Manfred Minimair, Ph.D.

Professor, Program Director for Computer Science, Cybersecurity, Data Science
Department of Mathematics and Computer Science

Manfred Minimair is a computer scientist and applied mathematician with strong interests in Cybersecurity, Data Science, Symbolic Computation, and Applied Computing. He teaches various topics from Computer Science, Cybersecurity and Data Science, including data mining, statistics, data visualization, big data, cloud computing, and computational mathematics.

He has spearheaded the development of several pioneering programs at Seton Hal University which he directs:

• M.S. in Data Science,
• Minor/Certificate in Cybersecurity, and
• Minor in Data Analytics.

In addition, he serves as the program director for the B.S. in Computer Science.

Dr. Minimair represents Seton Hall University in the Academic Engagement network of the US Cybercommand and in the New Jersey Big Data Alliance and is member of the Steering Committee of the NY/NJ Cyber Fraud Task Force of the US Secret Service. He is also co-chair of the Northern Jersey Chapter of IEEE Computational Intelligence Society. Dr. Minimair enjoys promoting engineering skills among his students and is also counselor for the IEEE Student Branch at Seton Hall University.

Dr. Minimair’s research spans areas from machine learning applications, data analytics, collaborative software design, to symbolic computation. A recent project studied machine learning methods to discover factors impacting performance in baseball pitching. Another work researched the design of software facilitating collaborative work for discoveries in data science and computational mathematics. The software enables teams of data scientists and computational mathematicians to develop scripts in languages such as R and Python and use these scripts for gaining insights from their data. Another one of his papers investigated and visualized biological data on fruit flies. 
His works in symbolic computation studied how to efficiently compute with large-scale systems of polynomial equations used in mathematical models of engineering and science. These investigations made use of given structures to efficiently simplify the systems and determine whether there is a solution for the models.