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This Week’s Events

Data-Aware Task-offloading Decision Making at the Network Edge

Group: Knowledge & Data Engineering Systems Group
Speaker: Dr Kostas Kolomvatsos, University of Thessaly
Date: 26 September, 2022
Time: 14:00 - 15:00
Location: Sir Alwyn Williams Building, 423 Seminar Room

Nowadays, the advent of the Internet of Things (IoT) provides a huge infrastructure where numerous devices collect and process data from their environment. Due to the limited computational capabilities of IoT devices, the adoption of the Edge Computing (EC) can provide an additional layer of processing to offer more computational resources.  In the EC, one can find an increased number of nodes that can collaborate each other and, collectively, support advanced processing activities very close to end-users enhancing the pervasiveness of services/applications. Usual collaborative activities can be placed around the exchange of data or services (e.g., data/services migration) or offloading actions for tasks demanding a specific processing workflow upon the collected data.  

In this talk, I will introduce the need for the adoption of proactive models that, based on data-aware reasoning mechanisms, are capable of efficiently managing data, services and tasks present at every individual EC node. I will elaborate on the characteristics that any future inference model should have and on the need of processing the surrounding contextual information. Additional requirements involve the presence of monitoring schemes for specific parameters to know when and how to perform data/tasks/services management activities in a collaborative manner. The aforementioned contextual information is not only related to the individual EC nodes, but also to the information related to the status of peer nodes. Any decision-making scheme could be built upon the minimum and sufficient information representing crucial parameters of the EC nodes behaviour to secure its viability without jeopardizing the performance of the network.  

SICSA DVF Talk: Data-Aware Task-offloading Decision Making at the Network Edge

Group: Scottish Informatics and Computer Science Alliance (SICSA)
Speaker: SICSA Event, SICSA
Date: 26 September, 2022
Time: 14:00 - 15:00
Location:University of Glasgow, Sir Alwyn Williams Building, SAWB 423, Glasgow , United Kingdom

Abstract Nowadays, the advent of the Internet of Things (IoT) provides a huge infrastructure where numerous devices collect and process data from their environment. Due to the limited computational capabilities of IoT devices, the adoption of the Edge Computing (EC) can provide an additional layer of processing to offer more computational resources. In the EC, one can find an increased number of nodes that can collaborate each other and, collectively, support advanced processing activities very close to end-users enhancing the pervasiveness of services/applications. Usual collaborative activities can be placed around the exchange of data or services (e.g., data/services migration) or offloading actions for tasks demanding a specific processing workflow upon the collected data. In this talk, I will introduce the need for the adoption of proactive models that, based on data-aware reasoning mechanisms, are capable of efficiently managing data, services and tasks present at every individual EC node. I will elaborate on the characteristics that any future inference model should have and on the need of processing the surrounding contextual information. Additional requirements involve the presence of monitoring schemes for specific parameters to know when and how to perform data/tasks/services management activities in a collaborative manner. The aforementioned contextual information is not only related to the individual EC nodes, but also to the information related to the status of peer nodes. Any decision-making scheme could be built upon the minimum and sufficient information representing crucial parameters of the EC nodes behaviour to secure its viability without jeopardizing the performance of the network. Short CV Dr Konstantinos (Kostas) Kolomvatsos received his BSc in Informatics from the Department of Informatics, Athens University of Economics and Business, MSc in Computer Science, New Technologies in Informatics and Telecommunications (2005), and PhD in Computer Science from the Department of Informatics and Telecommunications at the University of Athens (2013). He was a Marie Skłodowska Curie Fellow at the School of Computing Science, University of Glasgow, 2018-2020. Currently, he serves as an Assistant Professor in the Department of Informatics and Telecommunications, University of Thessaly. He is the founder of the Intelligent Pervasive Systems (iPRISM) research group and the Director of the Intelligent Systems for Orchestrating Pervasive Computing Applications (METIS). He serves as a PI in various National and EU/H2020 projects. His research interests are in the intersection of Intelligent Systems, Computational Intelligence, and Pervasive Data Science. He is the author of over 130 publications in the aforementioned areas.

