SLOBODAN VUCETIC


Professor and Chair

Department of Computer and Information Sciences
Temple University

Address:

1925 N. 12th St., 304A SERC
Philadelphia, PA 19122-6094

Telephone: (215) 204-5535
Fax: (215) 204-5082
Email: vucetic ( at ) temple.edu
Office: 314 SERC


Resume


News: Ranking of the U.S. Computer Science Doctoral Programs Our new study revealed that U.S. News ranking of CS doctoral programs, which is based purely on peer assessment, is surprisingly highly correlated with faculty citations. Our resulting ranking is available here.


Research Interests

Research Areas:

Data Science

Machine Learning

Big Data

I am interested in solving real-life knowledge discovery problems through development of novel machine learning algorithms. My research is driven by open data science problems in a wide array of disciplines such as Public Health, Medicine, Biology, Geosciences, Education, Marketing, Social Sciences, Traffic Engineering, and Industrial Engineering. 

Research Highlights:

-         CAREER award from National Science Foundation

-         Outstanding paper at IJCAI 2013: Best paper in the AI and Computational Sustainability Track

-         Leader of one of the top performing teams at CAFA 2010-12 and CAFA 2013-14 (Critical Assessment of Protein Function Annotation)

-         Member of a team with the best protein disorder predictor at CASP 5, CASP 6, and CASP 7 (Protein Structure Prediction Assessment)

Representative Funded Research Projects:

-        Deep Learning for Representation of Medical Claims (funded by the National Institutes of Health)    

-        Space-Time Models for Health Geographic Analysis (funded by the National Science Foundation)

-       Customizing Therapy for Individuals with Autism (funded by the National Science Foundation)

-       Discriminative Modeling of Spatial-Temporal Data in Remote Sensing (funded by the National Science Foundation)

-       Dynamic Evolution of Smart-Phone Based Emergency Communications Network (funded by the National Science Foundation)

-       Computational Advertising (supported by Yahoo! Faculty Research and Engagement Program)

-        Memory-Constrained Predictive Data Mining (funded by the National Science Foundation)

-        Machine Learning for Distributed Fault Diagnosis (supported by ExxonMobil)

-        Bioinformatics - Genomics, Analysis of Protein Disorder, Proteomics, Biomedical Text Mining (funded by Pennsylvania Department of Health, NIH

Program Committee member (in 2017):

-       34nd International Conference on Machine Learning (ICML)

-       23st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

-       20th International Conference on Artificial Intelligence and Statistics (AISTATS)

-       31th AAAI Conference on Artificial Intelligence (AAAI)

-       26th International Joint Conference on Artificial Intelligence (IJCAI)

-       (reviewer) 2017 Neural Information Processing Systems Conference (NIPS)

-       2017 IEEE International Conference on Data Mining 2017 (ICDM)

-       26th ACM International Conference on Information and Knowledge Management (CIKM)

-       2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

-       2017 IEEE International Conference on Big Data (BigData)


Recent Representative Publications (check the complete list with preprints)

-       Djuric, N., Kansakar, L., Vucetic, S., Semi-Supervised Combination of Experts for Aerosol Optical Depth Estimation, Artificial Intelligence Journal (AIJ), 2016.

-       Zhang, S., Vucetic, S., Sampling Bias in LinkedIn: A Case Study, International World Wide Web Conference (WWW), Montreal, Canada, 2016.

-       Zhang, K., Lan, L., Kwok, T.J., Vucetic, S., Parvim, B., Large Scale Semi-Supervised Learning via Sparse Nonparametric Prototype Model, IEEE Transactions on Neural Networks and Learning Systems, 2015.

-       Lan, L., Malbasa, V., Vucetic, S., Spatial Scan for Disease Mapping on a Mobile Population, AAAI Conference on Artificial Intelligence (AAAI), Quebec City, Canada, 2014.

