Thorsten Joachims Research Group

Cornell University

Clicks, choices, and text edits contain learning signals—but they also carry bias. Thorsten Joachims and his students develop machine-learning methods for using that feedback in search, recommendation, education, and language models. Their work connects counterfactual evaluation, policy learning, ranking, and personalization. POTEC tackles learning from logged decisions when the action space is large; coactive learning studies how a user’s edits can help personalize an LLM without requiring a perfect reference answer. This faculty-led research group offers a route into both the theory of interactive learning and its application to human-centered systems.