Dr. Mohammad Hajiaghayi

Biography:

Dr. Mohammad T. Hajiaghayi is the Jack and Rita G. Minker Associate Professor of Computer Science at the University of Maryland with a joint appointment in the University's Institute for Advanced Computer Studies (UMIACS). In addition, he holds a Research Affiliate position in MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and is a Permanent Member of Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) at Rutgers. Before joining the University of Maryland, he was a Senior Researcher in the Algorithms and Theoretical Computer Science group at AT&T Labs-- Research to which he is still a consultant. Before that, he was a one-year Postdoctoral Fellow in the School of Computer Science at Carnegie Mellon University (with ALADDIN project) and a one-year Postdoctoral Associate in MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) from which he also earned his Ph.D in 2005. During his Ph.D. studies, he spent some time at IBM Research centers and Microsoft Research centers. Dr. Hajiaghayi got his M.Sc. in Computer Science from the University of Waterloo in 2001 and his B.Sc. in Computer Engineering from Sharif University of Technology in 2000.

Dr. Hajiaghayi's research interests are algorithmic game theory and combinatorial auctions, network design, combinatorial optimizations and approximation algorithms, fixed-parameter algorithms, algorithmic graph theory, distributed and mobile computing, and computational geometry and embeddings. In the course of his professional career in these areas, he has published more than 110 papers in top conferences and journals of computer science, won a few best paper awards, and served in program committees or editorial boards of several well-known international conferences and journals. He has received an NSF CAREER Award in 2010, a Google Faculty Research Award in 2010, an ONR Young Investigator Award in 2011, and the University of Maryland Research and Scholarship Award (RASA) in 2011. He won best paper awards at the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) 2010, the International Symposium on Algorithms and Computation (ISAAC) 2006, and the Robocup 2001 Conference.

Dr. Mohammad T. Hajiaghayi is a Guggenheim Fellow (2019), an ACM Fellow (2018) (the youngest in the class of 2018 fellows), an AAAS Fellow (2023), an IEEE Fellow (2020), an EATCS Fellow (2020), a Blavatnik National Awards for Young Scientists Honoree (2020), a Washington Academy of Science Distinguished Career Awardee (2023), a University of Waterloo Alumni Achievement Medal (2022) Recipient, and the Jack and Rita G. Minker Professor of Computer Science Department at the University of Maryland at College Park. I am also affiliated Professor with Robert H. Smith School of Business, University of Maryland. In addition, Dr. Hajiaghayi hold a Research Affiliate position in MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also a Permanent Member of Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) at Rutgers. He was also a Visiting Scientist in the Simons Institute for the Theory of Computing at University of California, Berkeley, in Fall 2016, Spring 2018, and Fall 2018. 

Title:  Prize Collecting Framework: Classic Network Problems with Modern Techniques 

Abstract:  The Prize-collecting Framework encompasses classic network optimization problems where multiple demands seek to be served by the lowest-cost structure. If serving some demands proves too costly, they can be declined with a penalty paid instead. Within this framework, our primary focus lies on classic network design problems such as Minimum Spanning Tree, Steiner Tree, TSP, Steiner Forest, and Steiner Networks, among others and present state-of-the-art approximation algorithms for these problems. However, our approach integrates a robust and modern array of techniques ranging from clustering algorithms to recursive methodologies, offering solutions that extend beyond the confines of Prize-collecting Framework. We conclude by presenting very recent best approximation algorithms for Prize-collecting Steiner Forest and Prize-collecting Steiner Tree.