Simerjot is currently an AI Research Lead within JP Morgan AI Research (AiR) team. Her research interests include Natural Language Processing, deep learning, cognitive AI, knowledge graphs and neuro-symbolic AI. She has more than a decade of experience working at the intersection of Quantitative Finance and advanced computing and has been instrumental in helping financial institutions leverage deep learning and natural language processing for business transformation and growth. Previously, she worked at Bank of America where she built innovative solutions for continuous monitoring of market risk and front office controls. Simerjot finished her graduate studies in Artificial Intelligence from Stanford University and Operations Research from Columbia University.

Charese is an AI Research Lead on the AI Research team at J.P. Morgan. Her research interests center around computational linguistics and natural language processing. Before joining JPMorgan, Charese worked on the Vision & Language Technology team at Capital One where she led research to build complaint models for customer service channel dialogues and at Thomson Reuters, with the Research & Development group, building automatic summaries for Eikon financial software and natural language question-answering for Westlaw legal search, ultimately leading to two patents. Charese is a winner of Turing tests for creative arts, claiming 1st prize in Dartmouth’s 2017 PoetiX competition for machine-generated sonnet most indistinguishable from human poetry. She also won the 2018 LimeriX competition for machine-generated limericks. Charese holds a B.S. in Computer Science from Texas A&M University and a Ph.D. in Computational Linguistics from Indiana University.

Zhen is an AI Research Scientist on the AI Research team at J.P.Morgan. Her research interests are novel representations of financial data, uncertainty estimation, computer vision, and robotics. Zhen works on time series analysis and prediction with visual representations. Before joining J.P.Morgan, Zhen worked in robotics and enabling agents to perceive, understand and make decisions in the physical world through probabilistic modeling and machine learning. She received a Ph.D. in Electrical and Computer Engineering from the University of Michigan.

Rene is Fidelity's VP of Quant Index Solutions, focusing on developing index-based products and strategies for equity, fixed income, crypto assets, and multi-asset class structures with fundamental and machine learning based approaches. Previously, she worked at Quantopian as the Director of Data Science to evaluate, select and enhance systematic trading strategies using crowdsourced alpha. She holds a Ph.D. in Mathematics from Tufts University, specializing in machine learning, tensor algebra, and facial recognition.

Laura has recently joined Goldman Sachs QIS Systematic Trading, working on both research and production models. Her research interests are centered on high-frequency finance, using machine learning, deep neural networks, optimization, statistical, and econometric methods to study high-frequency trading data. The main topics she has worked on during her studies include optimal execution, market making, identification of institutional activity, and tail risk. Laura holds a Ph.D. in Operations Research and Financial Engineering from Princeton University, where she was awarded the Gordon Wu Fellowship, Princeton Teaching Awards, and the School of Engineering and Applied Science Award for Excellence.

Anastasia is currently an Assistant Professor at the University of Warwick in the Operations Research department and holds a visiting position at the mathematics department of Imperial College London. Her focus in research is on the design of effective learning dynamics of agents operating in high-dimensional, nonconvex settings using a combination of tools from stochastic processes, statistical mechanics, and mathematical modeling. She applies these methods in explainable and privacy-preserving machine learning, neuroscience, and finance. Prior to this she was a postdoctoral researcher at the Department of Computing at Imperial College London and the Scientific Computing Department at CWI Amsterdam and obtained her PhD cum laude from the University of Bologna in Financial Mathematics as part of a Marie-Curie ITN-EID project.

Annita is an AI Research Scientist on the AI Research team at J.P. Morgan. Her research interests include the application of monitoring, control and execution in robotics and financial systems as well as the utilization of computer vision in the financial domain. Before joining J.P.Morgan, Annita worked on developing machine learning solutions for healthcare applications and synthetic data generation models for production line manufacturing. Annita holds an MSc in Business Analytics from the Massachusetts Institute of Technology.

Cecilia is an AI Research Lead on the AI Research team at J.P. Morgan. Cecilia is a Natural Language Processing and Machine Learning researcher with an interdisciplinary background in cognitive science and artificial intelligence. She has a PhD in philosophy and neuroscience from Princeton University, where she researched symbolic and sub-symbolic models of cognition. At the University of Oxford, she managed and engaged in research on cutting edge technologies – including artificial intelligence, biotechnology, and nanotechnology – becoming an expert on the benefits and risks of future technology, the ethics of artificial intelligence, and forecasting technological development.