Eric Horvitz

Director, Microsoft Research

Focusing on the vibrance and vitality of Microsoft Research and its impact at Microsoft and across the greater research community. Pursuing principles of computational intelligence. Exploring opportunities to leverage the complementarities of human and machine reasoning. Passionate about harnessing computing advances to enhance the quality of peoples' lives.

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Research overview

I've long been curious about the computational foundations of intelligence: How do our minds work? What computational principles and architectures underly thinking and intelligent behavior? I've pursued answers via studies of machine perception, learning, reasoning, and decision making. Many questions remain unanswered and much research is to be done. On the way to a deeper understanding, I work with colleagues to field working systems that can immediately deliver value in the world. Projects include efforts in time-critical decisions, information retrieval, healthcare, urban infrastructure, sustainability, and development--with goals of understanding how computational models perform amidst real-world complexities, and of deploying systems that deliver value to people and society. A key focus of my work has been on opportunities to leverage the complementarities of human and machine intelligence. Related interests include machine learning and decision making for crowdsourcing and human computation, information triage and alerting that takes human attention into consideration, spanning work on notification systems, surprise modeling, multitasking, and psychological studies of interruption and recovery. On the more theoretical front, I've been long interested in offline and real-time optimization of the expected value of computational systems under limited and varying resources. Areas of concentration in this realm include flexible or anytime computation, ideal metareasoning for guiding computation, compilation for reducing real-time deliberation, ongoing, continual computation, and the construction of bounded-optimal reasoning systems--systems that maximize the expected utility of their behavior in an environment, given the expected costs of reasoning, the problems encountered over time, and assertions about a system's constitution. Research in this arena includes tackling hard reasoning problems with learning and decision making methods.


Serving on the scientific advisory boards of the Allen Institute for AI (AI2) and the Simons Institute, on the National Library of Medicine (NLM) Board of Regents, the Computer Science and Telecommunications Board (CSTB), the Ford Technical Advisory Committee, the National Security Commission on AI (NSCAI), the External Advisory Board of the Center for Causal Discovery (CCD), and Board of Sponsors of the Bulletin of the Atomic Scientists. In the realm of influences of AI on people and society, co-founder of the One Hundred Year Study on AI (AI100) and a project it spawned, the AI Index. Serving as founding chair of the Partnership on AI. Prior roles include service as president of the Association for the Advancement of Artificial Intelligence (AAAI), chair of the AAAS Section on Information, Computing, and Communication, and on the advisory board of NSF's Computer and Information Science and Engineering (CISE) Directorate, the Naval Research Advisory Committee (NRAC), and DARPA's Information Science & Technology (ISAT) Study Group.

Elected fellow of the AAAI, ACM, AAAS, National Academy of Engineering (NAE), American Academy of Arts and Sciences, American Philosophical Association, and American College of Medical Informatics (ACMI), and inducted into the CHI Academy.

Blast to the relevant past

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