Benjamin Bloem-Reddy

I am Associate Professor of Statistics at the University of British Columbia. I work on problems in statistics, machine learning, and causal inference. Most of my ongoing work is motivated by some form of (scientific) knowledge acquisition, and the interrelated roles of models, data, inference, and prediction. Some recent examples:

• Uses, benefits, and discovery of symmetry.
• Integrating probability, statistics, and data with scientific models.
• The role of causality, broadly construed, in learning from observations of and interactions with the world.

I also collaborate with researchers in the sciences on statistical problems arising in their research.
My research is/was supported by funding from NSERC, CANSSI, and UBC, and by computational resources and services provided by Advanced Research Computing at UBC.

I was a PhD student with Peter Orbanz at Columbia and a postdoc with Yee Whye Teh in the CSML group at the University of Oxford. Before moving to statistics and machine learning, I studied physics at Stanford University and Northwestern University.

Outside of being a professor, I enjoy spending time with my family and being outside (trail running, hiking, cycling, camping).

Contact: benbr at stat dot ubc dot ca
Office: Department of Statistics, Earth Sciences Building, Room 3168

Research group

Current (alphabetical order)

• Boyan Beronov, Computer Science PhD student (with Anne Condon)
• Hugh Dance, PhD student at Gatsby/UCL (with P. Orbanz)
• Gian Carlo Diluvi (with Trevor Campbell), PhD student
• Kevin Lam (with Yongjin Park), PhD student
• Johnny Xi, PhD student (NSERC CGS-D Scholarship)

Past (reverse chronological order) → next position

• Kenny Chiu, PhD student
• Andi Qian, MSc student
• Alex Sharp (with Chris Maddison), CANSSI Distinguished Postdoctoral Fellow
• Sebastian Gonzalez, Undergraduate RA → Amazon (AWS)
• Daniel Kennedy, NSERC USRA
• Liam Gilson, MSc student while on leave from Forestry PhD
• William Laplante (with Daniel J. McDonald), MSc student → PhD Student, UCL Statistical Science
• Johnny Xi, MSc student (2022) → PhD student, UBC Statistics
• Weijia (Grace) Yin, MSc (2022) → BC Centre for Excellence in HIV/AIDS
• Kenny Chiu, MSc (2021) → PhD student, UBC Statistics
• Gian Carlo Diluvi (with Trevor Campbell), MSc (2021) → PhD student, UBC Statistics
• Sean La (with Alexandre Bouchard-Côté), MSc (2021) → Data Scientist, Kabam Games

Working with me

• Prospective graduate students: If you're interested in working with me, you should apply to the UBC Statistics Department's MSc or PhD program (note that we now offer a fast-track MSc to PhD program, which is essentially equivalent to a US-based PhD program). In your application research/personal statement, indicate that you are interested in working with me and explain in detail why. (Not sure? You should be able to articulate your interests and long-term goals, and how working with me supports/aligns with them.) You are welcome to email me with the same information, though I am not able to respond to every such email. I look for students who are curious, creative, independent, and rigorous, with strong mathematical and coding abilities.

Papers

Pre-prints

• Debiased Counterfactual Generation via Flow Matching from Observations
  H. Dance, J. Xi, P. Orbanz, B. Bloem-Reddy
  [arxiv] [code]
• Counterfactual Cocycles: A Framework for Robust and Coherent Counterfactual Transports
  H. Dance and B. Bloem-Reddy
  [arxiv] [code]

Published and to appear

• Randomization Tests for Conditional Group Symmetry
  K. Chiu, A. Sharp, B. Bloem-Reddy
  Electronic Journal of Statistics 19(2): 5979-6039 (2025)
  [ejs] [arxiv] [code]
• CN-SBM: Categorical Block Modelling For Primary and Residual Copy Number Variation
  K. Lam, W. Daniels, J. M. Douglas, D. Lai, S. Aparicio, B. Bloem-Reddy, Y. Park
  Machine Learning in Computational Biology 2025
  [mlcb] [arxiv]
• Distinguishing Cause from Effect with Causal Velocity Models
  J. Xi, H. Dance, P. Orbanz, B. Bloem-Reddy
  ICML 2025
  [icml] [arxiv] [code]
• Identifying Metric Structures of Deep Latent Variable Models
  S. Syrota, Y. Zainchkovskyy, J. Xi, B. Bloem-Reddy, S. Hauberg
  ICML 2025
  [icml] [arxiv] [code]
• Mixed Variational Flows for Discrete Variables
  Gian Carlo Diluvi, B. Bloem-Reddy, and Trevor Campbell
  AISTATS 2024
  [arxiv] [code]
• On Neural Process Pooling Operator Choice and Posterior Calibration
  S. Naderiparizi, K. Chiu, B. Bloem-Reddy, F. Wood
  Canadian Conference on AI 2024
  [ccai]
• Indeterminacy in Generative Models: Characterization and Strong Identifiability
  J. Xi and B. Bloem-Reddy
  AISTATS 2023 (Oral presentation and notable paper)
  [aistats] [arxiv]
• Lossy Compression for Lossless Prediction
  Y. Dubois, B. Bloem-Reddy, K. Ullrich, C. J. Maddison
  NeurIPS 2021 (Spotlight)
  [neurips] [arxiv] [code] [Neural Compression Workshop @ ICLR 2021 version (oral presentation)]
• Probabilistic symmetry and invariant neural networks
  B. Bloem-Reddy and Y. W. Teh
  Journal of Machine Learning Research, 21(90): 1-61 (2020)
  [jmlr] [arxiv] [slides from a talk on this work] [Yee Whye talked about this in his IMS Medallion Lecture]
• Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks
  B. Bloem-Reddy, A. Foster, E. Mathieu, Y. W. Teh
  UAI 2018
  [uai] [arxiv] [code]
• Discussion of F. Caron and E. B. Fox, "Sparse graphs using exchangeable random measures"
  B. Bloem-Reddy
  Journal of the Royal Statistical Society, Series B (Statistical Methodology), 79(5)
  [jrss b] [pdf] [slides from discussion at RSS meeting]
• Random-walk models of networks formation and sequential Monte Carlo methods for graphs
  B. Bloem-Reddy and P. Orbanz
  Journal of the Royal Statistical Society, Series B (Statistical Methodology), 80(5): 871-898 (2018)
  [jrss b] [arxiv] [code]
  [A talk on this work] [Another talk on this work, by Peter Orbanz]
• Slice Sampling on Hamiltonian Trajectories
  B. Bloem-Reddy and J. P. Cunningham
  ICML 2016
  [pdf] [icml/jmlr] [A talk on this work]
• Superfluid Phase Stability of ³He in Axially Anisotropic Aerogel
  J. Pollanen, J. P. Davis, B. Reddy, K. R. Shirer, H. Choi, W. P. Halperin
  Journal of Physics: Conference Series, 150(3), 032084
  [iop]
• Stability of the axial phase of superfluid ³He in aerogel with globally anisotropic scattering
  J. P. Davis, J. Pollanen, B. Reddy, K. R. Shirer, H. Choi, W. P. Halperin
  Physical Review B 77, 140502(R)
  [aps] [arxiv]

