Heng Yang

Research Scientist at NVIDIA
(Incoming) Assistant Professor at Harvard SEAS
Robotics, Vision, Optimization, Learning

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Space Needle, 2022

I am an incoming (Fall 2023) Assistant Professor of Electrical Engineering in the School of Engineering and Applied Sciences (SEAS) at Harvard University. I obtained my Ph.D. in Robotics in 2022 from the Massachusetts Institute of Technology, where I was fortunate to work with Luca Carlone in the Laboratory for Information and Decision Systems.

I am currently a research scientist in the NVIDIA Autonomous Vehicle Research Group led by Marco Pavone.

Computational Robotics: I am building a computational robotics research group at Harvard University. My group is broadly interested in the algorithmic foundations of robot perception, action, and learning. Our vision is to enable safe and trustworthy autonomy for a broad range of high-integrity robotics applications, by designing tractable and provably correct algorithms that enjoy rigorous performance guarantees, developing fast implementations, and validating them on real robotic systems.

Current Interests: I am currently interested in three challenges.

  • Trustworthy robot perception with learned modules
  • Perception-based planning and control with performance guarantees
  • Convex optimization for estimation, decision-making, and learning

Read more about some of these topics in the sample research statement (November 2021).

Prospective Students: For PhD opportunities, please apply to the PhD program at Harvard SEAS (e.g., in EE/CS/AM/ME) and list me as a potential advisor. I am also open to taking (undergraduate or graduate) research interns.

Postdoc Fellow: We have open Postdoc Fellow positions in Learning, Optimization, Control, and/or Robotics. Please apply here.

I am interested in working with self-motivated candidates with (a) a strong theoretical and computational background (e.g., applied math, optimization and control, statistics, machine learning, and scientific computing), and/or (b) rich experiences in real robotic platforms (e.g., drones, manipulators, ground vehicles).

Due to the large volume of inquiries, I may not be able to reply to all emails, and I apologize for this.

News

Mar 22, 2023 New preprint on verification and synthesis of robust control barrier functions: you do not need iterative sum of squares
Feb 23, 2023 Talk at Yau Mathematical Sciences Center, Tsinghua University
Jan 1, 2023 Serving as an Associate Editor for the International Journal of Robotics Research
Nov 18, 2022 Paper on solving large-scale semidefinite relaxations of polynomial optimization accepted to Mathematical Programming! Check out the implementation STRIDE
Nov 2, 2022 Talk at Tsinghua University X-idea seminar series (slides)
Oct 24, 2022 Talk at Stanford Vision and Learning Lab
Oct 23, 2022 Talk at ECCV Workshop on 3D Perception for Autonomous Driving: “Perception with Confidence: A Conformal Prediction Perspective
Oct 22, 2022 Paper “Conformal Semantic Keypoint Detection with Statistical Guarantees” accepted to NeurIPS Workshop on Robot Learning: Trustworthy Robotics
Oct 7, 2022 Talk at IEEE ITSC Workshop on Safety Validation of Connected and Automated Vehicles
Jul 27, 2022 Talk at International Conference on Continuous Optimization (ICCOPT) (slides)

Awards & Recognitions

2022 MIT LIDS/ALL Magazine: Honing Robot Perception
2021 MIT News Spotlight: Making self-driving cars safer through keener robot perception
2021 Best Paper Award Finalist at Robotics: Science and Systems (RSS)
2021 Robotics: Science and Systems (RSS) Pioneer
2020 Graduated Non-Convexity (GNC) algorithm included in Matlab Navigation Toolbox and appeared in MathWorks News and Stories
2020 Best Paper Award Honorable Mention from IEEE Robotics and Automation Letters (RAL)
2020 Best Paper Award in Robot Vision at International Conference on Robotics and Automation (ICRA)
2019 MIT News Spotlight: Spotting objects amid clutter
2015 Tsinghua Principal Scholarship (Tsinghua News Spotlight)