Weitong Zhang

Weitong Zhang

Assistant Professor, School of Data Science and Society

CV

You can get a PDF version of my latest CV here

Education

Publications

* indicates Equal Contribution.

  1. Zhang, J., Zhang, W., Zhou, D., & Gu, Q. (2024). Uncertainty-Aware Reward-Free Exploration with General Function Approximation. ArXiv Preprint ArXiv:2406.16255.
  2. Sun, J., Zhang, W., Chen, Y., Hoar, B., Sheng, H., Yang, J., Costentin, C., Gu, Q., & Liu, C. (2024). What is the appropriate data representation of electrochemical impedance spectroscopy in machine-learning analysis?
  3. Zhang, W., Fan, Z., He, J., & Gu, Q. (2024). Settling Constant Regrets in Linear Markov Decision Processes. ArXiv Preprint ArXiv:2404.10745.
  4. Zhao, L., Deng, Y., Zhang, W., & Gu, Q. (2024). Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance. ArXiv Preprint ArXiv:2402.08680.
  5. Sheng, H., Sun, J., Rodrı́guez Oliver, Hoar, B. B., Zhang, W., Xiang, D., Tang, T., Hazra, A., Min, D. S., Doyle, A. G., & others. (2024). Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation. Nature Communications, 15(1), 2781.
  6. Lopez, V. K., Cramer, E. Y., Pagano, R., Drake, J. M., O’Dea, E. B., Adee, M., Ayer, T., Chhatwal, J., Dalgic, O. O., Ladd, M. A., & others. (2024). Challenges of COVID-19 Case Forecasting in the US, 2020–2021. PLoS Computational Biology, 20(5), e1011200.
  7. Deng, Y., Zhang, W., Chen, Z., & Gu, Q. (2023). Rephrase and respond: Let large language models ask better questions for themselves. ArXiv Preprint ArXiv:2311.04205.
  8. Hoar, B., Zhang, W., Chen, Y., Sun, J., Sheng, H., Zhang, Y., Yang, J., Costentin, C., Gu, Q., & Liu, C. (2023). Object-detecting deep learning for mechanism discernment in multi-redox cyclic voltammograms.
  9. Zhang, W., Wang, X., Nie, W., Eaton, J., Rees, B., & Gu, Q. (2023). MoleculeGPT: Instruction Following Large Language Models for Molecular Property Prediction. NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development.
  10. Zhang, W., He, J., Fan, Z., & Gu, Q. (2023). On the interplay between misspecification and sub-optimality gap in linear contextual bandits. International Conference on Machine Learning, 41111–41132.
  11. Zhang, J., Zhang, W., & Gu, Q. (2023). Optimal horizon-free reward-free exploration for linear mixture mdps. International Conference on Machine Learning, 41902–41930.
  12. Shea, K., Borchering, R. K., Probert, W. J. M., Howerton, E., Bogich, T. L., Li, S.-L., van Panhuis, W. G., Viboud, C., Aguás, R., Belov, A. A., & others. (2023). Multiple models for outbreak decision support in the face of uncertainty. Proceedings of the National Academy of Sciences, 120(18), e2207537120.
  13. Zhang, W., Wang, X., Smith, J., Eaton, J., Rees, B., & Gu, Q. (2023). Diffmol: 3d structured molecule generation with discrete denoising diffusion probabilistic models. ICML 2023 Workshop on Structured Probabilistic Inference {\Backslash&} Generative Modeling.
  14. Huang, Z., Hwang, J., Zhang, J., Baik, J., Zhang, W., Wodarz, D., Sun, Y., Gu, Q., & Wang, W. (2023). Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems. The Symbiosis of Deep Learning and Differential Equations III.
  15. Zhang, W., He, J., Zhou, D., Gu, Q., & Zhang, A. (2023). Provably efficient representation selection in low-rank Markov decision processes: from online to offline RL. Uncertainty in Artificial Intelligence, 2488–2497.
  16. Ji, K., Zhao, Q., He, J., Zhang, W., & Gu, Q. (2023). Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs. The Twelfth International Conference on Learning Representations.
  17. Hoar, B. B., Zhang, W., Xu, S., Deeba, R., Costentin, C., Gu, Q., & Liu, C. (2022). Electrochemical mechanistic analysis from cyclic voltammograms based on deep learning. ACS Measurement Science Au, 2(6), 595–604.
  18. Cramer, E. Y., Ray, E. L., Lopez, V. K., Bracher, J., Brennen, A., Castro Rivadeneira, A. J., Gerding, A., Gneiting, T., House, K. H., Huang, Y., & others. (2022). Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences, 119(15), e2113561119.
  19. Jia, Y., Zhang, W., Zhou, D., Gu, Q., & Wang, H. (2021). Learning Neural Contextual Bandits through Perturbed Rewards. International Conference on Learning Representations.
  20. Zhang, W., Zhou, D., & Gu, Q. (2021). Reward-free model-based reinforcement learning with linear function approximation. Advances in Neural Information Processing Systems, 34, 1582–1593.
  21. Bracher, J., Wolffram, D., Deuschel, J., Görgen, K., Ketterer, J. L., Ullrich, A., Abbott, S., Barbarossa, M. V., Bertsimas, D., Bhatia, S., & others. (2021). A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave. Nature Communications, 12(1), 5173.
  22. Zhang, W., Zhou, D., Li, L., & Gu, Q. (2020). Neural Thompson Sampling. International Conference on Learning Representations.
  23. Ray, E. L., Wattanachit, N., Niemi, J., Kanji, A. H., House, K., Cramer, E. Y., Bracher, J., Zheng, A., Yamana, T. K., Xiong, X., & others. (2020). Ensemble forecasts of coronavirus disease 2019 (COVID-19) in the US. MedRXiv, 2020–2008.
  24. Zou, D., Wang, L., Xu, P., Chen, J., Zhang, W., & Gu, Q. (2020). Epidemic model guided machine learning for COVID-19 forecasts in the United States. MedRxiv, 2020–2005.
  25. Wu, Y. F., Zhang, W., Xu, P., & Gu, Q. (2020). A finite-time analysis of two time-scale actor-critic methods. Advances in Neural Information Processing Systems, 33, 17617–17628.
  26. Liu, S., Zhang, W., Wu, X., Feng, S., Pei, X., & Yao, D. (2018). A simulation system and speed guidance algorithms for intersection traffic control using connected vehicle technology. Tsinghua Science and Technology, 24(2), 160–170.

Internship experience

Talks

Teaching

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Scholarships / Fellowships

I’m fortunate to be supported by the following scholarships and Fellowships