Publications

Peer-Reviewed Journals

  1. K. Hu, S. Yoon, V. Pavlovic, P. Faloutsos, M. Kapadia, “Predicting Crowd Egress and Environment Relationships to Support Building Design Optimization,” Computers & Graphics, Vol. 88, Pages 83-96, 2020. (SCI-E) [Elsevier]

  2. S. Yoon and S. Kim, “k-Top Scoring Pair Algorithm for Feature Selection in SVM with Applications to Microarray Data Classification,” Soft Computing, 14 (2):151-159, 2010. (SCI-E) [Springer]

  3. S. Yoon and S. Kim, “Mutual Information-based SVM-RFE for Diagnostic Classification of Digitized Mammograms,” Pattern Recognition Letters, 30 (16):1489-1495, 2009. (SCI-E) [Elsevier]

  4. S. Yoon and S. Kim, “AdaBoost-based Multiple SVM-RFE for classification of mammograms in DDSM,” BMC Medical Informatics and Decision Making, 9 (Suppl 1):S1, 2009. (SCI-E) [BMC]

Peer-Reviewed Conference Proceedings

  1. S. Sohn, H. Zhou, S. Moon, S. Yoon, V. Pavlovic, M. Kapadia, “Laying the Foundations of Deep Long-Term Crowd Flow Prediction,” The 16th European Conference on Computer Vision (ECCV), Online, 2020. [page]

  2. S. Yoon, “PyPlat: A Flexible Platform Game Project,” in T. Neller, et al., “Model AI Assignments,” The 10th Symposium on Educational Advances in Artifical Intelligence (EAAI) in conjunction with AAAI Conference on Artificial Intelligence (AAAI), New York, New York, USA, 2020. [pdf] [page]

  3. G. Qiao, H. Zhu, M. Kapadia, S. Yoon, and V. Pavlovic, “Scenario Generalization of Data-driven Imitation Models in Crowd Simulation,” The 12th ACM SIGGRAPH conference on Motion, Interaction and Games (MIG), Newcastle Upon Tyne, United Kingdom, 2019. [ACM]

  4. B. Sang and S. Yoon, “A Neural Network Approach for Birds of a Feather Solvability Prediction,” The 9th Symposium on Educational Advances in Artificial Intelligence (EAAI) in conjunction with AAAI Conference on Artificial Intelligence (AAAI), Honolulu, Hawaii, USA, 2019. [pdf] [code/data]

  5. S. Yoon and J. Kim, “Object-centric Scene Understanding for Image Memorability Prediction,” The 1st IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR), Miami, Florida, USA, 2018. [IEEE]

  6. G. Qiao, S. Yoon, M. Kapadia, and V. Pavlovic, “The Role of Data-driven Priors in Multi-agent Crowd Trajectory Estimation,” The 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA, 2018. [pdf]

  7. W. Liu, K. Hu, S. Yoon, V. Pavlovic, P. Faloutsos, and M. Kapadia, “Characterizing the Relationship between Environment Layout and Crowd Movement using Machine Learning,” The 10th ACM SIGGRAPH conference on Motion in Games (MIG), Barcelona, Spain, 2017. [ACM]

  8. S. Yoon, M. Kapadia, P. Sahu, and V. Pavlovic, “Filling in the Blanks: Reconstructing Microscopic Crowd Motion from Multiple Disparate Noisy Sensors,” 1st Workshop on Computer Vision Applications in Surveillance and Transportation (AVSTI) in conjunction with IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, 2016. [IEEE]

  9. C. Song, S. Yoon, and V. Pavlovic, “Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty,” The 30th AAAI Conference on Artificial Intelligence (AAAI), Phoenix, Arizona, USA, 2016. [pdf]

  10. B. Gholami, S. Yoon, and V. Pavlovic, “Decentralized Approximate Bayesian Inference for Distributed Sensor Network,” The 30th AAAI Conference on Artificial Intelligence (AAAI), Phoenix, Arizona, USA, 2016. [pdf] [code]

  11. S. Yoon and V. Pavlovic, “Sentiment Flow for Video Interestingness Prediction,” The 1st ACM Workshop on Human Centered Event Understanding from Multimedia (HuEvent) in conjunction with ACM International Conference on Multimedia (MM), Orlando, Florida, USA, 2014. [pdf] [code]

  12. J. Kim, S. Yoon, and V. Pavlovic, “Relative Spatial Features for Image Memorability,” The 21st ACM International Conference on Multimedia (MM), Barcelona, Cataluña, Spain, 2013. [pdf]

