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Professor of Computational Biology Emeritus at Carnegie Mellon University
Robert F. Murphy is Professor of Computational Biology Emeritus in the School of Computer Science at Carnegie Mellon University. He was the Ray and Stephanie Lane Professor of Computational Biology and Professor of Biological Sciences, Biomedical Engineering, and Machine Learning at Carnegie Mellon until his retirement in May 2021. He founded the Computational Biology Department in the School of Computer Science at Carnegie Mellon and served as its head from 2009 to 2020. He also 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 a Fellow of the IEEE and the American Institute of Medical and Biological Engineering. His interests and experience are in many aspects of machine learning, computational biology and biomedical research, especially in machine learning methods for biomedical image analysis and modeling and AI systems for driving experimental science.
Caltech
Doctor of Philosophy (Ph.D.), Biochemistry
January 1, 1974 – January 1, 1979
Columbia College, Columbia University
Bachelor of Arts (B.A.), Biochemistry
January 1, 1971 – January 1, 1974
Carnegie Mellon University
Professor of Computational Biology Emeritus
June 1, 2021 – Present
Quantitative Medicine LLC
Founder and Board Member
January 1, 2012 – June 1, 2021
Carnegie Mellon University
Head, Computational Biology Department
September 1, 2009 – June 1, 2021
Albert Ludwigs University of Freiburg, Germany
Honorary Professor and External Senior Fellow
January 1, 2008 – Present
Greater Freiburg Area
Carnegie Mellon University
Lane Professor of Computational Biology
September 1, 2007 – June 1, 2021
Carnegie Mellon University
Professor of Biological Sciences, Biomedical Engineering and Machine Learning
July 1, 2003 – June 1, 2021
Cultural Fit Analysis
The candidate's background is primarily in academia and scientific research, with a strong emphasis on computational biology and machine learning. While this demonstrates intellectual rigor and a problem-solving mindset, the direct cultural fit for a standard 'Software Engineer' role in a corporate environment is unclear. The experience is highly specialized and leadership-focused within a research context, which may differ significantly from the collaborative, product-driven, and often fast-paced culture of a typical software engineering team. The candidate's stated interest in 'stimulating new challenges' is positive, but the specific alignment with a software engineering culture needs further exploration.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, strategic thinking, and a deep commitment to scientific advancement. Their experience in founding and leading a department, as well as co-founding a company, indicates significant initiative and organizational skills. The focus on interdisciplinary research (computational biology, machine learning, biomedical engineering) suggests adaptability and a collaborative mindset. However, the resume does not provide direct evidence of operational fit for a typical software engineering role, as the experience is heavily academic and research-oriented rather than focused on software development lifecycle, agile methodologies, or specific programming practices in an industry setting.