Alexey Pozdnukhov

 http://scholar.google.com/citations?user=ORd-2JoAAAAJ&hl=en

  • Journal Papers
  • Batty M., Axhausen K., Giannotti F., Pozdnoukhov A., Bazzani A., Wachowicz M., Ouzonis G., and Portugali Y. Smart Cities of the Future. To appear in European Physical Journal Special Topics (accepted) (2012).
  • Kaiser C., Pozdnouhkov A. Enabling Real-time City Sensing with Kernel Stream Oracles and MapReduce. To appear in Pervasive and Mobile Computing (accepted), (2012).
  • Foresti L., Kanevski M. and Pozdnoukhov A. Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weather radar. IEEE Transactions on Geoscience and Remote Sensing, 50(8):2954-2967 (2012).
  • Foresti L., Pozdnoukhov A. Exploration of Alpine orographic precipitation patterns with radar image processing and clustering techniques. Meteorological Applications, DOI:10.1002/met.272 (2011).
  • Pozdnoukhov, A., Matasci, G., Kanevski, M., and Purves, R.S. Spatio-temporal avalanche forecasting with Support Vector Machines. Nat. Hazards Earth Syst. Sci., 11, 367-382 (2011).
  • Foresti L., Tuia D., Kanevski M., Pozdnoukhov A. (2010) Learning wind fields with multiple kernels.  Stochastic Environmental Research and Risk Assessment. ISSN 1436-3240, pp.1-16.
  • Tuia D., Ratle F., Pozdnoukhov A., Camps-Valls G. (2010), Multi-source composite kernels for urban image classification, IEEE Geoscience and Remote Sensing Letters. Vol. 7(1), pp 88-92.
  • Pozdnoukhov A., Foresti L. and Kanevski M., (2009) Data-driven topo-climatic mapping with machine learning algorithms. Natural Hazards Journal, Vol. 50, Number 3, pp. 497-518.
  • Pozdnoukhov A., Purves R.S., Kanevski M.. Applying Machine Learning Methods to Avalanche Forecasting. Annals of Glaciology, Vol.49, Number 1, pp. 107-113(7), 2008.
  • Buryak A., Zaubitzer F., Pozdnoukhov A. and Severin K., Indicator Displacement Assays as Molecular Timers J. of the American Chemical Society, 130, 11260-11261 (2008).
  • Buryak A., Pozdnoukhov A. and Severin K., Pattern-Based Sensing of Nucleotides in Aqueous Solution with a Multicomponent Indicator Displacement Assay. Chemical Communications, (23), 2366-2368 (2007).
  • Pozdnoukhov A., Kanevski M. Multi-scale Support Vector Regression for hot spot detection and Modelling. Stochastic Environmental Research and Risk Assessment, DOI 10.1007/s00477-007-0162-x, 14 pp., 2007.
  • Kanevski M., Pozdnoukhov A., Timonin V., Maignan M. Mapping of environmental data using kernel-based methods. Revue Internationale de Géomatique. Volume 17, Numbers 3-4, pp.309-331 (2007).
  • Pozdnoukhov A., Bengio, S., From Samples to Objects: Invariances in Kernel Methods. In 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.
  • Pozdnoukhov A. Support Vector Regression for Automated Robust Spatial Mapping of Natural Radioactivity. Applied GIS, Vol. 1, Num. 2, pp. 2101-2110, 2005.
  • Kanevski M., Parkin R., Pozdnukhov A., Timonin V., Maignan M., Demyanov V. and Canu S., Environmental data mining and modelling based on machine learning algorithms and geostatistics, Environmental Modelling & Software 19 (2004) (9), pp. 845–856.
  • Kanevski M., Pozdnukhov A., Canu S., Maignan M. Advanced Spatial Data Analysis and Modelling with Support Vector Machines.  International Journal of Fuzzy Systems, Vol. 4, No. 1, pp. 606-616, 2002.

