Zhuoyi (Zoey) Huang

I'm a Member of Technical Staff at xAI, where I work on scaling reinforcement learning for science and coding.

Previously, I worked at Microsoft Turing and Microsoft AI Development Acceleration Program on long-horizon computer-use RL and healthcare foundation models with Mass General Brigham. I also interned with the Meta Creation ML team, the Microsoft Research Visual Computing Group with Jingdong Wang, and Tencent AI Lab.

At Stanford, I worked with Jiajun Wu, Tobias Gerstenberg, Fei-Fei Li, Ehsan Adeli, Stephanie Chao, and Zhengyuan Zhou. Outside of work, I co-founded Currents.

Email / CV / Scholar / LinkedIn / X / Instagram / Blog

My work spans large scale RL, self-play, multimodal long-horizon reasoning, representation learning, and applications in embodied AI, media, and healthcare.

I keep returning to three problems: 🤖 real-world RL with sparse and subjective rewards; 🧪 simulations and AI for science, from virtual cells to digital twins; and 🧠 BCI and cognitive learning, or how AI can augment rather than replace humans.

I'm always happy to exchange ideas, explore collaboration, mentor junior researchers, and chat with peers and senior researchers. You can set up a 15-minute virtual coffee chat with me here.

News

2026 Jan	Joined xAI as Member of Technical Staff, working on reinforcement learning for science and coding.
2026 Feb	Currents joined the NVIDIA Inception Program.
2025 Dec	Attending NeurIPS in San Diego to present our work on synthetic ECG generation. It received the Best Research Paper Award at NeurIPS GenAI4H!
2025 Oct	Our work on learning ECG representations via poly-window contrastive learning appeared at BHI.
2024 Oct	MARPLE appeared at NeurIPS Datasets and Benchmarks 2024.
2024 May	Whodunnit? appeared at CogSci 2024.
2023 July	I joined MAIDAP as an Applied Scientist.
2023 June	I graduated from Stanford with an M.S. in Computer Science.

Research

	MARPLE: A Benchmark for Long-Horizon Inference Emily Jin, Zhuoyi Huang, Jan-Philipp Fränken, Weiyu Liu, Hannah Cha, Erik Brockbank, Sarah Wu, Ruohan Zhang, Jiajun Wu, Tobias Gerstenberg NeurIPS Datasets and Benchmarks, 2024 project page
	High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Training Zhuoyi Huang, Nutan Sahoo, Anamika Kumari, Girish Kumar, Kexuan Cai, Shixing Cao, Yue Kang, Tian Xia, Somya Chatterjee, Nicholas Hausman, Aidan Jay, Eric S. Rosenthal, Soundar Srinivasan, Sadid Hasan, Alex Fedorov, Sulaiman Vesal NeurIPS GenAI4H, 2025 (Oral, Best Research Paper Award)
	TrInk: Ink Generation with Transformer Network Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, Shujie Liu EMNLP, 2025
	Learning ECG Representations via Poly-Window Contrastive Learning Yi Yuan, Joseph van Duyn, Runze Yan, Zhuoyi Huang, Sulaiman Vesal, Sergey Plis, Xiao Hu, Gloria Hyunjung Kwak, Ran Xiao, Alex Fedorov BHI, 2025
	Whodunnit? Inferring what happened from multimodal evidence Sarah A. Wu, Erik Brockbank, Hannah Cha, Jan-Philipp Fränken, Emily Jin, Zhuoyi Huang, Weiyu Liu, Ruohan Zhang, Jiajun Wu, Tobias Gerstenberg CogSci, 2024
	Racial and Ethnic Disparities in Child Abuse Identification and Inpatient Treatment Fereshteh Salimi-Jazi, Norah E. Liang, Zhuoyi Huang, Lakshika Tennakoon, Talha Rafeeqi, Amber Trickey, Stephanie D. Chao JAMA Network Open, 2024
	Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI Emily Jin, Jiaheng Hu, Zhuoyi Huang, Ruohan Zhang, Jiajun Wu, Li Fei-Fei, Roberto Martín-Martín Agent Learning in Open-Endedness, 2023
	Disparities in Detection of Suspected Child Abuse Modupeola Diyaolu, Chaonan Ye, Zhuoyi Huang, Ryan Han, Hannah Wild, Lakshika Tennakoon, David A. Spain, Stephanie D. Chao Journal of Pediatric Surgery, 2023
	MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing Zelun Luo, Zane Durante, Linden Li, Wanze Xie, Ruochen Liu, Emily Jin, Zhuoyi Huang, Lun Yu Li, Jiajun Wu, Juan Carlos Niebles, Ehsan Adeli, Li Fei-Fei NeurIPS Datasets and Benchmarks, 2022
	More publications on Google Scholar

Experience

xAI · Member of Technical Staff · 2026–present

Microsoft · Senior Applied Scientist / Applied Scientist · 2023–2026

Stanford University · M.S. in Computer Science · 2021–2023

Stanford Medicine · Collaboration with Stephanie D. Chao

Teaching, interviews and activities

Over the years, I've been lucky to learn from generous mentors and inclusive communities. These experiences have made me want to give back through teaching, reviewing, and outreach.

Stanford CS 231N. Deep Learning for Computer Vision.
Course Assistant.

Stanford CS 131. Computer Vision: Foundations and Applications.
Course Assistant.

Stanford CS 224W. Machine Learning with Graphs.
Course Assistant.

Stanford CS 246. Mining Massive Data Sets.
Course Assistant.

Peer reviewer for MIDL 2024, ICML 2024, ICLR 2025, AAAI 2025 GenPlan Workshop, and ICLR 2026.

Website source from Jon Barron.