Computers are now predicting stock market changes, detecting cancer, and translating documents—all thanks to the power of machine learning and artificial intelligence. At the forefront of this field are Hopkins engineers and computer scientists, building better algorithms to tackle problems once thought to be too complex for computers to solve.

One of the department’s greatest strengths is applying data science and analytics to health care applications. Projects include using wearables data to monitor and prevent health problems, improving diagnostics, and turning patient care into precision medicine. Notably, our researchers quickly shifted focus to contribute to the fight against COVID-19 by developing prediction models to track the pandemic and analyzing data to understand health disparities, among other projects.

Research Centers and Labs

Data Science and AI Institute

The Johns Hopkins Data Science and AI Institute focuses on research and education in data science, machine learning, and artificial intelligence across diverse fields, including neuroscience, precision medicine, climate resilience, sustainability, public-sector innovation, and social sciences.

Institute for Data Intensive Engineering and Science

The IDIES mission is to coalesce data-intensive science efforts at Johns Hopkins into a well-focused center of activity and to propel various fields towards new discoveries and breakthroughs.

JHU Machine Learning Group

Our machine learning community seeks to push forward the state of the art in terms of theory, algorithms, models, and applications with an eye toward solving the most important challenges of our day.

Malone Center for Engineering in Healthcare

The Malone Center for Engineering in Healthcare brings together engineers, clinicians, and care providers who are leveraging data analytics in novel ways, pioneering new technologies, and applying systems engineering principles to speed the deployment of research-based innovations that will enhance the efficiency, effectiveness, and consistency of health care.

Mathematical Institute for Data Science

The Mathematical Institute for Data Science (MINDS) at Johns Hopkins University brings together a multidisciplinary team of mathematicians, statisticians, computer scientists, and engineers to develop the fundamental mathematical, statistical, and computational principles for the analysis and interpretation of massive amounts of complex, high-dimensional data.

Social Cognitive AI Lab

Working at the intersection of embodied AI, machine learning, and computational social cognition, SCAI’s goal is to advance human-centered AI by engineering machine social intelligence to build socially intelligent systems that can understand, reason about, and interact with humans in real-world settings.

People

Roy Adams

Assistant Professor, Psychiatry and Behavioral Sciences
Location:
204B 550 Building

Amitabh Basu

Professor, Applied Mathematics and Statistics
Location:
S438 Wyman Park Building

Muyinatu “Bisi” Bell

John C. Associate Professor of Electrical and Computer Engineering
Location:
208 Barton Hall

Tamás Budavári

Associate Professor, Applied Mathematics and Statistics & Physics and Astronomy
Location:
N437 Wyman Park Building

Adam Charles

Assistant Professor, Biomedical Engineering
Location:
301A Clark Hall

Rama Chellappa

Bloomberg Distinguished Professor of Computer Vision and Artificial Intelligence
Location:
301 Clark Hall

Najim Dehak

Associate Professor, Electrical and Computer Engineering
Location:
305 Barton Hall

Mateo Díaz

Assistant Professor, Applied Mathematics and Statistics
Location:
S429 Wyman Park Building

Mahyar Fazlyab

Assistant Professor, Electrical and Computer Engineering
Location:
224B Hackerman Hall

Sijia Geng

Assistant Professor, Electrical and Computer Engineering
Location:
207 Barton Hall

Kimia Ghobadi

John C. Malone Assistant Professor of Civil and Systems Engineering
Location:
202 Latrobe Hall

Jennifer Hu

Assistant Professor, Cognitive Science
Location:
167 Krieger Hall

Sanjeev Khudanpur

Associate Professor, Electrical and Computer Engineering
Location:
325 Hackerman Hall

Holden Lee

Assistant Professor, Applied Mathematics and Statistics
Location:
S403 Wyman Park Building

Nicolas Loizou

Assistant Professor, Applied Mathematics and Statistics
Location:
S434 Wyman Park Building

Enrique Mallada

Associate Professor, Electrical and Computer Engineering, Mechanical Engineering, & Applied Mathematics and Statistics
Location:
312 Barton Hall

Brice Ménard

Professor, Physics and Astronomy
Location:
529 Bloomberg Hall

Michael I. Miller

Bessie Darling Massey Professor of Biomedical Engineering
Location:
400W Wyman Park Building / 720 Ross Research Building

Tinoosh Mohsenin

Associate Professor, Electrical and Computer Engineering
Location:
309 Barton Hall

Casey Overby Taylor

Associate Professor, General Internal Medicine & Biomedical Engineering
Location:
217D Hackerman Hall

Vishal Patel

Associate Professor, Electrical and Computer Engineering
Location:
211 Barton Hall

Mihaela Pertea

Associate Professor, Biomedical Engineering & Genetic Medicine
Location:
S257 Wyman Park Building

Carey Priebe

Professor, Applied Mathematics and Statistics
Location:
301D Clark Hall

Jerry Prince

William B. Kouwenhoven Professor of Electrical and Computer Engineering
Location:
201C Clark Hall

Luana Ruiz

Assistant Professor, Applied Mathematics and Statistics
Location:
N452 Wyman Park Building

Jeremias Sulam

William R. Brody Faculty Scholar and Assistant Professor, Biomedical Engineering
Location:
320B Clark Hall

Soledad Villar

Assistant Professor, Applied Mathematics and Statistics
Location:
N433 Wyman Park Building

Joshua T. Vogelstein

Associate Professor, Biomedical Engineering, Biostatistics, Electrical and Computer Engineering, Applied Mathematics and Statistics, & Neuroscience
Location:
317C Clark Hall

John F. Wu

Associate Research Scientist, Center for Astrophysical Sciences; Assistant Astronomer, Space Telescope Science Institute

Louis Whitcomb

Professor, Mechanical Engineering
Location:
115 Hackerman Hall

Laurent Younes

Professor, Applied Mathematics and Statistics
Location:
324C Clark Hall

Putting trust to the test

Hopkins researchers unveil new uncertainty quantification methods in an effort to promote appropriate trust in AI use.