Mark Bishop


Mark Bishop is professor of Cognitive Computing at Goldsmiths, University of London, and former chair of the Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB). He has published widely in areas of Artificial Intelligence, Machine Learning and Neural Computing.

He is currently working at Tunsten Centre for Intelligent Data Analytics, a new commercial research project to develop a fully functional state-of-the art spend analytics system. Research includes:

  • Semantics
  • Automatic ontology generation
  • Trend analysis: prediction of future price fluctuations

Publications and seminars


  • COGS Seminar, University of Sussex, UK, (February 2010).
  • Mechanical Bodies, Mythical Minds, Middlesex University (October 2008), Hertfordshire University (October 2008), UK Cybernetics Society, Kings College (July 2007), Oxford Brookes University (April 2004), The Whitehead Lectures on Cognition, Computing & Creativity, Goldsmiths (November 2004), Warwick University (January 2004), Exeter University (February 2003)
  • Cry robot: the crocodile tears of a computing machine, Centre for Cognitive & Neural Systems, University of Warwick, UK, (December 2007).
  • An Introduction to Stochastic Diffusion Processes, Nottingham Trent University, Nottingham, UK, (January 2007).
  • An Introduction to Search and Optimisation using Stochastic Diffusion Processes, Department of Computing, University of Essex, UK, (February 2004).
  • Society Rules: the role of Stochastic Diffusion Processes in Cognition, Computation & Culture, Goldsmiths, University of London, (May 2003).
  • Dancing with Pixies: strong Artificial Intelligence & panpsychism, IBM Hursley Institute of Technology, Winchester (July 2003), Functional Imaging Laboratory, University College London (November 2001), Queen Mary College, University of London (March 2001), University of Oxford (November 2000), University of Reading Public Lecture Program, (October 2000).
  • Stochastic Diffusion Process: self organisation, search & intelligence, University of Cardiff (November 2001), University of York (March 2001), University of Kent (March 2001) and Imperial College, London, (November 1999)
  • Redcar Rocks: Strong artificial intelligence and panpsychism, McKay Institute of Communication & Neuroscience, University of Keele (June 1999).


  • Philosophy of the Information and Computing Sciences, The Lorentz, Leiden, Netherlands
  • 8th Annual Hatter Lecture, Zombie Robots & Killer Droids, ORT Seminars on Robotics & Technology, ORT House Camden (October 2009).
  • Symposium on Human and Robot, 39th International Symposium for Robotics, ISR 2008, COEX Convention Center in Seoul, Korea (October 2008).
  • Debate on Machine Consciousness, IEE Conference on Biologically Inspired Cognitive System, Stirling (August 2004).


  • Talk on Philosophy of Information, Lorentz Centre, Leiden University, Holland, (February 2010).
  • Seminars on the Chinese room, Microsoft Research, Seattle, USA, (2002).
  • The Restaurant Game, Alexandria University, EGYPT, (1997).
  • A Tutorial on Weightless Neural Networks, Alexandria University, EGYPT, (1997).

Mark d’Inverno


Mark d’Inverno is Professor of Computer Science and Pro-Warden for Research and Enterprise University at Goldsmiths, University of London, and for four years between 2007 and 2011 was head of the Department of Computing which has become one of Europe’s leading centres for interdisciplinary research and teaching especially in the arts increasingly in the social sciences.

He holds an MA in Mathematics and an MSc in Computation from the University of Oxford and a PhD from University College London entitled “Agents, Agency and Autonomy”.

He has published over 100 articles including authored and edited books, chapters in books, and journal and conference articles leading interdisciplinary computer science research projects in multi-agent systems, biological systems, music, art, design, education and social media.

He is currently working with Matthew Yee-King on the Music Circle System, an online educational social media system that promotes social learning.

This was part of a large €3 million European project which delivered courses for many thousands of students with partners including:

  • Goldsmiths’ Music and Computing departments
  • London Chamber Orchestra
  • coursera
  • Sussex University
  • Point Blank.

They are currently investigating a range of data-anaytics techniques to understand the nature of online social learning that has been supported through the music circle system. This is currently being spun out as the company Museifi Ltd. Mark was the lead scientist that put together the research team that provided the initial Country Check system that was bought by Thompson Reuters. About the Country Check system


Designing educational social machines for effective feedback
M. Yee-King, M. Krivenski, H. Brenton, A. Grimalt-Reynes, M. d’Inverno. 8th International Conference on e-learning. Lisbon, Portugal, 15-18 July, 2014.

