I would like to thank you for visiting my homepage. My nationality is Vietnamese-American. My primary research interests lie in Applied Mathematics and Computer Science, including harmonic analysis, optimization, and stochastic calculus, with applications in signal and image processing, machine learning, and stochastic dynamical systems. I will be a Research Collaborator V at Meta, USA, from Nov 2024, working on an AI project titled Inverse Reinforcement Learning from Human Feedback for Large Language Modeling. Previously, I was an Associate at SSC in the research project Partially Observable Markov Decision Process in Reinforcement Learning and Hierarchical Dirichlet Processes for Bayesian Object Tracking, contributing to the U.S. Naval Army.
Education:
Nov-2024: Research Collaborator V at Meta, USA
May 2024 - 2024: Associate in Signal Systems Corporation (SSC), USA
2022-2024: Research Associate in department of Biomedical Engineering at university of Virginia, USA
2021-2022: Post Doc Research Fellow in Geography and GeoInformation Science at George Mason University, USA
2016-2021: Post Doc Research Fellow in Department of Statistical Science at Duke university, USA, with Prof. D. Banks, S. Mukherjee, D. Dunson
2015-2016: Post Doc Research Fellow in The Statistical and Applied Mathematical Science Institute (SAMSI), USA, with Prof. R. Smith (UNC)
2011-2015: PhD in Institute for Mathematical Stochastics, University of Goettingen, Germany, with Prof. A. Munk and P. Mihailescu
2009-2011: Master in Bio-Mechatronics Engineering, Sungkyunkwan University, South Korea
2008-2009: Master in Control Theory, Faculty of Electrical - Electronics, Ho Chi Minh City University of Technology, Vietnam (not finished)
2008: Undergraduate in Control Theory (honor program), Faculty of Electrical - Electronics, Ho Chi Minh City University of Technology, Vietnam
Publications:
D.H. Thai. Bayesian Approach to Large POMDPs in System Identification. [in preparation].
D.H. Thai, A.L. Young and D.B. Dunson. Proximal Algorithms for Accelerated Langevin Dynamics. [in preparation].
A.H.M. Rubaiyat, D.H. Thai, G.K. Rohde, J.M. Nichols, M.N. Hutchinson. System Identification Using the Signed Cumulative Distribution Transform In Structural Health Monitoring Applications, 2023. [article] [arxiv] [code]
X. Fei, M.T. Le, D.H. Thai, K. Wessels and A. Zufle. Semi-Supervised Satellite Image Segmentation Using Spatial and Temporally Informed Poisson Segmentation. IGARSS 2023: 5642-5645.
D.H. Thai, X. Fei, M.T. Le, A. Zufle and K. Wessels. Riesz-Quincunx-Unet Variational Auto-Encoder for Satellite Image Denoising. IEEE Transactions on Geoscience and Remote Sensing, 2023. [article] [arxiv]
A. Susarla, A. Liu, D.H. Thai, M.T. Le and A. Zufle. Spatiotemporal Disease Case Prediction Using Contrastive Predictive Coding. SpatialEpi '22: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology, 2022. [article] [code]
D.H. Thai and D. Banks. Directional Mean Curvature for Textured Image Demixing. Applied Mathematical Modelling, Vol. 102, 2021. [article] [arxiv]
R. Richter, D.H. Thai and S. Huckemann. Generalized Intersection Algorithms with Fixpoints for Image Decomposition Learning, SIAM Journal on Imaging Sciences, Vol. 14, Iss. 3, 2021. [article] [arxiv]
R. Richter, D.H. Thai, C. Gottschlich and S. Huckemann. Filter Design for Image Decomposition and Applications to Forensics. Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2020. [article]
D.H. Thai, Hau-Tieng Wu and D. Dunson. Locally Convex Kernel Mixtures: Bayesian Subspace Learning. 18th ICMLA, 2019. [article]
R. Richter, C. Gottschlich, L. Mentch, D.H. Thai and S. Huckemann. Smudge Noise for Quality Estimation of Fingerprints and its Validation. IEEE Transactions on Information Forensics and Security, 2019. [article]
D.H. Thai and L. Mentch. Multiphase Segmentation For Simultaneously Homogeneous and Textural Images. Applied Mathematics and Computation, 335(15):146-181, October 2018. [article]
D.H. Thai and C. Gottschlich. Simultaneous Inpainting and Denoising by Directional Global Three-part Decomposition: Connecting Variational and Fourier Domain Based Image Processing. R.Soc.opensci.5: 171176, June 2018. [article]
D.H. Thai and C. Gottschlich. Directional Global Three-part Image decomposition. EURASIP Journal on Image and Video Processing, 2016(12):1-20, March 2016. [article]
D.H. Thai and C. Gottschlich. Global Variational Method for Fingerprint Segmentation by Three-Part Decomposition. IET Biometrics, 5(2):120-130, June 2016. [article]
D.H. Thai, S. Huckemann and C. Gottschlich. Filter Design and Performance Evaluation for Fingerprint Image Segmentation. PLoS ONE, 11(5):e0154160, May 2016. [article]
S.H. Cho, D.H. Thai, J.W. Han and H. Hwang. Multi-level Thresholding based on Non-Parametric Approaches for Fast Segmentation. Journal of Biosystems Engineering, 38(2):149-162, June 2013. [article]
D.H. Thai. Fourier and Variational Based Approaches for Fingerprint Segmentation. PhD thesis, University of Goettingen, 2015. [dissertation]