The medical school is holding a Seminar on Wednesday 8th January @1400-1530
New tools and methods to prevent blindness.
Seminar room 1, Medical and Biological Sciences Building
Abstract: In this talk, I will present a case study to illustrate how automated algorithm configuration can be used to gain insights into theoretical results on an evolutionary algorithm, namely the (1+(λ,λ)) Genetic Algorithm. This work is a collaboration with Carola Doerr.
The (1+(λ,λ)) Genetic Algorithm is an evolutionary algorithm that has interesting theoretical properties. It is the first algorithm where the benefit of crossover operator is rigorously proved. It is also the first example where self-adjusting parameter choice is proved to outperform any static parameter choice. However, it is not very well understood how the hyper-parameter settings influences the overall performance of the algorithm. Analyzing such multi-dimensional dependencies precisely is at the edge of what running time analysis can offer. In this work, we make a step forward on this question by presenting an in-depth study of the algorithm’s hyper-parameters using techniques in automated algorithm configuration.
Speaker bio: Dr Nguyen Dang is a post-doc in the Constraint Programming group at the University of St Andrews. Her main research focus is on automated algorithm configuration, algorithm selection and their applications in various contexts. These techniques make use of statistical methods and machine learning for fine-tuning of algorithm parameters, assessing parameters’ importance and building algorithm portfolios. Another line of her research is about solving combinatorial optimisation problems using metaheuristic algorithms.
Abstract: There is an ever-increasing burden on imaging departments to deliver high-throughput assessment of medical images. MRI in particular provides the advantage of full-body coverage and and a variety of quantitative imaging techniques, such as diffusion-weighted MRI, that can offer potent biomarkers for disease response and prognosis; with the advent of accelerated imaging techniques, many quantitative images can now be acquired in a single patient scan. Increases in computational power and the advent of methodologies such as deep-learning may help to deliver on the promise of truly personalised, image-guided therapies; by helping clinicians to better understand the complexities within multi-parametric MRI it may be possible to derive a non-invasive “digital biopsy” that can be monitored during treatment. In this presentation, we will review recent developments within the Computational Imaging group at the Institute of Cancer Research and Royal Marsden Hospital, demonstrating how novel algorithms and deep learning can be used to assist in the response assessment of advanced prostate cancer and soft-tissue sarcoma.
Speaker Bio: Dr Matt Blackledge is the team-leader of computational imaging at the ICR, where he has been developing computational techniques in MRI for over a decade. He is funded by both CRUK and Sarcoma UK to innovate novel approaches to image analysis from MRI and X-ray CT to improve cancer patient outcomes in a variety of disease types. He is particularly interested in how AI can be used to (i) further accelerate MR-image acquisition, (ii) understand cancer heterogeneity in images, and (iii) probe the link between quantitative imaging biomarkers and their underlying biology.
Abstract:
In this talk, I explore how group theory playing a crucial role in cyber security and quantum computation. At the same time, how computer science for example machine learning algorithms and computational complexity could help group theorists to tackle their open problems, as such this could help with cryptanalysis of the proposed primitives.
Symmetry is present in all forms in the natural and biological structures as well as man-made environments. Computational symmetry applies group-theory to create algorithms that model and analyze symmetry in real data set. The use of symmetry groups in optimizing the formulation of signal processing and machine learning algorithms can greatly enhance the impact of these algorithms in many fields of science and engineering where highly complex symmetries exist.
At the same time, Machine Learning techniques could help with solving long standing group theoretic problems. For example, in the paper [J. Gryak (University of Michigan, Data Science Institute), R. Haralick (The City University of New York, the prize recipient of International Association for Pattern Recognition), D. Kahrobaei, Solving the Conjugacy Decision Problem via Machine Learning, Experimental Mathematics, Taylor & Francis (2019)] the authors use machine learning techniques to solve the conjugacy decision problem in a variety of groups. Beyond their utilitarian worth, the developed methods provide the computational group theorist a new digital “sketchpad” with which one can explore the structure of groups and other algebraic objects, and perhaps yielding heretofore unknown mathematical relationships.