Don’t recommend the obvious: estimate probability ratios

Group: Information Retrieval (IR)
Speaker: Wenjie Zhao, Amazon
Date: 26 September, 2022
Time: 15:00 - 16:00
Location: Room 422, SAWB

Abstract

Sequential recommender systems are becoming widespread in the online retail and streaming industry. These systems are often trained to predict the next item given a sequence of a user’s recent actions, and standard evaluation metrics reward systems that can identify the most probable items that might appear next. However, some recent papers instead evaluate recommendation systems with popularity-sampled metrics, which measure how well the model can find a user’s next item when hidden amongst generally-popular items. We argue that these popularity-sampled metrics are more appropriate for recommender systems, because the most probable items for a user often include generally-popular items. If the probability that a customer will watch Toy Story is not much more probable than for the average customer, then the movie isn’t especially relevant for them and we should not recommend it. This paper shows that optimizing popularity-sampled metrics is closely related to estimating point-wise mutual information (PMI). We propose and compare two techniques to fit PMI directly, which both improve popularity-sampled metrics for state-of-the-art recommender systems. The improvements are large compared to differences between recently-proposed model architectures.

 

Bio

Wenjie Zhao is an applied scientist at Amazon Scotland Development Centre. She works on various topics in the area of recommenders systems. Her main focus is to build and improve large-scale deep learning recommenders for Amazon retail website. She received her MS in AI from the University of Edinburgh and her BS in Maths and Information Engineering from the Chinese University of Hong Kong.

FATA Seminar - Tutorial on dependently-typed functional programming

Group: Formal Analysis, Theory and Algorithms (FATA)
Speaker: Jan De Muijnck-Hughes, University of Glasgow
Date: 27 September, 2022
Time: 13:00 - 13:30
Location: Room 422, SAWB

These tutorials are intended to give all members the background necessary to follow future seminars. Jan De Muijnck-Hughes will give the first tutorial on dependently-typed functional programming.

Machine Learning at NHS National Services Scotland (Business Intelligence)

Group: Computing Technologies for Healthcare
Speaker: Dr Simon Rogers, NHS National Services Scotland
Date: 28 September, 2022
Time: 14:00 - 15:00
Location: https://uofglasgow.zoom.us/meeting/register/tJUkcOqsqz8vHdboNnP6P-_L3F5na54lHlsx

NHS NSS is a specialist health board, providing services to the other health boards and across the public sector. Within NSS, the Business Intelligence (and more specifically, the Artificial Intelligence Centre of Excellence) provide assistance, expertise, and a technology platform to support projects with an AI component. In this talk, I will introduce NSS BI (including our Seer platform), and discuss some of the work that we are undertaking, including work on the new version of the SPARRA model (predicting admission / re-admission for individuals in Scotland), work on how to safely export trained models from safe-haven environments, and some ongoing work on fairness / inequalities.

Biography:

Dr Simon Rogers is an AI data scientist at NHS National Services Scotland and Honorary Lecturer in Computer Science at University of Glasgow. His research involves the development of Machine Learning and Statistical techniques to help with the analysis of complex datasets. Among his long publication list he has also published a textbook on 'A First Course in Machine Learning', which is an introduction to modern (fairly probabilistic) Machine Learning.

This is a hybrid seminar: Join us at 423 Sir Alwyn Williams Building, School of Computing Science.

Registration is required:  https://uofglasgow.zoom.us/meeting/register/tJUkcOqsqz8vHdboNnP6P-_L3F5na54lHlsx

Coffee

Group: RA and PDRA Events
Speaker: Coffee
Date: 30 September, 2022
Time: 11:00 - 12:00
Location: Computing Science Common Room

Just our weekly coffee catch-up

Upwards seminar: How to establish a research strategy/agenda?

Group: Upwards - a seminar series about all aspects of research life
Speaker: Rodrerick Murray-Smith, School of Computing Science, University of Glasgow
Date: 30 September, 2022
Time: 14:30 - 15:30
Location: Sir Alwyn Williams Building, 422 Seminar Room

Hi everybody,

The next Upwards seminar will be on September 30th at 2.30 pm in Room 422 SAWB. For this time, we will have Rod Murray-Smith who will talk about

How to establish a research strategy/agenda?

