Informatics Seminars 2020/21

2020/21 Semester Two

  • Seminars will be held online until further notice. Please contact Effie Law or Thomas Erlebach to be added to the mailing list for receiving seminar announcements with the meeting links.
  • To get the link to the Teams meeting for the next seminar, please click here (UoL account login required)

Fri Jan 22, 14:00 (Host: Effie Law)

Javier Bargas-Avila (Google)

Title: The Design of Everyday Things / UX @ Google - What makes people love or hate the products we create?

Abstract: This talk will show you why we often fail using everyday things in our everyday life. No matter how smart you are, no matter how much experience you have, you will often feel inept as you fail to figure out the simplest things like whether you should push, pull, or slide a door. You will see why these mistakes are not “human error”, but origin in flawed product design that ignores the basic ways humans think and behave. You will see examples from everyday objects that will change the way you look at products around you in the future. You will learn to value good, usable designs and detect flawed ones. And you will not blame yourself for making mistakes but blame the designers that came up with complicated and unsuited solutions for simple problems.

Fri Jan 29, 14:00 (Host: Effie Law)

Per Ola Kristensson (University of Cambridge)

Title: TBA

Abstract: TBA

Fri Feb 5, 14:00 (Host: Thomas Erlebach)

Rayna Dimitrova (CISPA Helmholtz Center for Information Security)

Title: TBA

Abstract: TBA

Fri Feb 12, 14:00 (Host: Ashiq Anjum)

Frank McQuade (Director of Capability, Bloc Digital)

Title: TBA

Abstract: TBA

2020/21 Semester One

Fri Oct 9, 14:00 (Host: Effie Law)

Alan Blackwell (University of Cambridge)

TitleHow to design a programming language

Abstract: Programming languages - how to tell a computer what to do - are the core technology of the digital revolution, just as the invention of the wheel was the core technology enabling the design of land transportation systems. Wheels have been necessary, but not sufficient, for the design of effective cars. Beyond basic optimisation, and occasional innovation, research into wheel technologies may be important, but provides little practical guidance for successful products. In the same way, compilers and type systems are necessary, but not sufficient, for the design of effective programming languages. This talk draws lessons from the design of cars to propose principles and processes for programming language design, as well as research agendas that will support those principles and processes.

Speaker Bio: Alan Blackwell is Professor of Interdisciplinary Design at the University of Cambridge Computer Laboratory. Originally trained as a control engineer, his early career in industrial automation soon led to an interest in programming as a technical user interface. He implemented his first visual programming language in 1983 (an antecedent of Harel’s StateCharts) for specifying control of a cement batching plant in his hometown Wellington. Subsequent projects included a real-time expert systems language used to implement emergency response systems that now run on the trains of London Underground’s Central and Jubilee lines. After delivering his first conference keynote on programming language design in 1995, he realised that he knew nothing about the scientific causes that made one programming language more usable than another, so left his role as design lead of novel end-user programming languages at Hitachi to study for a PhD with Thomas Green at the MRC Applied Psychology Unit in Cambridge. Since becoming an academic, he and his team have contributed to the design of programming languages, tools and techniques at companies around the world, including Microsoft, Google, Intel, Nokia, Sony, AutoDesk and many others.

Fri Oct 16, 14:00 (Host: Eugene Zhang)

Yuankai Huo (Vanderbilt University)

Title: Machine Learning for Medical Image Analysis using Big Data

Abstract: Rapid developments in data sharing and computational resources are reshaping the medical imaging research field from small-scale (e.g., a cohort < 300 subjects) to large-scale (e.g., big data with thousands or more subjects). However, traditional medical image analysis techniques can be inadequate to overcome the new challenges in big data; including robustness of algorithm, inter-subject variabilities, computational resources etc. In this talk, I will (1) present an end-to-end large-scale lifespan brain image analyses on more than 5000 patients, (2) discuss the challenges and opportunities in machine learning for medical image analysis using big data.

Fri Oct 23, 14:00 (Host: Effie Law)

Jo Iacovides (University of York)

Title: Beyond play: exploring the complexity of player experience

Abstract: Gameplay frequently involves a combination of positive and negative emotions, where there is increasing interest in understanding more complex forms of player experience. In this talk I will present the findings of three different studies that consider overlooked aspects of gameplay. The first focuses on reflection as a core component of the player experience through exploring what sorts of reflection players engage in, when they do so and how they feel about reflection. The second study examines uncomfortable gameplay interactions across different commercial games to investigate how discomfort manifests and influences player engagement. Finally, the third study focuses on player motivation, through examining the role of games during difficult life experiences. Through exploring reflection, discomfort, and gaming as a form of coping, the talk will discuss how games can invoke powerful experiences that impact how we think and feel beyond the initial instance of play.

