Congratulations to Dr. Geoff Pleiss and his students on being awarded the AISTATS 2026 Best Student Paper Award.  

The paper, “We Still Don’t Understand High-Dimensional Bayesian Optimization,” was authored by Dr. Pleiss, his former research intern Colin Dumont, his current computer science student Donney Fan, and other collaborators.

Dr. Pleiss shared: "This paper was one of the most exciting I’ve ever worked on, and I’m honoured that the community found it impactful as well. I'm especially excited for my trainees who made this project possible through their hard work."

Congratulations Dr. Pleiss and team!

Congratulations to Rachel Lobay on being awarded a 2025 Graduate Teaching Award!

Rachel is currently a PhD Student in the department of Statistics. In addition to this award, she is the recipient of a Killam Graduate Teaching Assistant Award (2025 - 2026) and a Rick White Award (2024 - 2025).

The Graduate Teaching Award recognizes Rachel's commitment to teaching and the positive impact she has had on students.

Congratulations, Rachel!

Congratulations to PhD student Gian Carlo Diluvi on receiving a 2025 Graduate Teaching Assistant Award.

Gian Carlo is currently pursuing his PhD in Statistics at UBC, having previously completed his MSc in Statistics at UBC in 2021. 

Since joining the department in 2019, he has contributed extensively as a teaching assistant, and from 2021 to 2024 he has also served as a teaching assistant trainer.

This award recognizes Gian Carlo’s outstanding commitment to teaching and his meaningful impact on student learning.

Congratulations, Gian Carlo!

UBC Statistics lecturer Daniel Chen was recently featured on CityNews Vancouver for his creative project mapping Vancouver’s top cherry blossom viewing spots.

Using data from the Vancouver Open Data Portal, Dr. Chen created an interactive map highlighting the best streets in the city to view cherry blossoms. 

Congratulations to UBC Statistics PhD student Rachel Lobay on receiving the 2025–2026 Killam Graduate Teaching Assistant Award!

This award highlights the outstanding impact that graduate teaching assistants have on teaching and learning at UBC. Each year, up to nineteen graduate teaching assistants are selected for this honour — this year, Rachel is one of them.

Please join us in celebrating Rachel for this truly well‑earned achievement!

The UBC Statistics Department Colloquium Series features talks that are broad, accessible, and engaging—and open to everyone!

Our second talk of the series will take place on Tuesday, April 21st. We’re excited to welcome Dr. Edward Kennedy, Associate Professor in the Department of Statistics and Data Science at Carnegie Mellon University.

Date: Tuesday, April 21, 206
Time: 11 AM - 12 PM
Location: ESB 5104/5106

Title: Nonparametrics in causal inference: densities, heterogeneity, & beyond

Abstract: Much work in causal inference focuses on finite-dimensional targets like average treatment effects. However, many substantively important causal questions involve inherently infinite-dimensional objects, such as counterfactual outcome distributions, heterogeneous treatment effect surfaces, and continuous treatment curves. These targets occupy a hybrid space between classical parameter estimation and nonparametric function estimation. In this talk, I survey some recent work involving these infinite-dimensional causal estimands, highlighting both model-based and model-free nonparametric approaches. I discuss how, despite the impossibility of root-n-rate estimation, ideas from semiparametric theory (like double robustness) continue to play a central role. Throughout I emphasize the relevance of these methods in applications in social sciences and medicine.

This colloquium series is sponsored in part by the Constance van Eeden Endowment.

Future talks in this colloquium series:

Monday, June 8
Speaker: Dr. Stephanie Hicks (Johns Hopkins University)
Time: 3–4 PM
Location: ESB 5104/5106

Congratulations to Nikola Surjanovic on being awarded the 2025-2026 Marshall Prize.

Nikola is in his final year at UBC and will be graduating with a PhD in Statistics under the supervision of Dr. Alexandre Bouchard-Côté and Dr. Trevor Campbell. While completing his PhD, Nikola has taken on the role of Applied Scientist II (ML/AI) at Amazon, working on the supply chain optimization Technologies team.

Among his many achievements, Nikola was a core contributor and founding member of the Pigeons software project, which offers assistance with sampling and integration problems. He was also a co-chair and founding member of the Student Committee of the Western North American Region of the International Biometric Society. His academic excellence has been recognized through several distinctions, including the Alexander Graham Bell Canada Graduate Scholarship (Master’s level) from NSERC and the Governor General’s Silver Medal.

