Preprints and Working Papers (* indicates equal contributions, and indicates advisees)
  • DiP-GNN: Discriminative Pre-Training of Graph Neural Networks
    Simiao Zuo, Haoming Jiang, Qingyu Yin, Xianfeng Tang, Bing Yin and Tuo Zhao
    Preprint available on arXiv [Link]
  • Differentially Private Estimation of Hawkes Process
    Simiao Zuo, Tianyi Liu, Tuo Zhao and Hongyuan Zha
    Preprint available on arXiv [Link]
  • Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites
    Simiao Zuo, Qingyu Yin, Haoming Jiang, Shaohui Xi, Bing Yin, Chao Zhang andTuo Zhao
    Preprint available on arXiv [Link]
  • A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks
    Jie Wang, Minshuo Chen, Tuo Zhao, Wenjing Liao and Yao Xie
    Preprint available on arXiv [Link]
  • Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks
    Xiang Ji, Minshuo Chen, Mengdi Wang and Tuo Zhao
    Preprint available on arXiv [Link]
  • Homotopic Policy Mirror Descent: Policy Convergence, Implicit Regularization, and Improved Sample Complexity
    Yan Li, Tuo Zhao and George Lan
    Preprint available on arXiv [Link]
  • Block Policy Mirror Descent
    George Lan, Yan Li and Tuo Zhao
    Preprint available on arXiv [Link]
  • Learning Generalizable Vision-Tactile Robotic Grasping Strategy for Deformable Objects via Transformer
    Yunhai Han, Rahul Batra, Nathan Boyd, Tuo Zhao, Yu She, Seth Hutchinson and Ye Zhao
    Preprint available on arXiv [Link]
  • Implicit Regularization of Bregman Proximal Point Algorithm and Mirror Descent on Separable Data
    Yan Li, Caleb Ju, Ethan Fang and Tuo Zhao
    Preprint available on arXiv [Link]
  • Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
    Yan Li, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao and Hongyuan Zha
    Preprint available on arXiv [Link]
  • Reinforcement Learning for Adaptive Mesh Refinement
    Jiachen Yang, Tarik Dzanic, Brenden Petersen, Jun Kudo, Ketan Mittal, Vladimir Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio Kolev, Robert Anderson and Daniel Faissol
    Preprint available on arXiv [Link]
  • Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks
    Minshuo Chen*, Hao Liu*, Wenjing Liao and Tuo Zhao
    Preprint available on arXiv [Link]
  • Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation
    Minshuo Chen, Wenjing Liao, Hongyuan Zha and Tuo Zhao (Alphabetical order)
    Preprint available on arXiv [Link]
  • On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets and Beyond
    Xingguo Li, Junwei Lu, Zhaoran Wang, Jarvis Haupt and Tuo Zhao
    Preprint available on arXiv [Link]

