Awards

. 2008 Fargo-Moorhead Chamber of Commerce Distinguished Professorship.

. 2007 North Dakota State University Distinguished Professorship.

. 2007 Best Paper Award at the International Society of Computers and Their Applications (ISCA) Conference on Computers and Their Applications (CATA). The paper was entitled "Parameter Optimized, Vertical, Nearest Neighbor Vote and Boundary-Based Classification". The conference was held in Honolulu, Hawaii in March of 2007.

. 2006 Association of Computing Machinery (ACM) Knowledge Discovery and Data Mining (KDD) Cup Winning Team Leader Task 3. Negative Prediction of Computer Aided Detection (CAD) of Pulmonary Embolism from Computer Aided Tomography (CAT) data. Our team score was twice as high as the next closest competitor score ), see http://www.cs.unm.edu/kdd_cup_2006, http://www.cs.unm.edu/files/kdd-cup-2006-task-spec-final.pdf

. 2001-2003 Jordan A. Engberg Endowed Professorship of Computer Science.

. 2002 Association of Computing Machinery (ACM) Knowledge Discovery and Data Mining (KDD) Cup Winning Team Leader of the Broad Class, Task 2. Yeast Gene Regulation Prediction: There are now experimental methods that allow biologists to measure some aspect of cellular "activity" for thousands of genes or proteins at a time. A key problem that often arises in such experiments is in interpreting or annotating these thousands of measurements. This KDD Cup task focused on using data mining methods to capture the regularities of genes that are characterized by similar activity in a given high-throughput experiment. To facilitate objective evaluation, this task did not involve experiment interpretation or annotation directly, but instead it involved devising models that, when trained to classify the measurements of some instances (i.e. genes), can accurately predict the response of held aside test instances. The training and test data came from recent experiments with a set of S. cerevisiae (yeast) strains in which each strain is characterized by a single gene being knocked out. Each instance in the data set represents a single gene, and the target value for an instance is a discretized measurement of how active some (hidden) system in the cell is when this gene is knocked out. The goal of the task is to learn a model that can accurately predict these discretized values. Such a model would be helpful in understanding how various genes are related to the hidden system. See http://www.acm.org/sigs/sigkdd/kddcup/index.php?section=2002&method=res