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Slobodan Vucetic

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Slobodan Vucetic

  • College of Science and Technology

    • Computer and Information Sciences

      • Professor

Biography

RESEARCH INTERESTS
Machine Learning (Structured Learning, Representation Learning, Spatial-Temporal Data Analysis,
Online Learning, Sequential Pattern Mining, Hierarchical Bayes Models, Clustering, Feature Selection,
Dimensionality Reduction, Label Ranking, Semi-Supervised Learning, Multi-Task Learning, Multi-Label
Learning, Multi-Instance Learning, Multi-Source Learning, Support Vector Machines, Boosting, Neural
Networks, Regularization, Biased Data, Missing and Corrupted Data, Class Imbalance)
Data Mining Applications (Remote Sensing, Intelligent Transportation Systems, Medical Records,
Epidemiology, Computational Advertising, Crowdsourcing, Collaborative Filtering, Industrial Informatics, Financial Engineering, Power Systems, Precision Agriculture) Big Data (Learning on a Budget, Scalability, Sampling, Indexing, Distributed Learning, Data Intensive and Cloud Computing) Bioinformatics (Protein Function and Structure Prediction, Genomics, Drug Discovery)
Data Visualization, Convex Optimization, Data Compression, Algorithms, Nonlinear Systems, Signal Processing

Courses Taught

Number

Name

Level

CIS 5526

Machine Learning

Graduate

Selected Publications

Recent

  • McGee, F., Hauri, S., Novinger, Q., Vucetic, S., Levy, R., Carnevale, V., & Haldane, A. (2021). The generative capacity of probabilistic protein sequence models. Nature Communications, 12(1). doi: 10.1038/s41467-021-26529-9.

  • Egleston, B., Bai, T., Bleicher, R., Taylor, S., Lutz, M., & Vucetic, S. (2021). Statistical inference for natural language processing algorithms with a demonstration using type 2 diabetes prediction from electronic health record notes. Biometrics, 77(3), 1089-1100. doi: 10.1111/biom.13338.

  • Henry, K., Wiese, D., Maiti, A., Harris, G., Vucetic, S., & Stroup, A. (2021). Geographic clustering of cutaneous T-cell lymphoma in New Jersey: an exploratory analysis using residential histories. Cancer Causes and Control, 32(9), 989-999. doi: 10.1007/s10552-021-01452-y.

  • Dragut, E., Li, Y., Popa, L., & Vucetic, S. (2021). Data Science with Human in the Loop. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 4123-4124. doi: 10.1145/3447548.3469476.

  • Wiese, D., Stroup, A., Maiti, A., Harris, G., Lynch, S., Vucetic, S., Gutierrez-Velez, V., & Henry, K. (2021). Measuring neighborhood landscapes: Associations between a neighborhood’s landscape characteristics and colon cancer survival. International Journal of Environmental Research and Public Health, 18(9). doi: 10.3390/ijerph18094728.

  • Banjade, H., Hauri, S., Zhang, S., Ricci, F., Gong, W., Hautier, G., Vucetic, S., & Yan, Q. (2021). Structure motif centric learning framework for inorganic crystalline systems. Science Advances, 7(17). doi: 10.1126/sciadv.abf1754.

  • Hauri, S., Djuric, N., Radosavljevic, V., & Vucetic, S. (2021). Multi-modal trajectory prediction of NBA Players. Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021, 1639-1648. doi: 10.1109/WACV48630.2021.00168.

  • Wiese, D., Stroup, A., Maiti, A., Harris, G., Lynch, S., Vucetic, S., & Henry, K. (2020). Residential Mobility and Geospatial Disparities in Colon Cancer Survival. Cancer Epidemiology, Biomarkers & Prevention : a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology, 29(11), 2119-2125. doi: 10.1158/1055-9965.EPI-20-0772.

  • Wiese, D., Stroup, A., Maiti, A., Harris, G., Lynch, S., Vucetic, S., & Henry, K. (2020). Socioeconomic disparities in colon cancer survival: Revisiting neighborhood poverty using residential histories. Epidemiology, 31(5), 728-735. doi: 10.1097/EDE.0000000000001216.

  • Shapovalov, M., Dunbrack, R., & Vucetic, S. (2020). Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction. PLoS ONE, 15(5). doi: 10.1371/journal.pone.0232528.

  • Zhang, S., He, L., Vucetic, S., & Dragut, E. (2020). Regular expression guided entity mention mining from noisy web data. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, 1991-2000.

  • Maiti, A. & Vucetic, S. (2020). Spatial aggregation facilitates discovery of spatial topics. ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 252-262.

  • Djuric, N., Wang, Z., & Vucetic, S. (2020). Growing adaptive multi-hyperplane machines. 37th International Conference on Machine Learning, ICML 2020, PartF168147-4, 2545-2554.

  • Sondur, S., Kant, K., Vucetic, S., & Byers, B. (2019). Storage on the edge: Evaluating cloud backed edge storage in cyberphysical systems. Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019, 362-370. doi: 10.1109/MASS.2019.00050.

  • Zhang, S., He, L., Dragut, E., & Vucetic, S. (2019). How to invest my time: Lessons from human-in-the-loop entity extraction. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2305-2313. doi: 10.1145/3292500.3330773.

  • Bai, T. & Vucetic, S. (2019). Improving medical code prediction from clinical text via incorporating online knowledge sources. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, 72-82. doi: 10.1145/3308558.3313485.

  • Shapovalov, M., Vucetic, S., & Dunbrack, R. (2019). A new clustering and nomenclature for beta turns derived from high-resolution protein structures. PLoS Computational Biology, 15(3). doi: 10.1371/journal.pcbi.1006844.

  • Bai, T., Egleston, B., Bleicher, R., & Vucetic, S. (2019). Medical concept representation learning from multi-source data. IJCAI International Joint Conference on Artificial Intelligence, 2019-August, 4897-4903. doi: 10.24963/ijcai.2019/680.

  • Bai, T., Chanda, A., Egleston, B., & Vucetic, S. (2018). EHR phenotyping via jointly embedding medical concepts and words into a unified vector space. BMC Medical Informatics and Decision Making, 18. doi: 10.1186/s12911-018-0672-0.

  • Vucetic, S., Chanda, A., Zhang, S., Bai, T., & Maiti, A. (2018). Peer assessment of CS doctoral programs shows strong correlation with faculty citations. Communications of the ACM, 61(9), 70-76. doi: 10.1145/3181854.

  • Bai, T., Egleston, B., Zhang, S., & Vucetic, S. (2018). Interpretable representation learning for healthcare via capturing disease progression through time. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 43-51. doi: 10.1145/3219819.3219904.

  • Zhang, S., Pal, A., Kant, K., & Vucetic, S. (2018). Enhancing Disaster Situational Awareness via Automated Summary Dissemination of Social Media Content. 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings. doi: 10.1109/GLOCOM.2018.8647340.

  • Bai, T., Chanda, A., Egleston, B., & Vucetic, S. (2017). Joint learning of representations of medical concepts and words from EHR data. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, 2017-January, 764-769. doi: 10.1109/BIBM.2017.8217752.

  • Egleston, B., Sigurdson, E., Handorf, E.A., Ross, E.A., Vucetic, S., Wong, Y., & Bleicher, R.J. (2017). Pragmatic verification of cancer clinical trial efficacy findings using the surveillance epidemiology and end results (SEER) database. TRIALS, 18. Retrieved from http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000410814200084&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=abcd71df5a6dac31fd219478b0a9c638.