Han Zhao | Curriculum Vitae
3320 Siebel Center, 201 N Goodwin Ave, Urbana, IL – USA – 61801

 https://hanzhaoml.github.io/

hanzhao@illinois.edu

Æ (+1) 217-333-2064

Research Interests
Machine Learning: trustworthy machine learning, transfer and multitask learning, algorithmic fairness,domain adaptation/generalization
Artificial Intelligence: probabilistic circuits, graphical models

Professional Experience
University of Illinois at Urbana-Champaign
Department of Computer Science, Tenure-Track Assistant Professor
Aug. 2021 - Present
Department of Electrical and Computer Engineering, Assistant Professor (Affiliated)
Aug. 2021 - Present
Department of Computer Science, Adjunct Assistant Professor
Aug. 2020 - Aug. 2021
AmazonAmazon Visiting Academic

May 2022 - Present

The D. E. Shaw Group
Machine Learning Researcher

Mar. 2020 - Aug. 2021

Education
Carnegie Mellon University
Pittsburgh, PA, United States
PhD, Machine Learning Department, School of Computer Science
Sep. 2015 - Aug. 2020
○␣ Advisor: Prof. Geoff Gordon○␣ Thesis: Towards a Unified Framework for Learning and Reasoning, DOI: 10.13140/RG.2.2.10350.23363○␣ Thesis Committee: Geoffrey J. Gordon (chair), Ruslan Salakhutdinov, Barnabás Póczos and Tommi S. Jaakkola(Massachusetts Institute of Technology)
University of Waterloo
Master of Mathematics, Computer Science○␣ Advisor: Prof. Pascal Poupart○␣ Alumni Gold Medal Award, University of Waterloo

ON, Canada
Sep. 2013 - May. 2015

Tsinghua University
Bachelor of Engineering, Department of Computer Science○␣ Distinguished Graduate of Tsinghua University

Beijing, China
Aug. 2009 - July. 2013

University of Waterloo
Exchange Student, Computer Science○␣ Advisor: Prof. Pascal Poupart

ON, Canada
Sep. 2012 - May. 2013

Honors and Awards
Google Research Scholar Award
JSPS Fellow, Japan Society for the Promotion of Science○␣ AAAI New Faculty Highlights○␣ Kavli Fellow, National Academy of Sciences○␣ Teacher Ranked as Excellent, University of Illinois○␣ Facebook Research Award (Statistics for Improving Insights, Models, and Decisions)○␣ ICLR Outstanding Reviewer Award○␣ ICML Top Reviewer○␣ NeurIPS Highest-Scoring Reviewer
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2024
2024
2024
2023
2021-2023
2021
2021
2020
2018-2019

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Alumni Gold Medal Award, University of Waterloo
2015David R. Cheriton Graduate Scholarship, University of Waterloo
2013-2015○␣ International Masters Student Award, University of Waterloo
2013-2015○␣ Graduate Research Scholarship, University of Waterloo
2013-2015○␣ Mathematics Graduate Experience Award, University of Waterloo
2013○␣ Distinguished Graduate of Tsinghua University
2013○␣ Tsinghua University Scholarship for Academic Excellence
2010-2012○␣ Google Excellence Scholarship2012○␣ Liu Jimin Scholarship, Tsinghua University
2012○␣ Third Place in the poster competition of the 2012 REU Program, University of Notre Dame2012○␣ Second Prize in Contributions for Laboratory Construction, Tsinghua University
2011○␣ ICML Travel Grants
2015-2016, 2019
○␣ NIPS/NeurIPS Travel Grants
2016-2019○␣ ICLR Travel Grants
2018

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Research and Industry Experience
Simons Institute for the Theory of Computing, Berkeley
Long-Term Visitor○␣ Program: Modern Paradigms in Generalization

Aug. 2024 - Dec. 2024

Frontiers of Science Symposium, Japan Society for the Promotion of Science
JSPS Fellow○␣ Host: Prof. Kojiro Sho

July 2024 - Aug. 2024

Theoretical Sciences Visiting Program, Okinawa Institute of Science and Technology May 2024 - July 2024
Visiting Scholar○␣ Host: Prof. Makoto Yamada
Theoretical Sciences Visiting Program, Okinawa Institute of Science and Technology May 2023 - July 2023
Visiting Scholar○␣ Host: Prof. Makoto Yamada
Petuum, Inc.
Parttime Research InternJan. 2019 - May 2019

The D. E. Shaw Group
Quantitative Research Intern○␣ Mentor: Dr. Stephen Curran

May 2018 - Aug. 2018

Microsoft Research AI&R, RedmondResearch Intern○␣ Mentors: Dr. Ivan Tashev and Dr. Shuayb Zarar

May. 2017 - Aug. 2017

Baidu USA. Silicon Valley AI Lab
Research Intern○␣ Director: Prof. Andrew Ng. and Dr. Adam Coates

May. 2016 - Aug. 2016

Google Inc.
Software Engineer Intern○␣ Host: Jerred Costanzo

May. 2015 - Aug. 2015

Noah’s Ark Lab & The Chinese University of Hong Kong.
Research Intern○␣ Advisor: Dr. Zhengdong Lu and Dr. Hang Li

Sep. 2014 - Feb. 2015

Complex Network Lab, University of Notre Dame
Research Assistant○␣ Advisor: Prof. Tijana Milenković

