University of Toronto

STATSTRO 2026

Sampling, Simulation, and Scientific Discovery

July 16–17, 2026 · Toronto, ON

Registration opening soon

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

  • Sampling Techniques Day 1
    Modern MCMC methods for scientific applications, with a focus on scaling to high dimensions and large datasets.
  • Uncertainty Quantification Day 1
    Strategies for quantifying uncertainty across frequentist and Bayesian frameworks, including conformal prediction methods.
  • Machine Learning Day 2
    Deep learning for scientific applications, including neural networks as emulators and surrogates, interpolation, extrapolation, and generalizability.
  • Simulation-Based Inference Day 2
    Inference with intractable likelihoods using both traditional methods and neural approaches such as normalizing flows and diffusion models.

Speakers

Sampling Techniques

To Be Announced

Headline Speaker

To Be Announced

Tutorial Leader

To Be Announced

Invited Speaker

Uncertainty Quantification

To Be Announced

Headline Speaker

To Be Announced

Tutorial Leader

To Be Announced

Invited Speaker

Machine Learning

To Be Announced

Headline Speaker

To Be Announced

Tutorial Leader

To Be Announced

Invited Speaker

Simulation-Based Inference

To Be Announced

Headline Speaker

To Be Announced

Tutorial Leader

To Be Announced

Invited Speaker

Schedule

All times in Eastern Daylight Time (EDT, UTC-4)

9:15 – 10:00

Breakfast & Registration

Coffee, tea & light refreshments

10:00 – 10:10

Opening Remarks

10:10 – 11:10

Title to be announced

Headline

Speaker TBD

Sampling Techniques

Headline overview talk — 60 min

11:10 – 11:25

Break

Sampling Techniques

11:25 – 12:10

Title to be announced

Tutorial

Speaker TBD

Sampling Techniques

Hands-on coding tutorial — 45 min (Jupyter/Colab)

12:10 – 12:25

Lightning Talks

Lightning

Sampling Techniques

3 × 5-minute lightning talks

12:25 – 12:55

Title to be announced

Invited

Speaker TBD

Sampling Techniques

Invited science talk — 30 min

12:55 – 14:15

Lunch & Networking

Sampling Techniques

Group photo at 12:55, then catered lunch with poster session and networking

14:15 – 15:15

Title to be announced

Headline

Speaker TBD

Uncertainty Quantification

Headline overview talk — 60 min

15:15 – 15:30

Break

Uncertainty Quantification

15:30 – 16:15

Title to be announced

Tutorial

Speaker TBD

Uncertainty Quantification

Hands-on coding tutorial — 45 min (Jupyter/Colab)

16:15 – 16:30

Lightning Talks

Lightning

Uncertainty Quantification

3 × 5-minute lightning talks

16:30 – 17:00

Title to be announced

Invited

Speaker TBD

Uncertainty Quantification

Invited science talk — 30 min

17:00 – 17:30

Day 1 Wrap-Up

Uncertainty Quantification

Informal wrap-up and mingling

9:15 – 10:00

Breakfast & Registration

Uncertainty Quantification

Coffee, tea & light refreshments

10:00 – 11:00

Title to be announced

Headline

Speaker TBD

Machine Learning

Headline overview talk — 60 min

11:00 – 11:15

Break

Machine Learning

11:15 – 12:00

Title to be announced

Tutorial

Speaker TBD

Machine Learning

Hands-on coding tutorial — 45 min (Jupyter/Colab)

12:00 – 12:15

Lightning Talks

Lightning

Machine Learning

3 × 5-minute lightning talks

12:15 – 12:45

Title to be announced

Invited

Speaker TBD

Machine Learning

Invited science talk — 30 min

12:45 – 14:00

Lunch & Networking

Machine Learning

Catered lunch with poster session and networking

14:00 – 15:00

Title to be announced

Headline

Speaker TBD

Simulation-Based Inference

Headline overview talk — 60 min

15:00 – 15:15

Break

Simulation-Based Inference

15:15 – 16:00

Title to be announced

Tutorial

Speaker TBD

Simulation-Based Inference

Hands-on coding tutorial — 45 min (Jupyter/Colab)

16:00 – 16:15

Lightning Talks

Lightning

Simulation-Based Inference

3 × 5-minute lightning talks

16:15 – 16:45

Title to be announced

Invited

Speaker TBD

Simulation-Based Inference

Invited science talk — 30 min

16:45 – 17:00

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

Reed Essick

Co-Chair

Canadian Institute for Theoretical Astrophysics (CITA)

Maya Fishbach

Co-Chair

Canadian Institute for Theoretical Astrophysics (CITA)

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