News and Events
- Nov. 2021: Jiachen Yang has successfully defended his Ph.D. Dissertation: Cooperation in Multi-Agent Reinforcement Learning.
- Nov. 2021: Three Ph.D. positions are avaiable in my group. See more information here!
- Jul. 2021: I am co-organizing The First Workshop on Evaluations and Assessments of Neural Conversation Systems (EANCS) (co-located with EMNLP 2021) with a group of researchers from Google, Amazon, Microsoft, Facebook, Georgia Tech, Virginia Tech and National Taiwan University.
- Jul. 2021: Yujia Xie has successfully defended her Ph.D. Dissertation: On Computation and Applications of Optimal Transport.
- Apr. 2021: Zhehui Chen has successfully defended his Ph.D. Dissertation: Modern Statistical Methods for Optimization and Change-Point Detection.
- Apr. 2021: Haoming Jiang has successfully defended his Ph.D. Dissertation: Reducing Human Labor Cost in Deep Learning for Natural Language Processing.
- Apr. 2021: Tianyi Liu has successfully defended his Ph.D. Dissertation: Theoretical Analysis of Stochastic Gradient Descent in Nonconvex Optimization.
- Mar. 2021: Minshuo Chen wrote a blog post for our recent results in Towards Understanding Hierarchical Learning: Benefits of Neural Representations.
- Apr. 2020: Ethan Fang, Niao He, Junwei Lu, Zhaoran Wang, Zhuoran Yang and I are co-organizing an Online Seminar Series on Mathematical Foundation of Data Sciences. See more information here!
- Dec. 2019: Haoming Jiang's recent collabrative work with Microsoft Dynamics 365 AI and Microsoft Research AI (paper, code) achieves state-of-the-art results in 5 of 9 GLUE benchmark tasks and an overall GLUE task performance 89.9, which outperforms all existing models.
- Nov. 2019: Minshuo Chen wrote a blog post for our recent results in Efficient Approximation of Deep ReLU Networks for functions on Low Dimensional Manifolds and Nonparametric Regression on Low Dimensional Manifolds using Deep Neural Networks.
- Oct. 2019: Prof. Wenjing Liao and I are co-organizing a mini-symposium on Machine learning on Data with Low-dimensional Structures at the upcoming SIAM Conference on Mathematics of Data Sciences 2020.
- Oct. 2019: Prof. Yajun Mei, Prof. Yao Xie and I co-organized Georgia Statistics Day 2019.
Preprints and Working Papers (* indicates equal contributions, and ‡ indicates advisees)
- 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] - 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
Preprint available on arXiv [Link] - 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
Preprint available on arXiv [Link] - Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
Yuqing Wang, Minshuo Chen‡, Tuo Zhao and Molei Tao
Preprint available on arXiv [Link] - Adversarially Regularized Policy Learning Guided by Trajectory Optimization
Zhigen Zhao, Simiao Zuo‡, Tuo Zhao and Ye Zhao
Preprint available on arXiv [Link] - Self-Training with Differentiable Teacher
Simiao Zuo‡, Yue Yu, Chen Liang, Haoming Jiang, Siawpeng Er, Chao Zhang Tuo Zhao and Hongyuan Zha
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]
Recent Publications (* indicates equal contributions, and ‡ indicates advisees; Check my full publication list)
- 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 - 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 - 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] - Spectrum Truncation Power Iteration for Agnostic Matrix Phase Retrieval
Lewis Liu, Songtao Lu, Tuo Zhao and Zhaoran Wang
IEEE Transactions on Signal Processing 69:3991-4006, 2021 [PDF] - 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 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] - Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing
Haoming Jiang*‡, Chen Liang*‡, Chong Wang and Tuo Zhao
Annual Meeting of the Association for Computational Linguistics (ACL), 2020 [arXiv, Poster] - 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] - On Computation and Generalization of Generative Adversarial Imitation Learning
Minshuo Chen‡, Yizhou Wang‡, Tianyi Liu‡, Zhuoran Yang, Xingguo Li‡, Zhaoran Wang 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] - Meta Learning with Relational Information for Short Sequences
