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MIDAS Seminar Series Presents: Paul Bennett – Microsoft
November 16, 2020 @ 4:00 pm - 5:00 pm
Partner Research Manager, Microsoft
Robust and Transparent AI in Search and Recommendation
AI models power many search and recommendation tasks in many different domains including the web, enterprise, and e-commerce applications. Within these scenarios as well as other applications, there are key challenges to providing AI that is robust in the context of changing inputs or policies and in how results are surfaced to the people using the system. In this talk, I will overview recently published advances on three key challenges in these domains made by Microsoft researchers and our collaborators. In particular, how can we effectively customize models using few-shot learning and RL to new tasks or applications given very little labeled data; how can we learn more robust models in the presence of skewed data distributions or underlying toolchain nondeterminism, and how can we design UX interventions that improve the transparency to how the underlying AI system behaves? I will then conclude the talk with thoughts on future directions and opportunities.
Paul Bennett has been a researcher at Microsoft Research since 2006 where he is now Partner Research Manager for the Productivity + Intelligence group in Microsoft Research. His published research has focused on a variety of topics surrounding the use of machine learning in information retrieval – including deep learning for ranking and retrieval, ensemble methods and the combination of information sources, calibration, consensus methods for noisy supervision labels, active learning and evaluation, supervised classification and ranking, crowdsourcing, behavioral modeling and analysis, and personalization. Some of his work has been recognized with awards at SIGIR, CHI, ECIR, and ACM UMAP. Prior to joining MSR in 2006, he completed his dissertation in the Computer Science Department at Carnegie Mellon with Jaime Carbonell and John Lafferty. While at CMU, he also acted as the Chief Learning Architect on the RADAR project from 2005-2006 while a postdoctoral fellow in the Language Technologies Institute.