By
Annika Weder
/
Published
March 10, 2026
Benjamin Wandelt, a pioneer at the intersection of cosmology, statistics, and artificial intelligence, is transforming how we extract fundamental physics from astronomical data.
Driven by a deep curiosity about the origins and structure of the universe and a passion for developing the computational tools needed to unlock its secrets, Wandelt studies the fundamental physics of the universe using a combination of astronomical observations, large-scale data analysis, and novel computational techniques. His work, ranging from stars to the largest structures in the observable universe, has contributed significantly to our understanding of the universe’s structure and evolution.
Wandelt has joined Johns Hopkins University as the Bloomberg Distinguished Professor of Cosmology and Scientific AI. He comes to Johns Hopkins from the Institut d’Astrophysique de Paris, Sorbonne Université, and the Center for Computational Astrophysics at the Flatiron Institute in New York City.
About the BDP
Name: Benjamin Wandelt
Title: Bloomberg Distinguished Professor of Cosmology and Scientific AI
Appointments: Department of Physics and Astronomy, Krieger School of Arts and Sciences; Department of Applied Mathematics and Statistics, Whiting School of Engineering
Previous role: Institut d’Astrophysique de Paris, Sorbonne Université
Education: BSc in Physics, Imperial College London; Associateship of the Royal College of Science; Diploma of Imperial College; PhD in Astrophysics and Theoretical Physics, University of London
“I’m particularly interested in the connection between data and theory and how to make that as tight as possible,” Wandelt says. “My research focuses on fundamental physics using astronomical observations, so the data I’m looking at are astronomical and cosmological data sets—ways to probe our entire observable universe, such as the cosmic microwave background or galaxy surveys that map the galaxies in our universe. The ultimate goal is to extract from this data information about the big cosmic mysteries that we still face, such as ‘What is dark matter?’ ‘What is dark energy?’ ‘How did the universe begin?’ ‘What is it made of?’”
A leader in cosmological data science, Wandelt has played a key role in international collaborations such as the Planck satellite mission, where he co-led analyses probing the earliest imprints of structure in the universe. He is known for his work on the cosmic microwave background (the faint afterglow of the Big Bang that fills all space in the observable universe), large-scale structure of the universe (the web-like distribution of galaxies and galaxy clusters arranged in filaments), cosmic voids (vast, less dense regions of space that exist between filaments of the cosmic web), and cosmological data analysis methods. By combining theoretical models with sophisticated inference methods, Wandelt seeks to answer fundamental questions about cosmic origins, dark matter, and dark energy.
Wandelt has developed groundbreaking techniques and methodologies for analyzing astronomical data, transforming multiple areas of astronomy and cosmology. His current research is at the frontier of AI-driven scientific discovery—he is developing new AI systems to extract information from large datasets. While his own research will use these tools to answer questions about the universe, such new approaches could be used across many fields of research.
“I’m exploring new ways of creating AI collaborators and thinking about what discovery of artificially intelligent systems in the physical sciences would look like—how we would work with them, how we could learn from whatever they find, and how we can build them to find out new things that we can value and understand,” Wandelt says. “For example, traditionally in statistics, you compress detailed information into a shorter set of features that you then analyze. We’re developing automatic ways for a machine to learn to look at a full set of data and extract the most informative summaries that give you the tightest possible constraints from the parameters of the underlying physical model while being robust to modeling approximations.”
“We’re entering an era where the questions we can answer about the universe are limited not by data, but by our ability to extract information from it. That’s exactly the challenge I want to tackle.”
Benjamin Wandelt
Bloomberg Distinguished Professor
Originally trained as a theoretical physicist, Wandelt’s research interests evolved over time into computational physics and eventually into data analysis.
“I’ve always been drawn to interdisciplinary research because I’m fascinated by physics questions of how the universe began, or why it accelerates its expansion, and have also grown to love the mathematical, computational, and machine learning methods needed to confront theoretical models with data,” says Wandelt. “There’s a rich interplay between the methodology and the physical understanding we can extract, and that’s the space that I’m particularly interested in.”
Building bridges between cosmologists, computer scientists, and statisticians, Wandelt will strengthen Johns Hopkins University’s position as a leader in data-intensive physics. As a member of the newly established Johns Hopkins Data Science and AI Institute, Wandelt will help shape how AI transforms scientific discovery as part of the expansion of AI research at Johns Hopkins. He sees great potential for collaboration that will help move his research forward.
“Interdisciplinary work is highly valued at Johns Hopkins, and through my appointments across schools, I feel that there are no barriers to which aspects of my research I happen to be more excited about at any given time,” says Wandelt. “It has become clear to me how much exciting work can happen here with the right collaborations. I’m particularly excited to join Johns Hopkins in the early stages of the Data Science and AI Institute, while we’re still shaping what it will look like and coming up with ways to make it as productive, innovative, and exciting as possible.”
With major next-generation surveys and AI capabilities converging, Wandelt sees this as a pivotal moment.
“We’re entering an era where the questions we can answer about the universe are limited not by data, but by our ability to extract information from it,” Wandelt says. “That’s exactly the challenge I want to tackle.”
“Benjamin Wandelt’s use of data and computational methods to advance disciplines and address questions of significant societal impact makes him an excellent addition to the Data Science and AI Institute,” says Ed Schlesinger, dean of the Whiting School of Engineering. “We look forward to the innovations and collaborations he will develop at JHU.”
“Professor Wandelt has made remarkable contributions to theoretical, computational, and statistical astrophysics,” says Christopher Celenza, dean of the Krieger School of Arts and Sciences. “His methodological expertise, interdisciplinary mindset, and eagerness to collaborate with researchers from various backgrounds allow him to tackle profound questions about our universe. I am confident that his presence at Johns Hopkins University will open new frontiers of discovery, enrich our academic environment, and inspire the next generation of scientific leaders.”