Machine Learning, Data Mining and Predictive Analytics
Graduate project 1: Machine Learning for Distributed Fault Diagnosis (funded by ExxonMobil)
Graduate project 2: Memory Constrained Data Mining (NSF)
Teaching Instructor for:
[A*] Grbovic M., Zoeter O., Dance C., Guo S., Bouchard G., A model to use data streams of occupancy that are susceptible to missing data, US patent pending
 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.*, Djuric N.*, Vucetic S., Multi-prototype Label Ranking with Novel Pairwise to Total Rank Aggregation, International Joint Conference on Artificial Intelligence (IJCAI), 2013 [authors contributed equally]
 Grbovic M., Djuric N., Vucetic S., Learning from Pairwise Preference Data using Gaussian Mixture Model, Preference Learning Workshop, European Conference on Artificial Intelligence, (ECAI), 2012. [pdf]
 Grbovic M., Li W., Peng X., Usadi A. K., Vucetic S., Decentralized Fault Detection and Diagnosis via Sparse PCA based Decomposition and Maximum Entropy Decision Fusion, Journal of Process Control, 2012. In press [pdf]
 Grbovic M., Vucetic S., Tracking Concept Change using Incremental Boosting by Minimization of the Evolving Exponential Loss, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2011. [pdf] [video] [code]
 Grbovic M., Li W., Peng X., Usadi A. K., Vucetic S., A Boosting Method for Process Fault Detection with Detection Delay Reduction and Label Denoising, KDD Workshop on Data Mining for Service and Maintenance, The 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (ACM SIGKDD), 2011. [pdf]
 Grbovic M., Vucetic S., Statistical and Machine Learning Techniques for Fault Detection and Fault Classification in Industrial Systems, ExxonMobil Internal Technical Report, 2010.
1. Transactions on Pattern Recognition and Machine Intelligence (TPAMI)
2. SIAM International Conference on Data Mining (SDM) 2013
3. The Conference on Uncertainty in Artificial Intelligence (UAI) 2013
4. Neural Information Processing Systems (NIPS) 2013
5. International Conference on Machine Learning (ICML) 2014
1. September 2012 – present. Research Scientist at Yahoo! Labs, Sunnyvale, CA.
AREA: Computational advertising. Ads targeting, Look-alike modeling.
PROJECT 1: Large-scale behavioral targeting and look-alike modeling on 700M Yahoo! users
PROJECT 2: Outlier detection (with applications to Ad latency)
PROJECT 3: Multi-class email classification (automatic categorization of emails into categories: shopping, travel, financial, etc.)
2. June 2012 – September 2012. Research Scientist Intern at Akamai Technologies, Cambridge, MA.
AREA: Computational advertising. Ads modeling. Applying machine learning to online advertisement
PROJECT: Feature Selection for Display Advertising Purchase Prediction
3. February 2012 – May 2012. Research Intern at ExxonMobil Research and Engineering, Annandale, NJ.
PROJECT: Working on clustering and segmentation of large-scale data. Concentrating on applications of Sparse Principal Component Analysis
in specialized clustering and segmentation algorithms
4. May 2011 – October 2011. Research Scientist Intern at Xerox Research Center Europe, Grenoble, France
[A*] A model to use data streams of occupancy that are susceptible to missing data, US patent pending
PROJECT: “Image Identification using Hashing” [link]