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All entries are ordered by most recent first.
Last updated: 5 February 2025
Journal Publications
Journal Publications
2025
2025
130 | Use of artificial intelligence in soybean breeding and production
130 | Use of artificial intelligence in soybean breeding and production
A. K. Singh, S. E. Jones, L. Van der Laan, T. T. Ayanlade, J. Raigne, N. Saleem, S. Joshi, M. A. Arshad, H. Z. Mehrjerdi, A. Rairdin, J. Di Salvo, D. Elango, L. De Azevedo Peixoto, T. Z. Jubery, A. Krishnamurthy, A. Singh,S. Sarkar, B. Ganapathysubramanian
Advances in Agronomy, In Press, Corrected Proof, 2025
129 | Digital twins for the plant sciences
129 | Digital twins for the plant sciences
B. Ganapathysubramanian, S. Sarkar, A. Singh, A. K. Singh,
Trends in Plant Science, 2025
128 | Reinforcement learning optimization of a water resource recovery facility: Evaluating the impact of reward function design on agent training, control optimization, and treatment risk
128 | Reinforcement learning optimization of a water resource recovery facility: Evaluating the impact of reward function design on agent training, control optimization, and treatment risk
H. C. Croll, K. Ikuma, S. K. Ong, S. Sarkar
Journal of Water Process Engineering Volume 69, January 2025, 106658
127 | The impact of market orientation and brand storytelling on Shark Tank evaluations – a B2B and large language modeling perspective
127 | The impact of market orientation and brand storytelling on Shark Tank evaluations – a B2B and large language modeling perspective
P. Jayashankar, T. Roy, S. Chattopadhyay, M. A. Arshad, S. Sarkar
Journal of Business & Industrial Marketing
2024
2024
126 | InsectNet: Real-time identification of insects using an end-to-end machine learning pipeline
126 | InsectNet: Real-time identification of insects using an end-to-end machine learning pipeline
S. Chiranjeevi, M. Saadati, Z. K Deng, J. Koushik, T. Z. Jubery, D. Mueller, M. E. O'Neal, N. Merchant, A. Singh, A. K. Singh, S. Sarkar, A. Singh, B. Ganapathysubramanian
PNAS Nexus, 2024
125 | Multi-sensor and multi-temporal high-throughput phenotyping for monitoring and early detection of water-limiting stress in soybean
125 | Multi-sensor and multi-temporal high-throughput phenotyping for monitoring and early detection of water-limiting stress in soybean
S. E. Jones, T. T. Ayanlade, B. Fallen, T. Z. Jubery, A. Singh, B. Ganapathysubramanian, S. Sarkar, A. K. Singh
Frontiers in Plant Science 15:1484587
124 | Persistent monitoring of insect-pests on sticky traps through hierarchical transfer learning and slicing-aided hyper inference
124 | Persistent monitoring of insect-pests on sticky traps through hierarchical transfer learning and slicing-aided hyper inference
F. Fotouhi, K. Menke, A. Prestholt, A. Gupta, M. E. Carroll, H.-J. Yang, E. J. Skidmore, M. O’Neal, N. Merchant, S. K. Das, P. Kyveryga, B. Ganapathysubramanian, A. K. Singh, A. Singh, S. Sarkar
Frontiers in Plant Science 15:1484587