Peer-reviewed Conference Proceedings Papers (acceptance rate, %)

  • Kling F., Pozdnoukhov A., When a City Tells a Story: Urban Topic Analysis, To appear in Proc. of the 20th ACM SIGSPATIAL GIS, 2012.
  • McArdle G., Furey E., Lawlor A., Pozdnoukhov A., City-scale Traffic Simulation From Digital Footprints, UrbComp'12 at ACM SIGKDD, 2012.
  • Lawlor A., Coffey C., McGrath R., Pozdnoukhov A., Stratification structure of urban habitats, Pervasive Urban Apps at PERVASIVE'2012, 2012.
  • Pozdnoukhov A., Kaiser C. Scalable Local Regression for Spatial Analytics, Proc. Of the 19th ACM SIGSPATIAL GIS'2011 (2011) (22%).
  • Pozdnoukhov A., Kaiser C. Space-Time Dynamics of Topics in Streaming Text, Location Based Social Networks workshop at 19th ACM SIGSPATIAL GIS'2011 (30%) (2011) Best Paper Award.
  • Pozdnoukhov A., Kaiser C. Area-to-point Kernel Regression on Streaming Data, Geostreaming workshop at 19th ACM SIGSPATIAL GIS'2011 (2011).
  • Coffey C., Pozdnoukhov A., Calabrese F. Time of Arrival Predictability Horizons for Public Bus Routes, CTS workshop at 19th ACM SIGSPATIAL GIS'2011 (2011).
  • Walsh F., Pozdnoukhov A., Spatial structure and dynamics of urban communities, Pervasive Urban Applications workshop at PERVASIVE'2011 (2011) (25%).
  • Pozdnoukhov A. (2010), Spatial extensions to kernel methods, Proc. of the 18th ACM SIGSPATIAL GIS, 2010 (poster, acceptance rate 26%)
  • Kaiser C., Walsh F., Farmer C. and Pozdnoukhov A., (2010) User-centric time-distance representation of road networks. In Springer LNCS proc. of the GIScience’10 (oral, acceptance rate 25%).
  • Pozdnoukhov A. and Walsh F. (2010) Exploratory novelty identification in human activity data streams, GeoStreaming workshop at 18th ACM SIGSPATIAL GIS, 2010 (oral, acceptance rate 45%).
  • Foresti L., Kanevski M., and Pozdnoukhov A. Data-driven exploration of orographic enhancement of precipitation. European Meteorological Society annual meeting (solicited, 2010).
  • Pozdnoukhov A., Dynamic network data exploration through semi-supervised functional embedding. Proc. of the 17th ACM SIGSPATIAL GIS, 2009. (oral, acceptance rate 20%).
  • Foresti L., Tuia D., Pozdnoukhov A., and Kanevski M. (2009) Multiple Kernel Learning of Environmental Data. Case Study: Analysis and Mapping of Wind Fields. In proc. of ICANN’2009, LNCS, Springer, Volume 5769, pp.933-943. (oral, acceptance rate 40%)
  • Tuia, D., Ratle, F., Pacifici, F., Pozdnoukhov, A., Kanevski, M., Del Frate, F., Solimini, D., Emery, W.J. (2008), Active learning for very-high resolution optical imagery with SVMs, IEEE Geosciences and Remote Sensing Symposium IGARSS 2008, Boston, USA.
  • Tuia, D., Pacifici, F., Pozdnoukhov, A., Kaiser, C., Solimini, D., Emery, W.J. (2008), Very-high resolution image classification using morphological operators and SVM, IEEE Geosciences and Remote Sensing Symposium IGARSS 2008, Boston, USA. 
  • Pozdnoukhov A., Bengio S., Graph-based Transformation Manifolds for Invariant Pattern Recognition with Kernel Methods. Int Conf on Pattern Recognition, Hong Kong, 2006 (acceptance rate 34%).
  • Pozdnoukhov A., Bengio S., Semi-Supervised Kernel Methods for Regression Estimation. In proc. of Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP, finalist of Best Student Paper contest), Toulouse, France, 2006 (oral, acceptance rate 15%).
  • Pozdnoukhov A., Bengio S., Tangent Vector Kernels for Invariant Image Classification with SVMs. In proc. of Int. Conf. on Pattern Recognition (ICPR), Cambridge, UK, 2004 (oral, acceptance rate 15%).

Book

  • Kanevski M., Pozdnoukhov A., and Timonin V. Machine Learning Algorithms for Environmental Spatial Data. Theory, Applications and Software. ISBN: 978-2-940222-24-7, 368 p., EPFL press, 2009.