Joel Leja is the Dr. Keiko Miwa Ross Early Career Assistant Professor in the Department of Astronomy and Astrophysics at The Pennsylvania State University, with a co-hire through the Institute for Computational & Data Sciences (ICDS) and the Institute for Gravitation and the Cosmos. His research focuses on galaxy formation and evolution, combining JWST observations with advanced statistical and machine learning techniques. He earned his Ph.D. from Yale University in 2016 and a B.A. in Physics and Astrophysics from UC Berkeley in 2010. Leja leads the RUBIES and Blue Jay surveys, analyzing JWST data to study high-redshift galaxies, including 'Little Red Dots' and AGN populations. His work integrates computational models like Prospector-α to interpret galaxy photometry and spectra. Notable contributions include resolving discrepancies in cosmic star formation rates and uncovering ancient stellar populations in early galaxies. Affiliations: ICDS Faculty Fellow, Institute for Gravitation and the Cosmos member Education: Ph.D. Yale University (2016), B.A. UC Berkeley (2010) Key Projects: UNCOVER Treasury Survey, JWST deep field analysis, Prospector Bayesian framework He has been recognized as a Clarivate Highly Cited Researcher (2023) and received the Yale Brouwer Prize (2019). His outreach efforts include reverse science fairs and public lectures, such as the 2024 Ashtekar Frontiers of Science talk on JWST discoveries.
Eric Ford is a Distinguished Professor and ICDS Co-Hire in the Department of Astronomy and Astrophysics at Pennsylvania State University. His research focuses on exoplanet surveys, orbital dynamics, and astrostatistics. He is affiliated with the Institute for Computational & Data Sciences, Center for Exoplanets & Habitable Worlds, and Center for Astrostatistics. Ford earned his Ph.D. in Astrophysical Sciences from Princeton University and holds dual B.S. degrees in Physics and Mathematics from MIT. His NASA Hubble Fellowship (2006–2007) and Miller Institute Fellowship (2003–2006) underscore his contributions to theoretical astrophysics. Education highlights include a NASA Hubble Fellowship at Harvard-Smithsonian Center for Astrophysics and a Miller Research Fellowship at UC Berkeley. His work bridges theory and observation, emphasizing radial velocity surveys, exoplanet demographics, and high-precision data analysis. Notable collaborations include the NEID and HPF radial velocity projects, and NASA's Kepler mission. Research interests include exoplanet population studies, extreme precision radial velocity techniques, and the application of astrostatistics to exoplanet detection. His lab develops computational tools like GPLinearOdeMaker.jl and StellarSpectraObservationFitting.jl to model stellar activity and improve exoplanet characterization. Ford has led teams in analyzing Kepler data, uncovering planetary architectures and occurrence rates. His awards include the Simons Fellowship (2020–2021), Helen B. Warner Prize (2012), and Urey Prize (2011). Advising spans PhD and master’s students involved in NEID solar observations, granulation noise mitigation, and exoplanet dynamics. Grants support projects like the NEID Earth Twin Survey and EPRV research coordination networks. Lab affiliations include the Center for Exoplanets & Habitable Worlds and the NEID Science Team. Current work explores strategies to detect Earth-analog planets and mitigate stellar variability using 3.5 years of Sun-as-a-Star observations. Future research aims to advance data science applications in astronomy and prepare for direct imaging missions.
Catherine Zucker is an Astrophysicist and NASA Hubble Fellow at the Center for Astrophysics | Harvard & Smithsonian. Her research reconstructs the Milky Way's 3D structure using Gaia data, dust mapping, and interactive visualizations to connect star formation with galactic environments. Zucker specializes in interstellar medium characterization, molecular cloud dynamics, and galactic-scale gas structures. She leads simulation efforts for DESI collaboration, focusing on cosmological constraints through baryon acoustic oscillations. Her publications demonstrate advancements in k-nearest neighbor statistics for cosmological inference and development of SUNBIRD simulation models. Recent work includes full forward modeling of galaxy clustering and novel analyses of emission line galaxies. Honors include NASA Hubble Fellowship, NSF GRFP Fellowship, and Harvard Horizons recognition. She champions open-data sharing and interactive scientific visualizations.
Gustavo Medina is a Postdoctoral Fellow at the University of Toronto's Department of Astronomy & Astrophysics and affiliated with the Dunlap Institute for Astronomy & Astrophysics. His research reconstructs the Milky Way's formation using pulsating variable stars' positions, kinematics, ages, and chemical compositions. He works with data from proprietary surveys (HOWVAST, S⁵) and the Gaia mission, preparing for next-generation sky surveys. His expertise includes Galactic archaeology , RR Lyrae stars , and time-domain science . Medina's work bridges photometry/spectroscopy with astrostatistics , focusing on the Milky Way's halo structure. He is part of a collaborative research environment at the Dunlap Institute, which emphasizes innovative instrumentation and global partnerships.
