Organizers

Cynthia Rudin: Cynthia is a professor of computer science, electrical and computer engineering, and statistical science at Duke University. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. Her degrees are from the University at Buffalo and Princeton University. She is a three-time winner of the INFORMS Innovative Applications in Analytics Award. She has served on committees for INFORMS, the National Academies, the American Statistical Association, DARPA, the NIJ, and AAAI. She is a fellow of both the American Statistical Association and Institute of Mathematical Statistics. She is a Thomas Langford Lecturer at Duke University for 2019-2020.

Isabelle Moulinier: Isabelle is a Senior Director of Data Science at Capital One where she leads research and development in NLP. Before joining Capital One, she was a Director of Research at Thomson Reuters where she led research efforts in information retrieval and machine learning for the Legal and Tax verticals. Isabelle has been the Data Science program co-chair at the Grace Hopper Celebration of Women in Computing for the past 3 years, she was the 2014 SIGIR industry track co-chair, she co-organized workshops on Operational Text Classification (SIGIR, KDD) and has been a member of various program committees (AAAI, SIGIR, CIKM). https://www.linkedin.com/in/isabellemoulinier/

John Paisley: John is an Assistant Professor in the Department of Electrical Engineering at Columbia University and is an affiliated faculty member of the Data Science Institute at Columbia. He received the B.S., M.S. and Ph.D. degrees in Electrical and Computer Engineering from Duke University. He was then a postdoctoral researcher in the Computer Science departments at Princeton University and UC Berkeley. His research focuses on machine learning for signal and image processing applications and he is interested in Bayesian model and inference techniques for big data problems.

Senthil Kumar (primary organizer): Senthil 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 2019 NeurIPS Workshop on Robust AI in Financial Services.

Susan Tibbs: Susan 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.

Svitlana Vyetrenko: Svitlana is a Vice President and Artificial Intelligence Research Lead at JP Morgan Chase. She holds a PhD in Applied and Computational Mathematics from California Institute of Technology. She was previously a Vice President in Macro Linear Quantitative Research at JP Morgan Chase; and an Associate in Equity Strategies at Goldman Sachs. Her research interests broadly span applications of artificial intelligence and machine learning methods to trading; with current focus on using multi-agent simulations to model realistic markets for trading strategy and policy research.

Eren Kurshan is the Executive Head of AI & Machine Learning for Client Protection at BoA, where she is responsible for building custom AI and machine learning solutions for fraud and financial crime detection. Since joining BoA, she has led the development of the first set of internal AI/ML models for fraud detection across the bank's payment systems. Prior to this role, Eren has served in various AI and data science leadership roles at J.P. Morgan Corporate and Investment Bank, Columbia University, and IBM. Dr. Kurshan was a Visiting Industry Fellow at Princeton University (Center for Information Technology Policy) during 2015-2016 and has been serving as an Adjunct Professor of Computer Science at Columbia University since 2014. Dr. Kurshan has over 60 publications in peer reviewed conferences and journals, 100+ patents, and has been serving as the Associate Editor for IEEE and ACM journals on emerging technologies in computing. She received her Ph.D. in applied algorithms and theoretical computer science from the University of California.

C. Bayan Bruss: Bayan is the director of Applied Machine Learning research at Capital One’s Center for Machine Learning. His research experience spans many disciplines from risk modeling for critical infrastructure, computational psycholinguistics, cybersecurity, and representation learning. Most recently he has been focused on the application of graph machine learning to financial transactions. He has served on the program committee for several conferences and workshops (KDD ‘20, ICAIF ‘20, Usenix SCaiNet ‘19).

Simona Gandrabur: Simona has been working in the general field of AI for close to 20 years, most notably in areas related to processing of human languages – such as automatic speech recognition, natural language understanding, machine translation and conversational reasoning. Her experience ranges from many years in research, in the development of smart assistant applications, to defining strategy of AI-based offers. She is currently the head of AI strategy within the Wealth Management division of the National Bank of Canada.

Oluwatobi Olabiyi: Oluwatobi is a Senior Manager, Machine Learning Research for Conversational AI, at Capital One. His research interests are in the areas of sequential decision making under uncertainty, and include NLP, dialogue management, NLG, neural generative dialogue, adversarial networks, and reinforcement learning. Before joining Capital One, he was a Senior Research Scientist at Toyota Research Institute working on autonomous vehicle research. His background is in Signal Processing for Cognitive Communication Systems and earned his MS and PhD from Prairie View (Texas) A&M University. He has co-authored several peer-reviewed articles published in international conferences and highly referred journals.

Kevin Compher serves as SEC’s Division of Trading and Market’s (TM) Director of Innovation and Technical Solutions. Kevin is a strategic thought leader in cloud computing, software engineering and data science communities that has effectively developed strategic partnerships across the SEC, government, industry and academia. He holds science and engineering degrees from the University of Pennsylvania and the Naval Postgraduate School, as well as number of data science and technology certifications. Since joining the SEC in 2016, he has served as an applied researcher and financial engineer, focused on capital market structure, artificial intelligence, high performance computing, market data, as well as has lead technical activities for Consolidated Audit Trail systems. He is particularly interested in open source software, DEVOPS, explainable machine learning, automation and visualization capabilities, and leads numerous quant efforts and forums to foster collaboration between data science communities, including SEC’s Covid-19 Data Task Force. Finally, in collaboration with colleagues in the financial regulatory space, Kevin has developed, supported and performed several analyses addressing fairness, accountability and transparency of machine learning applications and standards in self-regulatory organizations used in the capital markets.

Cat Posey (panel moderator) is a Senior Tech Director on the leadership team in the Center for Machine Learning at Capital One, which focuses on research and strategic innovation initiatives in AI/ML, including the development of tools, technologies, frameworks, and partnerships with industry and academia. She leads the Advance ML organization which is accountable for ML applications in the Anti-Money Laundering (AML) space, the R&D investment strategy as well as talent pipeline development, ML learning and development and ML awareness and outreach. Cat is passionate about finding ways to shift the current dynamics within tech culture so the community can truly be a microcosm of the world, and a place where all of its members can thrive. She is also focused on developing and implementing AI/ML systems responsibly, and in ways that put humanity at the forefront. Cat founded Tech By Superwomen, a movement she started to shift the conversation to what works and what matters when it comes to creating a more inclusive tech industry. Prior to Capital One, she served as the head of strategic partnerships and alliances for the United States Digital Service, a tech startup founded by President Obama to change the technology infrastructure in order to better serve citizens. Cat is a recognized authority on issues impacting women in technology and an international speaker who has been featured in various national media publications. Link: www.linkedin.com/cathrynposey