School of Computing and Information Systems

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Inside Out: Improving Large Model Safety

Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities across diverse applications, yet they remain vulnerable to adversarial attacks through carefully crafted prompts and harmful visual inputs that circumvent safety mechanisms. Despite considerable efforts in reinforcement learning from human feedback (RLHF) and supervised fine-tuning, existing safeguards prove inadequate because these models operate as black-boxes without explanations for their decisions, making security vulnerabilities difficult to identify and eliminate. Addressing these challenges fundamentally requires understanding the inner safety mechanisms of these models to develop targeted mitigation strategies that can effectively defend against attacks. This dissertation presents four interconnected contributions to improve LLM and MLLM security through mechanistic understanding. We propose CASPER, a causality analysis framework operating at token, layer, and neuron levels that reveals how… Subtitle: PhD Dissertation Defense by ZHAO Wei. Contact: scisseminars@smu.edu.sg. Speaker Details: , ZHAO Wei PhD Candidate School of Computing and Information Systems Singapore Management University, Wei ZHAO is a Ph.D. Candidate in Computer Science at Singapore Management University, under the supervision of Professor Jun SUN. His research focuses on improving large model safety through understanding and enhancing the inner mechanisms of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). His PhD research addresses critical security vulnerabilities in these models, spanning… RSVP: . Reserve a seat: https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UMDgyWVA3R1YwVE8yRlU3SExISDRZRzNCNS4u. Type: Seminars & Workshops. Subject: Information Technology & Systems. Audience: Public. Current Student. Academic Community. Tuesday, January 6, 2026, 9:30 AM – 10:30 AM. Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902. For more info visit computing.smu.edu.sg.

Certifying AI with Robustness and Fairness

Machine learning is widely used in real-world applications, but its deployment raises serious safety concerns, including limited robustness and fairness. These shortcomings pose significant ethical and practical risks, especially in safety-critical systems. Although many empirical techniques aim to address these issues, they often fail under adaptive attacks, underscoring the need for methods with formal guarantees. Existing certified approaches, however, typically suffer from substantial utility loss. Moreover, the mechanisms underlying the trade-off between safety guarantees and accuracy are not well understood, limiting progress in this area. This dissertation investigates certified AI safety, with a focus on robustness and fairness. The first work leverages Bayes error to analyze robustness, studying the fundamental limits of certified robust accuracy under data distribution uncertainty. It derives an upper bound based on class-conditional distributions and their decision boundaries, with empirical resul… Subtitle: PhD Dissertation Defense by ZHANG Ruihan. Contact: scisseminars@smu.edu.sg. Speaker Details: , ZHANG Ruihan PhD Candidate School of Computing and Information Systems Singapore Management University, Ruihan ZHANG is completing her Ph.D. in Computer Science at SMU under the supervision of Prof. Sun Jun. She previously earned her Bachelor of (with Honours) from the Singapore University of Technology and Design. Her research focuses on trustworthy AI, where she uses formal methods to study and verify key properties such as robustness, fairness, and reliability in neural networks. Her work has been… RSVP: . Reserve a seat: https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UNlJKTDY5QVoyODNaMzJINlU4OERZN0FSWi4u. Type: Seminars & Workshops. Subject: Information Technology & Systems. Audience: Public. Current Student. Academic Community. Thursday, January 8, 2026, 10:00 AM – 11:00 AM. Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902. For more info visit computing.smu.edu.sg.

MITB Virtual Information Session (Singapore)

Public Events RSVP: . Reserve a seat: https://scispg.smu.edu.sg/acton/media/44865/mitbis0126. Type: Information Sessions. Subject: Analytics for Business, Consumer & Social Insights. Information Technology & Systems. Innovation & Entrepreneurship. International. Leadership. Learning & Professional Development. Organisational Behaviour. Operations Management. Strategic Management. Audience: Public. Saturday, January 17, 2026, 10:00 AM – 12:00 PM. Online This information session will be conducted virtually via Zoom. Please register for the session by clicking on the “Register Now” button that leads you to our event registration form.

MITB Coffee Session (Singapore)

Public Events RSVP: . Reserve a seat: https://scispg.smu.edu.sg/acton/media/44865/mitbcs0126. Type: Information Sessions. Subject: Analytics for Business, Consumer & Social Insights. Innovation & Entrepreneurship. Information Technology & Systems. International. Leadership. Learning & Professional Development. Strategic Management. Audience: Public. Professionals. Prospective Student. Wednesday, January 28, 2026, 7:00 PM – 8:00 PM. In-Campus SMU School of Computing and Information Systems Limited seats available! Complimentary Starbucks on us!