Roman Garnett

Roman Garnett

Assistant Professor
Department of Computer Science and Engineering
Washington University in St. Louis

email: garnett at wustl dot edu
office: Jolley Hall 504
phone: (314) 935–4992
address: Washington University in St. Louis
One Brookings Drive, CB 1045
St. Louis, MO 63130


I joined the CSE faculty at Wash U in January 2015. Before arriving here, I was a postdoctoral researcher in the Knowledge Discovery and Machine Learning research group at the University of Bonn (2012–2014) and the Auton Lab at Carnegie Mellon University (2010–2012). I completed my PhD as a member of the Machine Learning Research Group at the University of Oxford in 2010, and received an AB and MSc from Washington University in St. Louis in 2004.

You can find my CV here.


My primary research interest is developing new Bayesian machine-learning methods for sequential decision making under uncertainty. I am especially interested in algorithms targeting scientific discovery, including:

  • active search, where we seek to locate novel members of a rare, valuable class. Active search is a model for many settings in scientific discovery, such as drug discovery. [1, 2, 3, 4, 5]
  • Bayesian optimization, where we seek to globally optimize an expensive objective function. Bayesian optimization has seen widespread successful use across a range of application domains, including machine learning, where, e.g. optimizing the architecture of a deep neural network represents an extraordinarily expensive objective. DeepMind recently used Bayesian optimization to learn architectures as part of AlphaGo Zero. [1, 2, 3, and many applications below]

I am also passionate about applications of machine learning in natural science and engineering, and have made contributions in areas including astrophysics [1, 2], drug discovery [1, 2], surface science [1, 2], sensor network design [1], and animal behavior [1, 2, 3].


You may find a list of my publications here, or may wish to refer to my Google Scholar profile.

Working with me

Thanks for your interest! Here is some advice for those seeking to work with me:

  • Aspiring Ph.D. students: please note that the WUSTL CSE department considers all admissions collectively in a single pool. Students are initially admitted without a graduate advisor assigned. You can find application instructions here.
  • Master's and undergraduate students at Wash U: if you are seeking to complete a master's thesis, a master's project, or an undergraduate independent study with me, I strongly suggest you take my course on Bayesian machine learning. I have successfully completed numerous projects with both undergraduate and graduate students who have taken the course, and these collaborations have led to several publications.
  • Undergraduate students at any institution: The CSE department has an active REU program! We are currently focusing on big data analytics, and I have been heavily involved since joining the department. Consider applying!


CSE 515T – Bayesian Methods in Machine Learning: Spring 2018 Spring 2017 Spring 2015
CSE 511A – Introduction to Artificial Intelligence: Fall 2017 Fall 2016 Fall 2015