Selected Publications (* indicates equal contributions, and indicates advisees); Check my full publication list
  • On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds
    Biraj Dahal, Alexander Havrilla, Minshuo Chen, Tuo Zhao and Wenjing Liao
    Annual Conference on Neural Information Processing (NeurIPS),2022
  • Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint
    Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang and Tuo Zhao
    International Conference on Machine Learning (ICML), 2022 [arXiv]
  • PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance
    Qingru Zhang, Simiao Zuo, Chen Liang, Alex Bukharin, Pengcheng He, Weizhu Chen and Tuo Zhao
    International Conference on Machine Learning (ICML), 2022
  • MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation
    Simiao Zuo, Qingru Zhang, Chen Liang, Pengcheng He, Tuo Zhao and Weizhu Chen
    Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022 [arXiv]
  • CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data
    Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao and Chao Zhang
    Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022 [arXiv]
  • Self-Training with Differentiable Teacher
    Simiao Zuo, Yue Yu, Chen Liang, Haoming Jiang, Siawpeng Er, Chao Zhang Tuo Zhao and Hongyuan Zha
    Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) - Findings, 2022 [arXiv]
  • Adversarially Regularized Policy Learning Guided by Trajectory Optimization
    Zhigen Zhao, Simiao Zuo, Tuo Zhao and Ye Zhao
    Annual Learning for Dynamics & Control Conference (L4DC), 2022 [arXiv]
  • CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing
    Chen Liang, Pengcheng He, Yelong Shen, Weizhu Chen and Tuo Zhao
    Annual Meeting of the Association for Computational Linguistics (ACL), 2022 [arXiv]
  • No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models
    Chen Liang, Haoming Jiang, Simiao Zuo, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen and Tuo Zhao
    International Conference on Learning Representations (ICLR), 2022 [arXiv]
  • Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits
    Yan Li, Dhruv Choudhary, Xiaohan Wei, Baichuan Yuan, Bhargav Bhushanam, Tuo Zhao and Guanghui Lan
    International Conference on Learning Representations (ICLR), 2022 [arXiv]
  • Taming Sparsely Activated Transformer with Stochastic Experts
    Simiao Zuo, Xiaodong Liu, Jian Jiao, Young Jin Kim, Hany Hassan, Ruofei Zhang, Tuo Zhao and Jianfeng Gao
    International Conference on Learning Representations (ICLR), 2022 [arXiv]
  • Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
    Yuqing Wang, Minshuo Chen, Tuo Zhao and Molei Tao
    International Conference on Learning Representations (ICLR), 2022 [arXiv]
  • Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably
    Tianyi Liu, Yan Li, Enlu Zhou and Tuo Zhao
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 [arXiv]
  • Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning
    Jiachen Yang, Ethan Wang, Rakshit Trivedi, Tuo Zhao and Hongyuan Zha
    International Conference on Autonomous Agents and Multiagent Systems, 2022 [arXiv]
  • Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks
    Minshuo Chen, Haoming Jiang, Wenjing Liao and Tuo Zhao (Alphabetical order)
    Information and Inference: A Journal of the IMA, 2021 [arXiv, Poster]
  • Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL
    Minshuo Chen, Yan Li, Zhuoran Yang, Zhaoran Wang and Tuo Zhao
    Annual Conference on Neural Information Processing (NeurIPS),2021 [arXiv]
  • Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach
    Haoming Jiang, Bo Dai, Mengjiao Yang, Tuo Zhao and Wei Wei
    Conference on Empirical Methods in Natural Language Processing (EMNLP),2021 [arXiv]
  • Adversarial Training as Stackelberg Game: An Unrolled Optimization Approach
    Simiao Zuo, Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao and Tuo Zhao
    Conference on Empirical Methods in Natural Language Processing (EMNLP),2021 [arXiv]
  • Token-wise Curriculum Learning for Neural Machine Translation
    Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao and Tuo Zhao
    Conference on Empirical Methods in Natural Language Processing (EMNLP) - Findings,2021 [arXiv]
  • ARCH: Efficient Adversarial Regularized Training with Caching
    Simiao Zuo, Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao and Tuo Zhao
    Conference on Empirical Methods in Natural Language Processing (EMNLP) - Findings,2021
  • QUEACO: Query Attribute Value Extraction in E-commerce
    Danqing Zhang, Zheng Li, Tianyu Cao, Chen Luo, Tony Wu, Yiwei Song, Bing Yin, Tuo Zhao and Qiang Yang
    ACM International Conference on Information and Knowledge Management (CIKM), 2021 [arXiv]
  • A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization
    Tianyi Liu, Zhehui Chen, Enlu Zhou and Tuo Zhao
    Stochastic Systems (Accepted), 2021+ [arXiv, Poster]
  • COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction
    Siawpeng Er, Shihao Yang and Tuo Zhao
    Scientific Reports [arXiv]
  • Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks
    Hao Liu, Minshuo Chen, Tuo Zhao and Wenjing Liao
    International Conference on Machine Learning (ICML), 2021 [arXiv]
  • How Important is the Train-Validation Split in Meta-Learning?
    Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham Kakade, Huan Wang and Caiming Xiong
    International Conference on Machine Learning (ICML), 2021 [arXiv]
  • Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization
    Chen Liang, Simiao Zuo, Minshuo Chen, Haoming Jiang, Xiaodong Liu, Pengcheng He, Tuo Zhao and Weizhu Chen
