People

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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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On parental leave until Nov 2025.

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 analze 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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Antonio (Tony) is a postdoctoral fellow in the Department of Statistical Sciences. He works in the ART as part of a Canadian Statistical Sciences Institute (CANSSI) Collaborative Research Team led by Professor Gwendolyn Eadie, as well as in the Canadian Hydrogen Intensity Mapping Experiment (CHIME), which studies fast radio bursts. His primary research interest is in the study of data analysis methods for astrophysical data. Throughout his career, Antonio has researched a wide range of topics in physics and astronomy, including the evolution of the Universe, dark matter via gravitational lensing, the inference of parameters for gravitational waves, and the search of extrasolar planets using microlensing data. He received his Ph.D. in Physics and Astronomy from the University of Glasgow. Outside of work, he enjoys practicing pixel art, software development, and dancing.

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Jacqueline is a Banting 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. Prior to her Ph.D., she was a Clark Scholar at Texas Christian University and an intern at the Space Telescope Science Institute (STScI), where she worked with Kat Barger and Andrew Fox on the distances and chemical abundances of the Magellanic Stream's Leading Arm, respectively. 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.

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Mike is a Dunlap Postdoctoral Fellow who works on building AI foundation models to interpret astronomical images. His models have been used on images from every major telescope. He is leading the team measuring detailed galaxy morphology in Euclid. He is also Technical Lead of citizen science project Galaxy Zoo, which recruits hundreds of thousands of volunteers to annotate galaxy images. He did his Ph.D. at Oxford with Prof. Chris Lintott and then a postdoc at Manchester (UK) with Prof. Anna Scaife.

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Tanveer is an Arts & Sciences Postdoctoral Fellow at the David A. Dunlap Department of Astronomy & Astrophysics and a member of the Dark Energy Spectroscopic Instrument (DESI) collaboration and the Dark Energy Science Collaboration (DESC). 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. Previously, he has also extensively worked on target selection of emission-line galaxies for DESI, mitigation of observational systematics, as well as projects pertaining to the Milky Way and analysis of stellar rotation periods. Outside of research, Tanveer loves to learn languages, play board games, and read books.

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Kevin is a postdoctoral fellow at the David A. Dunlap Department of Astronomy & Astrophysics whose research focuses predominantly on understanding how the Milky Way formed and evolved through Galactic Archeology. On the dynamics side, he is currently using hierarchical Bayesian models to combine archival Hubble Space Telescope images with Gaia data to measure significantly improved astrometry, particularly for faint stars. For stellar parameters and chemistry, Kevin is working with the Sloan Digital Sky Survey V (SDSS-V) to help improve the APOGEE spectral reduction and abundance pipelines for the Milky Way Mapper program.

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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.

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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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Amanda is a Ph.D. candidate in the David A. Dunlap Department of Astronomy & Astrophysics supervised by Profs. Bryan Gaensler (UCSC), Gwen Eadie, and Paul Scholz (York University). She is also a member of the CHIME/FRB Collaboration. Amanda develops and employs statistical methods to solve astrophysics problems related to fast radio bursts, high-energy astrophysics, and circumgalactic media. Prior to entering graduate school, she received an honors mathematics and physics degree from McGill University, and held a variety of summer research positions including at NASA JPL and the University of Kyoto.

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David is a 5th-year Ph.D. candidate, CANSSI Ontario Multidisciplinary Doctoral (Mdoc) trainee, and Data Sciences Institute Doctoral Fellow in the Department of Statistical Sciences supervised by Profs. 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. Most recently, David and his fellow Ph.D.'s Ziang Zhang and Ziyi Liu have been working on a novel Bayesian computational method called Bayesian Optimization Sequential Surrogate (BOSS) that can serve as a fast and reliable alternative to Markov chain Monte Carlo (MCMC) methods for fitting highly complex and computationally intensive models.

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Sam is a 5th-year Ph.D. candidate in the David A. Dunlap Department of Astronomy & Astrophysics and Data Sciences Institute Doctoral Fellow, supervised by Gwen Eadie and Josh Speagle. Her research focuses on using globular cluster (GC) populations as a probe of galaxy formation and evolution, especially for low-mass galaxies. She is specifically interested in what deviations from the well-characterized scaling relationship between galaxy mass and GC populations can tell us about how clusters form and the varying evolutionary pathways of low-mass galaxies. She employs Bayesian hierarchical modeling techniques to answer these questions, and strives to introduce a wider range of statistical models in the astronomy literature.

