Course Listing

For a snapshot of courses being offered by Harvard School of Engineering over the next four years, visit our Multi-Year Course Planning tool.

Decision Theory

APMTH 231
2024 Spring

Demba Ba
Tuesday, Thursday
11:15am to 12:30pm

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.

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Learning, Estimation, and Control of Dynamical Systems

APMTH 232
2024 Spring

Na Li
Monday, Wednesday
9:45am to 11:00am

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.

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Flavor Molecules of Food Fermentation: Exploration and Inquiry

ENG-SCI 24
2024 Spring

Pia Sorensen
Monday, Wednesday
1:30pm to 2:45pm

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.

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Introduction to Electrical Engineering

ENG-SCI 50
2024 Spring

Marko Loncar
Monday, Wednesday
11:15am to 12:30pm

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.

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Entrepreneurship and Innovation: Practical and Academic Insights

ENG-SCI 94
2024 Spring

Josh Lerner, George Clay
Monday, Wednesday
11:15am to 12:30pm

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.

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Entrepreneurship and Innovation: Practical and Academic Insights

ENG-SCI 94
2024 Spring

Richard Bennett, Spencer Rascoff
Monday, Wednesday
12:45pm to 2:00pm

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.

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Startup R & D

ENG-SCI 95R
2024 Spring

Paul Bottino
Wednesday
3:45pm to 6:30pm

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.

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Engineering Problem Solving and Design Project

ENG-SCI 96
2024 Spring

Samir Mitragotri, Fawwaz Habbal
Monday, Wednesday
12:45pm to 3:30pm

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.

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Engineering Problem Solving and Design Project

ENG-SCI 96
2024 Spring

Chris Lombardo, Gu-Yeon Wei
Monday, Wednesday
12:45pm to 3:30pm

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.

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Engineering Design Projects

ENG-SCI 100HFB
2024 Spring

Katia Bertoldi

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.

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Humanitarian Design Projects

ENG-SCI 105HFR
2024 Spring

Chris Lombardo
Tuesday
6:00pm to 7:15pm

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.

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Computer Vision

ENG-SCI 143
2024 Spring

Todd Zickler
Tuesday, Thursday
9:45am to 11:00am

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.

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Probability with Engineering Applications

ENG-SCI 150
2024 Spring

Yue Lu
Tuesday, Thursday
12:45pm to 2:00pm

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.

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Applied Electromagnetism

ENG-SCI 151
2024 Spring

Evelyn Hu
Monday, Wednesday
12:45pm to 2:00pm

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).

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Signals and Communications

ENG-SCI 156
2024 Spring

Flavio du Pin Calmon
Tuesday, Thursday
11:15am to 12:30pm

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.

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Microfabrication Laboratory

ENG-SCI 177
2024 Spring

Kiyoul Yang
Monday
9:45am to 11:45am

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.  

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Foundations of Quantum Mechanics

ENG-SCI 200
2023 Fall

Federico Capasso
Monday, Wednesday
3:00pm to 4:15pm

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.

Course Website

Decision Theory

ENG-SCI 201
2024 Spring

Demba Ba
Tuesday, Thursday
11:15am to 12:30pm

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.

Course Website

Learning, Estimation, and Control of Dynamical Systems

ENG-SCI 202
2024 Spring

Na Li
Monday, Wednesday
9:45am to 11:00am

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.

Course Website

Informal Robotics

ENG-SCI 256
2024 Spring

Chuck Hoberman
Tuesday
1:30pm to 4:15pm

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

Course Website

Microfabrication Laboratory

ENG-SCI 277
2024 Spring

Kiyoul Yang
Monday
9:45am to 11:45am

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. 

Course Website

Professional Writing for Scientists and Engineers

ENG-SCI 297
2024 Spring

Suzanne Smith
Thursday
3:00pm to 5:00pm

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.

Course Website

Political Economy of the Global Semiconductor Industry: Technology, Markets and Policy

ENG-SCI 298R
2024 Spring

Woodward Yang
Thursday
3:45pm to 5:45pm

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.

Course Website

Special Topics in Engineering Sciences

ENG-SCI 299R
2024 Spring

Todd Zickler

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.

Course Website

An Introduction to Maker Skills

MIT ES .100
2024 Spring

Introduction to making and use of MIT's maker spaces intended to build skills needed for designing, conducting, and completing experiments and design projects, such as may be encountered in undergraduate classwork and research activities. Includes maker space training (i.e., wood shop, digital fabrication, and electronics fabrication) and open-ended design projects, with work evenly divided between class, homework, and maker space activities.
Course Website

ESG Undergraduate Teaching

MIT ES .201
2024 Spring

An opportunity to assist in the teaching of subjects in ESG in biology, chemistry, humanities and social sciences, mathematics, and physics. Student instructors may be involved in grading, running problem solving sessions, or teaching classes depending on experience and interest. Qualified students may also develop and teach undergraduate seminars under the supervision of an appropriate faculty or staff member. Student instructors meet every other week  with staff to discuss their teaching and cover a variety of topics related to effective teaching techniques.
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