**One paper was accepted to ICML 2021**

- "SMG: A Shuffling Gradient-Based Method with Momentum" - joint work with Trang H. Tran (Cornell) and Quoc Tran-Dinh (UNC Chapel Hill).

**I will serve as a Session Chair of an oral session at ICLR 2021**

**I will serve as a Session Chair of the session "Theory and Practice of Machine Learning" at AISTATS 2021**

**New paper!**

- "Federated Learning with Randomized Douglas-Rachford Splitting Methods" - joint work with Nhan H. Pham (UNC Chapel Hill), Dzung T. Phan (IBM Research), and Quoc Tran-Dinh (UNC Chapel Hill).

**New paper!**

- "Differential Private Hogwild! over Distributed Local Data Sets" - joint work with Marten van Dijk (Uconn & CWI), Nhuong V. Nguyen (Uconn), Toan N. Nguyen (Uconn), and Phuong Ha Nguyen (eBay).

**Our paper was accepted to The 2021 American Control Conference (ACC 2021)**

- "

__Regression Optimization for System-level Production Control__" - joint work with Dzung T. Phan (IBM Research), Pavankumar Murali (IBM Research), Nhan H. Pham (UNC), Hongsheng Liu (UNC), and Jayant R. Kalagnanam (IBM Research).

**One paper was accepted to AISTATS 2021**

- "Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes" - joint work with Nhuong V. Nguyen (Uconn), Toan N. Nguyen (Uconn), Phuong Ha Nguyen (eBay), Quoc Tran-Dinh (UNC Chapel Hill), and Marten van Dijk (Uconn & CWI).

**2020-11-24:**

### 2020

**New paper!**

- "Shuffling Gradient-Based Methods with Momentum" - joint work with Trang H. Tran (Cornell) and Quoc Tran-Dinh (UNC Chapel Hill).

**I will serve as an Area Chair of ICML 2021**

**I will organize the session "Recent Advances in Stochastic Gradient Algorithms for Machine Learning Applications" at INFORMS Annual Meeting 2020 on November 13, 2020.**

**New paper!**

- "Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes" - joint work with Marten van Dijk (Uconn & CWI), Nhuong V. Nguyen (Uconn), Toan N. Nguyen (Uconn), Quoc Tran-Dinh (UNC Chapel Hill), and Phuong Ha Nguyen (eBay).

**Our paper has been accepted for publication to Mathematical Programming**

- "A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization" - joint work with Quoc Tran-Dinh (UNC Chapel Hill), Nhan H. Pham (UNC Chapel Hill), and Dzung T. Phan (IBM Research).

**Two papers were accepted to NeurIPS 2020**

- "Hybrid Variance-Reduced SGD Algorithms for Nonconvex-Concave Minimax Problems" - joint work with Quoc Tran-Dinh (UNC Chapel Hill) and Deyi Liu (UNC Chapel Hill).

- "A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees" - joint work with Haoran Zhu (University of Wisconsin - Madison), Pavankumar Murali (IBM Research), Dzung T. Phan (IBM Research), and Jayant R. Kalagnanam (IBM Research).

**MIT-IBM proposal**"Hierarchical Disentangled Representations for Scalable Multi-agent Reinforcement Learning" (with Cathy Wu (MIT) and Lily Weng (MIT-IBM Lab)) has been accepted for a one year award.

**Our paper has been accepted for publication to Optimization Methods and Software**

- "Inexact SARAH Algorithm for Stochastic Optimization" - joint work with Katya Scheinberg (Cornell) and Martin Takac (Lehigh).

**One paper was accepted to ICDM 2020**

- "

__Pruning Deep Neural Networks with L0-constrained Optimization__" - joint work with Dzung T. Phan (IBM Research), Nam H. Nguyen (IBM Research), and Jayant R. Kalagnanam (IBM Research).

**I will serve as an Area Chair of AISTATS 2021**

**New paper!**

- "Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise" - joint work with Marten van Dijk (Uconn & CWI), Nhuong V. Nguyen (Uconn), Toan N. Nguyen (Uconn), Quoc Tran-Dinh (UNC Chapel Hill), and Phuong Ha Nguyen (Uconn).

**I will serve as an Area Chair of ICLR 2021**

**New paper!**

- "Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems" - joint work with Quoc Tran-Dinh (UNC Chapel Hill) and Deyi Liu (UNC Chapel Hill).

**I will be joining Journal of Machine Learning Research (JMLR) editorial board**

**One paper was accepted to ICML 2020**

- "Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization" - joint work with Quoc Tran-Dinh (UNC Chapel Hill) and Nhan H. Pham (UNC Chapel Hill).

**Our paper has been accepted for publication to Journal of Machine Learning Research (JMLR)**

- "ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization" - joint work with Nhan H. Pham (UNC Chapel Hill), Dzung T. Phan (IBM Research), and Quoc Tran-Dinh (UNC Chapel Hill).