Fluid gesture interaction design applications of continuous recognition for the design of modern gestural interfaces
Bruno Zamborlin, Frédéric Bevilacqua, Marco Gillies and Mark d’Inverno. ACM Transactions on Interactive Intelligent Systems, vol. 3, pp. 30-45, ACM Press, 2014, ISSN 2160-6455, [Article].

Automatic Group Interactive Radio Using Social Networks of Musicians
Benjamin Fields, Christophe Rhodes and Mark d’Inverno. ICWSM-11 Fifth International AAAI Conference on Weblogs and Social Media, pp. 478-481, Barcelona, ES, The AAAI Press, 2011, [Conference].

Analysis and Exploitation of Musician Social Networks for Recommendation and Discovery
Ben Fields, Kurt Jacobson, Christophe Rhodes, Mark d’Inverno, Mark Sandler and Michael A. Casey. IEEE Transactions on Multimedia, vol. 13, pp. 674-686, IEEE Circuits & Systems Society, 2011, ISSN 1520-9210, [Article].

Agents in bioinformatics computational and systems biology
Emanuela Merelli, Giuliano Armano, Nicola Cannata, Flavio Corradini, Mark d’Inverno, Andreas Doms, Phillip Lord, Andrew Martin, Luciano Milanesi, Steffen Möller, Michael Schroeder and Michael Luck. Briefings in Bioinformatics, vol. 8, pp. 45-59, Oxford University Press, 2007, ISSN 1467-5463, [Article].


PRAISE: Performance and pRactice Agents Inspiring Social Education
Aim: a system for enabling online communities to practice and perform together using state of art in music analysis gesture analysis, natural language and community management

  • Duration: 2013-2016
  • Funding Source: European Union FP7 Strep Project
  • Total Project value: €3,500,000
  • Partners: Artificial Intelligence Research Institute, Spanish Research Council, Spain; Sony Computer Science Laboratory, Paris; VUB University Brussels, Principal Investigator

ACE: Autonomic Agents For Online Cultural Experiences

  • Aim: to enable users to synchronously share their online experiences including social synchronous browsing and annotation
  • Duration: 2011-2013
  • Goldsmiths: €325,000 Euros
  • 1st call of the ERA-NET CHIST-ERA (European Coordinated Research on Long-term Challenges in Information and Communication Sciences\Technologies
    Partners: Artificial Intelligence Research Institute, Spanish Research Council, Spain; Toulouse Institute of Computer Science Research, France
    Principal Investigator

Creativeworks – AHRC Digital Economy Hub
Aim: build new partnerships and commercial opportunities between academia and the Creative Economy

  • Duration: 2012 – 2016
  • Total Project value: £4,000,000
  • Funding Source: Arts and Humanities Research Council
  • Partners: Queen Mary, University of London (lead institution); Birkbeck College; Central School of Speech and Drama; City University; the Courtauld Institute; Kingston University; Guildhall School of Music and Drama; King’s College London; Royal Holloway; School of Oriental and African Studies; Roehampton University; Trinity Laban Conservatoire of Music and Dance; University of the Arts
    Co-investigator and Goldsmiths lead

Vconect: Video Communications For Networked Communities
Aim: novel video communication technologies for communities building intelligent multi-camera and multi-location video communication combined with synchronous video share

  • Duration: 2011-2014
  • Total Project value: €5,500,000 Euros
  • Funding Source: European Union FP7 Strep Project
  • Partners: BT, Alcatel-Lucent (Belgium), Portugal Telecom, CWI (Netherlands), Fraunhofer (Germany), Joanneum Research (Austria), Eurescom (Germany), University College Falmouth (UK)

OMRAS2: A Distributed Research Environment for Music Informatics and Computational Musicology
Aim: framework for annotating and searching collections of both recorded music and digital score representations

  • Duration: 2007 – 2011
  • Funding Source: Engineering and Physical Sciences Research Council (EPSRC)
  • Project Value: £2,183,000
  • Goldsmiths Value: £735,000
  • Web (project):
  • Partners: Queen Mary, Kings College, Royal Holloway, Lancaster University, University of Surrey
    Principal Investigator

Ida Pu


Dr Ida Pu (PhD, University of Warwick) is a lecturer in Computer Science, Goldsmiths, University of London. Her research has centred on the design and analysis of algorithms, and crosses various application areas such as  mobile data communications, networks, data compression, computer security and MRI data analysis.

