I received my Ph.D. in Computer Engineering from Arizona State University. My recent research interests span the development of useful machine learning (ML) algorithms, particularly those supported by statistical theory, for the benefit of human society. To that end, I am primarily focused on applying ML approaches to propel scientific discovery as well as advancements in the healthcare domain. Initially, my research began with image processing and compression, evolved to the design of ML and deep learning algorithms, and finally led me to work at the intersection of the practical world and statistics.

Research Work

Machine Learning for Experimental Design

W. Li, V. Berisha, G. Dasarathy, “Advanced Tutorial: Label-Efficient Two-Sample Tests”, WSC'24. [PDF]

W. Li, P. Kadambi, P. Saidi, K. Ramamurthy, G. Dasarathy, V. Berisha, “Active Sequential Two-Sample Testing”, TMLR. [PDF]

W. Li, G. Dasarathy, K. Ramamurthy, V. Berisha, “A Label-Efficient Two-Sample Test”, UAI'22. [PDF]

Machine Learning Algorithm Design

W. Li, G. Dasarathy, K. Ramamurthy, V. Berisha, “Finding the Homology of Decision Boundaries with Active Learning”, NeurIPS'20. [PDF]

W. Li, G. Dasarathy, V. Berisha, “Regularization via Structural Label Smoothing”, AISTATS'20. [PDF]

Image Processing & Computer Vision

W. Li, "Adaptive Noise-Tolerant Network for Image Segmentation". [PDF]

W. Li, X. Qian, and J. Ji, “Noise-tolerant Deep Learning for Histopathological Image Segmentation”, ICIP'17. [PDF]

L. Wang, X. Zhou, C. Wang, and W. Li "The Effects of Image Dehazing Methods Using Dehazing Contrast-Enhancement Filters on Image Compression", KSII Transactions on Internet & Information Systems, 10(7). [PDF]

C. Tsai, W. Li, X. Qian, Y. Lin, “Image Co-saliency Detection and Co-segmentation via Progressive Joint Optimization”, IEEE Transactions on Image Processing, 28(1), 56-71. [PDF]