Chenyu (Monica) Wang

About Me

I am a fourth-year PhD student at MIT CSAIL, advised by Tommi Jaakkola. My research interests lie broadly in deep generative models, reinforcement learning, multi-modal learning, and AI for science. During my PhD, I was an research intern at Meta FAIR and Genentech. My research has been supported by the Citadel GQS PhD Fellowship.

Before my PhD, I obtained my Bachelor’s degree from Tsinghua University, working as a research assistant in Tsinghua Universal Machine Learning (THUML) Group under the supervision of Mingsheng Long. I was also fortunate to work as a research intern with Mengdi Wang at Princeton University and with Cyrus Shahabi at University of Southern California.

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Resume (Updated in Aug 2025)

Research

My research focuses on developing controllable and efficient generative models, via reinforcement learning, multi-modal learning, and representation learning. I work across various application domains, including language models, vision, and scientific data (e.g. biochemistry). My recent work explores:

• Reinforcement learning and inference-time alignment for deep generative models and language models. [7, 9, 11, 12]
• Multi-modal learning, especially multi-modal representations and their interactions with generative models. [4, 6, 8, 10, 15]
• Enhancing biochemistry discovery through generative models and agents. [3, 5, 13, 14]

Publications & Preprints

Full publication list can be found on Google Scholar.
(* Equal Contribution)

15. Learning Diffusion Models with Flexible Representation Guidance Chenyu Wang*, Cai Zhou*, Sharut Gupta, Zongyu Lin, Stefanie Jegelka, Stephen Bates, Tommi Jaakkola Preprint. Also Oral at ICML 2025 FM4LS workshop. [Paper] [Code]

14. SpatialAgent: An Autonomous AI Agent for Spatial Biology Hanchen Wang*#, Yichun He*, Paula P Coelho*, Matthew Bucci*, ..., Chenyu Wang, ..., Aviv Regev# Preprint. [Paper] [Code]

13. CellFlux: Simulating Cellular Morphology Changes via Flow Matching Yuhui Zhang*, Yuchang Su*, Chenyu Wang, Tianhong Li, Zoe Wefers, Jeffrey Nirschl, James Burgess, Daisy Ding, Alejandro Lozano, Emma Lundberg, Serena Yeung-Levy International Conference on Machine Learning. ICML 2025. [Paper] [Project Webpage]

12. Inference-time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review Masatoshi Uehara, Yulai Zhao, Chenyu Wang, Xiner Li, Aviv Regev, Sergey Levine, Tommaso Biancalani Preprint. [Paper] [Code]

11. GLID$^2$E: A Gradient-Free Lightweight Fine-tune Approach for Discrete Sequence Design Hanqun Cao*, Haosen Shi*, Chenyu Wang, Sinno Jialin Pan, Pheng-Ann Heng ICLR 2025 GenBio Workshop. [Paper]

10. Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation Yuhui Zhang*, Yuchang Su*, Yiming Liu, Xiaohan Wang, James Burgess, Elaine Sui, Chenyu Wang, Josiah Aklilu, Alejandro Lozano, Anjiang Wei, Ludwig Schmidt, Serena Yeung-Levy IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2025. [Paper] [Project Webpage] [Code]

9. Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design Chenyu Wang*, Masatoshi Uehara*, Yichun He, Amy Wang, Tommaso Biancalani, Avantika Lal, Tommi Jaakkola, Sergey Levine, Hanchen Wang, Aviv Regev International Conference on Learning Representations. ICLR 2025. [Paper] [Code]

8. An Information Criterion for Controlled Disentanglement of Multimodal Data Chenyu Wang*, Sharut Gupta*, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi Jaakkola, Caroline Uhler International Conference on Learning Representations. ICLR 2025. Also Oral and Honorable Mention Award at NeurIPS 2024 UniReps workshop. [Paper] [Code]

7. Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding Xiner Li, Yulai Zhao, Chenyu Wang, Gabriele Scalia, Gokcen Eraslan, Surag Nair, Tommaso Biancalani, Aviv Regev, Sergey Levine, Masatoshi Uehara Preprint. [Paper] [Code]

