Rate My Professor Arko Barman

Arko Barman, Ph.D., is an Associate Teaching Professor at Rice University’s Center for Transforming Data to Knowledge (D2K Lab), holding joint appointments in the Departments of Electrical and Computer Engineering, Statistics, and Computer Science. He currently serves as Director of the Data Science Minor and previously directed the Data Science Capstone Program from July 2022 to December 2023, mentoring over 60 sponsored student team projects connecting students with real-world data science challenges. Barman earned his Ph.D. in Computer Science from the University of Houston in 2018, M.S. in Signal Processing from the Indian Institute of Science in 2011, and B.E. in Electrical Engineering from Jadavpur University, India, in 2009. Prior to Rice University, he completed a two-year postdoctoral fellowship at the University of Texas Health Science Center at Houston (UTHealth), earning the inaugural Postdoctoral Service Award and Excellence in Teaching Award there. At Rice, Barman received the Curriculum Innovation Award recognizing his novel teaching and pedagogical approaches across undergraduate to postdoctoral levels.

Barman’s academic interests center on artificial intelligence for biomedicine, encompassing machine learning, deep learning, computer vision, medical image analysis, natural language processing, biomedical signal and image analysis, genomics, data mining, heuristic optimization, and machine learning applications for social sciences, paleontology, and ecology. A member of the Ken Kennedy Institute, he contributes to workgroups at the NIST U.S. Artificial Intelligence Safety Institute and has published, presented, and served on organizing committees for conferences including ACM SIGCSE and IEEE Frontiers in Engineering. His influence extends to a U.S. patent granted in 2022 for person re-identification using overhead view images and over 565 citations on Google Scholar. Key publications include “Detecting Large Vessel Occlusion at Multiphase CT Angiography by Combining Symmetric and Standard Deep Representations” (Radiology, 2020), “Machine Learning–Enabled Automated Determination of Acute Ischemic Stroke from Noncontrast CT” (Stroke, 2019), and “Deep learning-based magnetic resonance image segmentation for glioma analysis” (Frontiers in Medicine, 2023). A recent collaborative study received the best paper award at an IEEE international conference on data.