Simon Birrer is an Assistant Professor in Physics and Astronomy at Stony Brook University, specializing in cosmology and gravitational lensing. He holds a PhD from ETH Zurich (2016) and previously served as Kavli Fellow at Stanford University. Birrer leads research probing dark matter and dark energy using gravitational lensing phenomena. His group develops computational tools for analyzing strong gravitational lensing data to study cosmic expansion and dark matter distribution. Research areas include time-delay cosmography, Hubble constant measurements, and machine learning applications in astrophysics. Recent publications focus on multi-messenger gravitational lensing (2025), LSST survey applications (2025), and AI-powered lens modeling pipelines (2025). His work consistently addresses fundamental cosmological tensions like the Hubble constant discrepancy. Awards: Kavli Postdoctoral Fellowship (2019-2022) Kugelpyramide Lifetime Achievement Award Experimental Innovation Award (ETH Zurich) Research Group: Leads the SBU Strong Lensing group with 9+ graduate students and postdocs. The group participates in major collaborations including LSST Strong Lensing Science Collaboration (co-chair), LSST Dark Energy Science Collaboration, and TDCOSMO.
Dragan Huterer is a Professor of Physics and Associate Chair for the Graduate Program at the University of Michigan. His research focuses on cosmology, particularly dark energy and large-scale structure, utilizing data from the Dark Energy Survey (DES) and the Dark Energy Spectroscopic Instrument (DESI) collaborations. He earned his Ph.D. from the University of Chicago (2001) and B.S. from MIT (1996). His work explores the nature of dark energy through cosmological probes like Type Ia supernovae, galaxy clustering, and cosmic microwave background anisotropies. Key contributions include co-leading DESI's first-year cosmological analysis, revealing unprecedented constraints on dark energy and neutrino masses. He also investigates the statistical isotropy of the universe and authored the textbook A Course in Cosmology: From Theory to Practice . Awards include the Friedrich Wilhelm Bessel Research Award (2019) and the Chambliss Astronomical Writing Award (2025). He has advised numerous graduate and undergraduate students, and his funding includes DOE, NSF, and NASA grants. Current projects include the Michigan Cosmology Summer School and leadership in the DESI Collaboration.
Marilyn J Smith is the David S. Lewis Professor and Director of the Vertical Lift Research Center of Excellence (VLRCOE) at the Georgia Institute of Technology's Daniel Guggenheim School of Aerospace Engineering. She leads a seven-university consortium conducting vertical lift research for the U.S. Army, Navy, and NASA, and has secured over $200 million in collaborative research funding. Computational Nonlinear Computational Aeroelasticity Lab Director NASA FUN3D development team contributor Aerospace Systems Design Lab (ASDL) affiliate Her research spans unsteady aerodynamics, computational aeroelasticity, and sustainable energy applications across rotary-wing, fixed-wing, and launch vehicles. She serves on the Vertical Lift Consortium (VLC) Board of Directors and Vertical Flight Society (VFS) Board, while acting as VFS Deputy Technical Director for Aeromechanics and leading international NATO AVT panels on UAV aerodynamics. Recent publications focus on galaxy cluster cosmology, ship-helicopter dynamic interface modeling, and Type Ia supernova analysis. She has won prestigious awards including the AIAA Aerodynamics Award and multiple American Helicopter Society honors for research, mentoring, and service. 2022 AIAA Aerodynamics Award 2015 Best Paper Awards at AHS Forum 2014 & 2012 AHS Agusta-Westland International Fellowships Her laboratory work integrates high-performance computing with aerospace design and develops advanced turbulence models through partnerships with Georgia Tech Research Institute (GTRI). She contributes to public science communication with appearances on National Geographic, PBS, NPR, and local media.
Tamás Budavári is an Associate Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with joint appointments in Physics and Astronomy and a secondary appointment in Computer Science. He is affiliated with the Whiting School of Engineering and the Institute for Data-Intensive Engineering and Science (IDIES). His research focuses on computational and statistical methods for big data in astronomy and interdisciplinary applications such as urban blight analysis. Education: PhD in Astrophysics (2001), Eötvös Loránd University, Budapest Master’s in Theoretical Physics (1997), Eötvös Loránd University Research Interests: Budavári develops algorithms for handling large astronomical datasets, including Bayesian inference, streaming algorithms, and GPU-accelerated processing. His work includes SkyQuery (an online astronomy data tool), photometric redshift estimation, and cross-matching catalogs. He also applies computational methods to urban planning, such as optimizing strategies to address vacant housing in Baltimore City. Publications & Tools: Budavári’s recent work spans topics like deep learning for astronomical image restoration, combinatorial optimization for urban policy, and probabilistic catalog matching. His tools, such as CUDAHM and NWAY, enable scalable analysis of multi-epoch survey data and N-way catalog cross-identification. Awards & Grants: Recipient of the Gordon and Betty Moore Fellowship and SAMSI Research Fellowship Funded by NSF, STScI, NIH, and others Leadership & Outreach: He serves on the Steering Committee of the 21st Centuries Cities Initiative and is a founding editor of the Journal of Astronomy and Computing. His interdisciplinary work bridges astrophysics, data science, and urban systems.
