Teaching Excellence
Developing the next generation of leaders in AI, blockchain, and quantitative finance
Teaching Philosophy
Great teaching bridges theory and practice, making complex concepts accessible while maintaining intellectual rigor. My approach combines three core elements:
Conceptual Clarity
I break down sophisticated AI, blockchain, and finance concepts into fundamental principles, using intuitive examples and visualizations that build deep understanding rather than superficial knowledge.
Practical Application
Students work with real data, implement actual algorithms, and analyze live markets. Every theoretical concept connects to hands-on projects that mirror industry practice.
Critical Thinking
I challenge students to question assumptions, identify limitations, and think independently. The goal isn't memorization—it's developing the analytical skills to tackle novel problems.
Teaching Experience
Cornell University
Visiting Lecturer, Cornell Tech and Operations Research and Industrial Engineering (2017-present)
Developed and taught courses in reinforcement learning, machine learning, crypto, quantitative finance, data analytics, and algorithmic trading.
Johns Hopkins University
Adjunct Professor, (2026-Present), Crypto and Blockchains
An intensive course on cryptocurrencies, blockchain technology, digital asset lending, trading, infrastructure and societal benefits.
Executive Education
Multiple Institutions (2009-Present)
Developed and delivered executive programs on AI strategy, blockchain innovation, and algorithmic trading for Fortune 500 companies and financial institutions.
Courses Taught
Graduate Courses
Agentic AI Systems
Cornell Tech
Description: Advanced research seminar covering autonomous agent architectures, multi-agent reinforcement learning, LLM-based agents, and AI safety. Students complete original research projects culminating in conference submissions.
Prerequisites: Machine learning, probability theory
Blockchain & Decentralized Finance
Finance
Description: Technical deep dive into blockchain protocols, consensus mechanisms, smart contracts, and DeFi economics. Includes protocol analysis projects and mechanism design exercises.
Prerequisites: Distributed systems, game theory
Advanced Market Microstructure
Financial Engineering
Description: Rigorous treatment of market microstructure theory including asymmetric information models, limit order book dynamics, and high-frequency trading. Emphasis on current research and empirical methods.
Prerequisites: Econometrics, stochastic processes
Machine Learning for Finance
Financial Engineering
Description: Practical ML techniques for financial applications including return prediction, risk modeling, and portfolio optimization. Hands-on projects with real market data.
Prerequisites: Statistics, Python programming
Multi-Agent Systems
Financial Engineering
Description: Game theory, mechanism design, and coordination in multi-agent environments. Applications to markets, auctions, and distributed systems.
Prerequisites: Microeconomics, algorithms
Quantitative Trading Strategies
Financial Engineering
Description: Development, backtesting, and implementation of systematic trading strategies. Covers statistical arbitrage, momentum, and market-making approaches.
Prerequisites: Finance, statistics, programming
Data-Driven Decision Making with Unsupervised Learning
Financial Engineering
Description: Using data analytics and machine learning for business decisions. Covers predictive modeling, A/B testing, and causal inference with business cases.
Prerequisites: Statistics
Executive Education
AI Strategy for Executives
2-Day Program
Strategic framework for AI adoption covering competitive positioning, organizational change, ethics, and governance. Includes industry case studies and hands-on strategy workshops.
Blockchain Business Models
3-Day Program
Understanding blockchain applications beyond cryptocurrency. Explores supply chain, identity, finance, and other use cases with implementation considerations.
Quantitative Trading Intensive
5-Day Program
Comprehensive introduction to algorithmic trading for investment professionals. Covers strategies, backtesting, risk management, and technology infrastructure.
Digital Asset Management
2-Day Program
Institutional-grade approach to cryptocurrency investment. Covers market structure, portfolio construction, custody, and regulatory compliance.
M.S. Supervision (Financial Engineering)
I have supervised 40+ MS students to successful completion, with placements including:
- UBS
- Two Sigma (Quantitative Research)
- Citadel Securities (HFT)
- Jump Trading (Strategies)
- Jane Street (Research)
- MerCube
Current supervision areas include agentic AI for financial markets, DeFi mechanism design, optimal execution with ML, and blockchain scalability.
Teaching Innovation
Research Integration
Courses incorporate cutting-edge research, often leading to co-authored publications. Students learn by contributing to the frontier of knowledge.