Ge Liu (刘謌)

Assistant Professor in Computer Science, UIUC
Ph.D. in Computer Science, MIT
Ex Post-doc at Baker Lab, Institute for Protein Design, UW
Email:geliu@illinois.edu, geliu@csail.mit.edu
[Google scholar] [Github] [CV]
 

I am an Assistant Professor at the Department of Computer Science of the University of Illinois at Urbana-Champaign (UIUC). I am looking for PhD students starting in 2026 Fall (application due Dec 2025). I received my PhD from MIT EECS department, advised by professor David Gifford from Computer Science and Artificial Intelligence Laboratory (CSAIL).Prior to joining UIUC, I spent an amazing year at the Institute for Protein Design as a postdoctoral researcher advised by professor David Baker. My research develops multimodal, scalable, and geometry-aware deep generative models and sequential optimization techniques, as well as novel experiment frameworks and computational tools, for solving important problems in life sciences. I am especially interested in geometric DL and multi-modal flow matching & diffusion methods for computational design of functional and therapeutic molecules, including but not limited to antibody design, enzyme and catalyst design and drug discovery. Machine learning wise I'm working on geometry-aware multimodal flow matching/diffusion models, diffusion LLMs, sequential optimization and RL with generative priors, post-training and steering foundational generative models, active learning and model uncertainty. My PhD thesis won the MIT EECS George M. Sprowls Ph.D. Thesis Award in AI and Decision-Making in 2021.

I recieved my bachelor degree from Tsinghua University EE department, where I worked as a research assistant in Machine Learning and Computational Biology Group (IIIS), advised by Prof. Jianyang Zeng. I was a visiting scholar at CMU in 2014 summer and worked in Murphy Lab, Lane Center for Computational Biology, advised by professor Robert F. Murphy.

For Prospective Students and Interns: I am actively looking for highly-motivated Ph.D. students and interns to work on problems in the exciting frontiers of ML and AI4Science. If you are interested in joining my group, please send me an email at geliu[at]illinois[dot]edu with your CV and research summary. Students with strong motivation to do good science and make an impact in biomedicine while innovating in ML/CS are highly welcomed. Some of my research topics include but not limited to:
  • Deep generative models: multimodal flow matching on Riemannian manifolds and discrete data / geometry-aware, physics-informed deep generative modeling, and solving complex guided and conditional generation tasks and inverse problems with diffusion/FM prior.
  • RL post-training with generative priors, using uncertainty-aware learning, online learning algorithms (e.g. RLHF, bandits), active learning, Bayesian optimization.
  • Optimization: efficient algorithms for solving combinatorial optimization, discrete optimization, and black-box optimization problems in real-world biomedicine design problems.
  • Application to functional biological molecule design (antibodies, enzymes) and drug discovery.

News

  • [2025/9]Three papers accepted to NeurIPS 2025!
  • [2025/1]Three papers accepted to ICLR 2025!
  • [2024/9]Three papers accepted to NeurIPS 2024! And Protein Generator paper is out on Nature Biotech.
  • [2024/8]I started my position as an assistant professor in UIUC CS
  • [2023/10]I joined Baker Lab at the Institue for Protein Design at UW as a postdoc
  • [2023/5]I will be joining UIUC CS department as an assistant professor in 2024
  • [2023/4]Delighted to give an invited talk at UT Austin CS
  • [2023/3]Our Covid-19 vaccine design work is covered by MIT News and Boston Globe!
  • [2023/3]Delighted to give an invited talk at U Chicago CS Seminar
  • [2023/3]Delighted to give an invited talk at NYU Courant
  • [2023/2]Our paper on pan-variant Covid19 vaccine design with animal study is accepted to Frontiers in Immunology
  • [2023/2]Delighted to give an invited talk at UIUC CS Seminar
  • [2022/7]Our paper on antibody computational counter-panning is accepted by Cell Reports Methods
  • [2022/1]2 papers accepted to ICLR 2022

