People

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On leave November 2025-2026.

Gwen is an Assistant Professor of Astrostatistics, jointly appointed between the David A. Dunlap Department of Astronomy & Astrophysics (51%) and the Department of Statistical Sciences (49%). She is also a member of the Data Sciences Institute. Her research interests includes hierarchical Bayesian inference, generalized linear models, spatial point processes, and time series analysis for the study of the Milky Way Galaxy, ultra-diffuse galaxies, star clusters and globular clusters, stars and stellar flares, and fast radio bursts.

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Josh is an Assistant Professor of Astrostatistics, jointly appointed between the Department of Statistical Sciences (51%) and the David A. Dunlap Department of Astronomy & Astrophysics (49%). He is also an associate of the Dunlap Institute for Astronomy & Astrophysics and a member of the Data Sciences Institute. His work focuses on using a combination of astronomy, statistics, data science, and artificial intelligence (AI) to analyze massive datasets containing billions of stars and galaxies to understand how galaxies like our own Milky Way (and the stars within it) form, behave, and evolve over time.

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Jacqueline is a Dunlap Postdoctoral Fellow at the David A. Dunlap Department of Astronomy & Astrophysics with a joint affiliation at the University of Massachusetts, Amherst. She completed her Ph.D. at Texas A&M University, where she worked with Casey Papovich on searching for the most massive galaxies in the distant Universe. Jacqueline was born and raised in the beautiful West African country of Ghana and has been involved with organizing outreach events and programs such as LUMA.

Jacqueline also works closely with Adam Muzzin at York University.

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Tanveer is a Dunlap Postdoctoral Fellow at the David A. Dunlap Department of Astronomy & Astrophysics and a member of the DESI and DESC collaborations. His current research interests include developing frameworks to constrain cosmological models by cross-correlating multiple surveys and improving our ability to interpret these constraints in order to address various cosmological tensions. Outside of research, Tanveer loves to learn languages, play board games, and read books.

Tanveer also works closely with Renée Hložek at the Dunlap Institute.

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Kevin is an Eric and Wendy Schmidt AI in Science Fellow at the David A. Dunlap Department of Astronomy & Astrophysics whose research focuses on understanding how the Milky Way formed and evolved through Galactic Archeology. He combines hierarchical Bayesian models with Hubble Space Telescope and Gaia data to improve astrometry for faint stars, and works with SDSS-V to enhance APOGEE spectral pipelines.

Kevin also works closely with Aviad Levis in the Department of Computer Science.

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Biprateep is an Eric and Wendy Schmidt AI in Science, Canadian Institute for Theoretical Astrophysics (CITA), & Dunlap Postdoctoral Fellow. His work involves developing novel statistical machine learning tools to study the formation and evolution of galaxies and the Universe as a whole. He is interested in large astronomical surveys and has been deeply involved with the DESI collaboration. He is also passionate about developing a scientific community which is accessible and welcoming to all.

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Duo is an Eric and Wendy Schmidt AI in Science and Canadian Institute for Theoretical Astrophysics (CITA) Postdoctoral Fellow. His research interests focus on trying to use artificial intelligence (AI) to understand star formation, stellar feedback, and turbulence. He is also more broadly interested in developing foundational AI models for use across the sciences. Before joining the University of Toronto, Duo was a Virginia Initiatives on Cosmic Origins (VICO) Origin Postdoctoral Fellow at the University of Virginia. He received his Ph.D. from the University of Texas at Austin under the supervision of Stella Offner.

Duo also works closely with Peter Martin at the Canadian Institute for Theoretical Astrophysics (CITA).

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Ronan is a Dunlap Postdoctoral Fellow who works on novel observational techniques that broaden our knowledge of star formation within our galaxy. He aims to reconstruct recent star formation history using Gaia and Sloan Digital Sky Survey V (SDSS-V) data of young stars with a goal of creating the time-resolved star formation map in the last 50 Myrs. He received his Ph.D. at the University of Texas at Austin under the supervision of Adam Kraus. Outside of research, Ronan is interested in astronomy and science outreach programs. He also enjoys stargazing, amateur astronomy, and astrophotography.

