Thorsten Joachims

Thorsten Joachims is the Jacob Gould Schurman Professor of Computer Science and Information Science at Cornell University's Ann S. Bowers College of Computing and Information Science, where he serves as Interim Dean. He has held significant leadership roles including Associate Dean for Research and Chair of the Department of Information Science. His academic journey began with a Diplom in Computer Science from the University of Dortmund in 1997, followed by a PhD from the same institution in 2001. Before joining Cornell, he was a PostDoc at GMD's Institute for Autonomous Intelligent Systems and spent time as a visiting scholar at Carnegie Mellon University.

Joachims' research focuses on machine learning methods and theory, particularly learning from human behavioral data and implicit feedback, and machine learning applications for search engines, recommendation systems, education, and other human-centered tasks. His work bridges theoretical foundations with practical applications, emphasizing counterfactual evaluation and learning, fairness in AI, ranking algorithms, and bandit methods. His research has significant implications for how we build AI systems that interact with humans in meaningful ways while maintaining ethical standards.

The 15 most recent publications demonstrate a strong trajectory toward increasingly complex AI systems, with recent work focusing on large language models, multi-scale learning for long-term objectives, fairness in ranking under uncertainty, and novel approaches to recommendation systems using language-based user profiles. His research group consistently publishes in top venues across machine learning, information retrieval, and AI ethics.

• ACM Fellow
• AAAI Fellow
• Humboldt Fellow
• SIGIR Academy Member
• 11 Best Paper Awards
• 4 Test-of-Time Awards

Professor Joachims has advised over 25 PhD students and postdocs who have gone on to successful careers in academia and industry. His research has been supported by multiple NSF grants, including projects on counterfactual learning, fairness in ranking, and AI systems with steerable long-term dynamics. He has been instrumental in establishing Cornell's AI Initiative and Radical Collaboration, positioning the university at the forefront of responsible AI development.

His laboratory work focuses on developing machine learning systems that can learn effectively from human behavioral data while addressing critical issues of fairness, transparency, and long-term impact. The group maintains several open-source software projects including Propensity SVM-Rank, POEM, and SVM-struct that have been widely adopted in both research and industry.