Cesare Furlanello: CEO. Data scientist and entrepreneur, international expert of Applied Machine Learning and Artificial Intelligence. International collaborator of Riken (Japan), Wistar Institute (USA), US FDA for the development of Deep Learning models for Predictive Medicine. [Google Scholar profile]
Formerly Head of Data Science (2018-19) at Fondazione Bruno Kessler (FBK); Head of MPBA – Predictive Models for Biomedical and Environmental Data (1997-2019); national habilitation full professor in Biomedical Engineering (Dec. 2017); Coordinator of the FBK Data Analytics Research Line (2015-2017); Senior Researcher since 2006; tenured FBK researcher (1988); FBK junior Research Consultant (1987). MSc Math Univ Padua (1986). Visiting scientist: MIT (1990), Newton Institute Univ. Cambridge (1997). Contract professor Univ. Trento (1998-2003). Author of > 190 scientific papers, editor and reviewer of scientific journals and international grant programs. Principal Investigator in European and national research projects, industrial R&D contracts, > 80 projects funded and managed. International expert in machine learning for predictive biomarkers, and computing platforms for their reproducibility. Developer of integrative frameworks for machine learning on geospatial data, massive environmental data, Geographical Information Sciences, applied to Digital Agriculture Environmental Health, Security, Retail and Marketing. Winner of 2 Microsoft Azure Awards (Deep Learning for Precision Medicine, 2018; Deep Learning Models of Heat Waves and Climate Change impacts on Crop production, 2019). Organizing Chair 3rd Workshop or the MAQC Society, Theme: Reproducibility of Artificial Intelligence for Predictive Medicine, Riva del Garda (TN), 8-10 Apr 2019. Founder of WebValley interdisciplinary Data Science Summer School and Scientific Coordinator (2001-2020). President of the Massive Analysis and Quality Control (MAQC) Society (2018 – 19).
Tommaso Furlanello. PhD in Neuroscience USC (2014-2019); MSc Neuroscience Univ. Trento (2013-2014); Master in Behavioural Economics Univ Maastricht (2012-13); BSc Economics Univ. Pisa (2008-11). Internship in Deep Learning at Amazon Web Services (2017, 2018) and applied machine learning (S. Francisco, 2019). Expert in Deep Learning and its application to Neuroscience and Decision Processes. [Google Scholar profile]
Valerio Maggio: data science consultant and trainer, deep learning specialist, PhD in Computer Science Univ. Napoli (2010-13); MSc in Computer Science Univ. Napoli (2006-09); Postdoc Univ. Salerno (2014-15); Postdoc researcher FBK (2016-19), contributing to European Horizon 2020 projects amd European Institute of Technology in Environmental Risk, Retail, Security; Coordinating tutor for Data Science and Deep Learning (Keras, TensorFlow, PyTorch) in international training and at FBK WebValley (2016-19); Organizer of PyCon/PyData Italia, EuroSciPy (2015-2019); Microsoft Azure Cloud Research Software Engineer (2018); Honorary Senior Research Associate in Data Science and AI at MRC Integrative Epidemiology Unit, Univ. Bristol, UK (2019-2020). [Google Scholar profile]
Lorenzo Gorini: Junior Data Scientist. MSc in Theoretical Physics, Univ. Bologna (2018); Research assistant Max Planck Institute for Solid State Physics, Stuttgart (2017-18); Back-end Java Developer Accenture; Network Software testing (2019); Scientific software specialist for data analytics of environmental quality and molecular bioimaging (CNR, 2019-20)
Alessia Marcolini: Junior Data Scientist, working on machine learning/ deep learning frameworks to integrate multiple medical imaging modalities and different clinical data to get more precise prognostic/diagnostic biomarkers for human and veterinary health. Pythonista, PyCon Italy and EuroSciPy organizer, Django Girls coach. Currently Data Science MSc Degree – EIT Digital Master School (2020-present), Eindhoven University of Technology; BSc Computer Science, University of Trento (2017-2020); Junior Research Assistant FBK/MPBA Lab and manager of FBK-Artigianelli joint research lab (2017-2020). Developer of Histolab, Python package that standardizes WSI preprocessing, providing automatic identification of tissue and utilities for WSI preprocessing in a reproducible environment to support clinical and scientific research in the Digital Pathology. WebValley FBK fellow (2016), developing deep learning for Portable spectroscopy, for fruit quality analytics .