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Discovering the Future of AI

Penn AI is pleased to announce the first four awardees for the  Discovering the Future of AI awards. Fifty-four competitive applications were submitted in response to the request for proposals, representing creative and bold ideas in research and education across Penn’s schools.

In addition to the four awards totaling $450,000, an additional 31 faculty applicants representing eleven schools received awards for high performance computing needs supported by the Penn Advanced Research Computing Center  (PARCC)  for an estimated value of $852,000, bringing the total support to $1.3 million. Access to high-performance computing enables Penn researchers to run state-of-the-art AI models, analyze far larger datasets, and pursue bold, high-risk ideas without financial constraints.

The Discovering the Future award is designed to catalyze high-risk, high-reward research and education at the intersection of artificial intelligence and domain scholarship for the benefit of society.

We congratulate the following four awardees:

CASPER4D: Computer Assisted Surgical Performance Evaluation via Reconstruction

Daniel Hashimoto

Assistant Professor of Surgery, Perelman School of Medicine

(in collaboration with School of Engineering and Applied Sciences)

Website

CASPER4D is a collaborative research project using AI to reconstruct four-dimensional surgical environments from standard video to assess skill, predict risk, and improve patient outcomes.

 

The Penn AI Pedagogy Initiative: Building Capacity for Meaningful and Responsible Adoption at Scale

Seiji Isotani

Associate Professor, Graduate School of Education

(in collaboration with School of Arts & Sciences)

Website

The Penn AI Pedagogy Initiative supports faculty and students in co-designing AI-enhanced teaching practices, building a scalable framework for responsible AI adoption in education.

 

Molecule 3D Structure Informed Science Agentic LLM

Cesar de la Fuente

Presidential Associate Professor, Perelman School of Medicine (Microbiology)

(in collaboration with School of Engineering and Applied Sciences)

Website

ApexMol will integrate language and 3D molecular structure to enable AI systems to reason about and design new biomolecules, accelerating discovery.

 

EchoMFM: A Multimodal Foundation Model for Automated Clinical Interpretation of Echocardiograms

Julio Chirinos Medina

Professor, Perelman School of Medicine (Cardiovascular Medicine)

Website

EchoMFM will integrate imaging, EHR, reports, ECG, and MRI data to generate draft clinical interpretations and improve diagnostic efficiency.

Teacher and student experimenting with drones

Program Summary

The Discovering the Future of AI grants program provides faculty with critical resources to pursue paradigm-shifting research and education in AI and its applications. The goal is to foster synergies that pair the latest advances in AI with novel applications across disciplines to unlock new frontiers of discovery. This call will provide funding for one year (see below for details) but proposals that lead to successful outcomes, especially ones with broader impact at Penn, will be eligible for additional funding in subsequent years. A key goal of this program is to foster genuine collaboration between AI experts and domain experts. We strongly encourage proposals from all disciplines and schools.

Program Information

Proposals led jointly by two Co-Principal Investigators (Co-PIs) from different schools are particularly encouraged: one should be a core AI/ML faculty member, and the other a faculty expert in the relevant application domain. Proposals must be aligned with one or more of the strategic research thrusts of PennAI:

  • AI Foundations: Understanding the fundamental principles behind existing AI algorithms and developing the next generation of AI algorithms.
  • AI + Business: Exploring how AI will reshape industries, economies, and the future of work itself.
  • AI + Education: Developing educational programs or innovative in-class teaching (undergraduate or graduate) that leverage AI tools to transform the educational experience and/or advance Penn’s leadership in AI education.
  • AI + Health: Revolutionizing healthcare through AI-driven diagnostics, personalized medicine, computational biology, and optimization of clinical care.
  • AI + Science: Applying AI to accelerate discovery in the natural sciences, from discovering new materials and modeling climate change to unraveling the mysteries of the human mind.
  • AI + Society: Investigating the societal impact of AI, building trustworthy and ethical algorithms, or using AI to create new knowledge in the humanities and social sciences.

Examples of support include, but are not limited to:

  • Funding for the development of novel AI models and experiments.
  • Funding for acquiring or creating unique datasets necessary for transformative research.
  • Funding for graduate students or postdoctoral fellows for complex data analysis and model development.
  • Applied research that tackles real-world problems, or develops new products, technologies, or policies, especially research that incorporates community partnerships.
  • Funding for the development of educational programs or innovative in-class teaching.

Limitation: One (1) application per faculty member.

Eligibility:

For any sponsored research projects, the applicant must be eligible to serve as Principal Investigator for the project, unless otherwise noted in this opportunity. Please see Penn’s PI Eligibility requirements to ensure you are eligible.

For any educational program or course, applicants must provide a detailed explanation of how AI will enhance the course, impact students, and advance Penn’s leadership in AI/ML education.

Award Information:

An unrestricted research award of up to $200,000 for one (1) year. Awards are for direct costs only.

Internal Selection Process:

  • The Office of the Vice Provost for Research and the Penn AI Council invite Penn Faculty to submit one (1) application for consideration. Applications must include the following:
    1. Cover Page (InfoReady will autogenerate) including:
      • Candidate’s name, academic rank, department, email address, phone number, and campus address.
      • Project title
      • Project type
        • AI Research
        • AI Education
      • Research projects specify one of the following categories (refer to the Penn AI website for details at: https://ai.upenn.edu/ai-penn)
        • AI Foundations
        • AI + Business
        • AI + Education
        • AI + Health
        • AI + Society
        • AI + Science
    2. Abstract (maximum 1 page)
    3. Project description: (maximum 4 pages, not including references; single-spaced, 12-point font with one-inch margins)
      • All projects must identify a second audience outside of the academic community. Who are the primary and secondary audiences of this project, and how will the research or new educational methods benefit them?
    4. Brief Project Budget (maximum 1 page)
    5. Course syllabus: (Optional, if not relevant to proposal) Provide the course syllabus and/or a detailed explanation of how the AI educational funding will change the course, impact students, or broaden Penn as the leader in AI/ML education.
    6. Curriculum Vitae (CV) (maximum 2 pages)