Welcome to my personal page!

NEWS A paper on Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction between Feature Alignment and Target Fitting has been accepted to AISTATS-26. Our work establishes the first theoretical framework for understanding and developing cross-modal fine-tuning algorithms to adapt large foundation models to unseen data modalities. This is led by my undergraduate mentee Khiem Tran. Congrats Khiem!

NEWS A paper on Black-Box Optimization from Small Offline Datasets via Meta Learning with Synthetic Tasks has been accepted to AISTATS-26. This is led by my PhD students Azza Fadhel and The Hung Tran. Congrats Azza and The Hung!

NEWS A paper on Nanoporous Materials Discovery via Search Bias-Guided Surrogate Modeling has been accepted to AAAI-26, AI for Social Impact (AISI) Track. Our paper features an interesting application of offline optimization in discovering nanoporous materials with high adsorption properties. This is led by my PhD student Azza Fadhel. Congrats Azza!

NEWS A paper on ForeSWE: Forecasting Snow-Water Equivalent with an Uncertainty-Aware Attention Model has been accepted to AAAI-26, AI for Social Impact (AISI) Track. This is in collaboration with Professor Ananth Kalyanaraman and Professor Kirti Rajagopalan at WSU. The work is led by WSU’s talented PhD students: Krishu Thapa, Supriya Savalkar, and Bhupinderjeet Singh. Congrats all!

NEWS I and my colleagues will organize a ICLR-26 Workshop on Principled Design for Trustworthy AI: Interpretability, Robustness, and Safety across Modalities. This will take place April 26, 2026 in Rio de Janeiro, Brazil. We welcome contributions from the community and look forward to engaging discussions!

NEWS I and my colleagues (Jana Doppa, Aryan Deshwal, Minh Hoang) will give a tutorial on Black-Box Optimization with Offline Datasets at AAAI-26. Link to slides here.

NEWS A paper on Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge has been accepted for publication as spotlight in NeurIPS-25. This is led by my PhD student Hung Tran and undergaduate mentee Cuong Dao. Congrats Hung and Cuong!

NEWS A paper on Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data has been accepted for publication in NeurIPS-25. This is led by my mentee Duong Nguyen. Congrats Duong!

NEWS A paper on Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data has been accepted for publication in ICCV-25. This is led by my mentees Hang Phung and Duong Nguyen. Congrats Hang and Duong!

NEWS A paper on Federated Learning with Sparse and Scarce Data has been accepted for publication in IEEE Transactions on Computers. This is led by my mentee Hung Nguyen. Congrats Hung!

NEWS A paper on Probabilistic Federated Prompt-Tuning has been accepted for publication in NeurIPS-24. This is led by my PhD student Pei-Yau Weng. Congrats Pei-Yau!

NEWS A paper on Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques has been accepted for publication in NeurIPS-24. This is (again) led by my undergraduate mentee Cuong Dao. Congrats Cuong!

NEWS A paper on Boosting Offline Optimizers with Surrogate Sensitivity has been accepted for publication in ICML-24. This is led by my undergraduate mentee Cuong Dao. Congrats Cuong!

NEWS A paper on Learning Surrogates for Offline Black-Box Optimization via Gradient Matching has been accepted for publication in ICML-24. This is led by my PhD student Azza Fadhel and collaborator Minh Hoang. Congrats Minh and Azza!

NEWS A paper on Revisting Kernel Attention with Correlated Gaussian Process Representation has been accepted for publication in UAI-24. This is led by my PhD student Long Bui. Congrats Long!

NEWS I and my colleagues (Jana Doppa, Yan Yan, Ganapati Bhat, Taha Belkhouja) have given a tutorial on Advances in Robust Time-Series ML at AAAI-24.

I am currently a tenure-track Assistant Professor at the School of Electrical Engineering and Computer Science, Washington State University.

Prior to my current appointment, I was a Senior Research Scientist at the AWS AI Labs, Amazon (2020-2022) in Santa Clara, CA.

Before Amazon, I was a Research Staff Member of the MIT-IBM Watson AI Lab, IBM Research in Cambridge, MA (2018-2020).

Before IBM, I was a Postdoctoral Research Associate at the Laboratory for Information and Decision Systems (LIDS), MIT (2017-2018).

Prior to MIT, I was a Research Fellow at National University of Singapore (2015-2017) where I earned my PhD degree in Machine Learning (2010-2015).

My PhD adviser is Associate Professor Kian Hsiang Low.