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 lies at the intersection of NLP and AI for Social Impact as applied to: (i) globally important events arising in linguistically diverse regions requiring methods to tackle practical challenges involving multilingual, noisy, social media texts; (ii) polarization in the context of the current US political crisis; and iii) auditing AI systems and platforms for unintended harms. In addition to having his research been accepted at top artificial intelligence conferences and journals, his work has also received widespread international media attention that includes coverage from The New York Times, BBC, Wired, Times of India, The Daily Mail, VentureBeat, and Digital Trends. A detailed resume can be found here.
At RIT, Ashique is the PI of the Social Insight Lab. His current students are Sujan Dutta (PhD), Arka Dutta (PhD), Adel Khorramrouz (MSc), Mahbeigom Fayyazi (MSc), and Syed Mohammad Sualeh Ali (MSc).With three published collections of poems to his credit, an experience of directing music at a New York theater play, occasional dabbling at journalism and column-writing, and a recent success at swimming 50 meters underwater (a navy seal requirement), Ashique enjoys his multiple distractions that keep him away from work.