420 The UMAP Journal 22.4(2001) Presentation Some papers revealed tremendous analysis but lacked clarity in the pre- sentation. The strong papers presented the problem, discussed the data and explained their analysis, and finally revealed the development of their math- ematical methods/models. The big difference in papers was whether they informed the reader of what they did and, more important, how they did it. A clear presentation allowed the judge to comprehend their logic and reasoning One judge noted that he wished he was a mind reader because there was clearly lots of outstanding work; however, only the result was revealed. The strong papers revealed their analysis, not just the result Very broadly, we saw two types of weak presentations. The first consisted of reports that had a significant narrative, but no support in the form of math- ematical modeling or analysis. In these reports, the groups appeared to rely on qualitative observations and the information from the literature(web sites) to reach conclusions. The other type of poor-quality report was those that had a significant amount of mathematics in the form of tables and graphs, but no modeling or analysis to pull it together. These papers appeared to dump their computer runs into the report but did not really know what to do with them This year we noticed that the stronger teams clearly documented informa- tion they gathered from outside sources. When constructed models aligned very closely with models found in the open literature, it became difficult for judges to determine what was original work. Conclusion The effort and creativity of almost every team was inspiring. It appear however, that most teams can reason better than they can communicate. Often wonderful ideas were not revealed to the reader. The necessity to work with large data sets appeared much more difficult than anticipated. The top papers, however, did an amazing effort of blending and revealing the science research and mathematics. The best teams revealed the power of interdisciplinary prob lem solving About the author Gary Krahn received his Ph D in Applied Mathematics at the Naval Post- graduate School. He is currently the Head of the Dept of Mathematical Sci- ences at the u.s. military academy at West point. his current interests are in the study of generalized de bruijn sequences for communication and coding applications. He enjoys his role as a judge and Associate Director of the ICM420 The UMAP Journal 22.4 (2001) Presentation Some papers revealed tremendous analysis but lacked clarity in the pre￾sentation. The strong papers presented the problem, discussed the data and explained their analysis, and finally revealed the development of their math￾ematical methods/models. The big difference in papers was whether they informed the reader of what they did and, more important, how they did it. A clear presentation allowed the judge to comprehend their logic and reasoning. One judge noted that he wished he was a mind reader because there was clearly lots of outstanding work; however, only the result was revealed. The strong papers revealed their analysis, not just the results. Very broadly, we saw two types of weak presentations. The first consisted of reports that had a significant narrative, but no support in the form of math￾ematical modeling or analysis. In these reports, the groups appeared to rely on qualitative observations and the information from the literature (web sites) to reach conclusions. The other type of poor-quality report was those that had a significant amount of mathematics in the form of tables and graphs, but no modeling or analysis to pull it together. These papers appeared to dump their computer runs into the report but did not really know what to do with them. This year we noticed that the stronger teams clearly documented informa￾tion they gathered from outside sources. When constructed models aligned very closely with models found in the open literature, it became difficult for judges to determine what was original work. Conclusion The effort and creativity of almost every team was inspiring. It appears, however, that most teams can reason better than they can communicate. Often, wonderful ideas were not revealed to the reader. The necessity to work with large data sets appeared much more difficult than anticipated. The top papers, however, did an amazing effort of blending and revealing the science, research, and mathematics. The best teams revealed the power of interdisciplinary prob￾lem solving. About the Author Gary Krahn received his Ph.D. in Applied Mathematics at the Naval Post￾graduate School. He is currently the Head of the Dept. of Mathematical Sci￾ences at the U.S. Military Academy at West Point. His current interests are in the study of generalized de Bruijn sequences for communication and coding applications. He enjoys his role as a judge and Associate Director of the ICM