Rate My Professor Alberto Cano

Alberto Cano is an associate professor in the Department of Computer Science at Virginia Commonwealth University, where he joined as an assistant professor in 2015 and was promoted to associate professor with tenure in 2021. He also served as Faculty Director of the High Performance Research Computing Core from 2022 to 2024. Cano earned his Ph.D. in Computer Science from the University of Granada, Spain, in 2014; his dissertation, "New Classification Models through Evolutionary Algorithms," received the Best PhD Dissertation Award from the Spanish Association for Artificial Intelligence. Additional degrees include an M.Sc. in Intelligent Systems from the University of Cordoba (2013), an M.Sc. in Soft Computing and Intelligent Systems from the University of Granada (2011), a B.Sc. in Computer Science from the University of Cordoba (2010), and a B.Sc. in Computer Engineering from the same institution (2008).

Cano's research specializations encompass machine learning techniques for drifting data streams, imbalanced and multi-label classification, ensemble learning, evolutionary computation, big data analytics, and high-performance computing with GPUs and distributed systems like Hadoop and Spark. His work targets real-time adaptive algorithms for applications such as credit card fraud detection, spam filtering, and business intelligence. As principal investigator or co-principal investigator, he has obtained major grants including National Science Foundation MRI Track 1 for an NVIDIA DGX H100 GPU system ($299,621, 2023–2026), National Institutes of Health High Performance Computing Cluster for Biomedical Research ($749,998, 2025–2027), Higher Education Equipment Trust Fund awards ($1,442,280 in 2022–2023 and $744,038 in 2024–2025), and industry funding from Hamilton Beach Brands ($250,000 total, 2019–2022). Key publications include "ROSE: Robust Online Self-Adjusting Ensemble for Continual Learning on Imbalanced Drifting Data Streams" (Machine Learning, 2022), "Kappa Updated Ensemble for Drifting Data Stream Mining" (Machine Learning, 2020), "Early Dropout Prediction Using Data Mining: A Case Study with High School Students" (2016, 457 citations), and "A Survey on Learning from Imbalanced Data Streams" (2024, 215 citations). Cano contributes editorially as Associate Editor for IEEE Access (Outstanding Associate Editor 2020), Applied Intelligence, PeerJ Computer Science, and Information Fusion, and holds Senior Membership in IEEE (2019) and ACM (2023). His research has significantly impacted adaptive data stream mining and scalable machine learning methodologies.