Dr. Peter Sykacek
Dept. of Biotechnology
Boku University, Vienna
Muthgasse 18
A-1190 Wien, AUSTRIA
Phone:
+43-1-36006-6836
Email:
peter@sykacek.net
Web: http://www.sykacek.net/
Curriculum Vitae
Dr. Peter Sykacek
Objective
Addendum to Grant Proposal
Experience
01/2006Boku University Vienna
Group Leader Analytical Methods in Computational Biology
Vienna, AT
 Group Leader in the Bioinformatics Group, Department of Biotechnology,
Boku University, Vienna. I am currently establishing a research group with a
focus on analytical methods development in computational biology. We are
specialised in the application of highly structured stochastic systems to
optimally integrated models for analysis of molecular biological data.
04/2004-12/2005
University of Cambridge
Postdoctoral Research Fellow
Cambridge, UK
 Senior statistician in Dr. Micklem’s Group in the Department of Genetic and
the Cambridge Computational Biology Institute and senior member in the
Bioinformatics Group in the Department of Pathology. The responsibilities
in this post were methods developent, co-supervision of research students
and junior staff, analytical contributions to research grants in computatopnal
biology, data analysis and statistical consulting. My methodological research
interest is to provide fully probabilistic models to improve analysis of
microarray experiments focusing on aspects how to combine information
within multi experiment studies of different biological systems.
 Visiting Research Fellow in Prof. MacKay’s Inference Group.
09/2000–09/2003
University of Oxford
Postdoctoral Research Fellow
Oxford, UK
 Senior member in the Pattern Analysis and Machine Learning Research
Group working on Oxford’s BCI project; Collaboration with Research Dept.
of Putney Hospital for Neuro-Disabilities; Data analysis for evaluating
different cognitive tasks; Implementation and evaluation of an adaptive BCI;
Research on Bayesian latent space models, fixed form variational methods
and model selection for feature subset selection (work in progress);
Implementation of prototypes in MatLab and C++; Writing of project reports
for funding agency
 Tutoring for the Department of Eng. Sci. and for Pembroke College for
computer science and electrical engineering courses (Procedural
Programming, Functional Programming, Algorithm Design, Numerical
Methods for PDE’s, Pattern Recognition, Electrical Machines,
Curriculum vitae Dr. Peter Sykacek
1
Communication, Electromagnetic Field, Computer Architecture)
 Supervising student & visiting scientist projects for the BCI and
bioinformatics problems
03/1996–08/2003
Austrian Research Institute for AI
Postgraduate Research Assistant
Vienna, AT
 Researcher on two European sleep analysis projects; Collaboration with 16
European partners; Managing responsibility for the feature subset selection
task and for the implementation of the prototype sleep analyser; Research on
Bayesian classification and time series models; Implementations in MatLab
and C++; Major contribution to a funded research proposal; Writing of
project reports for funding agencies; Supervising student projects
 Lecturer at the University of Vienna (03/2000–06/2000) with a course on
Bayesian networks
03/1995–02/1996
Systems Engineer
Frequentis
Vienna, AT
 Software development for air traffic control equipment; Software
engineering in an ISO 9000 certified environment according to the German
V-model; Implementations in C in a real time environment
 Technical liaison with project partners and customers
09/1990–03/1999
Software Trainer
Wifi-Wien
Vienna, AT
 Software trainer (part time) for programming languages (Pascal, C, C++),
operating systems (all MS platforms) and databases
06/1988–03/1989
Software Engineer
MC-Software
Vienna, AT
 Software engineer in a cash register project; Development in Pascal
 Liaison with customer during first roll out
Education
10/1996–06/2000
Technical University of Vienna
Vienna, AT
 PhD in Computer Science / AI (part time)
 Thesis “Bayesian Inference for Reliable Biomedical Signal Processing”
 Graduated with a 1.0 equivalent degree (with distinction)
Since 1997
various institutions
 Three seminars at the Isaac Newton Institute for Mathematical Sciences,
Cambridge UK
 About two visits to scientific conferences and workshops per year
10/1984–03/1995
Technical University of Vienna
Vienna, AT
 Dipl Ing. (MSc.) in Electrical Engineering / Control (part time)
 Thesis “Parameter Optimisation for Fuzzy Control” (in German)
 Graduated with a 2.1 equivalent degree.
