The Data Science and AI Institute is a hub for data science and artificial intelligence that drives research and teaching in every corner of the university. The institute brings together world-class experts in artificial intelligence, machine learning, applied mathematics, computer engineering, and computer science to fuel data-driven discovery in support of research activities across the institution. The initiative is led by the Whiting School of Engineering, which will recruit 80 new faculty to join the Data Science and AI Institute, and in addition, 30 new Bloomberg Distinguished Professors will be recruited with substantial cross-disciplinary expertise to ensure the impact of the new institute is felt across the university. Of those, 22 BDPs will be allocated throughout the seven Data Science and AI Institute research clusters, 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 Data Science and AI Institute clusters were announced in December, 2024.

Artificial and Natural Intelligence

This cluster seeks to address key questions about the nature of intelligence in both natural and artificial systems, such as: How do current artificial intelligence systems contrast to natural intelligence theories and findings? Can natural intelligence theories improve modern AI systems? Are there novel computational theories of AI and natural intelligence that not only build on and account for natural intelligence findings but also result in much more effective AI? This cluster aims to 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.

This cluster’s investment in research includes 3 Bloomberg Distinguished Professorships.

Cluster 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

This BDP 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.

This cluster’s investment in research includes 3 Bloomberg Distinguished Professorships.

Cluster 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

This cluster aims to make Johns Hopkins a center for the development and theoretically rigorous use of cutting-edge computational tools to advance methodologic approaches to conducting research in the social and behavioral sciences, and to provide a rigorous quantitative analysis of issues such as inequality and heterogeneity, global warming and its impact on society and the economy, models of belief formation in a data rich environment. This cluster will be a hub of computational and big-data 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.

This cluster’s investment in research includes 3 Bloomberg Distinguished Professorships.

Cluster 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
• Robbie Shilliam, Professor and Chair of Political Science, Krieger School of Arts and Sciences
• Andy Perrin, Stavros Niarchos Foundation Agora Institute Professor of Sociology and Chair of Department of Sociology at 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

This cluster aims to advance medical AI by developing, evaluating, and scaling AI solutions for risk prediction, screening, and diagnosis. These solutions will not only be safe and effective, but also compatible with clinical workflows and scalable across diverse healthcare settings. The cluster integrates medical AI with multiple disciplines, including business of health (including health economics, policy, and services research), data and decision sciences, human-AI interaction, nursing, and public health, to improve health productivity, access, and equity. The focus on innovation, evaluation, and scaling of health AI will shift healthcare towards prevention and targeted care delivery via better risk-based and diagnostic assessments. This cluster will bring together multidisciplinary clinicians and researchers to work side-by-side to develop the new care models and position Johns Hopkins at the forefront of global innovation in medical AI.

This cluster’s investment in research includes 3 Bloomberg Distinguished Professorships.

Cluster 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

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.

This cluster’s investment in research includes 3 Bloomberg Distinguished Professorships.

Cluster 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

This cluster will build on Johns Hopkins’ exceptional strength in genomics, particularly in computational and statistical methods development. The cluster will address the need for new techniques to extract meaningful insights from genomic data as the quantity, complexity, and variety of these data being collected are growing dramatically. The cluster aims to 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.

This cluster’s investment in research includes 3 Bloomberg Distinguished Professorships.

Cluster 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

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.

This cluster’s investment in research includes 4 Bloomberg Distinguished Professorships.

Cluster 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