We are an interdisciplinary group working in computational Pathology. We build data driven, computational systems to analyze high-resolution histology images of diseased tissue and other clinical data sources. Artificial intelligence aided diagnosis in Pathology has the potential to improve clinical practice for treating cancer and other diseases by increasing diagnostic precision, reducing the costs of care, speeding up clinical workflows, and making diagnostic expertise accessible to more patients. Computational approaches also have the potential to advance the state of care by facilitating the discovery of novel biomarkers for disease prognosis and therapeutic response as well as advancing basic scientific investigation of disease processes.

The major focus of our research is analysis of the large amounts of high-resolution spatial-omics data that are starting to be generated in Pathology laboratories such as digitized H&E stained slides, multiplex immunofluorescence, spatially resolved transcriptomics, and 3D histology images. The scale and complexity of these data (e.g. whole slide images are typically 10s-100s of thousands of pixels in dimension and take years of medical training to interpret) often mean new algorithms need to be developed to fully harness the promise of these emerging tissue measurement technologies. Linking these different tissue-based data modalities together with other molecular, radiology, and electronic health record information presents significant opportunities for discovery, but also poses statistical and computational challenges. We work to solve these difficult biomedical data analysis problems by weaving together domain expertise, deep-learning, computer vision, statistical inference, and open-source software.

Team

Iain Carmichael
Assistant Professor of Pathology and Data Science, UNC-Chapel Hill
Visiting Assistant Professor, Department of Pathology, UCSF


Huong Vu
PhD student in Statistics, UC Berkeley


Neo Yin
PhD student in Statistics, UC Berkeley


Van Hovenga
PhD student in Statistics, UC Berkeley


Maggie Keying Kuang
PhD student in Biostatistics, UC Berkeley


Rodrigo Palmaka
Masters student in Statistics, UC Berkeley


Carolyn Dunlap
Masters student in the Information and Data Science program, UC Berkeley


Ian Laom
Undergraduate student in Math and Computer Science, UC Berkeley


Alexander Craig
Pathology Resident, UCSF


Daniel Qazi
Pathology Fellow, UCSF


Nabil Rahoui
Pathology Fellow, UNC-Chapel Hill


Michael Superdock
Oncology Fellow, UNC-Chapel Hill

Former team members

Shenghuan (Harry) Sun, 2024
Lead Machine Learning Scientist at Ruby Robotics

Sharan Shu, 2024
PhD Program in Statistics at Cornell University

Kaitlin Smith, 2023
Data scientist at AstraZeneca

Reya Vir, 2023
Undergraduate student in EECS at UC Berkeley

Sasmit Agarwal, 2023
Undergraduate student in EECS at UC Berkeley

Jerri Li, 2023
Citadel

Michael Sharpnack, 2023
Director, Pathologist, Department of Pathobiology at Gilead Sciences

Kevin Zhou, 2023
Masters program at UIUC