society and economy. This course aims to provide for undergraduates an introduction to entrepreneurship and its implications for innovation. The class will primarily consist of case study discussions, but will include some traditional lecture sessions that build on academic papers to provide more frameworks. As such, it draws primarily on materials from the introductory MBA course at Harvard Business School, “The Entrepreneurial Manager” (TEM). Students will be expected to come to class prepared to discuss the cases.

Students do field-based work in entrepreneurship to develop their existing startup and explore new ideas and opportunities for startup creation. The course is for student-founders seeking to advance their innovation experience in a supportive community of peer founders. Students may work individually; teams with a working history 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 and teaching staff.  Students share their work regularly and engage in a peer-to-peer feedback forum. Coursework is customized to the needs of each student and their startup role and includes development of product, technology, market, business, organization and leadership. See: https://tech.seas.harvard.edu/rad to apply for instructor permission to enroll.

Students do field-based work in entrepreneurship to develop their existing startup and explore new ideas and opportunities for startup creation. The course is for student-founders seeking to advance their innovation experience in a supportive community of peer founders. Students may work individually; teams with a working history 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 and teaching staff.  Students share their work regularly and engage in a peer-to-peer feedback forum. Coursework is customized to the needs of each student and their startup role and includes development of product, technology, market, business, organization and leadership. See: https://tech.seas.harvard.edu/rad to apply for instructor permission to enroll.

Students do field-based work in entrepreneurship to develop their existing startup and explore new ideas and opportunities for startup creation. The course is for student-founders seeking to advance their innovation experience in a supportive community of peer founders. Students may work individually; teams with a working history 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 and teaching staff.  Students share their work regularly and engage in a peer-to-peer feedback forum. Coursework is customized to the needs of each student and their startup role and includes development of product, technology, market, business, organization and leadership. See: https://tech.seas.harvard.edu/rad to apply for instructor permission to enroll.

Semester-long team-based project providing experience working with clients on complex multi-stakeholders real problems. Course provides exposure to problem definition, problem framing, qualitative and quantitative research methods, modeling, generation and co-design of creative solutions, engineering design trade-offs, and documentation/communication skills. Ordinarily taken in the junior year.

Semester-long team-based project providing experience working with clients on complex multi-stakeholders real problems. Course provides exposure to problem definition, problem framing, qualitative and quantitative research methods, modeling, generation and co-design of creative solutions, engineering design trade-offs, and documentation/communication skills. Ordinarily taken in the junior year.

Semester-long team-based project providing experience working with clients on complex multi-stakeholders real problems. Course provides exposure to problem definition, problem framing, qualitative and quantitative research methods, modeling, generation and co-design of creative solutions, engineering design trade-offs, and documentation/communication skills. Ordinarily taken in the junior year.

Semester-long team-based project providing experience working with clients on complex multi-stakeholders real problems. Course provides exposure to problem definition, problem framing, qualitative and quantitative research methods, modeling, generation and co-design 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. Each student will pursue an appropriate capstone project which involves both engineering design and quantitative analysis. This culminates in a final oral presentation and final report/thesis. Students must complete both parts of this course, fall and spring, in order to receive credit.

Individual engineering design projects which demonstrate mastery of engineering knowledge and techniques. Each student will pursue an appropriate capstone project which involves both engineering design and quantitative analysis. This culminates in a final oral presentation and final report/thesis. Students must complete both parts of this course, fall and spring, in order to receive credit.

Individual engineering design projects which demonstrate mastery of engineering knowledge and techniques. Each student will pursue an appropriate capstone project which involves both engineering design and quantitative analysis. This culminates in a final oral presentation and final report/thesis. Students must complete both parts of this course, fall and spring, in order to receive credit.

Multi-year long team projects that provide an engineering experience working with partner communities on real-world problems. Projects provide exposure to problem definition, quantitative analysis, modeling, generation of creative solutions utilizing appropriate technology, engineering design trade-offs, and documentation/communication skills. These projects will be implemented with our project partners after the appropriate design and approvals have been obtained.

