Dragomir Yankov

Ph. D. (Mar, 2008)

dyankovcs.ucr.edu
 

Department of Computer Science & Engineering

University of California, Riverside

Engineering Building Unit 2, Room 351

Riverside, CA 92521

 

Advisor: Dr. Eamonn Keogh
  Resume
 
  Dragomir_Yankov.pdf
  Research Interests
 
 

My areas of interest include Data Mining and Machine Learning in general. More specifically I am interested in their application for time series analysis. Some sub-areas in which I have worked and I am particularly excited about are:
 
  • Mining motifs and discords from large time series data sets
  • Manifold learning from high dimensional data (time series in particular) and in the presence of noise
  • Nonlinear dimensionality reduction techniques, again with focus on time series data
  • Nearest neighbor approaches for classification, anomaly detection and forecasting
  • Ensemble methods
  Dissertation
 
  Learning from Time Series in the Presence of Noise: Unsupervised and Semi-supervised Approaches [pdf]
  List of publications
 
 
Best Paper Award: D. Yankov, E. Keogh, U. Rebbapragada: Disk aware discord discovery: finding unusual time series in terabyte sized datasets. Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007) [Powerpoint]
 
D. Yankov, E. Keogh, K. Kan: Locally constrained Support Vector Clustering. Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007) [ Powerpoint]
 
D. Yankov, E. Keogh, J. Medina, B. Chiu, V. Zordan: Detecting time series motifs under uniform scaling. Proceedings of the 13th ACM SIGKDD international conference on Knowledge Discovery and Data mining (KDD 2007), p. 844-853 [Powerpoint]
 
D. Yankov, E. Keogh, L. Wei, X. Xi: Fast best-match shape searching in rotation invariant metric spaces. Proceedings of the 7th SIAM International Conference on Data Mining (SDM 2007)
 

D. Yankov, E. Keogh, L. Wei, X. Xi: Fast best-match shape searching in rotation invariant metric spaces (extended version). To appear in IEEE Transactions on Multimedia, Special Issue on Data Mining, 2007
 
D. Yankov, E. Keogh: Manifold clustering of shapes. Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), p. 1167-1171 [Powerpoint]
 
D. Yankov, D. DeCoste, E. Keogh: Ensembles of nearest neighbors forecasts. 17th European Conference on Machine Learning (ECML 2006), Proceedings. Lecture Notes in Computer Science, p. 545-556 [Powerpoint]
 
D. Yankov, E. Keogh, S. Lonardi, A. Fu: Dot plots for time series analysis. 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2005), p. 159-168 [Powerpoint]
  Projects
 
   
  A list for some of the projects that I have completed during the last few years can be found here.