Publications
All entries are ordered by most recent first.
Last updated: 4 September 2021
Last updated: 4 September 2021
Journal Publications
2021
79. Z. Jiang, A. Balu, C. Hedge, S. Sarkar, On Consensus-Optimality Trade-offs in Collaborative Deep Learning, Frontiers, 2021.
78. T Z Jubery, C. N. Carley, A. Singh, S. Sarkar, B. Ganapathysubramanian, A. K. Singh, Using Machine Learning To Develop A Fully Automated Soybean Nodule Acquisition Pipeline (SNAP), Plant Phenomics, 2021.
77. R. Singh, P. Singh, A. Sharma, O.R. Bingol, A. Balu, G. Balasubramanian, A. Krishnamurthy, S. Sarkar, Duane D. Johnson, Neural-network model for force prediction in multi-principal-element alloys, Computational Materials Science, Volume 198, 2021, 110693,
ISSN 09270256, https://doi.org/10.1016/j.commatsci.2021.110693.
76. L.G. Riera+, M. E. Carroll+, Z. Zhang+, J. Shook+, S. Ghosal+, T. Gao+, A. Singh, S. Bhattacharya, B. Ganapathysubramanian, A. K. Singh, and S. Sarkar, Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications, Plant Phenomics, 2021. DOI: https://doi.org/10.34133/2021/9846470
75. W. Guo, M. E. Carroll+, A. Singh, T. L. Swetnam, N. Merchant, S. Sarkar, A. K. Singh, and B. Ganapathysubramanian. UAS-Based Plant Phenotyping for Research and Breeding Applications, Plant Phenomics, 2021. DOI: https://doi.org/10.34133/2021/9840192
74. J. Shook+, T. Gangopadhyay+, L. Wu+, B. Ganapathysubramanian, S. Sarkar, A. K. Singh, Crop yield prediction integrating genotype and weather variables using deep learning, PLoS ONE 16(6): e0252402., 2021. DOI: https://doi.org/10.1371/journal.pone.0252402
73. K. Nagasubramanian+, T. Jubery+, F. F. Ardakani+, S.V. Mirnezami+, A.K. Singh, A. Singh, S. Sarkar, B. Ganapathysubramanian, How useful is active learning for image-based plant phenotyping?, Plant Phenome Journal. 2021; 4:e20020. DOI: https://doi.org/10.1002/ppj2.20020
72. X.Y. Lee, J.R. Waite, CH. Yang, B. Pokuri, A. Joshi, A. Balu, C. Hedge, B. Ganapathysubramanian, S. Sarkar. Fast inverse design of microstructures via generative invariance networks. Nature Computational Science, March 2021, DOI: https://doi.org/10.1038/s43588-021-00045-8
71. T. Gangopadhyay, V. Ramanan, A. Akintayo, P. K. Boor, S. Sarkar, S. R. Chakravarthy, S. Sarkar, 3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems, Energy and AI, March 2021, DOI: https://doi.org/10.1016/j.egyai.2021.100067
70. Y. Esfandiari, A. Balu, K. Ebrahimi, U. Vaidya, N. Elia, S. Sarkar, A fast saddle-point dynamical system approach to deep learning, Neural Networks, July 2021, DOI: https://doi.org/10.1016/j.neunet.2021.02.021
69. C. Liu, K.G. Lore, Z. Jiang, S. Sarkar, Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems, Knowledge-Based Systems, January 2021, DOI: https://doi.org/10.1016/j.knosys.2020.106527
2020
68. K.L. Tan, A. Sharma, S. Sarkar, Robust Deep Reinforcement Learning for Traffic Signal Control, Journal of Big Data Analytics in Transportation, December 2020, DOI:https://doi.org/10.1007/s42421-020-00029-6
67. Z. Jiang, C. Liu, B. Ganapathysubramanian, D. J. Hayes, S. Sarkar, Predicting county-scale maize yields with publicly available data, Scientific Reports, September 2020, DOI:https://doi.org/10.1038/s41598-020-71898-8
66. A. Singh, S. Jones, B. Ganapathysubramanian, S. Sarkar, D. Mueller, K. Sandhu, K. Nagasubramanian, Challenges and Opportunities in Machine- Augmented Plan Stress Phenotyping, Trends in Plan Science - Cell Press, August 2020, DOI:https://doi.org/10.1016/j.tplants.2020.07.010
65. S. V. Mirnezami, T. Young, T. Assefa, S. Prichard, K.Nagasubramanian, K. Sandhu, S. Sarkar, S. Sundararajan, M. E. O'Neal, B. Ganaphathysubramanian, A. Singh, Automated trichome counting in soybean using advanced image‐processing techniques, Applications in Plant Sciences.
64. X.Y. Lee, S. Saha, S. Sarkar, B. Giera, Companion Journal: Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures, Data in Brief, August 2020, 106119.
63. X.Y. Lee, S. Saha, S. Sarkar, B. Giera, Automated detection of part quality during two-photon lithography via deep learning, Additive Manufacturing, Volume 36, December 2020, 101444.
62. Kevin G. Falk, Talukder Zaki Jubery, Jamie A. O’Rourke, Arti Singh, Soumik Sarkar, Baskar Ganapathysubramanian, Asheesh K. Singh, Soybean root system architecture traits study through genotypic, phenotypic and shape-based clusters, Plant Phenomics, 2020 (accepted)
61. Z. Jiang, V. Chinde, A. Kohl, A. G. Kelkar, and S. Sarkar. Supervisory Control and Distributed Optimization of Building Energy Systems, Journal of Dynamic Systems, Measurement, and Control, October 2020, 142(10): 101008
60. H. Saha, C. Liu, Z. Jiang, and S. Sarkar. Data-driven performance monitoring of dynamical systems using Granger causal graphical models, in ASME journal of Dynamics, Systems and Control, published March 16, 2020.
59. Kevin G. Falk, Talukder Z. Jubery, Seyed V. Mirnezami, Kyle A. Parmley, Soumik Sarkar, Arti Singh, Baskar Ganapathysubramanian, and Asheesh K. Singh. Computer vision and machine learning enabled soybean root phenotyping pipeline. Plant Methods, 16, 5 (2020) doi:10.1186/s13007-019-0550-5
2019
58. Aditya Balu, Sahiti Nallagonda, Fei Xu, Adarsh Krishnamurthy, Ming-Chen Hsu, and Soumik Sarkar. A deep learning framework for diagnostics and patient-specific design of bioprosthetic heart valves. Scientific Reports, 9, 18560 (2019) doi:10.1038/s41598-019-54707-9
57. Kyle Parmley, Race Higgins, Baskar Ganapathysubramanian, Soumik Sarkar, and Asheesh Singh, Machine Learning Approach for Prescriptive Plant Breeding, Scientific Reports (Accepted)
56. B. S. S. Pokuri, S. Ghosal, A. Kokate, S. Sarkar, B. Ganapathysubramanian, Interpretable deep learning for guided microstructure-property explorations in photovoltaics, Nature (npj) Computational Materials, 2019.
55. K. Nagasubramanian, S. Jones, A. K. Singh, S. Sarkar, A. Singh, B. Ganapathysubramanian, Plant disease identification using explainable 3D deep learning on hyperspectral images, Plant Methods 15.1 (2019): 1-10.
54. X. Y. Lee, A. Balu, D. Stoecklein, B. Ganapathysubramanian, S. Sarkar, A Case Study of Deep Reinforcement Learning for Engineering Design: Application to Microfluidic Devices for Flow Sculpting, ASME Journal of Mechanical Design – Special Issue: Machine Learning for Engineering Design), November 2019; 141(11): 111401.
53. K. Parmley, K. Nagasubramanian, S. Sarkar, B. Ganapathysubramanian, A. K. Singh, Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions using Phenomics Assisted Selection in Soybean Plant Phenomics, Plant Phenomics (a Science Partner Journal), vol 2019, Article ID 5809404, 15 pages, 2019.
52. S. Ghosal, B. Zheng, S. C. Chapman, A. B. Potgieter, D. R. Jordan, X. Wang, A. K. Singh, A. Singh, M. Hirafuji, S. Ninomiya, B. Ganapathysubramanian, S. Sarkar, W. Guo, A weakly supervised deep learning framework for sorghum head detection and counting, Plant Phenomics (a Science Partner Journal), vol 2019, Article ID 1525874, 14 pages, 2019.
51. C. Liu, M. Zhao, A. Sharma, S. Sarkar, Traffic Dynamics Exploration and Incident Detection Using Spatiotemporal Graphical Modeling, Journal of Data Analytics in transportation (2019): pp 1-19
50. H.Saha, A.R.Florita , G.P.Henze, S.Sarkar, Occupancy sensing in buildings: A review of data analytics approaches, Energy and Buildings 118 (2019) : 278- 285
49. C. Liu, T. Han, L. Wu, S. Sarkar, D. Jiang, An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems, Mechanical Systems and Signal Processing 117 (2019): 170-187
2018
48. T. Gao, H. Emadi, H. Saha, J. Zhang, A. Lofquist, A. Singh, B. Ganapathysubramanian, S. Sarkar, A. Singh, and S. Bhattacharya. "A Novel Multirobot System for Plant Phenotyping" Robotics 7, no. 4 (2018): 61.
47. A. Ott, J. C. Schnable, C. Yeh, L. Wu, C. Liu, H. Hu, C. L. Dalgard, S. Sarkar and P. S. Schnable, Linked read technology for assembling large complex and polyploid genomes, BMC genomics 19, no. 1 (2018): 651
46. K. Nagasubramanian, S. Jones, S. Sarkar, A. K. Singh, A. Singh, B. Ganapathysubramanian, Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems, Plant Methods (2018)
45. A. K. Singh, B. Ganapathysubramanian, S. Sarkar, A. Singh, Deep learning for plant stress phenotyping: trends and future perspectives, Trends in Plant Science (2018)
44. A. Akintayo, G. Tylka, A. K. Singh, B. Ganapathysubramanian, A. Singh and S. Sarkar, A deep learning framework to discern and count microscopic nematode eggs, Scientific Reports 8, Article Number: 9145 (2018).
43. L. Wu, C. Liu, T. Huang, A. Sharma, S. Sarkar, Traffic sensor health monitoring using spatiotemporal graphical modeling, International Journal of Prognostics and Health Management, Vol 9 (1) 022, pages: 13, 2018.
42. A. Akintayo and S. Sarkar, Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data, Signal Processing, Volume 151, pp. 76-88, October 2018.
41. S. Ghosal, D. Blystone, A. K. Singh, B. Ganapathysubramanian, A. Singh and S. Sarkar, An explainable deep machine vision framework for plant stress phenotyping, Proceedings of the National Academy of Sciences of the United States of America (PNAS) May 1, 2018. 115 (18) 4613-4618; published ahead of print April 16, 2018.
40. S. Ghadai, A. Balu, A. Krishnamurthy and S. Sarkar, Learning Localized Features in 3D CADModels for Manufacturability Analysis of Drilled Holes, Computer Aided Geometric Design Journal, Elsevier, Volume 62, May 2018, Pages 263-275 , Presented at International Conference on Geometric Modeling & Processing (GMP 2018)
39. P. Chakraborty, Y. A. Gyamfi, S. Poddar, V. Ahsani, A. Sharma and S. Sarkar, Traffic Congestion Detection from Camera Images Using Deep Convolutional Neural Networks, Transportation Research Record (TRR), Journal of the Transportation Research Board, First Published June 11, 2018
38. T. Huang, C. Liu, A. Sharma, S. Sarkar, Traffic System Anomaly Detection using Spatiotemporal Pattern Networks, International Journal of Prognostics and Health Management: Vol 9 (1) 003, pages: 12, 2018
37. C. Liu, A. Akintayo, Z. Jiang, G.P. Henze, S. Sarkar, Multivariate Exploration of Non-Intrusive Load Monitoring via Spatiotemporal Pattern Network, Applied Energy, Volume 211, Pages 1106-1122, February 2018
36. K. G. Lore, D. Stoecklein, M. Davies, B. Ganapathysubramanian and S. Sarkar, A Deep Learning framework for Casual Shape Transformation, Neural Networks, Volume 98, Pages 305-317, February 2018
2017
35. Z. Jiang, K. Mukherjee, S. Sarkar, Generalized Gossip-based Subgradient Method for Distributed Optimization, International Journal of Control, November 2017.
34. C. Liu, S. Ghosal, Z. Jiang and S. Sarkar, An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling, Cyber- Physical Systems, Volume 3, Issue 1-4, 2017
33. Z. Jiang, C. Liu, A. Akintayo, G. Henze, S. Sarkar, Energy Prediction using Spatiotemporal Pattern Networks, Applied Energy, Volume 206, pp. 1022- 1039, November 2017.
32. V. Chinde, K. Kosaraju, A. Kelkar, R. Pasumarthy, S. Sarkar and N. Singh. A Passivity-based Power Shaping Control of Building HVAC Systems. Journal of Dynamic Systems, Measurement and Control, Article Number: 111007-111007-10 (2017).
31. T. Jubery, J. Shook, K. Parmley, J. Zhang, H. Naik, R. Higgins, S. Sarkar, A. Singh, A. Singh and B. Ganapathysubramanian. Deploying Fourier coefficients to unravel soybean canopy diversity. Frontiers in Plant Science, Vol.7, 2017.