Mairead is a 4th-year Ph.D. candidate 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.

Anika is a 3rd-year Ph.D. student at the David A. Dunlap Department of Astronomy & Astrophysics who works with Gwen Eadie 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 3rd-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 focuses on a combination of machine learning and stellar evolution. Specifically, he develops data-driven models to discover 'needles in a haystack': new, rare stellar populations in large-scale surveys. Subsequently, he attempts to understand the nature of these rare populations through a combination of follow-up observations and stellar evolution models. On the machine learning side, he has developed a purely data-driven model for low-resolution Gaia BP/RP spectra based on variational-autoencoders. On the stellar evolution side, he is focused on binary-stripped helium stars and carbon-enhanced metal poor stars.

Phil is a 3rd-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 exoplanet evolutionary analyses. He previously completed his B.Sc. at University of Waterloo in Earth Sciences with a Geophysics specialization, then worked in engineering consulting, software development, data engineering, and product management roles for several years before beginning his Ph.D. in Fall 2022.

Rodrigo is a 2nd-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 1st-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 approximate Bayesian computation to astronomical data, application of quantum computing to statistical problems, and Markov chain Monte Carlo (MCMC) methods. 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. 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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Steffani is a 5th-year Ph.D. candidate in the David A. Dunlap Department of Astronomy & Astrophysics working with Maria Drout and Jeremy Webb (York University). She is interested in 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 develops statistical and machine learning-driven approaches to try and search for such systems.

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Alicia is a Ph.D. student 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.

Jilani is a Pearson Scholar working with Gwen Eadie.

Skyler is an Astronomy & Astrophysics student working with Biprateep Dey.

Matt is an Astronomy & Astrophysics graduate (B.Sc. '24) working with Josh Speagle and Alicia Savelli to better understand how galaxies assemble and grow over time using data from a number of cosmological simulations.

Max is an Astronomy & Astrophysics graduate (B.Sc. '24) working with Josh Speagle, Steffani Grondin, and Alison Sills to explore our current understanding of how "multiple populations" form in globular clusters.

Isabelle is a 2nd-year Astronomy & Astrophysics student working with Josh Speagle, Mairead Heiger, Ting Li, and Biprateep Dey on a "Galactic Paleontology" project to uncover the assembly history of the Milky Way using its present-day surviving stars (i.e. "fossils").

Rosayla is a 2nd-year Astronomy & Astrophysics student working with Ting Li on compiling and (cross-)calibrating a wide range of measurements of stellar chemical abundances from spectroscopic surveys.

Alex (or Alejandro) is a 3rd-year Computer Science and Physics and Astrophysics student working with Alex Laroche, Josh Speagle, and Maria Drout on applying neural networks to understand how multi-modal models can lead to a better labeling of stars (especially variable stars). He really loves what he does and wants to go to graduate school!

Zack is a 4th-year Statistics and Computer Science student working with Josh Speagle, Andrew Saydjari, Ting Li, and Biprateep Dey to build a new state-of-the-art probabilistic machine learning-based method infer the chemical abundances of stars from their observed spectra using both theoretical (synthetic) data and real (noisy) observations.

Junbo is a 4th-year Statistical Sciences student working with Mike Walmsley on exploring the use of active learning strategies for improving the performance of machine learning morphological classifiers for the Euclid Space Telescope.

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Aarya 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. She is now a Legacy Survey of Space and Time (LSST) Discovery Alliance Catalyst Postdoctoral Fellow at the Max Planck Institute for Astronomy in Germany. She previously obtained her BEng in Computer Engineering from S. P. Pune University in India. Her current research lies at the intersection of astrophysics and statistics by developing novel data-driven tools that guide mathematical models of the Milky Way Galaxy.

Grace was a recent Computer Science graduate who worked with Ting Li and Josh Speagle on applying machine learning and statistical modelling techniques to model the distribution of faraway stars across the Milky Way. She is currently a Ph.D. student at Caltech.

Yihan was a recent Statistical Sciences graduate who worked with Josh Speagle, Ting Li, and Mairead Heiger on applying probabilistic machine learning methods to infer chemical abundances from stellar spectra in the DESI survey. She is currently an Master's student at Stanford University.