Aaron Roth

Aaron Roth
  • Henry Salvatori Professor of Computer and Cognitive Science
  • Professor of Statistics and Data Science

Contact Information

  • office Address:

    Room 406B, 3401 Walnut Street, Philadelphia, PA 19104

Teaching

Current Courses

  • CIS8990 - Doctoral Independent Study

    CIS8990034

  • CIS9990 - Thesis/dissertation Research

    For students working on an advanced research program leading to the completion of master's thesis requirements.

    CIS9990085

  • NETS4120 - Algorithmic Game Theory

    How should an auction for scarce goods be structured if the sellers wish to maximize their revenue? How badly will traffic be snarled if drivers each selfishly try to minimize their commute time, compared to if a benevolent dictator directed traffic? How can couples be paired so that no two couples wish to swap partners in hindsight? How can you be as successful as the best horse-racing expert at betting on horse races, without knowing anything about horse racing? In this course, we will take an algorithmic perspective on problems in game theory, to solve problems such as the ones listed above. Game theory has applications in a wide variety of settings in which multiple participants with different incentives are placed in the same environment, must interact, and each "player"'s actions affect the others.

    NETS4120001

Past 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.

  • CIS2620 - Automata,Comput.& Complx

    This course explores questions fundamental to computer science such as which problems cannot be solved by computers, can we formalize computing as a mathematical concept without relying upon the specifics of programming languages and computing platforms, and which problems can be solved efficiently. The topics include finite automata and regular languages, context-free grammars and pushdown automata, Turing machines and undecidability, tractability and NP-completeness. The course emphasizes rigorous mathematical reasoning as well as connections to practical computing problems such as test processing, parsing, XML query languages, and program verification.

  • CIS3200 - Intro To Algorithms

    How do you optimally encode a text file? How do you find shortest paths in a map? How do you design a communication network? How do you route data in a network? What are the limits of efficient computation? This course gives a comprehensive introduction to design and analysis of algorithms, and answers along the way to these and many other interesting computational questions. You will learn about problem-solving; advanced data structures such as universal hashing and red-black trees; advanced design and analysis techniques such as dynamic programming and amortized analysis; graph algorithms such as minimum spanning trees and network flows; NP-completeness theory; and approximation algorithms.

  • CIS5990 - Master's Indep Study

    For master's students studying a specific advanced subject area in computer and information science. Involves coursework and class presentations. A CIS 5990 course unit will invariably include formally gradable work comparable to that in a CIS 500-level course. Students should discuss with the faculty supervisor the scope of the Independent Study, expectations, work involved, etc.

  • CIS6200 - Adv Top in Mach Learning

    This course covers a variety of advanced topics in machine learning, such as the following: statistical learning theory (statistical consistency properties of surrogate loss minimizing algorithms); approximate inference in probabilistic graphical models (variational inference methods and sampling-based inference methods); structured prediction (algorithms and theory for supervised learning problems involving complex/structured labels); and online learning in complex/structured domains. The precise topics covered may vary from year to year based on student interest and developments in the field.

  • CIS7000 - Cis-Topics

    One time course offerings of special interest. Equivalent to a CIS 5XX level course.

  • CIS9990 - Master's Thesis

    For students working on an advanced research program leading to the completion of master's thesis requirements.

  • ESE0099 - 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.

  • NETS4120 - Algorithmic Game Theory

    How should an auction for scarce goods be structured if the sellers wish to maximize their revenue? How badly will traffic be snarled if drivers each selfishly try to minimize their commute time, compared to if a benevolent dictator directed traffic? How can couples be paired so that no two couples wish to swap partners in hindsight? How can you be as successful as the best horse-racing expert at betting on horse races, without knowing anything about horse racing? In this course, we will take an algorithmic perspective on problems in game theory, to solve problems such as the ones listed above. Game theory has applications in a wide variety of settings in which multiple participants with different incentives are placed in the same environment, must interact, and each "player"'s actions affect the others.

  • PPE3999 - Independent Study

    Student arranges with a faculty member to pursue a research project on a suitable topic. For more information about research and setting up independent studies, visit: https://ppe.sas.upenn.edu/study/curriculum/independent-studies

  • STAT9950 - Dissertation

    Dissertation

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