Michael Mior Headshot

Michael Mior

Assistant Professor
Department of Computer Science
Golisano College of Computing and Information Sciences

585-475-5810
Office Location
Office Mailing Address
102 Lomb Memorial Drive Rochester, NY 14623-5608

Michael Mior

Assistant Professor
Department of Computer Science
Golisano College of Computing and Information Sciences

Education

BS in Computing Science, University of Ontario Institute of Technology (Canada); MS in Computer Science, University of Toronto (Canada); Ph.D. in Computer Science, University of Waterloo (Canada)

Bio

Michael completed his Masters degree at the University of Toronto and received a PhD from the University of Waterloo. He. joined RIT as an Assistant Professor in 2018. His research revolves around schema design and management for NoSQL databases.

585-475-5810

Areas of Expertise

Currently Teaching

CSCI-620
3 Credits
This course provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. First, practical techniques used in exploratory data analysis and mining are introduced; topics include data preparation, visualization, statistics for understanding data, and grouping and prediction techniques. Second, approaches used to store, retrieve, and manage data in the real world are presented; topics include traditional database systems, query languages, and data integrity and quality. Case studies will examine issues in data capture, organization, storage, retrieval, visualization, and analysis in diverse settings such as urban crime, drug research, census data, social networking, and space exploration. Big data exploration and management projects, a term paper and a presentation are required. Sufficient background in database systems and statistics is recommended.
CSCI-729
3 Credits
This course examines current topics in Data Management. This is intended to allow faculty to pilot potential new graduate offerings. Specific course details (such as prerequisites, course topics, format, learning outcomes, assessment methods, and resource needs) will be determined by the faculty member(s) who propose a specific topics course in this area. Specific course instances will be identified as belonging to the Data Management cluster, the Security cluster, or both clusters.