| Sparse multiple kernel learning for signal processing applications N Subrahmanya, YC Shin IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (5), 788-798, 2009 | 111 | 2009 |
| Generalized practical models of cylindrical plunge grinding processes TJ Choi, N Subrahmanya, H Li, YC Shin International journal of machine tools and manufacture 48 (1), 61-72, 2008 | 66 | 2008 |
| Adaptive divided difference filtering for simultaneous state and parameter estimation N Subrahmanya, YC Shin Automatica 45 (7), 1686-1693, 2009 | 63 | 2009 |
| Granger causality for time-series anomaly detection H Qiu, Y Liu, NA Subrahmanya, W Li 2012 IEEE 12th international conference on data mining, 1074-1079, 2012 | 59 | 2012 |
| Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction P Sadowski, D Fooshee, N Subrahmanya, P Baldi Journal of Chemical Information and Modeling, 2016 | 39 | 2016 |
| Automated sensor selection and fusion for monitoring and diagnostics of plunge grinding N Subrahmanya, YC Shin Journal of manufacturing science and engineering 130 (3), 2008 | 32 | 2008 |
| Cold Start Approach For Data Driven Fault Detection M Grbovic, W Li, N Subrahmanya, A Usadi, S Vucetic Industrial Informatics, IEEE Transactions on, 1-1, 2013 | 31 | 2013 |
| Constructive training of recurrent neural networks using hybrid optimization N Subrahmanya, YC Shin Neurocomputing 73 (13-15), 2624-2631, 2010 | 28 | 2010 |
| A Bayesian machine learning method for sensor selection and fusion with application to on-board fault diagnostics N Subrahmanya, YC Shin, PH Meckl Mechanical systems and signal processing 24 (1), 182-192, 2010 | 24 | 2010 |
| Method for automatic control and positioning of autonomous downhole tools K Kumaran, NA Subrahmanya, PB Entchev, RC Tolman, RMA Boza US Patent 9,328,578, 2016 | 23 | 2016 |
| A data-based framework for fault detection and diagnostics of non-linear systems with partial state measurement N Subrahmanya, YC Shin Engineering Applications of Artificial Intelligence 26 (1), 446-455, 2013 | 22 | 2013 |
| A variational Bayesian framework for group feature selection N Subrahmanya, YC Shin International Journal of Machine Learning and Cybernetics 4 (6), 609-619, 2013 | 18 | 2013 |
| Multi-scale Graphical Models for Spatio-Temporal Processes F Janoos, H Denli, N Subrahmanya Advances in Neural Information Processing Systems 27, 316-324, 2014 | 12 | 2014 |
| Inner and outer recursive neural networks for chemoinformatics applications G Urban, N Subrahmanya, P Baldi Journal of chemical information and modeling 58 (2), 207-211, 2018 | 10 | 2018 |
| Model-Based Optimization of the OD Plunge Grinding Process via Generalized Intelligent Grinding Advisory System (GIGAS)”, N Subrahmanya, T Choi, YC Shin Transactions of the North American Manufacturing Research Institution 35 …, 2007 | 6 | 2007 |
| Improving Keyword Spotting and Language Identification via Neural Architecture Search at Scale PV Hanna Mazzawi, Xavi Gonzalvo, Aleks Kracun, Prashant Sridhar, Niranjan ... Interspeech 2019, 2019 | 5 | 2019 |
| Advanced Machine Learning Methods for Production Data Pattern Recognition N Subrahmanya, P Xu, A El-Bakry, C Reynolds SPE Intelligent Energy Conference & Exhibition, 2014 | 5 | 2014 |
| Online subspace and sparse filtering for target tracking in reverberant environment W Li, N Subrahmanya, F Xu 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM …, 2012 | 5 | 2012 |
| Determining Interwell Communication W Li, NA Subrahmanya, L Song, AK Usadi, K Kumaran, P Xu, ... US Patent App. 13/646,363, 2013 | 4 | 2013 |
| Enhanced production surveillance using probabilistic dynamic models A Tewari, S de Waele, N Subrahmanya International Journal of Prognostics and Health Management, 2018 | 3 | 2018 |
Yung C. ShinProfessor of Mechanical Engineering, Purdue UniversityVerified email at purdue.edu
