Senthil Kumar

Head of Emerging Research, AI Foundations, Capital One

Senthil Kumar is the Head of Emerging Research in AI Foundations 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. Recently, he co-organized the 2023 KDD Workshop on Machine Learning in Finance, and co-chaired the 2022 ACM International Conference on AI in Finance.

Naftali Cohen

Senior Data Scientist at Schonfeld and Lecturer at Columbia University

Dr. Naftali Cohen is a Senior Data Scientist at Schonfeld Strategic Advisors and an Adjunct Professor at the Tandon School of New York University. Prior to joining Schonfeld, he was a Vice President and Research Lead of AI Research at JP Morgan, where he established and led teams working on both the applied and research priorities of using advanced analytics and machine learning to solve complex financial problems. He also served as an academic researcher at the Lamont-Doherty Earth Observatory of Columbia University and the Department of Geology and Geophysics at Yale University, focusing on mathematical modeling of extreme-weather and data mining of climate change simulations.

Naftali completed his PhD at the Courant Institute of Mathematical Sciences at New York University, where he developed novel models to disentangle the complex dynamics of the climatological system.

Eren Kurshan

Head of Research and Methodology, Morgn Stanley

Eren Kurshan currently leads Research and Methodology efforts at Morgan Stanley, building emerging AI/ML tools and techniques towards serving the firm’s strategic initiatives. Prior to this role, she was the Executive Head of AI and Machine Learning for Client Protection at Bank of America Corporation, where she was responsible for leading the development of custom Machine Learning and Deep Learning solutions for fraud detection, prevention and operational improvement. Dr. Kurshan and her team built the first generation of in-house AI and Machine Learning models for Bank of America’s payment systems portfolio (including Credit Card, Debit Card, ATM, Wires, ACH, P2P Payments, Checks, Deposits, Online/Bill Pay transactions, Alert Processing and Prioritization etc). Dr. Kurshan has served as the technical lead for various AI and Data Science programs at Columbia University, J.P. Morgan Corporate and Investment Bank, and IBM. She was a Visiting Fellow at Princeton University Center for Information Technology Policy during 2015-2016 and served as 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. She has over 80 peer reviewed technical conference and journal publications and over 100 patents. She was the recipient of 2 Best Paper Awards from IEEE and ACM Conferences, Outstanding Research and Corporate Accomplishment Awards from IBM.

Ani Calinescu

Professor, Oxford University

Anisoara Calinescu is 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.

Terrence Bohan

Vice President, FINRA

Terrence Bohan is the Vice President of Investigations for Enforcement. In that capacity, he manages Enforcement's investigators located throughout the various FINRA offices nationwide as they investigate potential securities violations and, when warranted, bring formal disciplinary actions against firms and their associated persons. Mr. Bohan also oversees the Forensic Investigations and Litigation Support group, which supports FINRA's use of forensic tools and e-discovery technologies. He joined FINRA in 2018.

Before joining FINRA, Mr. Bohan spent over 23 years with the SEC's New York Regional Office's broker-dealer inspection program, primarily as a cause examination manager. In that role, Mr. Bohan identified and developed numerous significant cases involving fraudulent securities offerings, manipulations, Ponzi schemes, unregistered distributions of securities, money laundering and egregious sales practice schemes. He has worked extensively with law enforcement in the New York area, including the U.S. Attorney's Office, FBI, IRS, Postal Inspection Service and Manhattan District Attorney's Office. Mr. Bohan received his Bachelor of Science in finance from Fordham University.

Gideon Mann

Head of ML Product and Research, Bloomberg

Gideon Mann is the head of the ML Product and Research team in the Office of the CTO at Bloomberg LP. At Bloomberg, he guides corporate strategy for machine learning, natural language processing (NLP), information retrieval, and alternative data. His mandate includes building AI infrastructure (from GPUs to NLP libraries), incubating new technology (e.g., large language models), and new businesses (e.g., Bloomberg Second Measure). He has over 30 publications and more than 20 patents in machine learning and NLP. He served as a founding member of the Data for Good Exchange(D4GX). Before joining Bloomberg in 2014, he worked at Google Research NY, where his team carried out basic research, as well as developing machine learning products such as Colaborator. He holds a Ph.D. from The Johns Hopkins University. 

Clark Barrett

Professor, Co-Director Stanford Center for AI Safety

Clark Barrett is a Professor (Research) of Computer Science at Stanford University. Before coming to Stanford in 2016, he was an Associate Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University. His expertise is in automated reasoning and its applications. His PhD dissertation (Stanford, 2003) introduced a novel automated reasoning technique now known as Satisfiability Modulo Theories (SMT). He was an early pioneer in formal hardware verification as part of 0-in Design Automation (now part of Siemens/Mentor Graphics), where he helped build one of the first industrially successful assertion-based verification tool-sets for hardware. More recently, he has also pioneered techniques for applying formal methods to neural networks. His current work focuses on the development and application of automated reasoning techniques to improve reliability and security of software, hardware, and machine learning systems. He is the director of the Stanford Center for Automated Reasoning (Centaur) and co-director of the Stanford Center for AI Safety. He is an ACM Distinguished Scientist and a winner of the 2021 Computer Aided Verification (CAV) award.  

Paul Burchard

Managing Director, Goldman Sachs

Paul Burchard is a Managing Director and Head of Research & Development for Goldman Sachs. He focuses on the conception and development of creative mathematical ideas that advance leading-edge areas of technology and finance, Paul also designs and develops software to implement those ideas. Prior to Goldman Sachs, at the University of California Paul invented algorithms for the manufacture of integrated circuits with features smaller than the wavelength of light used to illuminate the lithographic masks. One conceptual advance was to show that the wavelength of light doesn’t actually limit the size of patterns that can be imaged, if we can have multiple exposures. When the desired pattern is smaller than the wavelength. The mask does not look like the pattern, but must instead become discovered by solving an inverse problem. To solve inverse problems efficiently, invented an algorithm for fast incremental convolution, based on the concept of fractal space-filling curves. This algorithm is as fundamental as the famous FFT, which allows fast convolution. As a visiting Assistant Professor at UC Paul invented the correct equation for processing vector data with features; for example, removing noise from color images containing edges. This equation is known as “Color TV” and has many applications.

Christopher Policastro

Data Scientist, Bank of New York Mellon

Christopher is a data scientist at Bank of New York Mellon, Innovation and Advanced Solutions. He has taught at New York University as an Industry Assistant Professor in the Tandon School of Engineering and the Center for Data Science. His research interests include machine learning, network analysis, and optimization. Christopher holds a Ph.D. in Mathematics from the University of California Berkeley.

Yu Yu

Director of Data Science at BlackRock

Yu Yu leads a team of data scientists to generate sales alpha for ~1000 salespeople across the Americas and EMEA, covering wealth and institutional clients. The charter for her team is to develop data science foundations in new domains, driving early-stage research and commercial value from high-potential areas for which data science has only begun to be applied. 

Prior to joining BlackRock, Yu Yu was a Director of Data Science at Bank of New York Mellon, where she built AI solutions that can help improve business outcomes for the bank as well as for its clients. Her project on liquidity forecasting won the 2020 Gartner Eye on Innovation Award in Americas Financial Services. Before BNYM, Yu also worked at Point72 and AIG. Yu was a tenure-track professor of marketing at Georgia State University for nearly five years prior to her industry careers.

Yu Yu grew up in China and received her BA in finance from NanKai University. She has a MA in Economics from Indiana University-Bloomington and a PhD in Quantitative Marketing from Cornell University.