Skills
Teaching
 About 9 years of experience teaching computer science subjects in a further
Curriculum vitae Dr. Peter Sykacek
2
education institution
 About 3 years of teaching experience on academic level in German and
English
 Experienced supervisor of student projects and thesis research
Research & Project Development
 About 10 years of research experience in Bayesian methods for machine
learning and signal processing
 Writing of research proposals and project reports for funding agencies
 Industrial experience
 Task management skills
Interests
Organising scientific workshops and seminars; Peer reviewing for several
European research councils, for scientific journals and conferences; Sports
(running, hiking, experienced ski instructor, wind surfing)
Personal
Date of Birth: 12/05/65
Marital status: married, one child
Publications1
Journal publications
(Sykacek et al. 2005)
P. Sykacek, R. Furlong and G. Micklem. A Friendly Statistics Package
for Microarray Analysis, In Bioinformatics, 21(21). 4096-4070, 2005.
(Mukherjee et al. 2004)
S.N. Mukherjee, P. Sykacek, S.J. Roberts and S.J. Gurr. Gene Ranking
Using Bootstrapped P-values. In ACM SIGKDD Explorations, Volume
5, Issue 2, Special Issue on Microarray Data Mining, 2004.
(Sykacek et al. 2004)
P. Sykacek, S. J. Roberts and M. Stokes. Adaptive BCI based on
variational Bayesian Kalman filtering: an empirical evaluation. In
IEEE Trans. Biomedical Engineering, 51(5). 719-729, 2004.
(Curran et al. 2004)
E. Curran, P. Sykacek, M. Stokes, S. J. Roberts, W. Penny, I.
Johnsrude and A. M. Owen. Cognitive tasks for driving a Brain
Compute Interfacing System. In IEEE Trans. Neural Systems and
Rehabilitation Engineering, 12 (1): 48-57, 2004.
(Sykacek et al. 2003)
P. Sykacek, S. J. Roberts, M. Stokes, E. Curran, M. Gibbs and L.
Pickup. Probabilistic methods in BCI research. In IEEE Trans. Neural
Systems and Rehabilitation Engineering, pages 192-195, 2003.
(Sykacek et al. 2002)
P. Sykacek, G. Dorffner, P. Rappelsberger and J. Zeitlhofer. Improving
bio-signal processing through modelling uncertainty: Bayes vs. nonBayes in sleep staging. Applied Artificial Intelligence, 16(5):395421,2002.
(Flexer et al. 2002)
A. Flexer, G. Dorffner, P. Sykacek, I. Rezek. An automatic,
continuous and probabilistic sleep stager based on a hidden Markov
model. Applied Artificial Intelligence, 16(3):199-207,2002.
(Rappelsberger et al. 2001)
P. Rappelsberger, E. Trenker, C. Rothman, G. Gruber, P. Sykacek, S.
1
Many preprints are available at http://www.sykacek.net/pubs.html.
Curriculum vitae Dr. Peter Sykacek
3
Roberts, G. Klösch, J. Zeitlhofer, P. Anderer, B. Saletu, A. Schlögl, A.
Värri, B. Kemp, T. Penzel, W. M. Herrmann, J. Hasan, M. J. Barbanoj,
D. Kunz, G. Dorffner. Das Projekt SIESTA. in Klinische
Neurophysiologie,32(2): pages 76-88, 2001.
(Rezek et al. 2000b)
I. Rezek, P. Sykacek and S. Roberts. Learning interaction dynamics
with coupled hidden Markov models. in IEE special issue proceedings
science measurement and technology, Vol. 147(5), pages 345-350,
2000.