Everybody is welcome to attend! Note that this session will be in-person 😊

For those new to the School, the Upwards seminars discuss all aspects of research life and provide a forum for everyone in the School to talk, start discussions and share best practices of colleagues who have been successful and unsuccessful in a particular aspect of research. The seminars are called Upwards because they aim to inspire and motivate, meet people you can approach for advice, and foster good research culture practices within the School.

PS. Apologies if you are receiving this email multiple times, but I want to ensure wider dissemination within the School.

Regards,
Gerardo

Upcoming events

Data-Aware Task-offloading Decision Making at the Network Edge

Group: Knowledge & Data Engineering Systems Group
Speaker: Dr Kostas Kolomvatsos, University of Thessaly
Date: 26 September, 2022
Time: 14:00 - 15:00
Location: Sir Alwyn Williams Building, 423 Seminar Room

Nowadays, the advent of the Internet of Things (IoT) provides a huge infrastructure where numerous devices collect and process data from their environment. Due to the limited computational capabilities of IoT devices, the adoption of the Edge Computing (EC) can provide an additional layer of processing to offer more computational resources.  In the EC, one can find an increased number of nodes that can collaborate each other and, collectively, support advanced processing activities very close to end-users enhancing the pervasiveness of services/applications. Usual collaborative activities can be placed around the exchange of data or services (e.g., data/services migration) or offloading actions for tasks demanding a specific processing workflow upon the collected data.  

In this talk, I will introduce the need for the adoption of proactive models that, based on data-aware reasoning mechanisms, are capable of efficiently managing data, services and tasks present at every individual EC node. I will elaborate on the characteristics that any future inference model should have and on the need of processing the surrounding contextual information. Additional requirements involve the presence of monitoring schemes for specific parameters to know when and how to perform data/tasks/services management activities in a collaborative manner. The aforementioned contextual information is not only related to the individual EC nodes, but also to the information related to the status of peer nodes. Any decision-making scheme could be built upon the minimum and sufficient information representing crucial parameters of the EC nodes behaviour to secure its viability without jeopardizing the performance of the network.  

SICSA DVF Talk: Data-Aware Task-offloading Decision Making at the Network Edge

Group: Scottish Informatics and Computer Science Alliance (SICSA)
Speaker: SICSA Event, SICSA
Date: 26 September, 2022
Time: 14:00 - 15:00
Location: University of Glasgow, Sir Alwyn Williams Building, SAWB 423, Glasgow , United Kingdom

Abstract Nowadays, the advent of the Internet of Things (IoT) provides a huge infrastructure where numerous devices collect and process data from their environment. Due to the limited computational capabilities of IoT devices, the adoption of the Edge Computing (EC) can provide an additional layer of processing to offer more computational resources. In the EC, one can find an increased number of nodes that can collaborate each other and, collectively, support advanced processing activities very close to end-users enhancing the pervasiveness of services/applications. Usual collaborative activities can be placed around the exchange of data or services (e.g., data/services migration) or offloading actions for tasks demanding a specific processing workflow upon the collected data. In this talk, I will introduce the need for the adoption of proactive models that, based on data-aware reasoning mechanisms, are capable of efficiently managing data, services and tasks present at every individual EC node. I will elaborate on the characteristics that any future inference model should have and on the need of processing the surrounding contextual information. Additional requirements involve the presence of monitoring schemes for specific parameters to know when and how to perform data/tasks/services management activities in a collaborative manner. The aforementioned contextual information is not only related to the individual EC nodes, but also to the information related to the status of peer nodes. Any decision-making scheme could be built upon the minimum and sufficient information representing crucial parameters of the EC nodes behaviour to secure its viability without jeopardizing the performance of the network. Short CV Dr Konstantinos (Kostas) Kolomvatsos received his BSc in Informatics from the Department of Informatics, Athens University of Economics and Business, MSc in Computer Science, New Technologies in Informatics and Telecommunications (2005), and PhD in Computer Science from the Department of Informatics and Telecommunications at the University of Athens (2013). He was a Marie Skłodowska Curie Fellow at the School of Computing Science, University of Glasgow, 2018-2020. Currently, he serves as an Assistant Professor in the Department of Informatics and Telecommunications, University of Thessaly. He is the founder of the Intelligent Pervasive Systems (iPRISM) research group and the Director of the Intelligent Systems for Orchestrating Pervasive Computing Applications (METIS). He serves as a PI in various National and EU/H2020 projects. His research interests are in the intersection of Intelligent Systems, Computational Intelligence, and Pervasive Data Science. He is the author of over 130 publications in the aforementioned areas.

