Daniel BoleyView profile
Professor
Daniel Boley is a Professor and Distinguished University Teaching Professor at the University of Minnesota, within the College of Science and Engineering, Department of Computer Science and Engineering. He serves as the Director of Graduate Studies for the Graduate Program in Data Science, which offers a Master's of Science and a Post-Baccalaureate Certificate. His office is located in Kenneth H. Keller Hall at 4-225C. Professor Boley's research spans computational methods in linear algebra, scalable data mining algorithms, and applications in systems biology and bioinformatics. His work focuses on scalable algorithms for convex optimization in machine learning, analysis of networks and graphs from metabolic biochemical networks, and wireless device networks. He has made significant contributions to numerical linear algebra methods for control problems, parallel algorithms, and iterative methods for matrix eigenproblems. His research interests also include algebraic models in systems and evolutionary biology, and biochemical metabolic networks. His recent publications demonstrate a strong focus on applying graph theory and network analysis to diverse domains including robot swarms, medical imaging (particularly for glioblastoma and COVID-19 diagnosis), and metabolic network analysis. His work bridges theoretical computer science with practical applications in biology and medicine, with a consistent emphasis on developing scalable computational methods. The trend shows increasing interdisciplinary collaboration, particularly with medical researchers. Distinguished Member by the ACM Top university award for post baccalaureate, graduate and professional education Distinguished University Teaching Professor title Professor Boley has advised numerous PhD students including Tatiana Lenskaia (2021), Shaozhe Tao (2018), Ham Ching Lam (2014), and others dating back to 1994. His research has been supported by various grants enabling work on scalable computation of elementary pathways through metabolic networks, Markov models of viral evolution, and scalable data mining algorithms for text analysis. He has developed software tools for clustering, dot plot visualization, and educational graphics. Professor Boley directs the Graduate Program in Data Science and has been involved in projects such as the Principal Direction Divisive Partitioning (PDDP) Project. His research group develops practical implementations of theoretical advances, including the PDDP clustering algorithm, Dot.py genome viewer, and various educational graphics tools for introductory programming courses. He maintains active collaborations across disciplines, particularly in bioinformatics and medical imaging applications.
