Scholar
Steve Hanneke
Google Scholar ID: fEhNO7YAAAAJ
Purdue University
Citations & Impact
Citations
3,045
H-index
25
i10-index
59
Publications
20
Co-authors
0
Contact
Publications
Resume (English only)
Academic Achievements
- Paper 'Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games' was runner up for the COLT 2021 Best Paper Award; 'Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound' received the ALT 2021 Best Paper Award; 'Proper Learning, Helly Number, and an Optimal SVM Bound' won the Best Paper Award at COLT 2020; 'VC Classes are Adversarially Robustly Learnable, but Only Improperly' received a Best Student Paper Award at COLT 2019; 'A Bound on the Label Complexity of Agnostic Active Learning' received Honorable Mention for the ICML 2017 Test of Time Award; Program Committee Chair for multiple international conferences.
Research Experience
- Currently an Assistant Professor in the Department of Computer Science at Purdue University; Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC) from 2018 to 2021; Independent scientist working in Princeton from 2012 to 2018, with a brief stint as a Visiting Lecturer at Princeton University in 2018; Visiting Assistant Professor in the Department of Statistics at Carnegie Mellon University from 2009 to 2012.
Education
- PhD in Machine Learning from Carnegie Mellon University in 2009, co-advised by Eric Xing and Larry Wasserman; B.S. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) from 2002 to 2005.
Background
- Research interests: Statistical analysis of machine learning, specifically what can be learned from empirical observation/experimentation and how much observation/experimentation is necessary and sufficient to learn it. Areas of expertise include statistical learning theory, artificial intelligence, statistical inference, etc.
Co-authors