Smith is Professor of Computer Science & Engineering at the University ofWashington, Adjunct in Linguistics, Affiliate of the Center for Statistics and the Social Sciences, and Senior Data Science Fellow at the eScience Institute. He is also Senior Research Manager for the AllenNLP team at the Allen Institute for Artificial Intelligence.Previously, he was Finmeccanica Associate Professor in the School of Computer Science at Carnegie Mellon University, completed his Ph.D. as a Hertz Foundation Fellow at Johns Hopkins University, and studied at the Universities of Maryland and Edinburghand Western Maryland College. He is an amateur clarinetist, tanguero, swimmer, cocktailenthusiast, and serves on the staff of two felines.

Abhinav Gupta is an Associate Professor at the Robotics Institute, Carnegie Mellon University and Research Manager at Facebook AI Research (FAIR). Abhinav's research focuses on scaling up learning by building self-supervised,lifelong and interactive learning systems. Specifically, he is interested in how self-supervised systems can effectively use data to learn visual representation, common sense and representation for actions in robots. Abhinav is a recipient of several awardsincluding ONR Young Investigator Award, PAMI Young Researcher Award, Sloan Research Fellowship, Okawa Foundation Grant, Bosch Young Faculty Fellowship, YPO Fellowship, IJCAI Early Career Spotlight, ICRA Best Student Paper award, and the ECCV Best Paper Runner-up Award. His research has also been featured in Newsweek, BBC, Wall Street Journal, Wired and Slashdot.

Dr Vaishak Belle is a Chancellor's Fellow and Faculty at the University of Edinburgh, an Alan Turing Institute Faculty Fellow, a Royal Society University Research Fellow, and a member of the RSE (Royal Society of Edinburgh) Young Academy of Scotland. At the University of Edinburgh, he directs a lab that specializes in the unification of symbolic systems and machine learning.
Vaishak's research is in artificial intelligence, and is motivated by the need to augment learning and perception with high-level structured, commonsensical knowledge, to enable AI systems to learn faster and more accurate models of the world. In particular, he has worked on areas such as knowledge representation, probabilistic inference, statistical relational learning, probabilistic programming, cognitive robotics, and his recent research has touched upon explainability and ethics in AI.
He has co-authored over 50 scientific articles on AI, at venues such as IJCAI, UAI, AAAI, MLJ, AIJ, JAIR, AAMAS, and along with his co-authors, he has won the Microsoft best paper award at UAI, and the Machine learning journal award at ECML-PKDD. In 2014, he received a silver medal by the Kurt Goedel Society. More information can be found at: https://vaishakbelle.com/lab

Joan Bruna is an Assistant Professor at Courant Institute, New York University (NYU), in the Department of Computer Science, Department of Mathematics (affiliated) and the Center for Data Science, since Fall 2016. He belongs to the CILVR group and to the Math and Data groups. From 2015 to 2016, he was Assistant Professor of Statistics at UC Berkeley and part of BAIR (Berkeley AI Research). Before that, he worked at FAIR (Facebook AI Research) in New York. Prior to that, he was a postdoctoral researcher at Courant Institute, NYU. He completed his PhD in 2013 at Ecole Polytechnique, France. Before his PhD he was a Research Engineer at a semi-conductor company, developing real-time video processing algorithms. Even before that, he did a MsC at Ecole Normale Superieure de Cachan in Applied Mathematics (MVA) and a BA and MS at UPC (Universitat Politecnica de Catalunya, Barcelona) in both Mathematics and Telecommunication Engineering. For his research contributions, he has been awarded a Sloan Research Fellowship (2018), a NSF CAREER Award (2019) and a best paper award at ICMLA (2018).

Nuno Vasconcelos is a Professor at the Electrical and Computer Engineering Department of the University of California, San Diego. He heads the Statistical Visual Computing Laboratory, which investigates problems in computer vision, machine learning, and multimedia. He is an IEEE Fellow, Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and regularly serves as area chair for the major conferences in vision and learning. He has received several best paper awards and other distinctions and co-authored more than 150 peer reviewed publications.

Victor Lempitsky leads the Vision, Learning, Telepresence (VIOLET) Lab at Samsung AI Center in Moscow, and also holds an associate professor position at Skolkovo Institute of Science and Technology (Skoltech). In the past, Victor was a researcher at Yandex, at the Visual Geometry Group (VGG) of Oxford University, and at the Computer Vision group of Microsoft Research Cambridge. He has a PhD ("kandidat nauk") degree from Moscow State University (2007). Victor's research interests are in various aspects of computer vision and deep learning. He has published extensively in top computer vision and machine learning venues, and has served as an area chair for top computer vision conferences (CVPR, ICCV, ECCV, ICLR) on multiple occasions. He has received a Scopus Russia Award for being a highly cited researcher in 2018.