June. 2012 - Aug. 2012

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Peer-Reviewed Conference Publications(∗ denotes equal contribution)[C1] H. Wang, Y. Lin, W. Xiong, R. Yang, S. Diao, S. Qiu, H. Zhao, T. Zhang, “Arithmetic Control of LLMs
for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards”, In
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Long Papers) (ACL2024)
[C2] R. Xian, Q. Li, G. Kamath, H. Zhao, “Differentially Private Post-Processing for Fair Regression”, In
Proceedings of the 41st International Conference on Machine Learning (ICML 2024) (27.5% acceptance rate)[C3] S. Liu, D. Zou, H. Zhao, P. Li, “Pairwise Alignment Improves Graph Domain Adaptation”, In Proceedingsof the 41st International Conference on Machine Learning (ICML 2024) (Spotlight) (3.5% acceptance rate)[C4] Y. He, S. Zhou, G. Zhang, H. Yun, Y. Xu, B. Zeng, T. Chilimbi, H. Zhao, “Robust Multi-Task Learning with
Excess Risks”, In Proceedings of the 41st International Conference on Machine Learning (ICML 2024) (27.5%
acceptance rate)[C5] Z. Gong, B. Usman, H. Zhao, D. I. Inouye, “Towards Practical Non-Adversarial Distribution Alignment via
Variational Bounds”, In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics(AISTATS 2024) (27.6% acceptance rate)[C6] G. Houry, H. Bao, H. Zhao, M. Yamada, “Fast 1-Wasserstein distance approximations using greedy
strategies”, In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS
2024) (27.6% acceptance rate)[C7] X. Han, J. Chi, Y. Chen, Q. Wang, H. Zhao, N. Zou, X. Hu, “FFB: A Fair Fairness Benchmark for InProcessing Group Fairness Methods”, In Proceedings of the 12th International Conference on Learning Representations (ICLR 2024) (30.8% acceptance rate)[C8] R. Xian, H. Zhuang, Z. Qin, H. Zamani, J. Lu, J. Ma, K. Hui, H. Zhao, X. Wang, M. Bendersky, “Learning
List-Level Domain-Invariant Representations for Ranking”. In Proceedings of the 37th Advances in Neural
Information Processing Systems (NeurIPS 2023) (Spotlight) (3.6% acceptance rate)[C9] Y. Hu, R. Xian, Q. Wu, Q. Fan, L. Yin, H. Zhao, “Revisiting Scalarization in Multi-Task Learning: A
Theoretical Perspective”. In Proceedings of the 37th Advances in Neural Information Processing Systems(NeurIPS 2023) (26.1% acceptance rate)[C10] S. Shin, I. Shomorony, H. Zhao, “Efficient Learning of Linear Graph Neural Networks via Node Subsampling”. In Proceedings of the 37th Advances in Neural Information Processing Systems (NeurIPS 2023) (26.1%
acceptance rate)[C11] C. Mavromatis, V. N. Ioannidis, S. Wang, D. Zheng, S. Adeshina, J. Ma, H. Zhao, C. Faloutsos, G. Karypis,“Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs”. In Proceedings of theEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECMLPKDD 2023) (24.0% acceptance rate)[C12] R. Xian, L. Yin, H. Zhao, “Fair and Optimal Classification via Post-Processing Predictors”. In Proceedingsof the 40th International Conference on Machine Learning (ICML 2023) (27.9% acceptance rate)[C13] Y. Hu, F. Wu, H. Zhang, and H. Zhao, “Understanding the Impact of Adversarial Robustness on Accuracy
Disparity”. In Proceedings of the 40th International Conference on Machine Learning (ICML 2023) (27.9%
acceptance rate)[C14] S. Liu, T. Li, Y. Feng, N. Tran, H. Zhao, Q. Qiu, and Pan Li, “Structural Re-weighting Improves Graph
Domain Adaptation”. In Proceedings of the 40th International Conference on Machine Learning (ICML 2023)(27.9% acceptance rate)[C15] Q. Jiang, C. Chen, H. Zhao, L. Chen, Q. Ping, S. Dinh Tran, Y. Xu, B. Zeng, T. Chilimbi, “Understanding
and Constructing Latent Modality Structures in Multi-modal Representation Learning”. In Proceedings ofthe IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023) (25.8% acceptance rate)
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[C16] S. Zeng, R. des Combes, H. Zhao, “Learning Structured Representations by Embedding Class Hierarchy”.
In Proceedings of the 11th International Conference on Learning Representations (ICLR 2023) (32.0% acceptancerate)[C17] S. Shin, H. Zhao, I. Shomorony, “Adaptive Power Method: Eigenvector Estimation from Sampled Data”. In
Proceedings of the 34th International Conference on Algorithmic Learning Theory (ALT 2023) (36.1% acceptancerate)[C18] Y. Shen, J. Du, H. Zhao, Z. Ji, C. Ma, M. Gao, “FedMM: Saddle Point Optimization for Federated Adversarial
Domain Adaptation”. In Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent
Systems (AAMAS 2023) (23.3% acceptance rate)[C19] J. Chi, W. Shand, Y. Yu, Kai-Wei Chang, H. Zhao, and Y. Tian, “Conditional Supervised Contrastive
Learning for Fair Text Classification”. In Findings of the Association for Computational Linguistics: EMNLP
2022 (EMNLP 2022 Findings) (14.0% acceptance rate)[C20] Z. Chen, R. Jiang, B. Duke, H. Zhao, and P. Aarabi, “Exploring Gradient-based Multi-directional Controls
in GANs”. In Proceedings of the European Conference on Computer Vision (ECCV 2022) (Oral) (2.7% acceptancerate)[C21] H. Wang, B. Li, H. Zhao, “Understanding Gradual Domain Adaptation: Improved Analysis, Optimal
Path and Beyond”. In Proceedings of the 39th International Conference on Machine Learning (ICML 2022)(21.9% acceptance rate)[C22] H. Wang, H. Si, B. Li, H. Zhao, “Provable Domain Generalization via Invariant-Feature Subspace Recovery”. In Proceedings of the 39th International Conference on Machine Learning (ICML 2022) (21.9% acceptancerate)[C23] R. Cheng, G. Balasubramaniam, Y. He, Y. H. Tsai, H. Zhao, “Greedy Modality Selection via Approximate
Submodular Maximization”. In Proceedings of the 38th conference on Uncertainty in Artificial Intelligence(UAI 2022) (32.3% acceptance rate)[C24] H. Shao, Y. Yang, H. Lin, L. Lin, Y. Chen, Q. Yang, H. Zhao, “Rethinking Controllable Variational
Autoencoders”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR
2022) (25.3% acceptance rate)[C25] R. Xian, H. Ji, H. Zhao, “Cross-Lingual Transfer with Class-weighted Language-Invariant Representations”. In Proceedings of the 10th International Conference on Learning Representations (ICLR 2022) (32.9%
acceptance rate)[C26] Y. H. Tsai, T. Li, M. Q. Ma, H. Zhao, K. Zhang, L-P. Morency, R. Salakhutdinov, “Conditional Contrastive
Learning with Kernel”. In Proceedings of the 10th International Conference on Learning Representations (ICLR
2022) (32.9% acceptance rate)[C27] J. Chi, J. Shen, X. Dai, W. Zhang, Y, Tian, H. Zhao, “Towards Return Parity in Markov Decision Processes”.
In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022) (29.2%
acceptance rate)[C28] S. Zhou, H. Zhao, S. Zhang, L, Wang, H Chang, Z. Wang, W. Zhu, “Online Continual Adaptation with
Active Self-Training”. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics(AISTATS 2022) (29.2% acceptance rate)[C29] B. Li, Y. Shen, Y. Wang, W. Zhu, C. Reed, J. Zhang, D. Li, K. Keutzer, H. Zhao, “Invariant Information
Bottleneck for Domain Generalization”. In Proceedings of the 36th AAAI Conference on Artificial Intelligence(AAAI 2022) (15.0% acceptance rate)[C30] G. Zhang, H. Zhao, Y. Yu, and P. Poupart, “Quantifying and Improving Transferability in Domain
Generalization”. In Proceedings of the 35th Advances in Neural Information Processing Systems (NeurIPS 2021)(25.7% acceptance rate)