-       Djuric, N., Grbovic, M., Radosavljevic, V., Bhamidipati, N., Vucetic, S., Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests, 28th AAAI Conference on Artificial Intelligence (AAAI), Quebec City, Canada, 2014.

-       Grbovic M., Vucetic S., Generating Ad Targeting Rules using Sparse Principal Component Analysis with Constraints, International World Wide Web Conference (WWW), Seoul, South Korea, 2014.

-       Coric, V., Djuric, N., Vucetic, S., Frugal Traffic Monitoring with Autonomous Participatory Sensing, SIAM Conference on Data Mining (SDM), Philadelphia, PA, 2014.

-       Djuric, N., Lan, L., Vucetic, S., Wang, Z., BudgetedSVM: A Toolbox for Scalable SVM Approximations, Journal of Machine Learning Research (JMLR), 2013. BudgetedSVM web site

-       Djuric, N., Vucetic, S., Efficient Visualization of Large-scale Data Tables through Reordering and Entropy Minimization, IEEE International Conference on Data Mining (ICDM), Dallas, TX, 2013.

-       Djuric, N., Kansakar, L., Vucetic, S., Semi-Supervised Learning for Integration of Aerosol Predictions from Multiple Satellite Instruments, International Joint Conference on Artificial Intelligence (IJCAI),, Beijing, China, 2013. (Outstanding IJCAI paper: the best paper in the AI and Computational Sustainability Track)

-       Grbovic, M., Djuric, N., Vucetic, S., Multi-Prototype Label Ranking with Novel Pairwise to Total Rank Aggregation, 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), 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, 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 one of the top performing teams; we are among co-authors of a joint CAFA paper: Radivojac, P., ..., Lan, L., Djuric, N., Guo, Y., Vucetic, S., ... 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.

-       Grbovic, M., Djuric N., Vucetic S., Supervised Clustering of Label Ranking Data, SIAM Conf. on Data Mining (SDM), Anaheim, CA, 2012 (Best of SDM 2012: a top 10 paper).

-       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:

Shanshan Zhang (Ph.D.)

Tian Bai (Ph.D.)

Aniruddha Maiti (Ph.D.)

Maxim Shapovalov (Ph.D.)

Ashis Chanda (Ph.D.)

Saman Enayati (Ph.D.)

Ziyu Yang (Ph.D.)

Graduated Ph.D. Students:

Vladimir Coric (Ph.D., 2014, first position: Lead Data Scientist, SEI Investments)

Nemanja Djuric (Ph.D., 2013, first position: Research Scientist, Yahoo! Labs, current: Uber ATC)

Liang Lan (Ph.D., 2012, first position: Research Scientist, Huawei Noah Ark Lab, current: Institute for Infocomm Research)

Mihajlo Grbovic (Ph.D., 2012, first position: Research Scientist, Yahoo! Labs, current: Airbnb)

Vuk Malbasa (Ph.D., 2011, first position: Postdoctoral Associate, Texas A&M University, current: University of Novi Sad)

Zhuang Wang (Ph.D., 2010, first position: Research Scientist, Siemens Corporate Research, current: Facebook)

For Prospective Students:

I am interested in mentoring highly motivated graduate students interested in machine learning and data science. I am looking both at the traditional CS students and at the non-CS students with a strong background in mathematics (with degrees such as electrical engineering, physics, operations research, applied math, statistics). 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.


Recent Teaching

Spring 2017: Principles of Data Science (CIS 3715)

Fall 2015: Machine Learning (CIS 5526)

Fall 2015: Math Concepts in Computing I (CIS 1166)

Spring 2015: Principles of Data Science (CIS 3715)

Spring 2015: Math Concepts in Computing II (CIS 2166)

Fall 2014: Machine Learning (CIS 5526)

Fall 2013: Introduction to Computer Science (CIS1001)

Fall 2013: Machine Learning (CIS 5526)

Spring 2013: Introduction to Computer Science (CIS1001)

Spring 2013: Mathematical Concepts in Computing II (CIS2166)