Graph theoretic problems have been of interest of theoretical computer scientists for many years, especially the computational complexity problems for such algorithmic problems. Such studies have been fruitful for one of the millennium problems (P vs NP) of the Clay Math Institute. Since graph groups are uniquely defined by a finite simplicial graph and vice versa, it is clear that there is a natural connection between algorithmic graph theoretic problems and group theoretic problems for graph groups. Since the graph theoretic problems have been of central importance in complexity theory, it is natural to consider some of these graph theoretic problems via their equivalent formulation as group theoretic problems about graph groups. The theme of the paper [Algorithmic problems in right-angled Artin groups: Complexity and applications, R. Flores, D. Kahrobaei, T. Koberda, J. of Algebra, Elsevier 2019.] is to convert graph theoretic problems for finite graphs into group theoretic ones for graph groups (a.k.a. right-angled Artin) groups, and to investigate the graph theory algebraically. In doing so, new approaches to resolving problems in complexity theory become apparent. The authors are primarily motivated by the fact that some of these group theoretic problems can be used for cryptographic purposes, such as authentication schemes, secret sharing schemes, and key exchange problems.
In the past couple of decades many groups have been proposed for cryptography, for instance: polycyclic groups for public-key exchanges, digital signatures, secret sharing schemes (Eick, Kahrobaei), hyperbolic groups for private key encryption (Chatterji-Kahrobaei), p-groups for multilinear maps (Kahrobaei, Tortora, Tota) among others. [J. Gryak, D. Kahrobaei, The Status of the Polycyclic Group-Based Cryptography: A Survey and Open Problems, Groups Complexity Cryptology, De Gruyter (2016).]
Most of the current cryptosystems are based on number theoretic problems such discrete logarithm problem (DLP) for example Diffie-Hellman key-exchange. Recently there has been some natural connections between algorithmic number theoretic and algorithmic group theoretic problems. For example, it has been shown that for a different subfamily of metabelian groups the conjugacy search problem reduces to the DLP. [J. Gryak, D. Kahrobaei, C. Martinez-Perez, On the conjugacy problem in certain metabelian groups, Glasgow Math. J., Cambridge Univ. Press (2019).]
In August 2015 the National Security Agency (NSA) announced plans to upgrade security standards; the goal is to replace all deployed cryptographic protocols with quantum secure protocols. This transition requires a new security standard to be accepted by the National Institute of Standards and Technology (NIST).
One goal of cryptography, as it relates to complexity theory, is to analyze the complexity assumptions used as the basis for various cryptographic protocols and schemes. A central question is determining how to generate intractible instances of these problems upon which to implement an actual cryptographic scheme. The candidates for these instances must be platforms in which the hardness assumption is still reasonable. Determining if the group-based cryptographic schemes are quantum-safe begins with determining the groups in which these hardness assumptions are invalid in the quantum setting.
In what follows we address the quantum complexity of the Hidden Subgroup Problem (HSP) to determine the groups in which the hardness assumption still stands. The Hidden Subgroup Problem (HSP) asks the following: given a description of a group G and a function f from G to X for some finite set X is guaranteed to be strictly H-periodic, i.e. constant and distinct on left (resp. right) cosets of a subgroup H < G, find a generating set for H.
Group-based cryptography could be shown to be post-quantum if the underlying security problem is NP-complete or unsolvable; firstly, we need to analyze the problem’s equivalence to HSP, then analyze the applicability of Grover’s search problem. [K. Horan, D. Kahrobaei, Hidden Subgroup Problem and Post-quantum Group-based Cryptography, Springer Lecture Notes in Computer Science 10931, 2018].
Speaker Bio:
I am currently the Chair of Cyber Security at the University of York, a position I have held since November 2018. While at York, I founded and am the director of the York Interdisciplinary Center for Cyber Security. Before coming to York, I was Full Professor at the City University of New York (CUNY) in New York City. I was at CUNY for 12 years, among other duties, I supervised 7 PhD computer science and mathematics students. In addition to my position at York, I am also an Adjunct Professor of Computer Science at the Center for Cyber Security at New York University (NYU). I have been an adjunct at NYU since 2016. I was a lecturer in Pure Mathematics at the University of St Andrews before New York.
