René Vidal

René Vidal
  • Rachleff and Penn Integrates Knowledge University Professor
  • Director of the Center for Innovation in Data Engineering and Science (IDEAS)

Contact Information

  • office Address:

    Levine Hall 4th Floor
    University of Pennsylvania
    3330 Walnut Street
    Philadelphia, PA 19104-6228

Research

Teaching

Current Courses

  • CIS9999 - Independent Study Research

    For Computer and Information Science doctoral students studying a specific advanced subject area. Students should discuss with the faculty supervisor the scope of the independent study/research and know the expectations and work involved.

    CIS9999077

  • ESE6450 - Deep Generative Models

    Generative models have found widespread applications in science and engineering. Recent progress in deep learning has enabled the application of generative models to complex high-dimensional data such as images, videos, text and speech. This course will cover state-of-the-art deep generative models, including variational autoencoders (VAEs), auto-regressive models, diffusion models, and generative adversarial networks (GANs). The course will also illustrate various applications of deep generative models to image and video generation, text and speech generation, image captioning, text-to-image generation, and inverse problems.

    ESE6450001

  • ESE9999 - Independent Study Research

    For Electrical and Systems Engineering doctoral students studying a specific advanced subject area. Students should discuss with the faculty supervisor the scope of the independent study/research and know the expectations and work involved.

    ESE9999020

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.

  • AMCS9950 - Dissertation

    Allows for a PhD student to be enrolled full-time to work exclusively on research, writing and preparing his/her doctoral thesis and defense. All required coursework (20 CUs) must be completed, and the student must have passed his/her thesis proposal/oral candidacy examination prior to being enrolled.

  • AMCS9990 - Masters Thesis

    For students writing a Master's Thesis to fulfill the program's requirements. All required coursework (8 CUs) must be completed prior to being enrolled.

  • AMCS9999 - Ind Study & Research

    Study under the direction of a faculty member.

  • CIS0099 - Ugrad Resrch/Ind Study

    An opportunity for the student to become closely associated with a professor (1) in a research effort to develop research skills and techniques 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 must submit a detailed proposal, signed by the independent study supervisor, to the SEAS Office of Academic Programs (111 Towne) no later than the end of the "add" period. Prerequisite: A maximum of 2 c.u. of CIS 0099 may be applied toward the B.A.S. or B.S.E. degree requirements.

  • 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.

  • CIS9990 - Master's Thesis

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

  • CIS9999 - Independent Study Research

    For Computer and Information Science doctoral students studying a specific advanced subject area. Students should discuss with the faculty supervisor the scope of the independent study/research and know the expectations and work involved.

  • ESE6450 - Deep Generative Models

    Generative models have found widespread applications in science and engineering. Recent progress in deep learning has enabled the application of generative models to complex high-dimensional data such as images, videos, text and speech. This course will cover state-of-the-art deep generative models, including variational autoencoders (VAEs), auto-regressive models, diffusion models, and generative adversarial networks (GANs). The course will also illustrate various applications of deep generative models to image and video generation, text and speech generation, image captioning, text-to-image generation, and inverse problems.

  • ESE6800 - Special Topcs in Ese

    Advanced and specialized topics in both theory and application areas. Students should check Graduate Group office for offerings during each registration period.

  • ESE9990 - Master's Thesis

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

  • ESE9999 - Independent Study Research

    For Electrical and Systems Engineering doctoral students studying a specific advanced subject area. Students should discuss with the faculty supervisor the scope of the independent study/research and know the expectations and work involved.

  • STAT9950 - Dissertation

    Dissertation

  • STAT9999 - Independent Study

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

In the News

Knowledge @ Wharton

Activity

Latest Research

Kwan Ho Ryan Chan, Yuyan Ge, Edgar Dobriban, Seyed Hamed Hassani, Rene Vidal Conformal Information Pursuit for Interactively Guiding Large Language Models.
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In the News

Oil, AI, and the Fed: What’s Next for Markets?

Jeremy Siegel, Wharton emeritus professor of finance, examines interest rates, energy markets, AI spending, tariffs, and the economic forces shaping investor outlook.…Read More

Knowledge @ Wharton - 2026/07/31
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