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Astrostat@UofTAstrostatistics Research TeamUniversity of Toronto


The ART is made up of researchers across astrophysics, statistics, and data science spanning a wide range of career stages. More information about individual members of the group can be found below.


A picture of Gwendolyn Eadie.
Gwendolyn Eadie

Personal Website

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.

A picture of Joshua Speagle.
Joshua S. Speagle (沈佳士)

Personal Website

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.

Postdoctoral Researchers

A picture of Antonio Herrera Martin.
Antonio Herrera Martin

Personal Website

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 PhD in Physics and Astronomy from the University of Glasgow. Outside of work, he enjoys practicing pixel art, software development, and dancing.

A picture of Jacqueline Antwi-Danso.
Jacqueline Antwi-Danso

Personal Website

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 PhD 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 PhD, 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.

A picture of Michael Walmsley.
Michael Walmsley

Personal Website

Mike is a Dunlap Postdoctoral Fellow at the Dunlap Institute for Astronomy & Astrophysics. He works on applying deep learning research breakthroughs to astrophysics with a focus on combining crowdsourcing and deep learning to do better science than with either alone and serve as Technical Lead for citizen science project Galaxy Zoo. He was previously a postdoc (PDRA) at the University of Manchester, supervised by Anna Scaife and working on the Foundation Model experiments that led to his current Fellowship. He did his DPhil at Oxford, where he developed a state-of-the-art active learning approach to classify million galaxies with crowdsourcing and Bayesian convolutional neural networks. Before that, he worked at fintech startup Cytora using machine learning and messy open data to price insurance.

A picture of Tanveer Karim.
Tanveer Karim

Personal Website

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 interpretability of the constraints 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.

A picture of Kevin McKinnon.
Kevin McKinnon

Personal Website

Kevin is a Canadian Institute for Theoretical Astrophysics (CITA) Postdoctoral Researcher 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.

Graduate Students

A picture of Amanda Cook.
Amanda Cook

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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.

A picture of David Li.
David Li

Personal Website

David is a 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 PhDs 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 methods for fitting highly complex and computationally intensive models.

A picture of Samantha Berek.
Samantha Berek

Sam is a 4th year PhD candidate in the David A. Dunlap Department of Astronomy & Astrophysics, 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.

A picture of Mairead Heiger.
Mairead Heiger

Mairead is a third-year graduate student 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.

A picture of Anika Slizewski.
Anika Slizewski

Anika is a second-year graduate 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.

A picture of Alex Laroche.
Alex Laroche

Personal Website

Alex is a second year graduate student in the David A. Dunlap Department of Astronomy & Astrophysics 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.

A picture of Phil Van-Lane.
Phil Van-Lane

Phil is a 2nd year PhD student in the David A. Dunlap Department of Astronomy & Astrophysics co-supervised by Drs. Eadie and Speagle and Dr. 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 BSc 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 PhD in Fall 2022.

A picture of Maggie Zhai.
Maggie Zhai

Maggie is a first-year PhD student in the Department of Statistical Sciences co-advised by Professors Gwendolyn Eadie and Joshua Speagle. Her research broadly focuses on citizen science projects to determine how to improve classification accuracy through volunteer user input, and is interested in using these methods to improve image classification accuracy within Galaxy Zoo. Prior to joining the University of Toronto, she completed her MS in Statistics from San Diego State University and BS in Mathematics from UC San Diego.

A picture of Rodrigo Barradas Herrera.
Rodrigo Barradas Herrera

Rodrigo is a first-year PhD student in the Department of Statistical Sciences co-supervised by Professors Vianey Leos Barajas and Gwendolyn Eadie. His research interests involve applying Hidden Markov Models (HMMs) to understand stellar flares and magnetic activity.

ART Associates

A picture of Huanqing Chen.
Huanqing Chen

Personal Website

Huanqing is a CITA Postdoctoral Fellow who studies the Epoch of Reionization (EoR), the period of time when the first galaxies form and grow. Her research focuses on quasar proximity zones -- large regions where quasar radiation dominates. She uses simulations, and more recently machine learning, to understand how the radiative feedback from quasars impacts surrounding galaxies and how we can recover the properties of the intergalactic medium (IGM) from observational data. When not doing research, she enjoys participating in public outreach, volunteering, playing hockey, and traveling to meet new people and animals.

A picture of Marta Reina-Campos.
Marta Reina-Campos

Personal Website

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).

A picture of Ioana Zelko.
Ioana Zelko

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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 (BA '14). After that, she completed her MA ('16) and PhD ('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.

A picture of Henry Leung.
Henry Leung

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Henry is a Ph.D. candidate and a Data Sciences Institute Doctoral Fellow at the David A. Dunlap Department of Astronomy & Astrophysics primarily supervised by Jo Bovy. His research focuses on adopting and adapting deep learning methods to analyze data from large-scale Galactic surveys like SDSS Milky Way Mappers and Gaia to further the understanding of our Galaxy. His current research interests include working on methods to build Foundation models like "Large Astronomy Models" trained on massive amounts of cross-domain and cross-survey data in astronomy.

A picture of Steffani Grondin.
Steffani Grondin

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Steffani is a PhD student 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.

A picture of Alicia Savelli.
Alicia Savelli

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Alicia is a PhD 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.

Undergraduate Students & Other Members

A picture of Yihan (Christine) Wang.
Yihan (Christine) Wang

Yihan is a 4th-year Statistical Sciences student working 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 extremely passionate about astrostatistics research and hopes to pursue further research opportunities in the future.

Qianyu (Rita) Fan

Qianyu is a 4th-year Statistical Sciences and Economics student working with Josh Speagle on exploring the application of new dimensionality reduction methods on noisy astrophysical data (in particular, chemical abundances measured from the APOGEE stellar spectra).

Matthew Kustec

Matt is a 4th-year Astronomy & Astrophysics student working with Josh Speagle and Alicia Savelli to better understand how galaxies assemble and grow over time by combining data from a number of cosmological simulations.

Maximilian Zabrodski

Max is a 4th-year Astronomy & Astrophysics student working with Josh Speagle and Steffani Grondin to conduct a literature review on the mysteries of "multiple populations" -- stars in globular clusters that appear to form at the same time but yet somehow have different chemical abundances.

A picture of Hannah Guo.
Hannah Guo

Hannah is a 4th-year Astronomy & Astrophysics student working with Gwen Eadie to conduct a literature review on flare time series data and flaring models for low-mass M-dwarf stars. She is also interested in how flares potentially affect exoplanet habitability and passionate about Bayesian approaches for modelling and predicting trends in stellar data and beyond.

Recent Alumni

A picture of Aarya Patil.
Aarya Patil (PhD '23)

Personal Website

Aarya completed her PhD 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.

A picture of Olga St-Onge.
Olga St-Onge (BA '23)

Olga is a recent graduate with a bachelor’s degree in physics and a minor in mathematics from Florida Atlantic University. Her primary interests are in extragalactic astronomy, cosmology, and relativity. She has experience with observational research into active galaxies & galaxy clusters and computational research into gravitational lensing. She is currently applying to graduate programs and seeking opportunities to participate in astrophysical research.