Don’t recommend the obvious: estimate probability ratios

Group: Information Retrieval (IR)
Speaker: Wenjie Zhao, Amazon
Date: 26 September, 2022
Time: 15:00 - 16:00
Location: Room 422, SAWB

Abstract

Sequential recommender systems are becoming widespread in the online retail and streaming industry. These systems are often trained to predict the next item given a sequence of a user’s recent actions, and standard evaluation metrics reward systems that can identify the most probable items that might appear next. However, some recent papers instead evaluate recommendation systems with popularity-sampled metrics, which measure how well the model can find a user’s next item when hidden amongst generally-popular items. We argue that these popularity-sampled metrics are more appropriate for recommender systems, because the most probable items for a user often include generally-popular items. If the probability that a customer will watch Toy Story is not much more probable than for the average customer, then the movie isn’t especially relevant for them and we should not recommend it. This paper shows that optimizing popularity-sampled metrics is closely related to estimating point-wise mutual information (PMI). We propose and compare two techniques to fit PMI directly, which both improve popularity-sampled metrics for state-of-the-art recommender systems. The improvements are large compared to differences between recently-proposed model architectures.

 

Bio

Wenjie Zhao is an applied scientist at Amazon Scotland Development Centre. She works on various topics in the area of recommenders systems. Her main focus is to build and improve large-scale deep learning recommenders for Amazon retail website. She received her MS in AI from the University of Edinburgh and her BS in Maths and Information Engineering from the Chinese University of Hong Kong.

FATA Seminar - Tutorial on dependently-typed functional programming

Group: Formal Analysis, Theory and Algorithms (FATA)
Speaker: Jan De Muijnck-Hughes, University of Glasgow
Date: 27 September, 2022
Time: 13:00 - 13:30
Location: Room 422, SAWB

These tutorials are intended to give all members the background necessary to follow future seminars. Jan De Muijnck-Hughes will give the first tutorial on dependently-typed functional programming.

Machine Learning at NHS National Services Scotland (Business Intelligence)

Group: Computing Technologies for Healthcare
Speaker: Dr Simon Rogers, NHS National Services Scotland
Date: 28 September, 2022
Time: 14:00 - 15:00
Location: https://uofglasgow.zoom.us/meeting/register/tJUkcOqsqz8vHdboNnP6P-_L3F5na54lHlsx

NHS NSS is a specialist health board, providing services to the other health boards and across the public sector. Within NSS, the Business Intelligence (and more specifically, the Artificial Intelligence Centre of Excellence) provide assistance, expertise, and a technology platform to support projects with an AI component. In this talk, I will introduce NSS BI (including our Seer platform), and discuss some of the work that we are undertaking, including work on the new version of the SPARRA model (predicting admission / re-admission for individuals in Scotland), work on how to safely export trained models from safe-haven environments, and some ongoing work on fairness / inequalities.

Biography:

Dr Simon Rogers is an AI data scientist at NHS National Services Scotland and Honorary Lecturer in Computer Science at University of Glasgow. His research involves the development of Machine Learning and Statistical techniques to help with the analysis of complex datasets. Among his long publication list he has also published a textbook on 'A First Course in Machine Learning', which is an introduction to modern (fairly probabilistic) Machine Learning.

This is a hybrid seminar: Join us at 423 Sir Alwyn Williams Building, School of Computing Science.

Registration is required:  https://uofglasgow.zoom.us/meeting/register/tJUkcOqsqz8vHdboNnP6P-_L3F5na54lHlsx

Coffee

Group: RA and PDRA Events
Speaker: Coffee
Date: 30 September, 2022
Time: 11:00 - 12:00
Location: Computing Science Common Room

Just our weekly coffee catch-up

Upwards seminar: How to establish a research strategy/agenda?

