About Me

Hello! I am a Senior Research Engineer at Samsung Research America. I received my Ph.D. in Electrical and Computer Engineering from the University of California, San Diego, where I was advised by Professor Xinyu Zhang.

Prior to my Ph.D., I worked as an engineer at Pi-Radio Inc., a startup spun off from NYU WIRELESS. Earlier, I spent two years as a hardware/systems engineer at Huawei in Shanghai building smartphones. I received my M.S. degree from New York University and my B.S. degree from Fudan University.

Research Interests
Wireless Sensing & Communication mmWave Radar Programmable Metasurfaces Neuromorphic & Low-Power IoT Software-Defined Radio

Latest News

  • Aug 2026 Our paper MetaCapsule: Toward In-Vivo Wi-Fi Sensing with a Smart Capsule has been accepted by SenSys 2027.
  • Aug 2026 Our work at Samsung Research, AutoBLE: Automating BLE Ranging Algorithm Design with Agentic Digital-Twin Optimization, has been accepted by DTWin'26.
  • Jun 2026 Our paper MetaVenom: Field Programmable Fully Passive Metasurfaces has been accepted by MobiCom 2026.
  • Nov 2025 Our paper Physically Accurate Differentiable Inverse Rendering for Radio Frequency Digital Twin has been accepted by MobiCom 2026.
  • Sep 2025 Our paper FlowForm: Scalable Passive Metasurface Network for mmWave Coverage Expansion has been accepted by SIGCOMM 2026.

Featured Research

MetaSurface Road Signs

mmWave MetaSurface Road Signs

We present MetaPolar, a fully passive mmWave metasurface tag for robust Infrastructure-to-Vehicle (I2V) communication with standard automotive radars. MetaPolar exploits the polarization domain via engineered split-ring resonators and a crossed-dipole layer to generate distinct 2×2 polarimetric RCS signatures. A semi-retroreflective design maintains reliable decoding across a ±7.5° field of view. Implemented on low-cost FR-4 PCBs (<$3/tag), MetaPolar achieves 2-bit encoding per tag (scalable to 2N bits with N tags), simultaneous multi-tag reading, and seamless integration into existing radar pipelines.

Hybrid Beamforming Radar

mmWave Phased-MIMO Radar with Massive Antennas

Millimeter-wave (mmWave) radar has become instrumental in diverse consumer applications. However, current mmWave radar suffers from limited angular resolution due to the limited number of antennas. In this research project, we introduce a novel mmWave phased-MIMO radar platform equipped with hundreds of antennas. We further study the layout of a sparse array of phased arrays to achieve high angular resolution.

Neuromorphic Radar

Neuromorphic Radar Sensing for IoT Systems

Radar sensors have recently been explored in the industrial and consumer Internet of Things (IoT). However, such applications often require self-sustainable or untethered operations, which are at odds with the high power consumption of radar. We propose a neuromorphic radar sensor that produces spikes and can directly interface with spiking neural networks emulated on neuromorphic computers, achieving superior energy efficiency.

Selected Recent Publications

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