AI and Machine Learning

Machine Learning Brickell Center Miami

AI and Machine Learning

Navigating oceans of data and making discoveries within is possible only with the use of efficient algorithms. As data grows in volume, velocity, variety, and veracity, so does the demand for efficiency in Data Science applications. AI and Machine Learning streamline algorithms based on algorithmic and methodological developments. The computational methods of AI aim to mimic human intelligence and also exceed human capabilities—assisting humans in decision making and solving complex problems.

The numerous branches of artificial intelligence encompass Machine Learning, Natural Language Understanding (spoken and written), Computer Vision, Data Mining, Human-Computer Interfaces, Data Visualization, and Deep Learning. In all these areas, data plays a crucial role as algorithmic innovations are developed from within while taking inspiration from mathematics, statistics, and physics. The applications of these major branches of AI are broad, reaching far beyond the traditional realm of science and engineering and ushering in crucial advances in medicine, the social sciences, business, and even the arts and humanities.

University of Miami Institute for Data Science and Computing AI and ML Director Mitsunori Ogihara

Mitsunori Ogihara, PhD
Professor, Department of Computer Science, College of Arts and Sciences
Director, IDSC Education
Director (Interim), IDSC AI and Machine Learning

Dr. Mitsunori Ogihara joined the University of Miami in 2007 as Professor in the Department of Computer Science and as Program Director of the Big Data Analytics & Data Mining Program for the Center for Computational Science (now IDSC). More recently, he served as Associate Dean for Digital Library Innovation in the College of Arts and Sciences. Dr. Ogihara holds secondary appointments in the Department of Electrical and Computer Engineering in the College of Engineering and the Department of Music Media and Industry in the Frost School of Music.

Dr. Ogihara obtained his PhD in Information Sciences from the Tokyo Institute of Technology in 1993. From 1994 to 2007, Dr. Ogihara was a Computer Science faculty member at the University of Rochester, where he was promoted to Associate Professor with tenure in 1998, and to Full Professor in 2002. He also served as Chair of the Department from 1999 to 2007.

His research interests are in data mining, information retrieval, network traffic data analysis, program behavior analysis, molecular computation, and music information retrieval. A prolific scholar, Dr. Ogihara has authored/co-authored three books The Complexity Theory CompanionMusic Data Mining, and Fundamentals of Java Programming, and the author of more than 200 peer-reviewed research papers. Many papers by Dr. Ogihara are through interdisciplinary collaborations. His articles appear in journals and conferences that cover many fields, including psychology, implementation science, library science, chemistry, biology, and digital humanities. He is currently serving as Editor-in-Chief for the Theory of Computing Systems Journal (Springer) and on the editorial board for the International Journal of Foundations of Computer Science (World Scientific).