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[C31] Z. Zhang, H. Wang, H. Zhao, H. Tong and H. Ji, “EventKE: Event-Enhanced Knowledge Graph Embedding”. In Findings of the Association for Computational Linguistics: EMNLP 2021 (EMNLP 2021 Findings)(11.8% acceptance rate)[C32] J. Chi, Y. Tian, G. Gordon and H. Zhao, “Understanding and Mitigating Accuracy Disparity in Regression”.
In Proceedings of the 38th International Conference on Machine Learning (ICML 2021) (21.5% acceptance rate)[C33] H. Wang, H. Zhao and B. Li, “Bridging Multi-Task Learning and Meta-Learning: Towards Efficient
Training and Effective Adaptation”. In Proceedings of the 38th International Conference on Machine Learning(ICML 2021) (21.5% acceptance rate)[C34] P. Liao∗ , H. Zhao∗ , K. Xu∗ , T. S. Jaakkola, G. Gordon, S. Jegelka and R. Salakhutdinov, “Information
Obfuscation of Graph Neural Networks”. In Proceedings of the 38th International Conference on Machine
Learning (ICML 2021) (21.5% acceptance rate)[C35] B. Li∗ , Y. Wang∗ , S. Zhang∗ , D. Li, T. Darrell, K. Keutzer and H. Zhao, “Learning Invariant Representations
and Risks for Semi-supervised Domain Adaptation”. In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR 2021) (23.7% acceptance rate)[C36] Y. H. Tsai, M. Q. Ma, M. Yang, H. Zhao, L-P Morency, R. Salakhutdinov, “Self-supervised Representation
Learning with Relative Predictive Coding”, In Proceedings of the 9th International Conference on Learning
Representations (ICLR 2021) (28.7% acceptance rate)[C37] P. Li, Y. Wang, H. Zhao, P. Hong, H. Liu, “On Dyadic Fairness: Exploring and Mitigating Bias in Graph
Connections”, In Proceedings of the 9th International Conference on Learning Representations (ICLR 2021)(28.7% acceptance rate)[C38] H. Zhao∗ , J. Chi∗ , Y. Tian and G. Gordon, “Trade-offs and Guarantees on Adversarial Representation
Learning for Information Obfuscation”, In Proceedings of the 34th Advances in Neural Information Processing
Systems (NeurIPS 2020) (20.1% acceptance rate)[C39] H. Zhao∗ , R. Combes∗ , Y.X. Wang and G. Gordon, “Domain Adaptation with Conditional Distribution
Matching and Generalized Label Shift”. In Proceedings of the 34th Advances in Neural Information Processing
Systems (NeurIPS 2020) (20.1% acceptance rate)[C40] J. Shen, H. Zhao, W. Zhang and Y. Yu. “Model-based Policy Optimization with Unsupervised Model
Adaptation”. In Proceedings of the 34th Advances in Neural Information Processing Systems (NeurIPS 2020)(Spotlight) (3.0% acceptance rate)[C41] Y. H. Tsai, H. Zhao, M. Yamada, L-P. Morency, R. Salakhutdinov, “Neural Methods for Point-wise Dependency Estimation”. In Proceedings of the 34th Advances in Neural Information Processing Systems (NeurIPS2020) (Spotlight) (3.0% acceptance rate)[C42] H. Zhao, J. Hu and A. Risteski, “On Learning Language-Invariant Representations for Universal MachineTranslation”. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020) (21.8%
acceptance rate)[C43] W. Wang, H. Zhao, H. Zhuang, N. Shah and R. Padman, “DyCRS: Dynamic Interpretable Postoperative
Complication Risk Scoring”. In The World Wide Web Conference (WWW 2020) (Oral) (19.2% acceptancerate)[C44] P. Li, H. Zhao and H. Liu, “Deep Fair Clustering for Visual Learning”. In Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition (CVPR 2020) (22.1% acceptance rate)[C45] H. Zhao, A. Coston, T. Adel and G. Gordon, “Conditional Learning of Fair Representations”. In Proceedingsof the 8th International Conference on Learning Representations (ICLR 2020) (Spotlight) (4.1% acceptance rate)[C46] T. Adel, H. Zhao and R. E. Turner, “Continual Learning with Adaptive Weights (CLAW)”. In Proceedingsof the 8th International Conference on Learning Representations (ICLR 2020) (26.5% acceptance rate)[C47] H. Zhao and G. Gordon, “Inherent Tradeoffs in Learning Fair Representations”. In Proceedings of the 33rd
