Alexei Pozdnoukhov

Position: Lecturer Photograph of Alexei Pozdnoukhov    
Office: TF12, 3rd Floor, John Hume Building
Telephone: +353 (0) 1 708 6146
Fax: +353 (0) 1 708 6456
email: Alexei.Pozdnoukhov@nuim.ie
Research Areas: Machine learning, geocomputation, sensor networks data processing, remote sensing and pattern recognition
Other Keywords: kernel methods, semi-supervised learning, novelty detection
Background: Alexei received a Ph.D. in computer science from EPFL, Switzerland, following his research in machine learning methods and computer vision that he carried out at IDIAP Research Institute in Martigny, Switzerland. He then joined the Institute of Geomatics and Analysis of Risk (IGAR), University of Lausanne, where he was developing approaches to apply machine learning techniques for geospatial data analysis.
Research: Alexei is interested in algorithms that can learn from empirical data. He is developing machine learning based approaches to process data streams from geo-refferenced sensors, detect unusual events, optimise the monitoring network and make predictions in time and space.

With Fergal Walsh, we are developing i2maps: a modular software system to automate collection, analysis and visualization of spatio-temporal data streams in real time. This system simplifies data handling and pre-processing and allows for fast prototyping, development and validation of intelligent data modelling methods and approaches.

Sample Embedding of IM communication network A variety of geospatial data analysis studies are being undertaken with this system, including statistical weather now-casting, dynamic mapping of GSM and 3G mobile phones usage, traffic intensity and congestion impact on the transportation system, world-wide instant messaging activity, etc. We develop novel data analysis methods, like the recent semi-supervised neural network-based functional embedding applied to real-time data on worldwide instant messaging on the internet.
Recent Publications: Pozdnoukhov A., Dynamic network data exploration through semi-supervised functional embedding. Proc. Of the 17th ACM SIGSPATIAL GIS ISBN:978-1-60558-649-6 2009. Link; PDF

Pozdnoukhov A., Foresti L. and Kanevski M., Data-driven topo-climatic mapping with machine learning algorithms. Natural Hazards Journal, Volume 50, Issue 3, pp. 497-518. 2009. Link; PDF

Tuia D., Ratle F., Pozdnoukhov A., Camps-Valls G. Multisource Composite Kernels for Urban-Image Classification. IEEE Geoscience and Remote Sensing Letters, In press, 2010. Abstract; PDF

Demyanov V., Pozdnoukhov A., Christie M., Kanevski M., Detection of Optimal Models in High Dimensional Parameter Space with Support Vector Machines. Springer Series: Quantitative Geology and Geostatistics; ISBN: 978-90-481-2321-6 2010. Link
Book
Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning Algorithms for Geospatial Data. Theory, Applications and Software. 377pp. EPFL press, 2009. Machine Learning for Spatial Environmental Data
Other publications

Pozdnoukhov A., Purves R.S., Kanevski M. Applying Machine Learning Methods to Avalanche Forecasting. Annals of Glaciology, vol. 49., pp. 107-113, 2008.

Pozdnoukhov A., Kanevski M. Multi-Scale Support vector Regression for hot spot detection and modeling. In Stochastic Environmental Research and Risk Assessment (SERRA), DOI 10.1007/s00477-007-0162-x, 14 pp., 2007.

Pozdnoukhov A., Bengio S. From Samples to Objects: Invariances in Kernel Methods. Pattern Recognition Letters Journal, Volume 27, Issue 10, pp. 1087-1097. 2006.

Pozdnoukhov A., Kanevski M. Monitoring Network Optimisation for Spatial Data Classification Using Support Vector Machines. Int. Journal of Environment and Pollution. Vol.28. 20 pp., 2006.
 
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