Keynote Talks and Speaker Bios

Keynote Talks


  • Daniela Rus, MIT CSAIL: Addressing Model Bias and Uncertainty via Evidential Deep Learning

  • Soheil Feizi, University of Maryland: Towards Understanding Foundations of Robust Learning

  • Michael Bronstein, Imperial College: Geometric Deep Learning: Grids, Graphs, Groups, Gauges

  • Danai Koutra, U. Michigan: Beyond Homophily in Graph Neural Networks

  • Lingfei Wu, JD.com: Deep Learning on Graphs for Natural Language Processing

  • Da Xu, Walmart Labs: Rethinking Product Embedding for E-commerce Machine Learning: the Application and Theoretical Perspectives

  • Polina Kirichenko, NYU: Applications of normalizing flows: semi-supervised learning, anomaly detection, and continual learning

  • Golnoosh Farnadi, MILA/HEC Montreal: Mitigating Algorithmic Discrimination in Machine Learning

Speaker Bios


  • Daniela Rus is the Director of MIT CSAIL and the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science. Rus’ research interests are in robotics, artificial intelligence, and data science. The focus of her work is developing the science and engineering of autonomy, toward the long-term objective of enabling a future with machines pervasively integrated into the fabric of life, supporting people with cognitive and physical tasks. Rus serves as Director of the Toyota-CSAIL Joint Research Center, whose focus is the advancement of AI research and its applications to intelligent vehicles. She is a MITRE senior visiting fellow, serves as a USA expert member for GPAI (Global Partnerships in AI), a member of the board of advisers for Scientific American, a member of the Defense Innovation Board, and a member of several other boards of technical companies. Rus is a Class of 2002 MacArthur Fellow, a fellow of ACM, AAAI and IEEE, and a member of the National Academy of Engineering, and the American Academy for Arts and Science. She earned her PhD in Computer Science from Cornell University.


  • Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. He also heads ML research in Project CETI, a TED Audacious Prize-winning collaboration aimed at understanding the communication of sperm whales. Michael received his PhD from the Technion in 2007. He has held visiting appointments at Stanford, MIT, Harvard, and Tel Aviv University, and has also been affiliated with three Institutes for Advanced Study (at TU Munich as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a visitor (2020)). Michael is the recipient of five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019). He has previously served as Principal Engineer at Intel Perceptual Computing and was one of the key developers of the Intel RealSense technology.


  • Danai Koutra is a Morris Wellman Assistant Professor in Computer Science and Engineering at the University of Michigan, where she leads the Graph Exploration and Mining at Scale (GEMS) Lab. Her research focuses on practical and scalable methods for large-scale real networks, and has applications in neuroscience, organizational analytics, and social sciences. Her research interests include large-scale graph mining, analysis of multi-source network data, graph summarization, similarity and matching, and anomaly detection. She won the SIGKDD Rising Star Award, a Facebook and a Google Faculty Award in 2020, an NSF CAREER award, an Amazon Research Faculty Award, and a Precision Health Investigator award in 2019, an ARO Young Investigator award and an Adobe Data Science Research Faculty Award in 2018, the 2016 ACM SIGKDD Dissertation award, and an honorable mention for the SCS Doctoral Dissertation Award (CMU). She holds one "rate-1" patent and has six (pending) patents on bipartite graph alignment. She has multiple papers in top data mining conferences, including 8 award-winning papers, and her work has been covered by the popular press, such as the MIT Technology Review. She is the Program Director of the SIAG on Data Mining and Analytics, an Associate Editor of ACM TKDD, a tutorial co-chair for KDD'19, and a demo co-chair for CIKM'19. At the University of Michigan, she led the "Explore Graduate Studies in CSE" workshop, which aims to broaden participation in computer science at the graduate level, in 2018-2019. She was a tutorial co-chair for KDD'19 and '20, tutorial co-chair for SDM'20, awards co-chair for ECML-PKDD'20, dissertation consortium co-chair for WSDM'20, demo co-chair for ICDM'18, SIGKDD Cup co-chair in '17, Ph.D. Forum co-chair for ICDM'17, publicity co-chair for SDM'17, and has co-organized 3 tutorials and 3 workshops. She has worked at IBM Hawthorne, Microsoft Research Redmond, and Technicolor Palo Alto/Los Altos. She earned her Ph.D. and M.S. in Computer Science from CMU in 2015 and her diploma in Electrical and Computer Engineering at the National Technical University of Athens in 2010.


