Margot and Tom Pritzker Prize for AI in Science Research Excellence
Established in 2024, with the generous support of the Margot and Tom Pritzker Foundation, the Margot and Tom Pritzker Prize for AI in Science Research Excellence recognizes outstanding contributions that jointly advance AI and the natural sciences or engineering. The prize is based on the quality and impact of the research. The prize may be awarded for a single notable achievement or for a collection of such achievements.
The Margot and Tom Pritzker Prize for AI in Science Research Excellence includes two grand prizes of $50,000.
Recipients will be reimbursed for reasonable travel expenses incurred in attending the prize ceremony during the University of Chicago and Caltech Conference on AI+Science.
Awardee must be a tenured or tenure-track academic based in a US institution of higher education.
Nominations are solicited from the academic community. Nomination should include:
Nomination statement (maximum 1000 words) addressing why the candidate should receive this prize and the candidate's impact on the broader community. Nominators must indicate in the nomination statement how the candidate consistently exemplifies moral, ethical, and professional conduct. The statement must include the nominator's name and email address.
Supporting letters from four endorsers. Endorsers should provide additional insights or evidence of the candidate's impact. Each letter must include the endorser's name and email address, and should focus on the candidate's contributions to AI and the natural sciences or engineering. The letter also should indicate how the candidate consistently exemplifies moral, ethical, and professional conduct. The nominator should collect the letters and bundle them for submission.
Name, address, and email address of the candidate (person being nominated).
Copy of the candidate's CV, listing publications, patents, honors, service contributions, etc.
Suggested citation (maximum of 25 words) to be used if the candidate is selected. This should be a concise statement describing the key accomplishment for which the candidate merits this prize.
The prize winner's work will reflect outstanding research or other contributions in AI and the natural sciences or engineering.
The prize winner's work will reflect outstanding research or other contributions in AI and the natural sciences or engineering. Criteria for selecting the prize winner will include:
the quality and impact of the research, considering impacts both on the scientific or engineering domain and the fields of AI and machine learning;
a single notable achievement or for a collection of such achievements;
the individual's specific contributions to the body of work, especially for large team efforts;
the individual's commitment to the community (e.g., through making their work accessible in well-structured and maintained public repositories, community building, or educational efforts)
Kyle Cranmer, Professor of Physics and David R. Anderson Director of the American Family Insurance Data Science Institute (DSI) at the University of Wisconsin-Madison
Professor Cranmer obtained his Ph.D. in Physics from the University of Wisconsin-Madison in 2005. Prior to joining UW-Madison in 2022, he was a faculty member at NYU for 15 years. He was awarded the Presidential Early Career Award for Science and Engineering in 2007 and the National Science Foundation's Career Award in 2009 for his work at the Large Hadron Collider. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in 2012. He was elected as a fellow of the American Physical Society in 2021.
Kyle is recognized for contributions to and advocacy for simulation-based inference, which have reshaped data analysis and experimental design in particle physics and other scientific domains.
Debora Marks, Professor in Systems Biology at Harvard Medical School
Professor Marks obtained her Ph.D. in Mathematical Biology from Humboldt University in 2010. In 2016 she received the Overton Award from the International Society for Computational Biology for outstanding accomplishments and significant contributions to the field of computational biology. In 2018 she was awarded the Chan Zuckerberg Initiative Ben Barres Early Career Acceleration Award. Her lab is interested in developing methods in deep-learning to address a wide range of biological challenges including predicting the effects of genetic variation and sequence design for biosynthetic applications.
Debora is recognized for contributions to the development and application of language modeling methods to understand evolutionary data with application to the prediction of phenotypes and molecular properties.