Michael S. Brown is a professor at York University and Canada Research Chair in Computer Vision. He is currently on leave from York and serving as a Senior Director at the Samsung AICentre in Toronto. His research focuses on physics-based computer vision and in-camera color processing. Dr. Brown is a recognized expert in color and camera processing and has given numerous tutorials and invited talks on the topic. From 2011-2016 hewas an Associate Editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence. He is currently on the editorial board for the International Journal of Computer Vision. Dr. Brown was the general chair for CVPR 2018 and will serve as generalchair for CVPR 2021 and 2023.

Alex Schwing is an Assistant Professor at the University of Illinois at Urbana-Champaign working with talented students on computer vision and machine learning topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016. His research interests are in the area of computer vision and machine learning, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing and generative modeling. His PhD thesis was awarded an ETH medal. For additional info, please browse to http://alexander-schwing.de.

Nic Lane is an Associate Professor in the Computer Science Department at the University of Oxford and Program Director (AI Systems) at the Samsung AI Center at Cambridge. Before joining Oxford, he held dual appointments at University College London (UCL) and Nokia Bell Labs; at Nokia, as a Principal Scientist, Nic founded and led DeepX – an embedded focused deep learning unit at the Cambridge location. Of late his research has specialized in the study of efficient machine learning under computational constraints, and over the last three years he has pioneered a range of embedded and mobile forms of deep learning. More generally, Nic’s research interests revolve around the modelling and systems challenges that arise when computers collect and reason over various types of complex real-world multi-modal sensor data. Nic has received multiple best paper awards, including ACM/IEEE IPSN 2017 and two from ACM UbiComp (2012 and 2015). In 2018 and 2019, he (and his co-authors) received the ACM SenSys Test-of-Time award and ACM SIGMOBILE Test-of-Time award for pioneering research, performed during his PhD thesis, that devised machine learning algorithms used today on devices like smartphones. This year Nic served as the PC-chair of ACM MobiSys 2019, a role he has performed also for ACM HotMobile and ACM SenSys in the past. Prior to moving to England, Nic spent 4-years at Microsoft Research based in Beijing as a Lead Researcher. He received his PhD from Dartmouth College in 2011.

Dr. Xue (Steve) Liu is a Full Professor in the School of Computer Science and a William Dawson Scholar (Chair Professor) at McGill University. He is Vice President R&D, Chief Scientist, and Co-Director of the Samsung AI Center Montreal. Dr. Liu is also a Professor (courtesy appointment) of Mathematics and Statistics at McGill University. Dr. Liu is also a faculty and associate member of Montreal Institute for Learning Algorithms (MILA). He served as the Chief Scientist of Tinder Inc. from 2016 to 2019, leading the research and innovation for the world's largest dating and social discovery app valued over 10 Billion US$.
At McGill University, Dr. Liu is also an associated member of the Center for Intelligent Machines (CIM), and an associated member of the Center for Advanced Systems and Technologies in Communications (SYTACom).

Tommaso Melodia is the William Lincoln Smith Chair Professor with the Department of Electrical and Computer Engineering at Northeastern University in Boston. He is also the Founding Director of the Institute for the Wireless Internet of Things and the Director of Research for the PAWR Project Office. He received his Laurea (integrated BS and MS) from the University of Rome - La Sapienza and his Ph.D. in Electrical and Computer Engineering from the Georgia Institute of Technology in 2007. He is an IEEE Fellow and recipient of the National Science Foundation CAREER award. He was named a College of Engineering Faculty Fellow in 2017 and received the Søren Buus Outstanding Research Award in 2018 - the highest research award in the College of Engineering at Northeastern University. Prof. Melodia is the Editor in Chief for Computer Networks, and has served as Associate Editor for IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, IEEE Transactions on Multimedia, among others. He has served as Technical Program Committee Chair for IEEE Infocom 2018, General Chair for IEEE SECON 2019, ACM Nanocom 2019, and ACM WUWNet 2014. Prof. Melodia is the Director of Research for the Platforms for Advanced Wireless Research (PAWR) Project Office, a $100M public-private partnership to establish 4 city-scale platforms for wireless research to advance the US wireless ecosystem in years to come. The PAWR Project Office is co-lead by Northeastern University and US Ignite and is overseeing the overall deployment and operation of the PAWR Program. Prof. Melodia’s research on modeling, optimization, and experimental evaluation of Internet-of-Things and wireless networked systems has been funded by the National Science Foundation, the Air Force Research Laboratory the Office of Naval Research, DARPA, and the Army Research Laboratory.

She is an associate professor of computer science at KAIST. She got PhD in 2008 from MIT and joined KAIST in the same year. Research interests are in developing and applying machine learning models for human social behavior data. In her research group, they look at various data such as news, social media, Wikipedia, and programming education.