About Me
Xinshi Chen (陈心诗)
I am a Research Scientist at ByteDance Seed, where I work on AI for Science with a focus on biomolecular structure prediction and protein design.
My recent work centers on the Protenix family of open-source models and tools, spanning high-accuracy structure prediction, efficient inference, benchmarking, and de novo binder design. More broadly, I am interested in principled machine learning, learning-based algorithm design, and scientific applications at the interface of machine learning and biology.
Previously, I received my Ph.D. in Machine Learning from Georgia Tech, where I was advised by Prof. Le Song. Before that, I received my B.S. and M.Phil. in Mathematics from the Chinese University of Hong Kong under the supervision of Prof. Eric Chung. I have also spent time at Oak Ridge National Laboratory, Ant Financial, Facebook AI, and MBZUAI.
Email: xinshi [dot] chen [at] gatech [dot] edu (still accessible)
Google Scholar | Twitter | LinkedIn
Selected Recent Work
Protenix-v2: Broadening the Reach of Structure Prediction and Biomolecular Design
Yuxuan Zhang†, Chengyue Gong†, Jinyuan Sun†, Jiaqi Guan†, Milong Ren†, Song Xue†, Hanyu Zhang†, Wenzhi Ma†, Zhenyu Liu†, Xinshi Chen⋆, Wenzhi Xiao⋆
Technical report 2026
Protenix-v1: Toward High-Accuracy Open-Source Biomolecular Structure Prediction
Protenix Team, Yuxuan Zhang, Chengyue Gong, Hanyu Zhang, Wenzhi Ma, Zhenyu Liu, Xinshi Chen, Jiaqi Guan, Lan Wang, Yanping Yang, Yu Xia, and Wenzhi Xiao
bioRxiv 2026
project
PXDesign: Fast, Modular, and Accurate De Novo Design of Protein Binders
Protenix Team, Milong Ren, Jinyuan Sun, Jiaqi Guan, Cong Liu, Chengyue Gong, Yuzhe Wang, Lan Wang, Qixu Cai, Wenzhi Ma, Yuxuan Zhang, Zhenyu Liu, Hanyu Zhang, Xinshi Chen, and Wenzhi Xiao
bioRxiv 2025
project
Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 Reproduction
Protenix Team, Xinshi Chen, Yuxuan Zhang, Chan Lu, Wenzhi Ma, Jiaqi Guan, Chengyue Gong, Jincai Yang, Hanyu Zhang, Ke Zhang, Shenghao Wu, Kuangqi Zhou, Yanping Yang, Zhenyu Liu, Lan Wang, Bo Shi, Shaochen Shi, and Wenzhi Xiao
bioRxiv 2025
project
Earlier Preprint & Workshop
A Deep Learning Approach to Recover Conditional Independence graphs
Harsh Shrivastava, Urszula Chajewska, Robin Abraham, Xinshi Chen
NeurIPS 2022 Workshop: New Frontiers in Graph Learning
paper
Graph Condensation via Receptive Field Distribution Matching
Mengyang Liu, Shanchuan Li, Le Song, Xinshi Chen
Arxiv Preprint 2022
paper
Efficient Dynamic Graph Representation Learning at Scale
Xinshi Chen, Yan Zhu, Haowen Xu, Mengyang Liu, Liang Xiong, Muhan
Zhang, Le Song
Arxiv Preprint 2021
paper
A Framework For Differentiable Discovery Of Graph Algorithms
Hanjun Dai, Xinshi Chen, Yu Li, Xin Gao, Le Song
NeurIPS 2020 Workshop in Learning Meets Combinatorial Algorithms, Oral
paper | talk
Can Graph Neural Networks Help Logic Reasoning?
Yuyu Zhang*, Xinshi Chen*, Yuan Yang*, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song
NeurIPS 2019 Workshop in Knowledge Representation & Reasoning Meets Machine Learning
paper | poster
Review: Ordinary Differential Equations For Deep Learning
Xinshi Chen
Arxiv Preprent 2019
paper
Conference & Journal
Provable Learning-based Algorithm For Sparse Recovery
Xinshi Chen, Haoran Sun, Le Song
International Conference on Learning Representations (ICLR) 2022
paper
Multi-task Learning of Order-Consistent Causal Graphs
Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song
Advances in Neural Information Processing Systems (NeurIPS) 2021
paper | github | talk | slides
Understanding Deep Architectures With Reasoning Layer
Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song
Advances in Neural Information Processing Systems (NeurIPS) 2020
paper | github | talk | slides
Learning To Stop While Learning To Predict
Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song
International Conference on Machine Learning (ICML) 2020
paper | github | talk | slides
GLAD: Learning Sparse Graph Recovery
Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Han Liu, Le Song
International Conference on Learning Representations (ICLR) 2020
paper | github | talk
RNA Secondary Structure Prediction By Learning Unrolled Algorithms
Xinshi Chen*, Yu Li*, Ramzan Umarov, Xin Gao, Le Song (*equal contribution)
International Conference on Learning Representations (ICLR) 2020, Oral.