Selected Publications

  1. Riemannian Consistency Model [PDF]
    Chaoran Cheng, Yusong Wang, Yuxin Chen, Xiangxin Zhou, Nanning Zheng, Ge Liu
    Advances in Neural Information Processing Systems (NeurIPS 2025).
  2. Adaptive Divergence Regularized Policy Optimization for Fine-tuning Generative Models [PDF]
    Jiajun Fan, Tong Wei, Chaoran Cheng, Yuxin Chen, Ge Liu
    Advances in Neural Information Processing Systems (NeurIPS 2025).
  3. Variational Supervised Contrastive Learning [PDF]
    Ziwen Wang, Jiajun Fan, Thao Nguyen, Heng Ji, Ge Liu
    Advances in Neural Information Processing Systems (NeurIPS 2025).
  4. Training Free Guided Flow Matching with Optimal Control [PDF]
    Luran Wang, Chaoran Cheng, Yizhen Liao, Yanru Qu, Ge Liu
    International Conference on Learning Representations (ICLR 2025).
  5. Online Reward-Weighted Fine-Tuning of Flow Matching with Wasserstein Regularization [PDF]
    Jiajun Fan, Shuaike Shen, Chaoran Cheng, Yuxin Chen, Chumeng Liang, Ge Liu
    International Conference on Learning Representations (ICLR 2025).
  6. Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension [PDF]
    Jiahan Li, Tong Chen, Shitong Luo, Chaoran Cheng, Jiaqi Guan, Ruihan Guo, Sheng Wang, Ge Liu, Jian Peng, Jianzhu Ma
    International Conference on Learning Representations (ICLR 2025).
  7. Categorical Flow Matching on Statistical Manifolds [PDF]
    Chaoran Cheng, Jiahan Li, Jian Peng, Ge Liu
    Advances in Neural Information Processing Systems (NeurIPS 2024) 37.
  8. Multistate and functional protein design using RoseTTAFold sequence space diffusion [PDF]
    Lisanza, S.L., Gershon, J.M., Tipps, S.W., Sims, J.N., Arnoldt, L., Hendel, S.J., Simma, M.K., Liu, G., Yase, M., ... & Baker, D.
    Nature Biotechnology, 2024.
  9. Neural P3M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs [PDF]
    Yusong Wang, Chaoran Cheng, Shaoning Li, Yuxuan Ren, Bin Shao, Ge Liu, Pheng-Ann Heng, Nanning Zheng
    Advances in Neural Information Processing Systems (NeurIPS 2024) 37.
  10. Optimal design for human preference elicitation [PDF]
    Subhojyoti Mukherjee, Anusha Lalitha, Kousha Kalantari, Aniket Deshmukh, Ge Liu, Yifei Ma, Branislav Kveton
    Advances in Neural Information Processing Systems (NeurIPS 2024) 37.
  11. Pessimistic Off-Policy Multi-Objective Optimization [PDF]
    Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu
    27th International Conference on Artificial Intelligence and Statistics (AISTATS 24).
  12. A pan-variant mRNA-LNP T cell vaccine protects HLA transgenic mice from mortality after infection with SARS-CoV-2 Beta [PDF]
    Brandon Carter, Pinghan Huang, Ge Liu, Yuejin Liang, Paulo JC Lin, Bi-Hung Peng, Lindsay McKay, Alexander Dimitrakakis, Jason Hsu, Vivian Tat, Panatda Saenkham-Huntsinger, Jinjin Chen, Clarety Kaseke,Gaurav D Gaiha, Qiaobing Xu, Anthony Griffiths, Ying K Tam, Chien-Te K Tseng, David K Gifford.
    Frontiers in Immunology, 2023.
  13. Maximum n-times Coverage for Vaccine Design. [PDF]
    Ge Liu,Alexander Dimitrakakis, Brandon Carter, and David K. Gifford
    Proceedings of the 10th International Conference on Learning Representations (ICLR 2022).
  14. Bridging Recommendation and Marketing via Recurrent Intensity Modeling [PDF]
    Yifei Ma, Ge Liu,Anoop Deoras.
    Proceedings of the 10th International Conference on Learning Representations (ICLR 2022).
  15. Sequence-graph duality: Unifying user modeling with self-attention for sequential recommendation [PDF]
    Zeren Shui, Ge Liu, Anoop Deoras, George Karypis
    New Frontiers in Graph Learning Workshop, NeurIPS 2022.
  16. Computational counterselection identifies nonspecific therapeutic biologic candidates [PDF]
    Sachit Dinesh Saksena, Ge Liu, Christine Banholzer, Geraldine Horny, Stefan Ewert, David K. Gifford
    Cell reports methods, 2022
  17. Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and their Augmentation by Compact Peptide Sets. [PDF]
    Ge Liu, Brandon Carter, and David K. Gifford
    Cell systems, 2021
  18. Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions. [PDF]
    Ge Liu, Brandon Carter, Trenton Bricken, Siddhartha Jain, Mathias Viard, Mary Carrington, and David K. Gifford
    Cell systems, 2020
  19. Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles. [PDF]
    Ge Liu*, Siddhartha Jain*, Jonas Mueller, and David K. Gifford,
    34th AAAI Conference on Artificial Intelligence (AAAI 2020).
  20. Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing (work done during intership at Google Brain, in collaboration with DeepMind). [PDF]
    Ge Liu, Heng-tze Cheng, Rui Wu, Jing Wang, Jayden Ooi, Sibon Li, Ang Li, Lihong Li, Craig Boutilier, Ed Chi,
    Deep Reinforcement Learning Workshop, NeurIPS 2019
    Optimization Foundations of RL workshop, NeurIPS 2019.
  21. Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning. [PDF]
    Ge Liu*, Haoyang Zeng*, Jonas Mueller, Brandon Carter, Ziheng Wang, Jonas Schilz, Geraldine Horny, Michael E Birnbaum, Stefan Ewert, David K. Gifford,
    Bioinformatics, 2019
  22. Information Condensation Active Learning. [preprint]
    Siddhartha Jain, Ge Liu, David K. Gifford,
    under review .
  23. Visualizing Complex Feature Interactions and Feature Fharing in Genomic Deep Neural Networks. [PDF]
    Ge Liu, Haoyang Zeng, David K. Gifford,
    BMC bioinformatics 20 (1), 1-14, 2019
  24. Visualizing Feature Maps in Deep Neural Networks using DeepResolve-A Genomics Case Study. [PDF]
    Ge Liu, David K. Gifford,
    Workshop on Visualization for Deep Learning, ICML 2017
  25. Convolutional Neural Network Architectures for Predicting DNA–protein Binding. [PDF]
    Haoyang Zeng, Matthew D. Edwards, Ge Liu, David K. Gifford,
    Bioinformatics, 32 (12), i121-i127, 2016

