Curriculum Vitae et Studiorum
My Vita in PDF format

Ph.D. in Computational Science and Informatics, Track in Computational Itelligence and Knowledge Mining
George Mason University, 2005

M.S. in Computer Science, Track in Artificial Intelligence and Machine Learning
George Mason University, 2000

B.S. in Computer Science, Track in Computer Networks
The Catholic University of America, 1998

In 2013 I received the 'Medaglia di Rappresentanza' from the Italian Ambassador on behalf of the president of the Italian Republic

Medaglia di Rappresentanza


My Ph.D. Lineage

PhD Tree



Research Interests
My formal background is in Computational Science and Remote Sensing, and my research focuses on the development and application of computational algorithms for the analysis of spatio-temporal remote sensing, numerical modeling and social media “Big Data” related to environmental hazards and renewable energy.  My research falls under the general description of CyberScience and can be summarized as a tripartite of tools, data and topics.

  • Tools: GeoInformatics is the framework that provides the geospatial analysis tools, primarily based on spatio-temporal data mining, machine learning and artificial intelligence.

  • Data: Remote sensing, numerical simulations, and social media are the primary source of ‘big data’ used in the analysis.

  • Topic: Environmental hazards and renewable energy are the challenging problems of high societal impact addressed.

Currently, I work on three projects, and I am constantly looking for new collaboration and new students.

  1. The fusion of remote sensing, numerical model and social media data during emergencies. Remote sensing is the de-facto standard in observing the Earth and its environment during emergencies, but gaps in the data are inevitable due to sensor limitations of atmospheric opacity. The goal is to ‘fill the gaps’ in remote sensing observations using social media data, other non-authoritative sources, and numerical models. This project is currently being funded by DOT and by ONR.

  2. Using GeoInformatics to optimize numerical model forecasts for renewable energy.  Given a single deterministic future forecast, and a history of past forecasts and associated observations, the goal is to build a probabilistic forecast that captures the risk of over- or under-producing electricity. This project is also partially supported by DoE and Xcel Energy through NCAR, and my code is being used operationally by Xcel Energy.

  3. The source  characterization of unknown and potentially toxic pollutants using remote sensing, numerical models and ground sensor measurements. I reconstructed the non-steady release rate for the radioactive leak at the Fukushima nuclear power plant. The methodology I developed uses an evolutionary (genetic) algorithm guided by machine learning rule induction. This work is currently being supported by ONR.  


Guido Cervone
Associate Professor
Department of Geography and Institute for CyberScience
The Pennsylvania State University
Affiliate Scientist
Research Application Laboratory
National Center for Atmospheric Research