About Ian Osband
Ian Osband is a prominent Research Scientist currently working at Google, focusing on foundational research for the next generation of AI. Previously, he was a Member of Technical Staff at OpenAI and a Research Scientist at DeepMind for over seven years. His core research expertise lies in reinforcement learning and decision-making under uncertainty, with notable contributions to deep exploration techniques like Randomized Value Functions and Posterior Sampling.
Education
Doctor of Philosophy (PhD)
Doctor of Philosophy (PhD) - Management Science and Engineering
MMath
MMath - Mathematics
BA Hons
BA Hons - Mathematics
King's Scholar
King's Scholar
Work Experience
Research Scientist
Foundational research for the next generation of AI.
Member of Technical Staff
Product inspired research at OpenAI, including ChatGPT and O-series.
Research Scientist
Built new "efficient agent" team with Benjamin Van Roy
- Epistemic neural networks (rethinking uncertainty in deep learning)
- Efficient agent development and alignment Research Scientist
Research Scientist
Focus on scalable approaches to exploration, with Daan Wierstra then David Silver.
- Ambitious, real-world applied projects
- Deep Exploration via Randomized Value Functions
- Algorithms behind data center cooling success Research Scientist
Data Scientist - Ads Metrics
Investigating targeted shopping ads and their effects on user experience and revenue.
Data Science Consultant
Devised a large scale learning algorithm for efficient product recommendations
Analyst - EM Credit Strategy
Graduate role combining quantitative and fundamental research.
Developed methods for efficient hedging under transaction costs.
Summer intern – Credit Derivatives Quant Research, Credit Trading
Created a client-facing prototype for valuing Credit Default Swaption portfolios.
Investigated strategies for systematic rich/cheap analysis in EM Bonds.
Summer Intern
Developed Excel templates for streamlined fund performance reports.
Identified and researched private equity managers in Pacific Rim emerging markets.
Research Focus and Expertise
Ian Osband is a leading researcher in Artificial Intelligence, specializing in decision-making under uncertainty and reinforcement learning. His work focuses on developing statistically and computationally efficient algorithms for deep exploration in complex environments.
Reinforcement Learning
Decision Making Under Uncertainty
Deep Exploration
Epistemic Neural Networks
Efficient Agent Development
OpenAI
DeepMind
Stanford University
Mathematics
Statistics
Machine Learning
Stochastic Calculus
Python
Professional Skills
Advanced mathematical concepts and quantitative methods, including stochastic processes and calculus, essential for complex modeling.
Expertise in developing and applying machine learning models, particularly in the context of sequential decision-making and reinforcement learning.
Proficiency in Python and Matlab for research and development, alongside financial tools like Bloomberg and VBA for quantitative finance applications.
Experience in financial modeling, fixed income, derivatives, and credit strategy from roles at JPMorgan Chase and other financial institutions.
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