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.

Evelyn Ruppert


Evelyn Ruppert is a Professor and Director of Research in the Department of Sociology at Goldsmiths, University of London. She is currently PI of an ERC funded Consolidator Grant project, Peopling Europe: How data make a people (ARITHMUS; 2014-19) and a recently completed ESRC funded project, Socialising Big Data (2013-14). She is Founding and Editor-in-chief of a SAGE open access journal, Big Data & Society: Critical Interdisciplinary Inquiries, launched in June 2014. Her book, Being Digital Citizens (with Engin Isin) was published in April 2015.

Evelyn is a data sociologist with interests in the sociology of governance specifically in relation to how different kinds of data are constituted and mobilised to enact and manage populations. She has undertaken research on how different socio-technical methods and forms of data (censuses, administrative databases, surveys, transactional, etc.) organise and make possible particular ways of constituting and governing populations and how digital devices and data call for reassembling social science methods.


Ruppert, Evelyn and Isin, Engin. 2015. Being Digital Citizens. London: Rowman & Littlefield International. ISBN 978-1-78348-055-5

Ruppert, Evelyn. 2015. ‘A baroque sensibility for Big Data visualisations.’ In Modes of Knowing: Resources from the Baroque, John Law and Evelyn Ruppert (eds). Mattering Press. (forthcoming)

Ruppert, Evelyn. 2016. ‘Big Data Economies and Ecologies.’ In: L. Ryan, L. McKie, eds. An End to the Crisis of Empirical Sociology? Trends and Challenges in Social Research. London: SAGE, pp. 1-16. (forthcoming)

Ruppert, Evelyn. 2013. ‘Not Just Another Database: The Transactions that Enact Young Offenders.’ Computational Culture, pp. 1-13. Avail at:

Ruppert, Evelyn, Law, John and Savage, Mike. 2013. ‘Reassembling Social Science Methods: the challenge of digital devices.’ Theory, Culture & Society, 30(4), pp. 22-46. ISSN 0263-2764

Ruppert, Evelyn and Savage, Mike. 2012. ‘Transactional Politics.’ The Sociological Review, 59(S2), pp. 73-92. ISSN 0038-0261

Ruppert, Evelyn. 2012. ‘The Governmental Topologies of Database Devices.’ Theory, Culture and Society, 29(4-5), pp. 1-21. ISSN 0263-2764

Daniel Stamate


Dr. Daniel Stamate has a PhD in Computer Science (University of Paris-Sud) and an MSc degree in Computer Science & Mathematics (University of Iasi). He currently leads the Data Science & Soft Computing Lab, and is the Director of the MSc Data Science programme at Goldsmiths, University of London.

Having previously worked in Statistical Databases, his present research focuses on Data Uncertainty Management, Sentiment Analysis & Stock Market Prediction, Scalable Machine Learning Approaches in Psychiatric Research – ongoing work in collaboration with Institute of Psychiatry at King’s College London, and Mobility Big Data Analytics – in particular analysing smart card data from Transport for London.


Latest publications

Sentiment and Stock Market Volatility Predictive Modelling – a Hybrid Approach.
Proceedings 2015 IEEE/ACM International Conference on Data Science and Advanced Analytics (DSAA’2015), with R. Olaniyan, D. Logofatu and L. Ouarbya

A Novel Statistical and Machine Learning Hybrid Approach to Predicting S&P 500 using Sentiment Analysis.
Proceedings of 8th International Conference of the ERCIM WG on Computational and Methodological Statistics, 2015, with Fionn Murtagh and Rapheal Olaniyan

Social Web-Based Anxiety Index’s Predictive Information on S&P 500, Revisited.
Proceedings of the 3rd International Symposium on Statistical Learning and Data Sciences (SLDS 2015), Springer LNAI, with R. Olaniyan and D. Logofatu

Scalable Distributed Genetic Algorithm for Data Ordering Problem with Inversion Using MapReduce.
Proceedings of the 10th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2014),
Springer 2014 IFIP Advances in Information and Communication Technology, with D. Logofatu

Improving Time-Efficiency in Blocking Expanding Ring Search for Mobile Ad Hoc Networks.
Journal of Discrete Algorithms, Volume 24, 2014, with I. Pu and Y. Shen

Data Mining in Interdisciplinary Research.
Invited talk, International Conference on Interdisciplinary Approaches to the Study of Individual Differences in Learning, London, 2013

