The Intelligent Systems Group periodically organises academic research seminars which usually take place in the Queens Building, normally on Mondays. (we also organise Problem Workshops with companies and other interested parties, see here for more info)
IMPORTANT: For many of the upcoming ISL research seminars, there will also be opportunities for booking individual (or small group) networking meetings with the visiting scholars. Links for booking such meetings will be made available in this page, along with the related seminar info. Stay tuned and don’t miss this opportunity!
Note that time and location of the seminars varies between the weeks.
Machine Learning Approach to Topological Data Analysis, Prof Kenji Fukumizu, The Institute of Statistical Mathematics, Japan. Date: 3pm-4pm 18th Sep 2018, Place: F.101a in Queen’s Building.
Abstract: Topological data analysis (TDA) is a recent methodology for extracting topological and geometrical features from data of complex geometric structures. Persistent homology, a new mathematical notion proposed by Edelsbrunner (2002), provides a multiscale descriptor for the topology of data, and has been recently applied to a variety of data analysis. In this talk I will introduce a machine learning framework of TDA by combining persistence homology and kernel methods. As an expression of persistent homology, persistence diagrams are widely used to express the lifetimes of generators of homology groups. While they serve as a compact representation of data, it is not straightforward to apply standard data analysis to persistence diagrams, since they consist of a set of points in 2D space expressing the lifetimes. We introduce a method of kernel embedding of the persistence diagrams to obtain their vector representation, which enables one to apply any kernel methods in topological data analysis, and propose a persistence weighted Gaussian kernel as a suitable kernel for vectorization of persistence diagrams. Some theoretical properties including Lipschitz continuity of the embedding are also discussed. I will also present applications to change point detection and time series analysis in the field of material sciences and biochemistry.
Abstract: In contrast to financial exchanges, Blockchain based crypto-currencies expose the entire transaction history to the public. By processing all transactions, we model the network with a high fidelity graph so that it is possible to characterize how the flow of information in the network evolves over time. We demonstrate how this data representation permits a new form of financial modeling — with the emphasis on the topological network structures to study the role of users, entities and their interactions in formation and dynamics of crypto-currency investment risk. In particular, we identify certain sub-graphs (‘chainlets’) that exhibit predictive influence on Bitcoin price and volatility and characterize the types of chainlets that signify bitcoin losses. This is joint work with Cuneyt Akcora, Yulia Gel and Murat Kantarcioglu.
Bio: Matthew Dixon holds a Ph.D. in Applied Mathematics from Imperial College (2007) and a MSc. in Scientific Computing from Reading University (2002). He began his research career as a visiting research fellow at the Center for Nonlinear Studies (LANL) in 2005 and 2006. This was followed by postdoctoral appointments at the Institute for Computational and Mathematical Engineering, Stanford University, and UC Davis, where he focused increasingly on the computational problems arising in large-scale predictive simulations. This led him to work with Silicon Valley and Finance firms, with an interest in theory and practical applications of machine learning and computational statistics. Matthew joined the Illinois Institute of Technology in 2015, as a tenure-track assistant professor, where he teaches computational finance and Bayesian modeling in the Mathematics and Finance Departments. His research in fintech is funded by Intel.
“On Monte-Carlo Tree Search and Reinforcement Learning“. Spyros Samothrakis, Institute for Analytics and Data Science. University of Essex. 23rd April 2018, 12:00 – 13:00, Room TBD.
“Book a meeting” link available soon
Abstract: Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved widespread adoption within the games community. Its links to traditional reinforcement learning (RL) methods have been outlined in the past; however, the use of RL semantics within tree search has not been thoroughly studied yet. In this talk we re-examine in depth this close relation between the two fields; we show that a straightforward adaptation of RL semantics within tree search can lead to a wealth of new algorithms, for which the traditional MCTS is only one of the variants. We confirm that planning methods inspired by RL in conjunction with online search demonstrate encouraging results on several classic board games and in arcade video game competitions, where our algorithm recently ranked first. Our study promotes a unified view of learning, planning, and search.