Multi-year long team projects that provide an engineering experience working with partner communities on real-world problems. Projects provide exposure to problem definition, quantitative analysis, modeling, generation of creative solutions utilizing appropriate technology, engineering design trade-offs, and documentation/communication skills. These projects will be implemented with our project partners after the appropriate design and approvals have been obtained.

Multi-year long team projects that provide an engineering experience working with partner communities on real-world problems. Projects provide exposure to problem definition, quantitative analysis, modeling, generation of creative solutions utilizing appropriate technology, engineering design trade-offs, and documentation/communication skills. These projects will be implemented with our project partners after the appropriate design and approvals have been obtained.

This class integrates perspectives from leading innovators with collaborative practice and theory of innovation to teach and inspire you to be more innovative in your life and career. Our approach is to engage with leaders and learn their perspectives and align this with innovation sprints where you learn the best tools, processes, and methods to innovate. You can see a course overview here https://youtu.be/CqfvXf33TCE.  Find out more information on Instagram @engsci139 or https://www.instagram.com/engsci139/

An introduction to the mathematical, optical, and computational foundations of computer vision, with a focus on applications in augmented reality and robotic perception. Topics include: camera optics, digital color photography pipelines, multi-camera geometry, image processing and manipulation, simultaneous localization and mapping, lighting and material estimation, and 3D scanning. Emphasis on combining mathematical modeling with robust algorithms for solving ill-posed problems.

This course introduces the fundamentals of probability theory for parameter estimation and decision making under uncertainty. It considers applications to information systems as well as other physical and biological systems. Topics include: discrete and continuous random variables, conditional expectations, Bayes’ rules, laws of large numbers, central limit theorems, Markov chains, Bayesian statistical inferences, and parameter estimations.

This course introduces the fundamentals of probability theory for parameter estimation and decision making under uncertainty. It considers applications to information systems as well as other physical and biological systems. Topics include: discrete and continuous random variables, conditional expectations, Bayes’ rules, laws of large numbers, central limit theorems, Markov chains, Bayesian statistical inferences, and parameter estimations.

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).

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).

This course introduces the fundamentals of circuit theory for the analysis of electrical circuits and the fundamentals of semiconductor devices for the understanding of transistors circuits and other useful actuators and sensors (i.e., transducers). Building on the principles from these two core fundamental areas of electrical engineering, the analog behavior of electronic circuits and physical devices will be modelled, analyzed, and applied. Lab assignments will focus on the design, implementation, and measurement of analog electronic circuits using real electrical components which interface to the physical world. This course complements and forms the basis for many of the abstractions that are used in digital computing systems such as in COMPSCI 141, COMPSCI 146, and COMPSCI 148.

This course and its follow-on course ENG-SCI 156 concern the fundamentals of information systems in the real world. Together they provide a comprehensive foundation in signal processing, systems design and analysis, control, and communications, while also introducing key linear-algebraic concepts in the context of authentic applications. The first course, ENG-SCI 155, focuses on the basic principles of feedback and its use as a tool for inferring and/or altering the dynamics of systems under uncertainty. Topics include linear algebra, the elemental representations of dynamic systems, stability analysis, the design of estimators (e.g., Kalman Filter) and feedback controllers (e.g., PID and Optimal Controller). The class includes both the practical and theoretical aspects of the topic.

This course is a follow-on to ENG-SCI 155 and continues to develop the fundamentals of information systems in the real world. It focuses on the analysis and manipulation of signals in the time and frequency domains in the context of authentic applications. Topics include: the sampling theorem, convolution, and linear input-output systems in continuous and discrete time. Further, students are introduced to transforms—including Fourier, discrete cosine, wavelet, and PCA / SVD ‘transforms’—that map between vector spaces via matrix multiplication as a method to ease analysis provided conditionalized knowledge. Randomness, noise, and filtering. Waves and interference in the context of communications; antennae, phasors, modulation, multiplexing. Applications in communications and data science.