    Annual Meeting of the Association for Computational Linguistics (ACL), 2021 [arXiv]
  • Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data
    Haoming Jiang, Danqing Zhang, Tianyu Cao, Bing Yin and Tuo Zhao
    Annual Meeting of the Association for Computational Linguistics (ACL), 2021 [arXiv]
  • Fine-Tuning Pre-trained Language Models with Weak Supervision: A Contrastive-Regularized Self-Training Approach
    Yue Yu*, Simiao Zuo*, Haoming Jiang, Wendi Ren, Tuo Zhao and Chao Zhang
    Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021 [arXiv]
  • Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network
    Siawpeng Er, Edward Liu, Minshuo Chen, Yan Li, Yuqi Liu, Tuo Zhao and Hua Wang
    International Microwave Symposium (IMS), 2021
    [The Finalist of IMS 2021 Best Student Paper Competition]
  • Learning to Defend by Learning to Attack
    Haoming Jiang*‡, Zhehui Chen*‡, Yuyang Shi, Bo Dai and Tuo Zhao
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 [arXiv, Poster]
  • Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization
    Tianyi Liu, Yan Li, Song Wei, Enlu Zhou and Tuo Zhao
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 [arXiv]
  • A Hypergradient Approach to Robust Regression without Correspondence
    Yujia Xie*, Yixiu Mao*, Simiao Zuo, Hongteng Xu, Xiaojing Ye, Tuo Zhao and Hongyuan Zha
    International Conference on Learning Representations (ICLR), 2021 [arXiv]
  • Towards Understanding Hierarchical Learning: Benefits of Neural Representations
    Minshuo Chen*‡, Yu Bai, Jason Lee, Tuo Zhao, Huan Wang, Caiming Xiong and Richard Socher
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2020 [arXiv, Poster]
  • Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspective
    Kaixuan Huang*‡, Yuqing Wang*, Molei Tao and Tuo Zhao
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2020 [arXiv, Poster]
  • Differentiable Top-k Operator with Optimal Transport
    Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei and Tomas Pfister
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2020 [arXiv, Poster]
  • Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
    Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao and Chao Zhang
    Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 [arXiv]
  • Deep Reinforcement Learning with Smooth and Robust Policy
    Qianli Shen*‡, Yan Li*‡, Haoming Jiang, Zhaoran Wang and Tuo Zhao
    International Conference on Machine Learning (ICML), 2020 [arXiv]
  • Transformer Hawkes Process
    Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao and Hongyuan Zha
    International Conference on Machine Learning (ICML), 2020 [arXiv]
  • BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision
    Chen Liang*‡, Yue Yu*, Haoming Jiang*‡, Siawpeng Er, Ruijia Wang, Tuo Zhao and Chao Zhang
    SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020 [arXiv]
  • SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
    Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao and Tuo Zhao
    Annual Meeting of the Association for Computational Linguistics (ACL), 2020 [arXiv]
  • Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers
    David Munzer, Siawpeng Er, Minshuo Chen, Yan Li, Naga Mannem, Tuo Zhao and Hua Wang
    IEEE Radio Frequency Integrated Circuits Symposium (RFIC), 2020 [arXiv]
  • On Generalization Bounds of a Family of Recurrent Neural Networks
    Minshuo Chen, Xingguo Li and Tuo Zhao
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 [arXiv, Poster]
  • Implicit Bias of Gradient Descent based Adversarial Training on Separable Data
    Yan Li, Ethan Fang, Huan Xu and Tuo Zhao
    International Conference on Learning Representations (ICLR), 2020 [arXiv, Poster]
  • Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds
    Minshuo Chen, Haoming Jiang, Wenjing Liao and Tuo Zhao (Alphabetical order)
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2019 [arXiv, Poster]
  • Towards Understanding the Importance of Shortcut Connections in Residual Networks
    Tianyi Liu*‡, Minshuo Chen*‡, Mo Zhou, Simon Du, Enlu Zhou and Tuo Zhao
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2019 [arXiv, Poster]
  • Towards Understanding the Importance of Noise in Training Neural Networks
    Mo Zhou*‡, Tianyi Liu*‡, Yan Li, Dachao Lin, Enlu Zhou and Tuo Zhao
    International Conference on Machine Learning (ICML), 2019 [arXiv, Poster]
  • On Computation and Generalization of Generative Adversarial Networks under Spectrum Control
    Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang and Tuo Zhao
    International Conference on Learning Representations (ICLR), 2019 [arXiv, Poster]
  • Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
    Jason Ge*‡, Xingguo Li*‡, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang and Tuo Zhao
    Journal of Machine Learning Research (JMLR), 20(44):1−5, 2019 [PDF, Software]
    [2016 ASA Best Student Paper Award on Statistical Computing]
  • Misspecified Nonconvex Statitical Optimization for Sparse Phase Retrival
    Zhuoran Yang*, Lin Yang*‡, Ethan Fang, Tuo Zhao, Zhaoran Wang and Matey Neykov
    Mathematical Programming Series Series B, 176(1-2):1-27, 2019 [arXiv]
  • Symmetry, Saddle Points and Global Optimization Landscape of Nonconvex Matrix Factorization
    Xingguo Li, Junwei Lu, Raman Arora, Jarvis Haupt, Han Liu, Zhaoran Wang and Tuo Zhao
    IEEE Transactions on Information Theory, 65(6):3489-3514, 2019 [arXiv]
  • Pathwise Coordinate Optimization for Nonconvex Sparse Learning: Algorithm and Theory
    Tuo Zhao, Han Liu and Tong Zhang
    The Annals of Statistics, 46(1):180-218, 2018 [arXiv, Software]

