Ruth W. T. Chew

I’m currently a Research Associate in the School of Computing in National University of Singapore (NUS). I work with Prof. Bryan Low in the GLOW.AI lab.

I’m interested in probabilistic reasoning, large language models (LLMs), and autonomous systems. My goal is to develop principled methods for reliable reasoning in AI systems, bridging theory and practice.

I have research background in Bayesian optimization, and over nine years of practical AI/deep learning experience across robotics, computer vision, and geospatial sensors. Previously, I was a part of BumbleBee Autonomous Systems (BBAS) from 2015-2019, a student-run team in NUS, building autonomous maritime robots as the Software Lead. Subsequently in 2024, I collaborated with BeeX, a start-up that spun-off from BBAS. I was also a Senior Member of Technical Staff in DSO National Laboratories and worked on computer vision for satellite imagery from 2019-2024.

I’m applying for PhD positions starting in 2026!

selected publications

ICML

BILBO: BILevel Bayesian Optimization

Ruth Wan Theng Chew, Quoc Phong Nguyen, and Bryan Kian Hsiang Low

In International Conference on Machine Learning, 2025

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Bilevel optimization is characterized by a two-level optimization structure, where the upper-level problem is constrained by optimal lower-level solutions, and such structures are prevalent in real-world problems. The constraint by optimal lower-level solutions poses significant challenges, especially in noisy, constrained, and derivative-free settings, as repeating lower-level optimizations is sample inefficient and predicted lower-level solutions may be suboptimal. We present BILevel Bayesian Optimization (BILBO), a novel Bayesian optimization algorithm for general bilevel problems with blackbox functions, which optimizes both upper- and lower-level problems simultaneously, without the repeated lower-level optimization required by existing methods. BILBO samples from confidence-bounds based trusted sets, which bounds the suboptimality on the lower level. Moreover, BILBO selects only one function query per iteration, where the function query selection strategy incorporates the uncertainty of estimated lower-level solutions and includes a conditional reassignment of the query to encourage exploration of the lower-level objective. The performance of BILBO is theoretically guaranteed with a sublinear regret bound for commonly used kernels and is empirically evaluated on several synthetic and real-world problems.
ICLR

Optimistic Bayesian optimization with unknown constraints

Quoc Phong Nguyen, Wan Theng Ruth Chew, Le Song, and 2 more authors

In The Twelfth International Conference on Learning Representations, 2024

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Though some research efforts have been dedicated to constrained Bayesian optimization (BO), there remains a notable absence of a principled approach with a theoretical performance guarantee in the decoupled setting. Such a setting involves independent evaluations of the objective function and constraints at different inputs, and is hence a relaxation of the commonly-studied coupled setting where functions must be evaluated together. As a result, the decoupled setting requires an adaptive selection between evaluating either the objective function or a constraint, in addition to selecting an input (in the coupled setting). This paper presents a novel constrained BO algorithm with a provable performance guarantee that can address the above relaxed setting. Specifically, it considers the fundamental trade-off between exploration and exploitation in constrained BO, and, interestingly, affords a noteworthy connection to active learning. The performance of our proposed algorithms is also empirically evaluated using several synthetic and real-world optimization problems.