About me
I’m a Research Scientist at Snowflake AI Research, where I train reasoning models and LLM agents, especially for structured data. Before that, I was a Research Scientist on Bytedance’s Seed‑Foundation‑Code team.
I earned my Ph.D. from the University of Illinois at Urbana‑Champaign under Prof. Ruoyu Sun, with co‑advising from Prof. Naira Hovakimyan. In my first year I collaborated with Prof. Justin Sirignano on deep‑learning for computational fluid dynamics (CFD), and during my master’s studies I worked with Prof. Kyle Smith on numerical simulation for energy storage systems.
Research interests: Reasoning Models; Agent; Efficient Deep Learning; Natural Language Processing; Computer Vision; Numerical Methods.
Hiring: Snowflake AI Research is seeking research scientist interns for remote, long-term positions (3-6 months, part-time) starting August 2025. Projects include:
Post‑training tool‑augmented LLMs
Development of personalized Text‑to‑SQL models
Deep Research on structured data
Developing and training of data‑science agents
If you are interested, don’t hesitate to send your resume to yite.wang@snowflake.com .
News
[April. 2025] I have joined Snowflake AI Research working on reasoning models and LLM Agents.
[May. 2024] I have joined ByteDance Seed-Foundation-Code as a Research Scientist working on LLM for code.
[Mar. 2024] I have successfully defended my Ph.D. thesis!
[Jan. 2024] Our paper on model expansion is accepted to ICLR’2024!
[Oct. 2023] I have received the NeurIPS 2023 Scholar Award!
[Sept. 2023] Our paper about dynamic sparse training for GANs is accepted to NeurIPS’2023!
[Jan. 2023] Our paper about foresight pruning is accepted to ICLR’2023!
[March. 2022] Our paper about neural architecture search for meta learning is accpeted to CVPR’2022!
Publications
( $\dagger$ denotes to equal contribution)
Fullstack bench: Evaluating llms as full stack coder [arXiv]
Seed-Foundation-Code Team, ByteDance
Designing Large Foundation Models for Efficient Training and Inference: A Survey [arxiv]
Dong Liu, Yanxuan Yu, Yite Wang, Jing Wu, Zhongwei Wan, Sina Alinejad, Benjamin Lengerich, Ying Nian Wu
LEMON: Lossless model expansion [arXiv, code]
Yite Wang, Jiahao Su, Hanlin Lu, Cong Xie, Tianyi Liu, Jianbo Yuan, Haibin Lin, Ruoyu Sun, Hongxia Yang
ICLR’2024：International Conference on Learning Representations
Balanced Training for Sparse GANs [arXiv, page, code]
Yite Wang$\dagger$, Jing Wu$\dagger$, Naira Hovakimyan, Ruoyu Sun
NeurIPS’2023: Neural Information Processing Systems
NTK-SAP: Improving neural network pruning by aligning training dynamics [arXiv, page, code]
Yite Wang, Dawei Li, Ruoyu Sun
ICLR’2023: International Conference on Learning Representations
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning [arXiv, code]
Haoxiang Wang$\dagger$, Yite Wang$\dagger$, Ruoyu Sun, Bo Li
CVPR’2022: The IEEE/CVF Computer Vision and Pattern Recognition Conference
Numerical investigation of convective transport in redox flow battery tanks: Using baffles to increase utilization [paper]
Yite Wang, Kyle Smith
Journal of Energy Storage