We aim to understand how transcription factors (TFs) ensure adequate and timely gene expression in human development. To do this, we combine chemical genetic tools to quantitatively manipulate TF function, sequence-to-activity deep learning approaches...
Our lab is interested in studying the mechanisms of spatio-temporal tumor evolution, progression and therapy response. Our current research focuses on scalable inference methods and modeling of single-cell genomic and in situ imaging data to study the co...
Our group develops and applies efficient and accurate statistical methods and computational tools to understand the genetic/genomic basis of human disease risk and progression using large-scale biobanks. We also leverage high-dimensional omics data to...
Our lab develops and applies computational methods in an emerging space we call “translational epigenomics”. This space encompasses efforts to advance precision oncology by deeply characterizing cancer epigenomes. By examining epigenetic changes in...
Dongwon Lee’s laboratory aims to understand how gene regulation contributes to the development and progression of human diseases, specifically focusing on kidney diseases. We use a combination of single-cell multiomics data, genetic data from disease...
The Lotter Lab performs interdisciplinary research with two core aims: 1) developing more robust computer vision algorithms using insights from human vision, and 2) optimizing these algorithms for medical imaging to deliver real-world impact. We aim to...
Arjun Manrai directs a research lab of machine learning scientists, clinicians, and biomedical data scientists working to improve medical decision making using computation. His group develops quantitative approaches to support physicians in making...
Pranav Rajpurkar is driven by a fundamental passion for building reliable artificial intelligence (AI) technologies for biomedical decision making. His lab approaches biomedical problems with a computational lens, developing AI algorithms, datasets, and...
Research:
AI holds tremendous promise in enabling scientific breakthroughs and discoveries in diverse areas. Dr. Zitnik investigates machine learning with a current focus on networked systems that require infusing structure and domain knowledge. This...