This course is a follow-on to ENG-SCI 155 and continues to develop the fundamentals of information systems in the real world. It focuses on the analysis and manipulation of signals in the time and frequency domains in the context of authentic applications. Topics include: the sampling theorem, convolution, and linear input-output systems in continuous and discrete time. Further, students are introduced to transforms—including Fourier, discrete cosine, wavelet, and PCA / SVD ‘transforms’—that map between vector spaces via matrix multiplication as a method to ease analysis provided conditionalized knowledge. Randomness, noise, and filtering. Waves and interference in the context of communications; antennae, phasors, modulation, multiplexing. Applications in communications and data science.

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 provides introduction to micro- and nano-fabrication processes used to realize photonic, electronic and mechanical devices. Lectures will introduce the state-of-the-art semiconductor fabrication processes, including lithography, deposition of metals and dielectrics, etching, oxidation, implantation, and diffusion of dopants. The fabrication component of the course will be carried out in a state-of-the-art cleanroom in the Center for Nanoscale Systems, where students will fabricate several electronic and photonic devices, including transistors, light-emitting diodes (LEDs), lasers and optical resonators.  Device characterization will be performed in a state-of-the-art teaching labs in SEC in Allston.  

The course provides introduction to micro- and nano-fabrication processes used to realize photonic, electronic and mechanical devices. Lectures will introduce the state-of-the-art semiconductor fabrication processes, including lithography, deposition of metals and dielectrics, etching, oxidation, implantation, and diffusion of dopants. The fabrication component of the course will be carried out in a state-of-the-art cleanroom in the Center for Nanoscale Systems, where students will fabricate several electronic and photonic devices, including transistors, light-emitting diodes (LEDs), lasers and optical resonators.  Device characterization will be performed in a state-of-the-art teaching labs in SEC in Allston.  

This course is an introduction to the foundations of quantum mechanics, with specific focus on the basic principles involved in the control of quantum systems. Experimental foundations of quantum mechanics. Superposition principle, Schrödinger’s equation, eigenvalue and time dependent problems, wave packets, coherent states; uncertainty principle. One dimensional problems: double well potentials, tunneling and resonant tunneling; WKB approximation. Hermitian operators and expectation values; time evolution and Hamiltonian, commutation rules, transfer matrix methods. Crystals, Bloch theorem, superlattices. Angular momentum, spin, Pauli matrices. Coherent interaction of light with two-level systems. Quantization of the EM field, spontaneous and stimulated emission; qubits, entanglement, teleportation.

ES 201/AM 231 is a course in statistical inference and estimation from a signal processing perspective. The course will emphasize the entire pipeline from writing a model, estimating its parameters and performing inference utilizing real data. The first part of the course will focus on linear and nonlinear probabilistic generative/regression models (e.g. linear, logistic, Poisson regression), and algorithms for optimization (ML/MAP estimation) and Bayesian inference in these models. We will play particular attention to sparsity-induced regression models, because of their relation to artificial neural networks, the topic of the second part of the course. The second part of the course will introduce students to the nascent and exciting research area of model-based deep learning. At present, we lack a principled way to design artificial neural networks, the workhorses of modern AI systems. Moreover, modern AI systems lack the ability to explain how they reach their decisions. In other words, we cannot yet call AI explainable or interpretable which, as a society, poses important questions as to the responsible use of such technology. Model-based deep learning provides a framework to develop and constrain neural-network architectures in a principled fashion. We will see, for instance, how neural-networks with ReLU nonlinearites arise from sparse probabilistic generative models introduced in the first part of the course. This will form the basis for a rigorous recipe we will teach you to build interpretable deep neural networks, from the ground up. We will invite an exciting line up of speakers. Time permitting, we will provide a model-based pespective of the building blocks of modern language and image generative models.