  • Soheil Feizi is an assistant professor in the Computer Science Department at University of Maryland, College Park. Before joining UMD, he was a post-doctoral research scholar at Stanford University. He received his Ph.D. from Massachusetts Institute of Technology (MIT). He has received the NSF CAREER award in 2020 and the Simons-Berkeley Research Fellowship on deep learning foundations in 2019. He is the 2020 recipient of the AWS Machine Learning Research award, and the 2019 recipients of the IBM faculty award as well as the Qualcomm faculty award. He is the recipient of a teaching award in Fall 2018 and Spring 2019 in the CS department at UMD. His work has received the best paper award of IEEE Transactions on Network Science and Engineering, over a three-year period of 2017-2019. He received the Ernst Guillemin award for his M.Sc. thesis, as well as the Jacobs Presidential Fellowship and the EECS Great Educators Fellowship at MIT.


  • Lingfei Wu earned his Ph.D. degree in computer science from the College of William and Mary in 2016. He currently serves as a Principal Scientist at JD.COM Silicon Valley Research Center. Previously, he was a research staff member at IBM Research and is leading a research team (10+ RSMs) for developing novel Graph Neural Networks for various tasks, which leads to the #1 AI Challenge Project in IBM Research and multiple IBM Awards including Outstanding Technical Achievement Award. He has published more than 70 top-ranked conference and journal papers and is a co-inventor of more than 40 filed US patents. Because of the high commercial value of his patents, he has received several invention achievement awards and has been appointed as IBM Master Inventors, class of 2020. He was the recipient of the Best Paper Award and Best Student Paper Award of several conferences such as IEEE ICC’19, AAAI workshop on DLGMA’20 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 (KDD, AAAI, IEEE BigData) and is the founding co-chair for Workshops of Deep Learning on Graphs (with AAAI’21, AAAI’20, KDD’20, KDD’19, and IEEE BigData’19). He has currently served as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, ACM Transactions on Knowledge Discovery from Data 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.


  • Da Xu is currently a senior machine learning engineer from the Search & Personalization team in Walmart Labs, leading the research projects on causal inference, robustness and theoretical understanding on using representation learning for e-commerce use cases. His research interest is mainly to bridge the theories and applications of using embeddings for ecommerce tasks. Da has multiple publications on prestige venues such as NeurIPS, ICLR, WSDM and ICASSP. He is also actively serving as reviewers for NeurIPS, ICLR, KDD, SIGIR, etc.

  • Golnoosh Farnadi is an assistant professor at the decision sciences department at HEC Montréal and a core academic member at Mila (Quebec AI Institute). She develops novel machine learning and AI models to tackle fairness and ethics in AI. Her recent work has mainly focused on addressing bias and algorithmic discrimination in decision making models. Golnoosh completed her PhD in Computer Science at KU Leuven and Ghent University in 2017, and then she became a postdoctoral researcher at the Statistical Relational Learning Group (LINQS) at the University of California, Santa Cruz. From 2018 to 2020, Golnoosh was a postdoctoral IVADO fellow at Université de Montréal and Mila, where she worked on fairness-aware sequential decision-making. In 2021, Golnoosh was appointed a Canada AI CIFAR chair. She has significant collaborative experience with both academia and industry including Microsoft research, UCLA, University of Washington, and Tsinghua University. These successful collaborations are reflected in over 40 publications in international conferences and journals. Among her numerous accomplishments, Golnoosh has received two paper awards for her work on statistical relational learning frameworks. She contributed to the community by giving invited talks, and organizing workshops and schools related to the topic of ethics in AI. She was one of the organizers of the 1st MILA/IVADO summer school on Bias and Discrimination in 2018 in Montreal and has been the scientific director of the online MOOC based on its content.


  • Polina Kirichenko is a third year PhD student at the Center for Data Science at New York University working with professor Andrew Gordon Wilson. She has been working on probabilistic deep learning, Bayesian machine learning, uncertainty estimation and generative modeling. Prior to joining NYU, she received her BS in Computer Science at Higher School of Economics University in Moscow and spent one year at Cornell's Operations Research department. She has previously interned at DeepMind, Google and EPFL university.