Devi Parikh is an Associate Professor in the School of Interactive Computing at Georgia Tech, and a Research Scientist at Facebook AI Research (FAIR). From 2013 to 2016, she was an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. From 2009 to 2012, she was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC), an academic computer science institute affiliated with University of Chicago. She has held visiting positions at Cornell University, University of Texas at Austin, Microsoft Research, MIT, Carnegie Mellon University, and Facebook AI Research. She received her M.S. and Ph.D. degrees from the Electrical and Computer Engineering department at Carnegie Mellon University in 2007 and 2009, respectively. She received her B.S. in Electrical and Computer Engineering from Rowan University in 2005. Her research interests are in computer vision, natural language processing, embodied AI, human-AI collaboration, and AI for creativity. She is a recipient of an NSF CAREER award, an IJCAI Computers and Thought award, a Sloan Research Fellowship, an Office of Naval Research (ONR) Young Investigator Program (YIP) award, an Army Research Office (ARO) Young Investigator Program (YIP) award, a Sigma Xi Young Faculty Award at Georgia Tech, an Allen Distinguished Investigator Award in Artificial Intelligence from the Paul G. Allen Family Foundation, four Google Faculty Research Awards, an Amazon Academic Research Award, a Lockheed Martin Inspirational Young Faculty Award at Georgia Tech, an Outstanding New Assistant Professor award from the College of Engineering at Virginia Tech, a Rowan University Medal of Excellence for Alumni Achievement, Rowan University’s 40 under 40 recognition, a Forbes’ list of 20 “Incredible Women Advancing A.I. Research” recognition, and a Marr Best Paper Prize awarded at the International Conference on Computer Vision (ICCV).
Timothy Hospedales is a Reader (UK Associate Professor) at the University of Edinburgh; Principal Researcher at Samsung AI Research Centre Cambridge; and Alan Turing Institute Fellow. He is Associate Editor of TPAMI, and has served as Area Chair of several major events (ICCV, ECCV, AAAI, ACL) and Program Chair of BMVC 2018. His work has been funded by UK EPSRC and the European Commission, and has led to over 75 publications in major venues, as well as best paper awards or nominations at ICML, ICPR and BMVC.
Adriana Kovashka is an Assistant Professor in Computer Science at the University of Pittsburgh. Her research interests are in computer vision and machine learning. She has authored sixteen publications in top-tier computer vision and artificial intelligence conferences and journals (CVPR, ICCV, ECCV, NeurIPS, IJCV, AAAI, ACL) and nine second-tier conference publications (BMVC, ACCV, WACV). She has served or is serving as an Area Chair for CVPR in 2018-2020. She has been on program committees for over twenty conferences or journals. She has co-organized five workshops at top-tier conferences. Her research is funded by the National Science Foundation, Google, Amazon and Adobe.
Rogerio Feris is the head of computer vision and multimedia research at IBM T.J. Watson Research Center. He joined IBM in 2006 after receiving a Ph.D. from the University of California, Santa Barbara. He has also worked as an Affiliate Associate Professor at the University of Washington and as an Adjunct Associate Professor at Columbia University. His work has not only been published in top AI conferences, but has also been integrated into multiple IBM products, including Watson Visual Recognition, Watson Media, and Intelligent Video Analytics. He currently serves as an Associate Editor of TPAMI, has served as a Program Chair of WACV 2017, and as an Area Chair of conferences such as NeurIPS, CVPR, and ICCV.
Qiuhong Ke received her PhD degree from The University of Western Australia in 2018. She is currently a Lecturer (equivalent to Assistant Professor in US university system) in The School of Computing and Information Systems at The University of Melbourne. She was a Postdoctoral Researcher in Max Planck Institute for Informatics in Germany from 2018 to 2019. Dr. Ke was awarded prestigious International Postgraduate Research Scholarship for doctoral studies in 2015. Her thesis “Deep Learning for Action Recognition and Prediction” has been awarded Dean’s List-Honourable mention by The University of Western Australia in 2018. She was also awarded Lise Meitner Award Postdoctoral Fellowship at Max Planck Institute for Informatics in 2018. She was awarded the 1962 Medal for her work in video recognition technology by the Australian Computer Society in 2019. Her research interests include computer vision and machine learning.
Lingfei Wu is a Research Staff Member in the IBM AI Foundations Labs, Ressoning group at IBM T. J. Watson Research Center. He earned his Ph.D. degree in computer science from the College of William and Mary in 2016. Lingfei Wu is a passionate researcher and responsible team leader, developing novel deep learning/machine learning models for solving real-world challenging problems. He has served as the PI in IBM for several federal agencies such as DARPA and NSF (more than $1.8M), as well as MIT-IBM Watson AI Lab. He has published more than 50 top-ranked conference and journal papers in ML/DL/NLP domains and is a co-inventor of more than 20 filed US patents. He was the recipient of the Best Paper Award and Best Student Paper Award of several conferences such as IEEE ICC’19 and KDD workshop on DLG’19. His research has been featured in numerous media outlets, including NatureNews, YahooNews, Venturebeat, TechTalks, SyncedReview, Leiphone, QbitAI, MIT News, IBM Research News, and SIAM News. Lingfei serves as an Associate Editor for ACM Transactions on Knowledge Discovery from Data. He has organized or served as Poster co-chairs of IEEE BigData’19, Tutorial co-chairs of IEEE BigData’18, Workshop co-chairs of Deep Learning on Graphs (with KDD’19, IEEE BigData’19, and AAAI’20), and regularly served as a SPC/TPC member of the following major AI/ML/DL/DM/NLP conferences including NIPS, ICML, ICLR, ACL, IJCAI, AAAI, and KDD.
Ranjay Krishna is a PhD Candidate in the Artificial Intelligence Lab at Stanford University, where he is co-advised by Professor Fei-Fei Li and Professor Michael Bernstein. His research interests lie at the intersection of computer vision, machine learning, and human-computer interaction. His research explores ways of developing never-ending learning visual systems that can organically grow knowledge bases by interacting and learning directly from people. He is also a teaching fellow at Stanford and designed and teaches a course on computer vision. He has a Masters of Science in Artificial Intelligence from Stanford University. Before that, he conferred a Double Bachelors of Science Degree in Electrical and Computer Engineering and a second degree in Computer Science from Cornell University. In the past, he has worked at Google AI, Facebook Artificial Intelligence Lab, Yahoo Research and Microsoft.