Bio : Spyros Samothrakis is a Lecturer and Assistant Director in the Institute for Analytics and Data Science at the University of Essex (IADS). Prior to his current role, he was a Senior Research Officer within the School of Computer Science and Electronic Engineering (CSEE). His research interests include Reinforcement Learning, Neural Networks and Causality. He obtained his PhD from the University of Essex (2014). He has won prizes in causality competitions (June 2013) and has published papers in all the areas above, in the relevant journals and conferences. He has been reviewing for a number of journals (IEEE Transactions on Evolutionary Computation, IEEE Transactions on Computational Intelligence and AI in Games) and has served as a programme committee member for a number of international conferences including FDG/DIGRA, IEEE CIG and GECCO. He has applied his knowledge of Reinforcement and Machine learning in widely different fields (e.g., natural language processing, game playing) with the explicit aim of showing that the underlying fundamental principles and methods remain the same, irrespective of the application domain. He is the academic supervisor in a number of KTP partnerships the University of Essex holds with local businesses and has recently won the Best KTP Academic award from the University for his work.
“Using Machine Learning for Cyber Security“. Harsha Kumara Kalutarage, Centre for Secure Information Technologies, Queen’s University Belfast. 16th April 2018, 12:00 – 13:00, Room TBD.
“Book a meeting” link available soon
Abstract: With the recent advancements in Machine Learning (ML), systems built on ML can be found in every domain. In spite of extensive academic research, however, such systems are not yet widely used in practice for Cybersecurity. This is because of some fundamental differences between Cybersecurity problems and other problems where ML usually finds much more success. This talk begins with an understanding of the behaviours of intruders with a particular focus on computer networks and then presents our recent work on this research area. It includes carefully deployed empirical analyses with number of attack scenarios on computer and control area networks. Finally, the talk concludes with a discussion on research challenges and provides necessary suggestions to move forward in this research line.
Bio : Harsha Kumara Kalutarage is currently a Senior Research Engineer of Security Data Analytics at the Centre for Secure Information Technologies, Queen’s University of Belfast, UK. His research interest is to improve Cybersecurity through the advancement and application of Data Science and Machine Learning. He wants to leverage his applied computer science research background to develop and evaluate new technologies in Cybersecurity. Harsha particularly enjoys tackling real world security problems not only for academic interest but also for generating useful tools to improve everyday life. To date the impact of Harsha’s research in this area includes over
20 publications, a patent (pending) and technology transfer. Harsha holds a Ph.D. in Computing (Cybersecurity), an M.Phil. in Computer Science (Speech Synthesis) and a B.Sc. Special degree (Statistics and Computing).
We investigate a simple model for social learning with two characters: a teacher and a student. The teacher’s goal is to teach the student the state of the world $Theta$, however, the teacher herself is not certain about $Theta$ and needs to simultaneously learn it and teach it. We examine several natural strategies the teacher may employ to make the student learn as fast as possible. Our primary technical contribution is analyzing the exact learning rates for these strategies by studying the large deviation properties of the sign of a transient random walk on $mathbb Z$.
“Theoretical support of machine learning debugging via weighted M-estimation“. Xiaomin Zhang, University of Wisconsin-Madison. 1st March 2018, 12:00 – 13:00, Room 0.3 MVB. *Cancelled, due to extreme weather*.
Abstract: We study a linear regression formulation of machine learning debugging, where data are obtained from two distinct pools of “clean” and “contaminated” data. The goal is to correctly identify the subset of buggy data contained in the contaminated data pool. We propose a novel weighted $M$-estimator that applies a Huber loss to the contaminated data and a squared error loss to the clean data, and derive rigorous statistical properties of the estimator. Our results reveal the dependence between the proper choice of relative weights; the sample sizes of the clean and contaminated data sets; and the ratio between the noise variances of the two datasets. Simulation studies demonstrate the success of our method when applied to debugging tasks involving synthetic and real datasets.
