Research
My research develops and evaluates methods and frameworks to improve the safety, security, and ethical reliability of AI systems, with a particular focus on large language models. I study how these systems behave in sensitive, high-risk settings such as healthcare, and design approaches to systematically assess and mitigate the associated risks. Alongside the technical work, I gather empirical evidence to help policymakers and other non-technical stakeholders make informed decisions that reduce algorithmic harm.
Responsible AI
Governance, evaluation, and accountability in deployed AI systems.
Safety, Security & Robustness
Failure modes, stress testing, and adversarial evaluation of machine learning models.
Natural Language Processing
Modeling and evaluation of large language systems.
Affective Computing
Understanding emotion, wellbeing, and human experience from data.
Stress Testing and Failure Analysis of Large Language Models
This line of research focuses on developing methods for stress testing large language models to systematically identify failure modes, including jailbreak vulnerabilities, unsafe outputs, and robustness breakdowns under distribution shift. The work emphasizes evaluation frameworks and adversarial testing approaches for characterizing model behavior in high-risk or deployment-relevant settings.
Responsible AI
Developed through close collaboration with interdisciplinary teams at NU’s Responsible AI Practice and industry partners, this work spans domains such as gaming, healthcare, and finance. Spanning domains such as gaming, healthcare, and finance, the work translates technical evaluation, monitoring, and guardrail methods into applied settings. Research outputs are informed by real-world deployment constraints and emphasize issues that emerge over the system lifecycle.
AI Systems and Suicide
This work examines how AI systems interact with suicide-related content, focusing on system behavior, responses, safeguards, and evaluation. Ongoing work includes (i) understanding how suicide is expressed through language and emotional expression, and (ii) identifying the medical and sociodemographic contexts that drive suicide risk. These findings are used as evidence to inform policy and deployment decisions involving AI systems.
Building an Actionable Framework to Responsible AI Integration in Clinical and Public Health Contexts
Building on the AI Ethics Box, this work adapts bioethical principles to healthcare settings and links them to concrete technical methods for evaluation, governance, and post-deployment monitoring. The framework addresses the gap between high-level ethical guidance and operational decision-making in clinical and public health contexts. It is refined through interdisciplinary collaboration and stakeholder co-design, supporting practical use by clinicians, health system leaders, and developers in real-world AI deployment.
RAI4MH: Responsible AI for Mental Health Initiative
RAI4MH is an international partnership developing evidence-based guidance for the responsible use of AI in mental health contexts. As the U.S. lead, I contribute to coordinating interdisciplinary collaboration across computer science, mental health, ethics, and policy. Key outputs include white papers that synthesize international expert consensus and contribute directly to policy discussions, including a POSTnote for the UK Parliament on responsible AI in mental health.
SHAPE-AI: Smart Health Analytics and Predictive Engagement with AI
This work studies how AI learning companions intersect with student mental health, cognition, and academic outcomes. It integrates multimodal data including biometric signals (sleep, activity, physiology), academic performance, and patterns of AI use to model wellbeing and learning trajectories. The study examines AI use as a digital phenotype of academic engagement and supports evidence-based guidance for intervention design and responsible AI deployment in higher education.
Data access: Available upon request
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