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Stefan A Robila
Montclair State University
Montclair, New Jersey

Stefan Robila is a professor of computer science at Montclair State University (Montclair, NJ). He completed his undergraduate studies in computer science at the University of Iasi, Romania in 1997 and then continued his education with an MS in Computer Science (2000) and a PhD in Computer Information Science (2002) at Syracuse University, Syracuse, NY.

His research work spans large interdisciplinary areas where computing is essential. One such area is remote sensing, where large amounts of data need to be collected and processed. Robila focused on processing spectral data, and designed, implemented, and tested new methods that improved both processing accuracy and computational efficiency. His work on separating different spectra for improved material identification and the proposed parallel and distributed algorithms were published in leading conference venues and journals. Expanding from here, he is now tackling diverse large data problems including environmental monitoring of data centers and sentiment analysis extraction. In parallel, Robila has also focused on ethical and cybersecurity problems, investigating for example the impact of social connections in phishing attacks, or societal implications of the use of remote sensing.

Stefan Robila has extensive mentoring experience, working with dozens of undergraduate and graduate students on independent studies, projects, and theses. Supported by two National Science Foundation awards, he managed for six years a Research Experience for Undergraduates program at Montclair that allowed 48 students to develop projects in imaging and computer vision and participate in activities focused on research process development and support for pursuing research careers. Robila is actively involved in the dissemination of knowledge related to undergraduate research. Involving CS faculty from eight different universities, he organized two panel discussions on undergraduate research presented at computing conferences, in addition to presenting on multidisciplinary research.

Having taught courses that span the majority of the computer science and information technology curriculum, Robila is also interested in how technology can help the learning process. In 2006, an HP grant brought to MSU tablet computers for use in computer courses, and allowed Robila and other faculty the opportunity to investigate tablet technology at an early stage. Support from SPIE allowed him to design novel courses in large data processing, and deliver imaging and optics outreach activities to visiting K-12 cohorts.

Stefan Robila is an ACM and IEEE senior member and IEEE Region I award recipient. He has been a reviewer for Computing Reviews since 2006.


Compressed sensing for distributed systems
Coluccia G., Ravazzi C., Magli E., Springer Publishing Company, Incorporated, New York, NY, 2015. 97 pp.  Type: Book

As the title suggests, this book focuses on recent advances in the research involving compressed sensing for distributed systems. It is written by three researchers with expert knowledge in the field. Compressed sensing refers to the p...


Learning group-based dictionaries for discriminative image representation
Lei H., Mei K., Zheng N., Dong P., Zhou N., Fan J. Pattern Recognition 47(2): 899-913, 2014.  Type: Article

Being able to correctly find and identify images based on either visual or language cues is an activity easily performed by humans. Unsupervised computing environments have yet to reach the same level of accuracy, as is evident from th...


WISEngineering: supporting precollege engineering design and mathematical understanding
Chiu J., Malcolm P., Hecht D., DeJaegher C., Pan E., Bradley M., Burghardt M. Computers & Education 67142-155, 2013.  Type: Article

Improving students’ understanding of science, technology, engineering, and mathematics (STEM) concepts is a critical goal of today’s society. Particularly in the US, encouraging students to pursue STEM-related profe...


 Unsupervised methods for the classification of hyperspectral images with low spatial resolution
Villa A., Chanussot J., Benediktsson J., Jutten C., Dambreville R. Pattern Recognition 46(6): 1556-1568, 2013.  Type: Article

Spectral imaging constitutes a fascinating type of data. Organized as a set of grayscale images, with each image recording the reflected light of the same scene under a different light wavelength range, a spectral image can reveal sign...


Visual enhancement of old documents with hyperspectral imaging
Joo Kim S., Deng F., Brown M. Pattern Recognition 44(7): 1461-1469, 2011.  Type: Article

Unlike regular color imaging that aims to represent a scene through a composition of the three fundamental colors (red, green, and blue), hyperspectral imagery (HSI) is identified as extreme since it is composed of tens or hundreds of ...


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