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From Data to Decisions: On Learning, Prediction, and Action in the Open World

Wednesday, February 27, 2013 - 4:00pm to 5:00pm
MIT CSAIL, Stata Center, Bldg 32 Kiva Conference Room, 4th Floor, G449
Speaker Name: 
Eric Horvitz, Microsoft

A confluence of advances has led to an inflection in our ability to collect, store, and harness large amounts of data to make predictions and guide decision making.  After discussing recent developments and trends in machine learning, I will present several representative efforts on learning and inference, including projects that have transitioned from the research lab into the open world.  I will first describe work to build and deploy predictive models that infer and forecast traffic flows in greater city regions. Then, I will present research on learning and fielding predictive models in healthcare.  Finally, I will review efforts to glean insights from large stores of behavioral data, covering projects that leverage anonymized streams of data gleaned from cell towers, search engines, and social media. 

Eric Horvitz is a Distinguished Scientist at Microsoft, where he serves as co-director of Microsoft Research's main Redmond lab. His research interests span theoretical and practical challenges with developing systems that perceive, learn, and reason. His contributions include advances in principles and applications of machine learning and inference, information retrieval, human-computer interaction,bioinformatics, and e-commerce. He has been elected a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and of the American Association for the Advancement of Science (AAAS), and was elected to the ACM CHI Academy in 2013. He currently serves on the NSF Computer & Information Science & Engineering (CISE) Advisory Board and on the council of the Computing Community Consortium (CCC). He received his PhD and MD degrees at Stanford University.