Daniel Reker

Daniel Reker serves as an Assistant Professor of Biomedical Engineering at Duke University's Pratt School of Engineering and is a member of the Duke Cancer Institute. His research integrates computational and experimental approaches to address critical challenges in drug discovery and delivery. With a strong emphasis on translational impact, his work bridges the gap between theoretical machine learning advancements and practical biomedical applications.

Dr. Reker's educational background includes a Sc.D. from the Swiss Federal Institute of Technology-ETH Zurich (Switzerland) in 2016. His academic journey has positioned him at the intersection of computer science, chemistry, and pharmacology, enabling his unique approach to biomedical engineering challenges.

Research in the Reker Lab centers on the tight integration of biomedical data science and wet-lab experiments for therapeutic development. The lab specializes in active machine learning workflows that guide automated experimentation to generate knowledge-rich datasets. Key research areas include predicting critical drug properties (efficacy, biodistribution, metabolism, toxicity), designing novel drug candidates and nanoparticles, and developing big data-driven protocols for precision medicine. The lab maintains both computational infrastructure and wet laboratory facilities, creating a truly interdisciplinary environment where students engage in both computational and experimental work.

Analysis of Dr. Reker's publication history reveals a consistent focus on machine learning applications in drug discovery, with particular emphasis on active learning methodologies. His recent work demonstrates increasing sophistication in integrating multiple data sources and developing specialized algorithms for pharmaceutical challenges. The publications span from fundamental algorithm development to translational applications, showing progression from in silico predictions to animal model validation and clinical relevance assessment.

Dr. Reker actively contributes to educational initiatives at Duke, having established a popular 'Machine Learning in Pharmacology' course and collaborating on a 'Biomedical Data Science Master's Certificate' program. His teaching portfolio includes multiple graduate and undergraduate courses in biomedical engineering, reflecting his commitment to training the next generation of interdisciplinary scientists.

The Reker Lab operates as a highly collaborative unit with established projects across Pharmacology, Biology, Chemistry, Biomedical Engineering, Environmental Engineering, and Immunology departments. This collaborative approach is amplified by Duke's unique proximity between university and hospital, enabling direct translational pathways for research findings. The lab's dual computational-experimental setup allows for rapid iteration between prediction and validation, creating a powerful engine for biomedical innovation.