Kaisey Mandel serves as Professor of Astrostatistics and Data Science at the University of Cambridge with a joint appointment between the Statistical Laboratory of the Department of Pure Mathematics and Mathematical Statistics (Faculty of Mathematics) and the Institute of Astronomy. Her astronomy office is located at the Kavli Institute for Cosmology. As of 2024, she is Past Chair of the Astrostatistics Interest Group of the American Statistical Association, a member of the Council of the International Astrostatistics Association, and a Turing Fellow Alumnus of The Alan Turing Institute. She founded and co-organizes the Cambridge Astro Data Science Discussion Group. University: University of Cambridge School: Faculty of Mathematics Departments: Department of Pure Mathematics and Mathematical Statistics, Institute of Astronomy Office Location: Kavli Institute for Cosmology (Kavli K03) Mandel's research focuses at the intersection of astrophysics, cosmology, statistics, and machine learning. Her primary research areas include supernova cosmology, astrostatistics, astronomical machine learning, astroinformatics, time-domain and transient astronomy, Bayesian modeling and inference, and statistical computation. She leads a research group that partners with the Young Supernova Experiment time-domain survey using Pan-STARRS telescopes and participates in LSST:UK, Time-Domain Extragalactic Survey (TiDES), International CHASC Astro-Statistics Collaboration, and MSCA-RISE ASTROSTAT-II Collaboration Network. Her recent publications (2023-2025) demonstrate a strong focus on Bayesian hierarchical modeling applied to supernova cosmology, particularly addressing standardization of Type Ia supernovae, dust extinction properties, and cosmological parameter estimation. Her work increasingly incorporates machine learning techniques while maintaining rigorous statistical foundations. The publications span top astronomy journals including Monthly Notices of the Royal Astronomical Society and Astrophysical Journal. European Research Council Consolidator Grant (2020) ISBA Savage Award for Outstanding Doctoral Dissertation in Applied Statistical Methodology (2011) Mandel has received significant research funding including an ERC Consolidator Grant and has mentored numerous students who have won awards in astrostatistics competitions. She teaches Astrostatistics courses for Part III Maths/Astrophysics students during Lent terms. Her research group actively collaborates with major astronomical surveys and has developed specialized statistical methodologies for time-domain astronomy. She maintains active research laboratories within both the Statistical Laboratory and the Institute of Astronomy, facilitating interdisciplinary work that bridges astronomy and statistics. Her group develops computational tools for astronomical data analysis and contributes to major international collaborations focused on cosmological parameter estimation.
Prof. Roberto Trotta is a Professor of Theoretical Physics at SISSA (Trieste, Italy) and Visiting Professor of Astrostatistics at Imperial College London. He leads the Theoretical and Scientific Data Science group at SISSA and directs its Interdisciplinary Lab. His research focuses on cosmology, dark matter/energy, and applying machine learning/AI to astrophysical data. He holds a PhD from the University of Geneva and an MSc from ETH Zurich. He has been a faculty member at Imperial College London (2008–2023) and a Visiting Professor at Gresham College (2019–2022). Education: PhD in Theoretical Physics, University of Geneva, Switzerland (2004) MSc (Hons) in Physics, ETH Zurich, Switzerland (2011) Research: Analyzes cosmological observations to study dark matter/energy, early universe physics, and particle-physics connections. Develops Bayesian methods, machine learning, and AI for data analysis. Leads projects like STAR NRE and StratLearn-z for improved astrophysical modeling. Collaborates on experiments like LISA, DARWIN, and EDGES. Public Engagement: Award-winning science communicator; authored The Edge of the Sky (2014) and STARBORN (2023). Public lectures at Gresham College and international festivals. Recognized with the Annie Maunder Medal (2020) and Foreign Policy's Global Thinkers (2014). Awards: Annie Maunder Medal (Royal Astronomical Society, 2020) Chair Georges Lemaître (2018) Foreign Policy 100 Global Thinkers (2014) Michelson Prize (2008) Leadership: Directed Imperial’s Centre for Languages, Culture, and Communication (2015–2020). Founded Data Fusion Consultants (2012–2020) for statistical consultancy. Collaborates with museums and artists on science communication. Labs/Teams: Leads the SISSA Data Science group and collaborates with the Theoretical and Scientific Data Science team at SISSA. Active in interdisciplinary projects like the AstroML group and DARWIN observatory collaborations.
Jon McAuliffe is an Assistant Professor at the Department of Statistics, University of California, Berkeley. He earned his Ph.D. in Statistics at Berkeley in 2005 under Michael Jordan, with a dissertation titled "Statistical Methods for Comparing Genomes." His research spans statistical inference, machine learning, and their applications in astronomy, computational biology, and theoretical statistics. Department: Statistics University: University of California, Berkeley Academic Rank: Assistant Professor Ph.D. Year: 2005 Advisor: Michael Jordan McAuliffe's research interests include Bayesian inference , variational methods , extreme event modeling , and scalable statistical algorithms . His work addresses challenges in astronomical image analysis, cancer genomics, and probabilistic modeling of complex datasets. Recent publications focus on time-uniform confidence sequences , deblending starfields , and applications in climate science . While no specific awards are listed, his contributions to methodological statistics and interdisciplinary applications are notable. He has not been explicitly linked to advising students in the provided texts but has collaborated on projects like the Celeste astronomical catalog , emphasizing computational efficiency and probabilistic modeling at petascale levels.
