Harry Hongyu Guo, Ph.D.
Email: hongyu.guo@uottawa.ca
Senior Researcher Officer, National Research Council Canada (NRC)
Adjunct Professor, School of Electrical Engineering and Computer Science, University of Ottawa
I am passionate about advancing machine learning and deep learning for transformative applications across scientific discovery, natural language processing, and computer vision.
I hold a BEng from Shanghai Jiao Tong University and a PhD from the University of Ottawa, both in computer science.
Recent News
- A molecule search project for health and climate solutions with Prof. Yoshua Bengio at Mila
- A molecule graph representation and generation project with Prof. Jian Tang at Mila
Service and Awards
- [Recent program committee members] ICLR19-22, NeurIPS16-21, ICML18-21, ACL21,19,17, EMNLP20,15-18, NAACL18,16,15
- [Research awards]
Excellence in Research Award at NRC-DT 2024
Best Paper Award at Canadian AI 2021
Best Paper Award at *SEM2015
Best Paper Award at MLDM2012
Research Breakthrough of the Year Award at NRC-IIT2011
Distinguished Papers Award at ECML-PKDD2006
Recent Preprints
- [AI for Materials] OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes.
Félix Therrien, Jamal Abou Haibeh, Divya Sharma, Rhiannon Hendley, Alex Hernández-García, Sun Sun, Alain Tchagang, Jiang Su, Samuel Huberman, Yoshua Bengio, Hongyu Guo, Homin Shin.
arXiv 2025 - [AI for Drugs] A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics.
Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Vignesh Bhethanabotla, Nakul Rampal, Omar Yaghi, Christian Borgs, Anima Anandkumar, Hongyu Guo, Jennifer Chayes.
arXiv 2023
Selected Publications [Full List]
- [AI for Drugs] Structure Language Models for Protein Conformation Generation.
Lu, J., Chen, X., Lu, S., Shi, C., Guo, H., Bengio, Y., Tang, J.
ICLR 2025 - [AI for Materials] AssembleFlow: Rigid Flow Matching with Inertial Frames for Molecular Assembly.
Guo, H., Bengio, Y., Liu, S.
ICLR 2025 - [AI for Drugs] A Text-guided Protein Design Framework.
Liu, S., Li, Y., Li, Z., Gitter, A., Zhu, Y., Lu, J., Xu, Z., Nie, W., Ramanathan, A., Xiao, C., Tang, J., Guo, H., & Anandkumar, A.
Nature Machine Intelligence 2025 - [AI for Drugs] Conversational Drug Editing Using Retrieval and Domain Feedback.
Liu, S., Wang, J., Yang, J., Wang, C., Liu, L., Guo, H., & Xiao, C.
ICLR 2024 - [Machine Learning] Calibration Attacks: A Comprehensive Study of Adversarial Attacks on Model Confidence.
Obadinma, S., Zhu, X., & Guo, H.
TMLR 2024 - [AI for Drugs] Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering.
Li, T., Guo, H., Grazioli, F., Gerstein, M., & Min, M.
NeurIPS 2023 - [AI for Drugs/Materials] Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials.
Liu, S., Du, W., Li, Y., Li, Z., Zheng, Z., Duan, C., Ma, Z., Yaghi, O., Anandkumar, A., Borgs, C., Chayes, J., Guo, H., & Tang, J.
NeurIPS 2023 - [AI for Drugs] A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining.
Liu, S., Du, W., Ma, Z., Guo, H., & Tang J.
ICML 2023 - [AI for Drugs] Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching.
Liu, S., Guo, H., & Tang, J.
ICLR 2023 - [Machine Learning] Over-Training with Mixup May Hurt Generalization.
Liu, Z., Wang, Z., Guo, H., & Mao, Y.
ICLR 2023 - [Machine Learning] Interpolating Graph Pair to Regularize Graph Classification.
Guo, H., & Mao, Y.
AAAI 2023 - [Machine Learning] f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning.
Lu, Y., Zhang, G., Sun, S., Guo, H., & Yu, Y.
TMLR 2023 - [Machine Learning] Information Bottleneck and Aggregated Learning.
Soflaei, M., Zhang, R., Guo, H., Al-Bashabsheh, A., & Mao, Y.
TPAMI 2023 - [AI for Drugs] T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precision Immunotherapy.
Chen, Z., Min, R., Guo, H., Cheng, C., Clancy, T., & Ning, X.
RECOMB 2023 - [AI for Drugs] Binding Peptide Generation for MHC Class I Proteins with Deep Reinforcement Learning.
Chen, Z., Zhang, B., Guo, H., Emani, P., Clancy, T., Jiang, C., Gerstein, M., Ning, X., Cheng, C., & Min, M.
Bioinformatics 2023 - [AI for Drugs] Pre-training Molecular Graph Representation with 3D Geometry.
Liu, S., Wang, H., Liu, W., Lasenby, J., Guo, H., & Tang, J.
ICLR 2022 - [AI for Drugs] Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction.