Software Packages
  • Picasso: Pathwise Calibrated Sparse Shooting Algorithm
    with Jason Ge, Xinguo Li, Haoming Jiang, Han Liu, Tong Zhang and Mengdi Wang
    [GitHub (Python), GitHub (R), Download (CRAN)]
  • PRIMAL: PaRametric sImplex Method for spArse Learning
    with Qianli Shen, Zichong Li, Yujia Xie [GitHub (R)]
  • Flare: Family of Lasso Regression
    with Xinguo Li, Lie Wang, Xiaoming Yuan and Han Liu [Download (CRAN)]
  • Huge: High-dimensional Undirected Graph Estimation
    with Haomingjiang, Xinyu Fei, Xingguo Li, Han Liu, Kathryn Roeder, John Lafferty and Larry Wasserman
    [GitHub (R), Download (CRAN)]
  • SAM: Sparse Additive Modeling
    with Haoming Jiang, Yukun Ma, Xinguo Li, Han Liu and Kathryn Roeder
    [GitHub (R), Download (CRAN)]

Selected Awards and Honors
  • Google Faculty Research Award [2020]
  • 2016 INFORMS SAS Best Paper Award on Data Mining [2016]
  • 2016 ASA Best Student Paper Award on Statistical Computing [2016]
  • Baidu Fellowship [2015]
  • Siebel Scholarship [2014, Siebel Scholar Profile]
  • Google Summer of Code Award [2011-2013]
  • Winner of INDI ADHD-200 Global Competition [2011]

Alchemists in My Group
  • Yan Li -- Ph.D. Student, ISyE, Georgia Tech (2018.12--Present, Coadvised by George Lan)
  • Chen Liang -- Ph.D. Student, ISyE, Georgia Tech (2018.8--Present)
  • Simiao Zuo -- Ph.D. Student, ISyE, Georgia Tech (2019.8--Present, Coadvised by Hongyuan Zha)
  • Qingru Zhang -- Ph.D. Student, CSE, Georgia Tech (2021.8--Present)
  • Alex Bukharin -- Ph.D. Student, ISyE, Georgia Tech (2021.8--Present, Coadvised by Yao Xie)
  • Yixiao Li -- Ph.D. Student, ISyE, Georgia Tech (2022.8--Present, Coadvised by Hua Wang)
  • Zhenghao Xu -- Ph.D. Student, ISyE, Georgia Tech (2022.8--Present, Coadvised by George Lan)
  • Zixuan Zhang -- Ph.D. Student, ISyE, Georgia Tech (2022.8--Present)