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David is a postdoctoral fellow and recent Ph.D. graduate working with Gwen Eadie, Patrick Brown, and Roberto Abraham. His research focuses on spatial statistics and Bayesian computation. Specifically, he is developing various spatial point process models to detect and study elusive Ultra-Diffuse Galaxies. He also designs fast, approximate Bayesian computational methods for large and complex statistical models.

Mairead is a 5th-year Ph.D. candidate and a Data Sciences Institute Doctoral Fellow in the David A. Dunlap Department of Astronomy & Astrophysics, supervised by Ting Li and Josh Speagle. She studies galactic chemical evolution using metal-poor stars and dwarf galaxies. Specifically, her research focuses on constraining nucleosynthetic yields (which remain one of the largest sources of uncertainty in chemical evolution models) with hierarchical modelling techniques. She is also interested in science communication and methods in stellar spectroscopy.

She also collaborates closely with Alex Ji at the University of Chicago.

Anika is a 4th-year Ph.D. student at the David A. Dunlap Department of Astronomy & Astrophysics who works with Gwen Eadie, Ting Li, and Jo Bovy on improving statistical methods of estimating masses of astronomical systems. They are interested in uncertainty analysis and have done work with outlier detection and dimensionality reduction of multiwavelength image data. Anika's long-term goal is to develop a consistent and accurate way to combine data from multiple types of populations to estimate properties of the Galaxy.

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Alex is a 4th-year Ph.D. candidate in the David A. Dunlap Department of Astronomy & Astrophysics and Data Sciences Institute Doctoral Fellow co-supervised by Josh Speagle and Maria Drout. His research combines machine learning and stellar evolution to discover rare stellar populations in large-scale surveys and understand their nature through follow-up observations. He focuses on binary-stripped helium stars and carbon-enhanced metal poor stars, and has developed data-driven models for Gaia spectra.

Phil is a 4th-year Ph.D. candidate in the David A. Dunlap Department of Astronomy & Astrophysics co-supervised by Gwen Eadie and Josh Speagle as well as Ryan Cloutier (McMaster). His research focuses on leveraging machine learning and statistical methods to develop stellar dating methods using rotation and magnetic activity data, with a focus on low-mass stars known as M-dwarfs. His long-term goal is to build a catalogue of M-dwarf ages and use these constraints to inform stellar activity and exoplanet evolutionary analyses.

Rodrigo is a 3rd-year Ph.D. student in the Department of Statistical Sciences co-supervised by Vianey Leos Barajas and Gwen Eadie. His research interests involve Hidden Markov Models (HMMs), including how they can be used to better understand stellar flares and magnetic activity.

Leo is a 2nd-year Ph.D. student in the Department of Statistical Sciences co-advised by Radu Craiu and Josh Speagle. He is broadly interested in applications of statistical learning, quantum computing, conformal prediction, and MCMC methods to Bayesian inference problems with astronomical data. He also completed his undergraduate degree at the University of Toronto.

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Marta Reina-Campos is a CITA Canada Postdoctoral Fellow at the Canadian Institute for Theoretical Astrophysics (CITA) and McMaster University. She is interested in learning how stellar clusters form and evolve as galaxies grow and merge, including by developing new physical models of star formation and feedback and testing them using large, complex numerical simulations of galaxy formation. She also tries to use old, massive stellar cluster populations as near-field cosmological tracers, including most recently using data from the James Webb Space Telescope (JWST).

