🎓 Teaching

My teaching spans deep learning, trustworthy foundation models, and the reliable deployment of modern AI systems. It connects with established research directions within Auditable AI Systems. Detection in data covers anomalies, outliers, and out-of-distribution inputs. Verification of output covers LLM evaluation, hallucination, and jailbreak detection. Auditing of action covers AI agent security, tool-call and MCP security, and audit trails. Modeling of risk is a developing direction on agent risk, loss, and deployment decisions.

USC (as instructor):

• CSCI 699: Auditing AI Systems: Foundation Models and Agents (Spring 2027 (scheduled))
• CSCI 566: Deep Learning and its Applications (Fall 2026 (scheduled); ~300 students); See Course website.
• CSCI 699: Adversarial and Trustworthy Foundation Models (Spring 2026; 40 students); See Course website.
• CSCI 566: Deep Learning and its Applications (Spring 2025; ~300 students); See Course website.
• CSCI 566: Deep Learning and its Applications (Spring 2024; ~300 students);