Speaker Bio: Dr Ioanna (Jo) Iacovides, is a Lecturer in Computer Science at the University of York, UK. Her research interests lie in Human Computer Interaction with a particular focus on understanding the role of learning within the player experience, and on investigating complex emotional experiences in the context of digital play. In addition, she is interested in exploring how games and playful technologies can created for a range of persuasive purposes, such as education and behaviour change. She has received awards for a work on examining reflection and gaming (best paper, CHI PLAY 2018), evaluating persuasive games (honourable mention, CHI 2015) and for the game Resilience Challenge, which encourages healthcare practitioners to consider how they adapt safely under pressure (first prize, 2017 Annual Resilience Healthcare Network symposium).

Fri Oct 30, 14:00 (Host: Effie Law)

Julien Maffre (Microsoft Research Cambridge)

Title: Overview of the Confidential Consortium Framework (CCF)

Abstract: In this talk, I present the Confidential Consortium Framework (CCF), Microsoft's open source framework (https://github.com/Microsoft/CCF) for building a new category of secure, highly available, and performant applications that focus on multi-party compute and data.

Fri Nov 6, 14:00 (Host: Eugene Zhang)

Shuai Liu (Hunan Normal University, China)

Title: Introduction of Advance in Single Target Tracking

Abstract: Computer vision is one of the main application scenarios of artificial intelligence, and visual tracking is the main research branch of computer vision. Therefore, based on traditional methods, our team uses human visual perception capabilities, Short-Long Term memory functions and attention characteristics by combining traditional feature models, and proposed corresponding visual tracking method/mechanism from the direction of target template matching and updates mechanism. Experiments show that the introduction of human vision can improve the tracking effect, and the proposed model can also propose a targeted development direction for human vision research.

Speaker Bio,: Prof. Shuai Liu received his Ph.D. degree from the Jilin University, 2011. From 2011 to 2018, lecturer to full Professor, he was on the faculty at the Inner Mongolia University, China. He joined the Department of Artificial Intelligence at Hunan Normal University in 2019, where he is now a director of this department. He was assigned the editorial role for many respected journals. His research interests include computer graphics, image processing, and computer vision.

Fri Nov 13, 14:00 (Host: José Miguel Rojas)

Syed Waqee Wali & Jan Ringert (University of Leicester)

Title: Semantic Comparisons of Alloy Models

Abstract: Alloy is a textual modeling language for structures and behaviors of software designs. The Alloy Analyzer provides various analyses making it a popular light-weight formal methods tool. While Alloy models can be analyzed, explored, and tested, there is little support for comparing different versions of Alloy models. We believe that these comparisons are crucial when trying to refactor, refine, or extend models. In this work we present an approach for the semantic comparisons of Alloy models. Our pair-wise comparisons include semantic model differencing and the checking of refactoring, refinement, and extension.  We enable semantic comparisons of Alloy models by a translation of two versions into a single model that is able to simulate instances of either one or of the versions. Semantic differencing and instance computation require only a single analysis of the combined model in the Alloy Analyzer. We implemented our work and evaluated it using 654 Alloy models from different sources including version histories. Our evaluation examines the cost of semantic comparisons using the Alloy Analyzer in terms of running times and variable numbers over individual models.

This work -- an extension of Syed’s MSc thesis published at the 23rd IEEE/ACM MoDELS conference -- has received the Best Foundation Paper Award and an ACM SigSoft Distinguished Paper Award.

Bio of Syed Waqee Wali:  Syed is a Senior Software Engineer at CGI UK, mostly focussed towards Web Engineering, IoT and Database Administration. He worked on the topic of this research paper as his final project to graduate with an MSc in Advanced Software Engineering with Industry. Syed won the Schools prize for the best software development MSc project. Syed was also part of the School's Driverleics project, involved in research and development with autonomous vehicles.

Bio of Jan Oliver Ringert:  Jan is a lecturer in Model-Based Software Development at University of Leicester. His research interests are in using formal methods for model-based software engineering with applications to autonomous systems. His work has been published in top software engineering and modeling conferences and journals.

