About the Workshop
STATSTRO (formerly Stellar Stats) is an annual interdisciplinary workshop that brings together statisticians, astronomers, members of the broader scientific community, and particularly early-career researchers working on interdisciplinary research in astrostatistics and astroinformatics.
Building on the Stellar Stats workshops (2021–2023) and previous STATSTRO editions (2024–2025), the workshop fosters connections between the Department of Statistical Sciences (DoSS), the Data Sciences Institute (DSI), and the astronomy communities of DADDAA, Dunlap, and CITA at the University of Toronto. This year's edition expands the workshop into an international 2-day gathering, with a focus on communities around the Great Lakes area in the US and across Canada — though it is open to participants from anywhere in the world.
This year's theme, Sampling, Simulation, and Scientific Discovery, explores how modern sampling methods, computational simulations, and statistical inference are driving new discoveries across the sciences. The extended format allows for longer tutorials, more in-depth discussions, stronger recruitment of statistics and machine learning researchers, and deeper engagement from both local and international participants.
Each of the four thematic sessions features a headline overview talk, a hands-on coding tutorial (Jupyter/Colab), lightning talks from early-career researchers, and an invited science presentation. A poster session runs throughout the workshop. There is no registration fee, and catered meals and networking activities provide ample opportunities for cross-disciplinary connections.
Thematic Sessions
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Sampling Techniques Day 1Modern MCMC methods for scientific applications, with a focus on scaling to high dimensions and large datasets.
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Uncertainty Quantification Day 1Strategies for quantifying uncertainty across frequentist and Bayesian frameworks, including conformal prediction methods.
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Machine Learning Day 2Deep learning for scientific applications, including neural networks as emulators and surrogates, interpolation, extrapolation, and generalizability.
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Simulation-Based Inference Day 2Inference with intractable likelihoods using both traditional methods and neural approaches such as normalizing flows and diffusion models.
Speakers
Sampling Techniques
To Be Announced
Headline SpeakerTo Be Announced
Tutorial LeaderTo Be Announced
Invited SpeakerUncertainty Quantification
To Be Announced
Headline SpeakerTo Be Announced
Tutorial LeaderTo Be Announced
Invited SpeakerMachine Learning
To Be Announced
Headline SpeakerTo Be Announced
Tutorial LeaderTo Be Announced
Invited SpeakerSimulation-Based Inference
To Be Announced
Headline SpeakerTo Be Announced
Tutorial LeaderTo Be Announced
Invited SpeakerSchedule
All times in Eastern Daylight Time (EDT, UTC-4)
Breakfast & Registration
Coffee, tea & light refreshments
Opening Remarks
Title to be announced
HeadlineSpeaker TBD
Sampling Techniques
Headline overview talk — 60 min
Break
Sampling Techniques
Title to be announced
TutorialSpeaker TBD
Sampling Techniques
Hands-on coding tutorial — 45 min (Jupyter/Colab)
Lightning Talks
LightningSampling Techniques
3 × 5-minute lightning talks
Title to be announced
InvitedSpeaker TBD
Sampling Techniques
Invited science talk — 30 min
Lunch & Networking
Sampling Techniques
Group photo at 12:55, then catered lunch with poster session and networking
Title to be announced
HeadlineSpeaker TBD
Uncertainty Quantification
Headline overview talk — 60 min
Break
Uncertainty Quantification
Title to be announced
TutorialSpeaker TBD
Uncertainty Quantification
Hands-on coding tutorial — 45 min (Jupyter/Colab)
Lightning Talks
LightningUncertainty Quantification
3 × 5-minute lightning talks
Title to be announced
InvitedSpeaker TBD
Uncertainty Quantification
Invited science talk — 30 min
Day 1 Wrap-Up
Uncertainty Quantification
Informal wrap-up and mingling
Breakfast & Registration
Uncertainty Quantification
Coffee, tea & light refreshments
Title to be announced
HeadlineSpeaker TBD
Machine Learning
Headline overview talk — 60 min
Break
Machine Learning
Title to be announced
TutorialSpeaker TBD
Machine Learning
Hands-on coding tutorial — 45 min (Jupyter/Colab)
Lightning Talks
LightningMachine Learning
3 × 5-minute lightning talks
Title to be announced
InvitedSpeaker TBD
Machine Learning
Invited science talk — 30 min
Lunch & Networking
Machine Learning
Catered lunch with poster session and networking
Title to be announced
HeadlineSpeaker TBD
Simulation-Based Inference
Headline overview talk — 60 min
Break
Simulation-Based Inference
Title to be announced
TutorialSpeaker TBD
Simulation-Based Inference
Hands-on coding tutorial — 45 min (Jupyter/Colab)
Lightning Talks
LightningSimulation-Based Inference
3 × 5-minute lightning talks
Title to be announced
InvitedSpeaker TBD
Simulation-Based Inference
Invited science talk — 30 min
Closing Remarks
Simulation-Based Inference
Wrap-up & next steps
Venue & Travel
Location
Department of Statistical Sciences
700 University Avenue, 9th–10th floors
Toronto, ON
WiFi
Visitors can connect via eduroam. UofT guest WiFi credentials will be provided at registration for those without eduroam access.
Tutorials
Please bring a laptop for the hands-on coding sessions (Jupyter/Colab). Setup instructions will be shared prior to the workshop.
Accessibility
The venue is wheelchair accessible. Please contact the organizers in advance if you have any accessibility requirements.
Getting There
700 University Avenue is located in downtown Toronto, easily accessible by TTC subway (Queen's Park station on Line 1) or streetcar (College St or Dundas St).
Registration
There is no registration fee, but space is limited. Registration will open soon — check back for updates or follow us for announcements.
All participants are expected to follow our Code of Conduct.
Meals & Refreshments
Lunches on both days are catered and designed for networking. Snacks and beverages are provided during all breaks.
Conference Dinner (Day 1)
A conference dinner will be held on Thursday evening. Space is limited — details and sign-up information will follow when registration opens.
Lightning Talks
We welcome 5-minute lightning talk submissions from early-career researchers and participants. Submission details will be available when registration opens.
Poster Session
A poster session runs throughout the workshop. We encourage all participants to present a poster. Details on format and submission will be provided at registration.
Organizing Committee
Josh Speagle
Co-Chair
Dept of Statistical Sciences / David A. Dunlap Dept of Astronomy & Astrophysics, UofT
Haowen Zhang
Committee Member
Kevin McKinnon
Committee Member
Biprateep Dey
Committee Member
Mairead Heiger
Committee Member
Past Editions
STATSTRO 2025
"Wrangling Data: Big and Small"
May 2025 — University of Toronto
STATSTRO 2024
"The AIstronomy Revolution"
2024 — University of Toronto
Stellar Stats
2021–2023
University of Toronto