Wayne Springer is a Professor in the Department of Physics & Astronomy at the University of Utah, with a career spanning over 25 years. He has been actively involved in experimental particle astrophysics, ultra-high-energy cosmic ray (UHECR) physics, and gamma-ray astronomy. Ph.D. in Physics from University of Maryland (1991) B.S. in Physics from University of Maryland (1985) Postdoctoral training at University of Maryland and University of Alberta His research focuses on particle astrophysics, cosmic ray detection, and gamma-ray astronomy. He has made significant contributions to the development of the HiRes and Telescope Array cosmic ray observatories, as well as the HAWC and SWGO gamma-ray observatories. His recent work includes deployment of the Trinity neutrino detector prototype and serving as SWGO project manager for Chile site infrastructure. Article trends show strong emphasis on TeV gamma-ray observations (HAWC, SWGO), cosmic ray diffusion mechanisms, dark matter searches, and high-energy astrophysical source characterization (pulsars, microquasars, supernova remnants). He has secured multiple NSF grants for particle astrophysics research and leads detector working groups in international collaborations. Professor Springer actively participates in astronomy outreach, co-developing observatories and implementing computational physics teaching tools with Gradescope auto-graders for enhanced pedagogy. His work bridges experimental high-energy physics, detector development, and multiwavelength astrophysical studies.
Hubert Wagner is an Assistant Professor in Data Science at the University of Florida's Department of Mathematics, part of the College of Liberal Arts and Sciences. He teaches courses such as Computational Applied Topology and Linear Algebra for Data Science. Prior to joining UF, he completed a postdoctoral fellowship at IST Austria under Herbert Edelsbrunner and earned his PhD from Jagiellonian University under Marian Mrozek. His research focuses on developing topological algorithms and tools for practical applications in fields like astrophysics and biomedicine. Notably, he received the 2022 Google Research Scholar Award in Algorithms & Optimization for his work on Bregman divergences and topological methods in high-dimensional data analysis. His research interests span computational geometry, topological data analysis, machine learning, and algorithm engineering. Recent projects include optimizing topological computations for large-scale imaging data (e.g., cosmic microwave background analysis) and detecting adversarial attacks on neural networks using persistent homology. He emphasizes practical applications through collaborations with industry and interdisciplinary research. Hubert is actively involved in academic service, including course development and mentoring. His work has been published in leading venues such as SoCG and NeurIPS, with a focus on bridging theoretical foundations and real-world computational challenges.
Ue-Li Pen is a Professor at the Canadian Institute for Theoretical Astrophysics (CITA), which is part of the Faculty of Arts & Science at the University of Toronto. His research focuses on theoretical astrophysics where basic physical effects can be isolated from astronomical complexities. His research interests include n-body and hydro simulations, origin of galaxy spin, dark energy studies through 21cm cosmology, baryon acoustic oscillations (BAO), absorber acceleration, and research on Fast Radio Bursts (FRBs) and pulsars related to gravitational waves, wave optics, and lensing. Current projects involve the non-linear dynamics of the cosmic neutrino background, 21cm intensity mapping, pulsar VLBI scintillometry, and the Canadian Hydrogen Intensity Mapping Experiment (CHIME). Analysis of recent publications shows Pen's work spans multiple cutting-edge areas in astrophysics, particularly focused on radio astronomy techniques, gravitational wave detection methods, black hole imaging, and cosmological measurements using 21cm radiation. His research often involves innovative applications of wave optics and interferometry to solve astrophysical problems. Professor Pen maintains an active research program with numerous recent publications in top astrophysics journals, demonstrating his continued leadership in the field of theoretical astrophysics and cosmology.
