SLOBODAN VUCETIC
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Resume
Research Interests
Big data
Machine learning
Data analytics
Spatio-temporal data mining
Bioinformatics
Biomedical informatics
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)
- CAREER award from National Science Foundation: 0546155 "CAREER: Memory-Constrained Predictive Data Mining"
- 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.
Students
Current Students:
Liang Lan (Ph.D.)
Nemanja Djuric (Ph.D.)
Vladimir Coric (Ph.D.)
Yi Jia (Ph.D.)
Lakesh Kansakar (Ph.D.)
Recently graduated:
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.
Teaching (since 2009)
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