I'm currently on the 2021-2022 academic job market. I'm looking for faculty openings in Robotics/Computer Vision/Optimization (ME/EE/CS/AA). Please feel free to contact me if you are interested. I'd also be thrilled to talk about my research in related seminars.
My name is Heng (Hank) Yang, and I am currently a PhD candidate at MIT Laboratory for Information and Decision Systems (LIDS) and Department of Mechanical Engineering (MechE). I am excited to be working with Prof. Luca Carlone in the the SPARK (Sensing, Perception, Autonomy, and Robot Kinetics) Lab on foundamental robotic perception, computer vision and optimization and learning algorithms.
I am broadly interested in robotics, computer vision, optimization, and machine learning. My long-term research vision is to enable safe and trustworthy autonomy for a broad range of high-integrity applications (e.g., autonomous driving, space robotics), by designing tractable and provably correct algorithms that enjoy rigorous performance guarantees, and validating them on real robotic systems.
My PhD research focused on designing what we call certifiable algorithms for outlier-robust geometric estimation in robot visual perception. Despite the NP-hardness of the mathematical optimization problems involved in outlier-robust geometric estimation, certifiable algorithms are polynomial-time algorithms that offer provable optimality guarantees. I have established the theoretical and computational foundations of certifiable perception based on a wide range of advanced tools (e.g., robust estimation, semidefinite relaxation, large-scale convex optimization), and successfully demonstrated the trustworthiness of certifiable algorithms on safety-critical applications such as self-driving and space robotics.
My future research aims to start from certifiable geometric perception, and reach my vision of system-level safe and trustworthy autonomy, by building an advanced toolbox combining theory, computation, and system validation. I plan to execute two important steps towards this vision, where the first step aims to integrate certifiable perception with deep feature learning to achieve safe perception, and the second step aims to integrate safe perception with safe action to construct system-level safety guarantees.
Before joining the SPARK Lab, I worked with Dr. Brian Anthony in the Device Realization Lab on designing a low-cost and portable ultrasound shear wave elastography device using external mechanical vibration (see Patents page), which is promising for bringing the currently limited and expensive advanced ultrasound elastography technique to the everyday life of patients.
Prior to MIT, I studied at Tsinghua University in Beijing, China. I was interested in using mathematical model to explain interesting phenomena in nature: how different animals drink and how they fly, and apply learnings from nature to engineering designs. Check out the Publications page for more detailed information.
Besides research, I enjoy running, working out, movies, music, hiking and travelling to keep a work-life balance.
[Last edit: 11/15/2021]