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ABOUT ME
ABOUT ME
Hi, I’m Donglin Zhan (詹东林)!
I am a final-year Ph.D student at Columbia University advised by James Anderson. My research interests generally lie in Machine Learning and Data Mining, as well as Optimization and Control Theory. Recently, my interests have shifted more towards agentic AI and world model, particularly for complex planning and algorithmic trading.
Previously, I received my bachelor's degree in Mathematics from Sichuan University.
I adore Icarus, a figure in ancient Greek mythology, and I wish I could always be brave in pursuing the life I want.
I am seeking jobs in trading industry (AI/ML researcher and quantitative researcher).
E-mail: donglin.zhan AT columbia.edu, icarusjanestephen AT gmail.com
SOME RECENT RESEARCH
SOME RECENT RESEARCH
Donglin Zhan, Haoting Zhang, Rhonda Righter, Zeyu Zheng, James Anderson Collaborative Bayesian Optimization with Wasserstein Barycenters IEEE Conference on Decision and Control 2025 (CDC) [paper]
Donglin Zhan, Leonardo F. Toso, James Anderson Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning IEEE Conference on Decision and Control 2025 (CDC) [paper]
Haoting Zhang*, Donglin Zhan*, James Anderson, Rhonda Righter, Zeyu Zheng Clustering then Estimation of the Spatio-Temporal Self-Exciting Processes, INFORMS Journal on Computing [paper]
Leonardo F. Toso, Donglin Zhan, James Anderson, Han Wang Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for the Model-free LQR, Learning for Dynamics and Control Conference 2024 (L4DC) (Best Paper Award) [paper]
Donglin Zhan, James Anderson Data-Efficient and Robust Task Selection for Meta-Learning, IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (CVPR) Workshop [paper]
Haoting Zhang, Haoxian Chen, Donglin Zhan, Hanyang Zhao, Henry Lam, Wenping Tang, David Yao, Zeyu Zheng SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations, Short Version on NeurIPS 2025 MLxOR Workshop
Haoting Zhang*, Donglin Zhan*, Yunduan Lin, Jinghai He, Qing Zhu, Max Zuo-Jun Shen, Zeyu Zheng Daily Physical Activity Monitoring—Adaptive Learning from Multi-source Motion Sensor Data, Conference on Health, Inference, and Learning (CHIL) 2024 [paper]
Donglin Zhan, Yusheng Dai, Yiwei Dong, Jinghai He, Zhenyi Wang, James Anderson Meta-Adaptive Stock Movement Prediction with Two-Stage Representation Learning, Workshop on Distribution Shift, Neural Information Processing System 2022 (NeurIPS), SIAM Conference on Data Mining 2024 (SDM) [paper]
Yunkai Zhang, Donglin Zhan, Haoting Zhang, Max Zuo-Jun Shen, Zeyu Zheng, Qing Zhu Does Attention Help Wildfire Prediction, INFORMS Data Mining and Data Analysis Workshop 2024 [paper]
Zhenyi Wang, Li Shen, Donglin Zhan, Qiuling Suo, Yanjun Zhu, Tiehang Duan, Mingchen Gao MetaMix: Towards Corruption-Robust Continual Learning with Temporally Self-Adaptation Data Transformation, IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (CVPR) [paper]
Zhenyi Wang, Li Shen, Tongliang Liu, Tiehang Duan, Yanjun Zhu, Donglin Zhan, David Doermann, Mingchen Gao Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training, Neural Information Processing Systems 2023 (NeurIPS) [paper]
Lizi Zhang*, Donglin Zhan* Stock Movement Prediction via Multi-source Transfer with Covariate Shift Detection, Workshop on ML in Finance, International Conference on Knowledge Discovery and Data Mining 2022 (SIGKDD) [paper]
Zhenyi Wang, Li Shen, Tiehang Duan, Donglin Zhan, Le Fang, Mingchen Gao Learning to Learn and Remember Super Long Multi-Domain Task Sequence, IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022 (CVPR) (Oral) [paper]
Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Donglin Zhan, Tiehang Duan, Mingchen Gao Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions, European Conference on Computer Vision 2022 (ECCV) [paper]
Haoting Zhang, Jinghai He, Donglin Zhan, Zeyu Zheng Neural Network-Assisted Simulation Optimization with Covariates, Winter Simulation Conference 2021 (WSC) [paper]
......
EXPERIENCE
EXPERIENCE
Machine Learning Engineer Intern, ByteDance, Bellevue, WA, Summer 2024
Research Scientist Intern, ByteDance, San Jose, CA, Summer 2023
Machine Learning Engineer Intern, JD.com AI Research, Beijing, China, Summer 2021
Research Assistant, University at Buffalo, Buffalo, NY, 2019 - 2020
Research Assistant, Brandeis University, Waltham, MA, 2018
PEER REVIEWER
PEER REVIEWER
Conference: NeurIPS, ICLR, ICML, CVPR, AISTATS, VLDB, ICDE, ECML-PKDD, ICASSP, L4DC
Journal: Neurocomputing, Pattern Recognition, IEEE Transactions on Vehicular Technology, IEEE Transactions on Automatic Control
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