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Pierini, Maurizio

Maurizio Pierini is a CERN physicist working on the CMS experiment at the Large Hadron Collider (LHC). His main interest is in new phenomena — new particles, new forces, new dimensions — that could be unveiled by studying proton collisions at the LHC.

To this purpose, he proposed novel data taking solutions aimed at dealing with the LHC's big data problem: how to analyse O (100Million) collisions per second with a system designed to store 1000 events per second. At present, he is working on developing AI solutions for CMS.

In particular, he is dreaming of an AI algorithm that could identify previously unseen phenomena as anomalies in the collected data, which could change the way we understand how the universe works.

Hiroshi Ishiguro received a D. Eng. in systems engineering from the Osaka University, Japan in 1991. He is currently Professor of Department of Systems Innovation in the Graduate School of Engineering Science at Osaka University (2009-) and Distinguished Professor of Osaka University (2017-)

He is also visiting Director (2014-) (group leader: 2002-2013) of Hiroshi Ishiguro Laboratories at the Advanced Telecommunications Research Institute and an ATR fellow. His research interests include sensor networks, interactive robotics, and android science.

He received the Osaka Cultural Award in 2011. In 2015, he received the Prize for Science and Technology (Research Category) by the Minister of Education, Culture, Sports, Science and Technology (MEXT). He was also awarded the Sheikh Mohammed Bin Rashid Al Maktoum Knowledge Award in Dubai in 2015. In 2020, he received Tateisi Prize.

Distinguished Professor Genevieve Bell is a renowned anthropologist, technologist, and futurist. Genevieve completed her PhD in cultural anthropology at Stanford University in 1998 and is best known for her work at the intersection of cultural practice and technology development.

She is currently the Director of the School of Cybernetics and 3A Institute(3Ai) at the Australian National University and is also a Vice President and a Senior Fellow at Intel Corporation.Genevieve joined the ANU in 2017 after spending 18 years in Silicon Valley guiding Intel's product development and social science and design research capabilities.

In 2017, Genevieve was appointed the inaugural Director of the 3A Institute, co-founded by the ANU and CSIRO's Data61. The Institute's mission is to establish a new branch of engineering to responsibly and sustainably scale AI-enabled cyber-physical systems. In 2021, she was appointed Director of the new School of Cybernetics at the ANU, which in addition to housing the 3A Institute, will build capacity in Systems and Design.

Oriol is a Principal Scientist at Google DeepMind, and a team lead of the Deep Learning group. His work focuses on Deep Learning and Artificial Intelligence. Prior to joining DeepMind, Oriol was part of the Google Brain team. He holds a Ph.D. in EECS from the University of California, Berkeley and is a recipient of the 2016 MIT TR35 innovator award.

His research has been featured multiple times at the New York Times, Financial Times, WIRED, BBC, etc., and his articles have been cited over 100,000 times. Some of his contributions such as seq2seq, knowledge distillation, or TensorFlow are used in Google Translate, Text-To-Speech, and Speech recognition, serving billions of queries every day, and he was the lead researcher of the AlphaStar project, creating an agent that defeated a top professional at the game of StarCraft, achieving Grandmaster level, also featured as the cover of Nature.

At DeepMind, he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, deep learning and reinforcement learning.

Dr. Vivienne Ming explores maximizing human capacity as a theoretical neuroscientist, delusional inventor, and demented author. Over her career she’s founded 6 startups, been chief scientist at 2 others, and launched the “mad science incubator”, Socos Labs, where she explores seemingly intractable problems—from a lone child’s disability to global economic inclusion—for free. Vivienne’s other companies apply machine learning to lessen the corrosive health effects of chronic stress in communities, fight bias in hiring and promotion, develop neurotechnologies to treat dementia and TBI, and promote learning at home and in school. As a visiting scholar at UC Berkeley's Redwood Center for Theoretical Neuroscience, she pursued her research in cognitive neuroprosthetics. In her free time, Vivienne designs AI systems to treat her son’s diabetes, predict manic episodes in bipolar sufferers, and reunite orphan refugees with extended family members. For relaxation, she writes science fiction and spends time with her wife and children. Vivienne was named one of “10 Women to Watch in Tech” by Inc. Magazine and one of the BBC’s 100 Women in 2017. She is featured frequently for her research and inventions in The Financial Times, The Atlantic, Quartz Magazine and the New York Times.

Flora is the Head of CV/AR at Streem. She specialises in AI applied to Computer Graphics and Vision problems faced in AR/VR. Her team at Streem is making the mobile phone's camera more intelligent, by building AI agents that can understand images/videos and augment them with relevant interactive virtual content.

She joined Streem, after it acquired her startup Selerio, which was spun out of her PhD work at Cambridge University. At Cambridge, Flora research focused on 3D shape retrieval using different query types such as 3D models, images/sketches and range scans. This work was awarded the 2013 Google Doctoral Fellowship in Computer Graphics and published in various top-tier venues, including ICCV and SIGGRAPH Asia.

She has served on several international program committees such as ICML, ICLR and Eurographics. Notably she was Paper Chair of the 2019 & 2020 Black in AI workshops, co-located with NeurIPS. She was recently named by Wired UK as one of the 32 innovators in the world who are building a better future.