Bio: I am a third year graduate student in CS Department at UW-Madison. My advisor is Po-Ling Loh. I am interested in the intersection of statistics, machine learning and optimization. Currently my research focuses on high-dimensional statistics.
“Modeling disease propagation in networks: source-finding and influence maximization“. Po-Ling Loh, University of Wisconsin-Madison. 29th January 2018, 12:00 – 13:00, QB F101c (Queen’s Building).
Abstract: We present several recent results concerning stochastic modeling of disease propagation over a network. In the first setting, nodes are infected one at a time, starting from a single infected individual, and the goal is to infer the source of the infection based on a snapshot of infected individuals. We show that if the underlying graph is a tree and possesses a certain regular structure, it is possible to construct confidence sets for the diffusion source with size independent of the number of infected nodes. Furthermore, the confidence sets we construct possess an attractive property of “persistence,” meaning they eventually settle down as the disease spreads over the network. In the second setting, nodes are infected in waves according to linear threshold or independent cascade models. We establish upper and lower bounds for the influence of a subset of nodes in the network, where the influence is defined as the expected number of infected nodes at the conclusion of the epidemic. We quantify the gap between our upper and lower bounds in the case of the linear threshold model and illustrate the gains of our upper bounds for independent cascade models in relation to existing results. Importantly, our lower bounds are monotonic and submodular, implying that a greedy algorithm for influence maximization is guaranteed to produce a maximizer within a 1-1/e factor of the truth. This is joint work with Justin Khim and Varun Jog.
Bio : Po-Ling grew up in the lovely town of Madison, Wisconsin. After graduating from Caltech with a BS in math and minor in English, she moved to UC Berkeley, where she subsequently earned an MS in computer science and PhD in statistics. From 2014–2016, She was an assistant professor in the Department of Statistics at the Wharton School of the University of Pennsylvania. She moved back to Madison in the summer of 2016.
“Event reasoning for transport video surveillance”. Huiyu Zhou, University of Leicester. 23rd January 2018, 13:00 – 14:00, SCEEM 1.11 (
Queens Building MVB).
Abstract: The aim of transport video surveillance is to provide robust security camera solutions for mass transit systems, ports, subways, city buses and train stations. As we have known, numerous security threats exist within the transportation sector, including crime, harassment, liability suits and vandalism. Possible solutions have been directed to insulate transportation system from security threats and to make the system safer for passengers. In this talk, I will introduce our solution to deal with several challenges in transports, in particular, city buses. In general, I will structure this talk into the following four sections: (1) The techniques that we have developed for automatically extracting and selecting features from face images for robust age recognition, (2) An effective combination of facial and full body measurements for gender classification, (3) Human tracking and trajectory clustering approaches to handle challenging circumstances such as occlusions and pose variations, and (4) event reasoning in smart transport video surveillance.
Bio: Dr. Huiyu Zhou obtained a Bachelor of Engineering degree in Radio Technology from Huazhong University of Science and Technology of China, and a Master of Science degree in Biomedical Engineering from University of Dundee of United Kingdom, respectively. He was then awarded a Doctor of Philosophy degree in Computer Vision from Heriot-Watt University, Edinburgh, United Kingdom. Dr. Zhou presently is a Reader at Department of Informatics, University of Leicester, United Kingdom. He has published widely in the field. He was the recipient of “CVIU 2012 Most Cited Paper Award”, “ICPRAM 2016 Best Paper Award” and shortlisted for “ICPRAM 2017 Best Student Paper Award” and “MBEC 2006 Nightingale Prize”. Dr. Zhou serves as the Editor-in-Chief of “Recent Advances in Electrical & Electronic Engineering” and Associate Editor of “IEEE Transaction on Human-Machine Systems”, and is on the Editorial Boards of several refereed journals. He is one of the Technical Committee of “Information Assurance & Intelligent Multimedia-Mobile Communication in IEEE SMC Society”, “Robotics Task Force” and “Biometrics Task Force” of the Intelligent Systems Applications Technical Committee, IEEE Computational Intelligence Society. He has given over 50 invited talks at international conferences, industry and universities, and has served as a chair for 30 international conferences and workshops. His research work has been or is being supported by UK EPSRC, EU ICT, MRC, Innovate UK, Leverhulme Trust, Invest NI and industry.
