Jonathan Masci
About Jonathan Masci
Jonathan Masci is the Co-Founder and Chief Scientist for Deep Learning at NNAISENSE, where he has contributed to the development of deep learning technologies since 2014. He holds a Bachelor's degree and a PhD in Computer Science, and has expertise in integrating machine learning solutions into industrial systems.
Work at NNAISENSE
Jonathan Masci serves as Co-Founder and Chief Scientist for Deep Learning at NNAISENSE, a position he has held since 2014. His work focuses on the development of deep learning technologies with an emphasis on industrial applications. He is actively involved in advancing the field through research and development initiatives. His contributions include extending neural networks to address structured problems in various scientific domains, such as biology, chemistry, and physics.
Education and Expertise
Jonathan Masci has an extensive academic background in computer science. He completed his Bachelor’s degree at Università degli Studi di Perugia from 2002 to 2006. He then pursued further studies at Università di Pisa, where he achieved a degree with a score of 110/110 from 2006 to 2009. He earned his Doctor of Philosophy (PhD) from USI Università della Svizzera italiana between 2010 and 2014. His expertise includes integrating machine learning solutions into real-world industrial systems and pioneering work in geometric deep learning.
Background
Before co-founding NNAISENSE, Jonathan Masci worked as a Postdoctoral Researcher at USI Università della Svizzera italiana from 2014 to 2016. He also completed his PhD studies at IDSIA from 2010 to 2014. His academic and research experiences have shaped his contributions to the field of deep learning, particularly in industrial contexts.
Achievements
Jonathan Masci has made significant contributions to the field of deep learning, particularly in the context of industrial applications. His work at NNAISENSE involves advancing deep learning technologies and integrating them into practical systems. He is recognized for his research in extending neural networks to structured problems across various scientific disciplines.