The Paul S. and Shirley Goodman award was established in 1997 by former MIS professor Dr. Seymour Goodman in honor of his parents Paul S. and Shirley Goodman.
The award is given to MIS doctoral students who excel professionally in the study of international developments in the field of computer science. Award amount varies from year to year.
Seymour (Sy) E. Goodman is professor of International Affairs and Computing at the Sam Nunn School of International Affairs and the College of Computing, Georgia Institute of Technology. He also serves as Co-Director of the Center for International Strategy, Technology, and Policy and Co-Director of the Georgia Tech Information Security Center.
Professor Goodman studies international developments in information technologies and related public policy issues. In this capacity, he has over 200 publications and served on many academic, government, and industry advisory, study, and editorial committees.
He has been the International Perspectives editor for the Communications of the ACM for the last nineteen years, and has studied computing on all seven continents in about one hundred countries.
He recently served as Chair of the Committee on Improving Cybersecurity Research in the United States, National Research Council, and as a member of the Computer Science and Telecommunications Board of the National Academies of Science and Engineering. [more]
Congratulations to the latest recipient of this award:
Hongyi Zhu - 2018
Hongyi Zhu is a Ph.D. student at the University of Arizona’s Artificial Intelligence (AI) Lab in the Management Information Systems (MIS) department. As a research associate in the lab, Hongyi has primarily worked on designing advanced unobtrusive mobile analytical frameworks for smart home care. His research focuses on recognition, extraction, and analysis of subjects’ in-house behaviors (e.g., motion, activities, object usage) from raw mobile sensors data. For example, Hongyi developed the major middleware of a National Science Foundation (NSF) funded sensor-based home monitoring/data collection system for senior care. He also developed a novel activity state representation for the Sequence-to-Sequence model to recognize Activities of Daily Living (ADLs) for arbitrary sensor combinations. This framework exploited the temporal behavioral patterns of the residents and is generalizable to the emerging smart home environments for daily activity surveillance. This work has been published in one of the major journals in health informatics and analytics. Hongyi further proposed a novel hierarchical, multi-phase, deep-learning based ADL recognition framework to extract motion semantics at various granularities (e.g., human-object interactions, gestures, ADLs) for enhanced interpretability. In addition to mobile health analytics, Hongyi is interested in other domains of research with high societal impact such as technology outcome/knowledge dissemination assessment and cybersecurity. He is dedicated to contributing to advanced business intelligence and data analytics methodologies.