Publications

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Research Interests:

deep neural networks, artificial intelligence, physiologic time-series analysis, medical imaging, case-based reasoning, game ai

Selected Publications:

2021

Jonathan Rubin, Alvin Chen, Anumod Odungattu Thodiyil, Raghavendra Srinivasa Naidu, Ramon Erkamp, Jon Fincke, Balasundar Raju. Efficient Video-Based Deep Learning for Ultrasound Guided Needle Insertion, International Conference on Medical Imaging with Deep Learning (MIDL 2021).

2020

Annamalai Natarajan, Yale Chang, Sara Mariani, Asif Rahman, Gregory Boverman, Shruti Vij and Jonathan Rubin. A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification, In Computing in Cardiology 2020.
*1st place PhysioNet Challenge 2020

Yumin Liu, Claire Zhao, Jonathan Rubin. Uncertainty Quantification in Chest X-Ray Image Classification using Bayesian Deep Neural Networks, European Conference on Artificial Intelligence (ECAI 2020), Knowledge Discovery in Healthcare Data Workshop, 2020.

2019

Yale Chang, Jonathan Rubin, Gregory Boverman, Shruti Vij, Asif Rahman, Annamalai Natarajan, Saman Parvaneh. A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series, In Computing in Cardiology 2019.
*2nd place PhysioNet Challenge Hackathon 2019

Xin Wang, Evan Schwab, Jonathan Rubin, Prescott Klassen, Ruizhi Liao, Seth Berkowitz, Polina Golland, Steven Horng and Sandeep Dalal. Pulmonary Edema Severity Estimation in Chest Radiographs Using Deep Learning, International Conference on Medical Imaging with Deep Learning (MIDL 2019).

Jonathan Rubin and S. Mazdak Abulnaga. CT-To-MR Conditional Generative Adversarial Networks for Improved Stroke Lesion Segmentation, Seventh IEEE International Conference on Healthcare Informatics (ICHI 2019).

Ruizhi Liao, Jonathan Rubin, Grace Lam, Seth Berkowitz, Sandeep Dalal, William Wells, Steven Horng, and Polina Golland. Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images., arXiv preprint arXiv:1902.10785 (2019).

2018

Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili, Jonathan Rubin and Una-May O'Reilly. Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection, Machine Learning for Health (ML4H) Workshop at NeurIPS 2018.

S. Mazdak Abulnaga & Jonathan Rubin. Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss, BrainLes 2018 MICCAI workshop.

Jonathan Rubin, Deepan Sanghavi, Claire Zhao, Kathy Lee, Ashequl Qadir, Minnan Xu-Wilson. Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks, arXiv preprint arXiv:1804.07839v2 (2018).

Jonathan Rubin, Saman Parvaneh, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018). Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings, International Society of Electrocardiology Conference (2018). Invited Talk

Jonathan Rubin, Cristhian Potes, Minnan Xu-Wilson, Junzi Dong, Asif Rahman, Hiep Nguyen, and David Moromisato. An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit, In International Journal of Medical Informatics (2018).

2017

Jonathan Rubin, Saman Parvaneh, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2017). Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings, In Computing in Cardiology 2017.

Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei and Kumar Sricharan (2017). Recognizing Abnormal Heart Sounds Using Deep Learning, In (IJCAI 2017) International Joint Conference on Artificial Intelligence, Knowledge Discovery in Healthcare Workshop.

2016

Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei and Kumar Sricharan (2016). Classifying Heart Sound Recordings using Deep Convolutional Neural Networks and Mel-Frequency Cepstral Coefficients, In Computing in Cardiology 2016.

Jonathan Rubin, Rui Abreu, Shane Ahern, Hoda Eldardiry and Daniel G. Bobrow (2016). Time, Frequency & Complexity Analysis for Recognizing Panic States from Physiologic Time-Series, In proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2016.

2015

Luis Cruz, Jonathan Rubin, Rui Abreu, Shane Ahern, Hoda Eldardiry and Daniel G. Bobrow (2015). A Wearable and Mobile Intervention Delivery System for Individuals with Panic Disorder, In proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia (MUM 2015).

Jonathan Rubin, Hoda Eldardiry, Rui Abreu, Shane Ahern, Honglu Du, Ashish Pattekar and Daniel G. Bobrow (2015). Towards a Mobile and Wearable System for Predicting Panic Attacks, In proceedings of the 2015 ACM Conference on Ubiquitous Computing, Ubicomp ’15.

2013

Jonathan Rubin & Ian Watson. (2013). Decision Generalisation from Game Logs in No Limit Texas Hold'em, In IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence.

Nolan Bard, John Alexander Hawkin, Jonathan Rubin and Martin Zinkevich. The Annual Computer Poker Competition. AI Magazine, 34(2):112–, 2013

Michael Silva, Silas McCroskey, Jonathan Rubin, Michael Youngblood and Ashwin Ram. (2013). Learning from Demonstration to Be a Good Team Member in a Role Playing Game, In Proceedings of the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2013.

2012

Jonathan Rubin & Ian Watson, Case-Based Strategies in Computer Poker, AI Communications, Volume 25, Number 1: 19-48, March 2012.

2011

Jonathan Rubin & Ian Watson. (2011). Successful Performance via Decision Generalisation in No Limit Texas Hold'em. In Case-Based Reasoning. Research and Development, 19th International Conference on Case-Based Reasoning, ICCBR 2011. Best Application Paper

Jonathan Rubin & Ian Watson. (2011). On Combining Decisions from Multiple Expert Imitators for Performance. In IJCAI-11, Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence.

J. Rubin, I. Watson, Computer poker: A review, Artificial Intelligence, 175(5-6):958-987, April 2011.

2010

Jonathan Rubin & Ian Watson. (2010). Similarity-Based Retrieval and Solution Re-use Policies in the Game of Texas Hold'em. In Case-Based Reasoning. Research and Development, 18th International Conference on Case-Based Reasoning, ICCBR 2010.

2009

Jonathan Rubin & Ian Watson. (2009). A Memory-Based Approach to Two-Player Texas Hold'em. In Proceedings of AI 2009: Advances in Artificial Intelligence, 22nd Australasian Joint Conference, pages 465-474, 2009.

Jonathan Rubin & Ian Watson. (2009). Memory and Analogy in Game-Playing Agents. Eighth International Conference on Case-Based Reasoning (ICCBR 2009), Workshop on Case-Based Reasoning for Computer Games.

2008

Ian Watson & Jonathan Rubin. (2008). Casper: a Case-Based Poker-Bot. In Proceedings of AI 2008: Advances in Artificial Intelligence, 21st Australasian Joint Conference on Artificial Intelligence, pages 594-600, 2008.

2007

Rubin, J. (2007). CASPER: Design and Development of a Case-Based Poker Player. Masters thesis, University of Auckland.

Rubin, J. & Watson, I. (2007). Investigating the Effectiveness of Applying Case-Based Reasoning to the game of Texas Hold’em. In, Proc. of the 20th. Florida Artificial Intelligence Research Society Conference (FLAIRS), Key West, Florida, May 2007. AAAI Press.

2005

Animation and Modelling of Cardiac Performance for Patient Monitoring, Jonathan Rubin, Burkhard C. Wuensche, Linda Cameron and Carey Stevens, Proceedings of IVCNZ '05, Dunedin, New Zealand, 28-29 November 2005, pp. 476-481.