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Ford Motor Company funding success for Dr. Tanaya Guha

Dr. Tanaya Guha (PI) has been awarded a research grant by Ford Motor Company through their Global University Research Program to develop the project "Multimodal Learning for In-Car Driver's Activity Monitoring". This 2-year project aims at developing an AI system that can monitor driver's and passengers' safety through audiovisual scene analysis integrated with short-term driving patterns. For example, by creating alerts when a driver is distracted. The project will be developed in collaboration with Ford's AI research at Michigan.

Wed 12 May 2021, 10:06 | Tags: People Grants Research

Identifying banknote fingerprints can stop counterfeits on streets

Shen Wang and Professor Feng Hao from the Systems and Security theme at the Department of Computer Science and Ehsan Toreini from Durham University, have had the paper ‘Anti-Counterfeiting for Polymer Banknotes Based on Polymer Substrate Fingerprinting’, published in the journal IEEE Transactions on Information Forensics and Security, in which they propose a novel technique called Polymer Substrate Fingerprinting, which can identify each banknote’s own unique, unclonable fingerprint.

The researchers have found that every polymer banknote has a unique "fingerprint", which is caused by the inevitable imperfection in the physical manufacturing process, whereby the opacity coating, a critical step during the production of polymer notes, leaves an uneven coating layer with a random dispersion of impurities in the ink. This imperfection results in random translucent patterns when a polymer banknote is back-lit by a light source.

Tue 23 Mar 2021, 12:33 | Tags: Research Data Science Systems and Security

Graham Cormode named 2020 ACM Fellow

Prof. Graham Cormode of the Department of Computer Science has been named among the 2020 Association for Computing Machinery (ACM) Fellows, for contributions to computer science. The ACM is the world's leading learned society for computer science. Prof. Cormode is recognised for his contributions to data summarisation and privacy enabling data management and analysis. His work on data streams and sketching has been widely implemented in many high tech companies and organisations.


Warwick Postgraduate Colloquium in Computer Science 2020

This year’s Warwick Postgraduate Colloquium in Computer Science (WPCCS) was held on Monday 14th December and marked the 18th edition of this beloved event. For the first time in its history, WPCCS took place online, on the communication platform MSTeams, to allow everyone to participate safely during the ongoing COVID-19 pandemic.

A cherished occasion to present one’s research, receive valuable feedback, and create connections within the department to develop new ideas, the Colloquium saw the participation of 50 PhD students who gave presentations spread across seven major themes, showcasing the quality and diversity of the research carried out in the Computer Science Department at Warwick. 22 PhD students also submitted longer, more detailed presentations which were made available to participants and attendees on the official WPCCS MSTeam, so to receive constructive in-depth comments.

Fri 18 Dec 2020, 09:22 | Tags: Conferences Research

EPSRC funding awarded to Prof. Yulan He and Prof. Rob Procter on developing an AI solution for tackling “infodemic”

Prof. Yulan He and Prof. Rob Procter have been awarded funding from the EPSRC under the UKRI’s COVID-19 call. During the COVID-19 pandemic, national and international organisations are using social media and online platforms to communicate information about the virus to the public. However, propagation of misinformation has also become prevalent. This can strongly influence human behaviour and negatively impact public health interventions, so it is vital to detect misinformation in a timely manner. This project aims to develop machine learning algorithms for automatic collection of external evidence relating to COVID-19 and assessment of veracity of claims.

The project is in collaboration with Prof. Maria Liakata and Dr. Arkaitz Zubiaga from the Queen Mary University of London.


Prof. Nasir Rajpoot awarded funding by Cancer Research UK to use machine learning to improve the early detection of oral cancer

Cancer Research UK is funding a study to examine the use of machine learning to assist pathologists and improve the early detection of oral cancer.

We are very excited to work on this project with Dr Khurram and his team at Sheffield. Early detection of cancer is a key focus area of research in our lab and this award by CRUK adds to the portfolio of research at the TIA lab on early detection of cancer.

The pilot project will pave the way towards the development of a tool that can help identify pre-malignant changes in oral dysplasia, crucial for the early detection of oral cancer. Successful completion of this project carries significant potential for saving lives and improving patient healthcare provision. -- Professor Nasir Rajpoot

The research is led by Dr Ali Khurram at the University of Sheffield with Professor Nasir Rajpoot from the University of Warwick as the co-Principal Investigator. Other co-investigators and collaborators include Professor Hisham Mehanna and Dr Paul Navkivell from the University of Birmingham and Dr Jacqueline James from Queen’s University Belfast.


WM5G funding awarded to Prof. Hakan Ferhatosmanoglu on machine learning based spatio-temporal forecasting

Warwick's Department of Computer Science has been awarded a new research grant to develop a machine learning solution for dynamic forecasting of available capacity on road networks. The developed software is planned to be integrated within the TfWM's Regional Transport Coordination Centre for adaptive route planning and traffic management mitigation against disruptions, incidents and roadworks.

The “5G Enabled Dynamic Network Capacity Manager” project is in collaboration with commercial partners, Blacc, Immense, one.network, and O2. The team has won the WM5G’s transport competition to leverage 5G networks for near real-time AI based modelling.

Prof. Hakan Ferhatosmanoglu is leading the development of the scalable ML solution to forecast residual capacities in a dynamic spatio-temporal graph. The solution is designed to benefit from high-granular and low-latency data feeds from 5G cellular and sensor data enabling congestion to be accurately monitored, modelled, and predicted.


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