From Data to Decisions: On Learning, Prediction, and Action in the Open World
Speaker: Eric Horvitz, Microsoft
Date: Wednesday, February 27 2013
Time: 4:00PM to 5:00PM
Location: 32-G449 (Patil/Kiva)
Host: Samuel Madden, CSAIL
Contact: Sheila Marian, x3-1996, email@example.comRelevant URL: http://research.microsoft.com/~horvitz.
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 Research. He co-directs the Microsoft Research-Redmond lab. His interests span theoretical and practical challenges with developing systems that perceive, learn, and reason, with a focus on inference and decision making under uncertainty and limited resources. He has been elected a Fellow of the Association for the Advancement of Artificial Intelligence, the American Academy of Arts and Sciences, and the National Academy of Engineering. He received PhD and MD degrees at Stanford University. More information about his research and collaborations can be found at http://research.microsoft.com/~horvitz.
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