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Poster Session
Nathalie Baracaldo · Seth Neel · Tuyen Le · Dan Philps · Suheng Tao · Sotirios Chatzis · Toyo Suzumura · Wei Wang · WENHANG BAO · Solon Barocas · Manish Raghavan · Samuel Maina · Reginald Bryant · Kush Varshney · Skyler D. Speakman · Navdeep Gill · Nicholas Schmidt · Kevin Compher · Naveen Sundar Govindarajulu · Vivek Sharma · Praneeth Vepakomma · Tristan Swedish · Jayashree Kalpathy-Cramer · Ramesh Raskar · Shihao Zheng · Mykola Pechenizkiy · Marco Schreyer · Li Ling · Chirag Nagpal · Robert Tillman · Manuela Veloso · Hanjie Chen · Xintong Wang · Michael Wellman · Matthew van Adelsberg · Ben Wood · Hans Buehler · Mahmoud Mahfouz · Antonios Alexos · Megan Shearer · Antigoni Polychroniadou · Lucia Larise Stavarache · Dmitry Efimov · Johnston P Hall · Yukun Zhang · Emily Diana · Sumitra Ganesh · Vineeth Ravi · · Swetasudha Panda · Xavier Renard · Matthew Jagielski · Yonadav Shavit · Joshua Williams · Haoran Wei · Shuang (Sophie) Zhai · Xinyi Li · Hongda Shen · Daiki Matsunaga · Jaesik Choi · Alexis Laignelet · Batuhan Guler · Jacobo Roa Vicens · Ajit Desai · Jonathan Aigrain · Robert Samoilescu

Fri Dec 13 03:30 PM -- 05:15 PM (PST) @
in Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy »

Author Information

Nathalie Baracaldo (IBM Research AI)

Nathalie Baracaldo leads the AI Security and Privacy Solutions team and is a Research Staff Member at IBM’s Almaden Research Center in San Jose, CA. Nathalie is passionate about delivering machine learning solutions that are highly accurate, withstand adversarial attacks and protect data privacy. Her team focuses on two main areas: federated learning, where models are trained without directly accessing training data and adversarial machine learning, where defenses are designed to withstand potential attacks to the machine learning pipeline. Nathalie is the primary investigator for the DARPA program Guaranteeing AI Robustness Against Deception (GARD), where AI security is investigated. Her team contributes to the Adversarial Robustness 360 Toolbox (ART). Nathalie is also the co-editor of the book: “Federated Learning: A Comprehensive Overview of Methods and Applications”, 2022 available in paper and as e-book in Springer, Apple books and Amazon. Nathalie's primary research interests lie at the intersection of information security, privacy and trust. As part of her work, she has also designed and implemented secure systems in the areas of cloud computing, Platform as a Service, secure data sharing and Internet of the Things. She has also contributed to projects to design scalable systems that monitor, manage performance and manage service level agreements in cloud environments. In 2020, Nathalie received the IBM Master Inventor distinction for her contributions to the IBM Intellectual Property and innovation. Nathalie also received the 2021 Corporate Technical Recognition, one of the highest recognitions provided to IBMers for breakthrough technical achievements that have led to notable market and industry success for IBM. This recognition was awarded for Nathalie's contribution to the Trusted AI initiative. Nathalie is associated Editor IEEE Transactions on Service Computing. Nathalie received her Ph.D. degree from the University of Pittsburgh in 2016. Her dissertation focused on preventing insider threats through the use of adaptive access control systems that integrate multiple sources of contextual information. Some of the topics that she has explored in the past include secure storage systems, privacy in online social networks, secure interoperability in distributed systems, risk management and trust evaluation. During her Ph.D. studies she received the 2014 Allen Kent Award for Outstanding Contributions to the Graduate Program in Information Science by the School of Information Sciences at the University of Pittsburgh. Nathalie also holds a master’s degree with Cum Laude distinction in computer sciences from the Universidad de los Andes, Colombia. Prior to that, she earned two undergraduate degrees in Computer Science and Industrial Engineering at the same university.

Seth Neel (University of Pennsylvania)

PhD student in statistics studying fairness and privacy in learning. Advised by Aaron Roth and Michael Kearns.

Tuyen Le (AgileSoDA Corp.)
Dan Philps (City, University of London. Rothko Investment Strategies)

Dan Philps is a Computer Scientist, head of Rothko Investment Strategies and is an artificial intelligence (AI) researcher. He has 20 years of quantitative investment experience. Prior to Rothko, he was a senior fund manager at Mondrian Investment Partners. Before 1998, Philps worked as an analyst/programmer at a number of investment banks, specializing in trading and risk models. He has a BSc (Hons) from King’s College London, is a CFA charterholder, a member of CFA Society of the UK and holds a post graduate research role at City, London University.