6. In-Context Symmetries: Self-Supervised Learning through Contextual World Models Sharut Gupta*, Chenyu Wang*, Yifei Wang*, Tommi Jaakkola, Stefanie Jegelka, Advances in Neural Information Processing Systems. NeurIPS 2024. Also Oral at NeurIPS 2024 SSL workshop. [Paper] [Code] [MIT CSAIL News]

5. Dirichlet Flow Matching with Applications to DNA Sequence Design Hannes Stark*, Bowen Jing*, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi Jaakkola, International Conference on Machine Learning. ICML 2024. Also Oral at ICLR 2024 MLGenX workshop. [Paper] [Code]

4. Removing Biases from Molecular Representations via Information Maximization Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi Jaakkola International Conference on Learning Representations. ICLR 2024. [Paper] [Code]

3. Tree-Based Neural Bandits for High-Value Protein Design Chenyu Wang*, Joseph Kim*, Le Cong, Mengdi Wang 56th Annual Conference on Information Sciences and Systems. CISS 2022. [Paper]

2. HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting Chenyu Wang*, Zongyu Lin*, Xiaochen Yang, Mingxuan Yue, Jiao Sun, Cyrus Shahabi AAAI Conference on Artificial Intelligence. AAAI 2022. (Oral Presentation) [Paper] [Code] [Talk at TGL]

1. Open Domain Generalization with Domain-Augmented Meta-Learning Yang Shu*, Zhangjie Cao*, Chenyu Wang, Jianmin Wang, Mingsheng Long IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2021. [Paper] [Code]

Education and Research Experience

Massachusetts Institute of Technology 2022.08-Present PhD student in Computer Science Advisor: Tommi Jaakkola

Meta FAIR 2024.05-2024.08 Research intern Advisor: Yuandong Tian, Bo Liu

Genentech 2024.05-2024.08 Research intern Advisor: Aviv Regev Mentor: Hanchen Wang, Masatoshi Uehara

Tsinghua University 2018.08-2022.06 B.S. in Economics Minor in Data Science and Technology Advisor: Mingsheng Long Mentor: Yang Shu

Princeton University 2021.06-2021.12 Research intern Advisor: Mengdi Wang, Le Cong Mentor: Joseph Kim, Huazheng Wang

University of Southern California 2021.01-2021.06 Research intern Advisor: Cyrus Shahabi Mentor: Jiao Sun, Mingxuan Yue

Selected Awards

• 2025    Citadel GQS PhD Fellowship (the only recipient in EECS)
• 2025    D. E. Shaw Research Doctoral Fellowship
• 2024    Honorable Mention Award, NeurIPS 2024 UniReps Workshop
• 2022    MIT EECS Great Educators Fellowship
• 2022    Outstanding Undergraduate in Tsinghua (2% in Tsinghua)
• 2022    Outstanding Undergraduate in Beijing
• 2022    Chen Daisun Scholarship (3 in Tsinghua SEM)
• 2022    Undergraduate Commencement Student Speaker of Tsinghua SEM
• 2021    Meritorious Winner in MCM/ICM Mathematical Contest in Modelling
• 2020    Tang Lixin Scholarship (50 in Tsinghua)
• 2019    National Scholarship (0.2% in China)
• 2019    Athletics Excellence Scholarship of Tsinghua
• 2018    Gold medalist of 50th International Chemistry Olympiad (4 in China, 6th place in the world)
• 2016    Silver medalist of 15th China Girl’s Mathematical Olympiad (50 in China)
  
  

Internship Experience

• Meta FAIR, Research Intern, May 2025 - Aug. 2025
• Genentech, Research Intern at Regev Lab, May 2024 - Aug. 2024
• Jane Street, Quantitative Trading Intern (return offer extended), Jun. 2021 - Sept. 2021
• WizardQuant Capital Management, Quantitative Research Intern, Jun. 2020 - Aug. 2020
• Techsharpe Quant Capital Management, Data Analyst Intern, Jan. 2020 - Feb. 2020
  
  

Services

Reviewer: ICLR 2025/2026, NeurIPS 2024/2025, ICML 2025, PLOS Computational Biology

Miscellaneous

I enjoy (and perhaps good at) doing sports. During undergrad, I was an active member in the track team and soccer team in my school, getting 1st place in 4*400m relay, 3rd place in 1500m, women’s soccer champion etc. I’m also a fan of literature and classical music. I enjoy travelling and tasting local delicacies.

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