Bloomberg Distinguished Professorships Research Clusters
The Data Science and AI Institute includes seven Bloomberg Distinguished Professorships (BDP) research clusters that bring together cross‑disciplinary expertise to drive new discoveries and strengthen the institute’s impact across Johns Hopkins University. These clusters help integrate data science and artificial intelligence into the university’s research enterprise in areas such as medical diagnosis, foundational machine learning, natural intelligence, neuroscience, genomics, cancer research, and the computational social sciences.
Established in 2013, the Bloomberg Distinguished Professorships (BDP) program bridges academic disciplines and opens novel fields of inquiry to address issues of global importance. BDPs serve as academic connectors across the institution, holding appointments in at least two schools or divisions and fostering collaboration that advances innovative, interdisciplinary research.
Artificial and Natural Intelligence
This cluster seeks to address key questions about the nature of intelligence in both natural and artificial systems. It will connect researchers working in vision, language, causal inference and their interaction, and will hire leaders that focus on understanding and building intelligent systems that include a combination of human behavior, the human brain, and state-of-the-art AI models.
Faculty cluster leads: Alan Yuille and Kyle Rawlins
Artificial Intelligence for Petascale Neuroscience
This cluster will provide crucial new computational resources and expand local intellectual capacity necessary to initiate a paradigm shift in our knowledge about the structure and function of the brain. The cluster will recruit next-generation, AI-based scientists to develop the tools needed to probe the functional organization of the brain across scales—from synapses to global brain networks. Insights into this organization will ultimately aid in the development of more efficient AI systems.
Faculty cluster leads: Dwight Bergles and Michael Miller
Big Data, Machine Learning, and Artificial Intelligence in Computational Social Sciences
This cluster aims to make Johns Hopkins a center for the development and theoretically rigorous use of cutting-edge computational tools to advance methodological approaches to conducting research in the social and behavioral sciences. It will also provide a rigorous quantitative analysis of issues such as inequality and heterogeneity, the societal and economic impacts of global warming, and models of belief formation in a data-rich environment. This cluster will be a hub of computational and bigdata social science that will carry out cutting-edge research while simultaneously discovering the social and ethical implications and the theoretical limits and possibilities of that research.
Faculty cluster leads: Francesco Bianchi, Hahrie Han, Robbie Shilliam, and Andy Perrin
Global Advances in Medical Artificial Intelligence: Creating, Evaluating, and Scaling New Care Models for Risk Prediction, Screening, and Diagnosis
This cluster aims to advance medical AI by developing, evaluating, and scaling AI solutions for risk prediction, screening, and diagnosis. It will integrate medical AI with multiple disciplines, including business of health, data and decision sciences, human-AI interaction, nursing, and public health, to improve health productivity, access, and equity. Multidisciplinary clinicians and researchers will work side by side to develop the new care models and position Johns Hopkins as a global leader in medical AI.
Faculty cluster leads: Kathryn McDonald and Tinglong Dai
Leveraging AI for High-Dimensional Spatially-Resolved Interrogation of Cancer
Advances in genomics, epigenomics, transcriptomics, and immune tumor microenvironmental profiling, together with digital imaging, have generated data on human cancers at an unprecedented scale and ushered in the era of precision medicine. This cluster will bring together experts with a focus on the application of state-of-the-art AI and machine learning techniques to interrogate spatially resolved, high-dimensional molecular data from human cancers, leveraging these data for clinical use to revolutionize the way cancer is diagnosed and treated.
Faculty cluster leads: Alex Baras, Ralph Hruban, Pablo Iglesias, and Tamara Lotan
Powering Biomedical Discovery with Data Science and AI for Genomics
This cluster will build on Johns Hopkins’ exceptional strength in genomics, particularly in computational and statistical methods development. It will address the need for new techniques to extract meaningful insights from genomic data as the quantity, complexity, and variety of data being collected are growing dramatically. It will integrate advanced data science methods, artificial intelligence, machine learning algorithms, and statistical models to make sense of the vast amount of genomic data available, which will ultimately aid in biological and medical research and likewise drive novel methods development.
Faculty cluster leads: Alexis Battle, Joel Bader, Michael Schatz, and Dan Arking
Theoretical Foundations of (Machine) Learning
This cluster aims to understand the theoretical foundations of machine learning, including how these systems learn, reason, and whether they are reliable. Fundamental artificial intelligence research is critical for sustainable progress and safety in AI and will pave the way for leveraging AI as a reliable tool for scientific explorations and engineering applications. Using a physics-based approach, this cluster will address fundamental questions about the universality, dynamics, scaling laws, and emergence of learning in both artificial and biological systems.
Faculty cluster leads: Brice Ménard, Alex Szalay, and Soledad Villar