Desmond Ong
University of Texas at Austin, TX·Computer Science·Early-career
Millions of people talk daily with AI chatbots built on large language models (LLMs). These systems seem to understand emotions and can even express empathy.
- Fields
Computer Science
Psychology
- Topics
affective computing
emotion understanding
social cognition
- Grants
- 1 known grant, through May 2030
What they work on
- Define types of emotional reasoning, such as predicting emotions from situations and mental states and reasoning about interventions that change emotions, drawing on psychological theory.
- Build question–answer benchmark datasets that measure social and affective cognition, including tasks that use facial and vocal expressions.
- Evaluate modern large language models on these benchmarks and develop techniques to improve their social and emotional reasoning.
- Produce policy guidelines for emotion-aware AI and educational materials that build AI literacy among teenagers and adults.
More about the research
Yet there are no rigorous definitions or tests of what social and emotional understanding in AI actually means. Without such measures, nobody can track progress, spot blind spots and harms, or improve these systems. This project defines that understanding and builds ways to measure it.
Why it matters
Objective tests of emotional understanding could help developers build conversational AI that is more capable and safer. They could also help reveal harms in systems that millions already use. The project's policy guidelines and AI-literacy materials aim to help designers, teenagers and adults use emotion-aware AI with awareness of its risks.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Lu Lin
Pennsylvania State University, PA·Computer Science
Identify critical LLM safety concerns, gaps in current evaluation and infrastructure needs through structured surveys and interviews with experts across computing disciplines.
Alexis Block
Case Western Reserve University, OH·Engineering·Early-career
Develop interpretable models that infer a person's emotional state from off-body signals such as heart rate, breathing rate, speech prosody (vocal rhythm and tone), facial thermal patterns, and posture.
Prerna Juneja
Seattle University, WA·Computer Science
Build a taxonomy of user interactions with AI companion apps, covering motivations, challenges and values, by analyzing large-scale social media data and user interviews.
The school
University of Texas at Austin: 49 labs on PI Pages, $1.1B a year on research, 871 research doctorates a year.
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