30. J. Zhang, H. Naik, T. Assefa, S. Sarkar, C. Reddy R. V, A. Singh, B. Ganapathysubramanian and A. Singh. Computer vision and machine learning for robust phenotyping in genome-wide studies. Scientific Reports 7, Article Number: 44048 (2017).
29. K. G. Lore, A. Akintayo, and S. Sarkar, LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement, Pattern Recognition, Volume 61, pp. 650-662, January 2017. Link to LLNet Code Repository
28. C. Liu, Y. Gong, S. Laflamme, B. Phares and S. Sarkar, Bridge damage detection using spatiotemporal patterns extracted from dense sensor network, Measurement Science and Technology (Special Feature on Dense Sensor Networks for Mesoscale SHM), Vol 61, No. 1, January 2017.
27. H. Naik, J. Zhang, A. Lofquist, T. Assefa, S. Sarkar, D. Ackerman, A. Singh, A. Singh and B. Ganapathysubramanian, A real-time Phenotyping Framework using Machine Learning for Plant Stress Severity rating in Soybean, Plant Methods 13:23 (2017).
26. D. Stoecklein, K. G. Lore, M. Davies, B. Ganapathysubramanian and S. Sarkar. Deep Learning for Flow Sculpting: Insights into Efficient Learning using Scientific Simulation Data. Scientific Reports 7, Article number: 46368 (2017).
2016
25. A. Akintayo, K. G. Lore, S. Sarkar, S. Sarkar. Prognostics of Combustion Instabilities from Hi-speed Flame Video using a Deep Convolutional Selective Autoencoder. International Journal of Prognostics and Health Management, 2016.
24. S. Sarkar and A. Srivastav, A Composite Discretization Scheme for Symbolic Indentification of Complex Systems, Signal Processing, Volume 125, pp. 156-170, August 2016.
23. A. Singh, B. Ganapathysubramanian, A. K. Singh, S. Sarkar, Machine Learning for High-Throughput Stress Phenotyping in Plants, Trends in Plant Sciences (TIPS), Volume 21, Issue 2, p110–124, 2016 .
22. S. Sarkar, V. Venugopalan, K. Reddy, J. Ryde, N. Jaitly and M. Giering, Deep Learning for Automated Occlusion Edge Detection in RGB-D Frames. Journal of Signal Processing Systems (Special Issue on Dynamic Data-driven Application Systems (DDDAS)), December 2016.
2015
21. K. G. Lore, D. Stoecklein, M. Davies, B. Ganapathysubramanian and S. Sarkar, Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns, JMLR: Workshop and Conference Proceedings 44 (2015). Proceedings of The 1st International Workshop on “Feature Extraction: Modern Questions and Challenges”, NIPS, pp. 213–225, 2015.
20. D. K. Jha, P. Chattopadhyay, S. Sarkar and A. Ray, Path Planning in GPS-denied Environments with Collective Intelligence of Distributed Sensor Networks, International Journal of Control, 2015.
19. S. Bengea, P. Li, S. Sarkar, S. Vichik, V. Adetola, K. Kang, T. Lovett, F. Leonardi, A. Kelman, Fault-Tolerant Optimal Control of a Building Heating, Ventilation and Air Conditioning System, Science and Technology for the Built Environment (formerly HVAC&R Research Journal), 2015. (won the Best Paper award)
2014
18. S. Sarkar and K. Mukherjee, Event-triggered Decision Propagation in Proximity Networks, Invited paper for the Inaugural issue of Frontiers in Robotics and AI: Sensor Fusion and Machine Perception (Part of the Nature Publishing Group), 2014.
17. S. Sarkar, S. Sarkar, N. Virani, A. Ray, M. Yasar, Sensor Fusion for Fault Detection & Classification in Distributed Physical Processes, Invited paper for the Inaugural issue of Frontiers in Robotics and AI: Sensor Fusion and Machine Perception (Part of the Nature Publishing Group), 2014.
2013
16. S. Sarkar, S. Sarkar, K. Mukherjee, A. Ray, and A. Srivastav, Multi-sensor Information Fusion for Fault Detection in Aircraft Gas Turbine Engines, Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, Vol. 227, No. 12, December 2013, pp. 1988-2001.
15. A. Ray, S. Phoha, and S. Sarkar, Behavior Prediction for Decision & Control in Cognitive Autonomous Systems, New Mathematics and Natural Computation: Special Issue on Engineering of the Mind, Cognitive Science and Robotics, Vol. 9, No. 3, November 2013, pp. 1-9.
14. S. Sarkar, K. Mukherjee, S. Sarkar, and A. Ray, Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines, Journal of Dynamic Systems, Measurement, and Control, Transactions of the ASME, Vol. 135, Issue 1. January 2013, pp. 14506 (6 pages).
13. S. Sarkar, K. Mukherjee, and A. Ray, Distributed Decision Propagation in Proximity Networks, International Journal of Control, Vol. 86, No.6, June 2013, pp. 1118-1130.
2012
12. S. Sarkar, K. Mukherjee, A. Ray, A. Srivastav, and T. Wettergren, Equilibrium Thermodynamics for Heterogeneous Packet Transmission in Communication Networks, IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol. 42, No. 4, August 2012, pp. 1083-1093.
11. S. Sarkar, K. Mukherjee, X. Jin, and A. Ray, Optimization of Symbolic Feature Extraction for Pattern Classification, Signal Processing, Vol. 92, No. 3, March 2012, pp. 625-635.
10. S. Chakraborty, S. Sarkar, and A. Ray, Symbolic Identification for Fault Detection in Aircraft Gas Turbine Engines, Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, Vol. 226, No. 4, April 2012, pp. 422-436.
2011
9. S. Sarkar, X. Jin, and A. Ray, Data-driven Fault Detection in Aircraft Engines with Noisy Sensor Measurements, Journal of Engineering for Gas Turbines and Power, Vol. 133, No. 8, August 2011, pp. 081602 (10 pages).
8. X. Jin, Y. Guo, S. Sarkar, A. Ray, and R.M. Edwards Anomaly Detection in Nuclear Power Plants via Symbolic Dynamic Filtering, IEEE Transactions on Nuclear Science, Vol. 58, No.1, February 2011, pp.277-288.
2009
7. S. Sarkar, C. Rao and A. Ray, Statistical Estimation of Multiple Faults in Aircraft Gas Turbine Engines, Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, Vol. 223, No.4, 2009, pp. 415-424.
6. S. Sarkar, K. Mukherjee and A. Ray, Generalization of Hilbert Transform for Symbolic Analysis of Noisy Signals, Signal Processing, Vol. 89, Issue 6, June 2009, pp. 1245-1251.
5. C. Rao, K. Mukherjee, S. Sarkar and A. Ray, Statistical Estimation of Multiple Parameters via Symbolic Dynamic Filtering, Signal Processing, in Vol. 89, Issue 6, June 2009, pp. 981-988
4. C. Rao, A. Ray, S. Sarkar and M. Yasar, Review and Comparative Evaluation of Symbolic Dynamic Filtering for Detection of Anomaly Patterns, Signal, Image, and Video Processing, Vol. 3, Issue 2 (2009), pp.101-114.
2008
3. S. Chakraborty, S. Sarkar, S. Gupta and A. Ray, Damage Monitoring of Refractory Wall in a Generic Entrained-Bed Slagging Gasification System, Proceedings of the I Mech E Part A: Journal of Power and Energy, Vol. 222 Part A, No. 8, October 2008, pp. 791-807.
2. S. Sarkar, M. Yasar, S. Gupta, A. Ray and K. Mukherjee, Fault Detection and Isolation in Aircraft Gas Turbine Engines: Part II Validation on a Simulation Test Bed, Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, Vol. 222 (G3), No. 3, May 2008, pp. 319-330.
1. S. Gupta, A. Ray, S. Sarkar and M. Yasar, Fault Detection and Isolation in Aircraft Gas Turbine Engines: Part I Underlying Concept, Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, Vol. 222 (G3), No. 3, May 2008, pp. 307-318.
79. Z. Jiang, A. Balu, C. Hedge, S. Sarkar, On Consensus-Optimality Trade-offs in Collaborative Deep Learning, Frontiers, 2021.
78. T Z Jubery, C. N. Carley, A. Singh, S. Sarkar, B. Ganapathysubramanian, A. K. Singh, Using Machine Learning To Develop A Fully Automated Soybean Nodule Acquisition Pipeline (SNAP), Plant Phenomics, 2021.
77. R. Singh, P. Singh, A. Sharma, O.R. Bingol, A. Balu, G. Balasubramanian, A. Krishnamurthy, S. Sarkar, Duane D. Johnson, Neural-network model for force prediction in multi-principal-element alloys, Computational Materials Science, Volume 198, 2021, 110693,
ISSN 09270256, https://doi.org/10.1016/j.commatsci.2021.110693.
76. L.G. Riera+, M. E. Carroll+, Z. Zhang+, J. Shook+, S. Ghosal+, T. Gao+, A. Singh, S. Bhattacharya, B. Ganapathysubramanian, A. K. Singh, and S. Sarkar, Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications, Plant Phenomics, 2021. DOI: https://doi.org/10.34133/2021/9846470
75. W. Guo, M. E. Carroll+, A. Singh, T. L. Swetnam, N. Merchant, S. Sarkar, A. K. Singh, and B. Ganapathysubramanian. UAS-Based Plant Phenotyping for Research and Breeding Applications, Plant Phenomics, 2021. DOI: https://doi.org/10.34133/2021/9840192
74. J. Shook+, T. Gangopadhyay+, L. Wu+, B. Ganapathysubramanian, S. Sarkar, A. K. Singh, Crop yield prediction integrating genotype and weather variables using deep learning, PLoS ONE 16(6): e0252402., 2021. DOI: https://doi.org/10.1371/journal.pone.0252402
73. K. Nagasubramanian+, T. Jubery+, F. F. Ardakani+, S.V. Mirnezami+, A.K. Singh, A. Singh, S. Sarkar, B. Ganapathysubramanian, How useful is active learning for image-based plant phenotyping?, Plant Phenome Journal. 2021; 4:e20020. DOI: https://doi.org/10.1002/ppj2.20020
72. X.Y. Lee, J.R. Waite, CH. Yang, B. Pokuri, A. Joshi, A. Balu, C. Hedge, B. Ganapathysubramanian, S. Sarkar. Fast inverse design of microstructures via generative invariance networks. Nature Computational Science, March 2021, DOI: https://doi.org/10.1038/s43588-021-00045-8
71. T. Gangopadhyay, V. Ramanan, A. Akintayo, P. K. Boor, S. Sarkar, S. R. Chakravarthy, S. Sarkar, 3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems, Energy and AI, March 2021, DOI: https://doi.org/10.1016/j.egyai.2021.100067
70. Y. Esfandiari, A. Balu, K. Ebrahimi, U. Vaidya, N. Elia, S. Sarkar, A fast saddle-point dynamical system approach to deep learning, Neural Networks, July 2021, DOI: https://doi.org/10.1016/j.neunet.2021.02.021
69. C. Liu, K.G. Lore, Z. Jiang, S. Sarkar, Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems, Knowledge-Based Systems, January 2021, DOI: https://doi.org/10.1016/j.knosys.2020.106527
2020
68. K.L. Tan, A. Sharma, S. Sarkar, Robust Deep Reinforcement Learning for Traffic Signal Control, Journal of Big Data Analytics in Transportation, December 2020, DOI:https://doi.org/10.1007/s42421-020-00029-6
67. Z. Jiang, C. Liu, B. Ganapathysubramanian, D. J. Hayes, S. Sarkar, Predicting county-scale maize yields with publicly available data, Scientific Reports, September 2020, DOI:https://doi.org/10.1038/s41598-020-71898-8
66. A. Singh, S. Jones, B. Ganapathysubramanian, S. Sarkar, D. Mueller, K. Sandhu, K. Nagasubramanian, Challenges and Opportunities in Machine- Augmented Plan Stress Phenotyping, Trends in Plan Science - Cell Press, August 2020, DOI:https://doi.org/10.1016/j.tplants.2020.07.010
65. S. V. Mirnezami, T. Young, T. Assefa, S. Prichard, K.Nagasubramanian, K. Sandhu, S. Sarkar, S. Sundararajan, M. E. O'Neal, B. Ganaphathysubramanian, A. Singh, Automated trichome counting in soybean using advanced image‐processing techniques, Applications in Plant Sciences.
64. X.Y. Lee, S. Saha, S. Sarkar, B. Giera, Companion Journal: Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures, Data in Brief, August 2020, 106119.
63. X.Y. Lee, S. Saha, S. Sarkar, B. Giera, Automated detection of part quality during two-photon lithography via deep learning, Additive Manufacturing, Volume 36, December 2020, 101444.
62. Kevin G. Falk, Talukder Zaki Jubery, Jamie A. O’Rourke, Arti Singh, Soumik Sarkar, Baskar Ganapathysubramanian, Asheesh K. Singh, Soybean root system architecture traits study through genotypic, phenotypic and shape-based clusters, Plant Phenomics, 2020 (accepted)
61. Z. Jiang, V. Chinde, A. Kohl, A. G. Kelkar, and S. Sarkar. Supervisory Control and Distributed Optimization of Building Energy Systems, Journal of Dynamic Systems, Measurement, and Control, October 2020, 142(10): 101008
60. H. Saha, C. Liu, Z. Jiang, and S. Sarkar. Data-driven performance monitoring of dynamical systems using Granger causal graphical models, in ASME journal of Dynamics, Systems and Control, published March 16, 2020.