Work Experience

  • AWS AI Labs, Senior Applied Scientist                    Sep 2020 - Oct 2023
    I am working with AWS science team on large scale deep sequential modeling, personalized recommender system,and modeling for time series.
  • Google Brain, Research Intern                    May 2019 - Aug 2019
    I was working with Mixel (SIR) team at Google Brain on inventing novel data-efficient training approach for Reinforcement Learning with Adaptive Behavior Policy Sharing.
  • Google Brain, Research SWE Intern                June 2018 - Aug 2018
    I was working with medical audio scribe modeling team at Google Brain on active learning for natural language processing tasks in health-care domain.

Honors and Awards

  • MIT EECS George M. Sprowls Ph.D. Thesis Award in AI and Decision-Making (awarded to 2 PhD students in EECS),  2021
  • The Data Open Datathon Global Championship, Finalist,  2019
  • The Data Open Datathon Boston-regional, top 3,  2018
  • David S. Y. Wong Fellowship at MIT,  2016
  • Friendship of Tsinghua-Sumsung Scholarship,  2013
  • Outstanding Social Work Scholarship,  2012
  • 2nd prize in Chinese Physics Olympiad for High school, Beijing,  2011
  • Massachusetts Math Olympiad Level One, Finalist, top 25 of Massachusetts State,  2008

Misc.

  • I am a Rock Band vocalist of JAM-SOUL, a student band at MIT