Valentina Franceschi

Valentina Franceschi is Associate Professor at the Department of Mathematics of the University of Padua. 

 

I graduated in Mathematics at the University of Bologna in 2012. Within my degree I was selected as international student for an exchange program at Temple University, Philadelphia in 2011. I obtained my PhD in Mathematics in 2016 at the University of Padova.  Between 2016 and 2020 I developed my research through post-doctoral experiences mainly in France, first as research collaborator of the Inria Paris team CAGE, then as Lecteur Hadamard at Université Paris-Sud in 2018 and finally as Marie Sk?odowoska Curie Fellow at Sorbonne Université within the individual project MesuR.

With a formation in pure mathematics, part of my research is now devoted to cortical inspired models for imaging and vision. I am a member of the group “Matematica delle immagini, della visione e delle loro applicazioni” within Unione Matematica Italiana and I participate to the international project RUBIN-VASE, funded by the French national research agency (ANR) on cortical inspired models for vision. I have been invited editor for a special issue on Color representation and cortical-inspired image processing on the of Journal of Mathematical Neuroscience, and I collaborated in the organization of conferences on mathematical imaging and vision.

 

Main Research Interests: 
Mean field cortical inspired models for vision with applications to visual illusions. 
Geometric Analysis with a focus on sub-Riemannian geometry.

 

Publications: 
https://scholar.google.com/citations?user=upg90zYAAAAJ&hl=it&oi=sra

Wolfgang Erb

Wolfgang Erb is an applied mathematician and Associate Professor in numerical analysis at the Department of Mathematics “Tullio Levi-Civita”, University of Padua (Padua, Italy).

 

He received his Ph.D in Mathematics at the Technical University of Munich (Munich, Germany) in 2010. He has been a post-doctoral research fellow at the University of Lübeck (Lübeck, Germany), at the University of Eichstätt-Ingolstadt (Eichstätt, Germany) and an assistant professor in mathematics at the University of Hawaii at Manoa (Honolulu, US).

 

His research interests include multivariate approximation linked to Lissajous curves, uncertainty principles on manifolds and graphs, kernel methods for signal processing and learning on networks, fast and efficient reconstruction algorithms for inverse problems, as well as applications in biomedical imaging, in particular Magnetic Particle Imaging.

 

He is teaching the course “Mathematical Models and Numerical Methods for Big Data” for the Data Science master’s program at the University of Padua.

 

Webpage: https://www.lissajous.it

Github: https://github.com/WolfgangErb

Researchgate: https://www.researchgate.net/pro?le/Wolfgang-Erb-2

Francesco Marchetti

Francesco Marchetti is a fixed-term researcher (RTDa) at the Department of Mathematics “Tullio Levi-Civita” of the University of Padova.

 

I got my master degree in Mathematics and my PhD in Health Planning Sciences at the University of Padova in 2016 and 2021, respectively.

In my master thesis and during my PhD, I worked in the field of Magnetic Particle Imaging (MPI), which is a novel tracer-based medical imaging technique, studying effective approximation schemes suitable for reconstructing signals along Lissajous curves, which are typical sampling trajectories in a MPI scanner. Moreover, I studied and applied kernel-based machine learning techniques in the context of rare diseases diagnosis and in the classification of patient-derived xenografts.

After the PhD, I started collaborating with the MIDA group of the University of Genova in the field of space weather forecasting, and I analysed and applied deep learning methods. Then, I won a one year postdoctoral grant sponsored by the Istituto Nazionale di Alta Matematica (INdAM). The research project linked to my current position concerns p-Laplacians on hypergraphs and related machine learning approaches.

 

My research interest is mainly in approximation theory, kernel-based approximation and machine learning, deep learning, medical imaging and space weather forecasting. I am part of the research groups CAA (Constructive Approximation and Applications) between the Universities of Padova and Verona, Rete Italiana di Approssimazione (RITA) and Methods for Image and Data Analysis (MIDA).

Francesco Rinaldi

Francesco Rinaldi is Full Professor at the Department of Mathematics “Tullio Levi-Civita”, University of Padova. He is also coordinator of the Data Science Master’s Programme at the University of Padova.

 

He received the M.S. degree in computer engineering and the Ph.D. degree in operations research from the Sapienza University of Rome, in 2005 and 2009, respectively.

He served as organizing/program committee member and as session/minisymposium organizer at many international conferences. He was also invited speaker at more than 40 international conferences and university seminars.

 

He received a number of research grants for his research (e.g., ARISLA 2021 grant).

 

He published over 50 papers in toptier academic journals including SIAM Journal on Optimization, Mathematical Programming Computation, Mathematics of Operations Research, Bioinformatics, IEEE Transactions, Molecular Neurodegeneration.

 

His current research interests include optimization for big data, network science, machine learning for medicine and biology.

 

Google scholar: https://scholar.google.com/citations?user=CQVb2IgAAAAJ&hl=en&oi=ao

Scopus: https://www.scopus.com/authid/detail.uri?authorId=7005406365

Personal Google Site: https://www.math.unipd.it/~rinaldi/

Marco Formentin

Marco Formentin is Associate Professor at the Department of Mathematics “Tullio Levi-Civita” of the University of Padova.

 

He graduated in Physics in 2004 and earned a doctorate in Mathematics in 2009 at the University of Padova. In 2017 he held the position of Assistant Professor (RTDb) at the Department of Mathematics “Tullio Levi-Civita” of the University of Padova and at present, he is Associate Professor.

 

His primary research topic is the understanding of systems made up of a large number of interacting agents and possible applications in complex systems ranging from ecology to interactive human dynamics.

 

Recent interests are:

1) mechanisms for the emergence of collective periodic behavior;
2) mathematical ecology: in particular mechanisms that enhance biodiversity;
3) modeling and statistics for complex human dynamics.

 

For more info visit https://www.researchgate.net/profile/Marco_Formentin