I am an associate editor of the of the Advances of Mathematics of Communication, published by the American Institute of Mathematical Sciences, the chief editor of the International Journal of Computer Mathematics: Computer Systems Theory, Taylor & Francis, and an associate editor of SIAM Journal on Applied Algebra and Geometry, The Society for Industrial and Applied Mathematics. I also have entrepreneurial experience as President and Co-founder of Infoshield, Inc., a computer security company.
My main research area is Post-Quantum Algebraic Cryptography, Information Security, Data Science, Applied Algebra. My research has been supported by grants from the US military: US Office of Naval Research, Canadian New Frontiers in Research Fund Exploration, American Association of Advancement in Sciences, National Science Foundation, National Security Agency, Maastricht-York Investment Fund, Research Foundation of CUNY, London Mathematical Society, the Edinburgh Mathematical Society, Swiss National Foundation, Institut Henri Poincare, and the Association for Women in Mathematics. I have 70 publications in prestigious journals and conference proceedings and several US patents. I have given about 240 invited talks in conferences and seminars around the world.
Abstract:
This talk will demonstrate how model checking based verification of compilers and runtime systems can increase the confidence of parallel execution of programming languages, using two case studies.
As HPC systems continue to increase in scale, their mean time between failure decreases meaning reliability has become a major concern. I will present HdpH-RS, a parallel HPC language. HdpH-RS has a formal semantics, and a fault tolerant work stealing scheduler that has been verified with the SPIN model checker. At embedded scale, program transformations on stateful dynamic code can introduce bugs, race conditions and deadlock. I will present a parallel refactoring tool for the CAL dataflow language. It is integrated with the TINA model checker that we use to identify parallelisable cyclo-static actors in dynamic dataflow programs.
The broader aim of this work is to integrate automated formal verification into parallelising compilers and parallel runtime systems for heterogeneous architectures.
Speaker bio: Dr Rob Stewart is an Assistant Professor at Heriot-Watt University. He’s interested in formalising, verifying and implementing dataflow, functional and domain specific programming languages for manycore architectures and programmable hardware.
Abstract: It is well known that older adults continue to lag behind younger adults in terms of their breadth of uptake of digital technologies, amount and quality of engagement in these tools and ability to critically engage with the online world. Can these differences be explained by older adults’ distrust of digital technologies? Is trust, therefore, a critical design consideration for appealing to older adults? In this talk I will argue that while distrust is not, in fact, determinative of non-use and therefore does not explain these differences in tech usage, it is nonetheless key for designers to understand older adult distrust in developing socially responsible technologies.
Speaker Bio: Bran is a lecturer in the Data Science Institute at Lancaster University. Her research explores the social impacts of computing, with a particular interest in trust, privacy, and ethics. Her recent work has explored these issues at both ends of the age spectrum, with projects such as IoT4Kids, looking at the privacy, security and ethical issues of enabling children to programme IoT devices; and Mobile Age, looking at developing mobile apps for older adults. Bran currently serves as a member of the ACM Europe Technology Policy Committee.
Abstract:
Two important aspects in hardware design are the safe routing of signals between modules, and ensuring that ports are correctly connected. Well-known hardware description languages such as SystemVerilog, provide nominal checking over these aspects. Thus, leaving correctness checks over module orchestration to be performed post-design-time using static analyses, testing, and during synthesis.
Using a mixture of dependent and quantitative typing, we can lift external correctness checks over module connections directly into the type-system. With this approach we can detect more errors at design time, enhance the safety of our hardware designs, and thus increase design productivity.
In this talk I will introduce and discuss LightClick, an orchestration language for hardware design that exemplifies our approach. LightClick uses quantitative typing to ensure linear usage of ports, and dependent types to ensure that port compatibility is a decidable compile-time check. I will show how LightClick can be used to model simple hardware designs, how SystemVerilog stubs are generated from designs using staged interpretation.
