Jianhui Sun
Research Scientist, Meta
js9gu AT virginia DOT edu
Bio
I am a Research Scientist at Meta.
I was a Ph.D. student in Department of Computer Science at University of Virginia, advised by Prof. Aidong Zhang.
I obtained my B.S. degree in Mathematics at Fudan University and I was in the PhD program in Department of Statistics at University of Virginia. I spent summers doing internships in Meta and Amazon.
My research covers a variety of topics in Machine Learning and Data Mining, including federated learning [NeurIPS'23, NeurIPS'23, AAAI'24], AutoML [TKDD'23, KDD'22, KDD'21], and adversarial attacks [KDD'23, KDD'20].
News
- [05/2024] One paper on dynamic graph transfer learning accepted to ICML'24.
- [01/2024] One paper on GenAI approach for Multi-modal Personalization accepted to ICLR'24.
- [12/2023] Attended NeurIPS'23 for our papers on 1) minimax optimization 2) conditional stochastic optimization, in federated learning settings, in New Orleans, LA.
- [12/2023] One paper on federated learning accepted to AAAI'24. See you in Vancouver!
- [11/2023] I will join Meta as a research scientist in Menlo Park, CA.
- [09/2023] Two papers on federated learning accepted to NeurIPS'23.
- [09/2023] Finished internship at Amazon Search, Palo Alto, CA as an Applied Scientist Intern working on LLM-empowered product widget title generation.
- [08/2023] Attended KDD'23 and presented our paper on adversarial training in Long Beach, CA.
- [05/2023] One paper on adversarial training accepted to KDD'23.
- [02/2023] Attended AAAI'23 for our paper on model interpretability in Washington, DC.
- [11/2022] Received Copenhaver Charitable Trust Bicentennial Fellowship.
- [10/2022] Attended CIKM'22 for FedGraph workshop in Atlanta, GA
- [09/2022] Finished internship at Meta AI, Menlo Park, CA as a Research Scientist Intern working on data augmentation of IG Reels ranking models.
- [08/2022] Attended KDD'22 and presented our paper on AutoML in Washington, DC.
Publications [Google Scholar]
‡ indicates equal contribution.
On the Role of Server Momentum in Federated Learning
AAAI'24: AAAI Conference on Artificial Intelligence. 2024.
Solving a Class of Non-Convex Minimax Optimization in Federated Learning
NeurIPS'23: Advances in Neural Information Processing Systems. 2023.
Federated Conditional Stochastic Optimization
NeurIPS'23: Advances in Neural Information Processing Systems. 2023.
Enhance Diffusion to Improve Robust Generalization
KDD'23: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2023.
Scheduling Hyperparameters to Improve Generalization: From Centralized SGD to Asynchronous SGD
TKDD: Transactions on Knowledge Discovery from Data. 2023.
Demystify Hyperparameters for Stochastic Optimization with Transferable Representations
KDD'22: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2022.
A Stagewise Hyperparameter Scheduler to Improve Generalization
KDD'21: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021.
Malicious Attacks against Deep Reinforcement Learning Interpretations
KDD'20: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020. Best Paper Runner-up
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
ICLR'24: International Conference on Learning Representations. 2024.
On the Role of Server Momentum in Federated Learning
AAAI'24: AAAI Conference on Artificial Intelligence. 2024.
Solving a Class of Non-Convex Minimax Optimization in Federated Learning
NeurIPS'23: Advances in Neural Information Processing Systems. 2023.
Federated Conditional Stochastic Optimization
NeurIPS'23: Advances in Neural Information Processing Systems. 2023.
Enhance Diffusion to Improve Robust Generalization
KDD'23: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2023.
Scheduling Hyperparameters to Improve Generalization: From Centralized SGD to Asynchronous SGD
TKDD: Transactions on Knowledge Discovery from Data. 2023.
Understanding and Enhancing Robustness of Concept-based Models
AAAI'23: AAAI Conference on Artificial Intelligence. 2023.
Shifting Episodic Prediction With Online Cognitive Bias Modification: A Randomized Controlled Trial
Clinical Psychological Science. 2023.
Dynamic Transfer Learning across Graphs
Demystify Hyperparameters for Stochastic Optimization with Transferable Representations
KDD'22: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2022.
Augmenting Knowledge Transfer across Graphs
ICDM'22: IEEE International Conference on Data Mining. 2022.
A Stagewise Hyperparameter Scheduler to Improve Generalization
KDD'21: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021.
Malicious Attacks against Deep Reinforcement Learning Interpretations
KDD'20: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020. Best Paper Runner-up
Correlation Networks for Extreme Multi-label Text Classification
KDD'20: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020.
GLIMA: Global and Local Time Series Imputation with Multi-directional Attention Learning
BigData'20: IEEE International Conference on Big Data. 2020.
Recurrent Imputation for Multivariate Time Series with Missing Values
ICHI'19: IEEE International Conference on Healthcare Informatics. 2019.
A Low-rank Multivariate General Linear Model for Multi-subject fMRI Data and a Non-convex Optimization Algorithm for Brain Response Comparison
NeuroImage. 2018.
Vitæ
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Meta, CA Jan 2024 - presentResearch Scientist
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Amazon Search, CA May 2023 - Sep 2023Applied Scientist, Intern
LLM-empowered Product Widget Title Generation Models -
Meta AI, CA May 2022 - Aug 2022Research Scientist, Intern
Data Augmentation Framework of Instagram Reels Ranking Models -
University of Virginia, VA Jan 2019 - presentPh.D. in Computer Science
Research Interests: Optimization, Federated Learning, AutoML, and Adversarial ML
M.S. in Computer Science, Dec 2022 -
University of Virginia, VA Aug 2016 – Jan 2019Ph.D. Program in Statistics
Research Interests: Brain Imaging Data Analysis, Bayesian Modeling
M.S. in Statistics, May 2018 -
National University of Singapore Jan-Jun 2015Exchange Student
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Fudan University, Shanghai 2012 - 2016B.S. in Mathematics
Minor in Economics