Workshop contributions

(Some of these have a more fully developed counterpart above)

• Triangular Monotonic Generative Models Can Perform Causal Discovery
  J. Xi, S. Gonzalez, B. Bloem-Reddy
  NeurIPS 2023 Workshop on Causal Representation Learning
  [paper] [poster]
• Hypothesis Tests for Distributional Group Symmetry with Applications to Particle Physics
  K. Chiu and B. Bloem-Reddy
  NeurIPS 2023 AI for Science Workshop
  [paper]
• Multiple Environments Can Reduce Indeterminacy in VAEs
  J. Xi and B. Bloem-Reddy
  NeurIPS Workshop on Causal Inference & Machine Learning: Why now? (WHY-21)
  [paper] [poster]
• Beauty in Machine Learning: Fluency and Leaps
  I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021 (selected for contributed talk)
  [paper] [poster]
• Generalization Bounds for Invariant Neural Networks
  C. Lyle, B. Bloem-Reddy, Y. Gal, M. Kwiatkowska
  NeurIPS 2019 Workshop on Machine Learning with Guarantees
• Neural network models of exchangeable sequences
  B. Bloem-Reddy and Y. W. Teh
  NeurIPS 2018 Workshop on Bayesian Deep Learning
  [pdf] [slides from a talk on this work]
• Sequential sampling of Gaussian process latent variable models
  M. Tegner, B. Bloem-Reddy, S. Roberts
  ICML 2018 Workshop on Tractable Probabilistic Models
  [arxiv]
• Sampling and inference for discrete random probability measures in probabilistic programs
  B. Bloem-Reddy*, E. Mathieu*, A. Foster, T. Rainforth, Y. W. Teh, M. Lomeli, H. Ge, Z. Ghahramani
  NeurIPS 2017 Workshop on Advances in Approximate Bayesian Inference
  [pdf] [poster]
• Random walk models of sparse graphs and networks
  B. Reddy and P. Orbanz
  NeurIPS 2014 Workshop on Networks: From Graphs to Rich Data. Best student poster award.
  

Technical reports

• Non-parametric Hypothesis Tests for Distributional Group Symmetry
  K. Chiu and B. Bloem-Reddy
  (Superseded by Randomization Tests for Conditional Group Symmetry)
  [arxiv] [pdf] [code]
• On the Benefits of Invariance in Neural Networks
  C. Lyle, M. van der Wilk, M. Kwiatkowska, Y. Gal, B. Bloem-Reddy
  [arxiv]
• Preferential Attachment and Vertex Arrival Times
  B. Bloem-Reddy and P. Orbanz
  [arxiv]
  (Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks develops inference methods and applies these models to data.)

Teaching

Current/upcoming

• Fall 2026: STAT 460/560, Theory of Statistical Inference I
  [course website]

• Fall 2026: STAT 548R, Seminar on Effective Research
  [course website]

• Spring 2027: STAT 305, Introduction to Statistical Inference
  Course website on Canvas

• Spring 2027: STAT 548S, Seminar on Effective Research
  [tbd]

• STAT 548A/B qualifying papers
  [guide, expectations, and list of potential papers]

Past courses

• Fall 2023, 2024, 2025: STAT 460/560, Theory of Statistical Inference I
  

• Fall 2019, 2020, 2021, 2022: STAT 547C, Topics in Probability
  [course website: 2021, 2022] [course notes]

• Fall 2024, 2025: STAT 535C, Computational Statistics

• Summer 2020, Spring 2021, 2022, 2023, 2024, 2025, 2026: STAT 305, Introduction to Statistical Inference

• Spring 2023: STAT 547S, Topics on Symmetry in Statistics and Machine Learning
  [course outline] [course notes]

Miscellaneous Notes

• Exchangeable random partitions and random discrete probability measures: a brief tour guided by the Dirichlet Process
  B. Bloem-Reddy
  Notes for a lecture given to Oxford PhD students (these are a work in progress)
  [pdf]