Quantitative Semantics for Uncertain Knowledge Bases.
Proceedings of the 14th International Conference on Information Processing and Management
of Uncertainty in Knowledge-Based Systems (IPMU 2012), Springer-Verlag (LNAI/CCIS series), 2012

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 (IPMU 2012), Springer-Verlag (LNAI/CCIS series), 2012, with I. Pu

Fixpoint Semantics for Extended Logic Programs on Bilattice based Multivalued Logics, and Applications.
Proceedings of ManyVal 2012 Conference, 2012, with I. Pu

Queries with Multivalued Logic based Semantics for Imperfect Information Fusion.
Proceedings of the 40th IEEE International Symposium on Multiple-Valued Logics (IEEE ISMVL 10), 2010

A Bilattice based Fixed Point Semantics for Integrating Imperfect Information.
Proceedings of the 6th Workshop on Fixed Points in Computer Science (FICS 09), 2009

Default Reasoning with Imperfect Information in Multivalued Logics.
Proceedings of the 38th IEEE International Symposium on Multiple-Valued Logics (IEEE ISMVL 08), 2008

Imperfect Information Representation through Extended Logic Programs in Bilattices.
Book chapter. In Uncertainty and Intelligent Information Systems, B.Bouchon-Meunier, R.R. Yager, C. Marsala, and M. Rifqi (Eds.), World Scientific, ISBN 978-981-279-234-1, 2008

Reduction in Dimensions and Clustering using Risk and Return Model
Proceedings of the IEEE International Symposium on Data Mining and Information Retrieval
(IEEE DMIR07), 2007, with S.W. Qaiyumi

Representing Imperfect Information through Extended Logic Programs in Multivalued Logics.
Proceedings of the 11th biennial Conference on Information Processing and Management of Uncertainty in
Knowledge-Based Systems (IPMU 06), 2006

Assumption based Multiple-Valued Semantics for Extended Logic Programs.
Proceedings of the 36th IEEE International Symposium on Multiple-Valued Logics (IEEE ISMVL 06), 2006

Extended Deductive Databases with Uncertain Information.
Proceedings of the 7th International Conference on Enformatika Systems Sciences and Engineering, 2005

Hypothesis-based Semantics of Logic Programs in Multivalued Logics.
Journal – ACM Transactions on Computational Logic 15(3), pp. 508-527, 2004, with Y. Loyer and N. Spyratos

Parameterized Semantics for Logic Programs – a Unifying Framework.
Journal – Theoretical Computer Science 308(1-3), pp. 429-447, 2003, with Y. Loyer and N. Spyratos

Hypothesis Support for Information Integration in Four-Valued Logics.
Proceedings of IFIP International Conference on Theoretical Computer Science,
LNCS No. 1872 (Springer Verlag), pp. 536-548, 2000, with Y. Loyer and N. Spyratos

Interfacing Decision Support Systems under Incomplete Information.
International Journal Information Theories and Applications 7(1), pp. 38-48, 2000, with Y. Loyer and N.Spyratos

Integration of Information in Four-Valued Logics under Non-Uniform Assumptions.
Proceedings of the 30th IEEE International Symposium on Multiple-Valued Logics, pp. 185-191, 2000, with Y. Loyer and N. Spyratos

Hypotheses Based Semantics for Information Integration in Four-valued Logics.
Proceedings of the FICS2000 Workshop (Fixed Points in Computer Science), 2000, with Y. Loyer and N. Spyratos

Interfacing Decision Support Systems under Incomplete Information.
Proceedings of the 25th International Conference on Information and Communication Technologies
and Programming, 2000, pp. 21-31, with Y. Loyer and N. Spyratos

Computing and Comparing Semantics of Programs in Four-valued Logics.
Proceedings of the the 24th International Symposium on Mathematical Foundations of Comp. Science,
LNCS No. 1672 (Springer Verlag), pp. 59-69, 1999, with Y. Loyer and N. Spyratos

Deterministic Enforcement of Constraints.
Journal – Programming and Computer Software 24, pp. 71-83, 1998, with D. Laurent and N. Spyratos

Semantics and Containment of Queries with Internal and External conjunctions.
Proceedings of the 6th International Conference on Database Theory, LNCS No. 1186
(Springer Verlag), pp. 71-82, 1997, with G. Grahne and N. Spyratos

Multivalued Stable Semantics for Updating Databases with Uncertain Information.
Information Modelling and Knowledge Bases, VIII, IOS Press, pp. 129-144, 1997, with N. Spyratos

Semantics and Containment of Queries in Multimedia Information Systems.
Proceedings of the 2nd International Workshop on Multimedia Information Systems,
West Point, USA, pp. 82-87, 1997, with G. Grahne and N. Spyratos

