An Ellipsoidal Branch and Bound Algorithm for Global Optimization. - William W. Hager; Dzung T. Phan (SIAM J. Optim., 2009)

A two-stage non-linear program for optimal electrical grid power balance under uncertainty. - Dzung T. Phan; Soumyadip Ghosh (WSC, 2011)

Gradient-Based Methods for Sparse Recovery. - William W. Hager; Dzung T. Phan; Hongchao Zhang (SIAM J. Imaging Sci., 2011)

A distributed scheme for fair EV charging under transmission constraints. - Dzung T. Phan; Jinjun Xiong; Soumyadip Ghosh (ACC, 2012)

Distributed methods for solving the security-constrained optimal power flow problem. - Dzung T. Phan; Jayant Kalagnanam (ISGT, 2012)

Lagrangian Duality and Branch-and-Bound Algorithms for Optimal Power Flow. - Dzung T. Phan (Oper. Res., 2012)

Fast Algorithms for Image Reconstruction with Application to Partially Parallel MR Imaging. - Yunmei Chen; William W. Hager; Feng Huang 001; Dzung T. Phan; Xiaojing Ye; Wotao Yin (SIAM J. Imaging Sci., 2012)

An exact algorithm for graph partitioning. - William W. Hager; Dzung T. Phan; Hongchao Zhang (Math. Program., 2013)

Two-stage stochastic optimization for optimal power flow under renewable generation uncertainty. - Dzung T. Phan; Soumyadip Ghosh (ACM Trans. Model. Comput. Simul., 2014)

A Resource Supply-Demand based Approach for Automatic MapReduce Job Optimization. - Jinjun Xiong; Dzung T. Phan; David Kung (HPCC/CSS/ICESS, 2015)

Multi-stage optimization for periodic inspection planning of geo-distributed infrastructure systems. - Dzung T. Phan; Yada Zhu (Eur. J. Oper. Res., 2015)

Predicting and mitigating congestion for an electric power system under load and renewable uncertainty. - Dzung T. Phan; Soumyadip Ghosh (ACC, 2016)

Change Detection Using Directional Statistics. - Tsuyoshi Idé; Dzung T. Phan; Jayant Kalagnanam (IJCAI, 2016)

Managing uncertainty in electricity generation and demand forecasting. - Kevin Warren; Ron F. Ambrosio; Bei Chen; Yuhui Fu 001; Soumyadip Ghosh; Dzung T. Phan; Mathieu Sinn; Chunhua Tian; Chandu Visweswariah (IBM J. Res. Dev., 2016)

Projection algorithms for nonconvex minimization with application to sparse principal component analysis. - William W. Hager; Dzung T. Phan; Jiajie Zhu (J. Glob. Optim., 2016)

Multi-task Multi-modal Models for Collective Anomaly Detection. - Tsuyoshi Idé; Dzung T. Phan; Jayant Kalagnanam (ICDM, 2017)

A Novel l0-Constrained Gaussian Graphical Model for Anomaly Localization. - Dzung T. Phan; Tsuyoshi Idé; Jayant Kalagnanam; Matt Menickelly; Katya Scheinberg (ICDM Workshops, 2017)

When Does Stochastic Gradient Algorithm Work Well? - Lam M. Nguyen; Nam H. Nguyen; Dzung T. Phan; Jayant R. Kalagnanam; Katya Scheinberg (CoRR, 2018)

Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD. - Marten van Dijk; Lam M. Nguyen; Phuong Ha Nguyen; Dzung T. Phan (ICML, 2019)

Efficient Protocol for Collaborative Dictionary Learning in Decentralized Networks. - Tsuyoshi Idé; Rudy Raymond; Dzung T. Phan (IJCAI, 2019)

ℓ0-Regularized Sparsity for Probabilistic Mixture Models. - Dzung T. Phan; Tsuyoshi Idé (SDM, 2019)

Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH. - Lam M. Nguyen; Marten van Dijk; Dzung T. Phan; Phuong Ha Nguyen; Tsui-Wei Weng; Jayant R. Kalagnanam (CoRR, 2019)

A Scale Invariant Flatness Measure for Deep Network Minima. - Akshay Rangamani; Nam H. Nguyen; Abhishek Kumar; Dzung T. Phan; Sang H. Chin; Trac D. Tran (CoRR, 2019)

A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization. - Quoc Tran-Dinh; Nhan H. Pham; Dzung T. Phan; Lam M. Nguyen (CoRR, 2019)

A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning. - Nhan H. Pham; Lam M. Nguyen; Dzung T. Phan; Phuong Ha Nguyen; Marten van Dijk; Quoc Tran-Dinh (AISTATS, 2020)

Pruning Deep Neural Networks with $ ell_{0}$-constrained Optimization. - Dzung T. Phan; Lam M. Nguyen; Nam H. Nguyen; Jayant R. Kalagnanam (ICDM, 2020)

