Bio:

Charles Elkan joined the UCSD faculty in 1990, after earning his Ph.D. that same year in computer science at Cornell University. He did his undergraduate degree at Cambridge University. Elkan was a postdoctoral fellow at the University of Toronto. In 1998-99, he was a visiting Associate Professor in computer science at Harvard University. While at Harvard, he was Senior Scientist at the software firm Knowledge Stream Partners. Elkan has consulted for Hewlett-Packard, SAIC, Sony, IBM, and Alcoa. He has won numerous best-paper awards, including first-place at the CoIL Challenge 2000 data mining competition. Elkan is the co-founder of UCSD's Artificial Intelligence Laboratory.

Professor Elkan has expanded his early interest in fuzzy logic to writing learning, search, and reasoning algorithms. He was an early pioneer in Web applications of artificial intelligence, notably in the area of information retrieval (e.g. how search engines process very general or very specific queries to yield the best matches possible). Elkan has developed algorithms for reasoning about database queries and updates, and methods of formalizing commonsense knowledge about causation. He also co-authored a powerful Web engine for comparing proteins and DNA--allowing biologists to detect shared features and evolutionary relationships among the flood of protein and DNA sequence data produced by the Human Genome Project. Elkan is currently co-directing the Knowledge and Data Engineering (KDE) effort within Calit2--a broad effort in database and data mining research to support applications involving massive data sets (initially from medical imaging and environmental sensor networks). He has also taken his work in AI and applied it to stock-market trading strategies. Elkan can also talk on how scientists and engineers communicate their ideas to the media and the public, having authored the CSE department's 'Notes on Giving a Research Talk.'