Jason Altschuler

Jason Altschuler
  • Assistant Professor of Statistics and Data Science
  • Assistant Professor of Computer and Information Science (secondary appointment)
  • Assistant Professor of Electrical and Systems Engineering (secondary appointment)

Contact Information

  • office Address:

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

Research Interests: optimization, probability, machine learning, mathematics of data science, optimal transport

Links: Personal Website, UPenn Optimization Seminar

Overview

Education

Ph.D. in Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2022

MS in Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2018

B.S. in Computer Science, Princeton University, 2016

Academic Positions Held

Faculty Fellow, New York University, 2022-2023

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Teaching

Current Courses (Fall 2024)

  • 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.

    STAT9910302 ( Syllabus )

All Courses

  • AMCS5999 - Independent Study

    Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.

  • AMCS9999 - Ind Study & Research

    Study under the direction of a faculty member.

  • STAT4300 - Probability

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

  • 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

    Dissertation

  • STAT9990 - Independent Study

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

Awards and Honors

  • George M. Sprowls Award for Best MIT PhD Thesis in Artificial Intelligence & Decision Making, 2022-2023
  • Two Sigma PhD Fellowship, 2020-2022
  • NSF Graduate Fellowship, 2016-2021
  • James Hayes-Edgar Palmer Prize in Engineering, Princeton University, 2016
  • Accenture Prize in Computer Science, Princeton University, 2015

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