John Dickerson

Assoc. Prof., UMD & Chief Scientic, Arthur AI

John Dickerson is an Associate Professor (as of July 2022) of Computer Science at the University of Maryland and co-founder and Chief Scientist of Arthur AI, an enterprise-focused AI/ML model monitoring company. He is recipient of awards such as the NSF CAREER Award, Google Faculty Research Award, and paper awards and nominations at venues such as AAAI and CHI. His research centers on solving practical economic problems using techniques from computer science, stochastic optimization, and machine learning. He has worked extensively on theoretical and empirical approaches to kidney exchange where his work has set policy at the UNOS nationwide exchange; worldwide blood donation markets with Facebook; game-theoretic approaches to counter-terrorism and negotiation, where his models have been deployed; and market design problems in industry (e.g., online advertising) through various startups. He has co-organized several large workshops including the Workshop on Dataset Curation and Security (at NeurIPS‘20), GAIW: Games, Agents, and Incentives (at AAMAS’20, ‘21, and ‘22), FAMAS: Fair Allocation in Multiagent Systems (FAMAS) (at AAMAS’19), and EXPLORE (at AAMAS’17).

Eren Kurshan

Exec. Head of AI/ML, BofA, Visiting Fellow, Princeton

Eren Kurshan is the Executive Head of AI and Data Science for Client Protection for Bank of America Corporation. In this role she is responsible for leading the development of custom Machine Learning and Deep Learning solutions for Fraud detection, prevention and operational improvement for Bank of Americas payment systems. Prior to her role at Bank of America, Eren has led various AI and Machine Learning Programs at Columbia University, J.P. Morgan Corporate and Investment Bank, and IBM Corporate Headquarters. Dr. Kurshan was a Visiting Fellow at Princeton University Center for Information Technology Policy in 2015-2016. She has been an Adjunct Professor of Computer Science at Columbia University since 2014. Dr. Kurshan received her Ph.D. in Applied Algorithms and Theoretical Computer Science from the University of California. Her research interests include application of deep learning in financial services, applied algorithms and efficient system design for AI. Eren has over 60 peer reviewed academic publications, over 100 patents. She was the recipient of 2 Best Paper Awards from IEEE and ACM Conferences, and a number of Outstanding Research and Invention Accomplishment Awards from IBM Research.

Jian Tang

Assoc. Prof., Mile-Quebec AI Institute & University of Montreal

Jian Tang is an associate professor at Mila-Quebec AI Institute and also at Computer Science Department and Business School of University of Montreal. He is a Canada CIFAR AI Research Chair. His main research interests are graph representation learning, graph neural networks, geometric deep learning, deep generative models, knowledge graphs and drug discovery. During his PhD, he was awarded with the best paper in ICML2014; in 2016, he was nominated for the best paper award in the top data mining conference World Wide Web (WWW); in 2020, he is awarded with Amazon and Tencent Faculty Research Award. He is one of the most representative researchers in the growing field of graph representation learning and has published a set of representative works in this field such as LINE and RotatE. His work LINE on node representation learning has been widely recognized and is the most cited paper at the WWW conference between 2015 and 2019. Recently, his group just released an open-source machine learning package, called TorchDrug, aiming at making AI drug discovery software and libraries freely available to the research community. He is an area chair of ICML and NeurIPS.

Peter Henstock

Lead, AI/ML, Pfizer

Peter Henstock is the Machine Learning and Artificial Intelligence Lead at Pfizer. His work has focused on the intersection of AI, visualization, statistics, and software engineering. His current mission is to advance the role of AI/ML across the company and pharma industry where it has the potential to add value across many subfields. Peter holds a Ph.D. in Artificial Intelligence from Purdue University along with 6 Master’s degrees, and is finishing his MBA. He was recognized as being among the top leaders in AI and Pharma globally by the Deep Knowledge Analytics group. He also currently teaches graduate AI and Software Engineering courses at Harvard.

Susan Tibbs

Vice President of Market Regulation, FINRA

Susan Tibbs is the Vice President of Market Regulation at FINRA. She has served in various roles of increasing responsibility leading to her present position. Susan leads the Trading Analysis, Market Manipulation Investigations, and Exchange Traded Products Surveillance and Investigations sections of Market Regulation. She has developed a specialty in complex products, new exchanges, and cross market surveillance and investigations. She oversees FINRA’s Cross Market Manipulation Surveillance Patterns and directs investigations for equity products both traded on and off exchange. Over the years she has been instrumental in the planning and implementation of surveillance patterns for various FINRA clients. Currently, she is part of the leadership team managing the implementation of machine learning in production surveillance patterns for Market Regulation. Susan holds a B.A. in International Affairs from the George Washington University and a Juris Doctorate from Western Michigan University Thomas M. Cooley Law School. Susan has participated in FINRA’s Leadership@Wharton program and the Center for Creative Leadership.

Armineh Nourbakhsh

Executive Director, J.P. Morgan AI Research

Armineh Nourbakhsh is a Director at J.P. Morgan AI Research, where she leads a team on multimodal document AI. Her career spans a decade of research in Natural Language Processing in areas such as targeted sentiment analysis, event detection and verification, information extraction, and social data mining. Prior to J.P. Morgan, Armineh was a Director of Data Science at S&P Global, where she led efforts to transform operational workflows related to the ingestion and processing of financial disclosures. In addition to numerous publications and patents, Armineh’s research has been deployed in award-winning AI-driven technologies such as Reuters Tracer, Westlaw Quick Check, and the SocialZ Verve index. She has previously organized workshops at AAAI and ICAIF, and served on the Program Committee of several conferences including IJCAI, AAAI, and ICAIF.