59. Kevin G. Falk, Talukder Z. Jubery, Seyed V. Mirnezami, Kyle A. Parmley, Soumik Sarkar, Arti Singh, Baskar Ganapathysubramanian, and Asheesh K. Singh. Computer vision and machine learning enabled soybean root phenotyping pipeline. Plant Methods, 16, 5 (2020) doi:10.1186/s13007-019-0550-5
2019
58. Aditya Balu, Sahiti Nallagonda, Fei Xu, Adarsh Krishnamurthy, Ming-Chen Hsu, and Soumik Sarkar. A deep learning framework for diagnostics and patient-specific design of bioprosthetic heart valves. Scientific Reports, 9, 18560 (2019) doi:10.1038/s41598-019-54707-9
57. Kyle Parmley, Race Higgins, Baskar Ganapathysubramanian, Soumik Sarkar, and Asheesh Singh, Machine Learning Approach for Prescriptive Plant Breeding, Scientific Reports (Accepted)
56. B. S. S. Pokuri, S. Ghosal, A. Kokate, S. Sarkar, B. Ganapathysubramanian, Interpretable deep learning for guided microstructure-property explorations in photovoltaics, Nature (npj) Computational Materials, 2019.
55. K. Nagasubramanian, S. Jones, A. K. Singh, S. Sarkar, A. Singh, B. Ganapathysubramanian, Plant disease identification using explainable 3D deep learning on hyperspectral images, Plant Methods 15.1 (2019): 1-10.
54. X. Y. Lee, A. Balu, D. Stoecklein, B. Ganapathysubramanian, S. Sarkar, A Case Study of Deep Reinforcement Learning for Engineering Design: Application to Microfluidic Devices for Flow Sculpting, ASME Journal of Mechanical Design – Special Issue: Machine Learning for Engineering Design), November 2019; 141(11): 111401.
53. K. Parmley, K. Nagasubramanian, S. Sarkar, B. Ganapathysubramanian, A. K. Singh, Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions using Phenomics Assisted Selection in Soybean Plant Phenomics, Plant Phenomics (a Science Partner Journal), vol 2019, Article ID 5809404, 15 pages, 2019.
52. S. Ghosal, B. Zheng, S. C. Chapman, A. B. Potgieter, D. R. Jordan, X. Wang, A. K. Singh, A. Singh, M. Hirafuji, S. Ninomiya, B. Ganapathysubramanian, S. Sarkar, W. Guo, A weakly supervised deep learning framework for sorghum head detection and counting, Plant Phenomics (a Science Partner Journal), vol 2019, Article ID 1525874, 14 pages, 2019.
51. C. Liu, M. Zhao, A. Sharma, S. Sarkar, Traffic Dynamics Exploration and Incident Detection Using Spatiotemporal Graphical Modeling, Journal of Data Analytics in transportation (2019): pp 1-19
50. H.Saha, A.R.Florita , G.P.Henze, S.Sarkar, Occupancy sensing in buildings: A review of data analytics approaches, Energy and Buildings 118 (2019) : 278- 285
49. C. Liu, T. Han, L. Wu, S. Sarkar, D. Jiang, An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems, Mechanical Systems and Signal Processing 117 (2019): 170-187
2018
48. T. Gao, H. Emadi, H. Saha, J. Zhang, A. Lofquist, A. Singh, B. Ganapathysubramanian, S. Sarkar, A. Singh, and S. Bhattacharya. "A Novel Multirobot System for Plant Phenotyping" Robotics 7, no. 4 (2018): 61.
47. A. Ott, J. C. Schnable, C. Yeh, L. Wu, C. Liu, H. Hu, C. L. Dalgard, S. Sarkar and P. S. Schnable, Linked read technology for assembling large complex and polyploid genomes, BMC genomics 19, no. 1 (2018): 651
46. K. Nagasubramanian, S. Jones, S. Sarkar, A. K. Singh, A. Singh, B. Ganapathysubramanian, Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems, Plant Methods (2018)
45. A. K. Singh, B. Ganapathysubramanian, S. Sarkar, A. Singh, Deep learning for plant stress phenotyping: trends and future perspectives, Trends in Plant Science (2018)
44. A. Akintayo, G. Tylka, A. K. Singh, B. Ganapathysubramanian, A. Singh and S. Sarkar, A deep learning framework to discern and count microscopic nematode eggs, Scientific Reports 8, Article Number: 9145 (2018).
43. L. Wu, C. Liu, T. Huang, A. Sharma, S. Sarkar, Traffic sensor health monitoring using spatiotemporal graphical modeling, International Journal of Prognostics and Health Management, Vol 9 (1) 022, pages: 13, 2018.
42. A. Akintayo and S. Sarkar, Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data, Signal Processing, Volume 151, pp. 76-88, October 2018.
41. S. Ghosal, D. Blystone, A. K. Singh, B. Ganapathysubramanian, A. Singh and S. Sarkar, An explainable deep machine vision framework for plant stress phenotyping, Proceedings of the National Academy of Sciences of the United States of America (PNAS) May 1, 2018. 115 (18) 4613-4618; published ahead of print April 16, 2018.
40. S. Ghadai, A. Balu, A. Krishnamurthy and S. Sarkar, Learning Localized Features in 3D CADModels for Manufacturability Analysis of Drilled Holes, Computer Aided Geometric Design Journal, Elsevier, Volume 62, May 2018, Pages 263-275 , Presented at International Conference on Geometric Modeling & Processing (GMP 2018)
39. P. Chakraborty, Y. A. Gyamfi, S. Poddar, V. Ahsani, A. Sharma and S. Sarkar, Traffic Congestion Detection from Camera Images Using Deep Convolutional Neural Networks, Transportation Research Record (TRR), Journal of the Transportation Research Board, First Published June 11, 2018
38. T. Huang, C. Liu, A. Sharma, S. Sarkar, Traffic System Anomaly Detection using Spatiotemporal Pattern Networks, International Journal of Prognostics and Health Management: Vol 9 (1) 003, pages: 12, 2018
37. C. Liu, A. Akintayo, Z. Jiang, G.P. Henze, S. Sarkar, Multivariate Exploration of Non-Intrusive Load Monitoring via Spatiotemporal Pattern Network, Applied Energy, Volume 211, Pages 1106-1122, February 2018
36. K. G. Lore, D. Stoecklein, M. Davies, B. Ganapathysubramanian and S. Sarkar, A Deep Learning framework for Casual Shape Transformation, Neural Networks, Volume 98, Pages 305-317, February 2018
2017
35. Z. Jiang, K. Mukherjee, S. Sarkar, Generalized Gossip-based Subgradient Method for Distributed Optimization, International Journal of Control, November 2017.
34. C. Liu, S. Ghosal, Z. Jiang and S. Sarkar, An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling, Cyber- Physical Systems, Volume 3, Issue 1-4, 2017
33. Z. Jiang, C. Liu, A. Akintayo, G. Henze, S. Sarkar, Energy Prediction using Spatiotemporal Pattern Networks, Applied Energy, Volume 206, pp. 1022- 1039, November 2017.
32. V. Chinde, K. Kosaraju, A. Kelkar, R. Pasumarthy, S. Sarkar and N. Singh. A Passivity-based Power Shaping Control of Building HVAC Systems. Journal of Dynamic Systems, Measurement and Control, Article Number: 111007-111007-10 (2017).
31. T. Jubery, J. Shook, K. Parmley, J. Zhang, H. Naik, R. Higgins, S. Sarkar, A. Singh, A. Singh and B. Ganapathysubramanian. Deploying Fourier coefficients to unravel soybean canopy diversity. Frontiers in Plant Science, Vol.7, 2017.
30. J. Zhang, H. Naik, T. Assefa, S. Sarkar, C. Reddy R. V, A. Singh, B. Ganapathysubramanian and A. Singh. Computer vision and machine learning for robust phenotyping in genome-wide studies. Scientific Reports 7, Article Number: 44048 (2017).
29. K. G. Lore, A. Akintayo, and S. Sarkar, LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement, Pattern Recognition, Volume 61, pp. 650-662, January 2017. Link to LLNet Code Repository
28. C. Liu, Y. Gong, S. Laflamme, B. Phares and S. Sarkar, Bridge damage detection using spatiotemporal patterns extracted from dense sensor network, Measurement Science and Technology (Special Feature on Dense Sensor Networks for Mesoscale SHM), Vol 61, No. 1, January 2017.
27. H. Naik, J. Zhang, A. Lofquist, T. Assefa, S. Sarkar, D. Ackerman, A. Singh, A. Singh and B. Ganapathysubramanian, A real-time Phenotyping Framework using Machine Learning for Plant Stress Severity rating in Soybean, Plant Methods 13:23 (2017).
26. D. Stoecklein, K. G. Lore, M. Davies, B. Ganapathysubramanian and S. Sarkar. Deep Learning for Flow Sculpting: Insights into Efficient Learning using Scientific Simulation Data. Scientific Reports 7, Article number: 46368 (2017).
2016
25. A. Akintayo, K. G. Lore, S. Sarkar, S. Sarkar. Prognostics of Combustion Instabilities from Hi-speed Flame Video using a Deep Convolutional Selective Autoencoder. International Journal of Prognostics and Health Management, 2016.
24. S. Sarkar and A. Srivastav, A Composite Discretization Scheme for Symbolic Indentification of Complex Systems, Signal Processing, Volume 125, pp. 156-170, August 2016.
23. A. Singh, B. Ganapathysubramanian, A. K. Singh, S. Sarkar, Machine Learning for High-Throughput Stress Phenotyping in Plants, Trends in Plant Sciences (TIPS), Volume 21, Issue 2, p110–124, 2016 .
22. S. Sarkar, V. Venugopalan, K. Reddy, J. Ryde, N. Jaitly and M. Giering, Deep Learning for Automated Occlusion Edge Detection in RGB-D Frames. Journal of Signal Processing Systems (Special Issue on Dynamic Data-driven Application Systems (DDDAS)), December 2016.
2015
21. K. G. Lore, D. Stoecklein, M. Davies, B. Ganapathysubramanian and S. Sarkar, Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns, JMLR: Workshop and Conference Proceedings 44 (2015). Proceedings of The 1st International Workshop on “Feature Extraction: Modern Questions and Challenges”, NIPS, pp. 213–225, 2015.
20. D. K. Jha, P. Chattopadhyay, S. Sarkar and A. Ray, Path Planning in GPS-denied Environments with Collective Intelligence of Distributed Sensor Networks, International Journal of Control, 2015.
19. S. Bengea, P. Li, S. Sarkar, S. Vichik, V. Adetola, K. Kang, T. Lovett, F. Leonardi, A. Kelman, Fault-Tolerant Optimal Control of a Building Heating, Ventilation and Air Conditioning System, Science and Technology for the Built Environment (formerly HVAC&R Research Journal), 2015. (won the Best Paper award)
2014
18. S. Sarkar and K. Mukherjee, Event-triggered Decision Propagation in Proximity Networks, Invited paper for the Inaugural issue of Frontiers in Robotics and AI: Sensor Fusion and Machine Perception (Part of the Nature Publishing Group), 2014.
17. S. Sarkar, S. Sarkar, N. Virani, A. Ray, M. Yasar, Sensor Fusion for Fault Detection & Classification in Distributed Physical Processes, Invited paper for the Inaugural issue of Frontiers in Robotics and AI: Sensor Fusion and Machine Perception (Part of the Nature Publishing Group), 2014.
2013
16. S. Sarkar, S. Sarkar, K. Mukherjee, A. Ray, and A. Srivastav, Multi-sensor Information Fusion for Fault Detection in Aircraft Gas Turbine Engines, Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, Vol. 227, No. 12, December 2013, pp. 1988-2001.
15. A. Ray, S. Phoha, and S. Sarkar, Behavior Prediction for Decision & Control in Cognitive Autonomous Systems, New Mathematics and Natural Computation: Special Issue on Engineering of the Mind, Cognitive Science and Robotics, Vol. 9, No. 3, November 2013, pp. 1-9.
14. S. Sarkar, K. Mukherjee, S. Sarkar, and A. Ray, Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines, Journal of Dynamic Systems, Measurement, and Control, Transactions of the ASME, Vol. 135, Issue 1. January 2013, pp. 14506 (6 pages).
13. S. Sarkar, K. Mukherjee, and A. Ray, Distributed Decision Propagation in Proximity Networks, International Journal of Control, Vol. 86, No.6, June 2013, pp. 1118-1130.
2012
12. S. Sarkar, K. Mukherjee, A. Ray, A. Srivastav, and T. Wettergren, Equilibrium Thermodynamics for Heterogeneous Packet Transmission in Communication Networks, IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol. 42, No. 4, August 2012, pp. 1083-1093.
11. S. Sarkar, K. Mukherjee, X. Jin, and A. Ray, Optimization of Symbolic Feature Extraction for Pattern Classification, Signal Processing, Vol. 92, No. 3, March 2012, pp. 625-635.
10. S. Chakraborty, S. Sarkar, and A. Ray, Symbolic Identification for Fault Detection in Aircraft Gas Turbine Engines, Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, Vol. 226, No. 4, April 2012, pp. 422-436.