Chris Matzner is a Professor and Associate Graduate Chair at the University of Toronto's Department of Astronomy and Astrophysics, affiliated with the Dunlap Institute for Astronomy & Astrophysics. He earned his Ph.D. from UC Berkeley in 1999. His research focuses on astrophysical fluid dynamics, particularly star formation processes (protostellar disks, molecular clouds, energy feedback) and stellar explosions (supernovae, gamma-ray bursts), employing analytical, numerical, and observational approaches. His research encompasses: Dynamics of protostellar outflows and molecular cloud interactions Models for supernova shocks and gamma-ray burst mechanisms Fragmentation in star and planet formation Massive black hole accretion processes Evolution of giant molecular clouds Stellar feedback in galactic environments Analysis of his 15 most recent publications reveals strong emphasis on supernova dynamics (particularly Type Ia explosions), star formation mechanisms in clusters and molecular clouds, shock wave physics in astrophysical contexts, and the development of astronomical instrumentation. The works demonstrate consistent focus on explosive transients, fluid dynamics in cosmic environments, and observational constraints on theoretical models. As Associate Graduate Chair, he oversees academic programs and student development. His laboratory affiliations include the Dunlap Institute's computational astrophysics and instrumentation groups. Current work involves modeling star cluster-galaxy interactions, tidal disruption events, and developing next-generation UV/IR detectors.
Kyle Dawson is a Professor of Physics and Astronomy at the University of Utah, where he has been employed since 2009. He currently serves as both a full Professor and Director of Graduate Studies in the Department of Physics and Astronomy, having progressed from Assistant Professor (2008-2015) to Associate Professor (2015-2019) before achieving his current position in 2019. His institutional affiliation places him within the College of Science at the University of Utah, a major research university in the western United States. Dawson earned his BA in Physics from Cornell University in 1998, followed by a PhD in Physics from the University of California, Berkeley in 2004. After completing his doctoral studies, he served as a postdoctoral researcher at the Lawrence Berkeley National Laboratory before joining the University of Utah faculty. His educational background in physics provided the foundation for his transition into observational cosmology, where he has made significant contributions through large-scale spectroscopic surveys. Professor Dawson's research focuses on observational cosmology through large spectroscopic surveys designed to measure the fundamental properties of the universe. He is currently the co-Spokesperson for the Dark Energy Spectroscopic Instrument (DESI), a major cosmological survey that has produced numerous high-impact publications in 2024-2025. Previously, he served as Principal Investigator for the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), which concluded in 2020 with final cosmological measurements. His work centers on measuring baryon acoustic oscillations to constrain cosmic expansion history, dark energy properties, neutrino masses, and to test General Relativity. His research group employs techniques including galaxy clustering analysis, quasar astrophysics, and large-scale structure mapping to address fundamental questions in cosmology. The analysis of Dawson's recent publications reveals a strong focus on extracting cosmological constraints from the DESI survey data. His work spans multiple aspects of cosmological analysis, including baryon acoustic oscillation measurements, full-shape power spectrum analysis, imaging systematics mitigation, and cross-correlation studies with cosmic microwave background data. The publications demonstrate collaborative work with large international teams and contribute to increasingly precise measurements of cosmological parameters, with particular attention to dark energy equation of state, neutrino masses, and potential deviations from General Relativity. Professor Dawson has secured significant research funding throughout his career, including multiple grants from the Department of Energy (DOE), NASA, and the National Science Foundation. His grant portfolio includes leadership roles in major cosmological surveys like DESI and eBOSS, as well as support for postdoctoral researchers and graduate students. His research group has mentored numerous students who have gone on to successful careers in academia, industry, and data science fields. Dawson leads a vibrant research group at the University of Utah focused on cosmological data analysis from large spectroscopic surveys. His current team includes two postdoctoral researchers (Angela Berti and Sarah Eftekharzadeh) and a graduate student (Allyson Brodzeller). The group specializes in galaxy clustering analysis, quasar astrophysics, and machine learning applications to spectroscopic data. The research environment fosters collaboration with international teams working on DESI and related cosmological surveys, providing students with opportunities to engage with cutting-edge cosmological research and large-scale data analysis techniques.
