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Daniel Reker
Assistant Professor at Duke University
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:
- Developing active machine learning workflows for automated experimentation
- Predicting critical drug properties such as efficacy, biodistribution, metabolism, toxicity, and side-effects
- Designing new drug candidates, nanoparticles, and pharmaceutical formulations
- 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.
Highlights
Only 50 days until the Biomedical Engineering Society #BMES2024 Annual Meeting and the lab is excited to come out to the Charm City in full force to present our latest work on #MachineLearning for #DrugDiscovery and #DrugDelivery! Mark your calendars for our presentations and make sure to say Hi if you would like to connect.
Thrilled to share our Nature Chemistry News & Views article led by Chloe Markey. We delve into the groundbreaking work of AstraZeneca, Google DeepMind, and the University of Cambridge directed by Lucy Colwell to design GCGR/GLP-1R dual agonists with machine learning. We hope our discussion can provide insights and context for a wide audience to appreciate this important work. https://rdcu.be/dQ6SR
