Ratna Kandala
Hello! I am Dr. Ratna. Welcome! Thank you for visiting my profile!
Research Interests
1) LLM Evaluation and Human-Centered AI
I study how large language models behave in complex human-facing settings where outputs may influence trust, judgment, emotion, and decision-making. I am especially interested in evaluation frameworks that move beyond fluency or accuracy to assess reliability, uncertainty, autonomy, sycophancy, and downstream human impact.
2) Computational Affective Science and Behavioral Data Science
I study how people express emotion, well-being, distress, uncertainty, and everyday experience in language. This work connects computational modeling with psychological measurement and human self-report data.
3) Health AI, Mental Health, and Decision Support
I am interested in AI systems that operate in health-relevant and emotionally sensitive contexts, including health misinformation, mental-health-relevant language, emotional support, and advice. A central question in this work is how AI can support understanding and decision-making while preserving uncertainty, human autonomy, and context.
4) Culturally Situated and Multilingual NLP
I study how language technologies perform across cultural, linguistic, and social contexts, especially when meaning depends on local beliefs, idioms, health practices, emotional norms, or low-resource communication settings. This includes work on multilingual health misinformation, culturally grounded AI evaluation, and low-resource conversational systems.
5) Computational Modeling of Human and Biological Systems
Building on my background in Systems Biology and earlier computational neuroscience work, I study human and biological systems as dynamic, adaptive, noisy, and context-dependent. This perspective informs my work on language, behavior, health-relevant data, and LLM evaluation, especially in settings where AI systems must interpret complex human experience and reason under uncertainty. I am interested in connecting language-based, behavioral, and self-report measures with biological, physiological, memory-related, and neural mechanisms.