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Richard Saldanha
BIO
Dr Richard Saldanha is a Teaching Fellow at Queen Mary University of London in the School of Economics and Finance. He is an expert in both statistical machine learning and quantitative finance.
Language(s)
English
Areas of expertise
- Artificial Intelligence
- Machine learning
- Data science
- Mathematics
- Fintech
- Legaltech
- Investment management
- Risk management
Profile
Dr Richard Saldanha has taught machine learning in finance at postgraduate level in the School of Economics and Finance at Queen Mary University of London since 2018. He also supervises MSc students for their Operations Research & Analytics industrial projects in the Mathematics Department at The London School of Economics and Political Science; and has been a guest lecturer in machine learning and finance on the MBA and Executive Diploma in AI for Business programmes at the Saïd Business School, University of Oxford.
Richard has worked in quantitative finance for over two decades and been successful in senior roles in both asset management and investment banking at major institutions in the City of London. His experience includes risk management at the most senior levels of the firm and the direct management of investments.
He now gives advice to companies about the practical and effective use of methods in artificial intelligence, machine learning and data science. Richard's speaking engagements have been numerous, everything from developing trading strategies to machine learning and fintech and, more recently, elucidating the nature of large language models.
Richard is a Fellow and Chartered Statistician of the Royal Statistical Society; a Science Council Chartered Scientist; and a Fellow and Advanced Practitioner in Artificial Intelligence of the Institute of Science and Technology. He attended Oriel College, University of Oxford, and holds a doctorate (DPhil) in statistics.
Types of Engagement
Videos
Recent Appearances
Article: AI forecaster can predict the future better than humans | New Scientist (March 2024)
Seminar: Large language models: A Statistician’s perspective | University of Oxford (December 2023)
Article: RangL: A reinforcement learning competition platform | arXiv:2208.00003 (July 2022)
Article: Creatures of the FCA Sandbox | Fintech Circle (December 2019)
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