Advances in Neural Information Processing Systems (NeurIPS 2019) (21.1% acceptance rate)
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[C48] H. Zhao∗ , Y. H. Tsai∗ , R. Salakhutdinov and G. Gordon, “Learning Neural Networks with Adaptive
Regularization”. In Proceedings of the 33rd Advances in Neural Information Processing Systems (NeurIPS 2019)(21.1% acceptance rate)[C49] H. Zhao, O. Stretcu, A. Smola and G. Gordon, “Efficient Multi-task Feature and Relationship Learning”.
In Proceedings of the 35th conference on Uncertainty in Artificial Intelligence (UAI 2019) (26.0% acceptance rate)[C50] H. Zhao∗ , Y. Xu∗ , X. Shi and N. B. Shah, “On Strategyproof Conference Peer Review”. In Proceedings of the
28th International Joint Conference on Artificial Intelligence (IJCAI 2019) (17.9% acceptance rate)[C51] H. Zhao, R. Combes, K. Zhang and G. Gordon , “On Learning Invariant Representation for Domain
Adaptation”. In Proceedings of the 36th International Conference on Machine Learning (ICML 2019) (LongOral) (2.3% acceptance rate)[C52] H. Zhao, J. Hu, Z. Zhu, A. Coates and G. Gordon, “Deep Generative and Discriminative Domain Adaptation”. In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems(AAMAS 2019) (24.3% acceptance rate)[C53] C. Liang, J. Ye, H. Zhao, B. Pursel and C. Lee Giles, “Active Learning of Strict Partial Orders: A Case
Study on Concept Prerequisite Relations”. In Proceedings of the 12th International Conference on Educational
Data Mining (EDM 2019) (34.6% acceptance rate)[C54] H. Zhao∗ , S. Zhang∗ , G. Wu, J. Costeira, J. Moura and G. Gordon, “Adversarial Multiple Source Domain
Adaptation”. In Proceedings of the 32nd Advances in Neural Information Processing Systems (NeurIPS 2018)(20.8% acceptance rate)[C55] H. Zhao and G. Gordon, “Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint”,
In Proceedings of the 34th conference on Uncertainty in Artificial Intelligence (UAI 2018) (30.8% acceptance rate)[C56] H. Zhao, S. Zarar, I. Tashev and C.-H. Lee, “Convolutional-Recurrent Neural Networks for Speech
Enhancement”. In Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP 2018) (Oral) (49.7% acceptance rate)[C57] H. Zhao and G. Gordon, “Linear Time Computation of Moments in Sum-Product Networks”. In Proceedings of the 31st Advances in Neural Information Processing Systems (NIPS 2017) (20.9% acceptance rate)[C58] T. Adel, H. Zhao and A. Wong, “Unsupervised Domain Adaptation with a Relaxed Covariate Shift
Assumption”. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI 2017) (24.6%
acceptance rate)[C59] H. Zhao, P. Poupart and G. Gordon, “A Unified Approach for Learning the Parameters of Sum-ProductNetworks”. In Proceedings of the 30th Advances in Neural Information Processing Systems (NIPS 2016) (23.6%
acceptance rate)[C60] P. Jaini, A. Rashwan, H. Zhao, Y. Liu, E. Banijamali, Z. Chen and P. Poupart, “Online Algorithms for
Sum-Product Networks with Continuous Variables”. In Proceedings of the 8th International Conference on
Probabilistic Graphical Models (PGM 2016)[C61] H. Zhao, T. Adel, G. Gordon and B. Amos, “Collapsed Variational Inference for Sum-Product Networks”.
In Proceedings of the 33rd International Conference on Machine Learning (ICML 2016) (24.0% acceptance rate)[C62] A. Rashwan, H. Zhao and P. Poupart, “Online and Distributed Bayesian Moment Matching for SPNs”. In
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016) (30.7%
acceptance rate)[C63] H. Zhao, M. Melibari and P. Poupart, “On the Relationship between Sum-Product Networks and Bayesian
Networks”. In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015) (26.0%
acceptance rate)[C64] H. Zhao, Z. Lu and P. Poupart, “Self-Adaptive Hierarchical Sentence Model”. In Proceedings of the 24th
International Joint Conference on Artificial Intelligence (IJCAI 2015) (28.6% acceptance rate)

