Sky Cao

Sky Cao
  • Assistant Professor of Statistics and Data Science

Contact Information

  • office Address:

    315 Academic Research Building
    265 South 37th Street
    Philadelphia, PA 19104

Research Interests: Probability, analysis, stochastic geometric analysis, Yang-Mills, Markov chain monte carlo

Links: Personal Website

Teaching

Current Courses (Fall 2026)

  • AMCS6481 - Probability Theory

    Measure theoretic foundations, laws of large numbers, large deviations, distributional limit theorems, Poisson processes, random walks, stopping times.

    AMCS6481401

  • MATH6480 - Probability Theory

    Measure theoretic foundations, laws of large numbers, large deviations, distributional limit theorems, Poisson processes, random walks, stopping times.

    MATH6480401

  • STAT9300 - Probability Theory

    Measure theoretic foundations, laws of large numbers, large deviations, distributional limit theorems, Poisson processes, random walks, stopping times.

    STAT9300401 ( Syllabus )

All Courses

  • AMCS6481 - Probability Theory

    Measure theoretic foundations, laws of large numbers, large deviations, distributional limit theorems, Poisson processes, random walks, stopping times.

  • MATH6480 - Probability Theory

    Measure theoretic foundations, laws of large numbers, large deviations, distributional limit theorems, Poisson processes, random walks, stopping times.

  • MATH8100 - Reading Seminar

    Reading of mathematical literature under the direction of a faculty member in a group of students. Hours and syllabus to be arranged with the supervising faculty member

  • STAT9300 - Probability Theory

    Measure theoretic foundations, laws of large numbers, large deviations, distributional limit theorems, Poisson processes, random walks, stopping times.

  • STAT9915 - 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.

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