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Research Scientist, Waymo |
Bio
I am currently a research scientist at Waymo. Previously, I received my Ph.D. from Georgia Tech, where I was advised by James Hays and Frank Dellaert. Prior to joining Georgia Tech, I completed my Bachelor’s and Master’s degrees in Computer Science at Stanford University, specializing in artificial intelligence.
Research
My interests resolve around machine learning for robotics and autonomy. Past and present research areas have included image understanding, 3D perception, SLAM, and simulation. I’ve been involved in research for self-driving vehicle development since 2017. Machine learning and computer vision for robot autonomy currently present (and will continue to present) enormous benefits for people all over the world, with implications for safer transportation and safer workplaces.
News
- September 2023: I will be speaking at the 2023 IROS Workshop on Traffic Agent Modeling for Autonomous Driving Simulation in October.
- September 2023: Our paper The Waymo Open Sim Agents Challenge is accepted to NeurIPS ‘23 as a Spotlight, in the Datasets & Benchmarks track.
- May 2023: The 2023 Waymo Open Dataset Challenges have concluded. Our whitepaper describing the 2023 Sim Agents challenge is on Arxiv.
- March 2023: The 2023 Waymo Open Dataset Challenges are live. More info available here.
- July 2022: Our paper SALVe: Semantic Alignment Verification for Floorplan Reconstruction from Sparse Panoramas has been accepted to ECCV 2022. [Project Page] [Paper]
- March 2022: I have joined Waymo Research as a research scientist.
- March 2022: I defended my PhD Thesis. Many thanks to Simon Lucey, Zsolt Kira, and Cedric Pradalier who joined my co-advisors, James Hays and Frank Dellaert, on my committee. The title of my thesis is “Deep Learning for Building and Validating Geometric and Semantic Maps” – coming soon to Arxiv.
Teaching
Aside from research, another passion of mine is teaching. I enjoy creating teaching materials for topics related to computer vision, a field which relies heavily upon numerical optimization and statistical machine learning tools. A number of teaching modules I’ve written can be found below: