News
2026-02
Patent Granted: our patent System and Method for Combining Data Selection and Reward Function for Tuning LLMs using Reinforcement Learning, joint work with Long Vu, Shankar Subramanian, and Todd Mummert from IBM Research, has been granted.
IBM Outstanding Technical Achievement Award 2026: Received the IBM Outstanding Technical Achievement Award for NL2Insights Impacting Products and Clients.
Multilingual Text2SQL Now in Production: I’m excited to announce that multilingual Text2SQL capabilities are now available across all IBM Cloud and AWS production regions for IBM watsonx.data intelligence SaaS. Users can now interact with data using natural language queries in English and Japanese (with more languages coming), making data insights accessible to a global workforce.
2026-01
Patent Granted: our patent Site-wide optimization for mixed regression models and mixed control variables, joint work with Dzung Phan and Lam Nguyen from IBM Research, has been granted.
2025-12
IBM Growth Award 2025: Received IBM Growth Award for advancing Text2SQL service within watsonx.data intelligence.
IBM Research Accomplishments 2025 - A-level: NL2Insights achieved Product and Client-0 Adoption & Impact recognition. Our fully automated Text2SQL pipeline now powers watsonx.data, BI Assistant, and Process Mining, generating over 200,000 SQL queries across 1,000+ databases at enterprise scale.
2025-05
IBM Outstanding Technical Achievement Award: Received the IBM Outstanding Technical Achievement Award for achieving first place on the BIRD Leaderboard with IBM Granite Text-to-SQL models.
Paper Accepted at UAI 2025: Our paper The Consistency Hypothesis in Uncertainty Quantification for Large Language Models has been accepted at the Forty-First Conference on Uncertainty in Artificial Intelligence (UAI 2025). Joint work with Q. Xiao, D. Bhattacharjya, B. Ganesan, R. Marinescu, K. Mirylenka, M. Glass, and J. Lee.
2024-12
IBM Research Accomplishments 2024 - A-level: IBM Granite fine-tuned Text-to-SQL models sweep top spots in BIRD Leaderboard. Our Granite-20B and Granite-34B models achieved first place in both tracks, outperforming larger models like GPT-4 and GPT-4o. This success drove renewed interest from major industry players including Google, Alibaba, and ByteDance. Models were featured at THINK’24 and deployed on BAM and Watsonx.ai platforms.
2024-04
Promotion to Staff Research Scientist: I have been promoted to Staff Research Scientist at IBM Research, Thomas J. Watson Research Center.
2023-09
Paper Accepted at ICDM 2023: Our paper Evaluating Robustness of Cooperative MARL: A Model-based Approach has been accepted at the 2023 IEEE International Conference on Data Mining (ICDM). This is joint work with Dr. Lam M. Nguyen, Dr. Jie Chen, Dr. Hoang Thanh Lam, Dr. Subhro Das, and Dr. Tsui-Wei Weng. [Paper]
2023-02-06
My collegues and I will be organizing a tutorial/lab forum “LSHA2: Automated AI for Decision Optimization with Reinforcement Learning” at AAAI 2023.
I will also be the Session Chair of ML: Optimization 1 at AAAI 2023 on Feb 10, 2023.
2022-09-28
The patent “A systematic approach for evaluating robustness of cooperative multi-agent reinforcement learning” has been filed on Sept 28, 2022. This is a joint work with Lam M. Nguyen, Jie Chen, Thanh-Lam Hoang, Subhro Das.
2021-12-28
From Jan 4th, I will be a Research Scientist at the IBM Thomas J. Watson Research Center in Yorktown Heights. Super excited for this new adventure!
2021-09-30
Our manuscript FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization. have been accepted at the 35th Conference on Neural Information Processing Systems. This is a joint work with Lam Nguyen, Dzung Phan (IBM Research) and Quoc Tran-Dinh (UNC Chapel Hill). [Preprint]
2021-08-08
I have completed my summer internship as a machine learning intern with Blue River Technology Inc. where I had a chance to work on interesting problems of applying machine learning for agriculture. I greatly appreciate all my colleges especially my mentors Chris Patwick and Ben Cline for their help during my internship.
2021-06-8
Together with Lam Nguyen, Dzung Phan (IBM Research) and Quoc Tran-Dinh (UNC Chapel Hill), we have completed the manuscript FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization. [Preprint]
2021-03-15
I will be a Machine Learning Intern at Blue River Technology Inc.. this summer. I am excited to help create smart products for agriculture.
2021-03-12
Together with Lam Nguyen, Dzung Phan (IBM Research) and Quoc Tran-Dinh (UNC Chapel Hill), we have completed the manuscript Federated Learning with Randomized Douglas-Rachford Splitting Methods. [Preprint]
2021-01-24
Our paper Regression Optimization for System-level Production Control - joint work with Dzung Phan (IBM Research), Lam Nguyen (IBM Research), Pavankumar Murali (IBM Research), Hongsheng Liu (UNC Chapel Hill), and Jayant Kalagnanam (IBM Research)- has been accepted to the 2021 American Control Conference (ACC). [Full paper]
2020-10-12
Our paper A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization - a joint work with Quoc Tran-Dinh (UNC Chapel Hill), Lam Nguyen and Dzung Phan (IBM Research) - has been accepted for publication at Mathematical Programming. [Full paper]
2020-08-14
I have completed my summer internship with IBM Research, Thomas J. Watson Research Center. It is indeed a fantastic experience. I greatly appreciate all my colleges especially my mentors (Dzung T. Phan and Lam M. Nguyen) and my supervisor (Roman Vaculin).
2020-06-01
Our paper Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization - a joint work with Lam Nguyen (IBM Research) and Quoc Tran-Dinh (UNC Chapel Hill)- has been accepted for publication at the International Conference on Machine Learning (ICML). [Full paper]
2020-05-03
Our paper ProxSARAH: An Efficient Algorithmic Framework For Stochastic Composite Nonconvex Optimization - a joint work with Lam Nguyen (IBM Research), Dzung Phan (IBM Research), and Quoc Tran-Dinh (UNC Chapel Hill) - has been accepted for publication at the Journal of Machine Learning Research (JMLR). [Full paper]
2020-02-09
Together with Lam Nguyen (IBM Research) and Quoc Tran-Dinh (UNC Chapel Hill), we have completed the manuscript Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization. [Preprint]
Another manuscript is out “Convergence Rates of Accelerated Markov Gradient Descent with Applications in Reinforcement Learning”, joint work with Thinh Doan (GA Tech), Lam Nguyen (IBM Research), Justin Romberg (GA Tech). [Preprint]
2020-01-23
Our manuscript ProxSARAH: An Efficient Algorithmic Framework For Stochastic Composite Nonconvex Optimization has been accepted for publication at Journal of Machine Learning Research (JMLR) with minor revision. [Preprint]
2020-01-07
Our paper A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning has been accepted for AISTATS 2020. This is a joint work with Lam Nguyen (IBM Research), Dzung Phan (IBM Research), Phuong-Ha Nguyen (UConn), Marten van Dijk (UConn), and Quoc Tran-Dinh (UNC Chapel Hill). [Full paper]
2019-10-19
This year I will travel to Seattle for the 2019 INFORMS Annual Meeting to present ProxSARAH: An Efficient Algorithmic Framework For Stochastic Composite Nonconvex Optimization, a joint work with Lam Nguyen (IBM Research), Dzung Phan (IBM Research), and Quoc Tran-Dinh (UNC Chapel Hill).
2019-08-15
I will be working with Quoc Tran-Dinh as a SAMSI (The Statistical and Applied Mathematical Sciences Institute) Research Fellow for the 2019 Fall Program on Deep Learning.
2019-07-08
Together with Lam Nguyen (IBM Research), Dzung Phan (IBM Research), and Quoc Tran-Dinh (UNC Chapel Hill), we have completed the manuscript A Hybrid Stochastic Optimization Framework for Composite Nonconvex Optimization. Checkout my research page for more details.
2019-02-15
Together with Lam Nguyen (IBM Research), Dzung Phan (IBM Research), and Quoc Tran-Dinh (UNC Chapel Hill), we have completed the manuscript ProxSARAH: An Efficient Algorithmic Framework For Stochastic Composite Nonconvex Optimization. Checkout my research page for more details.