2011
9. S. Sarkar, X. Jin, and A. Ray, Data-driven Fault Detection in Aircraft Engines with Noisy Sensor Measurements, Journal of Engineering for Gas Turbines and Power, Vol. 133, No. 8, August 2011, pp. 081602 (10 pages).
8. X. Jin, Y. Guo, S. Sarkar, A. Ray, and R.M. Edwards Anomaly Detection in Nuclear Power Plants via Symbolic Dynamic Filtering, IEEE Transactions on Nuclear Science, Vol. 58, No.1, February 2011, pp.277-288.
2009
7. S. Sarkar, C. Rao and A. Ray, Statistical Estimation of Multiple Faults in Aircraft Gas Turbine Engines, Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, Vol. 223, No.4, 2009, pp. 415-424.
6. S. Sarkar, K. Mukherjee and A. Ray, Generalization of Hilbert Transform for Symbolic Analysis of Noisy Signals, Signal Processing, Vol. 89, Issue 6, June 2009, pp. 1245-1251.
5. C. Rao, K. Mukherjee, S. Sarkar and A. Ray, Statistical Estimation of Multiple Parameters via Symbolic Dynamic Filtering, Signal Processing, in Vol. 89, Issue 6, June 2009, pp. 981-988
4. C. Rao, A. Ray, S. Sarkar and M. Yasar, Review and Comparative Evaluation of Symbolic Dynamic Filtering for Detection of Anomaly Patterns, Signal, Image, and Video Processing, Vol. 3, Issue 2 (2009), pp.101-114.
2008
3. S. Chakraborty, S. Sarkar, S. Gupta and A. Ray, Damage Monitoring of Refractory Wall in a Generic Entrained-Bed Slagging Gasification System, Proceedings of the I Mech E Part A: Journal of Power and Energy, Vol. 222 Part A, No. 8, October 2008, pp. 791-807.
2. S. Sarkar, M. Yasar, S. Gupta, A. Ray and K. Mukherjee, Fault Detection and Isolation in Aircraft Gas Turbine Engines: Part II Validation on a Simulation Test Bed, Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, Vol. 222 (G3), No. 3, May 2008, pp. 319-330.
1. S. Gupta, A. Ray, S. Sarkar and M. Yasar, Fault Detection and Isolation in Aircraft Gas Turbine Engines: Part I Underlying Concept, Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, Vol. 222 (G3), No. 3, May 2008, pp. 307-318.
Book Chapters
8. A. K. Singh, A. Singh, S. Sarkar, B. Ganapathysubramainan et. al., High-Throughput Phenotyping in Soybean, High-throughput Crop Phenotyping (part of a book series on Advanced Concepts and strategies in Plant Sciences (ACSPS)), Springer-Nature, 2021.
7. T. Gangopadhyay, S. Y. Tan, Z. Jiang, S. Sarkar, Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems, Dynamic Data Driven Application Systems (pp. 93-101), Springer, 2020.
6. T. Gangopadhyay, A. Locurto, J. B. Michael, S. Sarkar, Deep Learning Algorithms for Detecting Combustion Instabilities, Dynamics and Control of Energy Systems (pp. 283-300), Springer, 2020
5. A. Balu, S. Ghadai, G. Young, S. Sarkar, A. Krishnamurthy, A Machine Learning Framework for Decision Support in Design and Manufacturing, ASME press, 2019
4. K. G. Lore, D. Stoecklein, M. Davies, B. Ganapathysubramanian and S. Sarkar, Deep Learning for Engineering Big Data analytics, Big Data Analytics: From Planning to Performance, CRC Press, Taylor & Francis Group, USA 2017
3. S. Sarkar, Z. Jiang, A. Akintayo, S. Krishnamurthy and A. Tewari, Probabilistic Graphical Modeling of Distributed Cyber-Physical Systems, Cyber-Physical Systems: Foundations, Principles and Applications, Elsevier, 2016
2. S. Sarkar, S. Sarkar, and A. Ray, Data-enabled Health Management of Complex Industrial Systems, Fault Detection: Classification, Techniques and Role in Industrial Systems, Nova Science Publishers, December 2013
1. A. Srivastav, A. Tewari, B. Dong, S. Sarkar, and M. Gorbounov, ”Localized Uncertainty Quantification for Baseline Building Energy Modeling”, Automated Diagnostics for Facility Equipment, Systems, and Whole Buildings, Fairmont Press, 2014
7. T. Gangopadhyay, S. Y. Tan, Z. Jiang, S. Sarkar, Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems, Dynamic Data Driven Application Systems (pp. 93-101), Springer, 2020.
6. T. Gangopadhyay, A. Locurto, J. B. Michael, S. Sarkar, Deep Learning Algorithms for Detecting Combustion Instabilities, Dynamics and Control of Energy Systems (pp. 283-300), Springer, 2020
5. A. Balu, S. Ghadai, G. Young, S. Sarkar, A. Krishnamurthy, A Machine Learning Framework for Decision Support in Design and Manufacturing, ASME press, 2019
4. K. G. Lore, D. Stoecklein, M. Davies, B. Ganapathysubramanian and S. Sarkar, Deep Learning for Engineering Big Data analytics, Big Data Analytics: From Planning to Performance, CRC Press, Taylor & Francis Group, USA 2017
3. S. Sarkar, Z. Jiang, A. Akintayo, S. Krishnamurthy and A. Tewari, Probabilistic Graphical Modeling of Distributed Cyber-Physical Systems, Cyber-Physical Systems: Foundations, Principles and Applications, Elsevier, 2016
2. S. Sarkar, S. Sarkar, and A. Ray, Data-enabled Health Management of Complex Industrial Systems, Fault Detection: Classification, Techniques and Role in Industrial Systems, Nova Science Publishers, December 2013
1. A. Srivastav, A. Tewari, B. Dong, S. Sarkar, and M. Gorbounov, ”Localized Uncertainty Quantification for Baseline Building Energy Modeling”, Automated Diagnostics for Facility Equipment, Systems, and Whole Buildings, Fairmont Press, 2014
Articles
S. Sarkar, D. Vrabie, M. Krucinski, L. Bertuccelli, T. Lovett, S. Mijanovic, ”From Smart Homes to Green Cities: Role of Intelligent Diagnostics and Control in Energy Efficient Buildings”, Dynamic Systems & Control Magazine, ASME Mechanical Engineering, December 2013
Patents
3. S. Bengea, V. Adetola, M. Krucinski, S. Sarkar, A. Srivastav, T. Lovett, K. Mukherjee, A. Ghosh, M. Chen and P. Li, Automated Functional Tests for Diagnostics and Control, US Patent Application Serial No. 62/078,735, filed November 12, 2014, and International Application Docket No. PA-0022699- US
2. S. Sarkar, and A. Ray, Diagnosis and Estimation of Multiple Faults in Aircraft Gas Turbine Engines, PSU Invention Disclosure No. 2009-3602 (submitted)
1. S. Chakraborty, S. Sarkar, S. Gupta, and A. Ray, Method and System for Monitoring Refractory Walls In Slagging Gasification Systems, PSU Invention Disclosure No. 2009-3597, U.S. Patent Application Serial No. 61/265,272, Publication Date: December 03, 2009.
2. S. Sarkar, and A. Ray, Diagnosis and Estimation of Multiple Faults in Aircraft Gas Turbine Engines, PSU Invention Disclosure No. 2009-3602 (submitted)
1. S. Chakraborty, S. Sarkar, S. Gupta, and A. Ray, Method and System for Monitoring Refractory Walls In Slagging Gasification Systems, PSU Invention Disclosure No. 2009-3597, U.S. Patent Application Serial No. 61/265,272, Publication Date: December 03, 2009.
Conference Papers
2021
115. Balu, A., Botelho, S., Khara, B., Rao, V., Hegde, C., Sarkar, S., Adavani, S, Krishnamurthy, A. Ganapathysubramanian, B. (2021). Distributed Multigrid Neural Solvers on Megavoxel Domains, Supercomputing.
114. A. Saffari, S. Y. Tan, M. Katanbaf, H. Saha, J. R. Smith, S. Sarkar. 2021. Battery-Free Camera Occupancy Detection System. In Proceedings of the 5th International Workshop on Embedded and Mobile Deep Learning (Virtual, WI, USA)(EMDL’21). Association for Computing Machinery, New York, NY, USA.
113. J. Rade+, S. Sarkar, A. Krishnamurthy, J. Ren, A. Sarkar, AI Guided Measurement of Live Cells Using AFM, Modeling, Estimation and Control Conference (MECC), (Austin, TX), October 24-27, 2021.
112. S. Ghadai+, X. Y. Lee+, A. Balu+, S. Sarkar, A. Krishnamurthy, Multi-resolution 3D CNN for Learning Multi-scale Spatial Features in CAD Models, SIAM Conference on Geometric and Physical Modeling (GD/SPM21), Virtual, Sept 27-29, 2021.
111. Y. Esfandiari+, S. Y. Tan+, Z. Jiang, A. Balu+, E.D. Herron+, C. Hegde, S. Sarkar, Cross-Gradient Aggregation for Decentralized Learning from Non-IID data, International Conference on Machine Learning (ICML), Virtual, July 18-24, 2021.
110. A. Saffari+, S. Y. Tan+, M. Katanbaf+, H. Saha+, J. Smith, S. Sarkar, Battery-Free Camera Occupancy Detection System, 5th International Workshop on Embedded and Mobile Deep Learning (EMDL), Virtual, June 24-25, 2021.
109. A. Sarkar, J. Waite and S. Sarkar, Deep learning for fast Atomic Force Microscopy data analytics, 65th Biophysical Society Annual Meeting, Feb 22-26, 2021, Virtual.
108. A. Balu, Z. Jiang, SY. Tan, C. Hedge, Y. M. Lee, and S. Sarkar , Decentralized Deep Learning Using Momentum-Accelerated Consensus, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (Toronto, Canada), 2021
107. T. Gangopadhyay, S. Y. Tan, Z. Jiang, R. Meng, S. Sarkar, Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (Toronto, Canada), 2021
106. X.Y. Lee, Y. Esfandiari, K.L. Tan, S. Sarkar, Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents, International Conference on Cyber-Physical Systems (ICCPS), (Nashville, TN), 2021
2020
105. T. Gangopadhyay, J. Shook, A. K. Singh, S. Sarkar, Interpreting the Impact of Weather on Crop Yield Using Attention, NeurIPS Workshop on AI for Earth Sciences, 2020, Virtual
104. Y. Esfandiari, S.Y. Tan, Z.H. Jiang, A. Balu, C. Hegde, S. Sarkar, Local Gradient Aggregation for Decentralized Learning from Non-IID data, Optimization for Machine Learning Workshop, Neural Information Processing Systems (NeurIPS 2020), Virtual.
103. M. Cho, A. Joshi, X.Y. Lee, A. Balu, B. Ganapathysubramanian, S. Sarkar, C. Hegde, Differentiable Programming for Piecewise Polynomial Functions , Learning Meets Combinatorial Algorithms Workshop, Neural Information Processing Systems (NeurIPS 2020), Virtual
102. Z. Jiang, X.Y. Lee, S.Y Tan, A. Balu, Y.M. Lee, C. Hegde, S. Sarkar, Adaptive Gradient Tracking In Stochastic Optimization, Optimization for Machine Learning Workshop, Neural Information Processing Systems (NeurIPS 2020), Virtual.
101. X.Y. Lee, Y. Esfandiari, K.L. Tan, S.Sarkar, Targeted Query-based Action-Space Adversarial Policies on Deep Reinforcement Learning Agents, Deep Reinforcement Learning Workshop in Neural Information Processing Systems (NeurIPS 2020), Virtual.
100. T. Gangopadhyay, S. Y. Tan, Z. Jiang, S. Sarkar, Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems, International Conference on Dynamic Data Driven Application Systems (DDDAS), (Boston, MA), 2020.
99. B. Giera, X. Lee, S. Saha, S. Sarkar, Automated Detection of Part Quality During Two Photon Lithography via Deep Learning, Annual International Solid Freeform Fabrication Symposium (SFF Symp 2020), Austin, TX, August 17-19, 2020.
98. A. Joshi, B. Khara, S. Sarkar, B. Ganapathysubramanian, C. Hedge, Solving Linear PDEs with Generative Models, ASILOMAR 2020 Conference on Signals, Systems, and Computers, Pacific Grove, CA, 2020
97. A. Balu, S. Ghadai, S. Sarkar, A. Krishnamurthy, Orthogonal Distance Fields Representation For Machine-Learning Based Manufacturability Analysis, ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE), St. Louis, MO, 2020
96. L. Riera, K. Ozcan, J. Merickel, M. Rizzo, S. Sarkar, A. Sharma, Automatic Lane Detection Algorithm For Noisy Naturalistic Driving Data, IEEE Intelligent Vehicle Symposium (IV), (Las Vegas), 2020
95. S.Y. Tan, H. Saha, M. Jacoby, A.R. Florita, G.P. Henze, S. Sarkar, Granger Causality-based Hierarchical Time Series Clustering for State Estimation, IFAC World Congress, (Berlin, Germany), 2020
94. T. Gangopadhyay, S. Y. Tan, A. Locurto, J. B. Michael, S. Sarkar, Interpretable Deep Learning for Monitoring Combustion Instability, IFAC World Congress, (Berlin, Germany), 2020
93. K.L. Tan, Y. Esfandiari, X.Y. Lee, Aakanksha, S. Sarkar, Robustifying Reinforcement Learning Agents via Action Space Adversarial Training, Proceedings of American Control Conference (ACC 2020), Denver, CO.
