HPCGG: Bioinformatics

Isaac (Zak) Kohane is Associate Professor of Pediatrics and Health Sciences and Technology at Harvard Medical School and Director of the Children's Hospital Informatics Program. Dr. Kohane leads multiple collaborations at Harvard Medical School and its hospital affiliates in the elucidation of regulatory networks of genes and the interaction between genotype and phenotype using a variety of bioinformatics techniques. Application domains he is currently involved in include tumorigenesis, type 2 diabetes, neurodevelopment, neuro-endocrinology and transplantation biology. To aid in the diffusion of genomics into biomedical education, he has developed a course at Harvard Medical School entitled Genomic Medicine that was first offered in Spring 2003. Dr. Kohane's research builds on his doctoral work in computer science on decision support and subsequent research in machine learning applied to biomedicine. Dr. Kohane has also led the development of cryptographic health identification systems and automated personal health records and peer-to-peer pathology information networks. He was also the architect of the W3-EMRS distributed medical record system deployed at several hospitals. Dr. Kohane has published over 80 papers in biomedical informatics. He is the Principal Investigator and Co-Director of the Bioinformatics and Integrative Genomics training program at the Division of Health Science and Technology of Harvard/MIT (HST). His text on Microarrays for an Integrative Genomics was published by MIT Press last Summer. Dr. Kohane is also a practicing pediatric endocrinologist at Children's Hospital in Boston.

Marco F Ramoni is an Assistant Professor at Harvard Medical School and a Staff Scientist with the Informatics Program of Children's Hospital Boston. His current research interests focus on Bayesian methods for machine learning and their applications to structural and functional genomics. He has developed methods for the analysis of comparative microarray experiments, for clustering temporal microarray data, and for identifying haplotype tagging SNPs. He is currently involved in several genomic projects in the fields of stem cells identification, tumor classification and profiling, genomic survival analysis, and SNP analysis of autoimmune diseases. He is author of over 60 peer-reviewed publications in biomedical informatics, artificial intelligence and statistics. He is also co-founder of Bayesware, a software company developing machine-learning programs based on Bayesian methods. He is core faculty of the course Genomic Medicine at Harvard Medical School.

Dr. Park is Instructor at Harvard Medical School, with concurrent appointments as Research Staff at Children's Hospital and Instructor of Biostatistics at Harvard School of Public Health. His main research interest is in developing statistical techniques for microarray data, including how to correlate gene expression with other covariates such as patient survival times and how to derive insights into gene regulatory networks from expression data. Dr. Park's training is in applied mathematics (AB,SM, Harvard; PhD, Caltech), especially in computational methods for solving partial differential equations using massively parallel supercomputers. His interest in genomics began while he was a Postdoctoral Fellow in the biostatistics department at Harvard School of Public Health. In addition to his methodological research, he is currently involved in many collaborative projects with researchers from both basic biology and clinical laboratories.