I am looking for motivated students who are interested in research on the mathematical foundations of machine learning and information theory, with the goal of publishing in top-tier venues in these fields.

If this sounds like you, please feel free to reach out to me by email.

Brief Bio.

Lan V.  Truong (Senior Member, IEEE) received Ph.D. degree in Information Theory from the Department of Electrical and Computer Engineering, National University of Singapore (NUS), Singapore in 2018. He was an Operation and Maintenance Engineer with MobiFone Telecommunications Corporation, Hanoi, Vietnam for several years. From 2013 to 2015, he was a Lecturer with the Department of Information Technology Specialization, FPT University, Hanoi.  From 2018 to 2019, he was a Research Fellow with the Department of Computer Science, School of Computing, NUS.  From 2020 to 2023, he was a Research Associate with the Department of Engineering, University of Cambridge, U.K.  From 2023 to Jul. 2025, he was a Lecturer  (Assistant Professor) at the School of Mathematics, Statistics and Actuarial Science, University of Essex, U.K.  He is currently a Lecturer (Assistant Professor) at the the Faculty of Computer Science and Engineering (CSE),  Ho Chi Minh City University of Technology (HCMUT) - Vietnam National University, Ho Chi Minh City, Vietnam. His research interests include high-dimensional statistics, deep learning theory, statistical learning, and information theory.  Major research  topics of  interest include:

1. Mathematical Foundations of Deep Learning, Large Language Models, and Diffusion Models. Sample works:  "On Rademacher Complexity-based Generalisation Bounds for Deep Learning", "Generalization Error Bounds on Deep Learning with Markov Datasets", "Global Convergence Rate of Deep Equilibrium Models with General Activations", "On Rank-Dependent Generalisation Error Bounds for Transformers". 

2. Information Theory for Communications, Deep Learning, and Large Language Models. Sample works: "On Gaussian MACs With Variable-Length Feedback and Non-Vanishing Error Probabilities", "Moderate Deviations Asymptotics for Variable-Length Codes with Feedback", "Concentration Properties of Random Codes", "Concentration Properties of Random Codes", "Generalized Random Gilbert-Vashamov Codes: Typical Error Exponents and Concentration Properties".

3. Kernel Methods for Machine Learning and Deep Learning. Sample works: "Generalisation Bounds on Multiple-Kernel Learning with Mixed Datasets".

4. Multi-armed Bandits and Reinforcement Learning. Sample works: "Optimal Best-Arm Identification under Fixed Confidence with Multiple Optima", "On Gap-Based Lower Bounding Techniques for Best-Arm Identification".

5. High-Dimensional Statistics and Algorithms. Sample works:  "Fundamental limits and algorithms for sparse linear regression with sublinear sparsity", "Replica Analysis of the Linear Model with Markov or Hidden Markov Signal Priors", "Support Recovery in the Phase Retrieval Model:  Information-Theoretic Fundamental Limits".

Recent News

• I joined the Faculty of Computer Science and Engineering (CSE),  Ho Chi Minh City University of Technology (HCMUT) - Vietnam National University, Ho Chi Minh City, Vietnam as a Lecturer (Assistant Professor) in Sept. 2025. 

• Invited to serve on the Technical Program Committee of The IEEE International Symposium on Information Theory (ISIT 2026) 

• Paper "Optimal Best-Arm Identification under Fixed Confidence with Multiple Optima" was uploaded to Arxiv.

•  Paper "Global Convergence Rate of Deep Equilibrium Models with General Activations" was accepted to Transactions on Machine Learning Research (TMLR). 

• Invited to serve on the Technical Program Committee of The IEEE International Symposium on Information Theory (ISIT 2025) 

• Paper "On Rank-Dependent Generalisation Error Bounds for Transformers" was uploaded to Arxiv.

• Invited to serve on the Program Committee of  The 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025)

• Elevated to IEEE Senior Member in May 2024. 

• My recent work shows that the Rademacher complexity approach can lead to tight generalization bounds on CNNs for Binary Image Classifications. Check the latest version on Arxiv:

"On Rademacher Complexity-based Generalisation Bounds for Deep Learning". 

• Our paper "Generalized Random Gilbert-Vashamov Codes: Typical Error Exponents and Concentration Properties" was published in IEEE Transactions on Information Theory, Feb. 2024.  

• Our paper "Concentration Properties of Random Codes" was published in IEEE Transactions on Information Theory, Dec. 2023.

• Paper  "Replica Analysis of the Linear Model with Markov or Hidden Markov Signal Priors" was published in IEEE Transactions on Information Theory, Dec. 2023.

• I joined the School of Mathematics, Statistics and Actuarial Science at the University of Essex, U.K. as a Lecturer (Assistant Professor) in Sept. 2023. 

• Paper "Fundamental limits and algorithms for sparse linear regression with sublinear sparsity" was published in the Journal of Machine Learning Research (JMLR), Apr. 2023.

• Paper "Generalization Error Bounds on Deep Learning with Markov Datasets" was published in the Proc. of  The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS), Dec. 2022. 

• Recognized at a top reviewer in AISTATS 2022. 

• Paper "On Linear Models with Markov Signal Priors" was published in the Proc. of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), Mar.  2022.

• Our paper "On the All-Or-Nothing Behavior of Bernoulli Group Testing" was published in IEEE Journal on Selected Areas In information Theory (Special Issue On Estimation and Inference), Jan. 2021.

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