The annual BT-Pembroke Lecture, which will be held via Zoom on Monday 16th November from 6pm to 7pm. The lecture will be given by Dr Richard Caddis, Director of Health, Safety & Wellbeing, and Chief Medical Officer at BT. Richard will present ‘Black swan or new normal? The changing face of managing during COVID-19’.
BT has been a member of the Pembroke College Corporate Partnership Programme since 2003. This longstanding and mutually beneficial partnership is celebrated annually at the BT-Pembroke Lecture.
This year’s lecture focusses on the COVID-19 pandemic and the huge implications for us all. How should companies respond to a pandemic and what does that response mean for their employees? Dr Richard Caddis will explain the role of a chief medical officer in a large corporation and how BT has responded to the pandemic. He’ll delve into the role of companies in ensuring their employees’ health, safety & wellbeing, and will explore what this and past pandemics can tell us about pandemics in the future.
Please register to attend attend here and joining instructions will be sent nearer to the time. If you have any questions, please do not hesitate to contact Pembroke College at Corporate@pem.cam.ac.uk
Frank Carver has over a quarter of a century of experience in the software industry as an employee, consultant and entrepreneur, and is also a qualified teacher and trainer with experience teaching at college and university level. Recently, Frank has become increasingly concerned with the huge environmental impact of computer technology, combined with a general disregard for such issues in business and academia, and has taken a few years off work to research for a PhD in the area. Frank lives in Ipswich with his family and currently survives by coaching students on how to thrive in the complex and unfamiliar world of today’s university.
What do Software Developers Know About Sustainability?
Exact values vary, but some 2019 sources estimate that the internet and its associated technology consumes as much as 10% of the world’s electricity and has an environmental impact equivalent to the aviation industry. While the use of aviation has drastically reduced because of the COVID19 pandemic, use of the internet has greatly increased, so the current usage is probably considerably higher. The aim of this ongoing PhD research is to educate and inform developers to make smarter choices about the impact of their work, but in order to do that it is important to understand how software developers learn their skills and attitudes in the first place. That means examining how, and what, they are taught.
Dr Hannah Steventonis a Research Associate at the University of Suffolk, working on the DfT-funded Smarter Suffolk project with Suffolk County Council, BT and other partners. Smarter Suffolk is building a county-wide Live Lab of thousands of Internet of Things (IoT) sensors, measuring environmental and traffic conditions across the county to showcase competing innovative technologies. Big Data sets will be accumulated in a vendor-neutral data exchange for analytics applied to a range of Local Authority services. Existing and new communications networks are enabling the IoT deployment, in part using the existing powered lighting column infrastructure.
Initially a hydrogeologist, in her early career Hannah led regeneration projects for large civil engineering firms to assess and remediate contaminated land. Her professional experience includes sophisticated software analysis and modelling of large data sets, site project management of civils contractors, and laboratory chemical analysis. Her doctoral research showed how natural organic matter affects the movement of contaminants through the ground, and she taught Hydrogeology and Pollution at Birkbeck, University of London.
In recent years, as a computing educator and STEM Ambassador, Hannah taught hundreds of children coding, electronics and control systems, using the Raspberry Pi, Arduino, Crumble and Micro:Bit. Projects have included running children’s code on the International Space Station; developing an IoT device to win a major national competition; robots in school corridors; and a large class of 10 year olds soldering self-built games consoles.
Hannah holds a MA in Natural Sciences from the University of Cambridge, and an MSc and PhD in Hydrogeology from University College London. She is a Chartered Geologist and Fellow of the Geological Society, and a Raspberry Pi Certified Educator.
Interesting fact
When Hannah used to teach computing to ten year olds, the children wrote Python programmes to run on a Raspberry Pi to measure temperature and warn when it was too hot or cold. The children’s code was sent to run in space measuring temperature on the International Space Station and displaying the outcome to the astronauts. LinkedIn Poster presentation
Smarter Suffolk: Sensors and data for public services Download slides (.pdf)
Daniel O'Connor is a University College London (UCL) PhD student sponsored by BT, researching quantum annealing and its potential for practical implementation.
He enjoys a good cup of coffee, and a game of football. LinkedIn
Quantum annealing for network optimisation Download slides (.pdf)
Yi-Tun (Ethan) Lin is a Ph.D. student in the Colour & Imaging Lab, School of Computing Sciences, University of East Anglia, UK.
He received a joint M.Sc. degree in Colour Science in 2018, from University Jean Monnet (France), University of Granada (Spain) and University of Eastern Finland (Finland), and a B.Sc. degree in Physics in 2016, from National Taiwan University, Taiwan. His research interest is physics and machine learning-based spectral reconstruction. LinkedIn
My research focuses on automated, quantitative methods of processing large amounts of textual and other forms of unstructured data – mainly political texts and social media – and the methodology of text mining for social science. I have published on applications of measurement and the analysis of text as data on machine learning methods and deep learning. I am also applying machine learning and natural language processing techniques to the analysis of public policy. My substantive research interests centre on resilience and the role of public policies and institutions at different levels of governance in shaping it.
Chatsiou, K. and Mikhaylov, SJ., (2020). Deep Learning for Political Science. In: The SAGE Handbook of Research Methods in Political Science and International Relations. Editors: Curini, L. and Franzese, R., . SAGE Publications Ltd. 1053- 1078. 9781526459930
Political text classification using Neural Networks
We build a sentence-level political discourse classifier using existing human expert annotated corpora of political manifestos from the Manifestos Project (Volkens et al.,2020a) and applying them to a corpus of COVID-19 Press Briefings (Chatsiou,2020). We use manually annotated political manifestos as training data to train a local topic Convolutional Neural Network (CNN) classifier; then apply it to the COVID-19 Press Briefings Corpus to automatically classify sentences in the test corpus. We report on a series of experiments with CNN trained on top of pre-trained embeddings for sentence-level classification tasks. We show that CNN combined with transformers like BERT outperforms CNN combined with other embeddings (Word2Vec, Glove, ELMo) and that it is possible to use a pre-trained classifier to conduct automatic classification on different political texts without additional training.
I'm a PhD student researching quantum computer entangling gates in silicon at the London Center for Nanotechnology. Being part of the Advanced Characterisation of Materials CDT has linked me up with many like-minded PhD students here at University College London and Imperial College London. I'm also on IBM's community team for the open source quantum algorithm software Qiskit.