Ida’s interests in average case analysis of algorithmic performance falls in a challenging but rewarding research area. Apart from classical deterministic and sequential algorithmic work, she published also in the more difficult areas of parallel and randomised algorithms. Her earlier projects aim to develop parallel algorithms for uniform generation at random of combinatorial structures (e.g. paths, spanning trees, random graphs and strings) that occur in many applications. The constant worst case access time achieved in the matricial space economy project helps explain formally why the simple approach introduced seems to be so useful in practice. The error propagation project quantifies the errors in diffusion tensor imaging. The project of expanding ring search develops energy-efficient search algorithms for mobile Ad Hoc networks, and the brain functional imaging project develops MR imaging techniques to study brain characteristics and functions. Ida’s recent research interests include efficient algorithms for large data sets and collective intelligence in applications in body networks and brain imaging.

Ida has a wide technical experience in Computer Science. She has taught many main courses in Computer Science in areas of Artificial Intelligence, Computer Security, Data Compression, Data Structures, Algorithm Design, Computer Communication and Networks, Databases, Computer Programming (e.g. in Java, UNIX/Linux, Miranda, C, MATLAB, Pascal, Fortran, COBOL, Assembly Language), Electronics, and Discrete Mathematics. Many of these courses have been taught in Goldsmiths, both locally and internationally via the international programmes of the University of London. Some were taught overseas in Beijing, HK and Singapore. She has also published six subject guides for the University of London.

Ida has been invited and served as an external examiner for the Xian Liverpool Jaotong University, China; King’s College London; Queen Mary University of London; University of Liverpool; Liverpool John Moore University; Buckinghamshare New University; and the University of London, UK.

Authored Books

I M Pu, Fundamental Data Compression, Elsevier, 2006, ISBN 978-0-7506-6310-6, ISBN 0-7506-6310-3

I M Pu and S Miao, Laboratories for Modern Electronics, Beijing University of Technology Publisher, 1989, ISBN: 7-5639-0026-8 (in Chinese)

Other publications

Ida M Pu, Yuji Shen, ‘Analytical Studies of Energy-Time Efficiency of Blocking Expanding Ring Search’, Mathematics in Computer Science 3(4): 443–456 (2010) ISSN: 1661-8270

Ida M Pu, Yuji Shen, ‘A framework for chase strategies in recent energy or time efficient route discovery protocols for MANETs’, IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2011, ISBN: 978-1-4577-0352-2

Ida Pu, Yuji Shen, ‘Enhanced Blocking Expanding Ring Search in Mobile Ad Hoc Networks‘, in proceedings of the Third International Conference on New Technologies, Mobility and Security (NTMS 2009), pp451–455, 2009

Ida M. Pu, Daniel Stamate, Yuji Shen,
‘Improving time-efficiency in blocking expanding ring search for mobile ad hoc networks‘, Journal of Discrete Algorithms 24: 59-67 (2014)

Yuji Shen, Yi-Ching L. Ho, Rishma Vidyasagar, George Balanos, Xavier Golay, Ida M.Pu, Risto A. Kauppinen, ‘Gray matter nulled and vascular space occupancy dependent fMRI response to visual stimulation during hypoxic hypoxia’, NeuroImage 59(4): 3450-3456 (2012)

Y Shen, I M Pu, T Ahearn, M Clemence, C Schwarzbauer, ‘Quantification of venous vessel size in human brain in response to hypercapnia and hyperoxia using magnetic resonance imaging’, Magnetic Resonance in Medicine Volume 69, Issue 6, pages 1541–1552, June 2013

Ida Pu, Yuji Shen, ‘Efficient algorithms for noise propagation in diffusion tensor imaging’, in London Algorithmics 2008: Theory and Practice: (Texts in Algorithmics) eds. J Chan, J K Daykin, M S Rahman, 1 June 2009, ISBN: 978-1904987970

Daniel Stamate, Ida Pu, ‘Imperfect Information Fusion using Rules with Bilattice based Fixpoint Semantics’. Proceedings of the 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Springer-Verlag, IPMU (3) pp181-190 (2012)

Andy Freeman


Andy Freeman works with communities and artists to create digital and citizen science interventions to build awareness of digital, social and environmental issues. His practice has ranged from internet audio performance tools (earshot 1998-2002) through to citizen science based attempts to trace the movement of pollutants around the Thames estuary using sensor networks and digital mapping (Talking Dirty, 2015).