Group: Upwards - a seminar series about all aspects of research life
Speaker: Rodrerick Murray-Smith, School of Computing Science, University of Glasgow
Date: 30 September, 2022
Time: 14:30 - 15:30
Location: Sir Alwyn Williams Building, 422 Seminar Room

Hi everybody,

The next Upwards seminar will be on September 30th at 2.30 pm in Room 422 SAWB. For this time, we will have Rod Murray-Smith who will talk about

How to establish a research strategy/agenda?

Everybody is welcome to attend! Note that this session will be in-person 😊

For those new to the School, the Upwards seminars discuss all aspects of research life and provide a forum for everyone in the School to talk, start discussions and share best practices of colleagues who have been successful and unsuccessful in a particular aspect of research. The seminars are called Upwards because they aim to inspire and motivate, meet people you can approach for advice, and foster good research culture practices within the School.

PS. Apologies if you are receiving this email multiple times, but I want to ensure wider dissemination within the School.

Regards,
Gerardo

Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems

Group: Information Retrieval (IR)
Speaker: Ludovico Boratto, University of Cagliari
Date: 03 October, 2022
Time: 15:00 - 16:00
Location: Room 422, SAWB

Abstract

Considering the impact of recommendations on item providers is one of the duties of multi-sided recommender systems. Item providers are key stakeholders in online platforms, and their earnings and plans are influenced by the exposure their items receive in recommended lists. Prior work showed that certain minority groups of providers, characterized by a common sensitive attribute (e.g., gender or race), are being disproportionately affected by indirect and unintentional discrimination. However, there are situations where (i) the same provider is associated with multiple items of a list suggested to a user, (ii) an item is created by more than one provider jointly, and (iii) predicted user-item relevance scores are biasedly estimated for items of provider groups. In this talk, we assess disparities created by the state-of-the-art recommendation models in relevance, visibility, and exposure, by simulating diverse representations of the minority group in the catalog and the interactions. Based on emerged unfair outcomes, we devise a treatment that combines observation upsampling and loss regularization, while learning user-item relevance scores. Experiments on real-world data demonstrate that our treatment leads to lower disparate relevance. The resulting recommended lists show fairer visibility and exposure, higher minority item coverage, and negligible loss in recommendation utility.

 

Bio

Ludovico Boratto is a researcher at the Department of Mathematics and Computer Science of the University of Cagliari (Italy). His research interests focus on recommender systems and their impact on the different stakeholders, both considering accuracy and beyond-accuracy evaluation metrics. He has authored more than 60 papers and published his research in top-tier conferences and journals.  His research activity also brought him to give talks and tutorials at top-tier conferences and research centers (Yahoo! Research). He is editor of the book “Group Recommender Systems: An Introduction”, published by Springer. He is an editorial board member of the “Information Processing & Management” journal (Elsevier) and “Journal of Intelligent Information Systems” (Springer), and guest editor of several journals’ special issues. He is regularly part of the program committees of the main Web conferences, where he received three outstanding contribution awards. In 2012, he got his Ph.D. at the University of Cagliari (Italy), where he was a research assistant until May 2016. From May 2016 to April 2021, he joined Eurecat as Senior Research Scientist in the Data Science and Big Data Analytics research group. In 2010 and 2014, he spent ten months at Yahoo! Research in Barcelona as a visiting researcher. He is a member of ACM and IEEE.

Retrieval-Enhanced Language Models and Semantic-Driven Summarization for Biomedical Domains

Group: Information Retrieval (IR)
Speaker: Giacomo Frisoni, University of Bologna
Date: 03 October, 2022
Time: 15:00 - 16:00
Location: Room 422, SAWB

Abstract

In the last decade, deep learning advancements have boosted the development of many neural solutions for effectively analyzing biomedical literature—widely accessible through repositories such as PubMed, PMC, and ScienceDirect. Large pre-trained language models (PLMs) have become the dominant NLP paradigm, achieving unprecedented results in a panoply of tasks, from named entity recognition and semantic parsing to information retrieval and document summarization. However, the latest batch of research has highlighted several weaknesses of PLMs, including a black-box knowledge limited by weight matrices' dimensions and the scarce ability to separate discrete semantic relations from surface language structures.
This talk presents two papers riding different promising trends to solve these issues and draw a complementary path to architectural scaling: (i) equipping PLMs with the ability to attend over relevant and factual information from non-parametric external sources; (ii) infusing semantic parsing graphs into PLMs.
Specifically, in (i) we will see a T5 model empowered by differentiable access towards a large-scale text memory grounded on PubMed, while in (ii) we will explore a BART model for biomedical abstractive summarization augmented by event and AMR graphs, as well as a semantic-driven reinforcement learning signal.

