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Dr Braam Peter17/09/2018, 10:00
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Dr Maria Girone (CTO CERN OpenLab)17/09/2018, 10:05
The High Luminosity LHC (HL-LHC) represents an unprecedented computing challenge. For the program to succeed the current estimates from the LHC experiments for the amount of processing and storage required are roughly 50 times more than are currently deployed. Although some of the increased capacity will be provided by technology improvements over time, the computing budget is expected to be...
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Mr Stephen Pawlowski (VP of Advanced Computing Solutions, Micron)17/09/2018, 10:35
Abstract: Data is being created at an alarming rate and we need faster and more efficient machines and algorithms to make sense of this data. Though we will still need the performance of traditional high performance computing, there are characteristics and relationships in the data that are needing more non-traditional computing approaches for greater efficiency. This need is also coupled with...
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Dr Yujia Li (DeepMind)17/09/2018, 11:05
Abstract: Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs, which can capture both their structure and attributes. Our approach uses graph neural...
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17/09/2018, 11:50
Moderator: Dr Peter Braam
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Panelists: Dr Maria Girone, Dr Yujia Li, Dr Bojan Nikolic, Stephen Pawlowski -
Dr Shirley Ho17/09/2018, 14:00
Dr. Shirley Ho, Group Leader, Flatiron Institute, Cosmology X Data Science, CCA
Abstract: Scientists have always attempted to identify and document analytic laws that underlie physical phenomena in nature. The process of finding natural laws has always been a challenge that requires not only experimental data, but also theoretical intuition. Often times, these fundamental physical laws are...
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Dr David Rousseau17/09/2018, 14:30
Dr. David Rousseau, HEP Physicist (ATLAS software and Higgs Physics) at LAL, Orsay, France
Abstract: I will expand on two specific lines of effort to solve the computational challenge at the LHC. (i) LHC experiments need to reconstruct the trajectory of particles from the few precise measurements in the detector. One major process is to « connect the dots », that is associate together the...
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Dr Vladimir "Vava" Gligorov17/09/2018, 15:15
Dr. Vladimir “Vava” Gligorov, Research Scientist at CNRS/LPNHE
Abstract: LHCb is one of the four major experiments at the Large Hadron Collider (LHC) at CERN. It searches for particles and forces beyond our current physics theories, in particular
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by making highly precise measurements of the properties of the particles produced in the LHC collisions. Doing so requires analyzing an enormous... -
Dr Maurizio Pierini17/09/2018, 15:45
Dr. Maurizio Pierini, CERN Physicist working on the CMS experiment at the Large Hadron Collider
Abstract: The High-Luminosity LHC phase, scheduled for 2025, will challenge the commonly accepted solutions for tasks such as event reconstruction, real-time processing, large dataset simulation, etc. Deep Learning is considered as a strong candidate to solve this issue, by speeding up the task...
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17/09/2018, 16:45
Moderator: Dr Alan Barr
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Panelists: Dr. David Rousseau, Dr. Vladimir "Vava" Gligorov, Dr. Maurizio Pierini, Dr. Jennifer Thompson -
Prof. Richard McMahon18/09/2018, 10:00
Professor Richard McMahon, Professor of Astronomy, Director and Head of Department, Institute of Astronomy, University of Cambridge
Abstract: I will present an overview of non-radio imaging current and future challenges based on current ground and space based optical and near infrared imaging surveys; DES/VISTA/Gaia -> Euclid/LSST. Current surveys are producing PB scale imaging datasets at a...
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Prof. Stephen Smartt18/09/2018, 10:30
Prof. Stephen J. Smartt, Astrophysics Research Centre
School of Mathematics and Physics, Queen's University BelfastAbstract: Wide-field optical telescopes routinely employ large format detectors (CCDs) with 0.1 to 1 gigapixels which provide images every 30 seconds. Such facilities are capable of surveying the whole sky every night and the scientific exploitation requires immediate processing...
