Daniel Reker

Daniel Reker is an Assistant Professor of Biomedical Engineering at Duke University.12 He joined Duke in September 2020 after completing his postdoctoral fellowship at the Massachusetts Institute of Technology (MIT).1

Education and Early Career

Reker holds a Bachelor of Science degree from Technische Universität Darmstadt, as well as a Master of Science and a Doctor of Science (PhD) from ETH Zurich (Swiss Federal Institute of Technology).15 During his studies, he worked as a teaching assistant at TU Darmstadt and as an ABAP developer at b2tec Software Ltd.1

Research Focus

The Reker lab at Duke University integrates biomedical data science and wet-lab experiments to analyze and design therapeutic opportunities.57 Their research focuses on:

1. Developing active machine learning workflows for automated experimentation
2. Predicting critical drug properties such as efficacy, biodistribution, metabolism, toxicity, and side-effects
3. Designing new drug candidates, nanoparticles, and pharmaceutical formulations
4. Integrating clinical data analysis for precision medicine and personalized drug delivery

Appointments and Affiliations

• Assistant Professor of Biomedical Engineering at Duke University124
• Member of the Duke Cancer Institute246

Awards and Honors

Throughout his career, Reker has received several notable awards and recognitions, including:

• Forbes 30 under 30 Europe "Science and Healthcare" (2016)1
• MIT-IBM Watson AI Lab Fellowship (2018)1
• Koch Institute Image Award (2019)1
• Early and Advanced PostDoc Mobility Fellowships from the Swiss National Science Foundation1

Teaching

At Duke, Reker teaches various courses in the Biomedical Engineering department, including:

• Biomaterials
• Projects in Biomedical Engineering
• Special Topics with Lab
• Graduate Independent Study5

Daniel Reker's research combines computational approaches, particularly machine learning and artificial intelligence, with experimental work in drug discovery, development, and delivery.3 His interdisciplinary background in computer science, computational biology, and pharmaceutical sciences allows him to bridge the gap between data science and biomedical applications.