 

Bio

Giacomo Frisoni is a second-year Ph.D. student with competencies in Natural Language Understanding and Neuro-Symbolic Learning. He has a Bachelor's and Master's degree in Computer Science and Engineering from the University of Bologna, both with honors. He presented several original papers to journals and international peer-reviewed conferences—including top-tier venues like COLING, winning two Best Paper Awards. He participated in the Cornell, Maryland, Max Planck Pre-doctoral School 2020. In June 2022, he was selected as a member for the first HuggingFace Student Ambassador program.

FATA Seminar - Tutorials on algorithms and complexity and constraint programming

Group: Formal Analysis, Theory and Algorithms (FATA)
Speaker: David Manlove, Ciaran McCreesh, University of Glasgow
Date: 04 October, 2022
Time: 13:00 - 14:00
Location: Room 422, SAWB

These tutorials are intended to give all members the background necessary to follow future seminars. This weeks seminar includes two short tutorials. The first will be given by David Manlove on algorithms and complexity. The second tutorial will be on constraint programming and will be delivered by Ciaran McCreesh.

Coffee

Group: RA and PDRA Events
Speaker: Coffee
Date: 07 October, 2022
Time: 11:00 - 12:00
Location: Computing Science Common Room

Just our weekly coffee catch-up

Coffee

Group: RA and PDRA Events
Speaker: Coffee
Date: 14 October, 2022
Time: 11:00 - 12:00
Location: Computing Science Common Room

Just our weekly coffee catch-up

Coffee

Group: RA and PDRA Events
Speaker: Coffee
Date: 21 October, 2022
Time: 11:00 - 12:00
Location: Computing Science Common Room

Just our weekly coffee catch-up

SICSA DVF Talk: The Role of Artificial Intelligence in Education

Group: Scottish Informatics and Computer Science Alliance (SICSA)
Speaker: SICSA Event, SICSA
Date: 27 October, 2022
Time: 16:30 - 18:00
Location: University of Strathclyde, School of Education, Lord Hope Building, 141 St James Road, G4 0LT, Glasgow, United Kingdom

Abstract The talk, supported by the SICSA DVF scheme and Strathclyde Technology Enhanced Teaching and Learning (TETL) Network, will be given in the context of the ‘Postgraduate Certificate in Technology Enhanced Teaching and Learning (Digital Education)’ offered within the ‘MEd Education Studies’ programme at the School of Education, University of Strathclyde. The specific talk will provide a general introduction to the field of Artificial Intelligence and its adoption in Education. Short CV Dr Konstantinos (Kostas) Kolomvatsos received his BSc in Informatics from the Department of Informatics, Athens University of Economics and Business, MSc in Computer Science, New Technologies in Informatics and Telecommunications (2005), and PhD in Computer Science from the Department of Informatics and Telecommunications at the University of Athens (2013). He was a Marie Skłodowska Curie Fellow at the School of Computing Science, University of Glasgow, 2018-2020. Currently, he serves as an Assistant Professor in the Department of Informatics and Telecommunications, University of Thessaly. He is the founder of the Intelligent Pervasive Systems (iPRISM) research group and the Director of the Intelligent Systems for Orchestrating Pervasive Computing Applications (METIS). He serves as a PI in various National and EU/H2020 projects. His research interests are in the intersection of Intelligent Systems, Computational Intelligence, and Pervasive Data Science. He is the author of over 130 publications in the aforementioned areas.