A Class of Active Database Constraints.
Proceedings of the International Conference on Information Technology ’96, 1996,
with M. Halfeld Ferrari Alves, D. Laurent and N. Spyratos

Answer-Perturbation Techniques for the Protection of Statistical Databases.
Journal – Statistics and Computing 5, Springer, pp. 203-213, 1995, with H. Luchian

A General Model for the Answer-Perturbation Techniques.
Proceedings of the 7th International Working Conference on Scientific and Statistical Database
Management, pp. 90-96, 1994, with H. Luchian and B. Paechter

Statistical Protection for Statistical Databases.
Proceedings of the 6th International Working Conference on Scientific and Statistical Database
Management, Ascona, Switzerland, pp. 160-177, 1992, with H. Luchian

User-Oriented Approach for the Protection of Statistical Databases.
Scientific Annals of “Alexandru Ioan Cuza” University of Iasi, vol. 1 (Computer Science), pp. 41-55, 1992, with H. Luchian

Test d’Hypothèses pour l’Intégration d’Informations en Logique à Quatre Valeurs (In French)
Proceedings of JFPLC’2000 (Journées Francophones de Programmation Logique et Programmation
par Contraintes), Hermes, pp. 265-278, 2000, with Y. Loyer and N. Spyratos

Unification des Semantiques Usuelles de Programmes Logiques (In French)
Proceedings of JFPLC’98 (Journées Francophones de Programmation Logique et Programmation
par Contraintes), Hermes, pp. 135-150, 1998, with Y. Loyer and N. Spyratos

Bases de Données avec Informations Incertaines. Sémantique et Mises à Jour (In French)
Proceedings of JFPLC’96 (Journées Francophones de Programmation Logique et Programmation
par Contraintes), Hermes, pp. 49-63, 1996, with N. Spyratos


Applications of Multivalued Logics to Databases with Uncertain Information [In French: Applications des Logiques Multivaluées aux Bases de Données avec Informations Incertaines].
PhD Thesis in Computer Science, University of Paris-Sud, LRI (Laboratoire de Recherche en Informatique), 1999

A Branching Process Approach to Optimal Betting Strategies – Statistical Modeling and Computer Simulation Applications.
MSc Thesis in Computational Statistics, University of Iasi, Faculty of Mathematics, Computer Science Section

Data Science & Soft Computing Lab

– Our Lab organises the Data Science Talk Series at Goldsmiths, University of London
– The DSSC’s team designed the Data Science MSc Programme at Goldsmiths, University of London, launched 2014
– Summer school 23rd – 24th July 2015 : Prediction modelling and personalized medicine in psychiatric research using modern statistical methods, King’s College London
– The EVOLVE Conference, Iasi 2015:: Track: Evolving from Natural Computing and Data Mining
– The Congress of Romanian Mathematicians, 8th edition, Iasi 2015

Regular members

  • Dr Daniel Stamate, DSSC Lab Leader, Goldsmiths
  • Dr Ida Pu, Goldsmiths
  • Prof Doina Logofatu, Frankfurt University of Applied Sciences
  • Dr Mihaela Breaban, University of Iasi
  • Dr Marc Atlan
  • Dr Lahcen Ouarbya, Goldsmiths

Research and Masters students

  • Wajdi Alghamdi, PhD candidate, Goldsmiths
  • Rapheal Olaniyan, PhD candidate, Goldsmiths
  • Michael Ng, Data Science MSc student, Goldsmiths
  • Caroline Butler, Data Science MSc student, Goldsmiths

Undergraduate students

  • James Dewar, Computer Science BSc, Goldsmiths

Research Themes

  • Sentiment Analysis and Stock Market Trends Prediction
  • Data Analytics and Applications
  • Probability Distribution Forecasting and Applications in Wind Energy Forecasting and Risk Management
  • Soft Computing and Algorithms
  • Fuzzy Approaches to Imperfect Data Integration and Optimal Querying
  • Scalable Machine Learning Applications in Mining Treated Depression Patients Data

Read more Data Science & Soft Computing Lab

Christophe Rhodes


Christophe Rhodes is a lecturer in Computing, with interests in large-scale multimedia databases, applications of graph theory in humanities study, and mining social media for unexpected connections.

He combines his research and teaching with being co-founder and chair of Teclo Networks AG, a company supplying network equipment and analytics consultancy, an active classical music performance practice, and open source software maintenance and development.