The Marshall Prize honours Professor Albert Marshall for his seminal work in the theory of statistical reliability and for his contributions to the development of statistics at UBC. The prize is awarded to an outstanding M.Sc. or Ph.D. student in the Department of Statistics who has demonstrated excellence in the discipline of statistics as demonstrated by strength in the development and application of statistical methodology.

To learn more about this award and Professor Marshall and previous recipients, please visit: https://www.stat.ubc.ca/marshall-prize

UBC Statistics is launching a new Department Colloquium Series! These talks will be broad, accessible, and engaging — all are welcome!

Our inaugural talk will take place on Monday, March 16th. We’re excited to launch this new series by welcoming Dr. David Haziza, Professor in the department of mathematics and statistics at the University of Ottawa.

Date: Monday, March 16, 2026
Time: 3 - 4 PM
Location: ESB 5104/5106

Title: A Debiased Machine Learning Single-Imputation Framework for Item Nonresponse in Surveys

Abstract: Machine learning methods are now increasingly studied and used in National Statistical Offices, in particular to handle item nonresponse, where some survey respondents answer certain questions but leave others missing. In most surveys, item nonresponse affects key study variables, and imputation is routinely used to handle the resulting missing data. Standard parametric imputation methods can support rigorous inference when their modeling assumptions are approximately correct. However, when the imputation model is misspecified, the resulting inferences may be potentially misleading. Machine learning offers a flexible alternative by learning complex relationships between variables from the data, which can reduce the risk of misspecification. At the same time, this flexibility introduces new challenges for survey inference, since modern learning algorithms may converge more slowly than classical parametric models and may not automatically deliver valid uncertainty quantification. In this talk, I will present a survey sampling extension of the double/debiased machine learning framework of Chernozhukov et al. (2018). The proposed approach combines machine learning-based imputation with design-based survey weighting and an orthogonalized estimating strategy, leading to root-$n$ consistent and asymptotically normal estimation of population means under realistic conditions. We also develop a consistent variance estimator, yielding asymptotically valid confidence intervals while allowing the use of a wide range of machine learning algorithms. I will briefly discuss aggregation procedures and conclude with simulation results illustrating the performance of the proposed methodology.  

This colloquium series is sponsored in part by the Constance van Eeden Endowment.

Future talks in this colloquium series:

Tuesday, April 21
Speaker: Dr. Edward Kennedy (Carnegie Mellon University)
Time: 11 AM–12 PM
Location: ESB 5104/5106

Monday, June 8
Speaker: Dr. Stephanie Hicks (Johns Hopkins University)
Time: 3–4 PM
Location: ESB 5104/5106

Join us for the 3rd Annual UBC Life Sciences Symposium!

On Friday, April 10th, join 400+ researchers for a day of student talks, professional development workshops, and an EDI panel. The event is free for all UBC students and staff, and food will be provided all day!

Register or submit an abstract here: https://lss.lsi.ubc.ca/

Poster/Talk Deadline: March 20th
General Registration: April 3rd

Schedule:

9 am to 10 am: Faculty talk: Dr. Miller
10 am to 11 am: Session 1 (student talks)
11 am to 12 pm: Equity, diversity, inclusion panel
12 pm to 1 pm: Lunch break
1 pm to 2 pm: Session 2 (student talks)
2 pm to 3 pm: Professional development workshops
3 pm to 4 pm: Keynote speaker: Dr. Shendure
4 pm to 6 pm: Poster mixer, sponsor booths, award ceremony

Speakers: 

UBC Faculty Speaker: Dr. Freda Miller, Deputy Director, UBC Michael Smith Labs; Professor, Medical Genetics & SBME
Title: Repair and Regeneration of Mammalian Tissues via Endogenous Stem Cells.

Keynote speaker: Dr. Jay Shendure, Professor, Genome Sciences, University of Washington
Title: Molecular recording of mammalian development.
Abstract: Biology unfolds over time, within cells and tissues that are opaque to our eyes and instruments. Current molecular measurement paradigms are inherently limited: genomics is destructive and static, and imaging confined to a few channels in visually accessible systems. I will describe our efforts to develop an alternative—molecular recording—in which cells are programmed to write their own histories from within. I will focus on DNA Typewriter and ENGRAM, which record lineage and cellular state information into genomic DNA. Our long-term goal is to reframe phenotyping as an organism-wide, time-resolved measurement, capturing developmental statistics rather than static or tissue-restricted endpoints.

Workshops:

Session A: Science Outreach and Community Engagment
Session B: Hands-On Statistics with the Department of Statistics, facilitated by Grace Tompkins, Assistant Professor of Teaching, UBC Statistics

Register