Greg Bryan, Director
Greg Bryan is a professor of astronomy at Columbia University. He received a Ph.D. in astrophysics at the University of Illinois at Urbana-Champaign in 1996 and held positions at Princeton, MIT, and Oxford before joining the faculty at Columbia in 2004. He is a recipient of a Princeton Lyman Spitzer Fellowship, a Hubble Fellowship, an NSF CAREER award and the Leverhulme Trust Prize. His primary research focus involves the use of large-scale computational hydrodynamics and computational models to better understand astrophysical systems in a cosmological framework. He has applied such techniques to study the generation of large-scale structure in the universe, the formation of X-ray clusters, the evolution of galaxies and the birth of the first stars in the universe. He has also carried out numerical simulations used to generate visualizations for the Oscar-nominated IMAX film Cosmic Voyage, as well as planetarium shows at the American Museum of Natural History.
Lawrence Berkeley National Lab
Simone Ferraro is a faculty-level scientist at Lawrence Berkeley Lab and a senior member of the Berkeley Center for Cosmological Physics (BCCP). Previously he was a Miller and BCCP Fellow in the department of Astronomy at Berkeley. He completed my PhD in Astrophysics at Princeton and was an undergraduate at Cambridge and a master student at the University of Chicago. He is a cosmologist interested in many aspect of CMB and the evolution of the Large Scale Structure. His research spans from the very early Universe to the epoch of reionization and galaxy formation. He uses advanced statistical techniques, and a combination of theory and experimental data to help uncover the physics of the early Universe, the nature of Dark Matter and Dark Energy and the properties of neutrinos.
Lars Hernquist is the Mallinckrodt Professor of Astrophysics. His research interests include theoretical studies of dynamical processes in cosmology and galaxy formation/galaxy evolution. Numerical simulations of stellar dynamical and hydrodynamical systems. Investigations of the physics of compact objects, particularly neutron stars and the interplay between thermal and magnetic processes in strongly magnetized neutron stars.
Shirley Ho joined is a group leader of the Cosmology X Data Science group at CCA. Her research interests have ranged from fundamental cosmological measurements to exoplanet statistics to using machine learning to estimate how much dark matter is in the universe. Ho has broad expertise in theory, observation and data science. Ho’s recent interest has been on understanding and developing novel tools in statistics and machine learning techniques, and applying them to astrophysical challenges. Her goal is to understand the universe’s beginning, evolution and its ultimate fate.
Jens Jasche is an Associate Professor of Observational Cosmology of the Physics Department and the Oskar Klein Center at Stockholm University. He received his PhD degree in physics from the Ludwig Maximilian University of Munich. Before his faculty appointment at Stockholm University, he held a research fellowship at the Cluster of Excellence “Origin and Structure of the Universe” part of the Excellence Initiative of the Federation of German Federal States. He has also received a two-year Feodor Lynen fellowship of the Alexander von Humboldt Foundation to conduct postdoctoral research at the Institut d’astrophysique de Paris. His research focuses on computational astrostatistics and machine learning to analyze galaxy redshift surveys aiming at studying the origin and evolution of cosmic structure, galaxies’ formation, dark matter, and dark energy phenomenology. Jasche has pioneered the Bayesian forward modeling approach to reconstruct the cosmic initial density field from galaxy surveys. He is an elected fellow of the International Astrostatistics Association and has received funding from the Knut and Alice Wallenberg Foundation and the Swedish Research Council to research the dynamical evolution of the cosmic structure with next-generation galaxy surveys.
Institute d’Astrophysique de paris/CNRS
Guilhem Lavaux is a permanent CNRS scientist at the Institut d’Astrophysique de Paris. Before this, he was a CITA National Fellow at the University of Waterloo and Associate postdoctoral researcher at the Perimeter Institute for Theoretical Physics. He was also a Postdoctoral researcher at the University of Illinois at Urbana-Champaign, which included two long visiting researcher positions at Caltech and at Johns Hopkins University. He was a recipient of a 4-year fellowship for the bachelor program. He earned his Master in theoretical physics from the Ecole Normale Supérieure and received his doctoral degree from the Université Paris-Sud (now Paris-Saclay) for his work conducted at the Institut d’Astrophysique de Paris. He works on modeling of the dynamics of the Universe, the exploration of new statistical tools to constrain cosmological parameters. He stimulated, for cosmology, the use of cosmic voids, the development of forward modeling techniques and new machine learning formulations. He was granted an early career research award by the ANR to stimulate the scaling of Bayesian inference technique in cosmology.
universite de montreal
Laurence Perreault Levasseur completed her PhD in Applied Mathematics and Theoretical Physics from the University of Cambridge. She was a KIPAC Fellow at Stanford University, in the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), followed by a second postdoctoral fellowship at the Flatiron Institute in 2018. She has been the recipient of numerous awards and scholarships including the Vanier, Rouse Ball and MT Meyers awards. Since August 2019, she has been on faculty at the Department of Physics at the University of Montreal, as well as holding a joint position at the Quebec Artificial Intelligence Institute (Mila). Professor Levasseur’s research focuses on cosmology and in particular on the development of new machine learning techniques for data analysis and constraining cosmological parameters.