FLASH Alumni
  • Minshuo Chen -- Ph.D. in Machine Learning, Georgia Tech (2017.6--2022.7, Coadvised by Wenjing Liao)
    Current Position: Postdoctral Fellow, Princeton University
  • Siawpeng Er -- Ph.D. in Bioinformatics, Georgia Tech (2019.8--2022.7, Coadvised by Hua Wang)
    Current Position: Data Scientist, Home Depot
  • Jiachen Yang -- Ph.D. in Machine Learning, Georgia Tech (2020.01--2021.12, Coadvised by Hongyuan Zha)
    Current Position: Staff Research Scientist, Lawrence Livermore National Laboratory
  • Ethan Wang -- Undergraduate Student, CSE, Georgia Tech (2020.01--2021.11, Coadvised by Hongyuan Zha)
  • Yujia Xie -- Ph.D. in Computational Science and Engineering, Georgia Tech (2018.12--2021.8, Coadvised by Hongyuan Zha)
    Current Position: Research Scientist, Microsoft Cloud&AI
  • Zhehui Chen -- Ph.D. in Industrial Engineering, Georgia Tech (2016.8--2021.4, Coadvised by Jeff Wu)
    Current Position: Research Scientist, Didi Labs, Mountain View, CA
  • Haoming Jiang -- Ph.D. in Machine Learning, Georgia Tech (2017.8--2021.4)
    Current Position: Research Scientist, Amazon Search, Palo Alto, CA
  • Tianyi Liu -- Ph.D. in Operations Research, Georgia Tech (2017.9--2021.4, Coadvised by Enlu Zhou)
    Current Position: Research Scientist, Bytedance AML, Seattle, WA
  • Xingguo Li -- Visiting Student, Georgia Tech (2017.3--2018.6)
    Current Position: Quantitative Researcher, Radix Trading LLC
  • Lin Yang -- Visiting Student, Georgia Tech (2017.3--2017.6)
    Current Position: Assistant Professor, University of California Los Angeles
  • Xinyu Fei -- Visiting Student, Georgia Tech (2018.7--2018.9)
    Current Position: Ph.D. Student, University of Michigan
  • Mo Zhou -- Visiting Student, Georgia Tech (2018.7--2018.9)
    Current Position: Ph.D. Student, Duke University
  • Yizhou Wang -- Visiting Student, Georgia Tech (2019.1--2019.5)
    Current Position: Ph.D. Student, Northeastern University
  • Kaixuan Huang -- Visiting Student, Georgia Tech (2019.7--2019.9)
    Current Position: Ph.D. Student, Princeton University
  • Zichong Li -- Visiting Student, Georgia Tech (2019.7--2019.9)
    Current Position: Master Student, University of Science and Technology of China
  • Qianli Shen -- Visiting Student, Georgia Tech (2019.7--2019.9)
    Current Position: Ph.D. Student, National University of Singapore
  • Jie Lyu -- Undergraduate Student Researcher, Georgia Tech (2020.1--2020.5)
    Current Position: Software Engineer, Meta

About Alchemy
  • Back When We were Kids
    Ali Rahimi - NeurIPS 2017 Test-of-Time Award Presentation [Link]
  • My Take on Ali Rahimi's "Test of Time" Award Talk at NeurIPS
    Quoted from Yann LeCun's Facebook [Link]
  • Ali Rahimi's Response to Yann LeCun
    Quoted from Ali Rahimi's Facebook [Link]
  • An Addendum to Alchemy
    Quoted from Ben Recht's Blog [Link]
  • The Role of Theory in Deep Learning
    Quoted from David McAllester's Blog [Link]

Teaching
  • Basic Statistical Methods ISYE3030 -- 2019 Summer, 2019 Fall, 2020 Spring, 2020 Fall, Georgia Tech
  • Advanced Machine Learning ISYE8803 -- 2018 Spring, 2019 Spring, 2020 Fall, Georgia Tech
  • Introduction to Machine Learning ISYE4803 -- 2018 Fall, Georgia Tech
  • Machine Learning ISYE6740/CSE6740/CS7641 -- 2017 Spring, Fall, Georgia Tech

NSF Projects
  • IIS-1717916: Topics in Temporal Marked Point Processes: Granger Causality, Imperfect Observations and Intervention (2017.9 - 2021.8) [Link]
  • DMS-2012652: Deep Neural Networks for Structured Data: Regression, Distribution Estimation, and Optimal Transport (2020.9-2023.8) [Link]
  • IIS-2008334: Go Beyond Short-term Dependency and Homogeneity: A General-Purpose Transformer Recipe for Multi-Domain Sequential Data Analysis (2020.9-2023.8) [Link]
  • DMS-2134037: Bridging Statistical Hypothesis Tests and Deep Learning for Reliability and Computational Efficiency (2022.1-2024.12) [Link]
  • IIS-2226152: RI: Small: Taming Massive Pre-trained Models under Label Scarcity via an Optimization Lens (2022.9-2025.8) [Link]
Picture

Contact
Tuo Zhao
H. Milton Stewart School of Industrial and Systems Engineering
Groseclose 344
755 Ferst Dr. NW
Atlanta, GA 30332
Email: tourzhao (at) gatech (dot) edu