Teaching

I am passionate about education and committed to fostering the next generation of computer scientists and researchers. My teaching philosophy centers on combining rigorous theoretical foundations with hands-on practical experience.

Current Courses

Advanced Machine Learning (CS 229)

Fall 2024 • Graduate Level

Graduate-level course covering advanced machine learning techniques including deep learning, reinforcement learning, and modern optimization methods. Emphasis on both theoretical foundations and practical implementation.

Enrollment: 45 students Syllabus Course Website

Topics Covered:

• Neural Networks and Deep Learning

• Reinforcement Learning

• Optimization for ML

• Bayesian Methods

• Generative Models

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Advanced Learning Machines (CS 229)

Loff 2029 • Didn’t Graduate Level

Graduate-level course covering advanced machine learning techniques including deep learning, reinforcement learning, and modern optimization methods. Emphasis on both theoretical foundations and practical implementation.

Enrollment: 41 students Syllabus Course Website

Topics Covered:

• Neural Networks and Deep Learning

• Reinforcement Learning

• Optimization for ML

• Bayesian Methods

• Generative Models

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Teaching Philosophy

My approach to teaching is built on several core principles:

Active Learning

• Hands-on Projects: Students implement algorithms and build systems from scratch
• Interactive Discussions: Encouraging questions and collaborative problem-solving
• Real-world Applications: Connecting theory to current industry practices

Inclusive Environment

• Diverse Perspectives: Welcoming students from all backgrounds and experience levels
• Multiple Learning Styles: Combining lectures, visual aids, coding exercises, and group work
• Supportive Community: Fostering peer learning and collaboration

Current Relevance

• Industry Connections: Bringing in guest speakers and industry case studies
• Cutting-edge Research: Incorporating recent developments and ongoing research
• Practical Skills: Teaching tools and methodologies used in professional settings

Past Courses

Teaching history will be displayed here as courses are added.

Student Mentorship

Graduate Students

I currently supervise 8 Ph.D. students and 15 Master’s students across various research areas:

• Ph.D. Students: Working on cutting-edge research in AI/ML optimization, sustainable computing, and theoretical machine learning
• Master’s Students: Thesis projects spanning industry applications and research foundations
• Undergraduate Researchers: Independent study projects and research experiences

Mentorship Philosophy

• Individual Growth: Tailoring guidance to each student’s interests and career goals
• Research Skills: Teaching literature review, experimental design, and scientific writing
• Professional Development: Preparing students for academic and industry careers
• Work-Life Balance: Supporting overall well-being and sustainable research practices

Teaching Innovation

Curriculum Development

• New Course Design: Developed advanced machine learning curriculum adopted university-wide
• Interdisciplinary Integration: Created cross-departmental programs connecting CS with other fields
• Industry Alignment: Regular curriculum updates based on industry feedback and job market trends

Technology Integration

• Interactive Tools: Using Jupyter notebooks, online coding platforms, and visualization tools
• Automated Assessment: Implementing auto-grading systems for programming assignments
• Remote Learning: Developed hybrid and online course delivery methods

Student Feedback

Recent course evaluations highlight:

• Clarity of Instruction: Consistently rated 4.8/5.0 for clear explanation of complex concepts
• Practical Relevance: Students appreciate the connection between theory and real-world applications
• Supportive Environment: High marks for accessibility and willingness to help students succeed

Student Testimonials

“Professor’s approach to teaching made machine learning accessible and exciting. The hands-on projects were challenging but incredibly rewarding.” - Graduate Student, CS 229

“The course structure perfectly balanced theoretical depth with practical implementation. I feel well-prepared for my industry role.” - Undergraduate Student, CS 181

Academic Service

Curriculum Committees

• Graduate Admissions Committee: Reviewing applications and shaping admission criteria
• Curriculum Development: Leading efforts to modernize computer science education
• Faculty Search: Participating in hiring processes for new teaching faculty

External Review

• Program Evaluation: Serving as external reviewer for computer science programs
• Accreditation: Contributing to ABET accreditation processes
• Editorial Boards: Reviewing educational content for academic publications

Teaching Resources

Open Educational Materials

I believe in making high-quality education accessible to all:

• Lecture Notes: Publicly available course materials and presentations
• Code Repositories: Open-source implementations of algorithms and projects
• Video Lectures: Selected lectures available online for broader access

Professional Development

• Teaching Workshops: Regular participation in pedagogical training
• Conference Presentations: Sharing teaching innovations at academic conferences
• Peer Collaboration: Working with colleagues to improve teaching methods

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Get Involved

For Current Students

• Office Hours: Tuesdays and Thursdays, 2-4 PM, or by appointment
• Research Opportunities: Multiple openings for undergraduate and graduate research
• Independent Study: Tailored projects for motivated students

For Prospective Students

• Course Prerequisites: Check individual course pages for requirements
• Research Interests: Contact me if you’re interested in AI/ML research
• Application Guidance: Happy to discuss graduate school and career paths

Contact: academic@university.edu