92. Y. Esfandiari, A. Balu, K. Ebrahimi, U. Vaidya, N. Elia, S. Sarkar, A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning, acceptance at the Artificial Intelligence Safety (SafeAI) workshop in Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), New York, NY, 2020
91. X.Y. Lee, S. Ghadai, K.L. Tan, C. Hedge, S. Sarkar, Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents, Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), New York, NY, 2020
90. A. Joshi, M. Cho, V. Shah, B. Pokuri, S. Sarkar, B. Ganapathysubramanian, C. Hedge, InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models, Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), New York, NY, 2020
2019
89. T. Gangopadhyay, S. Y. Tan, A. Locurto, J. B. Michael, S. Sarkar, An Explainable Framework using Deep Attention Models for Sequential Data in Combustion Systems, NeurIPS Workshop on Machine Learning and the Physical Sciences, (Vancouver, Canada), 2019
88. Z. Jiang, A. Balu, S.Y. Tan, Y.M. Lee, C. Hedge, S. Sarkar, On Higher-order Moments in Adam, accepted in Beyond First Order Methods in Machine Learning workshop in 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
87. A. Mukherjee, A. Joshi, S. Sarkar, C. Hegde, Semantic Domain Adaptation for Deep Classifiers via GAN-based Datat Augmentation, accepted at the Machine Learning for Autonomous Driving workshop at Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
86. A. Joshi, V. Shah, S. Ghosal, B. Pokuri, S. Sarkar, B. Ganapathysubramanian, C. Hegde, Generative Models for Solving Nonlinear Partial Differential Equations, accepted at the Machine Learning and the Physical Sciences Workshop at Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
85. H. Saha, V. Venkataraman, A. Speranzon, S. Sarkar, A perspective on multi-agent communication for information fusion, Visually Grounded Interaction and Language at Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
84. X.Y. Lee, S. Ghadai, K.L. Tan, C. Hedge, S. Sarkar, Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents, Deep Reinforcement Learning Workshop in Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada.
83. T. Gangopadhyay, J. Shook, A. K. Singh, S. Sarkar, Deep Time Series Attention Models for Crop Yield Prediction and Insights, NeurIPS Workshop on Machine Learning and the Physical Sciences, (Vancouver, Canada), 2019
82. Aditya Balu, K.L. Tan, Michael C.H. Wu, Ming-Chen Hsu, Soumik Sarkar, Adarsh Krishnamurthy; Deep Learning for Dynamic Deformation Simulation of Bioprosthetic Heart Valves, USNCCM 15, Austin, TX, 2019.
81. A. Joshi, A. Mukherjee, S. Sarkar, C. Hegde, Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers, International Conference on Computer Vision (ICCV), Seoul, South Korea, Oct 27 – Nov 2, 2019.
80. Barnwal, A., Merickel, J., Riera-Garcia, L., Ozcan, K., Sarkar, S., Sharma, A., Rizzo, M. (2019). Linking age-related decline to driver behavior at signalized intersections. 2019 Association for the Advancement of Automotive Medicine’s (AAAM) 63rd Annual Scientific Conference, Madrid, Spain, 15 – 18 October 2019.
79. K.L. Tan, S. Poddar, S. Sarkar, A. Sharma, Deep Reinforcement Learning for Adaptive Traffic Signal Control. Proceedings of ASME 2019 Dynamic Systems and Control Conference (DSCC), (Park City, Utah), 2019.
78. Mukherjee A., Joshi A. , SarkarS., Hegde C., "Attribute-Controlled Traffic Data Augmentation Using Conditional Generative Models" accepted in Vision for All Seasons: Bad Weather and Nighttime workshop at IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Long Beach, CA), 2019.
77. Ghadai, S., Lee, X. Y., Balu, A., Sarkar, S., Krishnmaurthy, A., "Multi-level 3D CNN for Learning Multi-scale Spatial Features'', accepted and invited for talk in Deep Learning for Geometric Shape Understanding workshop at IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Long Beach, CA), 2019.
76. S.Y. Tan, H. Saha, A.R. Florita, G.P. Henze, S. Sarkar, A flexible framework for building occupancy detection using spatiotemporal pattern networks. Proceedings of American Control Conference, (Philadelphia, PA), 2019.
75. A. Balu, M. Hsu, S. Sarkar, A. Krishnamurthy, A Deep-Learning Framework for Diagnostics and Design of Bioprosthetic Heart Valves. BMES/FDA conference on Frontiers in Medical Devices : The Role of Digital Evidence to Support Personalized Patient Healthcare, (Washington, DC Metropolitan Area), 2019.
2018
74. R. Singh, A. Sharma, O. Bingol, A. Balu, G. Balasubramanian, D. D. Johnson and S. Sarkar, 3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys. Workshop on Machine Learning for Molecules and Materials (MLMM) at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018. [arXiv]
73. B.S.S. Pokuri, S. Ghosal, A. Kokate, B. Ganapathysubramanian, S. Sarkar, Interpretable deep learning for guided structure-property explorations in photovoltaics. Workshop on Machine Learning for Molecules and Materials (MLMM) at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018. [arXiv]
72. X. Lee, A. Balu, D. Stoecklein, B. Ganapathysubramanian, S. Sarkar, Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning, Deep Reinforcement Learning Workshop at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018.
71. R. Singh, V. Shah, B. Pokuri, S. Sarkar, B. Ganapathysubramanian, C. Hegde, Physics-aware Deep Generative Models for Creating Synthetic Microstructures, Workshop on Machine Learning for Molecules and Materials (MLMM) at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018.
70. T. Gangopadhyay, S. Y. Tan, G. Huang, S. Sarkar, Temporal Attention and Stacked LSTMs for Multivariate Time Series Prediction, Workshop on Modeling and decision-making in the spatiotemporal domain at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018.
69. A. Balu, S. Nallagonda, F. Xu, A. Krishnamurthy, M. Hsu, S. Sarkar, Machine Learning for Diagnostics and Patient-Specific Design of Bioprosthetic Heart Valves, Integrating Design and Analysis (IGA), (Austin, TX), 2018.
68. A. Havens, Z. Jiang, S. Sarkar, Online Robust Policy Learning in the Presence of Unknown Adversaries, Proceedings of Advances in Neural Information Processing Systems (NIPS), (Montreal, Canada), 2018.
67. H. Saha, C. Liu, Z. Jiang, and S. Sarkar, Exploring granger causality in dynamical systems modeling and performance monitoring, Conference on Decision and Control, (Miami Beach, FL), 2018.
66. L. Wu, V. Chinde, H. Sharma, U. Passe, S. Sarkar, A Data-driven Approach towards Integration of Microclimate Conditions for Predicting Building Energy Performance, 5th International High Performance Buildings Conference, (West Lafayette, IN), 2018.
65. Z. Jiang, A. Balu, C. Hedge, S. Sarkar, Incremental Consensus-based Collaborative Deep Learning, Workshop on Modern Trends in Nonconvex Optimization for Machine Learning at International Conference on Machine Learning (ICML), (Stockholm, Sweden), 2018 *Spotlight Presentation.*
64. T. Gangopadhyay, A. Locurto, P. Boor, J. B. Michael, S. Sarkar, Characterizing Combustion Instability Using Deep Convolutional Neural Networks, Proceedings of ASME 2018 Dynamic Systems and Control Conference (DSCC), (Atlanta, Georgia), 2018.
63. A. Locurto, T. Gangopadhyay, P. Boor, S. Sarkar, J. B. Michael, Mode decomposition and convolutional neural network analysis of thermoacoustic instabilities in a Rijke tube, 2018 Spring Technical Meeting, Central States Section of The Combustion Institute, (Minneapolis, Minnesota), 2018 .
62. P. Chakraborty, Y. A. Gyamfi, S. Poddar, V. Ahsani, A. Sharma and S. Sarkar, Traffic Congestion Detection from Camera Images Using Deep Convolutional Neural Networks, Transportation Research Board 97th Annual Meeting, (Washington, D.C.), 2018.
61. S. Poddar, K. Ozcan, P. Chakraborty, V. Ahsani, A. Sharma and S. Sarkar, Comparison of Machine Learning Algorithms to Determine Traffic Congestion from Camera Images, Transportation Research Board 97th Annual Meeting, (Washington, D.C.), 2018.
60. Z. Jiang, K. Mukherjee, S. Sarkar, On Consensus-Disagreement Tradeoff in Distributed Optimization, Proceedings of American Control Conference, (Milwaukee, WI), 2018.
59. C. Liu, Z. Jiang, A. Akintayo, G. P. Henze, S. Sarkar, Building Energy Disaggregation using Spatiotemporal Pattern Network, Proceedings of American Control Conference, (Milwaukee, WI), 2018.
58. Z. Jiang, T. Wilkie, S. Sarkar, Hierarchical Optimization for Building Energy Systems, Proceedings of American Control Conference, (Milwaukee, WI), 2018.
2017
57. Z. Jiang, A. Balu, C. Hegde, S. Sarkar, Collaborative Deep Learning in Fixed Topology Networks, Proceedings of Advances in Neural Information Processing Systems (NIPS), (Long Beach, CA), 2017.
56. A. Balu, T. V. Nguyen, A. Kokate, C. Hegde, S. Sarkar, A Forward-Backward Approach for Visualizing Information Flow in Deep Networks, Symposium on Interpretable Machine Learning at NIPS 2017.
55. K. Nagasubramanian, S. Jones, A. K. Singh, A. Singh, B. Ganapathysubramanian, S. Sarkar, Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency map, Workshop on Intrepreting, Explaining and Visualizing Deep Learning...now what?, NIPS 2017.
54. S. Ghosal, D. Blystone, A. K. Singh, B. Ganapathysubramanian, A. Singh and S. Sarkar, Interpretable Deep Learning applied to Plant Stress Phenotyping, Symposium on Interpretable Machine Learning at NIPS 2017.
53. S. Ghadai, A. Balu, A. Krishnamurthy, S. Sarkar, Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes, Symposium on Interpretable Machine Learning at NIPS 2017.
52. S. Ghosal, A. Akintayo, P. K. Boor, S. Sarkar, High Speed Video-based health monitoring using 3D deep learning, Proceedings of the Dynamic Data Driven Application Systems (DDDAS), (Cambridge, MA), 2017.
51. Z. Jiang, K. Mukherjee, S. Sarkar. Convergence and Noise Effect Analysis for Generalized Gossip-based Distributed Optimization. Proceedings of American Control Conference, (Seattle, WA), 2017.
50. L. Wu, C. Liu, T. Huang, A. Sharma, S. Sarkar, Traffic sensor health monitoring using spatiotemporal graphical modeling, Proceedings of the 2nd ACM SIGKDD Workshop on Machine Learning for Prognostics & Health Management. (Halifax, NS, Canada), 2017.
49. T. Huang, C. Liu, A. Sharma, S. Sarkar, Traffic System Anomaly Detection using Spatiotemporal Pattern Networks, Proceedings of the 2nd ACM SIGKDD Workshop on Machine Learning for Prognostics & Health Management. (Halifax, NS, Canada), 2017.
48. H. Saha, T. Gao, H. Emadi, Z. Jiang, A. Singh, B. Ganapathysubramanian, S. Sarkar, A. Singh, S. Bhattacharya, Autonomous mobile sensing platform for spatiotemporal plant phenotyping, Proceedings of ASME 2017 Dynamic Systems and Control Conference (DSCC) (Tysons, VA), 2017.
47. C. Liu, K. G. Lore, S. Sarkar, Data-driven root-cause analysis for distributed system anomalies, Proceedings of IEEE Conference on Decision and Control, (Melbourne, Australia), 2017.
2016
46. C. Liu, B. Huang, M. Zhao, S. Sarkar, U. Vaidya, A. Sharma. Data Driven Exploration of Traffic Network System Dynamics using High Resolution Probe Data. Proceedings of IEEE Conference on Decision and Control, (Las Vegas, NV), 2016.
45. A. Akintayo, K. G. Lore, S. Sarkar, S. Sarkar. Early Detection of Combustion Instabilities using Deep Convolutional Selective Autoencoders on Hi-speed Flame Video, Proceedings of the 22nd ACM SIGKDD Workshop on Machine Learning for Prognostics & Health Management. (San Francisco, CA). 2016.
44. A. Akintayo, N. Lee, V. Chawla, M. Mullaney, C. Marett, A. Singh, A. Singh, G. Tylka, B. Ganapathysubramanian & S. Sarkar. An End-to-end Convolutional Selective Autoencoder Approach to Soybean Cyst Nematode Eggs Detection. Proceedings of the 22nd ACM SIGKDD Workshop on Data Science for Food, Energy and Water. (San Francisco, CA). 2016.
43. V. Chawla, M.H. Hsiang, A. Akintayo, D. Hayes, P. Schnable, B. Ganapathysubramanian, S. Sarkar. A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert-knowledge. Proceedings of the 22nd ACM SIGKDD Workshop on Data Science for Food, Energy and Water. (San Francisco, CA). 2016.