Speaker Bio: Jan is a Research Associate at the University of Glasgow, who is interested in using state-of-the-art advances in programming language theory to build more trustworthy systems. Jan is currently involved in the Border Patrol project – a collaboration between the Universities of Heriot-Watt, Glasgow, and Imperial College London to explore how Dependent Typing and Session Typing can help make hardware design safer and secure.
Please note non-standard date and time for this talk
Abstract:
This talk will describe a range of our projects, utilising functional Near Infrared Spectroscopy (fNIRS) in HCI. As a portable alternative that’s more tolerate of motion artefacts than EEG, fNIRS measures the amount of oxygen in the brain, as e.g. mental workload creates demand. As opposed to BCI (trying to control systems with our brain), we focus on brain-based HCI, asking what brain data can tell us about our software, our work, our habits, and ourselves. In particular, we are driven by the idea that brain data can become personal data in the future.
Speaker Bio:
Dr Max L. Wilson is an Associate Professor in the Mixed Reality Lab in Computer Science at the University of Nottingham. His research focus is on evaluating Mental Workload in HCI contexts – as real-world as possible – primarily using functional Near Infrared Spectroscopy (fNIRS). As a highly tolerant form of brain sensor, fNIRS is suitable for use in HCI research into user interface design, work tasks, and everyday experiences. This work emerged from his prior research into the design and evaluation of complex user interfaces for information interfaces. Across these two research areas, Max has over 120 publications, including a Honourable Mention CHI2019 paper on a Brain-Controlled Movie – The MOMENT.
Please note non-standard date and time for this talk
Abstract: High-level programming languages such as Python are increasingly used to provide intuitive interfaces to libraries written in lower-level languages and for assembling applications from various components. This migration towards orchestration rather than implementation, coupled with the growing need for parallel computing (e.g., due to big data and the end of Moore’s law), necessitates rethinking how parallelism is expressed in programs.
Here, we present Parsl, a parallel scripting library that augments Python with simple, scalable, and flexible constructs for encoding parallelism. These constructs allow Parsl to construct a dynamic dependency graph of components from a Python program enhanced with a small number of decorators that define the components to be executed asynchronously and in parallel, and then execute it efficiently on one or many processors. Parsl is designed for scalability, with an extensible set of executors tailored to different use cases, such as low-latency, high-throughput, or extreme-scale execution. We show, via experiments on the Blue Waters supercomputer, that Parsl executors can allow Python scripts to execute components with as little as 5 ms of overhead, scale to more than 250000 workers across more than 8000 nodes, and process upward of 1200 tasks per second.
Other Parsl features simplify the construction and execution of composite programs by supporting elastic provisioning and scaling of infrastructure, fault-tolerant execution, and integrated wide-area data management. We show that these capabilities satisfy the needs of many-task, interactive, online, and machine learning applications in fields such as biology, cosmology, and materials science.
Slides: see here.
Speaker Bio: Daniel S. Katz is Assistant Director for Scientific Software and Applications at the National Center for Supercomputing Applications (NCSA), and Research Associate Professor in Computer Science; Electrical & Computer Engineering; and the School of Information Sciences at the University of Illinois Urbana-Champaign. For further details, please see his website here.
Abstract:
This talk provides an insight into the challenges involved in providing near real-time news search to Bloomberg customers. It starts with a picture of what’s involved in building such a backend, then delves into what makes up a search engine. Finally we discuss the challenges of scaling up for low-latency and high-load, and how we tackle them.
Speaker Bio:
Ankush leads the News Search infrastructure team at the Bloomberg Engineering office in London. After completing his Masters in Computer Science, he joined Bloomberg at their New York office in 2009. Later working from Washington DC, he led a team to build a web application leveraging Lucene/Elasticsearch for businesses to discover government contracting opportunities. In London, his team focuses on search infrastructure and services allowing clients to search news events from all over the globe with near real-time access and sub-second latencies.
