Srijan Kumar, Georgia Tech: Trustworthy Machine Learning for Fraud Detection
Mihai Cucuringu, Oxford University: Clustering signed and directed networks with applications to finance
Hima Lakkaraju, Harvard University: Towards Reliable and Robust Algorithmic Recourse
Neil Shah, Snap, Inc: Machine Learning on Graphs with Scarce Labels
Amar Gupta, MIT CSAIL: Machine Learning in Finance: Addressing Evolving Global Expectation
Pedro Bizarro, Feedzai: ML in Finance: Trends and Challenges in Business Critical Systems
Hamid Benbrahim, Thomas: Empowering Quant: Fundamental, and Thematic Investors with realtime industrial production data and AI
Mikhail Yurochkin, MIT-IBM Watson AI Lab: Black Loans Matter: Fighting Bias for AI Fairness in Lending
Mahashweta Das, Visa Research: Machine Learning for Financial Transaction Data: A Recommendation Use Case
Srijan Kumaris an Assistant Professor at the College of Computing at Georgia Institute of Technology. His research develops data science solutions to address the high-stakes challenges on the web and in the society. He has pioneered the development of user models and network science tools to enhance the well-being and safety of users. His methods are being used in production at Flipkart and taught at graduate level courses worldwide. He has received several awards including the Facebook Faculty Award, Adobe Faculty Award, ACM SIGKDD Doctoral Dissertation Award runner-up 2018, Larry S. Davis Doctoral Dissertation Award 2018, and 'best of' awards from WWW and ICDM. His research has been the subject of a documentary and covered in popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine. He completed his postdoctoral training at Stanford University, received a Ph.D. in Computer Science from University of Maryland, College Park, and B.Tech. from Indian Institute of Technology, Kharagpur.
Mihai Cucuringu is an Associate Professor in the Department of Statistics, and an Affiliate Faculty in the Mathematical and Computational Finance group at the Mathematical Institute, at University of Oxford. He is also a Lecturer at Merton College, and a Turing Fellow at The Alan Turing Institute in London. Previously, he was a CAM Assistant Adjunct Professor in the UCLA Dept. of Mathematics. He finished his Ph.D. in Applied Mathematics at Princeton University in 2012. His research pertains to the development and mathematical analysis of algorithms that extract information from massive noisy data sets, network analysis and certain inverse problems on graphs, such as clustering and ranking, with an eye towards extracting structure from time-dependent data which can be subsequently leveraged for prediction. His research interests in finance focus on statistical arbitrage, machine-learning for asset pricing, market microstructure in equity and crypto markets, synthetic data generation, and anomaly detection in financial networks.
Hima Lakkaraju is an Assistant Professor at Harvard University with appointments in Business School and Department of Computer Science. She leads the AI4LIFE research group at Harvard and I recently co-founded the Trustworthy ML Initiative (TrustML) to help lower entry barriers into trustworthy ML and bring together researchers and practitioners working in the field. Her research interests lie within the broad area of trustworthy machine learning. More specifically, her research spans explainable, fair, and robust ML. She is also very interested in reinforcement learning and causal inference. Her current research is being generously supported by NSF, Google, Harvard Data Science Initiative, and Bayer. Prior to her stint at Harvard, She received her PhD in Computer Science from Stanford University.
Hamid Benbrahim is Head of Data and AI at Thomas, the primary source for industrial sourcing in the U.S. and Canada. He is leading the transformation of Thomas Publishing into a data business, developing new financial indexes and alternative data products for the financial industry, streamlining data operations, and deploying advanced analytics capabilities for sales, marketing, and operations. He held senior leadership roles at Natural Numerix as CEO, TD Ameritrade as Chief Data Scientist, and Fidelity as Head of Applied Complexity Research leading business areas in systemic risk and data-driven strategy. He holds an MBA in Finance from Columbia Business School, and a Ph.D. in Engineering and Machine Learning from the University of New Hampshire.
Amar Gupta has spent the bulk of his career at MIT. He played the lead role in developing the patented AI-based technology for automated reading and processing of bank checks. After serving as Endowed Professor at another university and as Dean of Computer Science and Information Systems at a third university, he rejoined MIT. He has served as an advisor to large companies and international agencies and possesses a rich and diverse portfolio of research which has played a significant role in nucleating several technologies that are in broad use today. At MIT, he is currently affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL), the Department of Electrical Engineering and Computer Science (EECS), and the Institute for Medical Engineering & Science (IMES).
Neil Shah is a Sr. Research Scientist at Snap Inc, with interests spanning data mining, machine learning and computational social science, specifically in the contexts of graph-based modeling of user behavior and misbehavior. His work has resulted in 35+ conference and journal publications, in top venues such as KDD, ICDM, WWW, CIKM, SDM, AAAI, TKDD and more, including several best-paper awards. He has also served as an organizer, chair and on program committees at a number of these (PC Chair for WSDM Cup 2020, ASONAM 2019 Industrial Track; PC Chair and Workshop Chair for Cybersafety 2019-2020). He has had previous research experiences at Lawrence Livermore National Laboratory, Microsoft Research, and Twitch.tv. He earned a PhD in Computer Science in 2017 from Carnegie Mellon University's Computer Science Department, funded partially by the NSF Graduate Research Fellowship.
Mikhail Yurochkin is a Research Staff Member at IBM Research and MIT-IBM Watson AI Lab in Cambridge, Massachusetts. His research interests are Model fusion and federated learning; Algorithmic fairness; Applications of optimal transport in machine learning; Bayesian (nonparametric) modeling and inference. Before joining IBM, he completed Ph.D. in Statistics at the University of Michigan, where he worked with Long Nguyen. He received his bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology.
Pedro Bizarro is co-founder and Chief Science Officer at Feedzai. Pedro is a researcher turned entrepreneur: after a 10-year research career (Computer Science PhD at the University of Madison - Wisconsin, Fulbright Fellow, Marie Curie Fellow and winner of the BES Innovation National Competition) Pedro is now CSO at Feedzai where he leads the Research team in developing the best fraud prevention algorithms and tools.
Mahashweta Das is a Sr. Staff Research Scientist at Visa Research where she works on challenging real problems at the crossroads of tech and payment industry. Previously, she was employed as a Research Scientist at Hewlett Packard Labs where she designed and developed big data analytics solutions for HPE’s 'The Machine'. She has also held summer positions at Yahoo! Research, Technicolor Research Lab, and IBM Research. Mahashweta received her Ph.D. in Computer Science from the University of Texas at Arlington in 2013. Her research interests include machine learning, deep learning, data mining, and algorithms. She has published over fifteen refereed articles at premier international research conferences and journals, and regularly serves on the program committee of these conferences. Her PhD dissertation received Honorable Mention at ACM SIGKDD 2014 Doctoral Dissertation Award.