Leadership
René Vidal, Rachleff University Professor in the Department of Electrical and Systems Engineering in Penn Engineering and in the Department of Radiology in the Perelman School of Medicine, is the IDEAS Center’s inaugural director. Vidal’s own research focuses on the mathematical foundations of deep learning and its applications in computer vision and biomedical data science.
For General inquiries
René Vidal
Director of the Innovation in Data Engineering and Science Initiative
Faculty Recruitment
Thanks to a major $750 million investment in science, engineering and medicine, IDEAS is engaged in an aggressive hiring effort for multiple tenured or tenure-track faculty positions in Artificial Intelligence (AI), Machine Learning (ML) and Data Science (DS) with an emphasis in the following research areas:
AI Foundations
Advancing core AI/ML/DS methodology, including but not limited to generative AI, multimodal AI (e.g., vision language 3D), and the mathematical foundations of AI/ML/DS.
AI in Science
Data-driven approaches that can transform scientific discovery and modeling of new phenomena across engineering and science, including but not limited to integration of physical and data-driven models, inverse problems in imaging science, novel AI methods for discovering new materials, learning the structure of proteins or modeling global climate, new paradigms for bridging the gap between human and machine learning.
AI in Health
Data-driven approaches for analyzing complex multimodal biological and biomedical data, including but not limited to novel methods for learning from biological images, medical images, radiological and pathological reports, electronic health records, multiomic data.
Trustworthy AI
Design and engineering of fair, ethical, explainable, robust, safe, and trustworthy autonomous systems.
Accepting applications
Penn AI x Science Postdoctoral Fellows Program
In response to the rapid evolution of the AI landscape and the increasing importance of interdisciplinary collaboration, we are launching a unified postdoctoral fellows program that spans the sciences and AI. The program aims to seamlessly integrate the strengths of the Center for Innovation in Data Engineering and Science (IDEAS) and the Data Driven Discovery Initiative (DDDI) into a single home for data science postdoctoral fellows in SEAS and SAS. Both engineering and science oriented postdocs will benefit from sustained peer interactions within the program, broad exposure to faculty across schools, and opportunities for cross-school faculty mentorship.
Amy Gutmann Hall
Opening Fall 2024
Amy Gutmann Hall is slated to be the new home for data engineering and science at Penn, and will serve as a hub for cross-disciplinary collaborations that harness research and data across Penn’s 12 schools and numerous academic centers. Including active learning classrooms, collaborative spaces for student projects, and a data science hub for the entire Penn community, Amy Gutmann Hall will centralize resources that will advance the work of scholars across a wide variety of fields while making the tools and concepts of data analysis more accessible to the entire Penn community.
Learn more about how Amy Guttman Hall will serve as a hub for cross-disciplinary collaborations.
Data Science Hub
The IDEAS Data Science Hub will focus on infusing data-driven approaches into every discipline and sparkling new collaborations with academia, industry, and the next generation of data scientists in Philadelphia’s public schools.
News
Events
- ASSET Seminar: Yao Fu (University of Edinburgh)
- Herman P. Schwan Distinguished Lecture: “Engineering Proteins, Genomes, Viruses & Organs” (George Church, Harvard & MIT)
- Spring 2025 GRASP on Robotics: Ming C. Lin, University of Maryland at College Park, “Dynamics-Aware Learning: from Simulated Reality to Physical World”
- MEAM Seminar: “Flow Architectures, From Capillary Networks to Blood Transport through Organs”
- Spring 2025 GRASP on Robotics: Jan Peters, Technische Universität Darmstadt & German Research Center for Artificial Intelligence, “Inductive Biases for Robot Reinforcement Learning”