Suheng Tao (J.P.Morgan)
Sotirios Chatzis (Cyprus University of Technology)
Toyo Suzumura (IBM Thomas J. Research Center)
Wei Wang (Intuit)
WENHANG BAO (Columbia University)
Solon Barocas (Cornell University)
Manish Raghavan (Cornell University)
Samuel Maina (IBM Research)
Reginald Bryant (IBM Research)
Kush Varshney (IBM Research)
Skyler D. Speakman (IBM Research | Africa)

Skyler Speakman is a Research Scientist at IBM Research -- Africa. His projects use data science to impact the lives of millions of people on the continent. He believes that data collected through phones and drones will fundamentally change service delivery and African development in the next decade. Skyler completed a Ph.D. in Information Systems at Carnegie Mellon University as well as a M.S. in Machine Learning. He also holds masters in Mathematics, Statistics, and Public Policy. He lives in Nairobi, Kenya with his wife and two young sons.

Navdeep Gill (H2O.ai)

Navdeep Gill is a Senior Data Scientist/Software Engineer at H2O.ai where he focuses mainly on machine learning interpretability and previously focused on GPU accelerated machine learning, automated machine learning, and the core H2O-3 platform. Prior to joining H2O.ai, Navdeep worked at Cisco focusing on data science and software development. Before that Navdeep was a researcher/analyst in several neuroscience labs at the following institutions: California State University, East Bay, University of California, San Francisco, and Smith Kettlewell Eye Research Institute. Navdeep graduated from California State University, East Bay with a M.S. in Computational Statistics, a B.S. in Statistics, and a B.A. in Psychology (minor in Mathematics).

Nicholas Schmidt (BLDS, LLC)
Kevin Compher (InQTel)
Naveen Sundar Govindarajulu (RealityEngines.AI)
Vivek Sharma (Karlsruhe Institute of Technology)

I am a postdoctoral researcher and research scientist at MIT, Harvard and KIT working with Prof. Ramesh Raskar, Prof. Rajiv Gupta, Prof. Mauricio Santillana and Prof. Rainer Stiefelhagen. Previously, I worked with Prof. Luc Van Gool (KU Leuven/ ETH Zürich).

Praneeth Vepakomma (MIT)
Tristan Swedish (MIT)
Jayashree Kalpathy-Cramer (MGH/Harvard Medical School)
Ramesh Raskar (MIT)
Shihao Zheng (Eindhoven University of Technology)
Mykola Pechenizkiy (TU Eindhoven)
Marco Schreyer (University of St. Gallen)
Li Ling (JP Morgan Chase)
Chirag Nagpal (Carnegie Mellon University)
Robert Tillman (JPMorgan AI Research)
Manuela Veloso (JPMorgan and Carnegie Mellon University)
Hanjie Chen (University of Virginia)
Xintong Wang (University of Michigan)
Michael Wellman (University of Michigan)
Matthew van Adelsberg (Capital One)
Ben Wood (J.P.Morgan)
Hans Buehler (JPMorgan)

Dr. Hans Buehler has been working in Quantitative Finance since 2001, when he started as an intern in Deutsche Bank’s Equity Derivatives team. He became global head of that team in 2006, and moved to JPMorgan in Hong Kong in 2008. Hans now runs globally Equities and Investor Services Quantitative Research (QR) in JPMorgan, covering electronic trading, cash, derivatives, financing, prime, and clearing. Hans also leads QR’s IB-wide Data Analytics initiative, bringing data-driven process optimisation to the businesses and associated functions. The mandate is to drive change through data, using modern machine learning and AI methods. Hans studied Mathematics at Humboldt University to Berlin, and holds a PhD in Financial Mathematics from Technical University in Berlin. Hans is based in London

Mahmoud Mahfouz (J. P. Morgan AI Research / Imperial College London)

Self-driven, ambitious and highly motivated AI Research associate with a deep interest in the use of machine learning in financial trading applications. Previous applied data science and front-office technology experience. Part-time PhD student at Imperial College London investigating the applications of tensor decomposition techniques in deep and reinforcement learning. Graduated with a First-class MEng degree in the top 5% of the cohort. Strong electrical and electronic engineering background, programming experience, modeling skills, and engineering mind-set. Adaptable, always eager to learn and enjoy working in fast-paced and challenging environments.

Antonios Alexos (University of Thessaly)
Megan Shearer (University of Michigan, Ann Arbor)
Antigoni Polychroniadou (JPMorgan AI Research)

Antigoni is a cryptographer research lead at JPMorgan AI Research. She completed her PostDoc at Cornell and received her PhD from Aarhus University.

Lucia Larise Stavarache (IBM)

Larise, is a Senior Solution Architect with a demonstrated history of working in the information technology and services industry; skilled in architecture with Cloud, AI & Mobile focus, Larise has solid experience in consultancy (Mobile CoC, Innovation CoC) and technical hands-on global delivery engagements or product design (Assets, Apple & IBM).

