Greg Anderson is a PhD student at UT Austin, focusing on the intersection of program analysis and machine learning. Specifically, he is interested in developing formal approaches to verifying learning-enabled systems in order to increase their safety and reliability, and to allow such systems to be deployed more widely and more confidently.

Sam Anklesaria is a PhD student broadly interested in probabilistic programming, causal effect estimation, and approximate inference. Specifically, his research explores representations and inference algorithms for relational models.

Josh Hoffman is a PhD student at UT Austin broadly interested in deep learning and symbolic techniques for robotics. Specifically, he is working on techniques in reinforcement learning that can be deployed on mobile robots via low-shot learning. He is also a member of the Autonomous Mobile Robotics lab and co-advised by Joydeep Biswas.

Atharva Sehgal is a PhD student in the CS department at UT Austin. He’s broadly interested in making up for the inherent weaknesses in deep learning algorithms using techniques from programming languages and formal methods. He’s currently working at the intersection of program synthesis and perception.

Meghana Sistla is a PhD student at UT Austin in the Department of Computer Science, advised by Prof. Swarat Chaudhuri. She is broadly interested in the area of Programming Languages and Machine Learning. Before joining UT, she worked at Google after graduating from IIT Madras with Bachelors and Masters in Computer Science.

Yeming Wen is a graduate student at UT Austin at the computer science department, advised by Prof. Swarat Chaudhuri. His research focused on building a machine learning framework to generate code with human-like efficiency. Before joining UT Austin, he was a master student in computer science advised by Prof. Jimmy Ba at University of Toronto. He worked on the development of efficient learning algorithms for deep neural networks.

Chenxi Yang is a graduate student at UT-Austin. Her interests span the areas of programming language and machine learning, with a focus on interpretability and safety. Her current projects are about learning safe neurosymbolic programs and learning interpretable neurosymbolic programs for biology.