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Photograph of Roberto Corizzo

Roberto Corizzo Assistant Professor Computer Science

Roberto Corizzo
CAS | Computer Science
Don Myers Technology and Innovation Building 112E
PhD, Computer Science (University of Bari, Italy)

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

Roberto Corizzo conducts research at the intersection of big data computing, machine learning, and data mining. His research addresses analytical tasks such as sensor data forecasting, time series classification, anomaly detection, 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
For the Media
To request an interview for a news story, call AU Communications at 202-885-5950 or submit a request.


Spring 2024

  • CSC-208 Intro to Computer Science II

  • CSC-480 Introduction to Data Mining

Fall 2024

  • CSC-208 Intro to Computer Science II

  • CSC-483 Big Data Comp/Machine Learning

Scholarly, Creative & Professional Activities

Selected Publications

  • Corizzo, R., & Slenn, T. (2022, December). Distributed Node Classification with Graph Attention Networks. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 3720-3725) [Link
  • Faber, K., Corizzo, R., Sniezynski, B., & Japkowicz, N. (2022, October). Active Lifelong Anomaly Detection with Experience Replay. In 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-10) [Link]
  • Ding, L., Corizzo, R., Bellinger, C., & al. (2022, December). Imbalanced Multi-layer Cloud Classification with Advanced Baseline Imager (ABI) and CloudSat/CALIPSO Data. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 5902-5909) [Link
  • Ghosh, K., Bellinger, C., Corizzo, R., Branco, P., Krawczyk, B., & Japkowicz, N. (2022). The class imbalance problem in deep learning. Machine Learning, 1-57 [Link]
  • Corizzo, R., Baron, M., & Japkowicz, N. (2022). CPDGA: Change point driven growing auto-encoder for lifelong anomaly detection. Knowledge-Based Systems, 247, 108756 [Link]
  • 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) [Link]
  • Corizzo, R., Ceci, M., Fanaee-T, H., & Gama, J. (2021). Multi-aspect renewable energy forecasting. Information Sciences, 546, 701-722 [Link]
  • 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 [Link]

Media Appearances

Cover Story: "The UncertAInty of ChatGPT" - AU AWOL magazine - Issue 32 - Spring 2023 [Link]

Professional Services

Program Chair, 18th Conference on Computer Science and Intelligence Systems FedCSIS 2023 (IEEE #57573), AAIA track [Link]

Organizer, "4th Workshop on Deep Learning Practice and Theory for High-Dimensional Sparse and Imbalanced Data" DLP-KDD workshop, SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), August 14-18, 2022, Washington DC [Link]

Program Chair, 17th Conference on Computer Science and Intelligence Systems FedCSIS 2022 (IEEE #57573), AAIA track [Link]

Organizer, “S2D-OLAD: From shallow to deep, overcoming limited and adverse data” workshop, 9th International Conference on Learning Representations (ICLR 2021) [Link]


Congratulations to the lab member Ian Whitehouse (BS - Computer Science) for being a recipient of a 2023 Robyn Rafferty Mathias Student Research Conference award! [link]