Yujia Xie‡, Haoming Jiang‡, Feng Liu‡, Tuo Zhao and Hongyuan Zha
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] - Online Factorization and Partition of Complex Networks From Random Walks
Lin Yang‡, Vladimir Braverman, Tuo Zhao and Mengdi Wang
Annual Conference on Uncertainty in Artificial Intelligence (UAI), 2019 [arXiv, Poster] - On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About Its Nonsmooth Loss Function
Xingguo Li‡, Haoming Jiang‡, Jarvis Haupt, Raman Arora, Han Liu, Mingyi Hong and Tuo Zhao
Annual Conference on Uncertainty in Artificial Intelligence (UAI), 2019 [arXiv, Software, 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 Scalable and Efficient Computation of Large Scale Optimal Transport
Yujia Xie‡, Minshuo Chen‡, Haoming Jiang‡, Tuo Zhao and Hongyuan Zha
International Conference on Machine Learning (ICML), 2019 [arXiv, Poster] - On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization
Zhehui Chen*‡, Xingguo Li*‡, Lin Yang‡, Jarvis Haupt and Tuo Zhao
International Conference on Artificial Intelligence and Statistics (AISTATS), 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] - Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization
Minshuo Chen‡, Lin Yang‡, Mengdi Wang and Tuo Zhao
Annual Conference on Neural Information Processing Systems (NeurIPS), 2018 [arXiv, Poster] - Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Distributed Nonconvex Stochastic Optimization
Tianyi Liu‡, Shiyang Li‡, Jianping Shi, Enlu Zhou and Tuo Zhao
Annual Conference on Neural Information Processing Systems (NeurIPS), 2018 [arXiv, Poster] - Physical Systems behind Optimization Algorithms
Lin Yang‡, Raman Arora, Vladimir Braverman and Tuo Zhao
Annual Conference on Neural Information Processing Systems (NeurIPS), 2018 [arXiv, Poster] - Provable Gaussian Embedding with One Observation
Ming Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar and Zhaoran Wang
Annual Conference on Neural Information Processing Systems (NeurIPS), 2018 [arXiv, Poster] - 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] - On Local Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Regularized Sparse Learning in High Dimensions
Xingguo Li‡, Lin Yang‡, Jason Ge‡, Jarvis Haupt, Tong Zhang and Tuo Zhao
Annual Conference on Neural Information Processing Systems (NeurIPS), 2017 [arXiv, Poster] - Deep Hyperspherical Learning
Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao and Le Song
Annual Conference on Neural Information Processing Systems (NeurIPS), 2017 [arXiv] - Homotopy Parametric Simplex Method for Sparse Learning
Haotian Pang, Robert Vanderbei, Han Liu and Tuo Zhao
Annual Conference on Neural Information Processing Systems (NeurIPS), 2017 [arXiv] - On Faster Convergence of Cyclic Block
Coordinate Descent-type Methods for Strongly Convex Minimization
Xingguo Li*, Tuo Zhao*, Raman Arora, Han Liu and Mingyi Hong
Journal of Machine Learning Research (JMLR), 18(4):1-24 2018 [arXiv, Poster]
International Conference on Artificial Intelligence and Statistics (AISTATS), 2016 (short version) - Online Multiview Learning: Dropping Convexity for Better Efficiency
Zhehui Chen‡, Lin Yang‡, Chris Li and Tuo Zhao
International Conference on Machine Learning (ICML), 2017 [arXiv]
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
- Minshuo Chen -- Ph.D. Student, ISyE@Georgia Tech (2017.6--Present, Coadvised by Wenjing Liao)
- Siawpeng Er -- Ph.D. Student, ISyE, Georgia Tech (2019.8--Present, Coadvised by Hua Wang)
- 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)
- Ethan Wang -- Undergraduate Student, CSE, Georgia Tech (2020.01--Present, Coadvised by Hongyuan Zha)
- Simiao Zuo -- Ph.D. Student, ISyE, Georgia Tech (2019.8--Present, Coadvised by Hongyuan Zha)
FLASH Alumni
- Jiachen Yang -- Ph.D. Student, CSE, Georgia Tech (2020.01--2021.11, Coadvised by Hongyuan Zha)
Current Position: Staff Research Scientist, Lawrence Livermore National Laboratory - Yujia Xie -- Ph.D. Student, CSE, 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: Undergraduate Student, Georgia Tech
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) [Website, 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]
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