Robert F. Murphy is the Ray and Stephanie Lane Professor of Computational Biology Emeritus in the School of Computer Science at Carnegie Mellon University. He was also Professor of Biological Sciences, Biomedical Engineering, and Machine Learning at Carnegie Mellon until his retirement in May 2021. He founded the Computational Biology Department (originally the Ray and Stephanie Lane Center for Computational Biology) in the School of Computer Science at Carnegie Mellon and served as its head from 2009 to 2020. He also founded Carnegie MellonÕs B.S., M.S. and Ph.D. programs in Computational Biology, and the worldÕs first graduate program in Automated Science in 2018. He cofounded, and served on the board of directors of, Quantitative Medicine, LLC, which was acquired by Predictive Oncology Inc. in July 2020. He has been an editor for the three main bioinformatics journals, a member of the National Advisory General Medical Sciences Council and the NIH Council of Councils, and a member of a number of external advisory boards. He is an Honorary Professor of Biology at the Albert Ludwig University of Freiburg, Germany, was named as the first External Senior Fellow of the School of Life Sciences in the Freiburg Institute for Advanced Studies, and was the recipient of an Alexander von Humboldt Foundation Senior Research Award. He is a Fellow of the Institute of Electrical and Electronic Engineers IEEE) and the American Institute for Medical and Biological Engineering, and served as President of the International Society for Advancement of Cytometry. He was the first full-term chair of the Biodata Management and Analysis Study Section of the National Institutes of Health, and was a member of the National Advisory General Medical Sciences Council, and the National Institutes of Health Council of Councils.
Dr. Murphy has received research grants from the National Institutes of Health, the National Science Foundation, the American Cancer Society, the American Heart Association, the Arthritis Foundation, and the Rockefeller Brothers Fund. He has co-edited two books and three special journal issues on cell imaging, and has published over 230 research papers and has an h-index of 60.
Dr. MurphyÕs career has centered on combining fluorescence-based cell measurement methods with quantitative and computational methods. His group at Carnegie Mellon did extensive work on the application of flow cytometry to analyze endocytic membrane traffic beginning in the early 1980Õs. This work included the first (1) measurements of kinetics of rapid acidification of endocytosed material in early endosomes, (2) measurements of the kinetics of exposure of endocytosed material to hydrolytic enzymes in early endosomes in living cells, (3) demonstration in living cells of the regulation of early endosomal pH by the sodium, potassium ATPase, and (4) analysis and isolation of endocytic compartments by flow cytometry and sorting.
In the mid 1990Õs, his group pioneered the application of machine learning methods to high-resolution fluorescence microscope images depicting subcellular location patterns, and was the first to demonstrate superior machine performance in interpreting diverse patterns in biological images compared to visual interpretation. In 2003 he and B.S. Manjunath obtained major cooperative grants from the National Science Foundation to found Centers for Bioimage Informatics at Carnegie Mellon and the University of California, Santa Barbara. His groupÕs work over the past 20 years led to the development of the first (1) systems for automatically recognizing all major organelle patterns in 2D and 3D images, (2) system for building generative models of subcellular organization directly from images, (3) systems for calculating the fraction of proteins in different organelles using both supervised and unsupervised unmixing methods, and (4) systems for automatically recognizing all major subcellular patterns in tissue images. He has wide expertise in machine learning and its applications to biomedical problems.
Beginning in 2011, his group worked on applying and developing active machine learning methods for driving biomedical research campaigns. Working with Ph.D. students Armaghan Naik, Joshua Kangas and Devin Sullivan, he conducted the first Automated Science experiments involving modeling of complex phenotypes that were not known in advance. Automated Science is an iterative approach in which experiments are chosen by machine learning and executed using laboratory automation.
As Emeritus, he continues to oversee his research groupÕs work on funded research projects. His main areas of focus are machine learning methods for biomedical image analysis and AI systems for autonomously driving closed-loop experimental science campaigns.
Dr. Murphy received an A.B. in Biochemistry from Columbia College and a Ph.D. in Biochemistry from the California Institute of Technology. He was a Damon Runyon-Walter Winchell Cancer Foundation Postdoctoral Fellow with Dr. Charles Cantor at Columbia University.