teaching

Mentoring the next generation of AI researchers and engineers through hands-on learning and research collaboration.

Teaching Philosophy

I believe effective teaching combines rigorous theory with practical application. My approach emphasizes project-based learning, where students tackle real-world challenges using state-of-the-art ML frameworks. By mentoring students through complete research lifecycles—from ideation to publication—I help them develop both technical skills and research intuition.

Graduate Teaching

Johns Hopkins University

Graduate Teaching Assistant — Machine Learning
Spring 2025 200+ Students EN.605.649

Course: Big Data Machine Learning

Responsibilities:

  • Instructed graduate students in advanced ML, prompt engineering, and AI agents
  • Developed 6 project-based assignments solving real-world business problems with big data frameworks
  • Led weekly lab sessions on Scikit-learn and PyTorch with hands-on debugging support
  • Mentored students on ML system design, optimization, and deployment strategies

Impact:

  • 99% positive teaching evaluations for instructional support and assignment design
  • Boosted student coding efficiency by >40% through AI-assisted development practices

Topics: Big Data ML pipelines • Distributed computing • Model optimization • MLOps • Prompt engineering • AI-assisted development

Research Mentorship & Instruction

Mahdy Research Academy, North South University

AI Instructor & Research Mentor (Remote)
Jan 2024 – Present 150+ Students

Curriculum Development:

Research Supervision:

  • Supervised 15+ research projects (machine translation, medical imaging, clinical NLP)
  • >90% completion rate • 12 manuscripts • 9 Q1 journal submissions
  • Mentored students from ideation → experimentation → publication

Course Materials :

Topics: Transformers • Attention mechanisms • Neural MT • Medical imaging • Quantum neural networks • Knowledge distillation

BRAC Institute of Governance and Development (BIGD)

Prompt Engineering Instructor
Nov – Dec 2023 50+ Freelancers Dhaka, Bangladesh

Program: Designed and delivered training on AI-assisted workflows for data analysis, web development, and app development to beginner and expert freelancers

Impact: 70% productivity boost • $500/month average income increase

Course Materials :

Invited Talk: Responsible AI Practices in Prompt Engineering

Undergraduate Teaching

North South University

Undergraduate Teaching Assistant
Sep 2020 – Nov 2023 800+ Students 3 Departments

Courses:

  • Introduction to Programming (Python)
  • Object-Oriented Programming (Java)
  • Introduction to Machine Learning
  • Neural Networks and Pattern Recognition
  • Introduction to Natural Language Processing

Teaching Impact:

  • 10-15% improvement in student performance
  • 100% on-time project completion rate
  • Designed lab materials and led weekly hands-on programming sessions
  • Provided one-on-one mentorship for end-to-end term projects

Pedagogy: Hands-on coding labs • Debugging workshops • Project-based assessments • Individual mentoring

Guest Lectures & Invited Talks

2025

  • “Are Reasoning Capabilities Present in Base Models?”
    NLP Reading Group, JHU CLSP • Slides

  • “When (and why) RL is Effective for Reasoning Problems?”
    NLP Reading Group, JHU CLSP • Slides

  • “Evaluating an Ambient Clinical Scribe Technology”
    Mayo Clinic Research Symposium • Slides

2024

  • “Responsible AI Practices in Prompt Engineering”
    BIGD, BRAC University • Slides

Teaching Impact

1,200+
Students Taught

99%
Positive Evaluations

15+
Research Projects

12
Manuscripts Mentored

9
Q1 Journal Submissions

>90%
Project Completion

150+
Research Mentees

Open Educational Resources

All course materials are open-source and freely available to the global community.

GitHub Repositories

Deep Learning with PyTorch
Comprehensive tutorials covering image classification, object detection, NLP, and advanced architectures

Quantum Machine Learning
Hybrid quantum-classical models with PyTorch, Qiskit, and TorchQuantum

Course Slides

NLP Fundamentals
Transformers, attention, tokenization, embeddings

Generative Adversarial Networks
GAN architectures, training techniques, applications

Model Efficiency
Distillation, pruning, quantization for deployment

Quantum ML Intro
Quantum circuits, variational algorithms, hybrid models

What Students Say

“The best TA I’ve had at JHU. Clear explanations, always available for help, and assignments were challenging but rewarding.”

— Graduate Student
ML Course, Spring 2025

“The research mentorship completely changed my trajectory. I published my first paper as an undergraduate!”

— Research Mentee
Mahdy Research Academy

“Prompt engineering training doubled my freelancing income within 2 months.”

— Workshop Participant
BIGD Program, 2023