Dr. Dejing Dou received his bachelor degree in Electronic Engineering from Tsinghua University in 1996 and his Ph.D. degree in Artificial Intelligence from Yale University in 2004. His research areas include artificial intelligence, data mining, data integration, NLP, and health informatics. Dr. Dou has published more than 100 research papers, some of which appear in prestigious conferences and journals like AAAI, IJCAI, KDD, ICDM, ACL, EMNLP, CIKM, ISWC, JIIS and JoDS. His DEXA'15 paper received the best paper award. His KDD'07 paper was nominated for the best research paper award. He is on the Editorial Boards of Journal on Data Semantics, Journal of Intelligent Information Systems, and PLOS ONE. He has been serving as program committee members for various international conferences and as program committee co-chairs for five of them. Dr. Dou has received over $5 million PI research grants from the NSF and the NIH. Right now, Dr. Dou is on sabbatical leave from the University of Oregon and serving as the head of Big Data Lab (BDL).
Dr. Xiaolin Andy Li is a Partner of Tongdun Technology, heading the AI Institute and Cognization Lab. AI Institute is missioned to pioneer fundamental research and empower a broad spectrum of AI applications. AI Institute is composed of labs of deep learning, federated learning, reinforcement learning, computer vision, natural language processing, intelligent interaction (intelligent speech), and AI platforms and operating systems. He was a Professor and University Term Professor in Computer Engineering at the University of Florida. As the founding director, he founded National Science Foundation Center for Big Learning, the first national center on deep learning in the USA, along with dozens of colleagues at UF, CMU, UO, and UMKC, sponsored by NSF and over 30 industry members. He received a PhD degree in Computer Engineering from Rutgers University. His research interests include deep learning, cloud computing, security & privacy, IoT, FinTech, precision medicine, and logistics. He has published over 150 peer-reviewed papers in journals and conference proceedings, 5 books, and dozens of patents. He is a recipient of the NSF CAREER Award, NSF ICorp Top Team Award, Internet2 Innovative Application Award, and several best paper awards (IEEE SECON 2016, IEEE ICMLA 2016, ACM CAC 2013 and IEEE UbiSafe 2007). He has been a General Chair/Program Chair for dozens of conferences and workshops, a PC member for over 100 conferences/workshops, and associate editor for IEEE TPDS and JPDC among many other journals.
Dr. Ameet Talwalkar is an assistant professor in the Machine Learning Department at CMU, and also co-founder and Chief Scientist at Determined AI. His interests are in the field of statistical machine learning. His current work is motivated by the goal of democratizing machine learning, with a focus on topics related to scalability, automation, fairness, and interpretability of learning algorithms and systems. He led the initial development of the MLlib project in Apache Spark, is a co-author of the textbook 'Foundations of Machine Learning' (MIT Press), and created an award-winning edX MOOC on distributed machine learning. He is also one of the founders of the MLSys conference, serving as the inaugural Program Chair in 2018, General Chair in 2019, and currently as President of the MLSys Board.
Dr. Yanzhi Wang is currently an assistant professor in Dept. of ECE at Northeastern University, Boston, MA. He received the B.S. degree from Tsinghua University in 2009, and Ph.D. degree from University of Southern California in 2014. His research interests focus on model compression and platform-specific acceleration of deep learning applications. His research maintains the highest model compression rates on representative DNNs since 09/2018. His work on AQFP superconducting based DNN acceleration is by far the highest energy efficiency among all hardware devices. His recent research achievement, CoCoPIE, can achieve real-time performance on almost all deep learning applications using off-the-shelf mobile devices. His work has been published broadly in top conference and journal venues (e.g., ASPLOS, ISCA, MICRO, HPCA, PLDI, ICS, ISSCC, AAAI, ICML, CVPR, ICLR, IJCAI, ECCV, ICDM, ACM MM, DAC, ICCAD, FPGA, LCTES, CCS, VLDB, ICDCS, Infocom, TComputer, TCAD, JSAC, TNNLS, etc.), and has been cited above 6,700 times. He has received four Best Paper Awards, has another ten Best Paper Nominations and four Popular Paper Awards. He has received the ARO Young Investigator Award (YIP), Massachusetts Acorn Innovation Award, and other research awards from Google, MathWorks, etc. He is a senior member of IEEE. Three of his former Ph.D./postdoc students become tenure track faculty at Univ. of Connecticut, Clemson University, and Texas A&M University, Corpus Christi.
