Teaching
University at Buffalo, SUNY
Teaching & Mentoring
I have served as a Graduate Teaching Assistant across core courses in AI, machine learning, databases, and cybersecurity from Fall 2020 to Fall 2024. I also contribute to broadening AI security education through guest lectures, tutorials, and hands-on lab design at international conferences. A full CV is available here.
Recognition
CSE Best Graduate Teaching Award — Department of Computer Science and Engineering, University at Buffalo, 2022
Graduate TA · Fall 2020 – Fall 2024
Teaching Assistant Experience
Department of Computer Science and Engineering, University at Buffalo — responsible for designing and grading projects and labs, developing course materials, leading instructional components, and supporting students across AI, security, and database courses.
CSE 465 / 565
Computer Security
CSE 574
Introduction to Machine Learning
CSE 460 / 560
Data Models and Query Languages
CSE 368
Introduction to Artificial Intelligence
Invited Instruction
Guest Lectures & Tutorials
Selected invited instructional contributions at venues ranging from undergraduate seminars to international research conferences.
Tutorial: Machine Learning Based Online Abuse Defense — Platform, Research, and Hands-on Labs
ICWSM 2024 · 18th International AAAI Conference on Web and Social Media · Buffalo, NY
Co-presented a full tutorial covering the research landscape of online abuse defense, our custom moderation platform, and hands-on AI cybersecurity labs for conference attendees.
Guest Lecture: AI Security & Adversarial Machine Learning
CSE 4/565 Computer Security Seminar · University at Buffalo, SUNY · Buffalo, NY
Delivered multiple guest lectures on AI security and adversarial machine learning to over 140 graduate students enrolled in the Computer Security seminar.
Lightning Talk: AI Safety Issues and Research Directions
SEAS Ph.D. Seminar 2023 · School of Engineering and Applied Sciences, University at Buffalo · Buffalo, NY
Introduced current AI safety challenges and ongoing research directions to Ph.D. students across the School of Engineering and Applied Sciences.