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[C65] H. Zhao, P. Poupart, Y. Zhang and M. Lysy, “SoF: Soft-Cluster Matrix Factorization for Probabilistic Clustering”. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI 2015) (26.7% acceptancerate)[C66] T. Milenković, H. Zhao and F. Faisal, “Global Network Alignment in the Context of Aging". In Proceedingsof the 4th ACM International Conference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB
2013) (29.0% acceptance rate)

Journal Publications(∗ denotes equal contribution)[J1] V. Duong, Q. Wu, Z. Zhou, E. Zavesky, W-L. Hsu, H. Zhao, H. Shao, “A General-Purpose Multi-ModalOOD Detection Framework”. In Transactions on Machine Learning Research (TMLR 2024).
[J2] Y. He, H. Wang, B. Li, H. Zhao, “Gradual Domain Adaptation: Theory and Algorithms”, In Journal of
Machine Learning Research (JMLR 2024).
[J3] H. Wang, H. Si, H. Shao, H. Zhao, “Enhancing Compositional Generalization via Compositional Feature
Alignment”. In Transactions on Machine Learning Research (TMLR 2024).
[J4] Y. Yang; M. Lin, H. Zhao, Y. Peng, Z. Lu, “A Survey of Recent Methods for Addressing AI Fairness andBias in Biomedicine”, In Journal of Biomedical Informatics (JBI 2024).
[J5] Y. He, R. Cheng, G. Balasubramaniam, Y. H. Tsai, H. Zhao, “Efficient Modality Selection in Multimodal
Learning”, In Journal of Machine Learning Research (JMLR 2024).
[J6] X. Wang, H. Zhao, K. Nahrstedt, S. Koyejo, “Personalized Federated Learning with Spurious Features: An
Adversarial Approach”, In Transactions on Machine Learning Research (TMLR 2024).
[J7] J. Shen, H. Lai, M. Liu, H. Zhao, Y. Yu, and W. Zhang, “Adaptation Augmented Model-based Policy
Optimization”. In Journal of Machine Learning Research (JMLR 2023).
[J8] H. Zhao, “Costs and Benefits of Fair Regression”. In Transactions on Machine Learning Research (TMLR
2023).
[J9] J. Dong, S. Zhou, B. Wang, H. Zhao, “Algorithms and Theory for Supervised Gradual Domain Adaptation”.
In Transactions on Machine Learning Research (TMLR 2022).
[J10] H. Zhao∗ , C. Dan∗ , B. Aragam, T. Jaakkola, G. Gordon, and P. Ravikumar, “Fundamental Limits andTradeoffs in Invariant Representation Learning”. In Journal of Machine Learning Research (JMLR 2022).
[J11] H. Zhao and G. Gordon, “Inherent Tradeoffs in Learning Fair Representations”. In Journal of Machine
Learning Research (JMLR 2022).
[J12] S. Zhao, X. Yue, S. Zhang, B. Li, H. Zhao, B. Wu, R. Krishna, J. E. Gonzalez, A. L. Sangiovanni-Vincentelli,
S. A. Seshia and K. Keutzer, “A Review of Single-Source Deep Unsupervised Visual Domain Adaptation”.
In IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS 2020).
[J13] F. Faisal, H. Zhao and T. Milenković, “Global Network Alignment in the Context Of Aging”, In IEEE/ACM
Transactions on Computational Biology and Bioinformatics (IEEE/ACM TCBB 2014).

Workshop Papers(∗ denotes equal contribution)[W1] W. Chu, C. Xie, B. Wang, L. Li, L. Yin, H. Zhao, B. Li, “FOCUS: Fairness via Agent-Awareness forFederated Learning on Heterogeneous Data” In International Workshop on Federated Learning in the Age ofFoundation Models (NeurIPS 2023) (Oral)[W2] Z. Chen, H. Zhao, P. Aarabi, R. Jiang, “SC2GAN: Rethinking Entanglement by Self-correcting theCorrelated GAN Space”. In The 2nd Workshop and Challenges for Out-of-Distribution Generalization in
Computer Vision (ICCV 2023)
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[W3] G. Balasubramaniam, H. Wang, and H. Zhao, “Invariant Feature Subspace Recovery for Multi-ClassClassification”. In Workshop on Distribution Shifts: Connecting Methods and Applications (NeurIPS 2022)[W4] M. Q. Ma, Y.-H H. Tsai, P. P. Liang, H. Zhao, K. Zhang, R. Salakhutdinov, L.-P. Morency, “Conditional
Contrastive Learning for Improving Fairness in Self-Supervised Learning”. In Workshop on Self-Supervised
Learning – Theory and Practice (NeurIPS 2022)[W5] Y. He, H. Wang, and H. Zhao, “Generative Gradual Domain Adaptation with Optimal Transport”. In
Principles of Distribution Shift (PODS) (ICML 2022)[W6] H. Zhao, A. Coston, T. Adel and G. Gordon, “Conditional Learning of Fair Representations”. In Workshop
on Machine Learning with Guarantees (NeurIPS 2019).
[W7] H. Zhao∗ , J. Chi∗ , Y. Tian and G. Gordon, “Adversarial Privacy Preservation under Attribute Inference
Attack”. In Workshop on Machine Learning with Guarantees (NeurIPS 2019).
[W8] H. Zhao∗ , Y. H. Tsai∗ , R. Salakhutdinov and G. Gordon, “Approximate Empirical Bayes for Deep Neural
Networks”. In Uncertainty in Deep Learning workshop (UAI 2018).
[W9] H. Zhao∗ , S. Zhang∗ , G. Wu, J. Costeira, J. Moura and G. Gordon, “Multiple Source Domain Adaptation
with Adversarial Learning”. In Proceedings of the 6th International Conference on Learning Representations(ICLR 2018, workshop track).
[W10] Y. H. Tsai, H. Zhao, R. Salakhutdinov and N. Jojic, “Discovering Order in Unordered Datasets: Generative
Markov Networks”. In Time Series workshop (NIPS 2017, arXiv:1711.03167).
[W11] H. Zhao, O. Stretcu, R. Negrinho, A. Smola and G. Gordon, “Efficient Multi-task Feature and Relationship
Learning”. In Learning with Limited Labeled Data: Weak Supervision and Beyond workshop (NIPS 2017,arXiv:1702.04423).
[W12] H. Zhao and P. Poupart, “A Sober Look at Spectral Learning”. In Method of Moments and Spectral Learning
workshop (ICML 2014, arXiv:1406.4631).

