Teaching Philosophy
I believe in learning by doing and theory-practice integration. My teaching approach emphasizes:
- 🎯 Hands-on Learning: Every concept is reinforced with practical implementations
- 🧠 Critical Thinking: Encouraging students to question assumptions and explore alternatives
- 🤝 Collaborative Learning: Fostering peer-to-peer learning and group problem-solving
- 🌍 Real-world Applications: Connecting theoretical concepts to practical applications
Courses Taught
📊 Probabilistic Artificial Intelligence
ETH Zurich | Teaching Assistant | Fall 2022, 2023
Instructor: Prof. Andreas Krause
Students: ~200 Master's students
Rating: 4.8/5.0
Topics Covered:
- Bayesian inference and probabilistic modeling
- Gaussian processes and kernel methods
- Variational inference and MCMC
- Bayesian optimization and active learning
- Probabilistic deep learning
My Contributions:
- Designed and led weekly exercise sessions
- Created interactive Jupyter notebooks for hands-on learning
- Developed project assignments on real-world datasets
- Provided one-on-one mentoring during office hours
🎮 Model Predictive Control
ETH Zurich | Teaching Assistant | Spring 2022, 2023
Instructor: Prof. Melanie Zeilinger
Students: ~150 Master's students
Rating: 4.9/5.0
Topics Covered:
- Optimal control theory and dynamic programming
- Linear and nonlinear MPC formulations
- Robust and stochastic MPC
- Real-time implementation and computational aspects
- Safety-critical control applications
My Contributions:
- Led MATLAB/Simulink tutorials for control implementation
- Supervised final projects on autonomous vehicle control
- Created simulation environments for testing MPC algorithms
- Organized guest lectures from industry practitioners
🚗 Autonomous Mobility on Demand
ETH Zurich | Teaching Assistant | Fall 2021
Instructor: Prof. Emilio Frazzoli
Students: ~80 Master's students
Rating: 4.7/5.0
Topics Covered:
- Fleet management and routing algorithms
- Multi-agent coordination and game theory
- Demand prediction and dynamic pricing
- Autonomous vehicle technology stack
- Policy and regulatory considerations
My Contributions:
- Developed simulation framework for fleet optimization
- Mentored student teams on capstone projects
- Organized industry panel discussions
- Created case studies based on real deployment scenarios
Student Supervision
Master’s Thesis Projects
🛡️ Safe Reinforcement Learning for Autonomous Navigation
Student: Sarah Chen | Year: 2023
Developed safety-constrained RL algorithms for indoor robot navigation with formal safety guarantees.
Outcome: Published at IROS 2024
🎯 Multi-Agent Coverage Control with Communication Constraints
Student: Marco Rossi | Year: 2023
Extended our NeurIPS work to scenarios with limited inter-agent communication.
Outcome: Submitted to ICRA 2024
🧠 Submodular Optimization for Experiment Design
Student: Lisa Wang | Year: 2022
Applied submodular RL to active learning problems in scientific discovery.
Outcome: Best Thesis Award, Robotics Program
🏎️ Real-time Path Planning for Racing Applications
Student: Alex Mueller | Year: 2022
Developed high-speed trajectory optimization for autonomous racing.
Outcome: Deployed on AMZ racing car
📊 Bayesian Optimization for Hyperparameter Tuning
Student: Priya Sharma | Year: 2021
Novel acquisition functions for multi-objective hyperparameter optimization.
Outcome: Open-source library with 500+ GitHub stars
Semester Projects
Supervised 12+ semester projects covering topics such as:
- Reinforcement learning for robotics
- Computer vision for autonomous systems
- Optimization algorithms for control
- Machine learning for scientific applications
Workshops & Tutorials
🎓 International Workshops
Safe AI for Autonomous Systems
NeurIPS 2023 Workshop | Co-organizer & Tutorial Speaker
3-hour tutorial on safety-critical AI with 200+ attendees from academia and industry.
Submodular Optimization in Machine Learning
ICML 2023 Tutorial | Invited Speaker
Presented recent advances in submodular RL to 150+ researchers.
🏫 University Workshops
- ETH AI Center Seminar Series: Monthly talks on cutting-edge AI research
- Robotics Student Colloquium: Bi-weekly presentations by students and faculty
- Industry-Academia Bridge: Quarterly workshops connecting research with applications
Mentoring & Outreach
👩💻 Diversity & Inclusion
- ETH Women in AI: Mentor for 10+ female students in AI/ML
- Robotics Diversity Initiative: Organized summer school for underrepresented minorities
- High School Outreach: Guest lectures at local schools on AI and robotics
🌍 International Mentoring
- Remote Mentorship Program: Mentoring students from developing countries
- Open Source Contributions: Maintaining educational repositories with 1000+ users
- Online Tutorials: YouTube channel with 50+ educational videos
Teaching Materials & Resources
📚 Open Educational Resources
🐍 Reinforcement Learning Toolkit
Python library with implementations of modern RL algorithms
GitHub Stars: 800+ | Users: 50+ universities
📊 Interactive ML Notebooks
Jupyter notebooks for hands-on learning of ML concepts
Downloads: 10,000+ | Languages: English, German
🎮 Control Systems Simulator
Web-based simulator for learning control theory
Users: 5,000+ students | Institutions: 20+ universities
📖 Course Materials
- Lecture Slides: 500+ slides covering probabilistic AI and control theory
- Exercise Sets: 50+ problem sets with detailed solutions
- Video Lectures: 100+ hours of recorded content
- Assessment Tools: Automated grading systems for programming assignments
Student Feedback
"Manish's teaching style made complex concepts accessible. His real-world examples helped bridge theory and practice."
— Sarah M., Master's Student
"The hands-on projects were incredibly valuable. I learned more in one semester than in any other course."
— Alex K., PhD Student
"Manish's mentorship was instrumental in my research success. He always made time for questions and guidance."
— Lisa W., Former Thesis Student
Awards & Recognition
- 🏆 Outstanding Teaching Assistant Award - ETH Zurich (2023)
- 🌟 Best Mentor Award - ETH Women in AI (2022)
- 📚 Educational Innovation Prize - Robotics Program (2022)
- 👥 Student Choice Award - Highest rated TA (2021, 2022, 2023)
Interested in collaboration or have questions about my teaching approach? Contact me!