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Data Science and Machine Learning in Context

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Data driven approaches are providing unprecedented opportunities to predict, recommend, cluster, classify, transform, plan, and optimize. Catalyzed by advances in statistics, operations research, and particularly computing (e.g., machine learning), data driven techniques hold great promise in all fields of human endeavor. However, no technologies arrive without complications, and there are increasing societal concerns with their real, potential, and fictional implications.

Based in part on our (Spector, Norvig, Wiggins, Wing) recently published textbook, Data Science in Context (Cambridge Univ. Press), this talk proposes a rubric that can serve as a practical framework for guiding the specification and implementation of data-driven applications. It further describes the associated (seven) categories of challenges, and it discusses the needed role of ethics, economics, and political science as data-driven technologies have rapidly increasing impact. The talk is illustrated with many examples, which collectively serve to make its structural hypotheses more concrete.


Dr. Alfred Spector is a Visiting Scholar at MIT whose career began with innovation in large scale, networked computing systems (at Stanford, as a professor at CMU , and as founder of Transarc) and then transitioned to research leadership (first leading IBM Software Research, subsequently Google Research, and then as CTO of Two Sigma Investments). In addition to his managerial career, Dr. Spector has lectured widely on the growing importance of computer science across all disciplines (“CS+X”), and he has recently completed a book entitled Data Science in Context: Foundations, Challenges, and Opportunities (Spector, Norvig, Wiggins, and Wing; Cambridge University Press; 2022). He is a Fellow of the ACM and IEEE , and he is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, where he serves on its Council. Dr. Spector won the 2001 IEEE Kanai Award for Distributed Computing, was co-awarded the 2016 ACM Software Systems Award, and was a Phi Beta Kappa Visiting Scholar. He received a Ph.D. in Computer Science from Stanford (as a Hertz Fellow) and an A.B. in Applied Mathematics from Harvard.

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