Yu Kong

Assistant Professor
PhD Program
Golisano College of Computing and Information Sciences

2018 Submissions

Journal Paper

Kong, Yu, Zhiqiang Tao, and Yun Fu. "Adversarial Action Prediction Networks." IEEE Transactions on Pattern Analysis and Machine Intelligence. (2018): 1-1. Web. *

Published Conference Proceedings

Sun, Gan, et al. "Clustered Lifelong Learning via Representative Task Selection." Proceedings of the 2018 IEEE International Conference on Data Mining. Ed. n/a. n/a, n/a: n.p., Web. *

View All Submissions

Rui Li

Assistant Professor
PhD Program
Golisano College of Computing and Information Sciences

2018 Submissions

Published Conference Proceedings

Li, Rui, et al. "Sparse Covariance Modeling of Gene Regulatory Networks with Gaussian Processes." Proceedings of the NeurIPS. Ed. NeurIPS. Montreal, QC: n.p., 2018. Web. «

KC, Kishan, et al. "Learning Topology-preserving Embedding for Gene Interaction Networks." Proceedings of the European Conference on Computational Biology. Ed. ECCB. Athens, Greece: n.p., 2018. Web. «

View All Submissions

Linwei Wang

Professor
PhD Program
Golisano College of Computing and Information Sciences

2018 Submissions

Journal Paper

Alawad, Mohammed and Linwei Wang. "Learning Domain Shift in Simulated and Clinical Data: Localizing the Origin of Ventricular Activation from 12-lead Electrocardiograms." IEEE Transactions on Medical Imaging. (2018): DOI: 10.1109/TMI.2018.2880092 [Epub ahead of print]. Web. *

Dhamala, Jwala, et al. "Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo in Cardiac Electrophysiology." Medical Image Analysis 48. (2018): 43-57. Print. *

Wang, Linwei, et al. "Noninvasive Epicardial and Endocardial Electrocardiographic Imaging for Scar-related Ventricular Tachycardia." Europace 20. FI2 (2018): f263-f272. Web. *

Ghimire, Sandesh, et al. "Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential,." Medical Image Computing and Computer-Assisted Intervention. (2018): 508-516. Web. «

Dhamala, Jwala, et al. "High-dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model." Medical Image Computing and Computer-Assisted Intervention. (2018): 499-507. Web. «

Alawad, Mohammed and Linwei Wang. "Leveraging Simulation Data for Learning a Patient-specific Model to Localize the Origin of Ventricular Activation." IEEE International Symposium on Biomedical Imaging. (2018): 1318-1322. Web. *

Gyawali, Prashnna, et al. "Learning Disentangled Representation from 12-lead Electrocardiograms: Application in Localizing the Origin of Ventricular Tachycardia." AAAI Workshop on Health Intelligence,. (2018): 443-450. Web. «

Medina, Rebecca, et al. "Sensing Behaviors of Students in Online vs. Face-to-face Lecturing Contexts." IEEE Pervasive Computing Workshop. (2018): 77-82. Web. «

Coll-Font, Jaume, Linwei Wang, and Dana Brooks. "A Common-ground Review of the Potential for Machine Learning Approaches in Electrocardiographic Imaging Based on Probabilistic Graphical Models." Computing in Cardiology. (2018): in press. Web. *

Gharbia, Omar A, et al. "Noninvasive Electrocardiographic Imaging of Scar-related Ventricular Tachycardia: Association with Magnetic Resonance Imaging,Computing in Cardiology,." Computing in Cardiology. (2018): in press. Web. *

Ghimire, Sandesh and Linwei Wang. "Deep Generative Modeling and Analysis of Cardiac Transmembrane Potential." Computing in Cardiology. (2018): in press. Web. *

Journal Editor

Wang, Linwei, ed. Frontiers in Physiology. NA: n.p., 2018. Web.

Invited Keynote/Presentation

Wang, Linwei. "Active Surrogate Modeling for Uncertainty Quantification in Personalized Models." Distinguished seminar series. University of Utah. Salt Lake City, Utah. 26 Oct. 2018. Lecture.

Wang, Linwei. "High-dimensional Bayesian Active Learning for Personalized Modeling." Western New York Image and Signal Processing Workshop. Rochester Institute of Technology. Rochester, New York. 5 Oct. 2018. Keynote Speech.

Wang, Linwei. "Active Surrogate Modeling for Uncertainty Quantification in Personalized Models." RIT Center for Imaging Science Seminar. Rochester Institute of Technology. Rochester, New York. 5 Sep. 2018. Lecture.

View All Submissions