About me

Hi, my name is Trung Vu. I am a Postdoctoral Research Associate at Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, working with Dr. Tülay Adali. I completed my Ph.D. in Computer Science at Oregon State University, advised by Dr. Raviv Raich. My research focuses on optimization theory, matrix analysis, independent component analysis, and machine learning for signal processing.

News and Updates

• 2023
  • 12/13: Our papers “A Robust and Scalable Method with an Analytic Solution for Multi-Subject fMRI Data Analysis”, “Provable Randomized Coordinate Descent For Matrix Completion”, and “Subgroup Identification Through Multiplex Community Structure Within Functional Connectivity Networks” have been accepted to ICASSP 2024.
  • 11/10: I presented our recent work “Constrained Independent Vector Analysis with Reference for Multi-Subject fMRI Analysis” at IEEE Brain Discovery and Neurotechnology Workshop, Washington DC, USA. The arXiv version is available here.
  • 10/28: I presented two papers at ASILOMAR 2023, Pacific Grove, California, USA.
  • 10/13: Our journal paper “On Local Linear Convergence of Projected Gradient Descent for Unit-Modulus Least Squares” has been accepted to IEEE Transactions on Signal Processing.
  • 07/14: Our paper “On the Asymptotic Linear Convergence of Gradient Descent for Non-Symmetric Matrix Completion” has been accepted to ASILOMAR 2023.
  • 07/14: Our papers “Constrained Independent Vector Analysis with References: Algorithms and Performance Evaluation” and “Reproducibility in Joint Blind Source Separation: Application to fMRI Analysis” have been accepted to ASILOMAR 2023.
  • 03/13: Our journal paper “Identification of Homogeneous Subgroups from Resting-State fMRI Data” has been accepted to Sensors.
• 2022
  • 12/05: Our journal paper “On Asymptotic Linear Convergence Rate of Iterative Hard Thresholding for Matrix Completion” has been accepted to IEEE Transactions on Signal Processing.
  • 09/06: My dissertation - “Convergence Analysis Framework for Fixed-Point Algorithms in Machine Learning and Signal Processing” is now available on ScholarsArchive@OSU.
  • 07/12: Our journal paper “On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares” has been accepted to IEEE Transactions on Signal Processing. The arXiv version is available here.
  • 06/16: Our journal paper “A Closed-Form Bound on the Asymptotic Linear Convergence of Iterative Methods via Fixed Point Analysis” has been accepted to Optimization Letters. The arXiv version is available here.
• 2021
  • 05/24: Our journal paper “Perturbation Expansions and Error Bounds for the Truncated Singular Value Decomposition” has been accepted to Linear Algebra and Its Applications.
  • 01/29: Our paper “Exact Linear Convergence Rate Analysis for Low-Rank Symmetric Matrix Completion via Gradient Descent” has been accepted to ICASSP 2021.
• 2020
  • 04/09: Our journal paper “A Novel Attribute-based Symmetric Multiple Instance Learning for Histopathological Image Analysis” has been accepted to IEEE Transactions on Medical Imaging.
• 2019
  • 10/17: I presented one paper at MLSP 2019, Pittsburgh, Pennsylvania, USA and received the Student Paper Award (2nd place).
  • 08/01: Our paper “On Convergence of Projected Gradient Descent for Minimizing a Large-scale Quadratic over the Unit Sphere” has been accepted to MLSP 2019.
  • 05/12: I presented two papers at ICASSP 2019, Brighton, UK.
  • 02/15: I am fortunate to receive the IEEE Signal Processing Society Travel Grant for ICASSP 2019.
  • 02/01: Our two papers “Accelerating Iterative Hard Thresholding for Low-Rank Matrix Completion via Adaptive Restart” and “Local Convergence of the Heavy Ball method in Iterative Hard Thresholding for Low-Rank Matrix Completion” has been accepted to ICASSP 2019.
• 2018
  • 03/27: Our paper “Adaptive Step Size Momentum Method for Deconvolution” has been accepted to SSP 2018. This is my first paper since I came to OSU.
• 2017
  • 01/09: Starting from Winter 2017, I have joined a 2-year commercialization project with SmartVineyards in developing an intelligent decision support system to improve irrigation management in vineyards and other west coast crops. This project is funded by Oregon BEST and brings together researchers from different departments at Oregon State University and Washington State University.