Seminar

"Cluster Lensing in the Stage-IV Era"

Charlie Mpetha did his undergraduate at Lancaster University, then moved to a PhD in gravitational wave cosmology at the University of Edinburgh. Mid-way through his PhD, in 2023, he won a Leverhulme Study-Abroad scholarship to work at the WCA for a year with James Taylor on topics in cluster cosmology. Now, Charlie is in his first year as a NASA Postdoctoral Fellow at Goddard, working on cluster science for the Nancy Grace Roman space telescope.

Charles-Édouard Boukaré’s research focuses on understanding the deep interior structure and evolution of rocky planets. His work lies at the intersection of earth and planetary sciences, computational fluid mechanics, mineral physics, and high-pressure chemistry.

Dr Carlos Garcia Garcia is a cosmologist working at the interface of data and theory to understand the nature of dark energy. He received his PhD in 2020 from the University of Barcelona and has spent the past five years as a Beecroft Fellow at the University of Oxford. Carlos is active in the Vera C. Rubin Observatory’s Dark Energy Science Collaboration, where he has held different leadership roles, most recently serving as co-convener of the Weak Lensing and Large-Scale Structure working group. He is also a member of Euclid.

Jamie McCullough is a postdoctoral researcher in observational cosmology at Princeton University, using the measurements of galaxy shapes to trace the large-scale structure of the universe and the growth of that structure with cosmic time.

"Do Nearby Habitable-World Candidates Have an Atmosphere?"

Professor René Doyon’s research activities are focussed on the development of state-of-the-art astronomical instrumentation for various ground- and space-based observatories. He is also actively involved in various observational programs for detecting and characterizing brown dwarfs, exoplanets and young low-mass stars. Professor Doyon is the Director of the Trottier Institute for Research on Exoplanets and the Observatoire du Mont-Mégantic.

"Halo Clustering, Galaxy Clustering, and the Theory and Detection of Assembly Bias"

Andrew Zentner is Professor and Chair of the Department of Physics and Astronomy at the University of Pittsburgh, where he has been a member of the faculty since 2007. Originally, from Queens, in New York City, Andrew earned a B. S. in Electrical Engineering from The Cooper Union for the Advancement of Science and Art, a unique, tuition-free school in New York’s East Village, in 1998. He earned his Ph. D. in Physics from The Ohio State University in 2003. Subsequently, Andrew conducted postdoctoral research at the University of Chicago where he was a Kavli Fellow and a National Science Foundation Fellow before joining the faculty at Pittsburgh. 

"Modeling Galaxy Clusters for Cosmology"

Prof. Camille Avestruz is an assistant professor in the department of physics at the University of Michigan with research interests that span astrophysics, cosmology, and computation. Her primary focus is to understand the evolution of clusters of galaxies. Other aspects of her work prepare for the next decade of observations, which will produce unprecedented volumes of data. 

"From Science to Data Science: How to Land a Job in Industry."

Elena Massara is a Machine Learning Scientist at TD Bank and holds a Ph.D. in Astrophysics from SISSA (Italy, 2016). She previously held postdoctoral research positions at the University of California, Berkeley; the Flatiron Institute in New York; and the Waterloo Centre for Astrophysics in Canada.

"DESI DR2: Survey Overview, Cosmological Constraints from BAO, and Preparation for Full-Shape Analyses"

Ardaud de Mattia (CEA Saclay) is a member of the Dark Energy Spectroscopic Instrument (DESI) collaboration, where he has worked successively on photometric systematics (through image simulations), clustering catalogs and two-point statistics, and cosmological parameter inference. Arnaud is deeply involved in pipeline development for DESI — from catalog creation to cosmological constraints — leveraging GPU computing and automatic differentiation whenever relevant. He is also interested in galaxy field-level inference as a more comprehensive approach to cosmological analysis.

Dr Adrian Bayer (Princeton) works on the intersection of astrophysics and machine learning. Recent interests include: creating multi-probe cosmological simulations for galaxy, lensing, and CMB surveys; performing optimal and interpretable cosmological inference by sampling high-dimensional parameter spaces to reconstruct the Universe's initial conditions and using simulation-based inference; and developing statistical techniques to find astronomical signals in large and noisy spaces, such as for super massive black hole binaries and exoplanets.