Dr. Lingfei Wu earned his Ph.D. degree in computer science from the College of William and Mary in 2016. He is a research staff member at IBM Research and is leading a research team (10+ RSMs) for developing novel Graph Neural Networks for many AL/ML/NLP tasks, which leads to the #1 AI Challenge Project in IBM Research and multiple IBM Awards including IBM Outstanding Technical Achievement Award and IBM Invention Achievement Awards. He has published more than 70 top-ranked conference and journal papers and is a co-inventor of more than 30 filed US patents. He was the recipient of the Best Paper Award and Best Student Paper Award of several conferences such as AAAI workshop on DLGMA’20, IEEE ICC’19 and KDD workshop on DLG'19. His research has been featured in numerous media outlets, including NatureNews, YahooNews, Venturebeat, and TechTalks. He has co-organized 10+ conferences and workshops, including IEEE BigData'19, IEEE BigData'18, Workshops of Deep Learning on Graphs (with KDD’20, AAAI’20, KDD’19, and IEEE BigData’19). He has currently served as Associate Editor of ACM TKDD and International Journal of Intelligent Systems, and regularly served as a SPC/PC member of the following major AI/ML/NLP conferences including KDD, IJCAI, AAAI, NIPS, ICML, ICLR, and ACL.
Dr. Dimitris Papailiopoulos is an Assistant Professor of Electrical and Computer Engineering and Computer Sciences (by courtesy) at the University of Wisconsin-Madison, a faculty fellow of the Grainger Institute for Engineering, and a faculty affiliate at the Wisconsin Institute for Discovery. His research interests span machine learning, information theory, and distributed systems, with a current focus on communication-efficient training algorithms and coding-theoretic techniques that guarantee robustness during training and inference. Between 2014 and 2016, Dimitris was a postdoctoral researcher at UC Berkeley and a member of the AMPLab. Dimitris earned his Ph.D. in ECE from UT Austin in 2014, under the supervision of Alex Dimakis. In 2007 he received his ECE Diploma and in 2009 his M.Sc. degree from the Technical University of Crete, in Greece. Dimitris is a recipient of the NSF CAREER Award (2019), a Sony Faculty Innovation Award (2019), the Benjamin Smith Reynolds Award for Excellence in Teaching (2019), and the IEEE Signal Processing Society, Young Author Best Paper Award (2015). In 2018, he co-founded MLSys, a new conference that targets research at the intersection of machine learning and systems. In 2018 and 2020 he was program co-chair for MLSys, and in 2019 he co-chaired the 3rd Midwest Machine Learning Symposium.
Dr. Dapeng Oliver Wu is a professor at the Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL. He received University of Florida Term Professorship Award in 2017, University of Florida Research Foundation Professorship Award in 2009, AFOSR Young Investigator Program (YIP) Award in 2009, ONR Young Investigator Program (YIP) Award in 2008, NSF CAREER award in 2007, the IEEE Circuits and Systems for Video Technology (CSVT) Transactions Best Paper Award for Year 2001, and the Best Paper Awards in IEEE GLOBECOM 2011 and International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks (QShine) 2006. Currently, he serves as Editor in Chief of IEEE Transactions on Network Science and Engineering. He was the founding Editor-in-Chief of Journal of Advances in Multimedia between 2006 and 2008, and an Associate Editor for IEEE Transactions on Communications, IEEE Transactions on Signal and Information Processing over Networks, IEEE Signal Processing Magazine, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Wireless Communications and IEEE Transactions on Vehicular Technology. He is also a guest-editor for IEEE Journal on Selected Areas in Communications (JSAC), Special Issue on Cross-layer Optimized Wireless Multimedia Communications and Special Issue on Airborne Communication Networks. He has served as Technical Program Committee (TPC) Chair for IEEE INFOCOM 2012, and TPC chair for IEEE International Conference on Communications (ICC 2008), Signal Processing for Communications Symposium, and as a member of executive committee and/or technical program committee of over 100 conferences. He is an IEEE Fellow.
Technical Program Committee