Keynotes


Smita Krishnaswamy


Smita Krishnaswamy
Yale School of Medicine, USA

Short Bio.
Smita Krishnaswamy is an Assistant Professor in the department of Genetics at the Yale School of Medicine and Department of Computer Science in the Yale School of Applied Science and Engineering. She is also affiliated with the Yale Center for Biomedical Data Science, Yale Cancer Center, and Program in Applied Mathematics. Smita’s research focuses on developing unsupervised machine learning methods (especially graph signal processing and deep-learning) to denoise, impute, visualize and extract structure, patterns and relationships from big, high throughput, high dimensional biomedical data. Her methods have been applied variety of datasets from many systems including embryoid body differentiation, zebrafish development, the epithelial-to-mesenchymal transition in breast cancer, lung cancer immunotherapy, infectious disease data, gut microbiome data and patient data.

Smita teaches three courses: Machine Learning for Biology (Fall), Deep Learning Theory and applications (Spring), Advanced Topics in Machine Learning & Data Mining (Spring). She completed her postdoctoral training at Columbia University in the systems biology department where she focused on learning computational models of cellular signaling from single-cell mass cytometry data. She was trained as a computer scientist with a Ph.D. from the University of Michigan’s EECS department where her research focused on algorithms for automated synthesis and probabilistic verification of nanoscale logic circuits. Following her time in Michigan, Smita spent 2 years at IBM’s TJ Watson Research Center as a researcher in the systems division where she worked on automated bug finding and error correction in logic.

Eytan Ruppin


Eytan Ruppin
National Cancer Institute, USA

Eytan Ruppin, M.D., Ph.D., is a computational biologist whose research is focused on developing and harnessing data science approaches for the integration of multi-omics data to better understand the pathogenesis of cancer, its evolution and treatment. We collaborate with numerous experimental cancer labs, aiming to develop new computational approaches to jointly gain a network-level integrative view of the systems we study, focusing on cancer metabolism, genomics and immunotherapy. From a translational perspective, together with our collaborators, we aim to predict and test novel drug targets and biomarkers to treat cancer more effectively. After serving as a Computer Science and Medicine professor at TAU (Israel) and UMD (Maryland), Eytan has recently joined the NCI to setup and head its new Cancer Data Science Lab (CDSL).

Joel Saltz


Joel Saltz
Stony Brook University, USA

Dr. Saltz is a pioneer in developing Digital Pathology tools, methods and algorithms with the ultimate goal of extracting and leveraging digitalized Pathology information to better predict cancer outcome and to steer cancer therapy. He is also an expert in high-end computing and has developed a variety of highly cited systems software methods.

Dr. Saltz has served as founding chair of the Department of Biomedical Informatics at both Emory University, The Ohio State University and now Stony Brook University. A fellow of the American College of Medical Informatics, Dr. Saltz received his bachelor’s and master’s degrees in mathematics at the University of Michigan and then entered the MD/PhD program at Duke University, with his PhD studies performed in the Department of Computer Sciences. He later completed his residency in clinical pathology at Johns Hopkins School of Medicine.