  13. S. Yoon and V. Pavlovic, “Distributed Probabilistic Learning for Camera Networks with Missing Data,” Advances in Neural Information Processing Systems (NeurIPS) 25, Lake Tahoe, Nevada, USA, 2012. [pdf] [code]

  14. S. Yoon and S. Kim. “Multiple SVM-RFE Using Boosting for Mammogram Classification,” International Joint Conference on Computational Sciences and Optimization (CSO), Sanya, Hainan, China, 2009. [IEEE]

  15. S. Yoon and S. Kim. “AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM,” IEEE International Conference on Bioinformatics and Biomeidcine Workshops (BIBMW), Philadelphia, PA, U.S.A, 2008. [IEEE]

  16. S. Kim and S. Yoon. “BI-RADS Features-Based Computer-Aided Diagnosis of Abnormalities in Mammographic Images,” 6th International Special Topic Conference on Information Technology Applications in Biomedicine (ITAB), Tokyo, Japan, 2007. [IEEE]

  17. S. Kim and S. Yoon. “Mass Lesions Classification in Digital Mammography using Optimal Subset of BI-RADS and Gray Level Features,” 6th International Special Topic Conference on Information Technology Applications in Biomedicine (ITAB), Tokyo, Japan, 2007. [IEEE]

  18. S. Kim, S. Yoon, and D. Shin. “Computer-Aided Diagnosis of Cross-Institutional Mammograms Using Support Vector Machines with Feature Elimination,” Frontiers in the Convergence of Bioscience and Information Technologies (FBIT), Jeju City, Korea, 2007. [IEEE]

Extended Abstracts in Conference and Workshop Proceedings

  1. A. Viola and S. Yoon, “A Hybrid Approach for Video Memorability Prediction,” in proceedings of MediaEval 2019 Workshop, Sophia Antipolis, France, October 27-30, 2019. [pdf]

  2. A. Weiss, B. Sang, and S. Yoon, “Predicting Memorability via Early Fusion Deep Neural Network,” in proceedings of MediaEval 2018 Workshop, Sophia Antipolis, France, October 29-31, 2018. [pdf]

  3. S. Yoon, “TCNJ-CS@MediaEval2017: Predicting Media Interestingness,” in proceedings of MediaEval 2017 Workshop in conjunction with Conference and Labs of the Evaluation Forum (CLEF), Dublin, Ireland, September 13-15, 2017. [pdf]

  4. S. Yoon, “TCNJ-CS@MediaEval2017: Emotional Impact of Movies,” in proceedings of MediaEval 2017 Workshop in conjunction with Conference and Labs of the Evaluation Forum (CLEF), Dublin, Ireland, September 13-15, 2017. [pdf]

  5. B. Gholami, S. Yoon, and V. Pavlovic, “PhD Forum: Mean Field Variational Inference using Bregman ADMM for Distributed Camera Network,” International Conference on Distributed Smart Camera (ICDSC), Seville, Spain, 2015. [ACM]

Other Presentations and Technical Reports

  1. S. Sohn, S. Moon, H. Zhou, S. Yoon, V. Pavlovic, and M. Kapadia, “Deep Crowd-Flow Prediction in Built Environments,” Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop (AI+HADR) in conjunction with Neural Information Processing Systems (NeurIPS), Vancouver, British Columbia, Canada, December 13, 2019. [pdf]

  2. S. Yoon and J. Kim, “Improving Image Memorability Prediction via Coarse Scene Parsing,” Vision Meets Cognition Workshop in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017. (abstract)

  3. S. Yoon and V. Pavlovic, “Decentralized Probabilistic Learning for Sensor Network,” Symposium on Machine Learning for Characterization of Cognitive Communications and Radar in conjunction with IEEE Global Conference on Signal and Information Processing (GlobalSIP), Greater Washington, D.C., 2016. (invited talk)

  4. J. Kim, S. Yoon, and V. Pavlovic, “Relative Spatial Features for Image Memorability,” 3rd GNY Area Multimedia and Vision Meeting, NY, 2013. (abstract)

  5. S. Yoon and V. Pavlovic, “Distributed Probabilistic Learning for Camera Networks with Missing Data,” New York Academy of Sciences 7th Annual Machine Learning Symposium, NY, 2012. (abstract)

  6. S. Yoon and V. Pavlovic, “Distributed Probabilistic Learning for Camera Networks,” DCS-TR-696, Department of Computer Science, Rutgers University, June 2012. [pdf]

Thesis