Teaching

Teaching, mentoring, and building research confidence.

Teaching assistant work in graduate computer architecture and trustworthy AI hardware, plus hands-on mentorship for NSF REU students working at the intersection of hardware security and adversarial machine learning.

Courses

2 graduate TA assignments

Supported Spring 2026 Computer Architecture and Trustworthy AI Hardware through grading, exam coordination, project evaluation, and lecture support.

CEG 7350CEG 7900Spring 2026
Mentorship

2 NSF REU researchers

Mentored Cole Castronova and Ryan Dang from June 9 to August 7, 2025 on adversarially robust hardware Trojan detection with synthetic data augmentation.

NSF REU 2025June-AugustAdversarial ML
Program Support

18 NSF REU interns across two cohorts

Supported the WSU-led interview and onboarding process for the NSF REU program in 2025 and 2026. Each year, 9 interns are selected, with 4 hosted at Wright State and 5 hosted at AFIT.

InterviewsOnboardingFlyer Design

Courses

Teaching assistantship and course support.

My TA work sits close to the way I do research: clear rubrics, reproducible expectations, careful feedback, and a lot of help turning intimidating systems topics into concrete steps.

Spring 2026 CEG 7350-01

Computer Architecture

Teaching Assistant, Wright State University

Supported a graduate course covering processor design, RISC architectures, pipelining, hazards, superscalar execution, multiprocessor systems, and research-paper based presentation work.

  • Graded assignments and helped maintain consistent evaluation standards.
  • Coordinated exams, review logistics, and student-facing assessment details.
  • Evaluated paper-based class presentations and research-style project submissions.
  • Supported lectures and helped students during teaching/help hours when questions came up.
Spring 2026 CEG 7900

Trustworthy AI Hardware

Teaching Assistant and course-material contributor, Wright State University

Supported a graduate course on hardware security, embedded AI, adversarial AI threats, PUFs, hardware Trojans, side-channel analysis, and trustworthy ML for secure hardware platforms.

  • Handled assignment/lab grading, exam support, project evaluation, presentation grading, and student help hours.
  • Designed lecture, assignment, and lab material for security and AI-hardware modules.
  • Helped translate research papers into implementable classroom exercises and discussion prompts.
  • Supported hands-on topics involving adversarial attacks, side-channel data, PUF modeling, and Trojan detection workflows.

Course Design

Lecture, assignment, and lab modules.

For Trustworthy AI Hardware, I contributed material for modules that connected low-level security concepts with modern ML workflows.

Buffer overflow attacks Built teaching material around memory-safety failures, exploit intuition, and why secure hardware/software interfaces matter.
Intro to ML and deep learning Prepared foundations for model training, evaluation, and how ML becomes part of embedded and hardware-security systems.
AI attacks on PUF designs Helped students connect physical unclonable functions with model-building attacks, prediction risk, and authentication security.
AI for hardware Trojan detection and side-channel analysis Designed material around side-channel features, hardware Trojan detection pipelines, and ML-assisted classification.
Trustworthy ML and adversarial AI Trojan models Prepared content on adversarial robustness, model failure modes, and evaluation beyond nominal accuracy.

NSF REU Mentorship

Undergraduate research mentorship.

From June 9 to August 7, 2025, I mentored Cole Castronova and Ryan Dang on a hardware-security research project that continued past the summer into a journal submission.

June 9 - August 7, 2025 Cole Castronova and Ryan Dang

Adversarially Robust Hardware Trojan Detection with Synthetic Data Augmentation

The project studied whether machine-learning hardware Trojan detectors remain reliable when an attacker perturbs Ring Oscillator Network side-channel features. The baseline SVM detector could look strong under normal test data, but adversarial perturbations caused a collapse in recall.

We evaluated synthetic data augmentation strategies including SMOTE, CTGAN, and TVAE. TVAE produced high-fidelity tabular samples, widened the effective decision boundary, and maintained roughly 91% accuracy on clean data and about 88% accuracy under severe adversarial perturbation in the submitted manuscript.

Mentorship focus Guided literature review, experiment planning, result checking, interpretation discussions, and research communication.
Student outcome The students presented their work at the end of the REU, then the project continued into a Journal of Electronic Testing submission after the program.
Research habits Emphasized reproducible experiments, skepticism about headline accuracy, and careful comparison between nominal and adversarial performance.
My role Led methodology/software development and helped the students connect preliminary results to a publishable hardware-security research story.

REU Program Support

Recruiting, interviews, and onboarding.

NSF REU led by Wright State University and co-led by AFIT

I was actively involved in the WSU-run REU hiring process, including applicant interviews and onboarding for the 2025 and 2026 cohorts. Each year, the NSF REU program selects 9 interns through Wright State: 4 students hosted at WSU and 5 students hosted at AFIT, for 18 interns across the two cohorts.

  • Helped evaluate candidates and coordinate interview conversations.
  • Supported onboarding so students could quickly understand lab expectations, research themes, and communication workflows.
  • Helped connect undergraduate researchers with hardware security, AI security, and applied ML project directions.
  • Designed the REU 2026 recruiting flyer used to advertise the program.

Teaching materials are part of the work.

Lecture slides, labs, rubrics, and mentoring artifacts can be added as selected samples when they are ready for public sharing.

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