A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees. - Haoran Zhu; Pavankumar Murali; Dzung T. Phan; Lam M. Nguyen; Jayant Kalagnanam (NeurIPS, 2020)

ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization. - Nhan H. Pham; Lam M. Nguyen; Dzung T. Phan; Quoc Tran-Dinh (J. Mach. Learn. Res., 2020)

Regression Optimization for System-level Production Control. - Dzung T. Phan; Lam M. Nguyen; Pavankumar Murali; Nhan H. Pham; Hongsheng Liu; Jayant R. Kalagnanam (ACC, 2021)

A Scale Invariant Measure of Flatness for Deep Network Minima. - Akshay Rangamani; Nam H. Nguyen; Abhishek Kumar; Dzung T. Phan; Sang (Peter) Chin; Trac D. Tran (ICASSP, 2021)

Ensembling Graph Predictions for AMR Parsing. - Thanh Lam Hoang; Gabriele Picco; Yufang Hou; Young-Suk Lee 001; Lam M. Nguyen; Dzung T. Phan; Vanessa López; Ramón Fernandez Astudillo (NeurIPS, 2021)

Cardinality-Regularized Hawkes-Granger Model. - Tsuyoshi Idé; Georgios Kollias; Dzung T. Phan; Naoki Abe (NeurIPS, 2021)

FedDR - Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization. - Quoc Tran-Dinh; Nhan H. Pham; Dzung T. Phan; Lam M. Nguyen (NeurIPS, 2021)

Federated Learning with Randomized Douglas-Rachford Splitting Methods. - Nhan H. Pham; Lam M. Nguyen; Dzung T. Phan; Quoc Tran-Dinh (CoRR, 2021)

On the Solution of ℓ0-Constrained Sparse Inverse Covariance Estimation Problems. - Dzung T. Phan; Matt Menickelly (INFORMS J. Comput., 2021)

A Unified Convergence Analysis for Shuffling-Type Gradient Methods. - Lam M. Nguyen; Quoc Tran-Dinh; Dzung T. Phan; Phuong Ha Nguyen; Marten van Dijk (J. Mach. Learn. Res., 2021)

Interpretable Clustering via Multi-Polytope Machines. - Connor Lawless; Jayant Kalagnanam; Lam M. Nguyen; Dzung T. Phan; Chandra Reddy (AAAI, 2022)

StepDIRECT - A Derivative-Free Optimization Method for Stepwise Functions. - Dzung T. Phan; Hongsheng Liu; Lam M. Nguyen (SDM, 2022)

Finite-sum smooth optimization with SARAH. - Lam M. Nguyen; Marten van Dijk; Dzung T. Phan; Phuong Ha Nguyen; Tsui-Wei Weng; Jayant R. Kalagnanam (Comput. Optim. Appl., 2022)

AI-Based Real-Time Site-Wide Optimization for Process Manufacturing. - Jayant Kalagnanam; Dzung T. Phan; Pavankumar Murali; Lam M. Nguyen; Nianjun Zhou; Dharmashankar Subramanian; Raju Pavuluri; Xiang Ma; Crystal Lui; Giovane Cesar Da Silva (INFORMS J. Appl. Anal., 2022)

A hybrid stochastic optimization framework for composite nonconvex optimization. - Quoc Tran-Dinh; Nhan H. Pham; Dzung T. Phan; Lam M. Nguyen (Math. Program., 2022)

Optimal Control via Linearizable Deep Learning. - Vinícius Lima 002; Dzung T. Phan; Lam M. Nguyen; Jayant Kalagnanam (ACC, 2023)

An End-to-End Time Series Model for Simultaneous Imputation and Forecast. - Trang H. Tran; Lam M. Nguyen; Kyongmin Yeo; Nam Nguyen; Dzung T. Phan; Roman Vaculín; Jayant Kalagnanam (CoRR, 2023)

TabularFM: An Open Framework For Tabular Foundational Models. - Quan M. Tran; Suong N. Hoang; Lam M. Nguyen; Dzung T. Phan; Hoang Thanh Lam (IEEE Big Data, 2024)

Multi-polytope Machine for Classification. - Dzung T. Phan; Lam M. Nguyen; Jayant Kalagnanam; Chandra Reddy (SDM, 2024)

Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis. - Tsuyoshi Idé; Dzung T. Phan; Rudy Raymond (CoRR, 2024)

Convergence-Guaranteed Elastic Net Graphical Model Estimation with Applications to Anomaly Localization. - Dzung T. Phan; Matt Menickelly; Tsuyoshi Idé; Jayant Kalagnanam (SDM, 2025)

Toward a Trustworthy Optimization Modeling Agent via Verifiable Synthetic Data Generation. - Vinícius Lima 002; Dzung T. Phan; Jayant Kalagnanam; Dhaval Patel 002; Nianjun Zhou (CoRR, 2025)