Upcoming events

Biography

Dieter Fox is a Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. He grew up in Bonn, Germany, and received his Ph.D. in 1998 from the Computer Science Department at the University of Bonn. He joined the UW faculty in the fall of 2000.

His research interests are in robotics, artificial intelligence, and state estimation. The goal of his research is to enable systems to interact with people and their environment in an intelligent way. A lot of his work focuses on perception and its connection to control, where he develops techniques to extract relevant information from raw sensor data, with applications in human activity recognition, 3D mapping and tracking, and robot manipulation and control.

Fox shares his time between UW, where he is the head of the UW Robotics and State Estimation Lab (RSE-Lab), and the Allen Institute for AI (Ai2), where he focuses on building foundation models for robotics. Prior to Ai2, Fox spent a portion of his time at Nvidia, where he led the company’s Robotics Research Lab in Seattle for nearly eight years.

Fox is a Fellow of the AAAI, ACM, and IEEE, recipient of the IEEE RAS Pioneer Award and the IJCAI John McCarthy Award, and served as an editor of the IEEE Transactions on Robotics.

The Google Developer Group at UMN Twin Cities is hosting a career panel featuring industry professionals across the tech world. Panelists will discuss how to break into the field, what hiring teams actually look for and how students can prepare for getting internships and full-time roles. The event will include a Q&A session, as well as an opportunity to mingle with the panelists in smaller groups over pizza! 

Students can submit their resumes ahead of time or at the event, and panelists will give feedback on how to stand out to recruiters. 

Please submit your resume by Monday, November 2 at 11:59 p.m. 

Join us for a day-long showcase bringing together robotics research, innovation, students, faculty and industry.

Abstract

The Markov boundary of a response variable T is the minimal set of features that renders all other variables in the dataset statistically independent of T. While some data distributions admit a unique Markov boundary, others contain multiple distinct Markov boundaries for a response variable. The existence of multiple Markov boundaries results in the presence (and in some datasets, the abundance) of multiple predictively equivalent and optimal models. In this talk, I will introduce the foundational theory of multiple Markov boundaries and explore the potential mechanisms underlying their appearance in complex datasets, focusing specifically on biomedical applications. Furthermore, I will discuss the downstream implications of multiple Markov boundaries for predictive modeling, causal discovery, and model translation into real-world decision support systems.

Biography

This event is presented by the College of Science and Engineering Data Science Initiative.

At CodeFreeze 2027 — AgenticSE — we will explore how the rapid adoption of AI agents is fundamentally changing the way software is developed, verified, validated and maintained. AgenticSE is a one-day symposium that brings together leading industry practitioners, academic researchers and software engineers to examine the practical applications, breakthroughs and challenges of integrating agents into the software engineering lifecycle.