SLOBODAN VUCETIC

Associate Professor

Address:
Department of Computer and Information Sciences
Center for Data Analytics and Biomedical Informatics
Temple University
1805 N. Broad Street, 304 Wachman Hall
Philadelphia, PA 19122-6094

Telephone: (215) 204-5535
Fax: (215) 204-5082
Email: vucetic (at) ist.temple.edu
Office: 304 Wachman Hall

Resume

Research Interests

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 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 biochemistry, genomics, medicine, geosciences, traffic and industrial engineering, sociology, marketing, and finance.

Recent Research Projects:

- CAREER award from National Science Foundation: 0546155 "CAREER: Memory-Constrained Predictive Data Mining"

- 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)

Recent Selected Publications (check the complete list)

- 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, 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.

- Wang, Z., Crammer, K., Vucetic, S., Multi-Class Pegasos on a Budget, Int. Conf. on Machine Learning (ICML), Haifa, Israel, 2010.

- Radosavljevic, V., Vucetic., S., Obradovic, Z., Continuous Conditional Random Fields for Regression in Remote Sensing, European Conference on Artificial Intelligence (ECAI), Lisbon, Portugal, 2010.

- Wang, Z., Vucetic, S., Online Passive-Aggressive Algorithms on a Budget, JMLR W&C Proc. Int. Conf. on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy, 2010.

Students

Current Students:

Mihajlo Grbovic (Ph.D.)

Liang Lan (Ph.D.)

Vladimir Coric (Ph.D.)

Nemanja Djuric (Ph.D.)

Yi Jia (Ph.D.)

Lakesh Kansakar (Ph.D.)

Freshly graduated:

Vuk Malbasa (Ph.D.)

Zhuang Wang (Ph.D.)

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.