Meet the Mower Fellows

Dr. Homilius MaxDr. Max Homilius

Dr. Homilius received his MPhil degree in Computational Biology from the University of Cambridge and his PhD degree in Computer Science from Princeton University. His area of expertise is in Computational Biology, Biomedical Informatics and Machine Learning. At One Brave Idea, his research is focused on machine-learning approaches that relate clinical information from the electronic health record with molecular and genetic data in large patient cohorts, with a particular focus on cardiovascular disease. This includes models for the automated classification of clinical events and disease history from medical notes and the development of novel blood-based biomarkers with clinical relevance for patient stratification and improved personalization of treatment.


Dr. Shinichi GotoDr. Shinichi Goto

Dr. Goto is a cardiologist who graduated from Keio University School of Medicine and trained at Keio University School of Medicine and Kudanzaka Hospital in Japan. With deep expertise in data analytics and artificial intelligence, he specializes in quantitative and systematic approaches to understanding complex mechanisms using computer simulation. He has been a central part of the One Brave Idea work for several core projects. As a Mower fellow at One Brave Idea, Shinichi has been working on applying artificial intelligence techniques to medical data with the goal of transforming how we interpret genetic information and also use it in clinical practice.


Dr. Wandi ZhuDr. Wandi Zhu

Dr. Wandi Zhu is a biomedical engineer by training. She earned her Ph.D. Degree from Washington University in St. Louis. With expertise in cellular electrophysiology and engineering, she specializes in using interdisciplinary approaches to uncover precisely controlled cellular mechanisms underlying complex biological processes. After joining the One Brave Idea, she has been investigating how blood and vascular systems sense forces in the dynamic circulation in health and disease, using a combination of imaging, microfluidic devices, machine learning, and electrophysiology methods.