Andy has a wide experience of internet development for the media sector and currently teaches interdisciplinary courses in digital methods and visualisation for Goldsmiths’ Department of Computing. Current research interests include citizen science, digital mapping and tools for hyperlocal journalism and activism.

Rebecca Fiebrink


Dr Rebecca Fiebrink is a Lecturer in Computing at Goldsmiths. Her work lies at the intersection of human-computer interaction and machine learning; many of her projects focus on making machine learning and data mining techniques more usable by—and useful to—domain experts.

She is the author of the Wekinator software for real-time interactive machine learning, which enables musicians, composers, and interaction designers to apply machine learning to create new systems for real-time analysis and control. She has also published in the domain of music information retrieval, on the topic of automated musical audio analysis.

Fiebrink is a Co-I on the Horizon 2020-funded RAPID-MIX project on real-time adaptive prototyping for industrial design of multimodal expressive technology, which aims to produce improved machine learning and signal analysis tools for software developers creating music, games, and health applications.


Katan, S., M. Grierson, and R. Fiebrink. Using interactive machine learning to support interface development through workshops with disabled people. Proceedings of ACM CHI, April 18–23 2015.

Wolf, K. E., G. Gliner, and R. Fiebrink. A model for data-driven sonification using soundscapes. Proceedings of ACM Conference on Intelligent User Interfaces (IUI), March 29–April 1, 2015.

Hipke, K., M. Toomim, R. Fiebrink, and J. Fogarty. BeatBox: End-user interactive definition and training of recognizers for percussive vocalizations. Proceedings of AVI 2014 International Working Conference on Advanced Visual Interfaces, Como, Italy, May 27–30.

Laguna, C., and R. Fiebrink. 2014. Improving data-driven design and exploration of digital musical instruments. CHI’14 Extended Abstracts, 26 April–1 May.

Fried, O., and R. Fiebrink. Cross-modal sound mapping using deep learning. Proceedings of New Interfaces for Musical Expression (NIME), Daejeon, South Korea, May 27–30, 2013.

Fiebrink, R., and D. Trueman. End-user machine learning in music composition and performance. Presented at the CHI 2012 Workshop on End-User Interactions with Intelligent and Autonomous Systems. Austin, Texas, May 6, 2012.

Morris, D., and R. Fiebrink. Using machine learning to support pedagogy in the arts. Personal and Ubiquitous Computing, April 2012.

Fiebrink, R., P. R. Cook, and D. Trueman. Human model evaluation in interactive supervised learning. Proceedings of ACM CHI, Vancouver, May 7–12, 2011.



I am a CO-I on Horizon 2020-funded project Realtime Adaptive Prototyping for Industrial Design of Multimodal Interactive Expressive Technology (RAPID-MIX).

The RAPID–MIX consortium has devoted years of research to the design and evaluation of embodied, implicit and wearable human-computer interfaces. These interfaces, developed and applied through creative fields such as music and video games, provide natural and intuitive pathways between expressivity and technology.

RAPID–MIX will bring these innovations out of the lab and into the wild, directly to users, where they will have true impact. RAPID–MIX will bring cutting edge knowledge from three leading European research labs specialising in embodied interaction, to a consortium of five creative companies.


I am the author of the Wekinator software for real-time, interactive machine learning. Wekinator facilitates the use of machine learning as a prototyping and design tool, enabling composers, musicians, game designers, and makers to create new gestural interactions or semantic analysis systems from data.

The Wekinator has been downloaded over 3000 times and used in dozens of computer music performances utilising new musical instruments built with machine learning.

Nikolay Y. Nikolaev


Dr Nikolay Y. Nikolaev is lecturer in Computing Science at Goldsmiths, University of London, where he has taught courses in software engineering, language design, neural networks, artificial intelligence and the technology of thought.

Prior to this he was Assistant Professor of Computer Science at the American University in Bulgaria. He took his PhD in Artificial Intelligence, MSc in Computer Science and BSc in Computer Science at Sofia Technical University.



Nikolaev, N., and Iba, H. (2006). Adaptive Learning of Polynomial Networks: Genetic
Programming, Backpropagation and Bayesian Methods, Springer, New York

Nikolaev, N., and Iba, H. (2002). Genetic Programming of Polynomial Models for Financial Forecasting. In: Shu-Heng Chen (Ed.), Genetic Agorithms and Genetic Programming in Computational Finance, Chapter 5, Kluwer Academic Publ., Boston, MA, pp.103-123.