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Ioana is a CITA Postdoctoral Fellow and an Eric and Wendy Schmidt AI in Science Fellow. She is originally from Romania and came to the United States to pursue a physics degree at MIT (B.A. '14). After that, she completed her M.A. ('16) and Ph.D. ('21) in astronomy and astrophysics at Harvard University and was a postdoc at UCLA from 2022-2023. Her research focuses on trying to constrain the properties of dust and dark matter through a variety of observational probes, including strong galaxy lensing and large photometric surveys. Ioana is also passionate about outreach and mentorship and has been heavily involved with the United States Astronomy and Astrophysics Organisation/Olympiad for many years.

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Andrew is a NASA Hubble Postdoctoral Fellow in the Princeton Department of Astrophysical Sciences. His research focuses on combining astrophysics, statistics, and high-performance coding to study the chemical, spatial, and kinematic variations in the dust that permeates the Milky Way. He believes knowledge comes from data, and data comes from instruments - a view that shapes his approach to science. He is also passionate about scientific communication, open source software/data availability, and the replication crisis.

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Alicia is a 4th-year Ph.D. candidate in the David A. Dunlap Department of Astronomy & Astrophysics and at the Canadian Institute for Theoretical Astrophysics (CITA) working with Bart Ripperda and Norm Murray. Her research focuses on simulations of hot plasma in accretion disk coronae around active galactic nuclei. Specifically, she is working on incorporating more physics into the simulation code to compare against theory and observations. Along with Josh Speagle, she is also interested in studying galaxy evolution in cosmological simulations, with a focus on Milky Way Analogues.

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Mike is now joining Wayve to work on AI in industry. He was a Dunlap Postdoctoral Fellow from 2023-2025 who worked on building AI foundation models to interpret astronomical images. He led the team measuring detailed galaxy morphology in Euclid and was Technical Lead of citizen science project Galaxy Zoo. He did his Ph.D. at Oxford University with Chris Lintott and then a postdoc at the University of Manchester (UK) with Anna Scaife.

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Sam is now an NSF Astronomy and Astrophysics Postdoctoral Fellow at UMass Amherst working with Kate Whitaker. She completed her Ph.D. in 2025 in the David A. Dunlap Department of Astronomy & Astrophysics under the supervision of Gwen Eadie and Josh Speagle. Her research focuses on using globular cluster populations as a probe of galaxy formation and evolution, employing Bayesian hierarchical modeling techniques.

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Steffani is now an NSERC Postdoctoral Fellow at UC San Diego working with Floor Broekgaarden and Kyle Kremer. She completed her Ph.D. in 2025 in the David A. Dunlap Department of Astronomy & Astrophysics working with Maria Drout and Jeremy Webb (York University). Her research focuses on studying the evolution of massive stars in groups, including the evolution of post-common envelope binaries and the outcomes of dynamical interactions in dense stellar environments (such as globular clusters and open clusters). With Josh Speagle, she also developed statistical and machine learning-driven approaches to search for such systems.

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Amanda is now a Banting Postdoctoral Fellow at McGill University and a member of the CHIME/FRB Collaboration. She completed her Ph.D. in 2025 in the David A. Dunlap Department of Astronomy & Astrophysics under the joint supervision of Bryan Gaensler (UCSC), Gwen Eadie, and Paul Scholz (York University). Her research interests include fast radio bursts, high-energy follow up, and circumgalactic media, particularly in the context of astrostatistics. She previously obtained an honours BSc in mathematics and physics from McGill University in 2019.

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Aarya will be starting as an Assistant Professor at Penn State University. She is currently a Legacy Survey of Space and Time (LSST) Discovery Alliance Catalyst Postdoctoral Fellow at the Max Planck Institute for Astronomy in Germany. She completed her Ph.D. in 2023 in the David A. Dunlap Department of Astronomy & Astrophysics under the joint supervision of Gwen Eadie and Jo Bovy. Her research lies at the intersection of astrophysics and statistics by developing novel data-driven tools that guide mathematical models of the Milky Way Galaxy. She previously obtained her BEng in Computer Engineering from S. P. Pune University in India.