We run a seminar series within the department and also co-organise events with external partners. Please get touch if you are a member of the university and want to attend our seminar series. Our general events are listed below.
Thursday 25th August 2022 - 2pm
MS Teams link: To be sent to registered attendees 30min before start
Registration: Online Demand Response Programs for Constrained Local Electricity Systems Tickets, Thu 25 Aug 2022 at 14:00 | Eventbrite
Prof. Dr. Dirk Neumann
Head of the Institute of Economics and Business Administration
Chair of Information Systems Research
Renewable energies require new approaches to the operation of power systems. In this talk, I present a novel Demand Response (DR) program to mitigate grid congestion. The program stands out by adjusting electricity prices online and solely based on observed aggregate electricity consumption. It thereby avoids privacy concerns and expensive information system infrastructure investments. Additionally, it can cope with non-elastic and time-interdependent demand—which is typical for storage devices and industrial processes. ) model four flexible load types and provide a unified framework of load modeling under uncertainty and with time interdependencies.
Numerical experiments show that the DR program achieves considerable and stable cost savings of 40% to 60% in comparison to conventional DR programs. Moreover, the solution approach based on Deep Reinforcement Learning reaches these savings after a short learning period corresponding to only 25 simulation days.
Regarding load flexibility, I find that the responses of the four load types strongly differ depending on prices and the lag between the announcement and the application of price changes (`notification interval’) which is an important design parameter. The results imply that system operators should flexibilize their DR programs with the help of learning algorithms and tailor their program to the local load composition, congestion frequency, and forecasting quality.
Dirk Neumann has held the Chair of Information Systems Research at the Albert Ludwigs University of Freiburg as a Full Professor since 2008. He studied economics in Gießen and Milwaukee and read his PhD at KIT. The research focus of his working group is, among other things, AI in management. During his stays abroad, he worked with researchers at the University of New South Wales (UNSW) in Sydney, Concordia University in Montreal and Stanford University in Palo Alto. In his field of research, he is one of the most published researchers in Germany. He has received appointments at Humboldt University Berlin and Johannes Kepler University Linz (Austria).
The Centre for Machine Intelligence (CMI) at the University of Southampton invites you to a seminar on the topic of "Machine Intelligence for COVID-19". As the fight against the COVID-19 continues, a wide range of research efforts are taking place. We are hosting three talks about the latest academic research on how different aspects of machine intelligence are helping with this endeavour, followed by a live Q/A and panel session with the presenters.
14:00 - 14:05 - Welcome and introduction - Dr. Enrico Gerding (Director of CMI)
14:05 - 14:20 - The sounds of COVID-19: crowdsourcing and analysing respiratory signals for COVID-19 diagnostics - Prof. Cecilia Mascolo (University of Cambridge)
Abstract: In this talk I will describe the work we have been doing on crowdsourcing respiratory sounds (coughs, breathing and voice) through a mobile app (covid-19-sounds.org) and their analysis through audio based machine learning for COVID-19 diagnostics and disease progression. This work is part of a a bigger project which looks at how sounds of the human body (respiratory, cardiovascular, digestive) can be collected through wearables and mobile devices and analysed with the aim of improving automated and efficient medical diagnostics.
Bio: Cecilia Mascolo is the mother of a teenage daughter but also a Full Professor of Mobile Systems in the Department of Computer Science and Technology, University of Cambridge, UK. She is co-director of the Centre for Mobile, Wearable System and Augmented Intelligence and Deputy Head of Department for Research. She is also a Fellow of Jesus College Cambridge and the recipient of an ERC Advanced Research Grant. Prior joining Cambridge in 2008, she was a faculty member in the Department of Computer Science at University College London. She holds a PhD from the University of Bologna. Her research interests are in mobile systems and data for health, human mobility modelling, sensor systems and networking and mobile data analysis. She has published in a number of top tier conferences and journals in the area and her investigator experience spans projects funded by Research Councils and industry. She has received numerous best paper awards and in 2016 was listed in “10 Women in Networking /Communications You Should Know”. She has served as steering, organizing and programme committee member of mobile, sensor systems, networking, data science conferences and workshops. She has delivered a number of keynote talks at conferences and workshops in the area of mobility, data science, pervasive computing and systems.
14:20 - 14:35 - Emergency department admissions in COVID-19 and explainable machine learning to understand changes in clinical decision making - Dr. Chris Duckworth (IT Innovation Centre)
Abstract: The COVID-19 pandemic has created rapid and unprecedented changes in how services across the NHS are used. Emergency departments (EDs) have seen a 57% decrease (April 2020) in visits, with indications that patients in need of medical intervention have avoided coming to hospital for reasons unrelated to COVID, as COVID admissions have soared. This changing patient landscape has led to an overhaul in hospital operations and procedure. Decision support systems learn from historical ED operations, to enable early identification of patients who are at high risk of hospital admission, enabling ahead-of-time logistical planning. We test a machine learnt admission model's performance (trained pre COVID), pre COVID, and during the first wave, to understand how decision support systems are impacted by changing patient landscape and operational change. Using explainable machine learning, we understand how different hospital records (used as features) become more (or less) predictive of admission risk. We introduce the idea of using explainable admission models to track and understand changes in clinical decision making over time.
Bio: Chris is a Research Engineer (Healthcare) based in the IT Innovation Centre, as part of the School of Electronics and Computer Science. Since joining the centre in early 2021, his focus has been on the impact of COVID-19 on Emergency Department admissions and developing machine learning models for clinical decision making support. Chris' background in data science comes from a PhD (University of St Andrews) and employment at the Flatiron Institute (Simons Foundation) working in computational Astrophysics.
