Summary
Solaiman Salvi is an Assistant Teaching Professor and researcher with nine years of experience bridging multimodal AI research and undergraduate teaching, currently instructing Machine Learning and AI at UMBC. He earned a Ph.D. from Purdue focused on multimodal information extraction and situation-aware recommendation, collaborating with DARPA, Northrop Grumman, MIT, and other labs to deliver systems used in socially impactful domains like missing-persons search. His publications span top venues (VLDB, SIGMOD, AAAI, IEEE TransAI) and his doctoral work produced novel cross-modal retrieval and human-attribute recognition methods that scale across heterogeneous data sources. A committed mentor, he has supervised over a dozen undergraduates and master’s students and recently secured a campus SURE grant to support undergraduate research. Trained at BUET and Purdue, he blends rigorous academic research with practical teaching experience and a track record of building real-world, explainable AI systems.
9 years of coding experience
10 years of employment as a software developer
Higher Secondary Certificate (H.S.C) Science, Higher Secondary Certificate (H.S.C) Science at Dhaka College
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Purdue University
Secondary School Certificate (S.S.C) Science, Secondary School Certificate (S.S.C) Science at Ideal School and College
Bachelor of Science (B.Sc.) Computer Science and Engineering (CSE), Bachelor of Science (B.Sc.) Computer Science and Engineering (CSE) at Bangladesh University of Engineering and Technology