Faculty Scholars

Faculty Scholar Brian Dean, Ph.D. at  Clemson University, Clemson South Carolina

Brian C. Dean, Ph.D.

School of Computing
College of Engineering, Computing and Applied Sciences

Contact: 864-656-5866 or bcdean@clemson.edu  

Who is Professor Dean?

Brian Dean earned his undergraduate and graduate degrees in computer science from M.I.T. and is currently professor and chair of the computer science division of the Clemson School of Computing. Dean’s research is in applied algorithms, developing computational techniques and platforms that support the acquisition and analysis of data from a variety of areas of application, primarily biomedical domains. Due to his background in theoretical computer science and applied mathematics, his work uses a wide range of computational methods, such as optimization, machine learning, signal and image processing, and big data analytics. He currently collaborates with academic and clinical partners at Clemson, MUSC, and Prisma Health. Dean also serves as graduate program coordinator for the joint Clemson-MUSC graduate degree program in Biomedical Data Science and Informatics (BDSI).

For more information, see his departmental faculty profile.

How Professor Dean's research is transforming health care

Dean is currently involved in several projects that could have significant positive impact on health care; they are briefly summarized below:
  1. Novel algorithms for genome-wide association studies, and more generally feature selection in biomedical data sets, using techniques inspired by game theory as well as methods for fusion of analyses derived from multiple heterogenous data sets.
  2. Developing automated tools for understanding epilepsy. In a long-term collaboration with neurologists at MUSC, he has developed a web-based platform, called EEGnet, for collection and analysis of expert opinion on diagnosing Epilepsy. To date, dozens of neurologists have used this platform to help us analyze data from hundreds of subjects. Goals of the project include: understanding human inter-rater reliability issues and construction of automated machine learning models for diagnosis of Epilepsy from EEG data.
  3. Network analysis of the human brain. In collaboration with researchers at MUSC, he has designed novel algorithmic methods for analyzing high-resolution network models of the human brain, derived from MRI data. Through analysis of both structural and functional brain networks, he hopes to understand connectivity patterns indicative of Autism, Alzheimer’s, and traumatic brain injury, and how these relate to developmental changes in the brain.
  4. Recent pilot projects in collaboration with researchers at MUSC involve the use of AI and other techniques to classify lung nodules and perform 3D segmentation of heart valves.

    Health Research Expertise Keywords

    AI, Computational Neuroscience, Bioinformatics, Data Mining, Machine Learning, Medical Imaging, Biological Modeling