123 | In the Mix: A Workshop Merging Computational Chemistry and Electrochemistry Alongside Data Science
123 | In the Mix: A Workshop Merging Computational Chemistry and Electrochemistry Alongside Data Science
R. Duke, A. Sweet, N. C. Stumme, R. Ordikhani-Seyedlar, S. Chattopadhyay, C.-H. Yang, H.-J. Yang, A. S. Perera, L. Green, K. Endres, M. E. Losch, B. Ganapathysubramanian, S. E. Mason, S. Sarkar, S. K. Shaw, C. Risko, J. L. Jenkins, C. M. Teague
Journal of Chemical Education Article ASAP
122 | Evaluating Neural Radiance Fields for 3D Plant Geometry Reconstruction in Field Conditions
122 | Evaluating Neural Radiance Fields for 3D Plant Geometry Reconstruction in Field Conditions
M. A. Arshad, T. Jubery, J. Afful, A. Jignasu, A. Balu, B. Ganapathysubramanian, S. Sarkar, A. Krishnamurthy
Plant Phenomics. 2024;6:0235
121 | Metabolic Response to Small Molecule Therapy in Colorectal Cancer Tracked with Raman Spectroscopy and Metabolomics
121 | Metabolic Response to Small Molecule Therapy in Colorectal Cancer Tracked with Raman Spectroscopy and Metabolomics
G. Cutshaw, N. Joshi, X. Wen, E. Quam, G. Bogatcheva, N. Hassan, S. Uthaman, J. Waite, S. Sarkar, B. Singh, R. Bardhan
Angew. Chem. Int. Ed. 2024, e202410919
120 | A study of natural robustness of deep reinforcement learning algorithms towards adversarial perturbations
120 | A study of natural robustness of deep reinforcement learning algorithms towards adversarial perturbations
119 | Smart connected farms and networked farmers to improve crop production, sustainability and profitability
119 | Smart connected farms and networked farmers to improve crop production, sustainability and profitability
A. K. Singh, B. J. Balabaygloo, B. Bekee, S. W. Blair, S. Fey, F. Fotouhi, A. Gupta, A. Jha, J. C. Martinez-Palomares, K. Menke, A. Prestholt, V. K. Tanwar, X. Tao, A. Vangala, M. E. Carroll, S.l K. Das, G. DePaula, P. Kyveryga, S. Sarkar, M. Segovia, S. Silvestri, C. Valdivia
Frontiers in Agronomy, Volume 6, 2024
118 | Unified control of diverse actions in a wastewater treatment activated sludge system using reinforcement learning for multi-objective optimization
118 | Unified control of diverse actions in a wastewater treatment activated sludge system using reinforcement learning for multi-objective optimization
H. C. Croll, K. Ikuma, S. K. Ong, S. Sarkar
Water Research, Volume 263, 1 October 2024, 122179
117 | Diagnosis of pregnancy disorder in the first-trimester patient plasma with Raman spectroscopy and protein analysis
117 | Diagnosis of pregnancy disorder in the first-trimester patient plasma with Raman spectroscopy and protein analysis
A. P. Mathew, G. Cutshaw, O. Appel, M. Funk, L. Synan, J. Waite, S. Ghazvini, X. Wen, S. Sarkar, M. Santillan, D. Santillan, R. Bardhan
Bioeng Transl Med. 2024;e10691
116 | Class-specific data augmentation for plant stress classification
116 | Class-specific data augmentation for plant stress classification
N. Saleem, A. Balu, T. Z. Jubery, A. Singh, A. K. Singh, S. Sarkar, B. Ganapathysubramanian
The Plant Phenome Journal, 7, e20112.