Bi, H., Wang, H., Shi, C., Coley, C., Tang, J., & Guo, H.
ICML 2021 - [AI for Drugs] Self-supervised Graph-level Representation Learning with Local and Global Structure.
Xu, M., Wang, H., Ni, B., Guo, H., & Tang, J.
ICML 2021 - [Machine Learning] Symmetric Wasserstein Autoencoders.
Sun, S., & Guo, H.
UAI 2021 - [Machine Learning] Class-wise Calibration: A Case Study on COVID-19 Hate Speech.
Obadinma, S. G. H. Z. X.
CAI 2021 - [Machine Learning] Midpoint Regularization: from High Uncertainty Training Labels to Conservative Classification Decisions.
Guo, H.
ECML-PKDD 2021 - [Machine Learning] Nonlinear Mixup: Out-Of-Manifold Data Augmentation for Text Classification.
Guo, H.
AAAI 2020 - [Machine Learning] Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers.
Soflaei, M., Guo, H., Al-Bashabsheh, A., Mao, Y., & Zhang, R.
AAAI 2020 - [AI for Drugs] A Graph to Graphs Framework for Retrosynthesis Prediction.
Shi, C., Xu, M., Guo, H., Zhang, M., & Tang, J.
ICML 2020 - [NLP] Dynamic Graph Convolutional Networks for Entity Linking.
Wu, J., Zhang, R., Mao, Y., Guo, H., Soflaei, M., & Huai, J.
WWW 2020 - [Machine Learning] MixUp as Locally Linear Out-of-Manifold Regularization.
Guo, H., Mao, Y., & Zhang, R.
AAAI 2019 - [NLP] Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework.
Chen, J., Zhang, R., Mao, Y., Guo, H., & Xu, J.
EMNLP 2019 - [Machine Learning] Learning K-way D-dimensional Discrete Embedding for Hierarchical Data Visualization and Retrieval.
Liang, X., Min, M., Guo, H., & Wang, G.
IJCAI 2019 - [NLP] Modeling Noisy Hierarchical Types in Fine-Grained Entity Typing: A Content-Based Weighting Approach.
Wu, J., Zhang, R., Mao, Y., Guo, H., & Huai, J.
IJCAI 2019 - [NLP] A Neural Bag-of-Words Modelling Framework for Link Prediction in Knowledge Bases with Sparse Connectivity.
Kong, F., Zhang, R., Guo, H., Mensah, S., Hu, Z., & Mao, Y.
WWW 2019 - [NLP] Syntax Encoding with Application in Authorship Attribution.
Zhang, R., Hu, Z., Guo, H., & Mao, Y.
EMNLP 2018 - [Machine Learning] Parametric t-Distributed Stochastic Exemplar-Centered Embedding.
Min, M., Guo, H., & Shen, D.
ECML PKDD 2018 - [NLP] A Deep Network with Visual Text Composition Behavior.
Guo, H.
ACL 2017 - [Machine Learning] Exemplar-centered Supervised Shallow Parametric Data Embedding.
Min, M., Guo, H., & Song, D.
IJCAI 2017 - [Machine Learning] Accelerated Continuous Conditional Random Fields for load forecasting.
Guo, H.
ICDE 2016 - [NLP] DAG-Structured Long Short-Term Memory for Semantic Compositionality.
Zhu, X., Sobhani, P., & Guo, H.
NAACL 2016 - [NLP] Representation Based Translation Evaluation Metrics.
Chen, B., & Guo, H.
ACL 2015 - [Machine Learning] Long Short-Term Memory Over Recursive Structures.
Zhu, X., Sobhani, P., & Guo, H.
ICML 2015 - [NLP] The Unreasonable Effectiveness of Word Representations for Twitter Named Entity Recognition.
Cherry, C., & Guo, H.
NAACL 2015 - [NLP] Generating Text with Deep Reinforcement Learning.
Guo, H.
Deep Reinforcement Learning Workshop @NeurIPS 2015 - [Machine Learning] Accelerated Continuous Conditional Random Fields For Load Forecasting.
Guo, H.
TKDE 2015 - [NLP] An Empirical Study on the Effect of Negation Words on Sentiment.
Zhu, X., Guo, H., Mohammad, S., & Kiritchenko, S.
ACL 2014 - [Machine Learning] Modeling Short-Term Energy Load with Continuous Conditional Random Fields.
Guo, H.
ECML PKDD 2013 - [Machine Learning] Multirelational classification: a multiple view approach.
Guo, H., & Viktor, H.
KAIS 2008 - [Machine Learning] Mining relational data through correlation-based multiple view validation.
Guo, H., & Viktor, H.
KDD 2007 - [Machine Learning] Measuring to Fit: Virtual Tailoring Through Cluster Analysis and Classification.
Viktor, H., Paquet, E., & Guo, H.
ECML PKDD 2007 - [Machine Learning] Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach.
Guo, H., & Viktor, H.
KDD Explorations 2004