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Dr Bojan Nikolic18/09/2018, 10:50
Dr. Bojan Nikolic, Principal Research Associate at the Cavendish Laboratory, Cambridge
Abstract: I show that a software framework intended primarily for training of neural networks, PyTorch, is easily applied to a general function minimisation problem in science. The qualities of PyTorch of ease-of-use and very high efficiency are found to be applicable in this domain and lead to two...
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Prof. Jason McEwen (Astrophysics & Cosmology at Mullard Space Science Laboratory, University College London)18/09/2018, 11:10
Abstract: Over recent decades cosmology has transitioned from a data-poor to a data-rich field, which has lead to dramatic improvements in our understanding of the cosmic evolution of our Universe. Nevertheless, we remain ignorant of many aspects of the scenario that has been revealed. Little is known about the fundamental physics of structure formation in the early Universe or the formation...
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Dr Jennifer Thompson18/09/2018, 11:30
Dr. Jennifer Thompson, CERN
Abstract: The machine learning methods currently used in high energy particle physics often rely on Monte Carlo simulations of signal and background. A problem with this approach is that it is not always possible to distinguish whether the machine is learning physics or simply an artefact of the simulation. In this presentation I will explain how it is possible to...
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18/09/2018, 11:45
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18/09/2018, 12:00
Moderator: Prof. Richard McMahon
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Panelists: Dr. Shirley Ho -
Prof. Terry Lyons FLSW FRSE FRS18/09/2018, 14:00
Professor Terry Lyons FLSW FRSE FRS, Wallis Professor of Mathematics, Mathematical Institute, University of Oxford
Abstract: It often happens that measurements have redundant information over and above the invariants of interest; we might measure a rigid object in cartesian co-ordinates although we are only interested in the shape. Representing in an invariant way that does not carry...
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Dr Adam Elwood18/09/2018, 14:30
Dr. Adam Elwood
Abstract:"We introduce two new loss functions designed to directly optimise the statistical significance of the expected number of signal events when training neural networks to classify events as signal or background in the scenario of a search for new physics at a particle collider. The loss functions are designed to directly maximise commonly used estimates of the...
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Ms Vesna Lukic (PhD candidate University of Hamburg)18/09/2018, 14:45
Abstract: Machine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. A topic of current interest is to classify radio galaxy morphology, as it gives us insight into the nature of the AGN, surrounding environment and evolution of the host galaxy. The task of...
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Dr Hao Ni (Senior Lecturer in Financial Mathematics at UCL and the Turing Fellow at The Alan Turing Institute )18/09/2018, 15:00
Abstract: In this talk, we consider the supervised learning problem where the explanatory variable is a data stream. We provide an approach based on identifying carefully chosen features of the stream which allows linear regression to be used to characterise the functional relationship between explanatory variables and the conditional distribution of the response; the methods used to develop...
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Dr Andrey Ustyuzhanin18/09/2018, 15:30
Dr. Andrey Ustyuzhanin, Head of Yandex-CERN Joint Research Projects & Head of the Laboratory of Methods for Big Data Analysis at National Research University Higher School of Economics
Abstract: There is an exceptional way of doing data-driven research employing networked community. The following examples can illustrate the approach: Galaxy Zoo or Tim Gower’s blog. However many cases of...
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Dr Griffin Foster (University of Oxford, University of California at Berkeley)18/09/2018, 16:00
Abstract: Breakthrough Listen is the largest effort to date to search for techno-signatures from extraterrestrial civilizations. We use extensive computing power to search at high frequency and time resolution for transients events in petabytes of observational data from the Green Bank Telescope, Parkes Telescope, LOFAR, and soon MeerKAT. Given the diverse manifestations of transient signals...
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Dr Robert Lyon18/09/2018, 16:15
Abstract: To harness the discovery potential of data collected by the SKA, we require efficient and effective automated data processing methods. Machine learning tools have the potential to deliver this capability, as evidence via their successful application to similar problems in the astronomy domain. This talk introduces the machine learning required for successful time-domain data...
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18/09/2018, 17:00
Moderator: Prof. Paul Alexander
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Panelists: Dr. Peter Braam, Prof. Terry Lyons, Prof. Richard McMahon, Prof. Ian Shipsey
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