Dmitry Efimov (American Express)

Dmitry Efimov received his PhD in Mathematics from Moscow State University, Russia in 2007. In 2008 he joined the Department of Mathematical Analysis, Faculty of Mechanics and Mathematics, Moscow State University, as an Assistant Professor. His research interests included several important topics in functional and complex analysis (such as structural properties of spaces of holomorphic functions in the upper half-plane). In 2013 Dmitry with colleagues has published the monography "Boundary properties of analytic functions (further contribution)" with their main results. In 2012 he relocated to United Arab Emirates and joined Department of Mathematics and Statistics in American University of Sharjah as an Assistant Professor. During his work in UAE Dmitry became interested in Applied Machine Learning and started participating in Data Science competitions organized on Kaggle platform. During that time he solved more than 40 different problems and became winner or took prizes in the 11 competitions: 2016 3rd/5123 Santander Customer Satisfaction contest 2016 2nd/40 Cervical Cancer Screening contest 2015 3rd/59 Western Australia Rental Prices, Deloitte 2015 3rd/414 Avito Context Ad Clicks Challenge 2015 2nd/1306 West Nile Virus Prediction 2014 1st/44 Risky Business Challenge, American Express 2013 2nd/37 As the World Churns contest, Deloitte 2013 3rd/1687 Employee Access Challenge, Amazon 2013 2nd/553 KDD Cup: Author-Paper Identification 2013 4th/237 KDD Cup: Author Disambiguation Challenge 2012 2nd/12 Predicting the Strength of Social Ties contest Simultaneously, Dmitry has started a research work in Applied Machine Learning. His main research interest included feature selection and sensitivity analysis. Dmitry has created University course in Machine Learning as a part of Mathematics Master's program. In 2017 Dmitry has been invited to join American Express as a Director - Machine Learning and Data Science in London and relocated to New York on July 2018. He continues applying his Machine Learning knowledge to solve business problems and leads team of several Machine Learning and Data Science professionals in American Express. The additional information can be found by the following links: LinkedIn profile: www.linkedin.com/in/dmitry-efimov-b44a9335 Personal web page: efimov-ml.com Github: github.com/diefimov Kaggle profile: www.kaggle.com/efimov

Johnston P Hall (H2o.ai)

Patrick Hall is a senior director for data science products at H2O.ai where he leads responsible machine learning efforts. Patrick has been an invited speaker at international research and commercial conferences, he is an author of the popular e-booklet, "An Introduction to Machine Learning Interpretability," and a frequent contributor to O'Reilly Ideas on the subjects of transparency, model management, and security for machine learning. He's also a member of several financial services working groups focused on risks of artificial intelligence and an awarded lecturer in the Department of Decision Sciences at George Washington University. Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.

Yukun Zhang (ATB Financial)
Emily Diana (Wharton School, University of Pennsylvania)

Emily Diana is a second year doctoral student in the Department of Statistics at the University of Pennsylvania, where she works in the fields of differential privacy and statistical machine learning. She holds a Master of Science in Statistics from Stanford University and a Bachelor of Arts in Applied Mathematics from Yale University. Prior to graduate school, she worked for two years as a Scientific Software Developer at Lawrence Livermore National Laboratory, where she helped develop codes for massively parallel physics simulations.

Sumitra Ganesh (JPMorgan - AI Research)
Vineeth Ravi (JPMorgan AI Research)
Swetasudha Panda (Oracle Labs)
Xavier Renard (AXA)
Matthew Jagielski (Northeastern University)
Yonadav Shavit (Harvard)
Joshua Williams (Carnegie Mellon University)
Haoran Wei (University of Delaware)
Shuang (Sophie) Zhai (Iowa State University)
Xinyi Li (Columbia University)
Hongda Shen (University of Alabama in Huntsville)
Daiki Matsunaga (IBM Japan)
Jaesik Choi (Ulsan National Institute of Science and Technology)
Alexis Laignelet (Imperial College London)
Batuhan Guler (Imperial College London)
Jacobo Roa Vicens (JP Morgan Chase & Co)
Ajit Desai (Bank of Canada)

* Currently working as a Data Scientist in the economic and financial research department at the Bank of Canada. * Hold a Ph.D. in computational science and engineering from Carleton University and M.S. in aerospace engineering from IIT Madras. * My primary research focus is on utilizing artificial intelligence and machine learning tools and techniques on payments systems data. * In the past, I have worked on developing scalable algorithms for stochastic PDEs using high-performance computing. Also, I have focused on uncertainty quantification and the stochastic simulations of various engineering systems.

Jonathan Aigrain (AXA)
Robert Samoilescu (University Politehnica Bucharest)

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