Sebastian Bruch

Sebastian Bruch completed his Ph.D. in Computer Science at the University of Maryland, College Park in 2013. He is interested in randomized algorithms, sketching, and nearest neighbor search. He particularly enjoys conducting theoretical investigations of empirically-successful algorithms, and developing practical algorithms that are backed by theoretical guarantees.

Sebastian is the author of “Foundations of Vector Retrieval” and the co-author of “Efficient and Effective Tree-based and Neural Learning to Rank.” His published works have appeared in leading journals including ACM TOIS, IEEE TKDE, and FnT in Information Retrieval (IR). He has also contributed to the proceedings of and served on the program committees or as reviewer for conferences including NeurIPS, ICLR, SIGIR, WSDM, SIGKDD, and the Web Conference.