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.

This class integrates perspectives from leading innovators with collaborative practice and theory of innovation to teach and inspire you to be more innovative in your life and career. Our approach is to engage with leaders and learn their perspectives and align this with innovation sprints where you learn the best tools, processes, and methods to innovate. You can see a course overview here https://youtu.be/CqfvXf33TCE.  Find out more information on Instagram @engsci139 or https://www.instagram.com/engsci139/

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.

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.

This course introduces students to fundamental results and recently developed techniques in high-dimensional probability theory and statistical physics that have been successfully applied to the analysis of information processing and machine learning problems. Discussions will be focused on studying such problems in the high-dimensional limit, on analyzing the emergence of phase transitions, and on understanding the scaling limits of efficient algorithms. This course seeks to start from basics, assuming just a solid understanding of undergraduate probability theory. Students will take an active role by exploring and applying what they learn from the course to their own research problems.

This course teaches how to create original robotic devices made of light, compliant – informal – materials.

New fabrication techniques are transforming the field of robotics. Rather than rigid parts connected by mechanical connectors, robots can now be made of folded paper, carbon laminates or soft gels. They can be formed fully integrated from a 3D printer rather than assembled from individual components. Informal Robotics draws on cutting-edge research from leading labs, in particular, Harvard’s Micro Robotics Laboratory which has created unique designs for ambulatory and flying robots, end-effectors, medical instruments and other applications.

We will explore informal robotics from multiple perspectives, culminating with the design of original devices displaying animated intelligence in real-time. Going beyond traditional engineering approaches, we will also explore new opportunities for design at the product, architectural, and urban scales.

Techniques:
Hands-on:  Working with the GSD’s Fab Lab we are creating a kit of parts that will be available to all enrolled students. With the kit, you can create a wide range of folding mechanisms controlled by on-board miniature electronics.
Software / Simulation: Software workshops will be offered on Fusion 360 and Grasshopper to simulate robotic performance within a virtual environment.

Topics:
- Kinematics: design techniques for pop-ups, origami, and soft mechanisms.
- Fabrication: methods: for composite materials, laminated assembly, self-folding, and integrated flexures - the kit of parts will allow for hands-on exploration.
- Controls: how to actuate movement and program desired behavior. Topics include servos, linear actuators, and use of Arduino actuator control.
- Applications: takes us beyond purely technological concerns, contextualizing Informal Robotics within larger trends where materials, manufacturing and computation are starting to merge

This course teaches how to create original robotic devices made of light, compliant – informal – materials.

New fabrication techniques are transforming the field of robotics. Rather than rigid parts connected by mechanical connectors, robots can now be made of folded paper, carbon laminates or soft gels. They can be formed fully integrated from a 3D printer rather than assembled from individual components. Informal Robotics draws on cutting-edge research from leading labs, in particular, Harvard’s Micro Robotics Laboratory which has created unique designs for ambulatory and flying robots, end-effectors, medical instruments and other applications.

We will explore informal robotics from multiple perspectives, culminating with the design of original devices displaying animated intelligence in real-time. Going beyond traditional engineering approaches, we will also explore new opportunities for design at the product, architectural, and urban scales.

Techniques:
Hands-on:  Working with the GSD’s Fab Lab we are creating a kit of parts that will be available to all enrolled students. With the kit, you can create a wide range of folding mechanisms controlled by on-board miniature electronics.
Software / Simulation: Software workshops will be offered on Fusion 360 and Grasshopper to simulate robotic performance within a virtual environment.

