Professor of Computer Science, Associate Chair of Academic Affairs, and the Director of Bioinformatics LAB at University of Florida
Tamer Kahveci received his Ph.D. degree in Computer Science from University of California at Santa Barbara in 2004. He is currently a Professor and Associate Chair of Academic Affairs in the Computer and Information Science and Engineering Department at the University of Florida, serving as the Associate Chair of Academic affairs. Dr. Kahveci received the Ralph E. Powe Junior Faculty Enhancement award in 2006, CSB best paper award in 2008, the NSF Career award in 2009, the ACM-BCB (Bioinformatics and Computational Biology) best student paper award in 2010, ACM-BCB honorary best paper award in 2011, BiCoB best paper award in 2018, and ACM BCB best student paper award in 2022. His research focuses on bioinformatics. He has worked on indexing sequence and protein structure databases, sequence alignment and computational analysis of biological networks.
Dr. Kahveci has served as the PC co-chair of the ACM BCB conference in 2012 and 2017, the BioKDD workshop and the International Workshop on Robustness and Stability of Biological Systems and Computational Solutions in 2012, the Workshop on Epigenomics and Cell Function in 2013, and the Workshop on Computational Network Analysis from 2014 to 2020, the Workshops Chair of the ACM-BCB conference in 2014. He served as the Tutorials Chair of the ACM BCB and the IEEE BIBM conferences in 2015, and Workshop Chair in 2016. He is a member of the governing board of the ACM SIGBIO and the chair of the steering committee member of the ACM-BCB. He is a member of the editorial review board for of the journal International Journal of Knowledge Discovery in Bioinformatics (IJKDB). He was the lead guest editor of the Journal of Advances in Bioinformatics, special issue on “Computational analysis of biological networks” and associate editor in IEEE/ACM Transactions on Computational Biology and Bioinformatics. In addition to these, he has served on the program committees of numerous computational biology and database conferences.
Biological networks of an organism show how different bio-chemical entities, such as enzymes or genes interact with each other to perform vital functions for that organism. Dr. Kahveci’s lab is focusing on developing computational methods that will help in understanding the functions of large-scale biological networks. In this talk, we will discuss some of the recent research activities at Dr. Kahveci’s lab. More specifically, we will focus on how different network models, such as static, probabilistic, dynamic, and multilayer models, address various challenges in computational biology. We will first consider challenges centered on uncertainty in the topology of biological networks. We will discuss our new mathematical model, which represent probabilistic networks as collections of polynomials. We show that this is a powerful model that enables solving seemingly very tough computational problems on probabilistic networks efficiently and precisely. We will then discuss how the dynamic behavior of the network affects how we can approach to some of the fundamental computational problems on biological network analysis such as motif counting.