“A Linear-Time Kernel Goodness-of-Fit Test*”, Dr. Wittawat Jitkrittum (Gatsby Unit, UCL), Date: 18, Dec 2017. Place: Queens Building, Small Lecture Theater
Manifolds of Shape via Gaussian Process Latent Variable Models, Dr. Neill Campbell, University of Bath, 2nd of February, 15:00-16:00, MVB 1.06
Abstract: In this talk we will look at Gaussian Processes and Latent Variable Models, in particular focusing on how they may be used to learn generative, probabilistic models of shape. As well as looking at some of the theory behind the models I will show a number of real-world applications of such models with the domains of computer vision and graphics. I will also provide details of the challenges in this area and some early results of new work.
Bio: Neill CampbellI is a lecturer in the Department of Computer Science at the University of Bath in Computer Vision, Graphics and Machine Learning. He also hold an Honorary Lecturer position in the Virtual Environments and Computer Graphics Group in the Department of Computer Science at University College London where he was formerly a Research Associate working with Jan Kautz andSimon Prince on synthesizing and editing photorealistic visual objects funded by the EPSRC. Prior to this Neill was a Research Associate in the Computer Vision Group of the Machine Intelligence Laboratory, in the Department of Engineering at the University of Cambridge working on the EU Hydrosys Project led by Ed Rosten. Neill completed his PhD, in the Computer Vision Group at the University of Cambridge, under the supervision of Roberto Cipolla and the guidance of George Vogiatzis and Carlos Hernández.
Prof. Andrea Sgarro, University of Trieste, 9th of February, 14:00-15:00, MVB 1.06
Abstract: Back in 1967 the Croat linguist. Muljacic had used a fuzzy generalization of the Hamming distance between binary strings to classify Romance languages. In 1956 Cl. Shannon had introduced the notion of codeword distinguishability in zero-error information theory. Distance and distinguishability are subtly different notions, even if, with distances as those usually met in coding theory (with the exception of zero-error information theory, which is definitely non-metric), the need for string distinguishabilities evaporates, since the distinguishability turns out to be an obvious and trivial function of the distance. Fuzzy Hamming distinguishabilities derived from Muljacic distances, instead, are not that trivial, and must be considered explicitly. They are quite easy to compute, however, and we show how they could be applied in coding theory to channels with erasures and blurs. The new tool of fuzzy Hamming distinguishability appears to be quite promising to extend Muljacic approach from linguistic classification to linguistic evolution.
Bio: Andrea Sgarro is full professor of Theoretical Computer Science at the University of Trieste. His research interests include information theory and codes, cryptography, bioinformatics, soft computing, management of incomplete knowledge and computational linguistics. He is responsible for the scientific section of the Circolo della Cultura e delle Arti of Trieste, and is quite active in scientific communication: his books Secret Codes, Mondadori, and Cryptography, Muzzio, for the first time have introduced cryptology to an Italian-speaking audience. In his free time he enjoys languages, of which he speaks a dozen with varying degrees of competence, and plays the one-keyed transverse baroque flute.
