Contact 

School of Mathematics, Statistics and Actuarial Science,

University of Essex,

Colchester, CO4 3SQ

lan.truong@essex.ac.uk










If you are interested in researching on  Probability/Statistics/Mathematics for Information Theory or Machine Learning, please don't hesitate to contact me. Information for Ph.D. applicants to the University of Essex is available at https://www.essex.ac.uk/postgraduate/research/applying-to-essex.


Brief Bio.

Lan V.  Truong (Senior Member, IEEE) was born in Quang Binh province, Vietnam, where he studied at Vo Nguyen Giap Gifted High School from 1995 to 1998. He received the B.S.E. degree in electronics and telecommunications from the Posts and Telecommunications Institute of Technology (PTIT), Hanoi, Vietnam, in 2003, and the M.S.E. degree from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA, in 2011, and the Ph.D. degree 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, Purdue University, 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 currently a Lecturer  (Assistant Professor) at the School of Mathematics, Statistics and Actuarial Science, University of Essex, U.K. His research interests include high-dimensional statistics, deep learning theory, statistical learning, and information theory.  Major research  topics of  interest include:



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




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