Hi, I build AI systems that turn raw observations into transferable representations of human state and context — giving machines a reliable sense of what is happening around them. My research centers on foundation-model-based perception and multimodal/temporal learning for ambient intelligence, spanning presence, motion and activity, localization, and health-related sensing. Increasingly, I’m drawn to physical AI: extending this perception into systems that not only understand the world but act in it — helpfully and responsibly.

I care as much about shipping as publishing. At Origin, my research has gone from prototype to products deployed at scale (25M+ routers and 100M+ smart devices worldwide) across consumer and security markets — taking ideas from 0→1 all the way to global rollout. What drives me is turning frontier research into systems people actually use, and building the teams that make that happen. Before Origin, I earned my Ph.D. and M.S. from the ECE SIG at UMD , advised by Dr. K. J. Ray Liu.

Research interests:

• Foundation models for perception: large-scale representation learning over multimodal & temporal data, with strong transfer across tasks and modalities.

• Embodied & physical AI: grounding these representations in LLMs, agents, and robotic systems — reasoning, planning, and control that close the loop from perceiving the world to acting in it.

• Real-world reliability & human-centric intelligence: benchmarks and evaluation for generalization and perceptual limits, in service of understanding human state and context for safety, health, and spatial intelligence.

 I like cool and artistic research works but believe in those solving real-world problems. 

Outside of work, you’ll often find me on the tennis court 🎾, playing the piano 🎹, baking something semi-sweet 🍰, or soaking in nature’s beauty 🌳.