115 | Soybean Canopy Stress Classification Using 3D Point Cloud Data
115 | Soybean Canopy Stress Classification Using 3D Point Cloud Data
T. J. Young, S. Chiranjeevi, D. Elango, S. Sarkar, A. K. Singh, A. Singh, B. Ganapathysubramanian, T. Z. Jubery
Agronomy 2024, 16, 1181
114 | Zero-shot insect detection via weak language supervision
114 | Zero-shot insect detection via weak language supervision
B. Feuer, A. Joshi, M. Cho, S. Chiranjeevi, Z. K. Deng, A. Balu, A. K. Singh, S. Sarkar, N. Merchant, A. Singh, B. Ganapathysubramanian, C. Hegde
The Plant Phenome J. 2024;7:e20107
113 | Neural PDE Solvers for Irregular Domains
113 | Neural PDE Solvers for Irregular Domains
B. Khara, E. Herron, A. Balu, D. Gamdha, C.-H. Yang, K. Saurabh, A. Jignasu, Z. Jiang, S. Sarkar, C. Hegde, B. Ganapathysubramanian, A. Krishnamurthy
Computer-Aided Design Volume 172, July 2024, 103709
112 | NeuFENet: neural finite element solutions with theoretical bounds for parametric PDEs
112 | NeuFENet: neural finite element solutions with theoretical bounds for parametric PDEs
B. Khara, A. Balu, A. Joshi, S. Sarkar, C. Hegde, A. Krishnamurthy, B. Ganapathysubramanian
Engineering with Computers (2024)
111 | Latent Diffusion Models for Structural Component Design
111 | Latent Diffusion Models for Structural Component Design
E. Herron, J. Rade, A. Jignasu, B. Ganapathysubramanian, A. Balu, S. Sarkar, A. Krishnamurthy
Computer-Aided Design, Volume 171, June 2024, 103707
110 | AIIRA: AI Institute for Resilient Agriculture
110 | AIIRA: AI Institute for Resilient Agriculture
B. Ganapathysubramanian, J. M. P. Bell, G. Kantor, N. Merchant, S. Sarkar, P. S. Schnable, M. Segovia, A. Singh, A. K. Singh
AI Magazine 45: 94–98
109 | An LSTM-based approach to detect transition to lean blowout in swirl-stabilized dump combustion systems
109 | An LSTM-based approach to detect transition to lean blowout in swirl-stabilized dump combustion systems
T. Gangopadhyay, S. De, Q. Liu, A. Mukhopadhyay, S. Sen, S. Sarkar
Energy and AI, 2024
108 | Dominating Set Model Aggregation for communication-efficient decentralized deep learning
108 | Dominating Set Model Aggregation for communication-efficient decentralized deep learning
F. Fotouhi, A. Balu, Z. Jiang, Y. Esfandiari, S. Jahani, S. Sarkar
Neural Networks, 2023
107 | Roadway Weather Challenges Illuminate Real-World Driving Biomarkers of Dementia Risk
107 | Roadway Weather Challenges Illuminate Real-World Driving Biomarkers of Dementia Risk
Md Z. Hasan, G. Basulto-Elias, R. K. L. Tan, J. H. Chang, S. Sarkar, A. Sharma, S. Hallmark, M. Rizzo, J. Merickel
Alzheimer's & Dementia 19 (2023): e075742.
2023
2023
106 | 3D reconstruction of plants using probabilistic voxel carving
106 | 3D reconstruction of plants using probabilistic voxel carving
J. Feng, M. Saadati, T. Jubery, A. Jignasu, A. Balu, Y. Li, L. Attigala, P. S. Schnable, S. Sarkar, B. Ganapathysubramanian, A. Krishnamurthy
Computers and Electronics in Agriculture, Volume 213, October 2023, 108248
105 | Deep learning-based 3D multigrid topology optimization of manufacturable designs
105 | Deep learning-based 3D multigrid topology optimization of manufacturable designs
J. Rade, A. Jignasu, E. Herron, A. Corpuz, B. Ganapathysubramanian, S. Sarkar, A. Balu, A. Krishnamurthy
Engineering Applications of Artificial Intelligence, Volume 126, Part C, 2023, 107033
104 | Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy
104 | Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy
S. Ghazvini, S. Uthaman, L. Synan, E. C. Lin, S. Sarkar, M. K. Santillan, D. A. Santillan, R. Bardhan
Bioengineering & Translational Medicine, 2023; e10595
103 | Cyber-agricultural systems for crop breeding and sustainable production
