Jie Tang
Title: Self-supervised Learning and Pre-training on Graphs 
Abstract: TBD
Short Bio: Jie Tang is a Professor of the Department of Computer Science and Technology of Tsinghua University. He is a Fellow of the ACM, a Fellow of AAAI, and a Fellow of the IEEE. His research interests include artificial general intelligence (AGI), data mining, social networks , machine learning and knowledge graph, with an emphasis on designing new algorithms for information and social network mining. Recent years, He is super interested in designing new paradigms for artificial general intelligence. Similar to Open AI's GPT serials, he, together with a big research team, have designed GLM-130B, ChatGLM, CogView&CogVideo, CodeGeex, toward teaching machines to think like humans.


Wei Wang
Title: Multi-View Knowledge Graph Representation Learning
Abstract: Knowledge graphs are essential data structures that have been shown to improve several semantic applications, including semantic search, question answering, and recommender systems. Many knowledge graphs consist of both (1) an instance-view component, containing entity-entity relations, and (2) an ontology-view component, containing concept-concept and entity- concept relations. In addition, there are nodes involved in both entity-concept relations and entity-entity relations. In this talk, I will present our recent work on knowledge graph representation learning that models the two-view knowledge graphs by jointly embedding different views of the knowledge graphs in different geometric spaces, in order to better capture heterogeneous structures of the knowledge graphs. This model significantly outperforms previous state of the art models on knowledge graph completion and node typing tasks.
Short Bio: Wei Wang is the Leonard Kleinrock Chair Professor in Computer Science and Computational Medicine at University of California, Los Angeles and the director of the Scalable Analytics Institute (ScAi). She is also a member of the UCLA Jonsson Comprehensive Cancer Center, Institute for Quantitative and Computational Biology, and Bioinformatics Interdepartmental Graduate Program. She received her PhD degree in Computer Science from the University of California, Los Angeles in 1999. Dr. Wang's research interests include big data analytics, data mining, machine learning, natural language processing, bioinformatics and computational biology, and computational medicine. Dr. Wang received numerous awards in her career including an ACM fellow and an IEEE fellow. She is the chair of ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD).


Leman Akoglu
Title: Expressive, Scalable, and Interpretable Graph Embeddings
Abstract: Flattening graphs into vector representation, a.k.a. graph(level) embedding, transforms structural data to a form easy to learn with. The choice of an embedding method poses several considerations; including expressiveness, scalability, speed, and interpretability. In this talk, I will first present recent work on designing expressive Graph Neural Network (GNN) models that strike a balance between expressiveness and scalability; positioning between the highly scalable Message Passing Neural Networks (MPNNs) yet with expressiveness bounded by 1st-order Weisfeiler-Lehman isomorphism test (1-WL) and highly expressive models that come at the cost of scalability and sometimes generalization performance. Next I will shift to unsupervised graph embedding based on graph spectral density, that is very fast to compute, individually per graph and that lends itself to various interpretations. I will finish with a comparison of these types of approaches and discuss future directions.
Short Bio: Leman Akoglu is the Heinz College Dean's Associate Professor of Information Systems at Carnegie Mellon University. She has also received her Ph.D. from CSD/SCS of Carnegie Mellon University in 2012. Dr. Akoglu’s research interests are graph mining, pattern discovery and anomaly detection, with applications to fraud and event detection in diverse real-world domains. She is a recipient of the SDM/IBM Early Career Data Mining Research award (2020), National Science Foundation CAREER award (2015) and US Army Research Office Young Investigator award (2013). Her early work on graph anomalies has been recognized as the The Most Influential Paper (PAKDD 2020), which was previously awarded the Best Paper (PAKDD 2010), along with several “best paper” awards at top-tier conferences. Her research has been supported by the NSF, US ARO, DARPA, Adobe, Capital One Bank, Facebook, Northrop Grumman, PNC Bank, PwC, and Snap Inc.


Karthik Subbian
Title: Practical Challenges in Graph Representation Learning
Abstract: TBD
Short Bio: Karthik Subbian is a director and senior principal scientist at Amazon with more than 19 years of industry experience. He leads a team of scientists and engineers to improve search quality and trust. He was a research scientist and lead at Facebook, before coming to Amazon, where he had led a team of scientists and engineers to explore information propagation and user modeling problems using the social network structure and its interactions. Earlier to that, he was working at IBM T.J. Watson research center in the Business Analytics and Mathematical Sciences division. His areas of expertise include machine learning, information retrieval, and large-scale network analysis. More specifically, semi-supervised and supervised learning in networks, personalization and recommendation, information diffusion, and representation learning. He holds a masters degree from the Indian Institute of Science (IISc) and a Ph.D. from the University of Minnesota, both in computer science. Karthik has won numerous prestigious awards, including the IBM Ph.D. fellowship, best paper award at Siam Data Mining (SDM) conference 2013 and Informs Edelman laureate award 2013.