Journal papers

Mirikitani,D. and Nikolaev, N. (2011). Nonlinear Maximum Likelihood Estimation of Electricity Spot Prices using Recurrent Neural Networks, Neural Computing and Applications, vol.20, N:1, pp.79-89.

Mirikitani,D. and Nikolaev, N. (2010). Recursive Bayesian Recurrent Neural Networks for Time Series Modeling, IEEE Transactions on Neural Networks, vol.21, N:2, pp.262-274.

Mirikitani,D. and Nikolaev, N. (2010). Efficient Online Recurrent Connectionist Learning with the Ensemble Kalman Filter, Neurocomputing, vol.73, N:4-6, pp.1024-1030.

Nikolaev,N. and de Menezes, L. (2008). Sequential Bayesian Kernel Modelling with Non-Gaussian Noise, Neural Networks, vol.21. N:1, pp.36-47.

Conference papers

Nikolaev, N., Tino,P. and Smirnov, E.N. (2011). Time-Dependent Series Variance Estimation via Recurrent Neural Networks, In: T. Honkela et al (Eds.) Proc. Int. Conf. on Artificial Neural Networks, ICANN-2011, Espoo, Finland, LNCS-6971, Springer, pp.176-184.

Nikolaev, N., Mirikitani,D. and Smirnov, E.N. (2010). Unscented Grid Filtering and Elman Recurrent Networks, In: Proc. Int. Joint Conf. on Neural Networks IJCNN-2010, Barcelona, Spain, pp.1-7.

Nikolaev, N. and Smirnov, E. (2007). A One-Step Unscented Particle Filter for Nonlinear Dynamical Systems, In: Proc. Int. Conf. on Artificial Neural Networks, LNCS 4668, Springer, Berlin, pp.747-756.

Tino, P., Nikolaev, N. and Yao, X. (2005). Volatility Forecasting with Sparse Bayesian Kernel Models, In: Proc. 4th International Conference on Computational Intelligence in Economics and Finance, Salt Lake City, UT, pp.1150-1153.

Mihalis A. Nicolaou


Mihalis A. Nicolaou is lecturer in Computer Science at Goldsmiths, University of London.

Previously, Mihalis was a postdoctoral Research Associate at Imperial College London (Department of Computing), where he also completed his PhD in 2014. Before that, Mihalis obtained his MSc from the same department, and Ptychion (4Y BSc equiv.) from the Department of Informatics and Telecommunications at the University of Athens, Greece in 2008.

Mihalis’ research interests span the areas of machine learning and computer vision, particularly motivated by problems arising in the audio-visual analysis of affective behaviour under real-world conditions. Mihalis’ work revolves around probabilistic and robust methods, component analysis, predictive analysis, time-series analysis and alignment as well as the discovery of deep (hierarchical) non-linear representations.


Journal Papers

Conference Papers

Book Chapters


Automatic Sentiment Analysis in the Wild
European Commission Horizon 2020 Programme SEWA

The Automatic Sentiment Analysis in the Wild (SEWA) is a EC H2020 funded project. The main aim of SEWA is to deploy and capitalise on existing state-of-the-art methodologies, models and algorithms for machine analysis of facial, vocal and verbal behaviour, and then adjust and combine them to realise naturalistic human-centric human-computer interaction (HCI) and computer-mediated face-to-face interaction (FF-HCI).

This will involve development of computer vision, speech processing and machine learning tools for automated understanding of human interactive behaviour in naturalistic contexts. The envisioned technology will be based on findings in cognitive sciences and it will represent a set of audio and visual spatiotemporal methods for automatic analysis of human spontaneous (as opposed to posed and exaggerated) patterns of behavioural cues including continuous and discrete analysis of sentiment, liking and empathy.

Telepresence Reinforcement-learning Social Agent

European Commission FP7 TERESA project

The TERESA project aims to develop a telepresence robot of unprecedented social intelligence, thereby helping to pave the way for the deployment of robots in settings such as homes, schools, and hospitals that require substantial human interaction. In telepresence systems, a human controller remotely interacts with people by guiding a remotely located robot, allowing the controller to be more physically present than with standard teleconferencing. We are developing a new telepresence system that frees the controller from low-level decisions regarding navigation and body pose in social settings. Instead, TERESA will have the social intelligence to perform these functions automatically. In particular, TERESA will semi-autonomously navigate among groups, maintain face-to-face contact during conversations, and display appropriate body-pose behaviour.