Jeff M Phillips is a Professor in the Kahlert School of Computing at the University of Utah, specializing in algorithms for big data analytics, computational geometry, and machine learning. He holds a BS in Computer Science and Mathematics from Rice University (2003) and a PhD in Computer Science from Duke University (2009). He serves as Director of the Utah Center for Data Science, Director of the Data Science Program in the Kahlert School of Computing, and Faculty Co-Director of the One U Data Science Hub. His research focuses on geometric data analysis, coresets, sketches, and handling uncertainty in data. Education: BS/BA (Rice University, 2003), PhD (Duke University, 2009) CI Postdoctoral Fellow at University of Utah (2009–2011) His research interests include algorithms for big data analytics, computational geometry, machine learning, spatial statistics, and AI. He has led NSF-funded projects on spatial data analysis, cosmic origins via AI, and reactive flow data modeling. Phillips has advised numerous PhD and master’s students, contributing to topics like trajectory classification and bias mitigation in word embeddings. His publications span computational geometry, data science, and machine learning. Notable work includes coresets for kernel density estimates, bias mitigation in language models, and scalable spatial scan statistics. Phillips is also active in academic service, serving as co-PC chair for SoCG 2024 and on program committees for major conferences like NeurIPS and ICML.
Jonathan Blazek is an Assistant Professor of Physics at Northeastern University's College of Science, specializing in observational and theoretical cosmology. His research focuses on large-scale astronomical surveys to understand cosmic structure and dark energy, particularly through galaxy clustering and weak gravitational lensing. He is a key member of the Dark Energy Survey and Vera C. Rubin Observatory collaborations, leading efforts to combine multi-wavelength datasets for cosmological insights. Blazek earned his Ph.D. from UC Berkeley and completed postdoctoral fellowships at EPFL (Switzerland) and Ohio State University. Education: Ph.D. in Physics, University of California, Berkeley Postdoctoral Fellowships: EPFL (Switzerland), Ohio State University Research Interests: His work centers on cosmological modeling using galaxy surveys, particularly refining analytic and numerical methods to connect observations with theoretical frameworks. Key areas include: Weak gravitational lensing and galaxy clustering Combined-probe cosmology (integrating datasets across wavelengths) Dark matter and dark energy dynamics Large-scale structure formation Publications & Grants: Blazek has authored over 50 peer-reviewed articles, including foundational work on intrinsic alignment modeling and cosmic shear analysis. He leads the NSF CAREER grant project exploring dark sector physics with galaxy surveys. His recent publications address baryonic feedback effects, CMB lensing cross-correlations, and next-generation survey methodologies. Labs & Collaborations: He contributes to the Northeastern Cosmology Group and the Dark Energy Science Collaboration, advancing projects like the Legacy Survey of Space and Time (LSST) at Vera Rubin Observatory.
Daniel M. Scolnic is an Associate Professor of Physics at Duke University's Trinity College of Arts & Sciences and holds a joint appointment in the Department of Electrical and Computer Engineering at the Pratt School of Engineering. His research focuses on cosmology, particularly using Type Ia supernovae and near-infrared observations to probe dark energy and resolve the Hubble tension. Ph.D. (2013), Johns Hopkins University B.S. (2007), Massachusetts Institute of Technology As a leading figure in supernova cosmology, Scolnic works on refining the cosmic distance ladder, studying time-evolving dark energy, and analyzing systematic uncertainties in cosmological measurements. His work leverages data from the Dark Energy Survey (DES), Pantheon+ collaboration, and James Webb Space Telescope (JWST) to address discrepancies in the Hubble constant (H₀) derived from early- and late-universe observations. His recent publications highlight advancements in inverse distance ladder techniques, host galaxy dust modeling, and the role of photometric redshifts in cosmological analyses. Notably, his team's JAGB 2.0 study improves Hubble constant constraints using JWST. Defense Science Study Group (DSSG) Clarivate Most Highly Cited Scientists Fred Kavli Plenary Lectureship Sloan Research Fellowship in Physics Department of Energy Early Career Award Packard Fellowship Scolnic leads major grants from NASA, the Packard Foundation, and the Department of Energy, including a NASA Roman Project Infrastructure Team grant (2023-2028) and a Packard Fellowship (2019-2027) to investigate cosmological tensions. He actively collaborates with the Duke Cosmology Group and contributes to the Nancy Grace Roman Space Telescope's High-Latitude Time-Domain Survey.
