Carlo Bertolli is a PhD in Computing since 2008, with a specialisation in High-Performance computing, fault tolerance, compiler technologies and software optimisations. Lately, he has been working at Imperial College London on the HYDRA computational fluid dynamic application developed by Rolls Royce Plc which is used to simulate the behaviour of next generation turbomachineries. In particular the task of his research group is to accelerate the execution of HYDRA on clusters of GPUs and multicores, reducing the current large simulation times. His aim is to expand the current research field in compilers and software optimisations towards the definition of Domain-Specific Languages and intermediate languages to support next generation computational science applications. Specialties: High-Performance computing, High-Performance applications, fault tolerance, compilers, software optimisation.

Christopher Kanan is the Director AI Science at Paige, where he has been leading AI research activities at the company. they are creating clinical-grade state-of-the-art deep learning systems to better detect and treat cancer. He is an Associate Professor in the Carlson Center for Imaging Science at the Rochester Institute of Technology (RIT). He is also Associate Director of RIT’s Center for Human-aware AI (CHAI), and Affiliate Faculty in RIT’s Computer Science Department. He is a member of the McNair Scholars Advisory Board, part of RIT’s Division of Diversity and Inclusion. He directs the Machine and Neuromorphic Perception Laboratory (a.k.a., kLab). He is also a Visiting Associate Professor at Cornell Tech in New York City, where he teaches a course on Deep Learning. At RIT, his lab’s main focus is basic research in task-driven scene understanding and lifelong machine learning. Their recent work has focused on continual learning in deep networks, multi-modal vision and language systems, and creating more robust AI systems. They also do applied research in using deep learning to solve problems in computer vision. He has worked on computational pathology, semantic segmentation, object recognition, object detection, active vision, object tracking, and more. Beyond machine learning, he also has a strong background in eye tracking, primate vision, and theoretical neuroscience. He was previously affiliated with NASA Jet Propulsion Laboratory (JPL). He received his PhD from UC San Diego (UCSD) and was a postdoctoral scholar at Caltech.

Solène Neyret is a post-doctoral researcher at the École Nationale Supérieure des Arts et Métiers in France. She received her PhD in Neuroscience and Virtual Reality from the University of Barcelona, under the supervision of Prof Mel Slater. During her PhD, she investigated the impact of perspective changing on self-perception and perception of others in immersive virtual reality. Her interests are focused on the use of Virtual Reality for research in social neuroscience, cognitive psychology and psychology of perception. More specifically, she conducted experimental research about self-perception and self-representation, showing the impact of perspective changing on body perception and the benefits of having a conversation with oneself in virtual reality. She also worked on experimental research measuring pro-social behaviour using a reproduction of the Milgram experiment in virtual reality. After working on the topics of self-perception, group pressure, and body perception during her PhD, she is now developing an application of Virtual Reality for the emotional training of team sports players, under a project of the French National Agency for Research. She is as well teaching psychology of perception at École Nationale Supérieure des Arts et Métiers.

John M. Martinis received his B.S. in physics in 1980 and his Ph.D. in physics from the University of California, Berkeley. During his Ph.D., he investigated the quantum behaviour of a macroscopic variable, the phase difference across a Josephson tunnel junction. He joined the Commissariat à l'Energie Atomique in Saclay, France, for a first postdoc and then the Electromagnetic Technology division at NIST in Boulder, where he worked on superconducting quantum interference devices (SQUIDs) amplifiers. While at NIST he developed a technique of X-ray detection by using a superconducting transition-edge sensor microcalorimeter with electrothermal feedback. Since 2002 he has been working with Josephson-Junction qubits with the aim of building the first quantum computer. In 2004 he moved to the University of California Santa Barbara, where he now holds the Worster Chair in experimental physics. In 2014, Martinis' team was hired by Google to build the first useful quantum computer. On October 23, 2019, Martinis and his team published a paper on Nature with title "Quantum supremacy using a programmable superconducting processor", where they presented how they achieved quantum supremacy (hereby disproving the extended Church-Turing thesis) for the first time using a 53-qubits quantum computer. In April 2020, Wired magazine announced that Martinis reportedly resigned from Google after being reassigned to an advisory role. On September 29th, 2020, it was announced that Martinis had moved to Australia to join Silicon Quantum Computing, a start-up founded by Professor Michelle Simmons. In 2021, he received the John Stewart Bell Prize for Research on Fundamental Issues in Quantum Mechanics and Their Applications.

Alberto Massidda is a computer engineer with 13 years of experience, trained on high traffic and high availability mission critical architectures, on the Linux platform, with significant experience in the development and management of native cloud applications. His professional passion has shifted over time from infrastructures to machine learning: he developed his first experiences on information retrieval (2009) and machine translation (2014), and then embraced the rest of the statistical ML sector. He is currently pursuing a PhD in artificial intelligence. In the meantime, he has accumulated a varied background of experiences that help him in being extremely flexible: DevOps, startupper, NLP researcher, backend developer, Big Data engineer, fast prototyping, university thesis supervisor, conferences (longest streak: 5 editions of Codemotion, 2 edition of FOSDEM, Data Driven Innovation, Cerved Next, Linux Day), meetups (he held first Roman edition of “Tarallucci, Vino and ML”), university seminars (he was kindly hosted as guest lecturer in Machine Learning course and as teaching assistant for Artificial Intelligence, both at Roma3 University), in the fintech, travel sectors, language translation, open source consulting.

Alberto Montresor got his Ph.D. from the University of Bologna in 2000, where he also served as Research Assistant from 2002 to 2005. He joined University of Trento as associate professor in 2005, to become full professor in 2020. He teaches courses about algorithms and data structures, distributed algorithms and computer science education. His research is focused on large-scale decentralized systems, with some digressions in the field of centralized algorithms for graphs analysis. Very recently, he became interested in computer science education; for this reason, he actes as an intermediary between the local schools and his department. His research interests include distributed computing, fault-tolerance, peer-to-peer, gossip protocols and algorithms for the analysis of very large graphs. His main goal is to develop protocols and algorithms that are capable to scale to massive sizes and are able to survive to failures and to dynamism.