Achieving these goals requires advancing the state of the art in cognitive robotic systems. The project will not only generate new insights into socially normative robot behavior, it will produce new algorithms for interpreting social behavior, navigating in human-inhabited environments, and controlling body poses in a socially intelligent way. The project culminates in the deployment of TERESA in an elderly day centre. Because such day centres are a primary social outlet, many people become isolated when they cannot travel to them, e.g., due to illness. TERESA will provide a socially intelligent telepresence system that enables them to continue social participation.

Fun Robotic Outdoor Guide

European Research Council FP7 project FROG
FROG aspires to turn autonomous outdoor robots into viable location-based service providers. It will develop an outdoor guide robot, part of an emerging class of intelligent robot platforms.

Multimodal Analysis of Human Nonverbal Behaviour in Real-World Settings

European Research Council Starting Grant (FP7) MAHNOB
Project lifespan: 2008 – 2013

Existing tools for human interactive behaviour analysis typically handle only deliberately displayed, exaggerated expressions. As they are usually trained only on series of such exaggerated expressions, they lack models of human expressive behaviour found in real-world settings and cannot handle subtle changes in audiovisual expressions typical for such spontaneous behaviour.

The main aim of MAHNOB project is to address this problem and to attempt to build automated tools for machine understanding of human interactive behaviour in naturalistic contexts. MAHNOB technology will represent a set of audiovisual spatiotemporal methods for automatic analysis of human spontaneous (as opposed to posed and exaggerated) patterns of behavioural cues including head pose, facial expression, visual focus of attention, hands and body movements, and vocal outbursts like laughter and yawns.

As a proof of concept, MAHNOB technology will be developed for two specific application areas: automatic analysis of mental states like fatigue and confusion in Human-Computer Interaction contexts and non-obtrusive deception detection in standard interview settings.

A team of 5 Research Assistants (RAs), led by the PI and having the background in signal processing and machine learning will develop MAHNOB technology. The expected result after 5 years is MAHNOB technology with the following capabilities:

  • analysis of human behaviour from facial expressions, hand and body movements, gaze, and non-linguistic vocalizations like speech rate and laughter
  • interpretation of user behaviour with respect to mental states, social signals, dialogue dynamics, and deceit/veracity
  • near real-time, robust, and adaptive processing by means of incremental processing, robust observation models, and learning person-specific behavioural patterns
  • provision of a large, annotated, online dataset of audiovisual recordings providing a basis for benchmarks for efforts in machine analysis of human behaviour.

Matthew Yee-King


Matthew Yee-King gained a DPhil from the School of Informatics at Sussex University, wherein he investigated techniques for exploring the high dimensional space of synthetic timbre.

Since then he has worked on several research projects such as the PRAISE project, developing online, collaborative learning systems which have been used by many thousands of people. He has worked with the data resulting from the real world deployment of these systems to address questions such as:

  • What can be the impact of social, collaborative learning upon learning?
  • How can data be used to improve the design of online social learning systems?


Engineering Multiuser Museum Interactives, Engineering applications of artificial intelligence, Elsevier, p.1-24 (2015)
Roberto Confaloniera, Matthew Yee-King, Katina Hazelden, Mark d’Inverno, Dave de Jonge, Nardine Osmaa, Carles Sierra, Leila Agmoud, Henri Prade;

Multiuser Museum Interactives for Shared Cultural Experiences: an Agent Based Approach, AAMAS 2013, Saint Paul, Minnesota, USA, p.917-924 (2013)
Matthew Yee-King; Roberto Confalonieri; Dave de Jonge; Katina Hazelden; Carles Sierra; Mark d’Inverno; Leila Amgoud; Nardine Osman

Social machines for education driven by feedback agents, in Proceedings First International Workshop on the Multiagent Foundations of Social Computing, AAMAS-2014, Paris, France, May 6 2014
M. Yee-King ,M. d’Inverno, P. Noriega

Designing educational social machines for effective feedback. 8th International Conference on e-learning. Lisbon, Portugal, 15-18 July, 2014
M. Yee-King, M. Krivenski, H. Brenton, A. Grimalt-Reynes, M. d’Inverno.


PRAISE project: PRAISE is a social network for music education with tools for giving and receiving feedback. It aims to widen access to music education and make learning music more accessible and more social.

At its heart PRAISE will provide a supportive, social environment using the latest techniques in social networks, online community building, intelligent personal agents and audio and gesture analysis.

