Amir Najam

AMIR NAJAM   Citizenship Place of Birth: North Battleford, Saskatchewan, Canada Canadian citizen Back to top Degrees Ph.D. University of Toronto Computer Science 1973 M.Sc. University of Toronto Computer Science 1970 B.A. University of Saskatchewan Mathematics 1968   Doctoral Dissertation Title Algorithms for Analytic Approximation Back to top Employment History 2009—present Professor Emeritus Computer Science Univ of Waterloo 1991—  2008 Professor Computer Science Univ of Waterloo 2001—  2002 Associate Chair, Undergraduate Affairs Computer Science Univ of Waterloo 1996—  1998 Associate Dean, Computing & Director of MFCF Faculty of Mathematics Univ of Waterloo 1991—  1994 Associate Dean, Graduate Studies & Research Faculty of Mathematics Univ of Waterloo 1978—  1991 Associate Professor Computer Science Univ of Waterloo 1988—  1989 Associate Dean, Computing Faculty of Mathematics Univ of Waterloo 1980—  1982 Associate Chair, Graduate Affairs Computer Science Univ of Waterloo 1973—  1978 Assistant Professor Computer Science Univ of Waterloo 1973Summer Assistant Professor Computer Science Univ of Toronto Back to top Awards and Honours 2014 2014 Alumni of Influence Award University of Saskatchewan 2011 ACM/SIGSAM Richard D. Jenks Memorial Prize to The Maple Project For Excellence in Software Engineering Applied to Computer Algebra 2011 Journal of Symbolic Computation, 46 (7), July 2011 Special Issue in Honour of Keith Geddes on his 60th Birthday 2008 MICA 2008: Milestones in Computer Algebra A 60th birthday conference in honour of my career 2004 NSERC Synergy Award to U of W, Western & Maplesoft Recognizing productive collaboration between SCG, ORCCA & Maplesoft 1997 Paul G. Sorenson Distinguished Graduate Lecture University of Saskatchewan 1969—  1973 NRC Postgraduate Scholarship For graduate study at University of Toronto 1969 High Honours in Mathematics University of Saskatchewan 1968 Honours Scholarship (for 4th-year study) University of Saskatchewan    

My research interests are in the areas of machine learning, statistical learning theory, and reinforcement learning. I work on the theoreticalanalysis of computationally efficient methods for large or otherwise complex prediction problems. One example is structured prediction problems, where there is considerable complexity to the space of possible predictions. Such methods are important in a variety of application areas, including natural language processing, computer vision, and bioinformatics. A second area of interest is the analysis of prediction methods in a deterministic, game-theoretic setting. As well as being of interest in areas such as computer security, where an adversarial environment is a reasonable model, this analysis also provides insight into the design and understanding of prediction methods in a probabilistic setting. A third area of interest is the design of methods for large scale sequential decision problems, such as control of Markov decision processes. Again, computational efficiency is a crucial requirement. This is a common feature in all of these areas: the interplay between the constraint of computational efficiency and the statistical properties of a method.