DEEP LEARNING • ML SYSTEMS • PROBABILISTIC MODELING
Building AI systems from research to real-world deployment.
Foundational research behind Infima.io and WhiteRabbit.ai
Across ML, healthcare, finance, and applied modeling
ML models deployed in Amazon robots across US
I am a researcher and engineer working at the intersection of deep learning, probabilistic modeling, and ML systems, while also teaching at Stanford University. My work combines modern AI with optimization, stochastic modeling, and statistics to solve real-world problems across robotics, healthcare, finance, and operations. Research I led at Stanford helped form the technical foundations of two AI startups, Infima.io and WhiteRabbit.ai. I spent five years at Google Brain and now lead ML research for robotics at Amazon.
10+ years at Google Brain and Amazon Lab126, alongside academia at Stanford.
Adjunct Lecturer and graduate research mentor at Stanford University.
Revamped curriculum and served as Principal Instructor for 3 years, teaching graduate students across Stanford and industry professionals remotely via SCPD.
Mentoring PhD students and industry researchers across ML and applied mathematics.
Recent research and earlier work with lasting real-world impact.
Multimodal learning · 3D vision · robotics · sequence modeling
Improves motion–language alignment using LLM-based text canonicalization to reduce noisy caption supervision before contrastive learning, delivering stronger cross-dataset transfer and state-of-the-art motion–text retrieval.
Uses monocular depth cues from foundation models to improve 2D-to-3D human pose estimation without additional sensors, improving cross-dataset generalization across multiple pose architectures and benchmarks.
Learns socially appropriate robot parking locations from human preferences, aligning a robot’s objective with how people actually want it to behave in the home. Evaluated using preferences from people who lived with a social robot.
Combines permutation-invariant set representations with modern sequence models to learn temporal and cross-sectional structure across collections of time series, with applications to mortgage-risk prediction and portfolio optimization.
Foundational research behind two AI startups
Applied deep learning at massive scale to model nonlinear mortgage dynamics across 120M+ U.S. loans. The research helped form the technical foundation of Infima, later acquired by AD&Co.
Developed deep-learning computer vision models for automated grading of diabetic retinopathy from high-resolution retinal images. The research helped form the technical foundation of WhiteRabbit.ai.
