Machine Learning Research

Yaochen
(Ethan) Xie

Senior Applied Scientist @ Amazon Search

I work on making machine learning reliable and broadly useful — from image denoising and self-supervised learning to LLM post-training, reward modeling, and recommender systems.

About

I received my Ph.D. in Computer Science from Texas A&M University. Before that, I obtained my B.S. in Statistics from the School of the Gifted Young at the University of Science and Technology of China. My research aims to improve the applicability and reliability of machine learning to drive industrial applications and scientific discovery (AI4Science). I currently serve as an Area Chair for Language and Molecule @ ACL and Scaling Environments for Agents @ NeurIPS, and on the program committees of several conferences.

Research Interests

Language Models & Agentic AIPost-Training · Reward Modeling · Harness · Evolving Agent & Environment
Representation & Foundation ModelsSelf-Supervised Learning · Graph Neural Networks · Model Explainability · Multi-Modality
ApplicationsRecommender Systems & Search (Model Compression, RAG) · AI4Science (Geometric Deep Learning, Bioinformatics, Biomedical Imaging)

Selected Publications

* Equal contribution.

The World Won't Stay Still: Programmable Evolution for Agent Benchmarks

Guangrui Li, Yaochen Xie, Yi Liu, Ziwei Dong, Xingyuan Pan, Tianqi Zheng, Jason Choi, Michael J. Morais, Binit Jha, Shaunak Mishra, Bingrou Zhou, Chen Luo, Monica Xiao Cheng, Dawn Song

Preprint · 2025

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Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies

Yaochen Xie, Yuchao Lin, Ziqian Xie, Sheikh Muhammad Saiful Islam, Degui Zhi, Shuiwang Ji

Transactions on Machine Learning Research (TMLR) · 2024

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SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations

Xuan Zhang, Jacob Helwig, Yuchao Lin, Yaochen Xie, Cong Fu, Stephan Wojtowytsch, Shuiwang Ji

International Conference on Learning Representations (ICLR) · 2024

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Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Xuan Zhang*, Limei Wang*, Jacob Helwig*, Youzhi Luo*, Cong Fu*, Yaochen Xie*, … Shuiwang Ji

Preprint · 2023

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Task-Agnostic Graph Explanations

Yaochen Xie, Sumeet Katariya, Xianfeng Tang, Edward Huang, Nikhil Rao, Karthik Subbian, Shuiwang Ji

Neural Information Processing Systems (NeurIPS) · 2022

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Self-Supervised Representation Learning via Latent Graph Prediction

Yaochen Xie*, Zhao Xu*, Shuiwang Ji

International Conference on Machine Learning (ICML) · 2022

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Group Contrastive Self-Supervised Learning on Graphs

Xinyi Xu, Cheng Deng, Yaochen Xie, Shuiwang Ji

IEEE TPAMI · 2022

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Self-Supervised Learning of Graph Neural Networks: A Unified Review

Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, Shuiwang Ji

IEEE TPAMI · 2022

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Augmented Equivariant Attention Networks for Microscopy Image Transformation

Yaochen Xie, Yu Ding, Shuiwang Ji

IEEE Transactions on Medical Imaging (TMI) · 2022

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Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery

Zhengyang Wang*, Meng Liu*, Youzhi Luo*, Zhao Xu*, Yaochen Xie*, Limei Wang*, Lei Cai*, Qi Qi, Zhuoning Yuan, Tianbao Yang, Shuiwang Ji

Bioinformatics · 2022

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DIG: A Turnkey Library for Diving into Graph Deep Learning Research

Meng Liu*, Youzhi Luo*, Limei Wang*, Yaochen Xie*, Hao Yuan*, Shurui Gui*, Haiyang Yu*, … Shuiwang Ji

Journal of Machine Learning Research (JMLR) · 2021

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Global Voxel Transformer Networks for Augmented Microscopy

Zhengyang Wang*, Yaochen Xie*, Shuiwang Ji

Nature Machine Intelligence · 2021

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Noise2Same: Optimizing a Self-Supervised Bound for Image Denoising

Yaochen Xie, Zhengyang Wang, Shuiwang Ji

Neural Information Processing Systems (NeurIPS) · 2020

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Finding the Stars in the Fireworks: Deep Understanding of Motion Sensor Fingerprint

Xiang-Yang Li, Huiqi Liu, Lan Zhang, Zhenan Wu, Yaochen Xie, Ge Chen, Chunxiao Wan, Zhongwei Liang

IEEE/ACM Transactions on Networking · 2019

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Tutorial

Frontiers of Graph Neural Networks with DIG

Shuiwang Ji*, Meng Liu*, Yi Liu, Youzhi Luo, Limei Wang*, Yaochen Xie*, Zhao Xu*, Haiyang Yu*

ACM SIGKDD (KDD) · 2022

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Services

Area Chair

Language and Molecules @ ACL2024

Scaling Environments for Agents @ NeurIPS2025

Program Committee

NeurIPS2021–2023

ICML2022–2024

KDD2021–2022

ICLR2022–2023

Journal Reviewer

IEEE TPAMI · TIP · TNNLS

TMLR · Nature Communications · DAMI

Miscellaneous

Meet our family member Sesame, a bernedoodle born on 7/10/2023. Pup training overfits easily.

I love performance driving and am interested in auto engineering & tuning. My favorite drive is the Local 2.0, WA.