Featured Courses

Teaching Staff Lead
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.
Course White Paper MOOC Website Course Materials TinyML Open Education Initiative Discourse Forum

Head Teaching Fellow (Head TA)
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?
Syllabus Paper List Nuts & Bolts and Robotics Intro Lecture Slides

Head Teaching Fellow (Head TA)
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).
Syllabus Assignments Project Instructions Software Install Guide Robot Motion Planning Lecture Slides Section Notes

Featured Publications

In this paper, we detail the designs of three faster than state-of-the-art implementations of the gradient of rigid body dynamics on a CPU, GPU, and FPGA. Our optimized FPGA and GPU implementations provide as much as a 3.0x end-to-end speedup over our optimized CPU implementation by refactoring the algorithm to exploit its computational features, e.g., parallelism at different granularities.
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In this extended abstract we extend our previous work by using our Parallel DDP implementation for MPC on a physical Kuka arm. We demonstrated the feasibility of this approach in the presence of model discrepancies and communication delays between the robot and GPU and found that higher control rates generally lead to better tracking performance across a range of parallelization options.
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We analyze the benefits and tradeoffs of higher degrees of parallelization using a multiple-shooting variant of DDP implemented on a GPU. We describe our implementation strategy and present results demonstrating its performance compared to an equivalent multi-threaded CPU implementation using several benchmark control tasks. Our results suggest that GPU-based solvers can offer increased per-iteration computation time and faster convergence in some cases, but in general tradeoffs exist between convergence behavior and degree of algorithm-level parallelism.
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