Advisor: Andreas Pfenning
Irene received her B.S. in Mathematics from the Massachusetts Institute of Technology in 2010. There, she was involved in developing methods for leveraging protein-protein interaction networks to select hits in RNAi datasets and leveraging conservation information to predict exonic splicing enhancers. She then went to graduate school at Stanford University, where she received her Ph.D. in Computer Science in 2017. At Stanford, she developed methods to analyze novel high-throughput sequencing datasets to better understand the roles of DNA methylation and Cys2-His2 zinc finger transcription factor binding in transcriptional regulation. Irene is interested in developing computational approaches to integrate biological datasets in ways that will provide a more detailed understanding of transcriptional regulation. She is especially interested in elucidating the biological mechanisms behind the differences in gene regulation between different cell types.
Jose received his Ph.D. in Computer Science with a minor in Bioinformatics from Indiana University (IU) under the supervision of Predrag Radivojac. His doctoral research focused on the development of robust kernel methods for learning and mining on noisy and complex graph and hypergraph data. Prior to that, he received dual B.S. degrees in Computer Science and Mathematics at the University of Puerto Rico-Rio Piedras and M.S. degree in Computer Science at the University of California-San Diego. Additionally, he worked as a postdoctoral fellow in the Precision Health Initiative at IU where he developed computational approaches towards understanding protein function and how disruption of protein function leads to disease. Overall, Jose’s research interests include computational biology, machine learning and data mining.