42. S. Ghosal, V. Ramanan, S. Sarkar, S. R. Chakravarthy, S. Sarkar. Detection and Analysis of Combustion Instability from High-speed Flame Images using Dynamic Mode Decomposition. Proceedings of ASME 2016 Dynamic Systems and Control Conference (DSCC). (Minneapolis, MN), 2016.
41. C. Liu, Y. Gong, S. Laflamme, B. Phares, S. Sarkar. Damage Detection of Bridge Network with Spatiotemporal Pattern Network. Proceedings of ASME 2016 Dynamic Systems and Control Conference (DSCC). (Minneapolis, MN), 2016.
40. V. Chinde, A. Kohl, Z. Jiang, A. Kelkar, S. Sarkar. A VOLTTRON based implementation of Supervisory Control using Generalized Gossip for Building Energy Systems, Proceedings in the 4th International High Performance Buildings Conference, (West Lafayette, IN), 2016
39. Z. Jiang, V. Chinde, A. Kohl, S. Sarkar, A. Kelkar, Scalable Supervisory Control of Building Energy Systems using Generalized Gossip, Proceedings of the American Control Conference, (Boston, MA), 2016
38. V. Chinde, K. C. Kosaraju, A. Kelkar, R. Pasumarthy, S. Sarkar and N. M. Singh, Building HVAC Systems Control Using Power Shaping Approach, Proceedings of the American Control Conference, (Boston, MA), 2016
37. S. Sarkar, D. K. Jha, K. G. Lore, A, Ray and S. Sarkar, Multi-modal Spatiotemporal Fusion using Neural-symbolic Causal Modeling for Early Detection of Combustion Instability, Proceedings of the American Control Conference, (Boston, MA), 2016
36. K. G. Lore, S. Sarkar and D. K. Jha, Topology Control in Mobile Sensor Networks using Information Space Feedback, Proceedings of the American Control Conference, (Boston, MA), 2016
35. K. G. Lore, N. Sweet, K. Kumar, N. Ahmed, and S. Sarkar, Deep Value of Information Estimators for Collaborative Human-Machine Information Gathering. Proceedings of the International Conference of Cyber-Physical Systems, (Vienna, Austria), 2016 [arXiv]
34. C. Liu, S. Ghosal, Z. Jiang and S. Sarkar, An unsupervised spatiotemporal graphical modeling approach to Anomaly detection in Distributed CPS, Proceedings of the International Conference on Cyber-physical Systems (ICCPS 2016). [arXiv]
33. S. Ghosal, C. Liu, U. Passe, S. He, S. Sarkar, Data-driven persistent monitoring of Indoor Air Systems, Proceedings of the ASHRAE IAQ 2016 Defining Indoor Air Quality: Policy, Standards and Best Practices, (Alexandria, VA), 2016
2015
32. S. Sarkar, K. G. Lore and S. Sarkar, Early Detection of Combustion Instability by Neural-Symbolic Analysis of Hi-speed Video, Proceedings of the NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches; 29th Annual Conference on Neural Information Processing Systems (NIPS 2015).
31. Z. Jiang and S. Sarkar, Understanding Wind turbine interactions using Spatiotemporal Pattern Network, Proceedings of ASME Dynamics Systems and Control Conference, (Columbus, OH), 2015
30. Z. Jiang, S. Sarkar and K. Mukherjee, On Distributed Optimization using Generalized Gossip, Proceedings of IEEE Conference on Decision and Control, (Osaka, Japan), 2015
29. S. Sarkar, K. G. Lore, S. Sarkar, V. Ramanan, S. Chakravarthy and A. Ray, Early Detection of Combustion Instability from Hi-speed Flame Images via Deep Learning and Symbolic Time Series Analysis, Proceedings of Annual Conference of the Prognostics and Health Management Society, (San Diego, CA), 2015
28. S. Sarkar, V. Venugopalan, K. Reddy, J. Rayde, M. Giering and N. Jaitly, Occlusion edge detection in RGBD frames using Deep Convolutional Neural Networks, Proceedings of IEEE High Performance Extreme Computing Conference, (Waltham, MA), 2015
27. V. Chinde, J. C. Heylmun, A. Kohl, Z. Jiang, S. Sarkar and A. Kelkar, Comparative Evaluation of Control-oriented zone temperature Prediction modeling strategies in Buildings, Proceedings of ASME Dynamics Systems and Control Conference, (Columbus, OH), 2015
26. R. Georgescu, K. Reddy, N. Trcka, M. Chen, P. Qumiby, P. O'Neil, T. Khawaja, L. Bertuccelli, D. Hestand, S. Sarkar, O. Erdinc and M. Giering, Scalable Human-in-the-Loop Decision Support, IEEE Aerospace Conference, Big Sky, MT, March 2015
25. K. G. Lore, M. Davies, D. Stoecklein, B. Ganapathysubramanian and S. Sarkar, Deep Learning for Flow sculpting in Microfluidic platforms, NVIDIA GPU Technology Conference, Silicon Valley, 2015
24. S. Sarkar, V. Venugopalan, K. Reddy, J. Ryde, M. Giering and N. Jaitly, RGBD Occlusion detection via Deep Convolutional Neural Networks, NVIDIA GPU Technology Conference, Silicon Valley, 2015
23. P. Chattopadhyay, D. K. Jha, S. Sarkar and A. Ray, Path Planning in GPS-denied Environments: A Collective Intelligence Approach, Proceedings of American Control Conference, (Chicago, IL), 2015
22. A. Akintayo, S. Sarkar, A Symbolic Dynamic Filtering Approach to Unsupervised Hierarchical Feature Extraction from Time-Series Data, Proceedings of American Control Conference, (Chicago, IL), 2015
2014
21. S. Krishnamurthy, S. Sarkar, A. Tewari, Scalable anomaly detection and isolation in cyber-physical systems using Bayesian Networks, Proceedings of ASME Dynamical Systems and Control Conference, (San Antonio, TX), 2014
20. R. Khire, F. Leonardi, P. Quimby, S. Sarkar, (in alphabetical order) A Novel Human Machine Interface for Advanced Building Controls and Diagnostics, (3rd International High Performance Buildings Conference at Purdue), 2014
19. V. Adetola, S. Bengea, F. Borrelli, K. Kang, A. Kelman, F. Leonardi, P. Li, T. Lovett, S. Sarkar, S. Vichik, (in alphabetical order) Fault-Tolerant Optimal Control of a Large-Size, Commercial Building Heating, Ventilation and Air Conditioning System, (3rd International High Performance Buildings Conference at Purdue), 2014
2013
18. S. Sarkar, N. Virani, M. Yasar, A. Ray, and S. Sarkar, Spatiotemporal Information Fusion for Fault detection in Shipboard Auxiliary Systems, Proceedings of American Control Conference, (Washington, D.C.), 2013
17. S. Sarkar, A. Srivastav, and M. Shashanka, Maximally Bijective Discretization for Data-driven Modeling of Complex Systems, Proceedings of American Control Conference, (Washington, D.C.), 2013 (Best Session Paper Award)
2012
16. S. Sarkar, K. Mukherjee, S. Sarkar, and A. Ray, Symbolic Transient Time-series Analysis for Fault Detection in Aircraft Gas Turbine Engines, Proceedings of American Control Conference, (Montreal, Canada), 2012 (Best Session Paper Award)
15. S. Sarkar, K. Mukherjee, and A. Ray, Distributed Decision Propagation in Mobile Agent Networks, Proceedings of American Control Conference, (Montreal, Canada), 2012 (Best Session Paper Award)
2011
14. S. Sarkar, D. S. Singh, A. Srivastav, and A. Ray, Semantic Sensor Fusion for Fault Diagnosis in Aircraft Gas Turbine Engines , Proceedings of American Control Conference, (San Francisco, CA), 2011
13. D. S. Singh, S. Sarkar, S. Gupta, and A. Ray, Optimal Partitioning of Ultrasonic Data for Fatigue Damage Detection , Proceedings of American Control Conference, (San Francisco, CA), 2011
2010
12. S. Sarkar, K. Mukherjee, A. Srivastav, and A. Ray, Distributed Decision Propagation in Mobile Agent Networks , Proceedings of Conference on Decision and Control, (Atlanta, GA) 2010
11. S. Sarkar, K. Mukherjee, X. Jin, and A. Ray, Optimization of Time-series data Partitioning for Parameter Identification , Proceedings of ASME Dynamic Systems and Control Conference, (Cambridge, MA), 2010
10. S. Sarkar, K. Mukherjee, A. Srivastav, and A. Ray, Critical Phenomena and Finite-size Scaling in Communication Networks , Proceedings of American Control Conference, (Baltimore, MD), 2010.
9. S. Chakraborty, S. Sarkar, A. Ray and S. Phoha, Symbolic Identification for Anomaly Detection in Aircraft Gas Turbine Engines, Proceedings of American Control Conference, (Baltimore, MD), 2010.
2009
8. X. Jin, S.Sarkar, K. Mukherjee and A. Ray, Suboptimal Partitioning of Timeseries Data for Anomaly Detection, Proceedings of Conference on Decision and Control, (Shanghai, China), 2009.
7. S. Sarkar, K. Mukherjee, A. Srivastav, and A. Ray, Understanding phase transition in communication networks to enable robust and resilient control, Proceedings of American Control Conference, (St. Louis, MO), 2009. (Best Session Paper Award)
6. S. Sarkar, K. Mukherjee and A. Ray, Symbolic Analysis of Time Series Signals Using Generalized Hilbert Transform, Proceedings of American Control Conference, (St. Louis, MO), 2009.
5. S. Sarkar, C. Rao and A. Ray, Estimation of Multiple Faults in Aircraft Gasturbine Engines, Proceedings of American Control Conference, (St. Louis, MO), 2009. (Best Session Paper Award)
2008
4. C. Rao, S. Sarkar, A. Ray, and M. Yasar, Comparative Evaluation of Symbolic Dynamic Filtering for Detection of Anomaly Patterns, Proceedings of American Control Conference, (Seattle, WA), 2008.
3. C. Rao, K. Mukherjee, S. Sarkar, and A. Ray, Estimation of Multiple Parameters in Dynamical Systems, Proceedings of American Control Conference, (Seattle, WA), 2008.
2. S. Sarkar, K. Mukherjee , A. Ray, and M. Yasar, Fault Diagnosis and Isolation in Aircraft Gas Turbine Engines, Proceedings of American Control Conference, (Seattle, WA), 2008.
1. S. Chakraborty, S. Sarkar, and A. Ray, Symbolic Identification and Anomaly Detection in Complex Dynamical Systems, Proceedings of American Control Conference, (Seattle, WA), 2008
115. Balu, A., Botelho, S., Khara, B., Rao, V., Hegde, C., Sarkar, S., Adavani, S, Krishnamurthy, A. Ganapathysubramanian, B. (2021). Distributed Multigrid Neural Solvers on Megavoxel Domains, Supercomputing.
114. A. Saffari, S. Y. Tan, M. Katanbaf, H. Saha, J. R. Smith, S. Sarkar. 2021. Battery-Free Camera Occupancy Detection System. In Proceedings of the 5th International Workshop on Embedded and Mobile Deep Learning (Virtual, WI, USA)(EMDL’21). Association for Computing Machinery, New York, NY, USA.
113. J. Rade+, S. Sarkar, A. Krishnamurthy, J. Ren, A. Sarkar, AI Guided Measurement of Live Cells Using AFM, Modeling, Estimation and Control Conference (MECC), (Austin, TX), October 24-27, 2021.
112. S. Ghadai+, X. Y. Lee+, A. Balu+, S. Sarkar, A. Krishnamurthy, Multi-resolution 3D CNN for Learning Multi-scale Spatial Features in CAD Models, SIAM Conference on Geometric and Physical Modeling (GD/SPM21), Virtual, Sept 27-29, 2021.
111. Y. Esfandiari+, S. Y. Tan+, Z. Jiang, A. Balu+, E.D. Herron+, C. Hegde, S. Sarkar, Cross-Gradient Aggregation for Decentralized Learning from Non-IID data, International Conference on Machine Learning (ICML), Virtual, July 18-24, 2021.
110. A. Saffari+, S. Y. Tan+, M. Katanbaf+, H. Saha+, J. Smith, S. Sarkar, Battery-Free Camera Occupancy Detection System, 5th International Workshop on Embedded and Mobile Deep Learning (EMDL), Virtual, June 24-25, 2021.
109. A. Sarkar, J. Waite and S. Sarkar, Deep learning for fast Atomic Force Microscopy data analytics, 65th Biophysical Society Annual Meeting, Feb 22-26, 2021, Virtual.
108. A. Balu, Z. Jiang, SY. Tan, C. Hedge, Y. M. Lee, and S. Sarkar , Decentralized Deep Learning Using Momentum-Accelerated Consensus, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (Toronto, Canada), 2021
107. T. Gangopadhyay, S. Y. Tan, Z. Jiang, R. Meng, S. Sarkar, Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (Toronto, Canada), 2021
106. X.Y. Lee, Y. Esfandiari, K.L. Tan, S. Sarkar, Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents, International Conference on Cyber-Physical Systems (ICCPS), (Nashville, TN), 2021
2020
105. T. Gangopadhyay, J. Shook, A. K. Singh, S. Sarkar, Interpreting the Impact of Weather on Crop Yield Using Attention, NeurIPS Workshop on AI for Earth Sciences, 2020, Virtual
104. Y. Esfandiari, S.Y. Tan, Z.H. Jiang, A. Balu, C. Hegde, S. Sarkar, Local Gradient Aggregation for Decentralized Learning from Non-IID data, Optimization for Machine Learning Workshop, Neural Information Processing Systems (NeurIPS 2020), Virtual.