Proutskova, Polina; Rhodes, Christophe; Crawford, Tim and Wiggins, Geraint. 2013. Breathy, Resonant, Pressed – Automatic Detection Of Phonation Mode From Audio Recordings of Singing. Journal of New Music Research, 42(2), pp. 171-186. ISSN 0929-8215 [Article]

Whorley, Raymond; Rhodes, Christophe; Wiggins, Geraint and Pearce, Marcus. 2013. Multiple Viewpoint Systems: Time Complexity and the Construction of Domains for Complex Musical Viewpoints in the Harmonisation Problem. Journal of New Music Research, 42(3), pp. 237-266. [Article]

Fields, Ben; Jacobson, Kurt; Rhodes, Christophe; d’Inverno, Mark; Sandler, Mark and Casey, Michael A.. 2011. Analysis and Exploitation of Musician Social Networks for Recommendation and Discovery. IEEE Transactions on Multimedia, 13(4), pp. 674-686. ISSN 1520-9210 [Article]

Rhodes, Christophe; Crawford, Tim; Casey, Michael A. and d’Inverno, Mark. 2010. Investigating music collections at different scales with Audio DB. Journal of New Music Research, 39(4), pp. 337-348. ISSN 0929-8215 [Article]

Cannam, Chris; Sandler, Mark; Jewell, Michael O; Rhodes, Christophe and d’Inverno, Mark. 2010. Linked Data and you: Bringing music research software into the Semantic Web. Journal of New Music Research, 39(4), pp. 313-325. ISSN 0929-8215 [Article]

Dixon, Simon; Sandler, Mark; d’Inverno, Mark and Rhodes, Christophe. 2010. Towards a Distributed Research Environment for Music Informatics and Computational Musicology. Journal of New Music Research, 39(4), pp. 291-294. ISSN 0929-8215 [Article]

Rhodes, Christophe. 2010. Using Lisp Implementation Internals: Unportable but fun. Journal of Universal Computer Science, 18(2), pp. 315-339. ISSN 0948-695x [Article]

Casey, Michael A.; Rhodes, Christophe and Slaney, Malcolm. 2008. Analysis of Minimum Distances in High-Dimensional Musical Spaces. IEEE Transactions on Audio, Speech, and Language Processing, 16(5), pp. 1015-1028. ISSN 1558-7916 [Article]

Casey, Michael A.; Veltkamp, R.; Goto, M.; Leman, M.; Rhodes, Christophe and Slaney, M.. 2008. Content-Based Music Information Retrieval: Current Directions and Future Challenges. Proceedings of the IEEE, 96(4), pp. 668-696. ISSN 0018-9219 [Article]

Newton, Jim and Rhodes, Christophe. 2008. Custom specializers in Object-oriented Lisp. The Journal of Universal Computer Science, 14(20), pp. 3370-3388. ISSN 0948-695X [Article]


Transforming Musicology
Transforming Musicology is funded under the AHRC Digital Transformations in the Arts and Humanities scheme. It seeks to explore how emerging technologies for working with music as sound and score can transform musicology, both as an academic discipline and as a practice outside the university. The work is being carried out collaboratively between Goldsmiths College, Queen Mary College, Oxford University, the Oxford e-Research Centre, and Lancaster University with an international partner at Utrecht University.

Teclo Networks AG
Around the world Teclo is using its knowledge and experience in TCP/IP data acceleration to help mobile operators and businesses significantly improve the speed, stability and efficiency of data delivery across networks. Performance improvement can be significant – even where latest generation hi-speed technology is being used – and the benefits, immediate.

If you are are a business looking to improve the efficiency of information delivery into and around your workspace, take a look at our Enterprise Solutions section. If you are the operator of a mobile network then go to our Mobile Operator Solutions section.

Online Music Recognition And Searching (or Ontology-driven Music Retrieval & Annotation Sharing service) is a framework for annotating and searching collections of both recorded music and digital score representations such as MIDI. The project is funded by the EPSRC.

Andy Thomason


Andy Thomason teaches the MSc in Computer Games & Entertainment at Goldsmiths, University of London. He has worked in games and graphics since the  1970s and has recently left Sony Computer Entertainment (where he developed the compilers for the Playstation) to concentrate on academic work.

Recently he has been working with Imperial college on the Bioblox project visualising protein docking and at Kings on a website visualising gene expression in brain cells.

He has shifted his teaching focus to server-based visualisation fronted by WegGL enabled web pages, which allows much more complex animated visualisations to be created than in JavaScript alone.