Courses

Semester Long Courses

COMS-BC3159-SP23: Parallel Optimization for Robotics

Instructor of Record Spring 2023

Many stages of state-of-the-art robotics pipelines rely on the solutions of underlying optimization algorithms. Unfortunately, many of these approaches rely on simplifications and conservative approximations in order to reduce their computational complexity and support online operation. At the same time, parallelism has been used to significantly increase the throughput of computationally expensive algorithms across the field of computer science. And, with the widespread adoption of parallel computing platforms such as GPUs, it is natural to consider whether these architectures can benefit robotics researchers interested in solving computationally constrained problems online. This course will provide students with an introduction to both parallel programming on CPUs and GPUs as well as optimization algorithms for robotics applications. It will then dive into the intersection of those fields through case studies of recent state-of-the-art research and culminate in a team-based final project.

COMS-BC3997-SP23: Projects in Computer Science

Instructor of Record Spring 2023

This course is designed to explore topics and skills needed for the successful completion of large computer science projects. This will be done through a mix of lecture and group work led by both the course instructor as well as guest instructors from both industry and academia. Students will exercise their development of these skills by applying them in the context of a project. For Spring 2023, students are expected to bring a project to the course. The course staff will be able to provide general support for projects but may not have the technical expertise to support projects in depth. As such, these projects will ideally already have a (technical) mentor or client sponsor who can support the student. To document their projects and begin to build their personal portfolio, students will (learn how to and) develop a website, report, and presentation about both the final result of their project as well as the journey taken.
Robots are cyber-physical systems – leveraging computational intelligence to sense and interact with the real world. As such, robotics is a very diverse, cross-disciplinary field. This introductory course exposes learners to the vast opportunities and challenges posed by the interdisciplinary nature of robotics. While grounded and focused in computation this course also explores hands-on electromechanical and ethical topics that are an integral part of a real-world robotic system. Topics will include: a survey of the algorithmic robotics pipeline (perception, mapping, localization, planning, control, and learning), an introduction to cyber-physical system design, and responsible AI. The course will culminate in a team-based final project.

Harvard CS249r: Tiny Machine Learning (TinyML)

Head Teaching Fellow (Head TA) Fall 2020
Derek Bok Center Distinction in Teaching Award

An introductory course on Applied AI at the intersection of Machine Learning and Embedded IoT Devices. We provide background on both topics and then dive into the unique challenges faced at that intersection point with hands-on assignments using TensorFlow, Google Colab, and Arduino.

Harvard CS249r: Special Topics in Edge Computing - Autonomous Machines

Head Teaching Fellow (Head TA) Fall 2019
Derek Bok Center Distinction in Teaching Award

Modern embedded systems are intelligent devices that involve complex hardware and software to perform a multitude of cognitive functions collaboratively. Designing such systems requires us to have deep understanding of the target application domains, as well as an appreciation for the coupling between the hardware and the software subsystems.This course is structured around building “systems” for Autonomous Machines (cars, drones, ground robots, manipulators, etc.). For example, we will discuss what are all the hardware and software components that are involved in developing the intelligence required for an autonomous car?

Harvard CS 182: Introduction to Artificial Intelligence

Head Teaching Fellow (Head TA) Fall 2017, 2018
Derek Bok Center Distinction in Teaching Award

Artificial Intelligence (AI) is an exciting field that has enabled a wide range of cutting-edge tech-nology, from driverless cars to grandmaster-beating Go programs. The goal of this course is to introduce the ideas and techniques underlying the design of intelligent computer systems. Topics covered in this course are broadly be divided into 1) planning and search algorithms, 2) probabilistic reasoning and representations, and 3) machine learning (although, as you will see, it is impossible to separate these ideas so neatly).