Patents[P1] I. Tashev, S. Zarar, C-H. Lee, Y-H. Tu and H. Zhao, “Systems, Methods, and Computer-Readable Mediafor Improved Real-Time Audio Processing”. US Patent. US 15/952,353.

Conference and Invited Talks
Revisiting Scalarization in Multi-Task Learning○␣ Department of Computer Science, Fudan University, China○␣ International Chinese Statistical Association (ICSA), Wuhan, China○␣ Center for Machine Learning Research, Peking University, China○␣ Statistical Artificial Intelligence and Learning Group, Tsinghua University, China○␣ Deep Learning: Theory, Applications, and Implications (DL2024), Tokyo, Japan

July 2024
June 2024
June 2024
June 2024
Mar. 2024

Towards Foundation Models for Geospatial Data○␣ Department of Architecture and Urban Design, Kyushu University

Mar. 2024

Directional Preference Alignment with Multi-Objective Rewards for LLMs
○␣ 1st Workshop on Machine Learning for Large Models, MBZUAI, Abu Dhabi, UAE○␣ AI & Search Science Talk Series, Amazon

June 2024
Mar. 2024

Algorithmic Fairness and Robust Generalization from a Causal Perspective○␣ Technical Seminar, Radix Trading, LLC.
○␣ Spatial Omics Initiative, IGB Center for Artificial Intelligence and ModelingFeb. 2024
Feb. 2024

Trustworthy Machine Learning: Theory, Algorithms and Applications○␣ Machine Learning Summer School, Okinawa, Japan

Mar. 2024

Robust Learning under Distribution Shifts
8/13Advanced Controls Research Laboratory, University of Illinois Urbana-Champaign
Apr. 2024
Trustworthy AI workshop, Osaka University, Japan
Mar. 2024○␣ Machine Learning Summer School, Okinawa, Japan
Mar. 2024○␣ Kavli Frontiers of Science: Japanese-American-German Frontiers of Science Symposium (JAGFOS), Dresden,
Germany
Oct. 2023○␣○␣

Fair and Optimal Prediction via Post-Processing○␣ New Faculty Highlights, AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada Feb. 2024
○␣ Dagstuhl Seminars: Emerging Issues in Bioimaging AI Publications, GermanyJan. 2024○␣ Waterloo Artificial Intelligence Institute (Waterloo.ai), University of Waterloo
Nov. 2023○␣ Methods and Theory Seminar Series, Department of Statistical Sciences, University of Toronto
Nov. 2023○␣ Lunch & Learn Seminar Series, Modiface Inc., Toronto, Canada
Nov. 2023○␣ Computational Biology Branch, National Center for Biotechnology Information
Nov. 2023○␣ The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL), Northwestern University Oct. 2023
○␣ Computer Engineering Seminar, Purdue University
Oct. 2023○␣ The Information-Based Induction Sciences and Machine Learning (IBISML) Workshop, OIST, Japan July 2023
Robust Multi-Task Learning with Excess Risks○␣ AI & Search Science Talk Series, Amazon○␣ AWS Tech-Talk Series, Amazon○␣ Stores Foundational AI Tech Talk, Amazon

May 2023
June 2023
May 2024

Learning Structured Representations by Embedding Class Hierarchy○␣ M5 Tech-Talk Series, AI & Search Science, Amazon

Oct. 2023

Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning○␣ AI & Search Science Talk Series, Amazon
Aug. 2022
Provable Domain Generalization via Invariant-Feature Subspace Recovery○␣ TrustML Young Scientist Seminars, RIKEN, Japan○␣ Statistical Artificial Intelligence and Learning Group, Tsinghua University, China

Mar. 2023
July 2022

Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond○␣ Dagstuhl Seminars: Recent Advancements in Tractable Probabilistic Inference, Germany○␣ Microsoft Research, Distinguished Talk Series, USA

Apr. 2022
May 2022

Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation○␣ AI & Search Science Talk Series, Amazon
Mar. 2022
Costs and Benefits of Invariant Representation Learning○␣ University of California, Berkeley, ML & CV Seminar○␣ Google Research○␣ Tsinghua University, XuetangX Seminar○␣ (Virtual) Brown University, Department of Computer Science○␣ University of California, Santa Barbara, Department of Computer Science○␣ University of North Carolina, Chapel Hill, Department of Computer Science○␣ (Virtual) Hong Kong University of Science and Technology, Department of Computer Science○␣ University of Illinois at Urbana-Champaign, Department of Computer Science○␣ University of Virginia, Department of Computer Science○␣ Dartmouth College, Department of Computer Science○␣ University of Waterloo, David R. Cheriton School of Computer Science○␣ Borealis AI Lab, Toronto○␣ Pennsylvania State University, Department of Computer Science and Engineering○␣ Brandeis University

