Allison (Tsz Kwan) Lau
Hi! I am a final year undergraduate student in computer science and physics at the University of Toronto, under the mentorship of Prof. Rahul Krishnan.
I am currently working on alignment methods and side-channel attacks on Large Language Models (LLMs).
I seek to learn as broadly as possible — not just to master a subject, but to uncover new ways of seeing. I am an explorer at heart, driven by a curiosity to understand the world, humans, intelligent systems, and the relationship between the artificial and the natural. My work spans diverse research domains, including causal effect estimation and systems security in LLMs, computational sensing, and interdisciplinary science and instrumentation. I believe that computation should serve not just optimization, but meaning.
News
- 24' Dec 10:: I am in Vancouver for NeurIPS! I'll be presenting a poster on Dec 14 (Sat) at 4:30-5:30pm: Personalized Adaptation via In-Context Preference Learning @ AFM
Publications
[0] Personalized Adaptation via In-Context Preference Learning
Allison Lau, Younwoo Choi, Vahid Balazadeh, Keertana Chidambaram, Vasilis Syrgkanis, Rahul Krishnan
NeurIPS 2024 Workshop on Adaptive Foundation Models
We introduce the Preference Pretrained Transformer (PPT), a method for adaptive personalization using online user feedback. PPT combines offline training with a history-dependent loss and online adaptation through in-context learning to dynamically align with individual preferences.
[1] Analyzing the effect of undermining on suture forces during simulated skin flap surgeries with a three-dimensional finite element method
Wenzhangzhi Guo, Allison Lau, Joel C. Davies, Vito Forte, Eitan Grinspun, Lueder Alexander Kahrs
EG VCBM 2024
We developed a 3D mesh generation pipeline to model skin flap surgeries and systematically explored variations in undermining regions. We conducted suture force and suture line analyses to assess outcomes across these regions, and vsualized skin flap procedures using personalized 3D face scans for enhanced patient-specific planning.
[2] Beyond CCDs: Characterization of sCMOS detectors for optical astronomy
Aditya Khandelwal, Sarik Jeram, Ryan Dungee, Albert W.K. Lau, Allison Lau, Ethen Sun, Phil Van-Lane, Shaojie Chen, Aaron Tohuvavohu, Ting S. Li
SPIE Astronomical Telescopes + Instrumentation 2024
We highlight the suitability of sCMOS detectors for space imaging, evaluating detectors like the Teledyne Prime 95B for wide fields and Hamamatsu Orca-Quest for deep-sky imaging, which demonstrated exceptional performance and low noise levels.
Education
🐸 was at ETH Zurich (2025-2027, MS), University of Toronto (2021-2025, BS)