Professor
Computer Science, Information Science

Biography

Thorsten Joachims is a Professor in the Department of Computer Science and in the Department of Information Science at Cornell University. He joined Cornell in 2001 after finishing his Ph.D. as a student of Prof. Morik at the AI-unit of the University of Dortmund, from where he also received a Diplom in Computer Science in 1997. Between 2000 and 2001 he worked as aPostDoc at the GMD in the Knowledge Discovery Team of the Institute for Autonomous Intelligent Systems. From 1994 to 1996 he spent one and a half years at Carnegie Mellon University as a visiting scholar of Prof. Tom Mitchell. Working with his students and collaborators, his papers won 8 Best Paper Awards and 4 Test-of-Time Awards. Thorsten Joachims is an ACM Fellow, AAAI Fellow, and Humboldt Fellow.

Research Interests

• Machine Learning Methods and Theory
• Learning from Human Behavioral Data and Implicit Feedback
• Machine Learning for Search Engines, Recommendation, Education, and other Human-Centered Tasks

Teaching Interests

• CS4780/5780 Machine Learning. CS6784 Advanced Topics in Machine Learning.

Selected Publications

• Joachims, Thorsten, A. Swaminathan, T. Schnabel. 2017.“Unbiased Learning-to-Rank with Biased Feedback.”Paper presented at International Conferenc e on Web Search and Data Mining (WSDM), 2017. Best Paper Award.
• Schnabel, T., P. Bennett, S. Dumais, Thorsten Joachims. 2016.“Using Shortlists to Support Decision Making and Improve Recommender System Performance.”Paper presented at World Wide Web Conference (WWW)
• Chen, Shuo, Thorsten Joachims. 2016.“Modeling Intransitivity in Matchup and Comparison Data.”Paper presented at ACM Conference on Web Search and Data Mining (WSDM)
• Reddy, Siddharth , Igor Labutov, Thorsten Joachims. 2016.“Learning Student and Content Embeddings for Personalized Lesson Sequence Recommendation, Work in Progress.”Paper presented at ACM Conference on Learning at Scale (L@S)
• Schnabel, T., A. Swaminathan, A. Singh, N. Chandak, Thorsten Joachims. 2016.“Recommendations as Treatments: Debiasing Learning and Evaluation.”Paper presented at International Conference on Machine Learning (ICML)