Hai Fu is a Professor at the Department of Physics and Astronomy, University of Iowa. His research focuses on galaxy formation and evolution, with emphasis on massive galaxies, dual active galactic nuclei (AGN), and circumgalactic medium studies using multi-wavelength observations. PhD from University of Hawai’i (2008) Postdoctoral work at Caltech and UC Irvine Faculty at University of Iowa since 2013 His research interests span: Galaxy mergers and nuclear activity triggering Statistical methods for Tully-Fisher relations Observational cosmology with ALMA and MaNGA surveys Gas kinematics in high-redshift starbursts Gravitational lensing of submillimeter galaxies Recent publications include studies on: Statistical rectification of Tully-Fisher relations (2024) High-redshift cold gas streams detected via quasar absorption (2024) Binary AGN in galaxy mergers (2023) Gas fraction evolution in galaxies (2018) Scientific awards include: NSF Grant AST-2103251 for MaNGA Merger Project NSF funding for Gravbox augmented-reality sandbox He has advised multiple students including Dr. Joshua Steffen and Dr. Arran Gross, whose work contributed to understanding galaxy mergers and dual AGN systems. His team utilizes advanced instrumentation like the LISA Spectrograph at Van Allen Observatory and ALMA for high-resolution observations.
Jessi Cisewski-Kehe is an Associate Professor in the Department of Statistics at the University of Wisconsin–Madison, part of the School of Computer, Data & Information Sciences. Her research bridges statistics and astronomy, focusing on methodological development for complex, real-world datasets. Her primary research interests lie in astrostatistics , where she develops statistical tools to detect low-mass exoplanets using radial velocity data, disentangling true planetary signals from stellar activity noise. She also specializes in topological data analysis (TDA) , particularly persistent homology , and employs approximate Bayesian computation (ABC) and visualization techniques for interdisciplinary scientific challenges. Her recent publications reflect a strong trend in applying topological and Bayesian methods to astronomical data, especially in exoplanet discovery and stellar variability modeling. These works sit at the intersection of statistics, astronomy, and computational data science. Her scientific awards include the prestigious NSF CAREER Award and the Nellie McKay Fellowship at UW-Madison. She has been awarded multiple competitive grants from NSF and NASA, highlighting the impact and innovation of her research. Jessi has advised research projects and collaborates extensively across disciplines. Her funding includes multiple NSF grants (CAREER, CDS&E, AST) and NASA awards, supporting work in exoplanet detection, nanoparticle-protein interactions, and energy storage systems. She previously held positions at Carnegie Mellon University and Yale University. She leads research initiatives such as ExoStatistics and applies topological data analysis to challenges in astronomy and physical sciences. Her team develops open-source tools, including software for ABC methods and generalized landscapes in TDA.
Benjamin L'Huillier is an Assistant Professor in the Department of Physics and Astronomy at Sejong University, where he has been employed since 2021. Previously, he held positions as Research Professor at Yonsei University (2019-2021), Research Fellow at Korea Astronomy and Space Science Institute (2016-2019), and Research Fellow at Korea Institute for Advanced Study (2012-2016). Education: Ph.D. in Physics, Université Pierre et Marie Curie (2011) M.Sc., Université Paris Diderot (2008) Engineering degree, CentraleSupélec (2007) Research Focus: Dr. L'Huillier specializes in cosmology and large-scale structure formation, with particular expertise in dark energy, gravitational physics, and cosmological simulations. His research employs advanced statistical methods and artificial intelligence techniques to analyze cosmic structures and test fundamental cosmological models. Key areas include dark matter physics, general relativity applications, and baryon acoustic oscillations. Publication Trends: His recent publications demonstrate a strong focus on observational cosmology and theoretical astrophysics, particularly through large-scale collaborations like the Dark Energy Spectroscopic Instrument (DESI) and Legacy Survey of Space and Time (LSST). Works frequently address cosmological tensions in the ΛCDM model, galaxy formation mechanisms, and development of novel statistical approaches for astronomical data analysis.
Mehrdad Naderi is a Lecturer in Statistics at the Department of Mathematics, Physics, and Electrical Engineering , Northumbria University . His academic journey includes a PhD in Mathematical Statistics from Shahid Bahonar University of Kerman (2017) and postdoctoral research at National Chung Hsing University (Taiwan), Ferdowsi University of Mashhad (Iran), and University of Pretoria (South Africa). Education: PhD in Mathematical Statistics, Shahid Bahonar University of Kerman (2017) His research focuses on applied statistical inference with emphasis on classification , cluster analysis , factor analysis , finite mixture models , and EM algorithm for robust estimation. He has contributed to multivariate and matrix-variate analysis, particularly in handling outliers and asymmetrical data structures. Recent work includes three-way data clustering using matrix-variate normal distributions and robust Bayesian inference for censored mixture models. His publications demonstrate expertise in distribution theory, statistical computation, and applications to financial data, environmental modeling, and astrophysics. Current collaborations span multiple institutions, focusing on heavy-tailed distributions and computational methods for complex data structures.
