My research has focused on closed-loop methods for efficient adaptation, using a model's own performance as signal for improvement. This has spanned targeted training-data generation and gradient-free test-time adaptation, and, more recently, self-improving models.
My PhD was at EPFL supervised by Amir Zamir, on making models more reliable under changing environments. I was also a postdoc at the Singapore-MIT research centre working on neurosymbolic methods for adaptation. In my past life, I was a quant in New York and London and worked on creating systematic investment strategies (or, a glorified coin flipper).
Controlled Training Data Generation with Diffusion Models
T. Yeo*, A. Atanov*, H. Benoit^, A. Alekseev^, R. Ray, P. Esmaeil Akhoondi, A. Zamir
ViPer: Visual Personalization of Generative Models via Individual Preference Learning
S. Salehi, M. Shafiei, R. Bachmann, T. Yeo, A. Zamir
🔍 Spotlight
4M: Massively Multimodal Masked Modelling
D. Mizrahi, R. Bachmann, O. F. Kar, T. Yeo, M. Gao, A. Dehghan, A. Zamir
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback
T. Yeo, O. F. Kar, Z. Sodagar, A. Zamir
Task Discovery: Finding the Tasks that Neural Networks Generalize on
A. Atanov, A. Filatov, T. Yeo, A. Sohmshetty, A. Zamir
🎤 Oral
3D Common Corruptions and Data Augmentation
O. F. Kar, T. Yeo, A. Atanov, A. Zamir
🎤 Oral
🎤 Oral
Robust Learning Through Cross-Task Consistency
A. Zamir*, A. Sax*, T. Yeo, O. F. Kar, N. Cheerla, R. Suri, Z. Cao, J. Malik, L. Guibas
🎤 Oral
Iterative Classroom Teaching
T. Yeo, P. Kamalaruban, A. Singla, A. Merchant, T. Asselborn, L. Faucon, P. Dillenbourg, V. Cevher
EPFL
Ph.D. in Computer Science
Advisor: Amir Zamir, Pierre Dillenbourg
Thesis: Making Computer Vision Models Robust and Adaptive
University of Cambridge
M.Phil. in Machine Learning and Machine Intelligence
Thesis: Bayesian optimization for natural language processing
Postdoctoral Researcher — Singapore-MIT Alliance for Research and Technology
Teaching Assistant — EPFL
Spring 2018, 2019, 2020: EE559 Deep Learning
Fall 2019: CS433 Machine Learning