Nilah Ioannidis

Nilah Ioannidis is a Professor in the Department of Electrical Engineering and Computer Sciences (EECS) and the Center for Computational Biology (CCB) at University of California, Berkeley. Her research focuses on computational methods for analyzing personal genomes using machine learning and deep learning approaches.

Education:

• Ph.D. in Biophysics, Harvard University (2013)

Her work spans predicting the clinical impact of genetic variants, modeling gene expression dynamics, and developing tools like REVEL for variant pathogenicity prediction. Current research emphasizes genomic AI, non-coding variant interpretation, and cell-type-specific modeling.

Recent publications highlight advancements in sequence-based gene expression prediction, variant effect benchmarking, and the application of transfer learning to genomic datasets. These works bridge computational methods with clinical and molecular biology applications.

Scientific Awards:

• Chan Zuckerberg Biohub Investigator Award (2022-2027)
• Computational Transformation Fellow, UC Noyce Initiative (2022-2023)
• Okawa Foundation Research Grant (2021)
• NIH K99/R00 Pathway to Independence Award (2017-2022)

She has advised PhD students and collaborated with labs such as the Berkeley Artificial Intelligence Research Lab (BAIR) and the Center for Computational Biology (CCB).