Weng Kee Wong

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UCLA

United States

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About

Weng Kee Wong is a Professor at UCLA in the United States. His research focuses on metaheuristics for optimizing clinical trial designs, particularly in contexts such as lithium pharmacokinetics and patient recruitment in global studies. He has contributed significantly to the fields of evolutionary computation and experimental design in biosciences. His work includes applications of mathematical programming in toxicology and solutions for order-of-addition design problems.

Recent Grants

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Designing efficient designs under model uncertainty for biological studies

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Articles (21)

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2025

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2020

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2020

Collaborators (5)

Andrew Hooker

Professor of Pharmacometrics

Uppsala University

SWEDEN

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france Mentré

INSERM

FRANCE

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Yevgen Ryeznik

Senior Lecturer/Associate Professor

Uppsala University

SWEDEN

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Hongquan Xu

University of California

UNITED STATES

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Elin Svensson

Professor

Uppsala University

SWEDEN

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Weng Kee Wong

Research Interests

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Contact this professor

About

Recent Grants

Articles (21)

Design optimization of longitudinal studies using metaheuristics: Application to lithium pharmacokinetics

Nature-inspired metaheuristics for optimizing dose-finding and computationally challenging clinical trial designs

Adaptive Parameter Tuning of Evolutionary Computation Algorithms

Optimal experimental designs for big and small experiments in toxicology with applications to studying hormesis via metaheuristics

Model Parameters Estimation for the Biosciences Using Particle Swarm Optimization

Optimal Subdata Selection for Prediction Based on the Distribution of the Covariates

Metaheuristic Solutions to Order-of-Addition Design Problems

Mathematical programming tools for randomization purposes in small two‐arm clinical trials: A case study with real data

Frederic Schoenberg and Weng Kee Wong’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’

Yingnian Wu and Weng Kee Wong’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’

Optimizing Patient Recruitment in Global Clinical Trials using Nature-Inspired Metaheuristics

Constructing two-level $$Q_B$$-optimal screening designs using mixed-integer programming and heuristic algorithms

How continuing innovation, web-based tools and metaheuristics can further promote use of adaptive design in practice (Preprint)

Optimal designs for health risk assessments using fractional polynomial models

Particle swarm optimization for searching efficient experimental designs: A review

Orthogonal array composite designs for drug combination experiments with applications for tuberculosis

Spatial two-stage designs for phase II clinical trials

Pharmacometrics meets statistics—A synergy for modern drug development

Constructing robust and efficient experimental designs in groundwater modeling using a Galerkin method, proper orthogonal decomposition, and metaheuristic algorithms

Hybrid algorithms for generating optimal designs for discriminating multiple nonlinear models under various error distributional assumptions

On Optimal Designs for Clinical Trials: An Updated Review

Collaborators (5)

Andrew Hooker

france Mentré

Yevgen Ryeznik

Hongquan Xu

Elin Svensson

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