I am a first-year Ph.D. student in Computer Science at the University of Ottawa, EECS, supervised by Prof. Changjian Shui. Previously, I completed my M.Sc. in Electrical Engineering at McGill University and Mila — Quebec AI Institute under the supervision of Prof. Tal Arbel. I also received my B.Eng. in Computer Engineering from McGill University.
My research studies predictable AI, with an emphasis on developing theoretically grounded quantities that allow us to anticipate the reliability and consequences of AI systems before their outcomes are observed. My earlier work focused on predicting inference reliability through confidence calibration, my current work extends this perspective to the training process, asking whether the effect of data interventions on population generalization can itself be predicted before the intervention is performed.
📰 News
- 2025.11: Our paper “BayesAgent: Bayesian Agentic Reasoning Under Uncertainty via Verbalized Probabilistic Graphical Modeling” has been accepted to AAAI 2026.
- 2025.10: Our Calibration Guidance Prompt Generator 1.5B model is now live on Hugging Face 🤗 ! [Link]
📝 Publications
Xing Shen, Justin Szeto, Mingyang Li, Hengguan Huang, and Tal Arbel
The 28th International Conference on Medical Image Computing and Computer Assisted Intervention, 2025
Xing Shen, Hengguan Huang, Brennan Nichyporuk, and Tal Arbel
IEEE Transactions on Medical Imaging, 2025
Anita Kriz*, Elizabeth Laura Janes*, Xing Shen*, and Tal Arbel
(*equal contribution)
IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025
🎖 Honors and Awards
- University of Ottawa Ph.D. Research Fellowship (2026)
- Graduate Research and Enhancement Travel (GREAT) Award (2025)
- Healthy Brains, Healthy Lives (HBHL) Graduate Student Fellowship - funded via Canada First Research Excellence Fund (CFREF) (2024)
- McGill University Master’s Research Fellowship (2024)
- McGill Faculty of Engineering Class 2023 Distinction
- McGill Summer Undergraduate Research in Engineering (SURE) Award (2023)
🎓 Education
- 2026.01 – Present: Ph.D. in Computer Science, University of Ottawa, CGPA 10.0/10.0
- 2024.01 – 2026.05: M.Sc. in Electrical Engineering, McGill University and Mila — Quebec AI Institute, CGPA 4.0/4.0
- 2018.09 – 2023.05: B.Eng. in Computer Engineering, McGill University, Graduated with Distinction
💻 Professional Service
- Conference Reviewer: NeurIPS (2025–2026), AISTATS (2025–2026), AAAI (2027)
- Journal Reviewer: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Transactions on Machine Learning Research (TMLR), IEEE Journal of Biomedical and Health Informatics (JBHI), Frontiers in Artificial Intelligence, Frontiers in Radiology, Journal of Imaging Informatics in Medicine
- Teaching Assistant: CSI 4106 Introduction to Artificial Intelligence, CSI 3131 Operating Systems, University of Ottawa
📖 Blog
- 2026.08: Provable AI, Predictable AI, and Guardrails A proof is a conditional promise about inputs chosen in advance, while predictability is a forecast about the inputs a system actually meets. Neither property contains the other. A two-term bound makes visible exactly what a certificate leaves on the table, and guardrails turn out to attack only one of those two terms.
- 2026.08: Formatting Reference for Posts: A Reminder to Myself What is available when writing a post here: math, tables, figures, code, and footnotes, with no setup needed beyond dropping a Markdown file into the posts folder.