HarvardX: Tiny Machine Learning MOOC

Teaching Staff Lead Launched Fall 2020

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.

My Roles

  • Co-designed a free, hands-on, project-based professional certificate taught through three 6-week courses on the EdX platform covering the emerging field of Tiny Machine Learning (deploying machine learning onto microcontrollers for machine learning at the extreme edge) with the aim of democratizing access to this developing field
  • Over 35,000 students from over 160 countries enrolled as of March 2021 since the three courses launched in September 2020, December 2020, and February 2021 respectively
  • Served as the laboratory instructor both co-designing hands-on exercises as well as recording walkthroughs to aid in student learning and success
  • Managed the 10-person course staff to ensure that content was created, reviewed, and produced in a timely manner
  • Led and managed external relations for the course team coordinating with edX, Google, and Arduino
  • Co-designed course materials including short video lectures, readings, code walkthroughs, assessments, and discussion forums

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.

My Roles

  • Co-designed a new 40 student Applied AI course on the emerging field of Tiny Machine Learning (deploying machine learning onto microcontrollers for machine learning at the extreme edge)
  • Designed and gave lectures for the introduction to machine learning section of the course
  • Co-developed hands-on project-based assignments training TinyML models with Google Colab and deploying on Arduinos
  • Mentored student teams pursuing research-based final projects

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?

My Roles

  • Co-designed a new 45 student course at the intersection of robotics and computer architecture / systems serving as the robotics expert and instructor
  • Designed and gave lectures for the robotics section of the course
  • Co-developed course assignments and course infrastructure/tools (e.g., the online paper discussion forum)
  • Mentored student teams pursuing research-based final projects

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).

My Roles

  • Ran a team of 11 teaching fellows to ensure that sections and office hours were held, exams and homework assignments were graded, and student questions on the online forum were answered in a timely manner
  • Designed and gave two lectures titled “Introduction to Robotics and Path Planning I/II”
  • Co-Designed a new set of course section notes and exam review materials
  • Aided in the development of course assignments, and course infrastructure/tools (e.g., autograders)
  • Mentored student teams pursuing research-based final projects

MIT MAS.863 - How to Make Almost Anything

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

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.

My Roles

  • Led introductory sessions for various pieces of software and hardware used in the course (e.g., embedded programming for Atmel microcontrollers, CAD in Solidworks and Eagle)
  • Held office hours, aided students in lab work, machine usage, and project design

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!

My Roles

  • Worked with 9-12 teams of 4-6 students to teach programming concepts and robotic algorithm design through the completion of autonomous tasks in a Python/ROS environment using the MIT RACECAR hardware platform
  • Co-designed weekly challenges to ensure all teams developed the technical skills needed for the final race
  • Co-designed and co-built the final race track spanning an entire indoor ice hockey rink