I am a reseach scientist at Meta (Reality Labs). I obtained my Ph.D. in Computer Engineering from University of Wisconsin-Madison in 2023 (Advisor: Prof. Umit Y. Ogras). I also work closely with Prof. Yin Li. Prior to UW, I received my B.S. from University of Electronic Science and Technology of China (UESTC). During my Ph.D. study, I work as a research intern on computer vision field at Meta (Reality Labs), TikTok and Ambarella.
My research interests include ML/CV, human activity recognition (HAR), 3D human pose estimation (HPE), multi-modal sensing, NeRF, generative models, and smart healthcare algorithms.
Shoot me an email to email@example.com if you are interested in talking to me!
- September 2023: Joined Meta (Reality Labs) as a research scientist!
- August 2023: Defended my Ph.D.! What an exciting journey!
- February 2022: Two papers accepted to CVPR 2023 (PanoHead and Panic-3D ) and one paper accepted to DAC 2023!
- December 2022: Started research internship in Meta Reality Labs working on NeRF for device generalization!
- September 2022: mRI (multi-modal human pose estimation dataset) got accepted in NeurIPS 2022! Dataset and code are partially released, give it a try!!
- August 2022: Passed my prelim exam!
- June 2022: Started research internship in TikTok working on 3D-aware full-head GAN!
- February 2022: Paper accepted for publication in Design Automation Conference (DAC) 2022. See y’all in SF! (Acceptance rate: 23%) [paper]
- September 2021: Paper accepted for publication in ACM Transactions on Internet of Things (TIOT), and journal-track paper presentation in ACM SenSys 2021 (Acceptance rate: 8/49 ≈ 16%) [arXiv]
- July 2021: MARS got nominated for best paper award! [github]
- June 2021: Joined Ambarella as a computer vision intern.
- November 2020: Passed my qual exam!
- June 2020: Transferred to UW-Madison from Arizona State
- October 2019: Best paper award at EMBEDDED SYSTEMS WEEK 2019! (1/75)
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360°
mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors
MARS: mmWave-based Assistive Rehabilitation System for Smart Healthcare
MGait: Model-Based Gait Analysis Using Wearable Bend and Inertial Sensors
Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks
An ultra-low energy human activity recognition accelerator for wearable health applications
G Bhat, Y Tuncel, S An, HG Lee, UY Ogras. Best paper award at ESWEEK 2019!
A Systematic Survey of Research Trends in Technology Usage for Parkinson’s Disease