Team

Co-chairs

Prof. Massimiliano Barolo

max.barolo@unipd.it

Dr. Massimiliano Barolo is a Professor of Chemical Engineering at the University of Padova. After graduating with honors in Chemical Engineering, he worked as a process engineer in the largest petrochemical site in Italy. He earned the Ph.D. degree in Chemical Engineering in 1994. His main research interests are in the fields of process chemometrics, and of dynamics, modeling, monitoring, and control of chemical processes and biomedical systems. He is the author or co-author of 100+ peer-reviewed papers and of some teaching monographs.

Prof. Fabrizio Bezzo

fabrizio.bezzo@unipd.it

Dr. Fabrizio Bezzo is Professor of Chemical Engineering at the University of Padova. He received his degree in Chemical Engineering at the University of Padova and his PhD at Imperial College London (UK). He worked at Silicon Graphics (Minneapolis, U.S.A.) and at Process Systems Enterprise (London,UK). His research interests comprise process design and supply chain optimization of production systems for renewable energy and biofuels, and the modeling and scaling-up of chemical and pharmaceutical processes. He has published over 170 scientific papers in international research journals and conference proceedings. He co-chairs the Energy Section of the European Federation of Chemical Engineering.

Prof. Pierantonio Facco

pierantonio.facco@unipd.it

Dr. Pierantonio Facco is Associate Professor at the University of Padova, where he received his PhD in Chemical Engineering in 2009. His research interests are related to data analytics, machine learning, statistical and deep learning to support chemical and process engineering for: processes and product quality monitoring; product formulation and process development; process understanding; process, product and technology scale-up/down; design of experiments. His main activities are concerned with multivariate statistical process and quality monitoring and control, Quality-by-Design, adaptive soft-sensing, process analytical technologies, and development of artificial vision systems. He is author of more than 80 publications, among which papers in peer reviewed journals, conference proceedings and book chapters.

Post-doc researchers

Dr. Christopher Castaldello

christopher.castaldello@phd.unipd.it

Optimal design of dynamic experiments for process optimization: comparison between data-driven and knowledge-driven modeling methodologies

Ph.D students

Elia Arnese Feffin

elia.arnesefeffin@phd.unipd.it

An Industry 4.0 approach to the optimization of biopolymer production processes in territory-integrated biorefineries

Gianmarco Barberi

gianmarco.barberi@phd.unipd.it

Development of machine learning techniques for the scale-up of biopharmaceutical processes

Andrea Botton

andrea.botton.1@phd.unipd.it

A Machine Learning approach to plant scale-up/down

Francesca Cenci

francesca.cenci@phd.unipd.it

Estimability and confidence of system models for pharmaceutical manufacturing

Daniel Cristiu

daniel.cristiu@phd.unipd.it

Optimal design of green and blue hydrogen supply chains

Beatriz Felices Rando

beatriz.felicesrando@phd.unipd.it

Continuous microphotobioreactors for model identification of microalgae growth

Sergio García Carrión

sergio96gc@gmail.com

Latent-based multivariate data analytics tools for the optimization of industrial processes in Industry 4.0

Margherita Geremia

margherita.geremia@phd.unipd.it

Model-based design of experiments under uncertainty

Alberto Saccardo

alberto.saccardo.2@phd.unipd.it

A model-based approach to the design and optimization of microalgae cultivation processes

Francesco Sartori

francesco.sartori.5@phd.unipd.it

Approaching process reliability through the smart use of sensor data in the Industry 4.0 era

Luca Zanella

luca.zanella.7@phd.unipd.it

Modelling the role of neuroblastoma-derived exosomes in cancer dissemination

Qiang Zhu

q.zhu@stu.jiangnan.edu.cn

Implementing QbD initiatives to control the product quality with data-driven approaches

Research assistants

Luca Gasparini

luca.gasparini.9@studenti.unipd.it

Machine learning approaches to enhance biopharmaceutical process monitoring under small-data scenarios

CAPE-Mountain

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