**New paper!**

- "Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness" - joint work with Thinh T. Doan (GA Tech), Nhan H. Pham (UNC Chapel Hill), and Justin Romberg (GA Tech).

**New papers!**

- "A Unified Convergence Analysis for Shuffling-Type Gradient Methods" - joint work with Quoc Tran-Dinh (UNC Chapel Hill), Dzung T. Phan (IBM Research), Phuong Ha Nguyen (Uconn), and Marten van Dijk (Uconn).

- "Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization" - joint work with Quoc Tran-Dinh (UNC Chapel Hill) and Nhan H. Pham (UNC Chapel Hill).

**New paper!**

- "Convergence Rates of Accelerated Markov Gradient Descent with Applications in Reinforcement Learning" - joint work with Thinh T. Doan (GA Tech), Nhan H. Pham (UNC Chapel Hill), and Justin Romberg (GA Tech).

**One paper was accepted to AISTATS 2020**

- "A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning" - joint work with Nhan H. Pham (UNC Chapel Hill), Dzung T. Phan (IBM Research), Phuong Ha Nguyen (Uconn), Marten van Dijk (Uconn), and Quoc Tran-Dinh (UNC Chapel Hill).

**2019-11-06:**

### 2019

**Our paper has been accepted for publication to Journal of Machine Learning Research (JMLR)**

- "New Convergence Aspects of Stochastic Gradient Algorithms" - joint work with Phuong Ha Nguyen (Uconn), Peter Richtarik (KAUST), Katya Scheinberg (Cornell), Martin Takac (Lehigh), and Marten van Dijk (Uconn).

**I will serve as an Area Chair of ICML 2020**

**New paper!**

- "BUZz: BUffer Zones for Defending Adversarial Examples in Image Classification" - joint work with Phuong Ha Nguyen (Uconn), Kaleel Mahmood (Uconn), Thanh Nguyen (Iowa State University), and Marten van Dijk (Uconn).

**One paper was accepted to NeurIPS 2019**

- "Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD" - joint work with Phuong Ha Nguyen (Uconn) and Marten van Dijk (Uconn).

**I will organize a session at INFORMS Annual Meeting 2019 in Seattle, WA on October 21, 2019**

- "Fast and Provable Nonconvex Optimization Algorithms in Machine Learning" - with Quoc Tran-Dinh (UNC Chapel Hill) and speakers Yi Zhou (IBM Research), Nhan H. Pham (UNC Chapel Hill), and Cesar A. Uribe (MIT).

**New paper!**

- "A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization" - joint work with Quoc Tran-Dinh (UNC Chapel Hill), Nhan H. Pham (UNC Chapel Hill), and Dzung T. Phan (IBM Research).

**Our paper has been accepted for publication to Journal of Machine Learning Research conditioned on minor revisions**

- "New Convergence Aspects of Stochastic Gradient Algorithms" - joint work with Phuong Ha Nguyen (Uconn), Peter Richtarik (KAUST), Katya Scheinberg (Cornell), Martin Takac (Lehigh), and Marten van Dijk (Uconn).

**New paper!**

- "Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization" - joint work with Quoc Tran-Dinh (UNC Chapel Hill), Nhan H. Pham (UNC Chapel Hill), and Dzung T. Phan (IBM Research).

**Won Elizabeth V. Stout Dissertation Award for my PhD thesis.**

I have won the 2019 P.C. Rossin College of Engineering and Applied Science

*Elizabeth V. Stout Dissertation Award*for my PhD thesis "A Service System with On-Demand Agents, Stochastic Gradient Algorithms and the SARAH Algorithm". Thank for my PhD advisors Katya Scheinberg and Martin Takáč, and my previous advisor Alexander Stolyar. Also, thank for the supports of Marten van Dijk, Luis Vicente, Frank E. Curtis, and George Wilson.

**Two papers were accepted to ICML 2019**

- "Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD" - joint work with Marten van Dijk (Uconn), Phuong Ha Nguyen (Uconn), and Dzung Phan (IBM Research).

- "PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach" - joint work with Tsui-Wei (Lily) Weng (MIT), Pin-Yu Chen (IBM Research), Mark Squillante (IBM Research), Akhilan Boopathy (MIT), Ivan Oseledets (Skoltech), and Luca Daniel (MIT).

**New paper!**

- "ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization" - joint work with Nhan H. Pham (UNC Chapel Hill), Dzung T. Phan (IBM Research), and Quoc Tran-Dinh (UNC Chapel Hill).

**New paper!**

- "Finite-Sum Smooth Optimization with SARAH" - joint work with Marten van Dijk (Uconn), Dzung T. Phan (IBM Research), Phuong Ha Nguyen (Uconn), Tsui-Wei (Lily) Weng (MIT), and Jayant R. Kalagnanam (IBM Research).