103. M. Cho, A. Joshi, X.Y. Lee, A. Balu, B. Ganapathysubramanian, S. Sarkar, C. Hegde, Differentiable Programming for Piecewise Polynomial Functions , Learning Meets Combinatorial Algorithms Workshop, Neural Information Processing Systems (NeurIPS 2020), Virtual
102. Z. Jiang, X.Y. Lee, S.Y Tan, A. Balu, Y.M. Lee, C. Hegde, S. Sarkar, Adaptive Gradient Tracking In Stochastic Optimization, Optimization for Machine Learning Workshop, Neural Information Processing Systems (NeurIPS 2020), Virtual.
101. X.Y. Lee, Y. Esfandiari, K.L. Tan, S.Sarkar, Targeted Query-based Action-Space Adversarial Policies on Deep Reinforcement Learning Agents, Deep Reinforcement Learning Workshop in Neural Information Processing Systems (NeurIPS 2020), Virtual.
100. T. Gangopadhyay, S. Y. Tan, Z. Jiang, S. Sarkar, Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems, International Conference on Dynamic Data Driven Application Systems (DDDAS), (Boston, MA), 2020.
99. B. Giera, X. Lee, S. Saha, S. Sarkar, Automated Detection of Part Quality During Two Photon Lithography via Deep Learning, Annual International Solid Freeform Fabrication Symposium (SFF Symp 2020), Austin, TX, August 17-19, 2020.
98. A. Joshi, B. Khara, S. Sarkar, B. Ganapathysubramanian, C. Hedge, Solving Linear PDEs with Generative Models, ASILOMAR 2020 Conference on Signals, Systems, and Computers, Pacific Grove, CA, 2020
97. A. Balu, S. Ghadai, S. Sarkar, A. Krishnamurthy, Orthogonal Distance Fields Representation For Machine-Learning Based Manufacturability Analysis, ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE), St. Louis, MO, 2020
96. L. Riera, K. Ozcan, J. Merickel, M. Rizzo, S. Sarkar, A. Sharma, Automatic Lane Detection Algorithm For Noisy Naturalistic Driving Data, IEEE Intelligent Vehicle Symposium (IV), (Las Vegas), 2020
95. S.Y. Tan, H. Saha, M. Jacoby, A.R. Florita, G.P. Henze, S. Sarkar, Granger Causality-based Hierarchical Time Series Clustering for State Estimation, IFAC World Congress, (Berlin, Germany), 2020
94. T. Gangopadhyay, S. Y. Tan, A. Locurto, J. B. Michael, S. Sarkar, Interpretable Deep Learning for Monitoring Combustion Instability, IFAC World Congress, (Berlin, Germany), 2020
93. K.L. Tan, Y. Esfandiari, X.Y. Lee, Aakanksha, S. Sarkar, Robustifying Reinforcement Learning Agents via Action Space Adversarial Training, Proceedings of American Control Conference (ACC 2020), Denver, CO.
92. Y. Esfandiari, A. Balu, K. Ebrahimi, U. Vaidya, N. Elia, S. Sarkar, A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning, acceptance at the Artificial Intelligence Safety (SafeAI) workshop in Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), New York, NY, 2020
91. X.Y. Lee, S. Ghadai, K.L. Tan, C. Hedge, S. Sarkar, Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents, Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), New York, NY, 2020
90. A. Joshi, M. Cho, V. Shah, B. Pokuri, S. Sarkar, B. Ganapathysubramanian, C. Hedge, InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models, Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), New York, NY, 2020
2019
89. T. Gangopadhyay, S. Y. Tan, A. Locurto, J. B. Michael, S. Sarkar, An Explainable Framework using Deep Attention Models for Sequential Data in Combustion Systems, NeurIPS Workshop on Machine Learning and the Physical Sciences, (Vancouver, Canada), 2019
88. Z. Jiang, A. Balu, S.Y. Tan, Y.M. Lee, C. Hedge, S. Sarkar, On Higher-order Moments in Adam, accepted in Beyond First Order Methods in Machine Learning workshop in 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
87. A. Mukherjee, A. Joshi, S. Sarkar, C. Hegde, Semantic Domain Adaptation for Deep Classifiers via GAN-based Datat Augmentation, accepted at the Machine Learning for Autonomous Driving workshop at Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
86. A. Joshi, V. Shah, S. Ghosal, B. Pokuri, S. Sarkar, B. Ganapathysubramanian, C. Hegde, Generative Models for Solving Nonlinear Partial Differential Equations, accepted at the Machine Learning and the Physical Sciences Workshop at Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
85. H. Saha, V. Venkataraman, A. Speranzon, S. Sarkar, A perspective on multi-agent communication for information fusion, Visually Grounded Interaction and Language at Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
84. X.Y. Lee, S. Ghadai, K.L. Tan, C. Hedge, S. Sarkar, Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents, Deep Reinforcement Learning Workshop in Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada.
83. T. Gangopadhyay, J. Shook, A. K. Singh, S. Sarkar, Deep Time Series Attention Models for Crop Yield Prediction and Insights, NeurIPS Workshop on Machine Learning and the Physical Sciences, (Vancouver, Canada), 2019
82. Aditya Balu, K.L. Tan, Michael C.H. Wu, Ming-Chen Hsu, Soumik Sarkar, Adarsh Krishnamurthy; Deep Learning for Dynamic Deformation Simulation of Bioprosthetic Heart Valves, USNCCM 15, Austin, TX, 2019.
81. A. Joshi, A. Mukherjee, S. Sarkar, C. Hegde, Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers, International Conference on Computer Vision (ICCV), Seoul, South Korea, Oct 27 – Nov 2, 2019.
80. Barnwal, A., Merickel, J., Riera-Garcia, L., Ozcan, K., Sarkar, S., Sharma, A., Rizzo, M. (2019). Linking age-related decline to driver behavior at signalized intersections. 2019 Association for the Advancement of Automotive Medicine’s (AAAM) 63rd Annual Scientific Conference, Madrid, Spain, 15 – 18 October 2019.
79. K.L. Tan, S. Poddar, S. Sarkar, A. Sharma, Deep Reinforcement Learning for Adaptive Traffic Signal Control. Proceedings of ASME 2019 Dynamic Systems and Control Conference (DSCC), (Park City, Utah), 2019.
78. Mukherjee A., Joshi A. , SarkarS., Hegde C., "Attribute-Controlled Traffic Data Augmentation Using Conditional Generative Models" accepted in Vision for All Seasons: Bad Weather and Nighttime workshop at IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Long Beach, CA), 2019.
77. Ghadai, S., Lee, X. Y., Balu, A., Sarkar, S., Krishnmaurthy, A., "Multi-level 3D CNN for Learning Multi-scale Spatial Features'', accepted and invited for talk in Deep Learning for Geometric Shape Understanding workshop at IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Long Beach, CA), 2019.
76. S.Y. Tan, H. Saha, A.R. Florita, G.P. Henze, S. Sarkar, A flexible framework for building occupancy detection using spatiotemporal pattern networks. Proceedings of American Control Conference, (Philadelphia, PA), 2019.
75. A. Balu, M. Hsu, S. Sarkar, A. Krishnamurthy, A Deep-Learning Framework for Diagnostics and Design of Bioprosthetic Heart Valves. BMES/FDA conference on Frontiers in Medical Devices : The Role of Digital Evidence to Support Personalized Patient Healthcare, (Washington, DC Metropolitan Area), 2019.
2018
74. R. Singh, A. Sharma, O. Bingol, A. Balu, G. Balasubramanian, D. D. Johnson and S. Sarkar, 3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys. Workshop on Machine Learning for Molecules and Materials (MLMM) at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018. [arXiv]
73. B.S.S. Pokuri, S. Ghosal, A. Kokate, B. Ganapathysubramanian, S. Sarkar, Interpretable deep learning for guided structure-property explorations in photovoltaics. Workshop on Machine Learning for Molecules and Materials (MLMM) at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018. [arXiv]
72. X. Lee, A. Balu, D. Stoecklein, B. Ganapathysubramanian, S. Sarkar, Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning, Deep Reinforcement Learning Workshop at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018.
71. R. Singh, V. Shah, B. Pokuri, S. Sarkar, B. Ganapathysubramanian, C. Hegde, Physics-aware Deep Generative Models for Creating Synthetic Microstructures, Workshop on Machine Learning for Molecules and Materials (MLMM) at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018.
70. T. Gangopadhyay, S. Y. Tan, G. Huang, S. Sarkar, Temporal Attention and Stacked LSTMs for Multivariate Time Series Prediction, Workshop on Modeling and decision-making in the spatiotemporal domain at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018.
69. A. Balu, S. Nallagonda, F. Xu, A. Krishnamurthy, M. Hsu, S. Sarkar, Machine Learning for Diagnostics and Patient-Specific Design of Bioprosthetic Heart Valves, Integrating Design and Analysis (IGA), (Austin, TX), 2018.
68. A. Havens, Z. Jiang, S. Sarkar, Online Robust Policy Learning in the Presence of Unknown Adversaries, Proceedings of Advances in Neural Information Processing Systems (NIPS), (Montreal, Canada), 2018.
67. H. Saha, C. Liu, Z. Jiang, and S. Sarkar, Exploring granger causality in dynamical systems modeling and performance monitoring, Conference on Decision and Control, (Miami Beach, FL), 2018.
66. L. Wu, V. Chinde, H. Sharma, U. Passe, S. Sarkar, A Data-driven Approach towards Integration of Microclimate Conditions for Predicting Building Energy Performance, 5th International High Performance Buildings Conference, (West Lafayette, IN), 2018.
65. Z. Jiang, A. Balu, C. Hedge, S. Sarkar, Incremental Consensus-based Collaborative Deep Learning, Workshop on Modern Trends in Nonconvex Optimization for Machine Learning at International Conference on Machine Learning (ICML), (Stockholm, Sweden), 2018 *Spotlight Presentation.*
64. T. Gangopadhyay, A. Locurto, P. Boor, J. B. Michael, S. Sarkar, Characterizing Combustion Instability Using Deep Convolutional Neural Networks, Proceedings of ASME 2018 Dynamic Systems and Control Conference (DSCC), (Atlanta, Georgia), 2018.
63. A. Locurto, T. Gangopadhyay, P. Boor, S. Sarkar, J. B. Michael, Mode decomposition and convolutional neural network analysis of thermoacoustic instabilities in a Rijke tube, 2018 Spring Technical Meeting, Central States Section of The Combustion Institute, (Minneapolis, Minnesota), 2018 .
62. P. Chakraborty, Y. A. Gyamfi, S. Poddar, V. Ahsani, A. Sharma and S. Sarkar, Traffic Congestion Detection from Camera Images Using Deep Convolutional Neural Networks, Transportation Research Board 97th Annual Meeting, (Washington, D.C.), 2018.
61. S. Poddar, K. Ozcan, P. Chakraborty, V. Ahsani, A. Sharma and S. Sarkar, Comparison of Machine Learning Algorithms to Determine Traffic Congestion from Camera Images, Transportation Research Board 97th Annual Meeting, (Washington, D.C.), 2018.
60. Z. Jiang, K. Mukherjee, S. Sarkar, On Consensus-Disagreement Tradeoff in Distributed Optimization, Proceedings of American Control Conference, (Milwaukee, WI), 2018.
59. C. Liu, Z. Jiang, A. Akintayo, G. P. Henze, S. Sarkar, Building Energy Disaggregation using Spatiotemporal Pattern Network, Proceedings of American Control Conference, (Milwaukee, WI), 2018.
58. Z. Jiang, T. Wilkie, S. Sarkar, Hierarchical Optimization for Building Energy Systems, Proceedings of American Control Conference, (Milwaukee, WI), 2018.
2017
57. Z. Jiang, A. Balu, C. Hegde, S. Sarkar, Collaborative Deep Learning in Fixed Topology Networks, Proceedings of Advances in Neural Information Processing Systems (NIPS), (Long Beach, CA), 2017.
56. A. Balu, T. V. Nguyen, A. Kokate, C. Hegde, S. Sarkar, A Forward-Backward Approach for Visualizing Information Flow in Deep Networks, Symposium on Interpretable Machine Learning at NIPS 2017.
55. K. Nagasubramanian, S. Jones, A. K. Singh, A. Singh, B. Ganapathysubramanian, S. Sarkar, Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency map, Workshop on Intrepreting, Explaining and Visualizing Deep Learning...now what?, NIPS 2017.
54. S. Ghosal, D. Blystone, A. K. Singh, B. Ganapathysubramanian, A. Singh and S. Sarkar, Interpretable Deep Learning applied to Plant Stress Phenotyping, Symposium on Interpretable Machine Learning at NIPS 2017.
53. S. Ghadai, A. Balu, A. Krishnamurthy, S. Sarkar, Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes, Symposium on Interpretable Machine Learning at NIPS 2017.
52. S. Ghosal, A. Akintayo, P. K. Boor, S. Sarkar, High Speed Video-based health monitoring using 3D deep learning, Proceedings of the Dynamic Data Driven Application Systems (DDDAS), (Cambridge, MA), 2017.