Programme:

17-18 September 2021 | CERN, Meyrin | Forum and Public event

The Forum is by invitation only and the Public Event will be webcast and available to watch by all.

Language:

The public event will be held in English. French subtitles will be available for part of the event.

Locations:

Satellite viewing events: find out if a satellite viewing event is organized around you on our dedicated page.

Volunteers:

Volunteers will be a key part of the success of the event. Registration will open during the Summer 2021 for CERN members of personnel.

About Sparks!

As science becomes ever-more specialised, the complex problems facing society require knowledge and expertise from more than just one field. Scientific serendipity can no longer be taken for granted: it needs to be curated, and that is what Sparks! aims to do. Multidisciplinary discussion and collaboration is essential, yet few platforms exist offering opportunities for such interactions. As a centre of excellence in science and technology, one of the largest laboratories hosting collaborative research in the world, and a leader in fields as diverse as superconducting magnets and IT, CERN is ideally placed to host such multidisciplinary discussions and guide them to conclusions that will benefit society as a whole.

Sparks! will begin with a cycle of three pilot events, each focusing on a single theme to test the concept. Experience gained from this cycle will lead to a yearly event with multiple themes addressed each year. The event will become a flagship for CERN’s new Science Gateway, which is scheduled to open its doors to the public in 2022.

Looking beyond the events themselves, Sparks! will foster the development of an active multidisciplinary community of problem solvers. It will build on the team's experience of organizing successful highly-produced public events, adding a space for multidisciplinary scientific exploration.

Globe - debate

CERN operates some of the most complex scientific machinery ever built, relying on intricate control systems and generating petabyte upon petabyte of research data. Operating this equipment and performing analysis on the data gathered are both intensive tasks. Therefore, CERN is increasingly looking to the broad domain of artificial-intelligence (AI) research to address some of the challenges encountered in dealing with data, particle beam handling, and in the up keep of its facilities.

The modern domain of artificial intelligence came into existence around the same time that CERN did, in the mid-’50s. It has different meanings in different contexts, and CERN is mainly interested in task-oriented, so-called restricted AI, rather than general AI involving aspects such as independent problem-solving, or even artificial consciousness. Particle physicists were among the first groups to use AI techniques in their work, adopting Machine Learning (ML) as far back as 1990. Beyond ML, physicists at CERN are also interested in the use of Deep Learning to analyse the data deluge from the LHC.

Dealing with a data deluge

Even before the Large Hadron Collider began colliding high-energy beams of protons in 2010, the particle-physics community began to collect unprecedented quantities of data. Particles collide within the LHC’s detectors up to 40 million times a second, each collision event generating about a megabyte of data: far too much to store without some filtering.

Not only do the scientists have to programme their data acquisition systems to select the right events for further analysis while discarding the uninteresting data, they also have to examine trillions of stored collision events looking for signatures of rare physics phenomena. They have therefore turned to one sub-domain of AI, called machine learning (ML), to improve the efficiency and efficacy of these tasks. In fact, the LHC’s four major collaborations ‑ ALICE, ATLAS, CMS and LHCb ‑ have formed the Inter-experimental Machine Learning (IML) Working Group to follow developing trends in ML. Researchers are also collaborating with the wider data-science community to organise workshops to train the next generation of scientists in the use of these tools, and to produce original research in Deep Learning. ROOT, the software program developed by CERN and used by physicists around the world for analysing their data, also comes with machine-learning libraries.

Operating in extreme environments

Experimental facilities at CERN may be temporarily classified as high-radiation zones, preventing human intervention to perform repairs or to replace equipment. CERN has therefore developed autonomous robots to operate in these zones, which include the tunnel containing the LHC. The Engineering department at CERN, which builds and maintains these robots, uses AI techniques to help the robots navigate on their own and make decisions on what actions to take inside the radiation environments.

Machine Learning is also used in the CERN accelerator complex to predict and avoid equipment failures, as well as to optimize the quality of the high-energy beams of protons that CERN delivers to its experiments. Furthermore, physicists are also investigating how similar techniques could make the work of those who run accelerators more efficient, more reliable, and possibly even autonomous.

Bringing state-of-the-art techniques to CERN

In addition to using AI robotics for the maintenance of its complex machines, predicting component failure and for safety applications, CERN has recognised the importance of involving external AI expertise in projects undertaken at the laboratory.

Much of the collaboration with these experts is through CERN openlab, a public-private partnership that enables CERN to work with world-leading researchers and companies working on AI. CERN openlab has launched several machine-learning projects ranging from improving industrial control systems to simulating the conditions inside particle detectors following high-energy collisions to investigations of future Quantum Machine Learning (QML) algorithms. CERN openlab also contributes to the use of CERN’s AI resources for humanitarian operations and recently organised a conference on the ethics associated with our increasing use of AI in the world.

In addition, CERN is also interested in employing its AI knowhow to create positive impact in society as a whole. The CERN Knowledge Transfer Group works with players in the automotive, finance and pharmaceutical sectors towards this effort.

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