I am a co-founder of Trustworthy AI, which was acquired by Waymo in 2021. At Waymo, I continue Trustworthy AI's mission to build an automated test generation and risk modeling platform for safety-critical software. My work spans the intersection of machine learning, optimization, statistics, and control theory. Overall, I try to make computers faster and smarter on problems that matter to people.
Previously, I received a PhD at Stanford, where my advisor was John Duchi. My main research focused on distributionally robust optimization and rare-event simulation, and it involved many collaborations with Russ Tedrake at MIT. I was a member of the Machine Learning Group, and I also researched medical applications of machine learning as part of the Wearable Health Lab. I was supported by a Stanford Graduate Fellowship and a Hertz Fellowship.
Before Stanford, I received an MPhil in Information Engineering at the University of Cambridge on a Churchill Scholarship, where I was advised by Glenn Vinnicombe and Carl Rasmussen. I received a BSE in Mechanical and Aerospace Engineering at Princeton; my thesis advisor was Naomi Leonard, and I also worked in the research groups of Howard Stone and Lex Smits.
I have been fortunate to work at and collaborate with companies including Toyota Research Institute (TRI), Quantifind, Microsoft, and Merck.
aman at trustworthy dot ai, CV, Linkedin, Google scholar
Rate-informed discovery via Bayesian adaptive multifidelity sampling. Aman Sinha*, Payam Nikdel*, Supratik Paul, Shimon Whiteson. CoRL 2024. [link] [pdf] [arxiv]
Embedding synthetic off-policy experience for autonomous driving via zero-shot curricula. Eli Bronstein*, Sirish Srinivasan*, Supratik Paul*, Aman Sinha, Matthew O'Kelly, Payam Nikdel, Shimon Whiteson. CoRL 2022. Oral presentation. [link] [pdf] [arxiv]
Neural bridge sampling for evaluating safety-critical autonomous systems. Aman Sinha*, Matthew O'Kelly*, Russ Tedrake, John Duchi. NeurIPS 2020. [link] [pdf] [arxiv] [code] [poster] [slides] [talk] [press]
FormulaZero: distributionally robust online adaptation via offline population synthesis. Aman Sinha*, Matthew O'Kelly*, Hongrui Zheng*, Rahul Mangharam, John Duchi, Russ Tedrake. ICML 2020. [link] [pdf] [arxiv] [code] [slides] [talk]
Efficient black-box assessment of autonomous vehicle safety. Justin Norden*, Matthew O'Kelly*, Aman Sinha*. NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving, CVPR 2020 Workshop on Scalability in Autonomous Driving. [arxiv] [blog]
Digital biomarkers of spine and musculoskeletal disease from accelerometers: Defining phenotypes of free-living physical activity in knee osteoarthritis and lumbar spinal stenosis. Christy Tomkins-Lane, Justin Norden, Aman Sinha, Richard Hu, Matthew Smuck. The Spine Journal, 2019. Outstanding Paper Award. [link]
Scalable end-to-end autonomous vehicle testing via rare-event simulation. Matthew O'Kelly*, Aman Sinha*, Hongseok Namkoong*, John Duchi, Russ Tedrake. NeurIPS 2018. [link] [pdf] [arxiv] [code] [poster] [videos]
Certifying some distributional robustness with principled adversarial training. Aman Sinha*, Hongseok Namkoong*, John Duchi. ICLR 2018. Oral presentation. [link] [pdf] [arxiv] [code] [slides] [talk]
Objective measurement of function following lumbar spinal stenosis decompression reveals improved functional capacity with stagnant real-life physical activity. Matthew Smuck, Amir Muaremi, Patricia Zheng, Justin Norden, Aman Sinha, Richard Hu, Christy Tomkins-Lane. The Spine Journal, 2018. Outstanding Paper Award. [link]
Adaptive sampling probabilities for non-smooth optimization. Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John Duchi. ICML 2017. [link] [pdf] [code] [poster]
Objective measurement of free-living physical activity (performance) in lumbar spinal stenosis: are physical activity guidelines being met? Justin Norden, Matthew Smuck, Aman Sinha, Richard Hu, Christy Tomkins-Lane. The Spine Journal, 2017. Outstanding Paper Award Runner-up. [link]
Learning kernels with random features. Aman Sinha, John Duchi. NIPS 2016. [link] [pdf] [code] [poster]
Dynamic management of network risk from epidemic phenomena. Aman Sinha, John Duchi, Nick Bambos. IEEE CDC 2015. [link] [pdf] [slides]
Visualizing the very-large-scale motions in turbulent pipe flow. Leo Hellström, Aman Sinha, Lex Smits. Physics of Fluids, 2011. [link] [pdf]
Past and present research collaborators (alphabetically by last name in reverse chronological order): Payam Nikdel, Supratik Paul, Shimon Whiteson, Eli Bronstein, Matthew O'Kelly, Sirish Srinivasan, John Duchi, Russ Tedrake, Rahul Mangharam, Hongrui Zheng, Justin Norden, Richard Hu, Matthew Smuck, Christy Tomkins-Lane, Hongseok Namkoong, Amir Muaremi, Patricia Zheng, Steve Yadlowsky, Nick Bambos, Leo Hellström, Lex Smits
Safety-critical machine learning: development and testing. Aman Sinha. Stanford University PhD. Thesis, 2020. [link] [pdf] [slides] [talk]
Distributed gaussian process regression in networked systems. Aman Sinha. University of Cambridge MPhil. Thesis, 2014. [link] [pdf]
Distributed consensus protocols in adaptive multi-agent systems. Aman Sinha. Princeton University Undergraduate Thesis, 2013. Awarded Morgan W. McKinzie '93 Senior Thesis Prize for best senior thesis. [link] [pdf]
Single-particle motion in colloids: nonlinear fluctuations in the presence of hydrodynamic interactions. Aman Sinha. Princeton University Junior-Year Independent Study, 2012. [link]