Dr Lan Truong
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Email
lan.truong@essex.ac.uk -
Telephone
+44 (0) 1206 873656
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Location
2.524, Colchester Campus
Profile
Biography
Lan V. Truong received a Ph.D. 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, for several years. He spent one year as a Research Assistant with the NSF Center for Science of Information and the Department of Computer Science at Purdue University, U.S. in 2012. From 2013 to 2015, he was a University Lecturer with the Department of Information Technology Specialization, FPT University, Hanoi, Vietnam. 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. He is a Lecturer (Assistant Professor) at the School of Mathematics, Statistics and Actuarial Science, University of Essex, U.K. His research interests include probability, information theory, statistical learning, and deep learning theory.
Research and professional activities
Research interests
Information-theoretic methods in machine learning and data science
Theory of deep learning and representation learning
Multi-arm bandits and their applications
Kernel methods in machine learning
Concentration inequalities and their applications in machine learning and information theory
Shannon's theory for communication
High Dimensional Statistical Algorithms
Teaching and supervision
Current teaching responsibilities
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Data analysis and statistics with R (MA334)
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Optimisation (Linear Programming) (MA205)
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Databases and data processing with SQL (MA332)
Publications
Journal articles (14)
Truong, LV., Cocco, G., Font-Segura, J. and Guillén i Fàbregas, A., Concentration Properties of Random Codes. IEEE Transactions on Information Theory. 69 (12), 7499-7537
Truong, LV. and Fàbregas, AGI., (2024). Generalized Random Gilbert-Varshamov Codes: Typical Error Exponent and Concentration Properties. IEEE Transactions on Information Theory. 70 (2), 820-853
Truong, LV., (2023). Fundamental limits and algorithms for sparse linear regression with sublinear sparsity. Journal of Machine Learning Research. 24 (64), 1-49
Truong, LV., (2023). Replica Analysis of the Linear Model with Markov or Hidden Markov Signal Priors. IEEE Transactions on Information Theory. 69 (12), 7953-7975
Truong, LV., Aldridge, M. and Scarlett, J., (2020). On the All-or-Nothing Behavior of Bernoulli Group Testing. IEEE Journal on Selected Areas in Information Theory. 1 (3), 669-680
Truong, LV. and Scarlett, J., (2020). Support Recovery in the Phase Retrieval Model: Information-Theoretic Fundamental Limit. IEEE Transactions on Information Theory. 66 (12), 7887-7910
Truong, LV. and Scarlett, J., (2020). On Gap-Based Lower Bounding Techniques for Best-Arm Identification. Entropy. 22 (7), 788-788
Truong, LV. and Yamamoto, H., (2020). On the Capacity of Symmetric M-User Gaussian Interference Channels With Feedback. IEEE Transactions on Information Theory. 66 (2), 722-741
Truong, LV. and Tan, VYF., (2019). The Reliability Function of Variable-Length Lossy Joint Source-Channel Coding With Feedback. IEEE Transactions on Information Theory. 65 (8), 5028-5042
Truong, LV. and Tan, VYF., (2019). Moderate Deviation Asymptotics for Variable-Length Codes With Feedback. IEEE Transactions on Information Theory. 65 (7), 4364-4386
Truong, LV., (2019). Performance of Viterbi Decoding With and Without ARQ on Rician Fading Channels. IEEE Transactions on Communications. 67 (2), 903-914
Truong, LV. and Tan, VYF., (2018). On Gaussian MACs With Variable-Length Feedback and Non-Vanishing Error Probabilities. IEEE Transactions on Information Theory. 64 (4), 2333-2346
TRUONG, LV. and YAMAMOTO, H., (2017). Posterior Matching for Gaussian Broadcast Channels with Feedback. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. E100.A (5), 1165-1178
Truong, LV., Fong, SL. and Tan, VYF., (2017). On Gaussian Channels With Feedback Under Expected Power Constraints and With Non-Vanishing Error Probabilities. IEEE Transactions on Information Theory. 63 (3), 1746-1765
Conferences (16)
Truong, LV. and Guillén i Fàbregas, A., (2023). Concentration Properties of Generalized Random Gilbert-Varshamov Codes
Truong, LV., (2022). Generalization Error Bounds on Deep Learning with Markov Datasets
Truong, LV., (2022). On Linear Model with Markov Signal Priors
Truong, LV., Font-Segura, J., Cocco, G. and Fabregas, AGI., (2022). Convergence in Distribution of the Error Exponent of Random Codes at Zero Rate
Truong, LV., Cocco, G., Font-Segura, J. and Guillen i Fabregas, A., (2021). Concentration of Random-Coding Error Exponents
Truong, LV., (2021). Linear Models with Hidden Markov Sources via Replica Method
Truong, LV. and Scarlett, J., (2019). On the Information-Theoretic Limits of Noisy Sparse Phase Retrieval
Truong, LV. and F. Tan, VY., (2018). The Reliability Function of Lossy Source-Channel Coding of Variable-Length Codes with Feedback
Truong, LV., (2018). Performance of Viterbi Decoding on Interleaved Rician Fading Channels
Truong, LV. and Tan, VYF., (2017). On the Gaussian MAC with stop-feedback
Truong, LV. and Tan, VYF., (2017). Error exponent of the common-message broadcast channel with variable-length feedback
Truong, LV., Fong, SL. and Tan, VYF., (2016). On second-order asymptotics of AWGN channels with feedback under the expected power constraint
Truong, LV., Fong, SL. and Tan, VYF., (2016). The ∊-capacity region of AWGN multiple access channels with feedback
Truong, LV. and Yamamoto, H., (2015). On the capacity of symmetric Gaussian interference channels with feedback
Truong, LV., (2014). Posterior matching scheme for Gaussian multiple access channel with feedback
Truong, LV. and Szpankowski, W., (2013). Capacity of a Structural Binary Symmetric Channel
Thesis dissertation (1)
Truong, LV., (2018). Refined Asymptotics for the Fundamental Limits of Communications with Feedback
