Scholar
Zois Boukouvalas
Google Scholar ID: Kmm3ZbMAAAAJ
American University
Citations & Impact
Citations
1,461
H-index
14
i10-index
20
Publications
20
Co-authors
0
Contact
Publications
Resume (English only)
Academic Achievements
- Published multiple academic articles, including a recent article critically examining state-of-the-art approaches to tackling misinformation; received two awards from the Energetics Technology Center for the development of human-assisted machine learning and natural language processing approaches for energetics and for the discovery of energetic materials using machine learning.
Research Experience
- Served as a lead principal investigator on several research projects; participated as an expert panelist in international workshops; co-organized a EUSIPCO special session on Latent Variable Methods: Theoretical Advances and Applications in the Age of Machine Learning; collaborated with Dr. Japkowicz on an interdisciplinary project using machine learning techniques to detect misinformation on Twitter.
Education
- 2004-2008: BS in Mathematics, University of Patras (Greece); 2009-2011: MS in Applied and Computational Mathematics, Rochester Institute of Technology; 2011-2013: MS in Applied Mathematics, UMBC; 2013-2017: PhD in Applied Mathematics, UMBC, under the supervision of Dr. Tülay Adali.
Background
- Associate Professor at the Mathematics and Statistics Department of American University. Research focuses on developing interpretable machine learning models and algorithms for multi-modal data analysis, combining aspects from information geometry, mathematical statistics, and numerical optimization. His work involves different types of data, including biomedical images for studying psychiatric illnesses, social and linguistic data for understanding political and social trends, and chemical data for drug discovery and materials design. Communicates research findings through peer-reviewed publications, invited talks, and podcast interviews.
Miscellany
- Aims to create effective environments that encourage and support the success of underrepresented students for entry into machine learning careers through his research and teaching activities.
Co-authors