Teaching & Mentoring
Teaching & Mentoring
I teach through active, student-centered learning, designing each course around what students must be able to do rather than what I must cover. I want students to leave able to judge a claim on their own: what a method assumes, how to build it, where it breaks, and how to evaluate it.
Lecturer, Department of Computer Science and Engineering
Port City International University, Bangladesh 2018–2021
I taught twelve undergraduate computer science courses with classes of 20 to 60 students, and I taught the companion lab in nine of them. I used think-pair-share in lectures and pair programming in labs.
AI and machine learning
Programming
Algorithms and theory
Systems
Graduate Teaching Assistant, Department of Information Systems
University of Maryland, Baltimore County 2022–2024
- Teaching assistantIS 636 · Structured Systems Analysis and Design Graduate
Fall 2022 and Spring 2023. Held office hours, graded assignments and exams, and advised student project teams. I found TEAMMATES, an online peer evaluation tool, set it up for the course, and ran every team peer evaluation in both semesters, from enrolling students and forming teams to scheduling each evaluation session.
- Teaching assistantIS 734 · Data Analytics for Cybersecurity Graduate
Tutored students, gave written feedback, and graded coursework.
- Teaching assistantIS 603 · Decision Making Support Systems Graduate
Tutored students, gave written feedback, and graded coursework.
- Teaching assistantIS 320 · Advanced Business Applications Undergraduate
Taught the weekly lab sessions, designing exercises and assessing student work.
Guest lectures and workshops
Teaching from my research
- Spring 2026IS 790 · Causal AI Graduate
Causal representation learning. Why statistical models fail under distribution shift; structural causal models; the independent causal mechanisms and sparse mechanism shift principles; disentangled representations; and case studies of CausalVAE and SCM-VAE, with in-class quizzes. I designed the lecture from recent papers and my own research, and afterward students wrote to me about using the methods in their own work.
- Mar 2026Explainable AI workshop · iHARP Artificial Intelligence Series
I led and presented the workshop, developed with Dr. Md Osman Gani: interpretable models and post-hoc explanations; LIME and SHAP; Grad-CAM and attention maps; causal and counterfactual explanations with DiCE; evaluation metrics such as faithfulness and stability; and hands‑on notebooks.
- Fall 2024, Fall 2025IS 733 · Data Mining Graduate
Deep sequence modeling. Sequence modeling tasks; recurrent neural networks and backpropagation through time; vanishing gradients; and self-attention and Transformers, with in‑class quizzes.
Mentoring
Mentoring
I enjoy helping students turn an interest into a real research question. I ask every mentee to name the assumption under which their method would fail, and to release their code and data with the paper.
- 2020, 2021Md Saiful Islam
A review of FOREX currency prediction, Algorithms (2020), and foreign exchange rate prediction with a GRU-LSTM hybrid network, Soft Computing Letters (2021).
- 2021Polash Dey
Recurrent neural networks for stock price prediction across frequency domains, Algorithms.
- 2021Tasnim Afroze
Glaucoma detection with Inception-v3, International Conference on Applied Intelligence and Informatics.
- 2019, 2020Dipankar Gupta
Bangla voice command recognition, IEEE RAAICON (2019), and a gesture-based mouse and keyboard, ICO (2020).