Brian Ziebart

Brian Ziebart

Associate Professor

I am primarily interested in machine learning and its applications to problems in robotics, assistive technologies, and human-computer interaction. I develop and apply new techniques for predicting structured data. I was awarded a Ph.D. in Machine Learning from Carnegie Mellon University in 2010.


Email: (Please see Frequently Asked Questions first)
Office: 3190H Daley Library (3rd Floor, North)
Lab: 4211 Science and Engineering Laboratories


CS 411: Artificial Intelligence I -- Spring 2018, Spring 2019
CS 401: Computer Algorithms I -- Fall 2014, Fall 2017
CS 412. Introduction to Machine Learning -- Fall 2015, Spring 2016, Fall 2016, Spring 2017
CS 491: Introduction to Machine Learning -- Spring 2013, Spring 2014, Spring 2015.
CS 594: Advanced Topics in Machine Learning: Structured Prediction -- Fall 2013.


Adversarial prediction: Approximating our training data and optimizing over the exact performance measure to provide greater flexibility for:
  • Learning under covariate shift (input distribution bias) and active learning;
  • Cost-sensitive classification and inductive optimization of univariate performance measures;
  • Learning to optimize for F-measure, discounted cumulative gain, and other multivariate performance measures; and
  • Structured prediction problems over sequences, trees, graphs, etc.
Inverse optimal control: Using maximum entropy structured prediction techniques to forecast future human behavior for intelligent robotics and vehicle navigation applications.

Awards and Honors

My research is supported by the following grants:

  • NSF CAREER (RI)-1652530: Adversarial Machine Learning for Structured Prediction
  • NSF EAGER (SCH)-1650900: The Virtual Assistant Health Coach: Summarization and Assessment of Goal-Setting Dialogues with Barbara Di Eugenio, Ben Gerber, Bing Liu, and Lisa Sharp
  • NSF IIS-1526379: Robust Optimization of Loss Functions with Application to Active Learning with Lev Reyzin
  • NSF III-1514126: Computational tools for extracting individual, dyadic, and network behavior from remotely sensed data with Tanya Berger-Wolf and Meg Crofoot (resources)
  • Future of Life Institute: Towards Safer Inductive Learning
  • NSF NRI-1227495: Purposeful Prediction: Co-robot Interaction via Understanding Intent and Goals (sub-contract) with Drew Bagnell, Martial Hebert, Anind Dey, Dieter Fox, Josh Tenenbaum

Best Paper Runner-Up, European Conference on Computer Vision (ECCV) 2012
Best Paper Nominee, International Conference on User Interfaces (IUI) 2012
Best Paper Award, International Conference on Machine Learning (ICML) 2011
Honorable Mention, Carnegie Mellon University School of Computer Science Dissertation Award 2011
Best Student Paper Runner-Up, International Conference on Machine Learning (ICML) 2010


(See Google Scholar for an up-to-date listing.)

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