103 | Cyber-agricultural systems for crop breeding and sustainable production
S. Sarkar, B. Ganapathysubramanian, A. Singh, F. Fotouhi, S. Kar, K. Nagasubramanian, G. Chowdhary, S. K. Das, G. Kantor, A. Krishnamurthy, N. Merchant, A. K. Singh
Trends in Plant Science Special issue: 21st century tools in plant science
102 | First Trimester Prediction of Preterm Birth in Patient Plasma with Machine-Learning-Guided Raman Spectroscopy and Metabolomics
102 | First Trimester Prediction of Preterm Birth in Patient Plasma with Machine-Learning-Guided Raman Spectroscopy and Metabolomics
L. Synan, S. Ghazvini, S. Uthaman, G. Cutshaw, C-Y Lee, J. Waite, X. Wen, S. Sarkar, E. Lin, M. Santillan, D. Santillan, R. Bardhan
ACS Applied Material & Interfaces 2023
101 | Self-supervised learning improves classification of agriculturally important insect pests in plants
101 | Self-supervised learning improves classification of agriculturally important insect pests in plants
S. Kar, K. Nagasubramanian, D. Elango, M. E. Carroll, C. A. Abel, A. Nair, D. S. Mueller, M. E. O'Neal, A. K. Singh, S. Sarkar, B. Ganapathysubramanian, A. Singh
The Plant Phenome Journal Volume 6, Issue 1
100 | Systematic Performance Evaluation of Reinforcement Learning Algorithms Applied to Wastewater Treatment Control Optimization
100 | Systematic Performance Evaluation of Reinforcement Learning Algorithms Applied to Wastewater Treatment Control Optimization
99 | “Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies
99 | “Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies
T. J. Young, T. Z. Jubery, C. N. Carley, M. Carroll, S. Sarkar, A. K. Singh, A. Singh, B. Ganapathysubramanian
Frontiers in Plant Science 14 (2023)
98 | Reinforcement learning applied to wastewater treatment process control optimization: Approaches, challenges, and path forward
98 | Reinforcement learning applied to wastewater treatment process control optimization: Approaches, challenges, and path forward
H. C. Croll, K. Ikuma, S. K. Ong, S. Sarkar
Critical Reviews in Environmental Science and Technology
97 | A physics-informed feature weighting method for bearing fault diagnostics
97 | A physics-informed feature weighting method for bearing fault diagnostics
H. Lu, V. P. Nemani, V. Barzegar, C. Allen, C. Hu, S. Laflamme, S. Sarkar, A. T. Zimmerman
Mechanical Systems and Signal Processing, Volume 191, 15 May 2023, 110171
96 | Few-shot deep learning for AFM force curve characterization of single-molecule interactions
96 | Few-shot deep learning for AFM force curve characterization of single-molecule interactions
2022
2022
95 | Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean
95 | Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean
A. Rairdin, F. Fotouhi, J. Zhang, D. S. Mueller, B. Ganapathysubramanian, A. K. Singh, S. Dutta, S. Sarkar, A. Singh
Frontiers in Plant Science, 2022
94 | Physics-Aware Machine Learning Surrogates for Real-time Manufacturing Digital Twin
94 | Physics-Aware Machine Learning Surrogates for Real-time Manufacturing Digital Twin
A. Balu, S. Sarkar, B. Ganapathysubramanian, A. Krishnamurthy
Manufacturing Letters, 2022
93 | Deep Learning for Live Cell Shape Detection and Automated AFM Navigation
93 | Deep Learning for Live Cell Shape Detection and Automated AFM Navigation
J.Rade, J.Zhang, S.Sarkar, A.Krishnamurthy, J.Ren, A.Sarkar
Bioengineering 2022, 9(10), 522
92 | Multi-fidelity machine learning models for structure-property mapping of organic electronics
92 | Multi-fidelity machine learning models for structure-property mapping of organic electronics
C-H. Yang, B. S. S. Pokuri, X. Y. Lee, S. Balakrishnan, C. Hedge, S. Sarkar, B. Ganapathysubramanian
Computational Materials Science, Volume 213, October 2022, 111599
