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Olagunju, Amos
St Cloud State University
St Cloud, Minnesota
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Amos Olagunju is a professor in the Department of Computer Science and Information Technology at St. Cloud State University (SCSU) in Minnesota. He previously served as the interim dean of undergraduate studies for two years at SCSU. Prior to that position, he served as the dean of the School of Graduate Studies and chief research officer at Winston Salem State University in North Carolina. Amos served as the chair of the Mathematics and Computer Science Department, and later the Computing and Information Sciences Department, at Delaware State University (Dover, DE). Before that, he taught in the Asian Division at the University of Maryland University College, North Carolina A&T State University, and Michigan State University.

A faculty fellow and later a senior faculty fellow selected jointly by the American Society of Engineering Education and the Navy, Amos developed manpower mobilization and data-mining algorithms for monitoring the retention behaviors of personnel. As a member of the technical staff at Bell Communications Research (now Telcordia), he developed an architecture for a generalized C transaction environment, quantitative models for system workload projection and characterization, software metrics, and managerial decision support systems.

Amos developed statistical methods for the determination of content validity to obtain his doctorate in educational research and evaluation from the University of North Carolina at Greensboro. He investigated a distributed model as a basis for keyword detection to earn his master’s in computer and information sciences from Queen’s University (Canada). He received a bachelor’s degree in mathematics and computer science from Ahmadu Bello University in Nigeria. Amos was designated as an ACM senior member in 2007. His current research interests are in the areas of bioinformatics, quantitative security risk assessments, numerical computing, and artistic storytelling of breakthrough computing algorithms and technologies. He has been a reviewer for Computing Reviews since 2005, and has written over 100 reviews.

Date Reviewed  
- 10 of 197 reviews

   People, ideas, milestones: a scientometric study of computational thinking
Saqr M., Ng K., Oyelere S., Tedre M.  ACM Transactions on Computing Education (TOCE) 3(21): 1-17, 2021. Type: Article

The fascinating debate over the definition, scope, tools, and environments for advocating computational thinking (CT) promotes interdisciplinary educational collaborations and discoveries among scientists worldwide. But what should innovative advo...

Dec 30 2021  
  Trustworthy AI
Wing J.  Communications of the ACM 10(64): 64-71, 2021. Type: Article

Artificial intelligence (AI) techniques are useful in creating effective computing tools for diverse applications in areas such as transportation, agriculture, medicine, and justice systems. Yet the credibility of AI is still an interesting subjec...

Dec 14 2021  
   Data-driven anomaly detection with timing features for embedded systems
Lu S., Lysecky R.  ACM Transactions on Design Automation of Electronic Systems 24(3): 1-27, 2019. Type: Article

The Internet of Things (IoT) continues to usher in the joys of connecting several house appliances and electronic devices via wired and wireless networks. But how should the rooted systems that support IoT provide security and privacy for online u...

Oct 20 2021  
   Curriculum learning for speech emotion recognition from crowdsourced labels
Lotfian R., Busso C.  IEEE/ACM Transactions on Audio, Speech and Language Processing 27(4): 815-826, 2019. Type: Article

In computer applications such as synergistic games, gratifying robots, and speech recognition systems, the ability to identify emotions is invaluable. But how should effective algorithms and systems be designed for discerning emotions from diverse...

Aug 20 2021  
  Multigrid for matrix-free high-order finite element computations on graphics processors
Kronbichler M., Ljungkvist K.  ACM Transactions on Parallel Computing 6(1): 1-32, 2019. Type: Article

Discretization is a method for transforming continuous variables, equations, functions, and models into their discrete equivalents. A multigrid technique uses a hierarchy of discretization to solve elliptic partial differential equations (PDEs). C...

Apr 7 2021  
   Aspect aware learning for aspect category sentiment analysis
Zhu P., Chen Z., Zheng H., Qian T.  ACM Transactions on Knowledge Discovery from Data 13(6): 1-21, 2019. Type: Article

Do you like (or dislike) “fruit flies like a banana,” but not “time flies like an arrow”? How should humans and computerized systems accurately distinguish between predefined and undefined categories of words used in senten...

Apr 6 2021  
   Data-driven model-based detection of malicious insiders via physical access logs
Cheh C., Thakore U., Fawaz A., Chen B., Temple W., Sanders W.  ACM Transactions on Modeling and Computer Simulation 29(4): 1-25, 2019. Type: Article

Employees with security clearance will perhaps continue to pose the ultimate security threat to businesses, organizations, and security researchers. What kinds of data and algorithms should be effectively used to monitor and thwart risky employees...

Mar 8 2021  
   DeepXplore: automated whitebox testing of deep learning systems
Pei K., Cao Y., Yang J., Jana S.  Communications of the ACM 62(11): 137-145, 2019. Type: Article

Many of us use trustworthy electronic systems, from self-driving car owners to online bankers and shoppers. How should real-life computer systems be methodically tested for nearly all potential faults and malware threats, to instill confidence in ...

Jan 12 2021  
   Modern debugging: the art of finding a needle in a haystack
Spinellis D.  Communications of the ACM 61(11): 124-134, 2018. Type: Article

The rapidly emerging smart Internet of Things (IoT) hardware and devices that make our lives comfortable and secure require faultless and reliable software. But what techniques and tools should programmers and software engineers be cognizant of to...

Oct 26 2020  
   A unified framework with multi-source data for predicting passenger demands of ride services
Wang Y., Lin X., Wei H., Wo T., Huang Z., Zhang Y., Xu J.  ACM Transactions on Knowledge Discovery from Data 13(6): 1-24, 2019. Type: Article

Ride-sharing service customers look for and deserve fair fares. However, with the use of the Internet to access competing fares when booking shared rides, how should ride-sharing providers forecast passenger demands in order to remain competitive?...

Sep 15 2020  
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