J. Michael Steele

J. Michael Steele
  • C.F. Koo Professor Emeritus of Statistics

Contact Information

Research Interests: applications of probability, mathematical finance, modeling of price processes, statistical modeling

Links: Personal Website

Overview

Education

PhD, Stanford University, 1975
BA, Cornell University, 1971

Career and Recent Professional Awards

President, Institute for Mathematical Statistics, 2010
Fellow, Institute for Mathematical Statistics, 1984
Fellow, American Statistical Association, 1989
Frank Wilcoxon Prize, American Society for Quality Control and the American Statistical Association, 1990

Academic Positions Held

Wharton: 1990-present (named C.F. Koo Professor, 1991).

Previous appointments: Princeton University; Carnegie Mellon University; Stanford University; University of British Columbia.

Visiting appointments: University of Chicago, Columbia University

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Research

  • V. Pozdnyakov and J. Michael Steele, “Scan Statistics: Pattern Relations and Martingale Methods”. In Handbook of Scan Statistics, edited by (J. Glaz, et al), Springer Verlag, (2019)

  • A. Arlotto and J. Michael Steele (2018), A Central Limit Theorem for Costs in Bulinskaya’s Inventory Management Problem When Deliveries Face Delays, Methodology and Computing in Applied Probability: Special Issue in Memory of Moshe Shaked, 41 (4), pp. 1448-1468.

  • J. Michael Steele (2016), The Bruss-Robertson Inequality: Elaborations, Extensions, and Applications, Mathematica Applicanda (Annales Societatis Mathematicae Polonae Series III), 44 (1), pp. 3-16.

  • A. Arlotto and J. Michael Steele (2016), A Central Limit Theorem for Temporally Non-Homogenous Markov Chains with Applications to Dynamic Programming, Mathematics of Operations Research, 41 (4), pp. 1448-1468.

  • Peichao Peng and J. Michael Steele (2016), Sequential Selection of a Monotone Subsequence from a Random Permutation, Proceedings of the American Mathematics Society, 144 (11), pp. 4973-4982.

  • A. Arlotto, Elchanan Mossel, J. Michael Steele (2016), Quickest Online Selection of an Increasing Subsequence of Specified Size, Random Structures and Algorithms, 49, pp. 235-252.

  • A. Arlotto and J. Michael Steele (2016), Beardwood-Halton-Hammersly Theorem for Stationary Ergodic Sequences: a Counter-example, Annals of Applied Probability, 26 (4), pp. 2141-2168.

  • V. Posdnyakov and J. Michael Steele (2016), Buses, Bullies, and Bijections, Mathematics Magazine, 89 (3), pp. 167-176.

  • S. Bhamidi, J. Michael Steele, T. Zaman (2015), Twitter Event Networks and the Superstar Model, Annals of Applied Probability, 25 (5), pp. 2462-2502.

  • A. Arlotto, V. Nguyen, J. Michael Steele (2015), Optimal Online Selection of a Monotone Subsequence: A Central Limit Theorem, Stochastic Processes and their Applications, 125, pp. 3596-3622.

Teaching

All Courses

  • CIS8990 - PhD Independent Study

    For doctoral students studying a specific advanced subject area in computer and information science. The Independent Study may involve coursework, presentations, and formally gradable work comparable to that in a CIS 5000 or 6000 level course. The Independent Study may also be used by doctoral students to explore research options with faculty, prior to determining a thesis topic. Students should discuss with the faculty supervisor the scope of the Independent Study, expectations, work involved, etc. The Independent Study should not be used for ongoing research towards a thesis, for which the CIS 9990 designation should be used.

  • MATH5460 - Adv Applied Probability

    The required background is (1) enough math background to understand proof techniques in real analysis (closed sets, uniform covergence, fourier series, etc.) and (2) some exposure to probability theory at an intuitive level (a course at the level of Ross's probability text or some exposure to probability in a statistics class).

  • STAT4330 - Stochastic Processes

    An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes.

  • STAT5330 - Stochastic Processes

    An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes.

  • STAT9550 - Stoch Cal & Fin Appl

    Selected topics in the theory of probability and stochastic processes.

  • STAT9910 - Sem in Adv Appl of Stat

    This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

  • STAT9950 - Dissertation

  • STAT9990 - Independent Study

    Written permission of instructor and the department course coordinator required to enroll.

Awards and Honors

  • Wharton Undergraduate Excellence in Teaching Award, 2010
  • Frank Wilcoxon Prize, American Society for Quality Control and the American Statistical Association, 1990
  • Fellow, American Statistical Association, 1989
  • Fellow, Institute for Mathematical Statistics, 1984

Activity

Latest Research

V. Pozdnyakov and J. Michael Steele, “Scan Statistics: Pattern Relations and Martingale Methods”. In Handbook of Scan Statistics, edited by (J. Glaz, et al), Springer Verlag, (2019)
All Research