Prof. Mark Girolami, University College London, 23rd of February, 14:00-15:00, MVB 1.06 Abstract: Ambitious mathematical models of highly complex natural phenomena are challenging to analyse, and more and more computationally expensive to evaluate. This is a particularly acute problem for many tasks of interest and numerical methods will tend to be slow, due to the complexity of the models, and potentially lead to sub-optimal solutions with high levels of uncertainty which needs to be accounted for and subsequently propagated in the statistical reasoning process. This talk will introduce our contributions to an emerging area of research defining a nexus of applied mathematics, statistical science and computer science, called “probabilistic numerics”. The aim is to consider numerical problems from a statistical viewpoint, and as such provide numerical methods for which numerical error can be quantified and controlled in a probabilistic manner. This philosophy will be illustrated on problems ranging from predictive policing via crime modelling to computer vision, where probabilistic numerical methods provide a rich and essential quantification of the uncertainty associated with such models and their computation.
Bio: Mark Girolami is Professor of Statistics in the Department of Statistical Science at Imperial College London. Prior to joining Imperial College, Mark held Chairs in Computing and Inferential Science at the University of Glasgow, in Statistics at UCL and subsequently Warwick University. In 2011 he was elected to the Fellowship of the Royal Society of Edinburgh when he was also awarded a Royal Society Wolfson Research Merit Award. He was one of the founding Executive Directors of the Alan Turing Institute for Data Science from 2015 to 2016. He is an EPSRC Established Career Research Fellow and Director of the Lloyds Register Foundation-Turing Programme on Data Centric Engineering of The Alan Turing Institute. He is currently an Associate Editor for J. R. Statist. Soc. C, Journal of Computational and Graphical Statistics, Statistics & Computing, and Area Editor for Pattern Recognition Letters. He is a member of the Research Section of the Royal Statistical Society.
Problem workshop with Piccadilly Group, 23rd of March, 15:00-16:00, MVB 1.06
Abstract: In this session, we”ll hear from the CEO of Piccadilly Group, Dan Hooper and CTO, Adam Smith, who will outline the underlying issues and challenges in the management of software testing and technology delivery within banking, and how we see AI addressing many of these challenges.
Problem Statement: The group discussion will focus on the practical challenges
of developing artificial intelligence and machine learning for use in this
About Piccadilly Group:Piccadilly Group is the UK’s leading Test and Intelligence Agency dedicated to Financial Services, providing specialist skills, bespoke product development
and expert consultancy knowledge across the entire test landscape.
Indian Buffet process for model selection in convolved multiple-output Gaussian processes, Dr Mauriciou Alvarez, University of Sheffield, 4th of May, 15:00-16:00, MVB 1.06
Abstract: Multi-output Gaussian processes have received increasing attention during the last few years as a natural mechanism to extend the powerful flexibility of Gaussian processes to the setup of multiple output variables. The key point here is the ability to design kernel functions that allow exploiting the correlations between the outputs while fulfilling the positive definiteness requisite for the covariance function. Alternatives to construct these covariance functions are the linear model of coregionalization and process convolutions. Each of these methods demands the specification of the number of latent Gaussian processes used to build the covariance function for the outputs. We propose the use of an Indian Buffet process as a way to perform model selection over the number of latent Gaussian processes. This type of model is particularly important in the context of latent force models, where the latent forces are associated with physical quantities like protein profiles or latent forces in mechanical systems. We use variational inference to estimate posterior distributions over the variables involved and show examples of the model performance over artificial data and several real-world datasets.
Bio: Dr. Álvarez received a degree in Electronics Engineering (B. Eng.) with Honours, from Universidad Nacional de Colombia in 2004, a master degree in Electrical Engineering (M. Eng.) from Universidad Tecnológica de Pereira, Colombia in 2006, and a Ph.D. degree in Computer Science from The University of Manchester, UK, in 2011. After finishing his Ph.D., Dr. Álvarez joined the Department of Electrical Engineering at Universidad Tecnológica de Pereira, Colombia, where he was appointed as a Faculty member until Dec 2016. From January 2017, Dr. Álvarez was appointed as Lecturer in Machine Learning at the Department of Computer Science of the University of Sheffield, UK.