Coffee

Group: RA and PDRA Events
Speaker: Coffee
Date: 28 October, 2022
Time: 11:00 - 12:00
Location: Computing Science Common Room

Just our weekly coffee catch-up

Networking and Systems Research Theme Event: Scottish Autonomous Networks Systems Event

Group: Scottish Informatics and Computer Science Alliance (SICSA)
Speaker: SICSA Event, SICSA
Date: 12 December, 2022
Time: 09:00 - 20:30
Location: University of Glasgow, James Watt School of Engineering, James Watt South Building, Level 6 - Creativity Suite, Glasgow, G12 8QQ, United Kingdom

This is the event to bring together the Scottish research community working the space of autonomous networked systems. This event will be held both in-person and online. The in-person meeting will be in the level 6 'creativity suite' @ James Watt School of Engineering, University of Glasgow. Topics The Scottish Autonomous Networked Systems (SANS) event is an opportunity to bring together researchers from different areas to share ideas and learn about ongoing work in relation to autonomous networked systems. Topics can include: Applying novel AI & ML techniques in an innovative manner to create evolvable and autonomous compute and network infrastructure. Repurposing robotic operation techniques to distributed and federated software control to achieve runtime operational assurance. Investigating new programming interactions between user, system, and network to achieve trust for autonomous operation. The use of formal methods to help achieve trustworthy operation in the face of dynamic and adaptable operational environments. Attending If you would like to attend physically, please register on the Doodle. There is no need to register if you would like to attend online. Information on how the seminar will be streamed is coming soon. COVID-19 Following Scottish Government and University guidance, we ask participants displaying potential COVID-19 symptoms not to attend physically, and encourage participants to wear masks in indoor areas should they become busy. The University supports the Distance Aware scheme, and can provide appropriate lanyards and badges. We strongly encourage participants to take a Lateral Flow Test (LFT) prior to the event; although the national testing programme has now finished, LFTs can be purchased from most major supermarkets and pharmacies. Programme The following is a tentative schedule for this edition of SANS. Note that the call for talks, demos, and posters is currently open, feel free to get in touch here to share your ongoing work. Schedule Monday 12th December 09:00-09:30 Welcome / Registration 09:30-11:00 Academic Keynote Speaker: Wolfgang Keller 11:00-11:30 Coffee 11:30-13:00 Session: Short Talks & Demos 13:00-14:00 Lunch 14:00-15:30 Session: Short Talks & Demos 15:30-16:00 Coffee 16:00-17:00 Session: Short Talks & Demos 17:00-18:00 Session: Discussion Tuesday 13th December 09:00-09:30 Welcome / Registration 09:30-11:00 Industrial Keynote Speaker: Rakuten Symphony 11:00-11:30 Coffee 11:30-13:00 Session: Short Talks & Demos 13:00-14:00 Lunch 14:00-15:30 Session: Short Talks & Demos 15:30-16:00 Coffee 16:00-17:00 Session: Posters 17:00-17:30 Closing Remarks 19:00-20:30 Social Event Location: TBA Talks Wolfgang Keller, Technical University of Munich TBA TBA, Rakuten Symphony TBA Organisers The organisers of this edition of SANS are Paul Harvey, Jeremy Singer, Colin Perkins, Marc Roper, Blesson Varghese, and Philip Rodgers. Feel free to contact any of the organisers about queries regarding this seminar.

Healthcare Seminar: TBC

Group: Computing Technologies for Healthcare
Speaker: Dr. Tanaya Guha, University of Glasgow
Date: 25 January, 2023
Time: 14:00 - 15:00
Location: https://uofglasgow.zoom.us/meeting/register/tZAscuCgrDgvGNEB9piiUZf_mCbyPsEBu4c2

Biography: 

Dr. Tanaya Guha is a Senior Lecturer (Associate Professor) in University of Glasgow and a member of the Glasgow Interactive Systems (GIST) section. Her research focuses on developing machine intelligence capabilities to understand human activities and behaviour combining machine learning, computer vision and signal/speech processing. She received her PhD in Electrical and Computer Engineering from the University of British Columbia (UBC), Vancouver. Among other awards and honours, she is a recipient of Warwick Global Research Priority award, ICME Outstanding Area Chair award and Mensa Canada Woodhams memorial scholarship. She is also a member of ISCA, IEEE, an elected member of IEEE MSA Technical Committee and an Executive Committee member of AAAC. She is actively involved in the Organizing and Program Committees of several conferences. 

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