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Daniel Reker

Assistant Professor of Biomedical Engineering
Biomedical Engineering

Overview


The Reker lab tightly integrates biomedical data science and wet-lab experiments for the analysis and design of therapeutic opportunities. Automated experimentation can be guided by active machine learning to generate knowledge-rich datasets. A key aspect of our research is improving our understanding of the most effective active machine learning workflows to enable the broad deployment of adaptive machine learning and automated experimentation.

We focus our adaptive model development on critical drug properties such as efficacy, biodistribution, metabolism, toxicity, and side-effects. Prospective applications of these predictions enable us to better understand limitations of currently approved medications as well as design new drug candidates, nanoparticles, and pharmaceutical formulations. By integrating clinical data analysis, we can rapidly validate the translational relevance of our predictions and conceive big data-driven protocols for precision medicine and personalized drug delivery.

Current Appointments & Affiliations


Assistant Professor of Biomedical Engineering · 2021 - Present Biomedical Engineering, Pratt School of Engineering
Member of the Duke Cancer Institute · 2022 - Present Duke Cancer Institute, Institutes and Centers

In the News


Published December 3, 2024
Research & Innovation Seed Grants Total Nearly $2 Million
Published July 31, 2023
Allowing Machine Learning to Ask Questions Can Make It Smarter
Published August 19, 2022
Five Questions for Dan Reker on Bioengineering Better Drug Delivery

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Recent Publications


Taking a deep dive with active learning for drug discovery.

Journal Article Nature computational science · October 2024 Full text Cite

Yoked learning in molecular data science

Journal Article Artificial Intelligence in the Life Sciences · June 1, 2024 Active machine learning is an established and increasingly popular experimental design technique where the machine learning model can request additional data to improve the model's predictive performance. It is generally assumed that this data is optimal f ... Full text Cite
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Recent Grants


Designing Personalized Formulations with Machine Learning

ResearchPrincipal Investigator · Awarded by National Institutes of Health · 2023 - 2028

NSF Engineering Research Center for Precision Microbiome Engineering (PreMiEr)

ResearchInvestigator · Awarded by National Science Foundation · 2022 - 2027

University Training Program in Biomolecular and Tissue Engineering

Inst. Training Prgm or CMEMentor · Awarded by National Institutes of Health · 1994 - 2027

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Education, Training & Certifications


Swiss Federal Institute of Technology-ETH Zurich (Switzerland) · 2016 Sc.D.