Ph.D. position on Machine Learning @ UT Austin

[le...@andrew.cmu.edu]

The Department of Information, Risk, and Operations Management (IROM) at the University of Texas at Austin McCombs School of Business invites applications for its new fully-funded Ph.D. position on Use-Inspired AI.  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 denoising diffusion probabilistic 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: Human-centered machine intelligence, large language models, causal Inference, data-driven sequential decision-making, statistical learning theory, AI & economics, responsible AI, personalization

Maria De-Arteaga: Human-AI collaboration, algorithmic fairness, responsible AI, human-centered machine learning

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: 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.  

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. 

Deadline: December 15th, 2023

Questions: If you have any logistics questions regarding the application, please contact maddison...@mccombs.utexas.edu. For any other inquiries regarding the position itself, please reach out to Maytal.Saar...@mccombs.utexas.edu.