Dan Boley is known for his past work on numerical linear algebra methods for control problems, parallel algorithms, iterative methods for matrix eigenproblems, inverse problems in linear algebra, as well as his more recent work on computational methods in statistical machine learning, data mining, and bioinformatics. His current interests include scalable algorithms for convex optimization in machine learning, the analysis of networks and graphs such as those arising from metabolic biochemical networks and networks of wireless devices.

Research Division
Data Science and Machine Learning

Current Research
Google Scholar

Ph.D. Graduates
Tatiana Lenskaia (2021, BICB); Shaozhe Tao (2018, ISyE; co-advised with Shuzhong Zhang); Ham Ching Lam (2014, Scientific Computation); Dimitrije Jevremovic (2013); Dongwei Cao (2007); Kenneth C Reinert (2006, Neuroscience; co-advised with Tim Ebner); David Littau (2005); Todd Goehring (1999); Seung-Woo Kim (1997); Jose Ignacio Aliaga (1995, Systems Theory, Poly. Univ. of Valencia (Spain); co-advised with Prof. V.  Hernandez); M. M. Wang (1994, Control Science; co-advised with E. B. Lee).