Spatio-temporal data mining
I am interested in solving real-life knowledge discovery and statistical analysis problems through development of novel machine learning algorithms. My research focuses on efficient learning and knowledge discovery from large-scale heterogeneous, multi-source, biased, dependent, distributed, and online data. It is driven by open big-data problems in disciplines such as genomics, medicine, geosciences, traffic engineering, industrial engineering, marketing, and finance.
Representative Funded Research Projects:
- A Discriminative Modeling Framework for Mining of Spatio-Temporal Data in Remote Sensing (funded by National Science Foundation)
- Machine Learning for Distributed Fault Diagnosis (funded by ExxonMobil)
- Bioinformatics - Microarray Data Analysis, Analysis of Protein Disorder, Proteomics, Biomedical Text Mining (funded by Pennsylvania Department of Health, NIH)
PC member/reviewer (in 2013):
- 30th International Conference on Machine Learning (ICML), Atlanta, GA, USA, June 16-21, 2013.
- 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Chicago, IL, USA, August 11-14, 2013.
- 16th International Conference on Artificial Intelligence and Statistics (AISTATS), Scottsdale, AZ, USA, April 29 - May 1, 2013.
- 2013 IEEE International Conference on Big Data, Silicon Valley, CA, USA, October 6-9, 2013.
- 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, Australia, July 21-26, 2013.
- 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shanghai, China, December 18-21, 2013.
Recent Selected Publications (check the complete list)
- Djuric, N., Kansakar, L., Vucetic, S., Semi-Supervised Learning for Integration of Aerosol Predictions from Multiple Satellite Instruments, 23rd International Joint Conference on Artificial Intelligence (IJCAI),, Beijing, China, 2013.
- Grbovic, M., Djuric, N., Vucetic, S., Multi-Prototype Label Ranking with Novel Pairwise to Total Rank Aggregation, 23rd International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, 2013.
- Ristovski, K., Radosavljevic, V., Vucetic, S., Obradovic, Z., Continuous Conditional Random Fields for Efficient Regression in Large Fully Connected Graphs, 27th AAAI Conference on Artificial Intelligence (AAAI), Bellevue, WA, 2013.
- Grbovic M., Djuric, N., Guo, S., Vucetic S., Supervised Clustering of Label Ranking Data using Label Preference Information, Machine Learning Journal (MLJ), in press, 2013.
- Grbovic M., Li W., Subrahmanya N. A., Usadi A. K., Vucetic S., Cold Start Approach for Data Driven Fault Detection, IEEE Transactions on Industrial Informatics, in press, 2013.
- Lan, L., Djuric, N., Guo, Y., Vucetic, S., MS-kNN: Protein Function Prediction by Integrating Multiple Data Sources, BMC Bioinformatics, 2013. (reporting about our CAFA/AFP 2011 participation where we were a top performing team; we are among co-authors of a joint CAFA paper Radivojac, P., ..., Lan, L., Djuric, N., Guo, Y., Vucetic, S., et al., Friedberg, I., A Large-scale Evaluation of Computational Protein Function Prediction, Nature Methods, 2013.)
- Wang, Z., Crammer, K., Vucetic, S., Breaking the Curse of Kernelization: Budgeted Stochastic Gradient Descent for Large-Scale SVM Training, Journal of Machine Learning Research (JMLR), 2012.
- Grbovic, M., Dance, C., Vucetic, S., Sparse Principal Component Analysis with Constraints, AAAI Conference on Artificial Intelligence (AAAI), Toronto, Canada, 2012.
- Djuric, N., Grbovic, M., Vucetic, S., Convex Kernelized Sorting, AAAI Conference on Artificial Intelligence (AAAI), Toronto, Canada, 2012.
- Wang, Z., Lan, L. Vucetic, S., Mixture Model for Multiple Instance Regression and Applications in Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing (TGARS), 2012.
- Ristovski, K., Vucetic, S., Obradovic, Z., Uncertainty Analysis of Neural Network-Based Aerosol Retrieval, IEEE Transactions on Geoscience and Remote Sensing (TGARS), 2012.
- Wang, Z., Djuric, N., Crammer, K. Vucetic, S., Trading Representability for Scalability: Adaptive Multi-Hyperplane Machine for Nonlinear Classification, ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), San Diego, CA, 2011. (link to software)
- Malbasa, V., Vucetic., S., Spatially Regularized Logistic Regression for Disease Mapping on Large Moving Population, ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), San Diego, CA, 2011.
Liang Lan (Ph.D.)
Nemanja Djuric (Ph.D.)
Vladimir Coric (Ph.D.)
Yi Jia (Ph.D.)
Lakesh Kansakar (Ph.D.)
Mihajlo Grbovic (Ph.D., 2012, first position: Research Scientist, Yahoo!)
Vuk Malbasa (Ph.D., 2011, first position: Postdoc, Texas A&M)
Zhuang Wang (Ph.D., 2010, first position: Research Scientist, Siemens)
For Prospective Students:
Research assistantships are available for highly qualified and motivated graduate students interested in machine learning and data mining. Interested students are encouraged to contact me by e-mail. Please, send me your CV and write a short description about your academic background and research interests.
Spring 2013: Introduction to Computer Science (CIS1001)
Spring 2013: Mathematical Concepts in Computing II (CIS2166)
Fall 2012: Introduction to Computer Science (CIS1001)
Spring 2012: Introduction to Computer Science (CIS1001)
Spring 2012: Mathematical Concepts in Computing II (CIS2166)
Fall 2011: Machine Learning (CIS8526)
Spring 2011: Data Mining (CIS4350).
Fall 2010: Machine Learning (CIS8526)
Spring 2010: Introduction to Computer Science (CIS1001)
Fall 2009: Machine Learning (CIS8526).
Spring 2009: Introduction to Computer Science (CIS1001)
Spring 2009: Paradigms of Scientific Knowledge