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Exoplanet Validation with Machine Learning: 50 new validated Kepler planets

Dr Theo Damoulas (Department of Computer Science) along with Dr David Armstrong (Department of Physics) and Jevgenij Gamper (Department of Mathematics) have developed probabilistic machine learning algorithms that can separate out real planets from fake ones in the large samples of thousands of candidates found by telescope missions such as NASA’s Kepler and TESS. The results of which have led to fifty new confirmed planets, the first to be not only ranked but also probabilistically validated by machine learning.

The paper "Exoplanet Validation with Machine Learning: 50 new validated Kepler planets" has been accepted to the Monthly Notice of the Royal Astronomical Society, DOI: 10.1093/mnras/staa2498

Thu 10 Sep 2020, 13:30 | Tags: Research Artificial Intelligence

Wearable IoT Electronic Nose for Urinary Incontinence Detection

Work performed by Computer Systems Engineering student Michael Shanta for his 3rd year project, supervised by Dr. Marina Cole and Dr. Siavash Esfahani in the School of Engineering, was written up in a paper that was recently accepted for presentation at the IEEE Sensors 2020 Conference.

For his 3rd year project Michael worked on developing machine learning techniques for an Electronic Nose in order to classify odours based on the sensor responses. The system aims to detect incontinence incidents, allowing alerts to be sent to relevant personnel from an IoT network via a cloud server.


Grasshopper jumping on a sphere gives new quantum insights

Bloch sphere with grasshopper Dr Dmitry Chistikov and Professor Mike Paterson, together with physicists Olga Goulko (Boise State University) and Adrian Kent (Cambridge), have published an interdisciplinary paper Globe-hopping, solving a probabilistic puzzle on the sphere that has applications to quantum information theory.

Suppose a lawn must cover exactly half the area of a sphere. A grasshopper starts from a random position on the lawn and jumps a fixed distance in a random direction. What shape of lawn maximizes the chance that the grasshopper lands back on the lawn? A natural guess would be that a hemispherical lawn is best. It turns out, however, that this is nearly never the case — there are only a few exceptional jump sizes.

This work involving spherical geometry, probability theory, basic number theory, and theoretical physics appears in the Proceedings of the Royal Society A and shows, apart from concern for the well-being of grasshoppers, that there are previously unknown types of Bell inequalities. The Bell inequality, devised by physicist John Stewart Bell in 1964, demonstrated that no combination of classical theories with Einstein's special relativity is able to explain the predictions (and later actual experimental observations) of quantum theory.

A University press release can be found here.

Tue 11 Aug 2020, 11:51 | Tags: People Research Theory and Foundations

PETRAS SRF award to Dr Arshad Jhumka to investigate trust in IoT systems

Dr Arshad Jhumka from the department’s Artificial Intelligence research theme has been awarded a grant as PI, under the PETRAS SRF programme, to develop and deploy a trusted edge-based Internet of Things (IoT) network. IoT networks are expected to be deployed as solutions to problems in a wide variety of contexts, from non-critical applications such as smart city monitoring to providing support to emergency services such as critical communications. As IoT devices are resource constrained, execution of resource-hungry applications will be offloaded to edge networks for quick response. Such an infrastructure is open to cyber-attacks and needs to be resilient to attack.

Fri 22 May 2020, 18:16 | Tags: Grants Research Artificial Intelligence

EPSRC funding for Florin Ciucu

Florin Ciucu has been successful with a 491K EPSRC grant application ‘Practical Analysis of Parallel and Networked Queueing Systems’. The project will run for 4 years and will address some fundamental queueing problems at the core of modern computing and communication systems with parallel or network structures. The technical objective is to develop novel martingale-based models and techniques circumventing the historical Poisson assumption on the systems’ input, which has been convincingly shown to be highly misleading for practical purposes. The proposal was supported by IBM Research, Microsoft Research, and VMware.


Dr Criseida Zamora joined the department as a Research Fellow

Dr Criseida Zamora has joined the department to work together with Dr Yulia Timofeeva, Prof Kirill Volynski (UCL) and a number of other world-leading experimental laboratories on an MRC-funded project "Virtual presynaptic nerve terminal". This project aims to develop a unified computational modelling framework which will allow the neuroscience community to explore mechanisms of synaptic transmitter release that cannot be directly determined experimentally.

Criseida is a Bionic engineer working in the Systems Biology field. She received a PhD degree in Biomedical Engineering and Physics working on the analysis of biochemical noise in synthetic genetic circuits at the Center for Research and Advanced Studies of the National Polytechnic Institute in Mexico. Her academic background and research experience have focused hitherto on building in silico models to study emergent properties of molecular systems to answer physiological questions. She has also worked as a postdoctoral scholar at Okinawa Institute of Science and Technology in Japan and the University of Bristol.

Tue 12 May 2020, 00:46 | Tags: People Research Applied Computing

DASA award to Dr Victor Sanchez to improve security at airports

Dr Victor Sanchez (PI) from the department's Artificial Intelligence research theme and Prof. Carsten Maple (Co-I) from Warwick Manufacturing Group have been further awarded a research grant by the Defence and Security Accelerator (DASA), which is part of the Ministry of Defence, to continue with Phase 2 of the project R-DIPS - "Real-time Detection of Concealment of Intent for Passenger Screening." The project, which began on October 2019 and ends on February 2021, aims at developing a machine learning and computer vision solution to track, in real-time, multiple individuals across a set of non-overlapping surveillance cameras to detect those with suspicious behaviours and movements within an airport. The project will improve the screening process of passengers to detect those attempting to mask nefarious intent. The R-DIPS project is an international collaboration with Prof. Chang-Tsun Li who is also affiliated with Deakin University, Australia.

Fri 01 May 2020, 11:57 | Tags: Grants Research Artificial Intelligence

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