معرفی
Brian Cleary serves as an Assistant Professor in Boston University's Faculty of Computing & Data Sciences (CDS), with cross-appointments in Biology and Biomedical Engineering departments. He is a core faculty member in the Bioinformatics Program and the Biological Design Center at the Rajen Kilachand Center for Integrated Life Sciences & Engineering, conducting interdisciplinary research at the intersection of computer science and biology.
His educational trajectory includes dual undergraduate degrees in Biology and Business, Economics, and Management from Caltech, followed by 8 years developing trading algorithms in finance before returning to academia. He completed his PhD in Computational and Systems Biology at MIT in 2019.
Cleary's research pioneers computational approaches to decipher spatial gene expression patterns in tissues, focusing on theoretical frameworks that transform cellular and tissue physiology understanding. His lab implements paired computational-experimental methodologies to study organ development (particularly brain and ovary), disease progression mechanisms, and tissue organization principles through machine learning and statistical innovations.
Analysis of his recent publications reveals dominant themes in compressed sensing techniques for high-throughput biological interrogation, spatial transcriptomics optimization, and scalable genetic screening methods. His work consistently bridges algorithmic innovation with biological discovery across reproductive biology, cardiovascular disease, and infectious disease diagnostics.
Scientific recognition includes:
- Independent Broad Fellow at the Broad Institute of MIT and Harvard
Cleary actively recruits PhD students and postdocs for his Algorithmic Lens on Biology Laboratory, leveraging both computational and wet-lab approaches. His research program emphasizes experimental design informed by statistical learning theory to overcome scalability limitations in biological measurement systems.
The Algorithmic Lens on Biology Laboratory operates across two physical locations: the Center for Computing and Data Sciences (15th floor) and the Biological Design Center (6th floor in CILSE), employing random composite experiments and low-dimensional feature learning to study cellular pathways and tissue organization at unprecedented scales.
