Talks and Presentations

(*) indicates the presenting author

• D. Cacciarelli*. Active learning and adaptive sampling for regression data streams. ENBIS Annual Meeting, Leuven, Belgium, 09/2024.

• D. Cacciarelli*, M. Kulahci and J.S. Tyssedal. Real-time sampling strategies for data streams. INFORMS Annual Meeting – Quality, Statistics and Reliability Best Student Poster Competition, Phoenix, US, 10/2023.

• D. Cacciarelli*, M. Kulahci and J.S. Tyssedal. Stream-based active learning for regression with dynamic feature selection. IEEE International Conference on AI for Industries, Los Angeles, US, 09/2023.

• S.O.N. Topalian*, H.H. Hansen, D. Cacciarelli and M. Kulahci. pySPC: A Python library for multivariate statistical process control and dynamic principal component analysis. ENBIS Annual Meeting, Valencia, Spain, 09/2023.

• D. Manjah*, D. Cacciarelli, B. Standaert , M. Benkedadra, G. Rotsart, S. Galland,  B. Macq and C. De Vleeschouwer (2023). Stream-Based Active Distillation for Scalable Model Deployment. CVPR Workshop on Learning with Limited Labelled Data for Image and Video Understanding, Vancouver, Canada, 06/2023.

• D. Cacciarelli*, M. Kulahci and J.S. Tyssedal. Invited seminar on online active learning. Baker Hughes, virtual, 11/2022.

• D. Cacciarelli*, M. Kulahci and J.S. Tyssedal. Poster on real-time process monitoring with label scarcity. Danish Data Science Academy Annual Conference, Billund, Denmark, 11/2022.

• D. Cacciarelli*, M. Kulahci and J.S. Tyssedal. Presentation on online active learning in the presence of outliers. INFORMS Annual Meeting - QSR Workshop, Indianapolis, IN, 10/2022.

• D. Cacciarelli*, M. Kulahci and J.S. Tyssedal. Poster on online active learning for soft Sensor development using semi-supervised autoencoders. ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World, Baltimore, MD, 07/2022. 

• D. Cacciarelli*, M. Kulahci and J.S. Tyssedal. Invited talk on stream-based active learning. ENBIS Annual Meeting, Trondheim, Norway, 06/2022.

• D. Cacciarelli*, M. Kulahci and J.S. Tyssedal. Active learning: training predictive models with less data. Math meets Industry, Trondheim, Norway, 06/2022.

• D. Cacciarelli* and M. Kulahci. A novel fault detection and diagnosis approach based on orthogonal autoencoders. ENBIS Annual Meeting, virtual, 09/2021.

• D. Cacciarelli* and M. Boresta. Machine Learning algorithms for predicting donor responses to direct marketing campaigns. AIRO Young Workshop, Bolzano, Italy, 02/2020.