CDS Welcomes First Incoming PhD Medical Track Cohort
This Fall, the Center for Data Science is pleased to welcome its first incoming cohort of medical track PhD students: Daniel Im, Taro Makino, Boyang Yu, and Weicheng Zhu. Launched this Fall 2020, the CDS and NYU School of Medicine track program aims to train students in the application of core data science skills to healthcare and medicine. CDS Medical Track PhD students follow the core Data Science PhD curriculum and then take additional courses and work with faculty in the School of Medicine in areas including imaging, population health, systems biology/genetics, bioinformatics, neuroscience, and cardiovascular disease. These incoming students are embarking on an important new advance in the partnership between data science methodology and crucial medical research, which has already yielded important breakthroughs in areas such as COVID-19 tracking and breast cancer detection.
“Data science for medicine is where both the greatest potential and peril of data science are at,” says CDS Associate Professor Kyunghyun Cho, who was one of the early proponents of the medical track initiative. “The medical track … has been designed to and will train you to fully realize this great potential of data science for medicine while preemptively minimizing and addressing any negative consequences, by combining the state-of-the-art data science methods and medical expertise.”
The incoming medical track students focus on a wide range of domains and research interests. Daniel Im joined CDS medical track program in order to study explainable AI to build trust AI-based medical diagnosis systems. Daniel is interested in developing methods for explainable artificial intelligence (AI) with the intent of making an AI model’s decisions more transparent and interpretable. “The outcome of an ML model can lead to serious consequences in the application to healthcare,” he says. “It is important that we understand how the ML model reaches its conclusion.”
Taro Makino is interested in researching robustness and explainability in deep learning, in the context of medical images. “I am excited to participate in the medical track at CDS because it is a unique opportunity to collaborate with researchers at the forefront of integrating machine learning into clinical practice,” says Taro. “This requires expertise from both the machine learning and clinical sides, which I feel very privileged to be able to leverage for my own research.”
Weicheng Zhu, too, is focused on deep learning for medical imaging. “I am excited about applying machine learning to optimize clinical operations and expand healthcare capacities, and I am also interested in unmasking and solving inherent problems in medical data with novel machine learning methodologies,” he says. “The support from both CDS and NYU Langone enables me to extend my research in the interdisciplinary field.” Weicheng’s recent work includes interpreting graph representations from electronic health records.
Boyang Yu joined the medical track because, she says, “Healthcare is the best place where intricacy meets interpretability.” Boyang has studied the association between body composition and mortality of lung cancer patients, and is broadly concerned with the intersection of machine learning and healthcare.
This intrepid group of students are the trailblazers in building a lasting model for cutting-edge medical and machine learning advancement. CDS Assistant Professor Carlos Fernandez-Granda is another tireless advocate for the track who is now seeing his efforts come to fruition. Fernandez-Granda agrees in the groundbreaking potential of this effort. “Data science and machine learning have the potential to revolutionize medicine. In order to fully realize this potential, it is critical to combine cutting-edge methodology with advanced medical expertise and real-world data,” he says. “This is the goal of the PhD Medical Track.”
