Ashique KhudaBukhsh is currently an Assistant Professor at the Golisano College of Computing and Information Sciences (GCCIS), Rochester Institute of Technology (RIT). Prior to this role, he was a Project Scientist at the School of Computer Science, Carnegie Mellon University (CMU) mentored by Prof. Tom Mitchell. Prior to this role, he was a postdoc mentored by Prof. Jaime Carbonell at CMU. His PhD thesis (Computer Science Department, Carnegie Mellon University, also advised by Prof. Jaime Carbonell) focused on referral networks, an emerging area at the intersection of Active Learning and Game Theory. His Master's thesis at the University of British Columbia (UBC), advised by Prof. Kevin Leyton-Brown and Prof. Holger H. Hoos, focused on automated algorithm design for combinatorial hard problems. His current research focus is in Computational Social Science. In this field, he is interested in analyzing globally important events in South East Asia and developing methods for noisy social media texts generated in this linguistically diverse region. His other broad focus is US politics; he has offered a graduate course (team-taught with Prof. Mark S. Kamlet and Prof. Tom M. Mitchell) at the intersection of machine learning and political science focusing on the 2020 US election. A detailed resume can be found here.