Peer reviewed conference publications and book chapters
(Sykacek et al. 2004)
P. Sykacek, I. Rezek and S. J. Roberts. Bayes Consistent Classification
of EEG Data by Approximate Marginalisation. In S. J. Roberts and R.
Dybowski editors Applications of Probabilistic Models for Medical
Informatics and Bioinformatics, to appear, Springer Verlag, 2004.
(Sykacek & Roberts 2003)
P. Sykacek and S. J. Roberts. Adaptive classification by variational
Kalman filtering. In S.Thrun, S. Becker and K. Obermayer, editors,
Advances in Neural Information Processing Systems 15, pages 737744, MIT press, 2003.
(Rezek et al. 2002)
I. Rezek, S. J. Roberts and P. Sykacek,(2002). Ensemble Coupled
Hidden Markov Models for Joint Characterisation of Dynamic Signals.
Ninth International Workshop on Artificial Intelligence and Statistics
2003.
(Sykacek & Roberts 2002)
P. Sykacek and S. J. Roberts. Bayesian time series classification. In T.
G. Dietterich, S. Becker and Z. Ghahramani, editors, Advances in
Neural Information Processing Systems 14, 937-944, MIT press, 2002.
(Sykacek et al. 2001)
P. Sykacek, S. J. Roberts, I. Rezek, A. Flexer and G. Dorffner. A
probabilistic approach to high resolution sleep analysis. In G. Dorffner,
K. Hornik and H. Bischof, editors, Proceedings of the International
Conference on Neural Networks (ICANN), pages 617-624, Springer
Verlag, 2001.
(Dorffner et al. 2000)
G. Dorffner, P. Sykacek and C. Schittenkopf. Modelling Uncertainty in
Biomedical Applications of Neural Networks. in Proceedings of
Artificial Neural Networks in Medicine and Biology 1, Göteborg,
Sweden, pages 18-26, Springer Verlag, 2000.
(Flexer et al. 2000)
A. Flexer, P. Sykacek, I. Rezek and G. Dorffner. Using hidden Markov
models to build an automatic, continuous and probabilistic stager, in S.
I. Amari etal., Proceedings of the IEEE-INNS-ENNS International
Joint Conference on Neural Networks, IJCNN 2000, Como Italy, IEEE
Computer Society, Vol. III, 627-631, 2000.
(Rezek et al. 2000a)
I. Rezek, P. Sykacek and S. Roberts. Coupled hidden Markov models
for biosignal interaction modelling. in Proceedings of Medsip-2000,
International Conference on Advances in Medical Signal and
Information Processing, pages 672-679, 2000.
(Sykacek 2000)
P. Sykacek.. On input selection with reversible jump Markov chain
Monte Carlo sampling. in S. A. Solla and T. K. Leen and K. R. Müller
editors, Advances in Neural Information Processing Systems 12, pages
638-644, MIT press, 2000.
Curriculum vitae Dr. Peter Sykacek
4
(Sykacek 1998)
P. Sykacek. Outliers and Bayesian Inference. in M. Heiss editor,
Proceedings of NC 98 Vienna, pages 973-978, 1998.
(Cristianini et al. 1998)
N. Cristianini, J. Shawe-Taylor and P. Sykacek. Bayesian Classifiers
are Large Margin Hyperplanes in a Hilbert Space. in Proc. ICML 98,
pages 109-117, 1998.
(Sykacek et al. 1998)
P. Sykacek, G. Dorffner, P. Rappelsberger and J. Zeitlhofer.
Experiences with Bayesian learning in a real world application. in M.
I. Jordan and M. J. Kearns and S. Solla editors, Advances in Neural
Information Processing Systems 10, pages 964-970, 1998.
(Sykacek 1997)
P. Sykacek. Equivalent error bars for neural network classifiers trained
by Bayesian inference. in Proceedings of the European Symposium on
Artificial Neural Networks (Bruges, 1997), pages 121-126, 1997.
Curriculum vitae Dr. Peter Sykacek
5