Fri Nov 20, 14:00 (Host: Effie Law)

Nicholas Cummins (King's College London)

Title: Speech analysis for mental health: opportunities and challenges

Abstract: The production of speech is remarkably complex, combining conscious and subconscious cognitive thought with the physical actions of the respiratory system. This complexity means that changes in our health state can affect speech, often at a subconscious level. Such changes can then alter both the linguistic, acoustic and prosodic content in ways that are potentially measurable through intelligent signal processing techniques. The multifaceted nature of speech means it is uniquely placed as a signal of interest in remote monitoring (RMT) mobile health (mHealth) studies. No other RMT signal contains such a combination of cognitive, neuromuscular and physiological information. However, considerable research efforts are required to realise the potential of speech as a mHealth biomarker. This talk will consist of three parts, a general introduction to speech production, an overview of current works in this area and an outlook on the challenges and opportunities associated with this fascinating and unique health signal.

Speaker Bio.: Nicholas (Nick) Cummins is a lecturer in AI for speech analysis for health at the Department of Biostatistics and Health Informatics at King’s College London. Nick’s current research interests include speech processing, affective computing and multisensory signal analysis. He is fascinated by the application of machine learning techniques to improve our understanding of different health conditions and mental health disorders in particular. Nick is actively involved in RADAR-CNS project in which he assists in the management of Work Package 8: Data Analysis & Biosignatures. Nick was awarded his PhD in electrical engineering from UNSW Australia in February 2016 for his thesis ‘Automatic assessment of depression from speech: paralinguistic analysis, modelling and machine learning’. After completing his PhD, he was a postdoctoral researcher at the Chair of Complex and Intelligent Systems at the University of Passau, Germany. Most recently, he was a habilitation candidate at the Chair of Embedded Intelligence for Health Care and Wellbeing at the University of Augsburg, also in Germany. During his time in Germany, he was involved in the DE-ENIGMA, RADAR-CNS, TAPAS and sustAGE Horizon 2020 projects. He also wrote and delivered courses in speech pathology, deep learning and intelligent signal analysis in medicine.

Fri Nov 27, 14:00 (Host: Huiyu Zhou)

Hui Wang (University of Ulster)

Title: Small Data Analytics

Abstract: Big data is a key characteristic of the modern times. Very large data sets arise in various domains at low costs – for example, data being captured from sensors, which are widely available, or crowd sourced from the public. However, large data sets with annotations (labelling, structuring etc) are very rare, as proper annotation must be done by experts, and this is very expensive. This is a small data challenge in the big data age. Machine learning, especially deep learning, can effectively learn with large data sets. However, it cannot effectively learn with small data sets due to various issues, e.g. overfitting, noise, outliers and sampling bias, which can render the learned model less generalisable. In this talk, I will review various approaches to effective learning with small data sets such as data augmentation, transfer learning, regularisation and visualisation, and knowledge-based learning. I will also present an overview of our approach, lattice machine.

Biography: Hui Wang is Professor of Computer Science at Ulster University. His research interests are machine learning, knowledge representation and reasoning, combinatorial data analytics, and their applications in image, video, spectra and text analyses. He has over 270 publications in these areas. He is principal investigator of a number of regional, national and international projects in the areas of image/video analytics (EPSRC funded MVSE 2021-2024, Horizon 2020 funded DESIREE and ASGARD, FP7 funded SAVASA, Royal Society funded VIAD), spectral data analytics (EPSRC funded VIPIRS 2020-2022), text analytics (INI funded DEEPFLOW, Royal Society funded BEACON), and intelligent content management (FP5 funded ICONS); and is co-investigator of several other EU funded projects.

He is Head of the AI Research Centre at Ulster University, Director of Research (Computer Science and Informatics, 2018-2020) in School of Computing, Ulster University. He is an associate editor of IEEE Transactions on Cybernetics, and an associate editor of The Computer Journal. He is the Chair of IEEE SMCS Northern Ireland Chapter (2009-2018), and a member of IEEE SMCS Board of Governors (2010-2013).

Fri Dec 4, 14:00 (Host: Huiyu Zhou)

Andrew M. Wallace (Heriot-Watt University)

Title: PervAsive low-TeraHz and optical sensing for Car Autonomy and Driver assistance (PATHCAD)

Abstract:

What if it rains?

While media coverage about the future of the autonomous car changes from the naïve acceptance of a disruptive technology to the damning realisation that it may actually have the occasional accident due to adverse weather, adversarial attack, and hardware/software malfunction, the vast majority of trials have taken place in good weather with considerable pre-mapping and detailed route planning.

