Dr. Daniel Reker

Biography

Reker's lab meticulously integrates biomedical data science with wet-lab experimental analysis, aiming to design personalized therapeutic opportunities. He employs automated experimentation driven by active machine learning to generate rich datasets, which are essential for understanding and improving effective active machine learning workflows. A significant aspect of his research focuses on developing adaptive models to predict critical drug properties such as efficacy, biodistribution, metabolism, toxicity, and side effects. These predictions are pivotal for comprehending the limitations of currently approved medications and for designing new drug candidates and nanoparticles. Reker is also involved in integrating clinical data analysis to rapidly validate the translational relevance of these predictions, striving to conceive data-driven protocols for precision medicine and personalized drug delivery. He holds an Sc.D. from the Swiss Federal Institute of Technology-ETH Zurich, and his work reflects a commitment to advancing the intersection of computational methods with practical biomedical applications.

Research Interests

Courses