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Today, Johns Hopkins University announced the results of a year-long, faculty-led process which has identified seven Bloomberg Distinguished Professorships research clusters to drive interdisciplinary discoveries as a central part of the university's new Data Science and AI Institute.
The Bloomberg Distinguished Professorships (BDP) program, made possible through a gift from Hopkins alumnus Michael R. Bloomberg, was established in 2013 as an ambitious investment in interdisciplinary research to bridge academic disciplines and open novel fields of inquiry to address issues of critical global importance. Starting from an initial cohort of 50 individual BDP positions, the program has more than doubled in size through a faculty-led process for identifying areas of inquiry that are best pursued through new research clusters. Recruitment is ongoing for BDP clusters in crucial fields such as technology policy, neuroscience, the business of health, quantum science, and sustainable energy. Last year, Johns Hopkins announced an expansion of this successful program as a signature element of the Johns Hopkins Data Science and Artificial Intelligence Institute.
As part of this latest expansion, 30 new BDP positions will be affiliated with the Data Science and AI Institute. Of those, 22 BDPs will be allocated through the research clusters announced today, weaving data science, data-driven research, and AI even more fully into the fabric and future of the university in areas such as medical diagnosis, foundational machine learning, natural intelligence, neuroscience, genomics, cancer research, and the computational social sciences.
"The BDP program has fueled impactful research in areas as diverse as machine learning, health equity, and cancer immunology by harnessing powerful insights that transcend disciplinary boundaries," JHU President Ron Daniels said. "The Data Science and AI Institute, and its embedded BDPs, will bring this tested approach to cross-disciplinary and cross-divisional collaboration, allowing us to harness the power and potential of AI to open new and powerful avenues of research that drive solutions to daunting societal challenges and aid human flourishing."
Like existing BDPs, this new Data Science and AI Institute cohort will serve as academic bridges by holding appointments in at least two schools or divisions across the institution. Recent pioneering work by current BDPs includes using big data to model and detect cancerous tumors; testing the efficacy of SMS text-based interventions to reduce childhood obesity; characterizing the inner cores of distant exoplanets; and identifying links between school surveillance and academic outcomes.
"The exceptional response to our call for proposals to identify these new BDP research clusters reflects the tremendous vision, ambition, and energy around data science and AI already thriving among Johns Hopkins faculty," Provost Ray Jayawardhana said. "These interdisciplinary domains will amplify exciting work underway on our campuses and catalyze new research directions that will continue to transform our institution and enhance our impact on the world for decades."
The seven selected clusters resulted from a rigorous, faculty-led selection process that began last winter. Johns Hopkins called upon its faculty to submit creative and exciting ideas for clusters, resulting in 38 letters of intent spanning multiple domains and 12 university units. Thirteen teams were invited to submit full proposals, which were reviewed by external panels of preeminent scholars in the relevant fields. These diverse review committee members included named professors, MacArthur Fellows, and elected members of National Academies of Medicine, Science, and Engineering from esteemed universities across the U.S. and abroad.
Recruitment of BDPs for the new clusters will kick off in early 2025. The remaining eight BDP positions not allocated within the clusters will be recruited based on the evolving strategic research priorities of the Institute.
The full list of selected clusters and their faculty leads are:
Artificial and Natural Intelligence
Leads: Alan Yuille, Bloomberg Distinguished Professor of Cognitive Science and Computer Science, Krieger School of Arts and Sciences & Whiting School of Engineering; Kyle Rawlins, Associate Professor of Cognitive Science, Krieger School of Arts and Sciences
Artificial Intelligence for Petascale Neuroscience
Leads: Dwight Bergles, Diana Sylvestre & Charles J. Homcy Professor, Department of Neuroscience, School of Medicine, and Director of the Kavli Neuroscience Discovery Institute; Michael Miller, Bessie Darling Massey Professor and Director of Biomedical Engineering, Whiting School of Engineering & Medicine
Big Data, Machine Learning, and Artificial Intelligence in Computational Social Sciences
Leads: Francesco Bianchi, Louis J. Maccini Professor and Department Chair of Economics, Krieger School of Arts and Sciences; Hahrie Han, Inaugural Director of the Stavros Niarchos Foundation Agora Institute and Professor of Political Science, Krieger School of Arts and Sciences; Andy Perrin, SNF-Agora Professor of Sociology and Chair of Department of Sociology, Krieger School of Arts and Sciences; Robbie Shilliam, Professor and Chair of Political Science, Krieger School of Arts and Sciences
Global Advances in Medical Artificial Intelligence: Creating, Evaluating, and Scaling New Care Models for Risk Prediction, Screening, and Diagnosis
Leads: Kathy McDonald, Bloomberg Distinguished Professor of Nursing and Medicine, School of Nursing & School of Medicine; Tinglong Dai, Bernard T. Ferrari Professor of Business, Carey Business School and Professor of Nursing, School of Nursing
Leveraging AI for High-Dimensional Spatially-Resolved Interrogation of Cancer
Leads: Tamara Lotan, Professor of Pathology, Oncology and Urology, School of Medicine; Alex Baras, Associate Professor of Pathology, Oncology and Urology, School of Medicine; Ralph Hruban, Director and Professor of Pathology, School of Medicine; Pablo Iglesias, Interim Department Head and Edward J. Schaefer Professor of Electrical and Computer Engineering, Whiting School of Engineering
Powering Biomedical Discovery with Data Science and AI for Genomics
Leads: Alexis Battle, Professor of Biomedical Engineering, Computer Science and Genetic Medicine, Whiting School of Engineering/School of Medicine; Joel Bader, Professor of Biomedical Engineering, Computer Science and Oncology, Whiting School of Engineering/School of Medicine; Michael Schatz, Bloomberg Distinguished Professor of Computer Science, Biology and Oncology, Whiting School of Engineering, Krieger School of Arts and Sciences & School of Medicine; Dan Arking, Professor of Genetic Medicine, School of Medicine
Theoretical Foundations of (Machine) Learning
Leads: Brice Ménard, Professor of Physics & Astronomy, Krieger School of Arts and Sciences; Alex Szalay, Bloomberg Distinguished Professor, Physics & Astronomy and Computer Science, Krieger School of Arts and Sciences & Whiting School of Engineering; Mark Dredze, John C. Malone Professor of Computer Science, Whiting School of Engineering; Soledad Villar, Assistant Professor of Applied Mathematics and Statistics, Whiting School of Engineering