51. Z. Jiang, K. Mukherjee, S. Sarkar. Convergence and Noise Effect Analysis for Generalized Gossip-based Distributed Optimization. Proceedings of American Control Conference, (Seattle, WA), 2017.
50. L. Wu, C. Liu, T. Huang, A. Sharma, S. Sarkar, Traffic sensor health monitoring using spatiotemporal graphical modeling, Proceedings of the 2nd ACM SIGKDD Workshop on Machine Learning for Prognostics & Health Management. (Halifax, NS, Canada), 2017.
49. T. Huang, C. Liu, A. Sharma, S. Sarkar, Traffic System Anomaly Detection using Spatiotemporal Pattern Networks, Proceedings of the 2nd ACM SIGKDD Workshop on Machine Learning for Prognostics & Health Management. (Halifax, NS, Canada), 2017.
48. H. Saha, T. Gao, H. Emadi, Z. Jiang, A. Singh, B. Ganapathysubramanian, S. Sarkar, A. Singh, S. Bhattacharya, Autonomous mobile sensing platform for spatiotemporal plant phenotyping, Proceedings of ASME 2017 Dynamic Systems and Control Conference (DSCC) (Tysons, VA), 2017.
47. C. Liu, K. G. Lore, S. Sarkar, Data-driven root-cause analysis for distributed system anomalies, Proceedings of IEEE Conference on Decision and Control, (Melbourne, Australia), 2017.
2016
46. C. Liu, B. Huang, M. Zhao, S. Sarkar, U. Vaidya, A. Sharma. Data Driven Exploration of Traffic Network System Dynamics using High Resolution Probe Data. Proceedings of IEEE Conference on Decision and Control, (Las Vegas, NV), 2016.
45. A. Akintayo, K. G. Lore, S. Sarkar, S. Sarkar. Early Detection of Combustion Instabilities using Deep Convolutional Selective Autoencoders on Hi-speed Flame Video, Proceedings of the 22nd ACM SIGKDD Workshop on Machine Learning for Prognostics & Health Management. (San Francisco, CA). 2016.
44. A. Akintayo, N. Lee, V. Chawla, M. Mullaney, C. Marett, A. Singh, A. Singh, G. Tylka, B. Ganapathysubramanian & S. Sarkar. An End-to-end Convolutional Selective Autoencoder Approach to Soybean Cyst Nematode Eggs Detection. Proceedings of the 22nd ACM SIGKDD Workshop on Data Science for Food, Energy and Water. (San Francisco, CA). 2016.
43. V. Chawla, M.H. Hsiang, A. Akintayo, D. Hayes, P. Schnable, B. Ganapathysubramanian, S. Sarkar. A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert-knowledge. Proceedings of the 22nd ACM SIGKDD Workshop on Data Science for Food, Energy and Water. (San Francisco, CA). 2016.
42. S. Ghosal, V. Ramanan, S. Sarkar, S. R. Chakravarthy, S. Sarkar. Detection and Analysis of Combustion Instability from High-speed Flame Images using Dynamic Mode Decomposition. Proceedings of ASME 2016 Dynamic Systems and Control Conference (DSCC). (Minneapolis, MN), 2016.
41. C. Liu, Y. Gong, S. Laflamme, B. Phares, S. Sarkar. Damage Detection of Bridge Network with Spatiotemporal Pattern Network. Proceedings of ASME 2016 Dynamic Systems and Control Conference (DSCC). (Minneapolis, MN), 2016.
40. V. Chinde, A. Kohl, Z. Jiang, A. Kelkar, S. Sarkar. A VOLTTRON based implementation of Supervisory Control using Generalized Gossip for Building Energy Systems, Proceedings in the 4th International High Performance Buildings Conference, (West Lafayette, IN), 2016
39. Z. Jiang, V. Chinde, A. Kohl, S. Sarkar, A. Kelkar, Scalable Supervisory Control of Building Energy Systems using Generalized Gossip, Proceedings of the American Control Conference, (Boston, MA), 2016
38. V. Chinde, K. C. Kosaraju, A. Kelkar, R. Pasumarthy, S. Sarkar and N. M. Singh, Building HVAC Systems Control Using Power Shaping Approach, Proceedings of the American Control Conference, (Boston, MA), 2016
37. S. Sarkar, D. K. Jha, K. G. Lore, A, Ray and S. Sarkar, Multi-modal Spatiotemporal Fusion using Neural-symbolic Causal Modeling for Early Detection of Combustion Instability, Proceedings of the American Control Conference, (Boston, MA), 2016
36. K. G. Lore, S. Sarkar and D. K. Jha, Topology Control in Mobile Sensor Networks using Information Space Feedback, Proceedings of the American Control Conference, (Boston, MA), 2016
35. K. G. Lore, N. Sweet, K. Kumar, N. Ahmed, and S. Sarkar, Deep Value of Information Estimators for Collaborative Human-Machine Information Gathering. Proceedings of the International Conference of Cyber-Physical Systems, (Vienna, Austria), 2016 [arXiv]
34. C. Liu, S. Ghosal, Z. Jiang and S. Sarkar, An unsupervised spatiotemporal graphical modeling approach to Anomaly detection in Distributed CPS, Proceedings of the International Conference on Cyber-physical Systems (ICCPS 2016). [arXiv]
33. S. Ghosal, C. Liu, U. Passe, S. He, S. Sarkar, Data-driven persistent monitoring of Indoor Air Systems, Proceedings of the ASHRAE IAQ 2016 Defining Indoor Air Quality: Policy, Standards and Best Practices, (Alexandria, VA), 2016
2015
32. S. Sarkar, K. G. Lore and S. Sarkar, Early Detection of Combustion Instability by Neural-Symbolic Analysis of Hi-speed Video, Proceedings of the NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches; 29th Annual Conference on Neural Information Processing Systems (NIPS 2015).
31. Z. Jiang and S. Sarkar, Understanding Wind turbine interactions using Spatiotemporal Pattern Network, Proceedings of ASME Dynamics Systems and Control Conference, (Columbus, OH), 2015
30. Z. Jiang, S. Sarkar and K. Mukherjee, On Distributed Optimization using Generalized Gossip, Proceedings of IEEE Conference on Decision and Control, (Osaka, Japan), 2015
29. S. Sarkar, K. G. Lore, S. Sarkar, V. Ramanan, S. Chakravarthy and A. Ray, Early Detection of Combustion Instability from Hi-speed Flame Images via Deep Learning and Symbolic Time Series Analysis, Proceedings of Annual Conference of the Prognostics and Health Management Society, (San Diego, CA), 2015
28. S. Sarkar, V. Venugopalan, K. Reddy, J. Rayde, M. Giering and N. Jaitly, Occlusion edge detection in RGBD frames using Deep Convolutional Neural Networks, Proceedings of IEEE High Performance Extreme Computing Conference, (Waltham, MA), 2015
27. V. Chinde, J. C. Heylmun, A. Kohl, Z. Jiang, S. Sarkar and A. Kelkar, Comparative Evaluation of Control-oriented zone temperature Prediction modeling strategies in Buildings, Proceedings of ASME Dynamics Systems and Control Conference, (Columbus, OH), 2015
26. R. Georgescu, K. Reddy, N. Trcka, M. Chen, P. Qumiby, P. O'Neil, T. Khawaja, L. Bertuccelli, D. Hestand, S. Sarkar, O. Erdinc and M. Giering, Scalable Human-in-the-Loop Decision Support, IEEE Aerospace Conference, Big Sky, MT, March 2015
25. K. G. Lore, M. Davies, D. Stoecklein, B. Ganapathysubramanian and S. Sarkar, Deep Learning for Flow sculpting in Microfluidic platforms, NVIDIA GPU Technology Conference, Silicon Valley, 2015
24. S. Sarkar, V. Venugopalan, K. Reddy, J. Ryde, M. Giering and N. Jaitly, RGBD Occlusion detection via Deep Convolutional Neural Networks, NVIDIA GPU Technology Conference, Silicon Valley, 2015
23. P. Chattopadhyay, D. K. Jha, S. Sarkar and A. Ray, Path Planning in GPS-denied Environments: A Collective Intelligence Approach, Proceedings of American Control Conference, (Chicago, IL), 2015
22. A. Akintayo, S. Sarkar, A Symbolic Dynamic Filtering Approach to Unsupervised Hierarchical Feature Extraction from Time-Series Data, Proceedings of American Control Conference, (Chicago, IL), 2015
2014
21. S. Krishnamurthy, S. Sarkar, A. Tewari, Scalable anomaly detection and isolation in cyber-physical systems using Bayesian Networks, Proceedings of ASME Dynamical Systems and Control Conference, (San Antonio, TX), 2014
20. R. Khire, F. Leonardi, P. Quimby, S. Sarkar, (in alphabetical order) A Novel Human Machine Interface for Advanced Building Controls and Diagnostics, (3rd International High Performance Buildings Conference at Purdue), 2014
19. V. Adetola, S. Bengea, F. Borrelli, K. Kang, A. Kelman, F. Leonardi, P. Li, T. Lovett, S. Sarkar, S. Vichik, (in alphabetical order) Fault-Tolerant Optimal Control of a Large-Size, Commercial Building Heating, Ventilation and Air Conditioning System, (3rd International High Performance Buildings Conference at Purdue), 2014
2013
18. S. Sarkar, N. Virani, M. Yasar, A. Ray, and S. Sarkar, Spatiotemporal Information Fusion for Fault detection in Shipboard Auxiliary Systems, Proceedings of American Control Conference, (Washington, D.C.), 2013
17. S. Sarkar, A. Srivastav, and M. Shashanka, Maximally Bijective Discretization for Data-driven Modeling of Complex Systems, Proceedings of American Control Conference, (Washington, D.C.), 2013 (Best Session Paper Award)
2012
16. S. Sarkar, K. Mukherjee, S. Sarkar, and A. Ray, Symbolic Transient Time-series Analysis for Fault Detection in Aircraft Gas Turbine Engines, Proceedings of American Control Conference, (Montreal, Canada), 2012 (Best Session Paper Award)
15. S. Sarkar, K. Mukherjee, and A. Ray, Distributed Decision Propagation in Mobile Agent Networks, Proceedings of American Control Conference, (Montreal, Canada), 2012 (Best Session Paper Award)
2011
14. S. Sarkar, D. S. Singh, A. Srivastav, and A. Ray, Semantic Sensor Fusion for Fault Diagnosis in Aircraft Gas Turbine Engines , Proceedings of American Control Conference, (San Francisco, CA), 2011
13. D. S. Singh, S. Sarkar, S. Gupta, and A. Ray, Optimal Partitioning of Ultrasonic Data for Fatigue Damage Detection , Proceedings of American Control Conference, (San Francisco, CA), 2011
2010
12. S. Sarkar, K. Mukherjee, A. Srivastav, and A. Ray, Distributed Decision Propagation in Mobile Agent Networks , Proceedings of Conference on Decision and Control, (Atlanta, GA) 2010
11. S. Sarkar, K. Mukherjee, X. Jin, and A. Ray, Optimization of Time-series data Partitioning for Parameter Identification , Proceedings of ASME Dynamic Systems and Control Conference, (Cambridge, MA), 2010
10. S. Sarkar, K. Mukherjee, A. Srivastav, and A. Ray, Critical Phenomena and Finite-size Scaling in Communication Networks , Proceedings of American Control Conference, (Baltimore, MD), 2010.
9. S. Chakraborty, S. Sarkar, A. Ray and S. Phoha, Symbolic Identification for Anomaly Detection in Aircraft Gas Turbine Engines, Proceedings of American Control Conference, (Baltimore, MD), 2010.
2009
8. X. Jin, S.Sarkar, K. Mukherjee and A. Ray, Suboptimal Partitioning of Timeseries Data for Anomaly Detection, Proceedings of Conference on Decision and Control, (Shanghai, China), 2009.
7. S. Sarkar, K. Mukherjee, A. Srivastav, and A. Ray, Understanding phase transition in communication networks to enable robust and resilient control, Proceedings of American Control Conference, (St. Louis, MO), 2009. (Best Session Paper Award)
6. S. Sarkar, K. Mukherjee and A. Ray, Symbolic Analysis of Time Series Signals Using Generalized Hilbert Transform, Proceedings of American Control Conference, (St. Louis, MO), 2009.
5. S. Sarkar, C. Rao and A. Ray, Estimation of Multiple Faults in Aircraft Gasturbine Engines, Proceedings of American Control Conference, (St. Louis, MO), 2009. (Best Session Paper Award)
2008
4. C. Rao, S. Sarkar, A. Ray, and M. Yasar, Comparative Evaluation of Symbolic Dynamic Filtering for Detection of Anomaly Patterns, Proceedings of American Control Conference, (Seattle, WA), 2008.
3. C. Rao, K. Mukherjee, S. Sarkar, and A. Ray, Estimation of Multiple Parameters in Dynamical Systems, Proceedings of American Control Conference, (Seattle, WA), 2008.
2. S. Sarkar, K. Mukherjee , A. Ray, and M. Yasar, Fault Diagnosis and Isolation in Aircraft Gas Turbine Engines, Proceedings of American Control Conference, (Seattle, WA), 2008.
1. S. Chakraborty, S. Sarkar, and A. Ray, Symbolic Identification and Anomaly Detection in Complex Dynamical Systems, Proceedings of American Control Conference, (Seattle, WA), 2008