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Photograph of Roberto Corizzo

Roberto Corizzo Asst Professor Department of Computer Science

Contact
Send email to Roberto Corizzo
CAS - Computer Science
Don Myers Building - 212
Degrees
PhD, Computer Science (University of Bari, Italy)

MSc, BSc, Computer Science (University of Bari, Italy)

Bio
Roberto Corizzo conducts research on high-performance computing for big data analytics. His research addresses analytical tasks such as sensor data forecasting, time series classification, predictive modeling, and feature extraction tailored to real-world applications in fields such as energy, cybersecurity, astrophysics, and social networks.


Before coming to American University, he was a postdoctoral research fellow in the Department of Computer Science at University of Bari, Italy, and a research intern at the INESC TEC research institute in Porto, Portugal.
See Also
Personal Website
Publications
For the Media
To request an interview for a news story, call AU Communications at 202-885-5950 or submit a request.

Teaching

Spring 2022

  • CSC-208 Intro to Computer Science II

  • CSC-208 Intro to Computer Science II

Fall 2022

  • CSC-480 Introduction to Data Mining

  • CSC-496 Selected Topics: Non-Recurring: High-Performance Computing

Scholarly, Creative & Professional Activities

Selected Publications

  • Faber, K., Corizzo, R., Sniezynski, B., Baron, M., & Japkowicz, N. (2021, December). WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data. In 2021 IEEE International Conference on Big Data¬†(pp. 4450-4459). IEEE.
  • Corizzo, R., Ceci, M., Fanaee-T, H., & Gama, J. (2021). Multi-aspect renewable energy forecasting. Information Sciences, 546, 701-722.
  • Corizzo, R., Ceci, M., Pio, G., Mignone, P., & Japkowicz, N. (2021, October). Spatially-aware autoencoders for detecting contextual anomalies in geo-distributed data. In International Conference on Discovery Science (pp. 461-471). Springer, Cham.
  • Corizzo, R., Ceci, M., Zdravevski, E., & Japkowicz, N. (2020). Scalable auto-encoders for gravitational waves detection from time series data. Expert Systems with Applications, 151, 113378.