Any member of any community can post audio to any community for which they are a member and ask for specific kinds of feedback on various regions of that audio. Any community member can respond with text, or with other audio to emphasize a particular point about style or performance for example.

Katayoun Farrahi


Katayoun Farrahi is a lecturer at Goldsmiths, University of London, and is involved in designing course material for the Data Science masters programme, particularly on big data applications.

The focus of her research is on methods (based on machine learning) for mining meaningful information from large scale data. Much of her research has focused on mobile phone data. She also works on mobile phone sensed data-driven applications – applications which can make use of knowledge about human behaviour extracted from mobile sensed data. One example is the simulation of epidemics from mobile phone sensed physical proximity data.

Prior to joining Goldsmiths, Katayoun was a research assistant at Idiap Research Institute. She obtained her PhD in Computer Science from Swiss Federal Institute of Technology in Lausanne (EPFL) in 2011.


Epidemics & Mobile Phone Data
What if you use more realistic human interaction patterns from mobile phones (Bluetooth) to simulate epidemics? We take this one step further and propose to use communication logs obtained by mobile phones for contact tracing. Mobile phones have the potential to provide a “global sensor” for research in epidemiology and my work explores different methods for integrating this data in this domain.
Sequence Modeling with Latent Topic Models
The particular problem of mining long sequences from large-scale location data is of relevance for problems relating to mobile sensing and Reality Mining applications. Latent topic models are of particular interest for big data mining applications due to their unsupervised nature and ability to handle noise. The distant n-gram topic model (DNTM), visualized, is an extension of Latent Dirichlet Allocation (LDA), which can incorporate sequence data. This model has been tested on location data, particularly GPS and cell tower connection data to mine long duration sequences of location patterns.


Journal Articles

K. Farrahi, R. Emonet, M. Cebrian
Epidemic Contact Tracing via Communication Traces
PLoS ONE 9(5): e95133. May 1, 2014. doi: 10.1371/journal.pone.0095133

K. Farrahi and D. Gatica-Perez
A Probabilistic Approach to Mining Mobile Phone Data Sequences
Personal and Ubiquitous Computing, published online Feb. 2013, Vol. 18, No. 1, pp. 223-238, Jan. 2014

K. Zia, A. Riener, K. Farrahi, A. Ferscha
An Agent-Based Parallel Geo-Simulation of Urban Mobility during City-scale Evacuation
Simulation: Transactions of the Society for Modeling and Simulation, SAGE, May 2013

A. Ferscha, K. Farrahi, J. van den Hoven, D. Hales, A. Nowak, P. Lukowicz, D. Helbing
Socio-inspired ICT
The European Physical Journal Special Topics, Springer, Vol. 214, No. 1, pp. 401-434, Nov. 2012

A. Madan, M. Cebrian, S. Moturu, K. Farrahi, and A. Pentland
Sensing the `Health State` of our Society
IEEE Pervasive Computing, Vol. 11, No. 4, Oct.-Dec. 2012

K. Farrahi and D. Gatica-Perez
Discovering Routines from Large-Scale Human Locations using Probabilistic Topic Models
ACM Transactions on Intelligent Systems and Technology, Special Issue on Activity Recognition, Vol. 2. No. 1, 2011

K. Farrahi and D. Gatica-Perez
Probabilistic Mining of Socio-Geographic Routines From Mobile Phone Data
IEEE Journal of Selected Topics in Signal Processing, Special Issue on Signal and Information Processing for Social Networks, Vol. 4, No. 4, pp. 746 – 755, Aug. 2010


K. Farrahi
A Probabilistic Approach to Socio-Geographic Reality Mining Ecole Polytechnique Federale de Lausanne, Thèse No. 5018, March 2011.

Conference Articles

K. Farrahi, R. Emonet, M. Cebrian
Predicting a Community’s Flu Dynamics with Mobile Phone Data
in CSCW, Vancouver, Canada, March 2015

M. Schedl, D. Hauger, K. Farrahi, M. Tkalcic
On the Influence of User Characteristics on Music Recommendation
in ECIR, Vienna, Austria, March 2015

K. Farrahi, M. Schedl, A. Vall, D. Hauger, M. Tkalcic
Impact of Listening Behavior on Music Recommendation
in ISMIR, Taipei Taiwan, Oct 2014

M. Schedl, A. Vall, K. Farrahi
User Geospatial Context for Music Recommendation in Microblogs
in ACM Special Interest Group On Information Retrieval (SIGIR), Australia, July 2014

