Nilah Ioannidis

Nilah Ioannidis is an Adjunct Assistant Professor in the Department of CS at the University of California, Berkeley. Her research focuses on Biosystems & Computational Biology (BIO) and Artificial Intelligence (AI). She has a joint appointment in EECS and the Center for Computational Biology (CCB). Her group develops computational methods to analyze and interpret personal genomes, including machine learning and deep learning techniques to predict the clinical impact of genome variation. Dr. Ioannidis previously served as a postdoctoral scholar at Stanford University and earned her Ph.D. in Biophysics from Harvard University. She is also affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR).

Nilah Monnier Ioannidis is an Assistant Professor at the University of California, Berkeley, focusing on computational and experimental approaches to understand genetic variation and its impact on disease. Her research spans the prediction of pathogenic variants, analysis of molecular dynamics in live cells, and identification of susceptibility loci for genetic disorders. She develops statistical and machine learning methods to interpret genomic data, with applications in cancer genetics and molecular biology. Her work includes studies on mRNA dynamics, translation hot-spots, and the role of genetic variants in disease risk. Ioannidis also explores the functional implications of genomic data through cross-protein transfer learning and Bayesian statistical models.