July 2020
July 2020
June 2020
Apr. 2020
Mar. 2020
Mar. 2020
Mar. 2020Feb. 2020Feb. 2020Feb. 2020Feb. 2020Feb. 2020Feb. 2020Feb. 2020

Inherent Tradeoffs in Learning Invariant Representations○␣ Massachusetts Institute of Technology○␣ Brandeis University

Oct. 2019Nov. 2019

Learning Neural Networks with Adaptive Regularization○␣ ShanghaiTech University

Aug. 2019

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New York University, Shanghai
Tencent AI Lab○␣ Noah’s Ark Lab, Huawei○␣ Toutiao AI Lab, ByteDance○␣ Amazon AWS AI Lab, Shanghai○␣ Borealis AI Lab, Waterloo○␣ University of Waterloo, Waterloo

Aug. 2019
Aug. 2019
Aug. 2019
Aug. 2019
Sep. 2019
Sep. 2019
Sep. 2019

On Strategyproof Conference Peer Review○␣ International Joint Conference on Artificial Intelligence

Aug. 2019

On Learning Invariant Representations for Domain Adaptation○␣ Microsoft Research Seminar series, Microsoft Research Montreal Lab, Canada○␣ International Conference on Machine Learning

May. 2019
June 2019

Multiple Source Domain Adaptation with Adversarial Learning○␣ AI seminar, Carnegie Mellon University○␣ Microsoft Research Seminar series, Microsoft Research Montreal Lab, Canada○␣ Technical seminar, Petuum Inc.
○␣ Technical seminar, Pony. AI

Apr. 2018
Sep. 2018
Oct. 2018
Nov. 2018

High-Accuracy Neural-Network Models for Speech Enhancement○␣ Microsoft Research Seminar series, Microsoft Research Redmond Lab○␣ IEEE International Conference on Acoustics, Speech and Signal Processing

Aug. 2017
Apr. 2018

Sum-Product Networks: A New Probabilistic Inference Machine○␣ AI seminar, Carnegie Mellon University

Mar. 2017

Collapsed Variational Inference for Sum-Product Networks○␣ International Conference on Machine Learning

June 2016

On the Relationship between Sum-Product Networks and Bayesian Networks○␣ AI seminar, University of Waterloo○␣ International Conference on Machine Learning

Mar. 2015
July 2015

Self-Adaptive Hierarchical Sentence Model○␣ AI seminar, University of Waterloo○␣ Technical seminar, Google○␣ International Joint Conference on Artificial Intelligence○␣ AI lunch, Jump Trading

Mar. 2015
Apr. 2015
July 2015
Nov. 2017

SoF: Soft-Cluster Matrix Factorization for Probabilistic Clustering○␣ Association for the Advancement of Artificial IntelligenceJan. 2015○␣○␣

Professional Activities
National Science Foundation (NSF) review panelist○␣ CISE: Core Programs: IIS○␣ OAC: OAC Core
○␣ GEO/RISE: CAIG

2022
2023
2024

Mentoring Program○␣ Mentorship Program (ICLR)

2024

Conference Area Chair/Senior Program Committee○␣ Area Chair, International Conference on Artificial Intelligence and Statistics (AISTATS)○␣ Senior Program Committee, AAAI Conference on Artificial Intelligence (AAAI)○␣ Area Chair, International Conference on Machine Learning (ICML)○␣ Area Chair, Neural Information Processing Systems (NeurIPS)○␣ Area Chair, International Conference on Learning Representations (ICLR)

2024
2021-2025
2020-2024
2021-2024
2024

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Conference Reviewer/Program Committee Member○␣ IEEE International Symposium on Information Theory (ISIT)
2021○␣ Advances in Neural Information Processing Systems (NIPS/NeurIPS)
2016, 2018-2021○␣ International Conference on Machine Learning (ICML)
2017-2021○␣ International Conference on Artificial Intelligence and Statistics (AISTATS)
2017-2021○␣ International Conference on Learning Representations (ICLR)
2019-2021○␣ International Conference on Computer Vision (ICCV)2019○␣ Conference on Computer Vision and Pattern Recognition (CVPR)
2020○␣ European Conference on Computer Vision (ECCV)2020○␣ Time Series Workshop (ICML)
2021○␣ IEEE Workshop on Statistical Signal Processing (SSP)2016○␣ Workshop on Adaptive & Multitask Learning: Algorithms & Systems (ICML)
2019○␣ Workshop on Statistical Deep Learning in Computer Vision (ICCV)2019○␣ AAAI Conference on Artificial Intelligence (AAAI)
2019-2020○␣ International Joint Conference on Artificial Intelligence (IJCAI)
2020-2021○␣ Asian Conference on Machine Learning (ACML)
2019○␣ Deep Generative Models Workshop, ICML
2018○␣ Workshop on Tractable Probabilistic Models, ICML
2018○␣ Workshop on Principled Approaches to Deep Learning, ICML
2017○␣ Workshop on Learning with Rich Experience: Integration of Learning Paradigms, NeurIPS
2019
Journal Reviewer○␣ SIAM Journal on Mathematics of Data Science (SIMODS)○␣ IEEE Computational Intelligence Magazine○␣ Neural Computing○␣ Transactions on Audio, Speech and Language Processing (TASLP)○␣ Pattern Analysis and Applications○␣ PLoS One○␣ IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)○␣ Machine Learning Journal (MLJ)○␣ IEEE Access○␣ Journal of Artificial Intelligence Research (JAIR)○␣ Journal of Machine Learning Research (JMLR)○␣ Transactions on Machine Learning Research (TMLR)(Co-) Organizers○␣ ML for Large Models Workshop, Mohamed bin Zayed University of Artificial Intelligence: MBZUAI○␣ CVPR 2021 Responsible Computer Vision Workshop○␣ KDD 2021 Machine Learning for Consumers and Markets Workshop