Dr. Álvarez is interested in machine learning in general, its interplay with mathematics and statistics, and its applications. In particular, his research interests include probabilistic models, kernel methods, and stochastic processes. He works on the development of new approaches and the application of Machine Learning in areas that include applied neuroscience, systems biology, and humanoid robotics.
Probabilistic and Bayesian deep learning, Dr Andreas Damianou, Amazon Research, 15th of May, 14:00-15:00, MVB 1.06
Abstract: In this talk I will firstly motivate the need for introducing probabilistic and Bayesian flavor to “traditional” deep learning approaches. For example, Bayesian treatment of neural network parameters is an elegant way of avoiding overfitting and “heuristics” in optimization, while providing a solid mathematical grounding. Moreover, introducing ideas from Bayesian uncertainty treatment and probabilistic graphical models, allows for a higher level of reasoning which is needed for solving non-perceptual tasks, such as transfer/unsupervised learning and decision making. In the talk I will highlight the deep Gaussian process family of approaches, which can be seen as non-parametric Bayesian neural networks. Unfortunately, combining deep nets with probabilistic reasoning is challenging, because uncertainty needs to be propagated across the neural network during inference. This comes in addition to the (easier) propagation of gradients (e.g. back-propagation). Therefore, as part of my talk I will talk about approximation methods to tackle the aforementioned computational issue, such as variational, amortized and black-box inference.
Bio: Andreas Damianou completed my PhD studies under Neil Lawrence in Sheffield, and subsequently pursued a post-doc in the intersection of machine learning and bio-inspired robotics. He have now moved to the industry as a machine learning scientist, based in Cambridge, UK. His area of interest is machine learning, and more specifically: Bayesian non-parametrics (focusing on both data efficiency and scalability), representation learning, uncertainty quantification, big data. In a recent work he seeks to bridge the gap between representation learning and decision-making, with applications in robotics and data science pipelines. Personal website.
Deep probabilistic models for weakly supervised structured prediction, Diane Bouchacourt, University of Oxford, 8th of June, 15:00-16:00, MVB 1.06
Abstract: Structured prediction refers to the prediction of a structured, complex output given an input value. This task is challenging as there is often uncertainty on the output. In this setting, deep probabilistic networks are powerful tools to learn the distribution of the structure to predict. Such models parametrise the distribution of the data with a neural network. This allows reasoning under uncertainty and decision making, according to the task at hand. However, while we can easily gather a large amount of data observations, retrieving ground-truth values of the output to predict is costly, if not infeasible. In this talk, I will present how to employ deep probabilistic models to perform structured prediction for computer vision tasks; both in the supervised and weakly supervised setting when only part of the ground-truthlabelingis available.
Bio: Diane Bouchacourt is a PhD student in the Optimization for Vision and Learning (OVAL Group) at the Department of Engineering Science at University of Oxford. She works under the co-supervision of M Pawan Kumar at the University of Oxford and Sebastian Nowozin at Microsoft Research Cambridge. Her research focuses on developing novel optimization algorithms and deep probabilistic models for structured output prediction. She is currently focusing on unsupervised and supervised learning of generative models based on neural networks.
ISL also organises problem workshops with companies and other interested parties. These are talks by industrialists, companies in the area of finance, healthcare companies, and many other areas who have an application which would involve machine learning or computational statistics. They are keen to establish a collaborative link with ISL members. They have typically indicated that they wish to co-invest in support of this objective. Because the latter are not our regular academic seminars they can be of much shorter duration than the usual 50 minute duration and typically consist in the presentation of the topic of interest, and discussion of data they have available. The presentation is informal and followed by a discussion. Given the nature of these talks, no Abstract is given and the title may be omitted. ISL members, affiliates and UoB academic staff from other faculties are welcome to attend and we are always keen to facilitate developing contacts.