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Soon Ae Chun
City University of New York
Staten Island, New York
 

Soon Ae Chun is a City University of New York (CUNY) College of Staten Island (CSI) professor and director of its Information Systems and Informatics (ISI) program. She also teaches at the CUNY Graduate Center (GC) in both the computer science PhD program and the Master’s program in data science. She is the director of the National Science Foundation (NSF)-sponsored Information Security Research and Education Lab (iSecure Lab). In 2018, she was awarded a Fulbright Senior Scholarship. She received the CSI President’s Dolphin Award for Outstanding Scholarly Achievement in 2014.

Dr. Chun applies data management, data analytics, machine learning, and semantic web and knowledge-based approaches to security, privacy, digital government, smart cities, and digital health. She served as president of the Digital Government Society from 2016 to 2017, and is a founding editor-in-chief of Digital Government: Research and Practice (DGOV).

Her research has been funded by NSF, the National Oceanic and Atmospheric Administration (NOAA), New Jersey state government agencies, the National Research Foundation of Korea, and the Professional Staff Congress (PSC-CUNY). She is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM).

She has been a CR reviewer since 2013.


     

Deep item-based collaborative filtering for top-N recommendation
Xue F., He X., Wang X., Xu J., Liu K., Hong R.  ACM Transactions on Information Systems 37(3): 1-25, 2019. Type: Article

Recommender systems are an essential component in digital platforms, nudging consumers toward more efficient decision making by predicting and presenting products and services in a personalized ranking order that is of top interest. By filtering o...

 

Coding-data portability in systematic literature reviews: a W3C’s open annotation approach
Díaz O., Medina H., Anfurrutia F.  EASE 2019 (Proceedings of the Evaluation and Assessment on Software Engineering, Copenhagen, Denmark,  Apr 15-17, 2019) 178-187, 2019. Type: Proceedings

Systematic literature reviews (SLRs) involve several steps: the planning step, which identifies the scope of literature according to the research goals, and develops a coding protocol; the analysis step, which performs searching for relevant liter...

 

Understanding movement in context with heterogeneous data
Derin O., Mitra A., Stroila M., Custers B., Meulemans W., Roeloffzen M., Verbeek K.  MOVE 2019 (Proceedings of the 1st ACM SIGSPATIAL International Workshop on Computing with Multifaceted Movement Data, Chicago, IL,  Nov 5, 2019) 1-4, 2019. Type: Proceedings

Mobility studies on humans, vehicles, and animals involve trajectory data, which allows for location tracking over time per entity. Trajectory data can help with analyzing and understanding the location information of an entity or a crowd, the flo...

 

 Searching for global employability: can students capitalize on enabling learning environments?
Isomöttönen V., Daniels M., Cajander Å., Pears A., Mcdermott R.  ACM Transactions on Computing Education (TOCE) 19(2): 1-29, 2019. Type: Article, Reviews: (2 of 2)

Today’s higher education systems need to produce graduates with global employability that exhibits creativity and innovation, that is, the ability to solve open-ended problems in different cultural settings, but also domain-specific skill se...

 

Linked open knowledge organization systems: definition of a method for reducing the traversing
Chicaiza J., Tapia-Leon M., Piedra N., Lopez-Vargas J., Tovar-Caro E.  APPIS 2019 (Proceedings of the 2nd International Conference on Applications of Intelligent Systems, Las Palmas de Gran Canaria, Spain,  Jan 7-9, 2019) 1-6, 2019. Type: Proceedings

Knowledge organization systems (KOS) allow for establishing and accessing a common vocabulary and concepts in a domain. Linked data refers to a semantic web knowledge organization method that links concepts by relationships on the web to facilitat...

 
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