Professor (CS, ECE)
Office: University Crossings 139
- Ph.D. Computer Science, Ohio State University
- M.S. Computer Science, University of Delaware
- B.A. Mathematics, University of Wisconsin-Madison
Computer algebra, algebraic algorithms, automatic performance tuning, high performance computing
Jeremy Johnson is Professor of Computer Science and Electrical and Computer Engineering at Drexel University. He served as the first Department Head for the newly formed Computer Science department from 2002-2012. His research interests include algebraic algorithms, computer algebra systems, problem solving environments, programming languages and compilers, high performance computing, and automated performance tuning. He directs Drexel's Applied Symbolic Computation lab which focuses on the use of symbolic computation to derive and optimize the implementation of algorithms with mathematical structure. He currently serves as chair of the ACM special interest group on symbolic and algebraic manipulation (SIGSAM) and serves on the editorial board of the journal of Applicable Algebra in Engineering, Communication and Computing. He previously served on the steering committee of the International Symposium on Symbolic and Algebraic Computation (ISSAC). He was guest editor of two issues of the journal of symbolic computation devoted to Computer Algebra and Signal Processing and a collection of papers related to ISSAC 2009 for which he was general chair. He also co-edited a book on Quantifier Elimination and Cylindrical Algebraic Decomposition. He was a founding member of the SPIRAL project on the automatic generation and optimization of digital signal processing algorithms and was a key part of the DARPA funded Very High Dimensionality Study, which developed the mathematical framework and a prototype domain specific language (Tensor Product Language) and special purpose compiler that led to the SPL language. He was also a member of the DoE funded Power grid project which developed hardware to accelerate load flow computation and various sparse linear algebra kernels.