Topics:
- Kinematics: design techniques for pop-ups, origami, and soft mechanisms.
- Fabrication: methods: for composite materials, laminated assembly, self-folding, and integrated flexures - the kit of parts will allow for hands-on exploration.
- Controls: how to actuate movement and program desired behavior. Topics include servos, linear actuators, and use of Arduino actuator control.
- Applications: takes us beyond purely technological concerns, contextualizing Informal Robotics within larger trends where materials, manufacturing and computation are starting to merge

This course will discuss electro-optics and nonlinear physics in nanophotonic devices. Topics include key building blocks of integrated photonics such as interferometers, microresonators, Bragg gratings, and photonic crystals; device physics and design of silicon photonics-based electro-optic modulators; second-order nonlinear photonics including parametric oscillation/ amplification, electro-optic modulation/ combs, harmonic generation, quasi-phase matching, and frequency mixing; third-order nonlinear photonics including supercontinuum generation, parametric oscillation, soliton frequency combs, and techniques for dispersion engineering and pulse shaping; Brillouin lasing and Raman scattering.

The course provides introduction to micro- and nano-fabrication processes used to realize photonic, electronic and mechanical devices. Lectures will introduce the state-of-the-art semiconductor fabrication processes, including lithography, deposition of metals and dielectrics, etching, oxidation, implantation, and diffusion of dopants. The fabrication component of the course will be carried out in a state-of-the-art cleanroom in the Center for Nanoscale Systems, where students will fabricate several electronic and photonic devices, including transistors, light-emitting diodes (LEDs), lasers and optical resonators.  Device characterization will be performed in a state-of-the-art teaching labs in SEC in Allston. 

The course provides introduction to micro- and nano-fabrication processes used to realize photonic, electronic and mechanical devices. Lectures will introduce the state-of-the-art semiconductor fabrication processes, including lithography, deposition of metals and dielectrics, etching, oxidation, implantation, and diffusion of dopants. The fabrication component of the course will be carried out in a state-of-the-art cleanroom in the Center for Nanoscale Systems, where students will fabricate several electronic and photonic devices, including transistors, light-emitting diodes (LEDs), lasers and optical resonators.  Device characterization will be performed in a state-of-the-art teaching labs in SEC in Allston. 

This class leads students to develop their skills in the critical reading and writing of science and engineering. Genres will include research articles, grant proposals, school/fellowship/job applications, or lay abstracts & press releases for the non-scientific public. Crucially, students will be empowered not only to achieve their own writing goals, but also to break down these learned skills and impart them to others, as effective collaborators and mentors of younger students.

This class leads students to develop their skills in the critical reading and writing of science and engineering. Genres will include research articles, grant proposals, school/fellowship/job applications, or lay abstracts & press releases for the non-scientific public. Crucially, students will be empowered not only to achieve their own writing goals, but also to break down these learned skills and impart them to others, as effective collaborators and mentors of younger students.

This class leads students to develop their skills in the critical reading and writing of science and engineering. Genres will include research articles, grant proposals, school/fellowship/job applications, or lay abstracts & press releases for the non-scientific public. Crucially, students will be empowered not only to achieve their own writing goals, but also to break down these learned skills and impart them to others, as effective collaborators and mentors of younger students.

This is a SAT/UNSAT seminar course focused on design thinking, analysis, planning, and executing the development of engineered systems. Weekly meetings will include discussions and assigned readings of case studies and examples of the systems surrounding the developing technical system. Organizing and executing research, innovation, and product design at the scales from academic group, to startup, to major industry will be discussed. The course is designed to allow the engineer and designer to integrate technical knowledge into an executable framework as an individual or leader of a design team.

This graduate seminar offers an in-depth exploration of the global semiconductor industry which is currently at the forefront of technological innovation and geopolitical dynamics.  Central to our study is the semiconductor industry's remarkable growth and evolution, driven by a complex and interdependent global network encompassing a diverse array of companies and research institutions. We will also engage in a multifaceted analysis of the semiconductor industry which will involve examining the technological limits and breakthroughs, assessing the economic factors underpinning the industry, and understanding the current geopolitical landscape, especially focusing on the recent tensions between China and the United States.

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

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

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