UT Austin | Use-inspired AI Ph.D. position on Machine Learning

The successful candidate will be given the opportunity to pursue a broad research agenda on developing AI/ML methodologies to address challenges arising in a particular real-world context and which directly applies to this context. Some example topics include: how to develop principled methods to engage humans in the development of machine learning systems; how to build reliable generative models (e.g., LLMs and diffusion-based generative models) that better serve their end users for specific use cases; how to develop effective and reliable AI partners for human decision-makers; how to perform data-driven sequential decision-making in a reliable manner; what policy interventions one could develop for mitigating adverse effects of algorithmic decision-making and large-scale machine learning systems; what are some failure modes of existing AI systems and how should we address them.

AI/ML at IROM

IROM has a strong community among faculty and students working on machine learning. Here is a non-exhaustive list of faculty at IROM who are core members of UT Austin’s Machine Learning Lab:

• Leqi Liu: large language models, safety and alignment, causal Inference, data-driven sequential decision-making, statistical learning theory, AI & economics, responsible AI, personalization
• Yan Leng: Social networks, graph neural networks, causal inference, game theory
• Deepayan Chakrabarti: Network, graph, statistical learning theory, robust optimization
• Maytal Saar-Tsechansky: Human-AI collaboration, responsible and fair AI, machine learning from imperfect and biased humans, AI for high-risk decisions, AI in healthcare
• Mingyuan Zhou: Generative models, probabilistic methods, approximate inference, deep neural networks, Bayesian analysis, reinforcement learning

In addition, we expect to have a growing Ph.D. cohort in this area, given our commitment to continuously developing this new Ph.D. position and to lead the future growth of Use-Inspired AI.

AI/ML at UT Austin

UT Austin has a collaborative and vibrant research community in AI/ML under university-wide initiatives including the Machine Learning Laboratory and the Institute for Foundations of Machine Learning. These initiatives span multiple departments including Department of Computer Science, Department of Electrical & Computer Engineering, Department of Statistics & Data Science, Department of Information, Risk, and Operations Management, Department of Linguistics, and Department of Psychology.

Austin is a lively city renowned for its live music, tech industry, cultural diversity, and welcoming community. Given its proximity to the beautiful Hill Country, Austin offers a dynamic blend of creativity and outdoor adventures.

Requirement

The candidate should have a Bachelor’s or Master’s degree in computer science, statistics & data science, mathematics, engineering, or related fields. GRE waiver can be requested for this specific application cycle.

Application information

Please submit the application through https://www.mccombs.utexas.edu/graduate/phd/admissions/apply/. In your personal statement, please indicate your interests in the Use-Inspired AI position, past academic and research experiences, current research interests, and 1-2 sentences on potential advisers at IROM. Please also indicate clearly at the end of your statement that you want to be considered for the use-inspired AI Ph.D. position.

Deadline

December 15th, 2025

Questions

If you have any logistics questions regarding the application, please contact IROMPhDAdmissions@mccombs.utexas.edu. For any other inquiries regarding the position itself, please reach out to leqiliu@utexas.edu and Maytal.Saar-Tsechansky@mccombs.utexas.edu.

We will be at NeurIPS 2025. If you want to reach out to us, please contact leqiliu@utexas.edu.

Source and more details: https://docs.google.com/document/d/1iCg9LOQSFOeFIY7HwEUN4NAraCqBpLrP-re2jERKYME/edit?tab=t.0