Description
After more than 30 years in academia researching in the area of AI, both as a student and as a faculty member, I joined J.P. Morgan to create and head an AI research group. In this talk, I will present several concrete examples of the projects we are pursuing in connection with J.P. Morgan's lines of business. I will focus on areas related to data, learning from experience, explainability, and ethics. I will conclude with a discussion of my current understanding of the transformational impact that AI can have in the future of financial services.
Manuela Veloso is the Head of J.P. Morgan AI Research and is on leave from Carnegie Mellon University, where she is the Herbert A. Simon University Professor in the School of Computer Science, and former head of the Machine Learning Department. At Carnegie Mellon University, she has led research in AI, with a focus on robotics and machine learning, having concretely researched and developed a variety of autonomous robots. At J.P. Morgan AI Research, Manuela pursues research on data mining and cryptography, machine learning, explainability, and human-AI interaction.
Theoretically Speaking is a lecture series highlighting exciting advances in theoretical computer science for a broad general audience. Events are free and open to the public. No special background is assumed. The lecture will be held via Zoom webinar. Please use the Q&A feature to ask questions.
This talk was not recorded. The papers mentioned in the talk are listed below.
- Trading via Image Classification
- The Effect of Visual Design in Image Classification
- Privacy-Preserving Dark Pools
- Generating Synthetic Data in Finance: Opportunities, Challenges and Pitfalls
- Heuristics for Link Prediction in Multiplex Networks
- AI pptX: Robust Continuous Learning for Document Generation with AI Insights
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