Organizers
Workshop Committee
Armineh Nourbakhsh
Executive Director, JP Morgan
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.
Naftali Cohen
Senior Data Scientist @ Schonfeld + Faculty @ Columbia University
Naftali Cohen is a senior data scientist at Schonfeld Strategic Advisors and a faculty member at the Industrial Engineering and Operations Research department of Columbia University. In his role, he is responsible for designing and implementing machine learning, optimization, and modeling approaches for real-time trading. Before joining Schonfeld, he was at JP Morgan AI Research, where he developed and applied advanced machine learning and AI algorithms to various financial applications. He has published over 20 papers and holds about ten patents. He recently co-organized the Time-series in Finance in ICAIF-2021 and the AI in Financial Services: Adaptiveness, Resilience, and Governance in AAAI-2022. Naftali holds a Ph.D. in Applied Mathematics from the Courant Institute -- New York University, and an M.Sc. and a B.Sc. from the Hebrew University of Jerusalem.
Eren Kurshan
Executive Head of AI and Data Science, Bank of America
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.
Bayan Bruss
Director of Applied Machine Learning research, Capital One
Bayan Bruss is the director of Applied Machine Learning research at Capital One’s Center For Machine Learning. His team is currently focused on Graph Machine Learning, Decision Theory, Machine Learning for Data and Privacy and Explainable AI. Prior to Capital One Bayan has over a decade of experience in academia, startups and consulting. He has participated in the organizing committees and program committees of several conferences and workshops at KDD, ICAIF, and NeurIPs. He holds an Adjunct Position at Georgetown University.
Senthil Kumar
Chief Scientist, Center for Machine Learning at Capital One
Senthil Kumar is the Chief ML Scientist at the Center for Machine Learning at Capital One where he applies Machine Learning and AI to various business problems. Prior to joining Capital One, he was at Bell Laboratories where he developed new technologies and managed several successful products that have been licensed around the world. He has published over 30 papers and holds 6 patents. Most recently, he co-organized the 2020 KDD Workshop on Machine Learning in Finance, and the 2020 NeurIPS Workshop on Fair AI in Finance.
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.
Anisoara Calinescu
Associate Professor, University of Oxford
Anisoara Calinescu is Associate Professor of Computer Science and Deputy Head of Department (Teaching), in the Department of Computer Science of the University of Oxford. She has a 5-year (MSc equivalent) Computer Science degree from the Technical University of Iasi, Romania, and a DPhil in Engineering Science from the University of Oxford. Ani's main research area is Modeling and Reasoning about Complex Systems. Her research interests are fundamentally interdisciplinary, and include: complex systems and complexity metrics; supply chains and financial systems; agent-based modeling; IoT-based Digital Twins; systemic risk. Her recent work includes applying Machine Learning techniques to identify behavioral patterns in supply chain and financial market data; and building, validating and calibrating large-scale agent-based models of complex systems. Ani is currently a Principal Investigator on "A demonstrator and reference framework IoT-based Supply Chain Digital Twin" Pitch-In project, in collaboration with Cambridge University and Schlumberger, and a Co-investigator on two projects funded by JP Morgan Chase AI Faculty Research Awards.