The PATHCAD project explored alternatives to vehicle sensing for scene mapping and actor recognition in adverse conditions, based on video, LiDAR and radar technologies. I shall present work to develop a higher resolution radar system, to recognise and track actors and predict their behaviour in radar images, and discuss how active LiDAR imaging can penetrate bad weather by use of full waveform processing, potentially aided by concurrent radar sensing.

As time allows, I will present some additional material on how fast, eye-safe, full wave, automotive LiDAR systems can be built from improvements in solid state semiconductor arrays, allied to random sampling, compressed sensing and approximate computing. i Much of the work was supported by Jaguar Land Rover and EPSRC (EP/N012402/1) as part of the TASCC programme, and was carried out by the Universities of Birmingham, Edinburgh and Heriot-Watt.

Fri Dec 11, 14:00 (Host: José Miguel Rojas/ Mohammad Mousavi)

Michael Fisher (University of Manchester)

TITLE: Responsible Autonomous Systems?

ABSTRACT: Autonomous systems can make decisions and take actions without direct human intervention. These systems are increasingly advocated for use across robotics, "driverless" cars, unmanned air systems, etc. Yet the title of this seminar is deliberately vague. Do we mean to ask whether autonomous systems are responsible for certain actions or decisions, or whether the autonomous systems are developed and used in a responsible way? We actually aim to address both: if we want truly autonomous systems then some actions or decisions must be made by the system; if we want to develop and deploy autonomous systems responsibly then we must surely know what they will do and why.

In this talk I will describe how the answer to both questions can be provided through a combination of software architectures and formal verification. Having an appropriate autonomous systems architecture allows us to identify nodes/components that make the key decisions. Formally verifying these nodes/components then allows us to examine, and provide guarantees for, their decision-making processes. Once we move to deploying autonomous systems in unknown environments then the decisions the systems might have to make often cannot be predicted beforehand. And here it becomes crucial to verify not what decisions the system will make but why it makes the decisions it does.

BIOGRAPHY: Michael Fisher is Professor of Computer Science at the University of Manchester, holding a Royal Academy of Engineering Chair in Emerging Technologies. His research centres on the safety, reliability, ethics, and verification of autonomous systems, he is involved in BSI and IEEE standards for autonomous systems and robotics, and is Co-Chair of the IEEE Technical Committee on the Verification of Autonomous Systems. (At least until this year) He is involved in public engagement activities and has spoken on autonomous systems within key international organisations such as the International Committee of the Red Cross and the Global Forum on AI for Humanity.

Fri Dec 18, 14:00 (Host: Eugene Zhang)

Yuyin Zhou (John Hopkins University/Stanford University, US)

Title: Medical Machine Intelligence: Data-Efficiency and Knowledge-Awareness

Abstract: While deep learning has largely advanced the field of computer-aided diagnosis by offering an avenue to deliver automated medical image analysis, there remain several challenges towards medical machine intelligence, such as unsatisfactory performance regarding challenging small targets, insufficient training data, high annotation cost, the lack of domain-specific knowledge, etc. These challenges have affected the applicability of such models being deployed in safety-critical medical scenarios. In this talk, I will first briefly summarize the latest progress of deep learning in medical image analysis, with a focus on the aforementioned challenges. I will then present different data-efficient and knowledge-aware deep learning approaches that can facilitate the model generalization to different medical tasks without requiring intensive manual labeling efforts via incorporating domain-specific knowledge in the learning process. Finally, I will discuss how to make neural networks approach the real clinical expertise in general and how this can benefit further diagnostics and procedures.

Biosketch: Yuyin Zhou is an incoming postdoctoral scholar at Stanford University. She has just completed her Ph.D. degree of Computer Science at the Johns Hopkins University, under the supervision of Bloomberg Distinguished Professor Alan Yuille. Before that, Yuyin received her M.S. degree from the University of California, Los Angeles (UCLA) in 2016, and a B.S. degree from Huazhong University of Science and Technology in 2014. Yuyin’s research interests span in the fields of medical image computing, computer vision, and machine learning, especially the intersection of them. Her project with Johns Hopkins Medicine on organ segmentation has been featured on National Public Radio. Yuyin has published over 20 peer-reviewed papers including top-tier venues such as CVPR, ICCV, AAAI, MICCAI, TMI, and MedIA, and was the runner-up in MICCAI 2018 - Computational Precision Medicine: Pancreatic Cancer Survival Prediction Challenge. She also worked at Google Cloud AI and Google Brain.

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Dept of Informatics
University of Leicester
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