14:35 - 14:50 - Indirect disease mitigation using targeted interventions - Dr. Edoardo Manino (University of Manchester)
Abstract: The current COVID-19 pandemic is fought on two fronts: reducing the spread of the disease, and educating the population on the importance of vaccines and face masks. Both contribute to curbing the total number of cases, but how do these two factors interact? In this talk I present a mathematical model of coupled disease-awareness systems and propose strategies for optimal control. This is joint work with Dr Markus Brede (University of Southampton).
Bio: Edoardo Manino is a Research Associate in the Computer Science department of the University of Manchester. He is part of the EnnCore project and focuses on automated verification of neural network architectures. His background is in Bayesian machine learning, a topic he recently got awarded a PhD from the University of Southampton. His other research interests range from network science to algorithmic game theory and reinforcement learning.
14:50 - 15:30 - Panel discussion and Q/A - Chair: Dr. Kate Farrahi (University of Southampton)
Machine Intelligence experts will demonstrate the latest advances from revolutionary new technologies in a packed showcase event at the University of Southampton.
A distinguished panel of AI pioneers and government chiefs will discuss the research field’s expected impact on the UK economy and the necessary strategies to fulfil this potential at the all-day event, hosted by the University’s Centre for Machine Intelligence (CMI). The showcase takes place on Tuesday 22nd of October 2019.
Machine Intelligence, which includes the development of Artificial Intelligence (AI), Machine Learning and Autonomous Systems, is using science to build a safer and smarter society. Researchers from Southampton’s School of Electronics and Computer Science (ECS) are leaders in several pioneering live projects and outlined the latest progress before an audience of around 200 industry attendees, government representatives and academic peers in our last year's event.
With attendees ranging from researchers, local authorities, SMEs and large corporates the event brough together a line up of lightning talks and breakout sessions dealing with the cutting edge challenges and opportunities of AI and how we can work together. Breakout sessions included Data Bias and Inclusion in AI; Privacy, Law and AI; Data Provenance and Governance; and Data Sharing Platforms – technological, legal and ethical challenges.
In line with the UK’s Industrial Strategy, we’re keen to foster academic-industry partnerships with a socio-technical angle and will be providing seed-funding opportunities for building collaborative teams from the sparks of shared interest we hope to generate at the event. Attendees included the Office for AI, Southampton Connect, Hampshire County Council, Shell and a number of local SMEs.
Across many disciplines at the university of Southampton, there is interest in the applications of machine learning to scientific research. The symposium on interdisciplinary machine learning was a morning event designed to bring together researchers and facilitate a discussion about this growing field. Talks covered a range of topics from archeology to biology with a view to understanding the benefits provided by machine learning and their limitations. The event attracted over 200 sign-ups and talks were well received by the audience
The CMI PhD evening consisted of a series of talks detailing the PhD process, and specific areas of interest across the different core Machine Intelligence Research Groups within ECS. In particular, we introduced the new Centre for Doctoral Training on Machine Intelligence and Nano-Electronic Devices (MINDS CDT) and the opportunities to get involved with this exciting training programme.
The Machine Intelligence Showcase was a great success. Head over to the event page to find out who was speaking and to watch the showcase video!
When you read about AI in the news, you're more likely than not to actually be reading about a new advancement in Machine Learning. In the past 10 years, machine learning has started to find its way into everyday technology and revolutionise both products and industry. The CMI evening on machine learning attracted over 150 attendees including members of the public, students, and researchers. Talks from industry and academia were well received by the audience and led to very exciting discussions at the end.
The panel session received many questions about the use of data from the NHS and other questions around the techniques used to manage large datasets.
The evening closed with a very successful networking session.
CMI Evening on Robots and their use in Warfare - 02/05/2018
We held an evening seminar on Robots and their use in Warfare on the 2nd of May. With over 130 signups, the event was a great success. World experts in the legal aspects of warfare, Prof. Christian Enemark and Dr. Regina Rauxloh gave an overview of the key legal, moral, and ethical considerations. Dr. Andrea Munafo from the National Oceanography Centre, along with Dr. Tarapore and Dr. Ramchurn from ECS, then provided an overview of the capabilities built into the next generation of autonomous systems, whether under-water, ground, or aerial. The evening concluded with a networking session that we hope will bring many more collaborations across the University via the CMI.
CMI organised an evening of talks on Artificial Intelligence and the ethical and societal challenges associated with large scale use of such technology. The evening consisted of a number of talks by leading academics within the Centre for Machine Intelligence as well as guests from leading tech companies. Prof. Chris Reed from Dundee and Dr. Isabel Sargent from Ordnance Survey were guest speakers.
We had over 60 attendees from within the University, industry, and the general public. The panel led to a number of questions raised around the use of argumentation machines to discuss ethical questions, the use of AI in defence and how we can work with policy makers to develop regulation for autonomous systems.
The event was a great success with over 60 signups and a number of talks detailing topics in AI, Machine Learning, and Autonomous Systems as well as funding opportunities.
This first open event organised by the CMI attracted over 100 attendees from across ECS, the Faculty of Engineering and Physical Sciences, as well as industry and the general public. The event was sponsored by Oxford Innovation and was an exciting mix of both academic presentations and hands-on use-case discussions from industry.