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

Past Courses

  • CIS 899 - PHD INDEPENDENT STUDY

    For doctoral students studying a specific advanced subject area in computer and information science. The Independen t Study may involve coursework, presentations, and formally gradable work comparable to that in a CIS 500 or 600 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 999 designation should be used.

  • ESE 099 - INDEPENDENT STUDY

    An opportunity for the student to become closely associated with a professor in (1) a research effort to develop research skills and technique and/or (2) to develop a program of independent in-depth study in a subject area in which the professor and student have a common interest. The challenge of the task undertaken must be consistent with the student's academic level. To register for this course, the student and professor jointly submit a detailed proposal to the undergraduate curriculum chairman no later than the end of the first week of the term.

  • MATH546 - ADVANCED 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). After a summary of the necessary results from measure theory, we will learn the probabist's lexicon (random variables, independence, etc.). We will then develop the necessary techniques (Borel Cantelli lemmas, estimates on sums of independent random variables and truncation techniques) to prove the classical laws of large numbers. Next come Fourier techniques and the Central Limit Theorem, followed by combinatorial techniques and the study of random walks.

  • MATH547 - STOCHASTIC PROCESSES

    Markov chains, Markov processes, and their limit theory. Renewal theory. Martingales and optimal stopping. Stable laws and processes with independent increments. Brownian motion and the theory of weak convergence. Point processes.

  • STAT399 - INDEPENDENT STUDY

  • STAT430 - PROBABILITY

    Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.

  • STAT433 - 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.

  • STAT510 - PROBABILITY

    Elements of matrix algebra. Discrete and continuous random variables and their distributions. Moments and moment generating functions. Joint distributions. Functions and transformations of random variables. Law of large numbers and the central limit theorem. Point estimation: sufficiency, maximum likelihood, minimum variance. Confidence intervals.

  • STAT533 - 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.

  • STAT899 - INDEPENDENT STUDY

  • STAT930 - PROBABILITY

    Measure theory and foundations of Probability theory. Zero-one Laws. Probability inequalities. Weak and strong laws of large numbers. Central limit theorems and the use of characteristic functions. Rates of convergence. Introduction to Martingales and random walk.

  • STAT931 - STOCHASTIC PROCESSES

    Markov chains, Markov processes, and their limit theory. Renewal theory. Martingales and optimal stopping. Stable laws and processes with independent increments. Brownian motion and the theory of weak convergence. Point processes.

  • STAT955 - STOCH CAL & FIN APPL

    Selected topics in the theory of probability and stochastic processes.

  • STAT991 - 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.

  • STAT995 - DISSERTATION

  • STAT999 - INDEPENDENT STUDY

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

In the News

Knowledge @ Wharton

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

In the News

Polling the Polling Experts: How Accurate and Useful Are Polls These Days?

Turn on the Internet, pick up your telephone or cell phone, read a newspaper or watch television: No matter what the communication vehicle is, polls and the reporting of poll results are ubiquitous. Yet how accurate are polls? Can they be manipulated? How do the Internet and the proliferation of cell phone users affect both marketing and political polls? And which polls are the most reliable? Knowledge@Wharton interviewed the experts.

Knowledge @ Wharton - 2007/11/14
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Awards and Honors

Wharton Undergraduate Excellence in Teaching Award 2010
All Awards