Professional Membership○␣○␣Member of the Association for the Advancement of Artificial Intelligence
Member of Association for Computing Machinery

2023 - Present
2021 - Present

Student Advising
Ph.D. Students○␣ Weixin Chen (UIUC CS PhD)○␣ Yifei He (UIUC CS PhD)○␣ Yuzheng Hu (UIUC CS PhD)
○␣ Seiyun Shin (UIUC ECE PhD, co-advised with Ilan Shomorony, Mavis Future Faculty Fellows, 2023-2024)○␣ Haozhe Si (UIUC ECE PhD)○␣ Haoxiang Wang (UIUC ECE PhD, co-advised with Bo Li → Research Scientist, Nvidia) Mavis Future Faculty
Fellows, 2022-2023○␣ Ruicheng Xian (UIUC CS PhD)

11/13

○␣

Siqi (Cindy) Zeng (UIUC CS PhD)

Thesis-based Master Students○␣ Gargi Balasubramaniam (UIUC MSCS → Research Engineer, Google DeepMind) Siebel Scholar, Class of ’23○␣ Yifei He (UIUC MSCS → UIUC CS PhD)○␣ Haozhe Si (UIUC ECE MS → UIUC ECE PhD)
○␣ Aditya Sinha (UIUC MSCS → Research Scientist, Netflix)○␣ Qilong Wu (UIUC MSCS → UIUC CS PhD)
Undergraduate Mentoring○␣ Samuel Schapiro (UIUC)○␣ Sixian Du (Peking University → Stanford MSEE)○␣ Siqi (Cindy) Zeng (CMU → UIUC CS PhD)○␣ Haozhe Si (UIUC → UIUC ECE Master) Thesis: ISR: Invariant Subspace Recovery○␣ Peiyuan Liao (CMU → CTO of Cyber Manufacture Co.)○␣ Bo Li (Harbin Institute of Technology → Ph.D. student, National University of Singapore)
Ph.D. Thesis Committee○␣ Chulin Xie, University of Illinois Urbana-Champaign○␣ Qingyun Wang, University of Illinois Urbana-Champaign○␣ Liliang Ren, University of Illinois Urbana-Champaign○␣ Kung-Hsiang (Steeve) Huang, University of Illinois Urbana-Champaign○␣ Tianshi Wang, University of Illinois Urbana-Champaign○␣ Qian Jiang, University of Illinois Urbana-Champaign○␣ Jun Wu, University of Illinois Urbana-Champaign○␣ Jian Kang, University of Illinois Urbana-Champaign○␣ (Vicki) Qi Zeng, University of Illinois Urbana-Champaign○␣ Olawale Elijah Salaudeen, University of Illinois Urbana-Champaign○␣ Shiji Zhou, Tsinghua University○␣ Jianfeng Chi, University of Virginia

Advisor: Bo Li
Advisor: Heng Ji
Advisor: Chengxiang Zhai
Advisor: Heng Ji
Advisor: Tarek Abdelzaher
Advisor: Minh N. Do
Advisor: Jingrui He
Advisor: Hanghang Tong
Advisor: Heng Ji
Advisor: Sanmi Koyejo
Advisor: Wenwu Zhu
Advisor: Yuan Tian

Teaching Experience
Instructor, University of Illinois at Urbana-Champaign○␣ CS 446: Machine Learning
Spring 2024
○␣ CS 442: Trustworthy Machine Learning (Teacher Ranked as Excellent, University of Illinois)
Fall 2023○␣ CS 598: Transfer Learning
Spring 2023
○␣ CS 498: Trustworthy Machine Learning
Fall 2022○␣ CS 442: Trustworthy Machine Learning (Teacher Ranked as Excellent, University of Illinois) Spring 2022
○␣ CS 598: Transfer Learning (Teacher Ranked as Excellent, University of Illinois)
Fall 2021
Instructor, TechX Academy○␣ Advanced Introduction to Deep Learning
Teaching Assistant, Carnegie Mellon University○␣ Introduction to Machine Learning○␣ Convex Optimization○␣ Undergraduate Computational Complexity Theory

Summer 2019
Spring 2016
Fall 2017
Spring 2018

Teaching Assistant, Tsinghua University○␣ Introduction to Information Retrieval

Fall 2012

Teaching Assistant, University of Waterloo○␣ Designing Functional Programs

Fall 2013

Department and University Service
University of Illinois at Urbana-Champaign
12/13

Department of Computer Science, PhD Qualification Exam Committee
Department of Computer Science, PhD Admission Committee○␣ Department of Computer Science, Academic Appeals Committee○␣ Department of Computer Science, Faculty Search Sub-Committee○␣○␣Carnegie Mellon University○␣ Machine Learning Department, PhD Admission Committee○␣ Machine Learning Department, PhD Speaking Skills Committee○␣ School of Computer Science, coordinator of AI Seminar

Fall 2021 Fall 2020 Spring 2022 Spring 2023 20172018 - 20202018 - 2020

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