K. Farrahi, K. Zia, A. Sharpanskykh, A. Ferscha, L. Muchnik
Agent Perception Modeling for Movement in Crowds
in Int. Conf. on Practical Applications of Agents and Multi-Agent Systems (PAAMS) Salamanca, May 2013

K. Farrahi, R. Emonet, and A. Ferscha
Socio-Technical Network Analysis from Wearable Interactions
in Proc. IEEE Int. Symp. on Wearable Computers (ISWC), Newcastle, Jun. 2012

K. Farrahi and D. Gatica-Perez
Extracting Mobile Behavioral Patterns with the Distant N-Gram Topic Model
in Proc. IEEE Int. Symp. on Wearable Computers (ISWC), Newcastle, Jun. 2012
Best Paper Award Nominee

A. Madan, K. Farrahi, D. Gatica-Perez, and A. Pentland
Pervasive Sensing to Model Political Opinions in Face-to-Face Networks
in Proc. Int. Conf. on Pervasive Computing (Pervasive), San Francisco, Jun. 2011

K. Farrahi and D. Gatica-Perez
Mining Human Location-Routines using a Multi-Level Approach to Topic Modeling
in IEEE Int. Conference on Social Computing, Symposium on Social Intelligence and Networking (SocialCom-SIN), Minneapolis, Aug. 2010

K. Farrahi and D. Gatica-Perez
Learning and Predicting Multimodal Daily Life Patterns from Cell Phones
in Proc. Int. Conf. on Multimodal Interfaces (ICMI-MLMI), Cambridge, Nov. 2009

K. Farrahi and D. Gatica-Perez
What Did You Do Today? Discovering Daily Routines from Large-Scale Mobile Data
in Proc. ACM Int. Conf. on Multimedia (MM), Vancouver, Oct. 2008

K. Farrahi and D. Gatica-Perez
Discovering Human Routines from Cell Phone Data with Topic Models
in Proc. IEEE Int. Symposium on Wearable Computers (ISWC), Pittsburgh, Sep. 2008

K. Farrahi and D. Gatica-Perez
Daily Routine Classification from Mobile Phone Data
in Proc. Workshop on Machine Learning and Multimodal Interaction (MLMI), Utrecht, Sep. 2008

Fionn Murtagh


Fionn Murtagh has worked in data analytics throughout his career. His first employment, following his primary degrees in Mathematics and Engineering Science, was as statistician-programmer in educational research, overseeing the regular national-level ability and attainment testing, as well as analytics research.

His MSc in Information Retrieval was followed by a PhD in Mathematical Statistics. After an initial period as lecturer in Computer Science, Fionn worked as a visiting researcher in nuclear reactor safety, at the European Joint Research Centre. He served with the Space Science Department of the European Space Agency for 12 years, on data analytics and databases, image and signal processing, and networking, for the Hubble Space Telescope.

He has published over 300 papers, approximately 150 in leading journals, and is author of eight books (with another four underway or to be published imminently).

Fionn Murtagh was a partner in a number of Framework Programme projects, KTPs, a COST Action, and projects funded by EPSRC, BBSRC and STFC (PPARC). Funded by the latter with approx. £9 million was Astrogrid, for datagrid middleware, for which Fionn was a Lead Investigator and a founder member.


Starck, J.-L., Murtagh, F. and Fadili, J., Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity, Cambridge University Press, 2nd edn., 2015. (Chinese version in preparation.)
F. Murtagh, M. Pianosi, R. Bull, Semantic mapping of discourse and activity, using Habermas’s Theory of Communicative Action to analyze process, Quality and Quantity, 2015, in press.
P. Contreras and F. Murtagh, Fast, linear time hierarchical clustering using the Baire metric, Journal of Classification, 29, 118–143, 2012.
F. Murtagh, The new science of complex systems through ultrametric analysis: Application to search and discovery, to narrative and to thinking, Journal of p-Adic Numbers, Ultrametric Analysis and Applications, vol 5, no. 4, 326-337, 2013.
F. Murtagh, The remarkable simplicity of very high dimensional data: application to model-based clustering, Journal of Classification, 26, 249-277, 2009.
F. Murtagh and P. Contreras, Random projection towards the Baire metric for high dimensional clustering, A. Gammerman, V. Vovk and H. Papadopoulos, Eds, Statistical Learning and Data Sciences, Springer Lecture Notes in Artificial Intelligence (LNAI) Volume 9047, 424-431, 2015.