Timothy A. McKay serves as the Arthur F. Thurnau Professor of Physics, Astronomy, and Education at the University of Michigan's College of Literature, Science, and the Arts (LSA), where he also holds the administrative role of Associate Dean for Undergraduate Education. His dual expertise bridges astrophysics research and educational innovation, with significant contributions to both observational cosmology and learning analytics. His educational background includes: B.S. in Physics from Temple University (1986) Ph.D. in Physics from the University of Chicago (1992) McKay's research spans two interconnected domains. In observational cosmology, he pioneered work with major astronomical surveys including the Sloan Digital Sky Survey (SDSS), Robotic Optical Transient Search Experiment (ROTSE), and Dark Energy Survey (DES), focusing on galaxy clusters, cosmic rays, and large-scale structure. Since 2015, he has strategically shifted toward learning analytics, applying data science to transform STEM education. His innovative projects include E 2 Coach (a personalized student support system) and the NSF-funded REBUILD initiative, which creates intergenerational research teams to develop evidence-based teaching practices across physics, chemistry, astronomy, biology, and mathematics. Analysis of his publication trajectory reveals a deliberate pivot from astrophysics to educational research around 2015. While his early work centered on galaxy clusters and cosmological phenomena, recent publications (2020-2024) overwhelmingly focus on systemic equity gaps in STEM education, data-driven interventions, and multi-institutional collaborations. This evolution demonstrates how his data science methodology transitions seamlessly between cosmic structures and educational ecosystems. His scientific recognition includes: Prestigious Arthur F. Thurnau Professorship (awarded for exceptional undergraduate teaching) McKay directs the NSF-funded REBUILD project and the Digital Innovation Greenhouse, securing substantial research funding while mentoring undergraduate and graduate students in interdisciplinary teams. His work with the Big Ten Academic Alliance (CIC) has generated cross-institutional studies on grading patterns, performance disparities, and student support systems, with practical applications implemented across multiple universities. He actively collaborates with faculty across STEM disciplines to develop scalable educational technologies. His research infrastructure includes the Digital Innovation Greenhouse (an educational technology incubator) and REBUILD project teams, which integrate undergraduates, graduate students, postdocs, and faculty in evidence-based educational research. These teams operate at the intersection of data science and pedagogy, developing tools that analyze institutional datasets to personalize student support while maintaining rigorous scientific methodology.
James E. Aguirre is an Associate Professor in the Department of Physics and Astronomy at the University of Pennsylvania. His research focuses on understanding galaxy formation, cosmology, and large-scale structure through advanced instrumentation and observational techniques. He leads projects such as HERA (Hydrogen Epoch of Reionization Array) and TIM (Terahertz Intensity Mapper), dedicated to studying the early universe and distant star-forming galaxies. Aguirre’s work involves cutting-edge millimeter-wave and radio instrumentation design, including Z-Spec, PAPER, and MUSTANG. He has contributed to significant discoveries, such as detecting massive water reservoirs around quasars and determining distances to gravitationally lensed galaxies. Supported by NSF grants, his research bridges observational astronomy with cosmological theory. Education: Ph.D. in Astrophysics (thesis work on TopHat balloon-borne telescope). Teaching: ASTR011 Introduction to Astrophysics I. Current Projects: HERA, TIM, Simons Observatory, and PAPER. Grants: NSF Grant No. 0807990 and others. His research group collaborates on instrumentation like the Bolocam Galactic Plane Survey and explores techniques for mitigating calibration errors and improving signal analysis in radio interferometry. Aguirre’s efforts advance both observational methods and our understanding of cosmic evolution from the epoch of reionization to present-day galaxy formation.
Elena D'Onghia is an Associate Professor in the Department of Astronomy at the University of Wisconsin-Madison. Her research focuses on unraveling the dynamical processes that shape the stellar structure of the Milky Way and nearby galaxies using analytical models and high-resolution numerical simulations. She leverages data from the GAIA satellite to study galactic evolution, emphasizing the importance of understanding our cosmic environment beyond the Solar System. University: University of Wisconsin-Madison Department: Astronomy Contact: edonghia@astro.wisc.edu | 4504 Sterling Hall Research Interests D'Onghia's work spans galactic dynamics, stellar structure formation, and interstellar medium processes. She investigates how gravitational interactions, stellar bars, and supernova-driven outflows influence galactic morphology. Her studies often integrate multi-wavelength observations with cosmological simulations to trace the evolution of galaxies like the Milky Way and Magellanic Cloud analogs. Recent Publications Her recent articles highlight galactic disk corrugations, starburst outflows in the LMC, and the role of classical bulges in shaping stellar bars. Collaborative projects include the Sloan Digital Sky Survey and ALMA-based studies of high-redshift galaxies. Scientific Awards 2018-2020 Vilas Associate Professor Research Fellow 2013-2017 Alfred P. Sloan Research Fellow 2013 Kavli Fellow Frontiers of Science 2009-2012 Keck Fellowship (Harvard) 2005-2006 Max-Planck Research Fellowship