paper | github | talk | slides | news
Efficient Probabilistic Logic Reasoning with Graph Neural Networks
Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song
International Conference on Learning Representations (ICLR) 2020
paper | github | talk
Particle Flow Bayes' Rule
Xinshi Chen*, Hanjun Dai*, Le Song (*equal contribution)
International Conference on Machine Learning (ICML) 2019
paper | github | talk | slides | poster
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Song
International Conference on Machine Learning (ICML) 2019
paper | github | talk | slides | poster
A distinct class of vesicles derived from the trans-Golgi mediates secretion of xylogalacturonan in the root border cell
Pengfei Wang, Xinshi Chen, Cameron Goldbeck, Eric Chung, Byung-Ho Kang
The Plant Journal 2017
paper
Parametric Finite Element Method for Shape Optimization applied to Golgi Stack
CUHK Theses & Dissertations Collection 2017 [arxiv]
Committee: Prof. Raymond Honfu Chan, Prof. Ronald Lok Ming Lui, Prof. Eric Chung
Education
Ph.D. in Machine Learning
2017 - 2022
• Georgia Institute of Technology (Advisor: Prof. Le Song)
• Dissertation: Duality Between Deep Learning And Algorithm Design
M.Phil. (Master of Philosophy) in Mathematics
September 2015 - July 2017
• The Chinese University of Hong Kong (Advisor: Prof. Eric Chung)
• Thesis: Parametric Finite Element Method for Shape Optimization [PDF]
B.Sc. in Mathematics
September 2011 - July 2015
• The Chinese University of Hong Kong
• Exchange in math department at ETH Zurich, Switzerland January 2014 - June 2014
Experience
Research Scientist
Present
• ByteDance Seed, AI for Science
• Working on the Protenix family of open-source models and tools for biomolecular structure prediction and protein design.
Research Assistant
Feb 2021 - July 2021
• Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), United Arab Emirates
Research Intern
June 2020 - August 2020
• Facebook AI, Menlo Park.
Research Assistant or Teaching Assistant
September 2017 - 2022
• Teach in School of Mathematics, Georgia Institute of Technology.
• Work in Machine Learning Group, supervised by Prof. Le Song.
Research Intern
June 2018 - August 2018
• AI department, Ant Financial (affiliate company of Alibaba), Hangzhou, China
Teaching Assistant
August 2015 - June 2017
• Department of Mathematics, The Chinese University of Hong Kong
REU Research Intern
June 2014 - August 2014
• Oak Ridge National Laboratory, United States
• Mentor: Dr. Joshua Fu, Dr. John Drake and Dr. Kwai Wong
• Solve diffusion-convection equation based on finite element method.
• Title: Modeling Chemical Transport with Galerkin Methods[Project link]
Academic Service
• Program Committee / Reviewer: AAAI 2020-22, ICLR 2020-22, AISTAT 2020-22, MSML 2020-21, ICML 2020-21, NIPS 2020-21, IJCAL 2021
Teaching
School of Computational Science and Engineering, GaTech
• CSE6740 Computational Data Analysis, (Two Guest Lectures), Fall 2019
School of Mathematics, GaTech
• MATH2551 Multivariable Calculus, (Recitation Teaching), Spring 2018 & Fall 2017
Department of Mathematics, CUHK
• MATH3240 Numerical Methods for Differential Equations, (Tutorial), Spring 2016
• MATH3230 Numerical Analysis, (Tutorial), Fall 2016 & Fall 2015
• MATH2010 Advanced Calculus I, (Tutorial), Spring 2016
• MATH1510 Calculus for Engineers, (Tutorial), Fall 2015
Enrichment Programme for Young Mathematics Talents, Hong Kong
• SAYT1054 Mathematical Analysis, (Discussion Group), Fall 2013
Award
• Google PhD Fellowship, 2020-2022
• ICLR Travel Award, 2020
• ICML Travel Award, 2019
• Postgraduate Studentship, CUHK, 2015-2017
• Best oral presentation in 3rd AoE(Area of Excellence) Symposium, 2016
• Professor Charles K. Kao Research Scholarship, 2013-14
• College Head’s list - for outstanding academic performance, 2013-14
• Undergraduate Exchange Scholarship, 2013
Extra-Curriculum
Volunteer Experience
• Bronze Award for Volunteer Service (Individual), 2012, issued by HK Social Welfare Department
• Gold Award for Volunteer Service (Group), 2012, issued by HK Social Welfare Department
• Overall Best Mainland Service Project 2011-12, Caring Heart Community Service Project Scheme
Certificates
• Completion of the Mental Health First Aid Course, certified by MHFA International
• Advanced Open Water Diver, certified by PADI