Eve Ostriker is the Lyman Spitzer, Jr., Professor of Theoretical Astrophysics at Princeton University. Prior to joining the Princeton faculty, she was Professor of Astronomy at the University of Maryland. She received her bachelor’s and doctoral degrees in Physics from Harvard University and the University of California, Berkeley, and was a postdoctoral fellow at the Harvard-Smithsonian Center for Astrophysics. Ostriker’s research is in the area of theoretical and computational astrophysics, with main scientific interests in the process of star formation; the dynamics, thermodynamics, and chemistry of the interstellar medium and circumgalactic medium; the structure and evolution of galaxies; and the physics of accretion and outflows. She is also active in the development of numerical methods and tools for computational fluid dynamics, including magnetic fields, radiation, and cosmic rays. A particular emphasis of her work has been on quantifying the role of turbulence and analyzing how the many forms of energy returned by massive stars to their surroundings leads to self-regulated star formation and powerful galactic winds. Ostriker is a Simons Investigator in Astrophysics and a member of the American Academy of Arts and Sciences, and was recipient of fellowships from the Guggenheim Foundation and the Miller Institute.
carnegie mellon university
Aarti Singh is an Associate Professor in the Machine Learning Department in the School of Computer Science at Carnegie Mellon University. Before this, she was a Postdoctoral Research Associate at the Program in Applied and Computational Mathematics at Princeton University and prior to that received her Ph.D. degree in Electrical Engineering from the University of Wisconsin-Madison. Her research lies at the intersection of machine learning, statistics and signal processing, and focuses on designing intelligent statistically and computationally efficient algorithms that use feedback to guide learning and decision-making. She is particularly interested in applications of machine learning for enabling social and scientific discoveries. Her work is recognized by an NSF Career Award, a United States Air Force Young Investigator Award, A. Nico Habermann Junior Faculty Chair Award, Harold A. Peterson Best Dissertation Award, and three best student paper awards. Her service honors include serving as member of the National Academy of Sciences (NAS) committee on Applied and Theoretical Statistics, Program Chair for the International Conference on Machine Learning (ICML) 2020 and Artificial Intelligence and Statistics (AISTATS) 2017 conferences, guest editor for Electronic Journal of Statistics, and Associate Editor of the IEEE Transactions on Information Theory.
rachel somerville is a Group Leader at the Center for Computational Astrophysics. Before this, she was a Distinguished Professor and held the George A. and Margaret M. Downsbrough Chair in astrophysics at Rutgers University. She has also previously held positions at the University of Michigan, the Space Telescope Science Institute, the Max Planck Institute for Astronomy in Heidelberg, Germany, and Johns Hopkins University. She earned her bachelor’s degree from Reed College, received her Ph.D. from the University of California, Santa Cruz and did postdoctoral work at the Hebrew University in Jerusalem and the Institute of Astronomy at the University of Cambridge. The main goal of her research is to understand how galaxies and supermassive black holes form and evolve within a cosmological context. Somerville uses semi-analytic modeling, numerical simulations and observations to approach these problems. Her work has been recognized with the 2013 Dannie Heineman Prize for Astrophysics and a 2014 Simons Investigator Award.
max-planck-institute for astrophysics
Prof. Volker Springel is a scientific director of the Max-Planck-Institute for Astrophysics (MPA) in Garching, Germany, where he leads the department of computational astrophysics. One of his central interests lies in studying cosmic structure formation with magnetohydrodynamic simulations of galaxy formation. He investigates, in particular, magnetogenesis in galaxies through dynamo processes, and the relevance of particle transport processes in the diffuse intragalactic plasma for galaxy evolution. Before joining the MPA, Springel has been professor at Heidelberg University and a group leader at the Heidelberg Institute for Theoretical Studies. He is a member of the German National Academy of Sciences Leopoldina and a foreign member of the US National Academy of Sciences.
institut d’astrophysique de paris/Flatiron
Professor Wandelt is the International Chair of Theoretical Cosmology at Sorbonne University and the Institut d’Astrophysique de Paris. His research in theoretical, computational, and statistical astrophysics connects fundamental physics and cosmology with astronomical data ranging from stars to the largest scales accessible to observations. From 2011, he was the founding associate director of the Institut Lagrange de Paris in cosmology, astro-particle, and theoretical physics and became its director in 2014. Professor Wandelt has held long-term visiting faculty positions at the Max Planck Institute for Astrophysics; Caltech; Princeton University; the Institute for Advanced Studies; and NYU; and most recently is a Senior Research Scientist at the Center for Computational Astrophysics at the Flatiron Institute.