Assistant Professor, Industrial Engineering
Research: Machine Learning and Large-scale Data Analysis, Artificial Intelligence (AI), Information Retrieval, Social Media, Recommender Systems, Sequential Decision Optimization, Operations Research, Smart Cities Applications.
Laboratory: Data-Driven Decision Making Lab (D3M)
- Information Engineering
Scott Sanner is an Assistant Professor in the Department of Mechanical & Industrial Engineering. Previously Scott was an Assistant Professor at Oregon State University and before that he was a Principal Researcher at National ICT Australia (NICTA) and Adjunct Faculty at the Australian National University. Scott earned a PhD in Computer Science from the University of Toronto (2008), an MS in Computer Science from Stanford University (2002), and a double BS in Computer Science and Electrical and Computer Engineering from Carnegie Mellon University (1999).
Scott's research spans a broad range of topics from the data-driven fields of Machine Learning and Information Retrieval to the decision-driven fields of Artificial Intelligence and Operations Research. Scott has applied the analytic and algorithmic tools from these fields to diverse application areas such as recommender systems, interactive text visualization, and Smart Cities applications including transport optimization.
Scott has served as Program Co-chair for the 26th International Conference on Automated Planning and Scheduling (ICAPS), member of the Editorial Board for the Artificial Intelligence Journal (AIJ) and the Machine Learning Journal (MLJ), and Electronic Editor for the Journal of Artificial Intelligence Research (JAIR). Scott was a co-recipient of the 2014 AIJ Prominent Paper Award.