- Ashwin Rao
- Adjunct Professor, ICME
- Stanford University
- Office: Huang Engineering Center, ICME Mezannine Level, Room M05
- Email: ashwin.rao@stanford.edu

My academic background is in Algorithms Theory and Abstract Algebra.

My current academic interests lie in the broad space of A.I. for Sequential Decisioning under Uncertainty.

I am particularly interested in Deep Reinforcement Learning applied to Financial Markets and to Retail Businesses.

More details on my background and work are described on my LinkedIn page.

I'm teaching CME 241 (Reinforcement Learning for Stochastic Control Problems in Finance) next in Winter 2022.

- Overview (CME 241 Lecture Slides)
- Design Paradigms for Applied Mathematics implementations in Python

- Chapter 1: Markov Processes and Markov Reward Processes (CME 241 Lecture Slides)
- Chapter 2: Markov Decision Processes (CME 241 Lecture Slides)
- Chapter 3: Dynamic Programming Algorithms (CME 241 Lecture Slides)
- Chapter 4: Function Approximation and Approximate Dynamic Programming (CME 241 Lecture Slides)

- Chapter 5: Utility Theory (CME 241 Lecture Slides)
- Chapter 6: Dynamic Asset Allocation and Consumption (CME 241 Lecture Slides)
- Chapter 7: Derivatives Pricing and Hedging (Arbitrage/Completeness Slides, Deep Hedging Slides, Optimal Exercise Slides)
- Chapter 8: Order-Book Trading Algorithms (Order Execution Slides, Market Making Slides)

- Chapter 9: Monte-Carlo (MC) and Temporal-Difference (TD) for Prediction (CME 241 Lecture Slides)
- Chapter 10: Monte-Carlo and Temporal-Difference for Control (CME 241 Lecture Slides)
- Chapter 11: Experience Replay, Least-Squares Policy Iteration and Gradient TD (Experience-Replay and Batch RL Slides, Value Function Geometry Slides)
- Chapter 12: Policy Gradient Algorithms (CME 241 Lecture Slides)
- (In Progress) Chapter 13: Multi-Armed Bandits: Exploration versus Exploitation (CME 241 Lecture Slides)
- Chapter 14: Blending Learning and Planning
- Chapter 15: RL in Real-World Finance: Reality versus Hype, Present versus Future

- Moment-Generating Function and it's Applications
- Function Approximations as Vector Spaces
- Portfolio Theory (Lecture Slides)
- Introduction to and Overview of Stochastic Calculus Basics (Lecture Slides)
- The Hamilton-Jacobi-Bellman (HJB) Equation
- Black-Scholes Equation and it's Solution for Call/Put Options
- Conjugate Priors for Gaussian and Bernoulli Distributions