MIT MAS.863 - How to Make Almost Anything

Teaching Assistant for the Harvard Section Fall 2017, 2018, 2019, 2021

This course provides a hands-on introduction to the resources for designing and fabricating smart systems, including CAD/CAM/CAE; NC machining, 3-D printing, injection molding, laser cutting; PCB layout and fabrication; sensors and actuators; analog instrumentation; embedded digital processing; wired and wireless communications. This course also puts emphasis on learning how to use the tools as well as understand how they work. By the end of the course you will know how to make… almost anything.

MOOCs

HarvardX: Tiny Machine Learning MOOC

Teaching Staff Lead Launched Fall 2020 - Winter 2022

In this exciting Professional Certificate program offered by Harvard University and Google TensorFlow, you will learn about the emerging field of Tiny Machine Learning (TinyML), its real-world applications, and the future possibilities of this transformative technology. TinyML is a cutting-edge field that brings the transformative power of machine learning (ML) to the performance-constrained and power-constrained domain of embedded systems. The program will emphasize hands-on experience and is a collaboration between expert faculty at Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS) and innovative members of Google’s TensorFlow team.

Workshops / Summer Courses

SciTinyML-22 was a, five day, hands-on, virtual workshop exploring how embedded ML (tinyML) can impact the developing world through hands-on activities using embedded hardware devices. SciTinyML-22 was run regionally with seperate workshops for Africa (187 participants from 29 countries), Asia, and Latin America. This program was a collaboration led by the Abdus Salam International Centre for Theoretical Physics (ICTP), the Harvard John A. Paulson School of Engineering and Applied Sciences, and TinyMLedu.
Mind the Gap: Opportunities and Challenges in the Transition Between Research and Industry is aimed at bridging the gap between academia and industry. For researchers, this workshop will help lift the curtain on the realities of academic to industry tech transfer. For industry experts, this workshop provides an opportunity to influence the direction of academic research. For both, we hope to provide an venue for integrated dialogue and identification of new potential collaborations.

EASI-22: Edge AI Summer Institute

A TinyMLedu Workshop

Lead Organizer Summer 2022

EASI-22 was a 3-day, hands-on workshop for high school teachers and students exploring real-world applications of artificial intelligence at the edge through hands-on examples of Tiny Machine Learning (TinyML). This program was a collaboration between Navajo Technical University, the Harvard John A. Paulson School of Engineering and Applied Sciences, and Barnard College, Columbia University.
SciTinyML-21 was a, five day, hands-on, virtual, global (216 participants from 48 countries) workshop exploring how embedded ML (tinyML) can impact the developing world through hands-on activities using the Edge Impulse cloud platform and a smartphone. This program was a collaboration between the Abdus Salam International Centre for Theoretical Physics (ICTP), the Harvard John A. Paulson School of Engineering and Applied Sciences, and TinyML4D.
CRESTLEX 3.0 was a first-of-its-kind, 4-day, hands-on workshop for high school teachers and students exploring real-world applications of artificial intelligence through hands-on examples of Tiny Machine Learning (TinyML). This program was a collaboration between Navajo Technical University, the Harvard John A. Paulson School of Engineering and Applied Sciences, Google, and Edge Impulse.

MIT Beaverworks Summer Institute: Autonomous RACECAR Grad Prix

Associate Instructor (TA) Summer 2016, 2017, 2018, 2019

Driverless vehicle technology has been growing at an exponential pace since the DARPA Grand and Urban Challenges pushed the state of the art to demonstrate what was already possible. Commercial interest and aggressive development are being driven by Google, Toyota, Tesla, Continental, Uber, Apple, NVidia, and many other companies. There is no single technology or “killer app” available to solve the myriad perception, understanding, localization, planning, and control problems required to achieve robust navigation in highly variable, extremely complex and dynamically changing environments. This summer, Beaver Works Summer Institute will offer nine teams of five students, each with its own MIT-designed RACECAR (Rapid Autonomous Complex Environment Competing Ackermann steeRing) robot, the opportunity to explore the broad spectrum of research in these areas, learn to collaborate, and demonstrate fast, autonomous navigation in a Mini Grand Prix to Move… Explore… Learn…Race!