I am a Ph.D. candidate in Electrical and Computer Engineering at New York University, advised by Prof. Sundeep Rangan at NYU WIRELESS.
My research develops spatially aware, uncertainty-aware wireless intelligence for embodied autonomy and adaptive 6G systems. I build algorithms, wireless digital twins, and physical FR3/mmWave testbeds that turn sparse RF and multimodal observations into calibrated spatial beliefs and closed-loop decisions.
Ph.D. in Electrical and Computer Engineering
2022
Expected May 2027
New York University
M.S. in Computer Engineering
2020
2022
New York University
B.E. in Electrical Engineering and Automation
2015
2019
China Agricultural University
My work starts from a simple problem: future wireless and robotic systems rarely see the world through clean measurements. A receiver may observe only a few multipath components; a robot may have partial visual context; a handset may only measure the bands and antenna modules it chooses to activate. In these settings, a single point estimate is often less useful than a belief over competing spatial hypotheses.
I use this view to connect four threads: posterior RF localization through MC-CLE and LOCUS-DT, wireless digital twins for zero-shot robot navigation and SLAM, UE-centric multi-band adaptation under mobility and blockage, and object-centric spatial memory for wearable and embodied agents. The long-term goal is to make wireless systems not only communicate, but also reason about space, uncertainty, and action.
Posterior localization methods that retain multimodal spatial hypotheses for 6G and robotics.
Ray-tracing priors for zero-shot indoor navigation, wireless SLAM, and robot policies.
FR3/mmWave RFSoC/Pi-Radio testbeds with TurtleBot4, Jackal UGV, D48 pan-tilt, and linear-track motion.
Multi-cell multi-band handset digital twins for array, band, and rate prediction under mobility.
Sparse egocentric sensing and semantic 3D object memories for wearable and embodied agents.
Wireless systems and embodied AI pipelines that move from probabilistic inference to real-world experiments.
Posterior RF localization methods that turn sparse AoA/SNR and multipath observations into calibrated spatial beliefs.
RFSoC/Pi-Radio FR3 channel-sounding platform for robotic localization and navigation experiments with TurtleBot4 and Jackal UGV.
Ray-tracing digital-twin priors and physics-informed RL for zero-shot wireless navigation, localization, and wireless SLAM.
UE-centric multi-cell multi-band handset digital twins for closed-loop array, band, and rate prediction under mobility.
Adaptive sparse sensing and object-centric semantic 3D memory for wearable and embodied spatial intelligence.
I am proud to be part of NYU WIRELESS, a leading 6G research center at NYU Tandon and the home base for my work on wireless sensing, localization, digital twins, and robotic measurement systems. The center gives my research a rare mix of theory, simulation, RF hardware, and mobile robotic platforms.