91 | A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems
91 | A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems
90 | A modular vision language navigation and manipulation framework for long horizon compositional tasks in indoor environment
90 | A modular vision language navigation and manipulation framework for long horizon compositional tasks in indoor environment
H. Saha, F. Fotouhi, Q. Liu, S. Sarkar
Frontiers in Robotics and AI: Computational Intelligence in Robotics, 2022
89 | Plant Phenotyping with Limited Annotation: Doing More with Less
89 | Plant Phenotyping with Limited Annotation: Doing More with Less
K. Nagasubramanian, A.K. Singh, A. Singh, S. Sarkar, B. Ganapathysubramanian
Plant Phenome Journal, 2022
88 | Generative Semantic Domain Adaptation for Perception in Autonomous Driving
88 | Generative Semantic Domain Adaptation for Perception in Autonomous Driving
A. Mukherjee, A. Joshi, A. Sharma, C. Hegde, S. Sarkar
Journal of Big Data Analytics in Transportation, 2022
87 | Sugar and stops in drivers with insulin-dependent type 1 diabetes
87 | Sugar and stops in drivers with insulin-dependent type 1 diabetes
A. Barnwal, P. Chakraborty, A. Sharma, L. Riera-Garcia, K. Ozcan, S. Davami, S. Sarkar, M. Rizzo, J. Merickel
Accident Analysis & Prevention, Volume 173, August 2022, 106692
86 | NURBS-Diff: A Differentiable Programming Module for NURBS
86 | NURBS-Diff: A Differentiable Programming Module for NURBS
A. D. Prasad, A. Balu, H. Shah, S. Sarkar, C. Hegde, A. Krishnamurthy
Computer-Aided Design, Volume 146, May 2022, 103199
85 | Multimodal sensor fusion framework for residential building occupancy detection
85 | Multimodal sensor fusion framework for residential building occupancy detection
S. Y. Tan. M. Jacoby, H. Saha, A. Florita, G. Henze, S. Sarkar
Knowledge-Based Systems, 2022,107593
84 | The Stochastic Augmented Lagrangian method for domain adaptation
84 | The Stochastic Augmented Lagrangian method for domain adaptation
Z. Jiang, C. Liu, Y.M. Lee, C. Hegde, S.Sarkar, D. Jiang
Knowledge-Based Systems, 2022,107593
2021
2021
83 | WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction
83 | WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction
Jacoby M, Tan SY, Katanbaf M, Saffari A, Saha H, Kapetanovic Z, Garland J, Florita A, Henze G, Sarkar S, Smith J
Journal of Sensor and Actuator Networks. 2021; 10(4):71
82 | A high-fidelity residential building occupancy detection dataset
82 | A high-fidelity residential building occupancy detection dataset
81 | Algorithmically-consistent deep learning frameworks for structural topology optimization,
81 | Algorithmically-consistent deep learning frameworks for structural topology optimization,
J. Rade, A. Balu, E. Herron, J. Pathak, R. Ranade, S. Sarkar, A. Krishnamurthy,
Engineering Applications of Artificial Intelligence, Volume 106, 2021, 104483
80 | Multi-resolution 3D CNN for learning multi-scale spatial features in CAD models
80 | Multi-resolution 3D CNN for learning multi-scale spatial features in CAD models
S Ghadai, XY Lee, A Balu, S Sarkar, A Krishnamurthy
Computer Aided Geometric Design, 102038
79 | On Consensus-Optimality Trade-offs in Collaborative Deep Learning
79 | On Consensus-Optimality Trade-offs in Collaborative Deep Learning
Z. Jiang, A. Balu, C. Hedge, S. Sarkar
Frontiers in artificial intelligence, p 130, 2021.
78 | Using Machine Learning To Develop A Fully Automated Soybean Nodule Acquisition Pipeline (SNAP)
78 | Using Machine Learning To Develop A Fully Automated Soybean Nodule Acquisition Pipeline (SNAP)
T Z Jubery, C. N. Carley, A. Singh, S. Sarkar, B. Ganapathysubramanian, A. K. Singh
Plant Phenomics, 2021
77 | Neural-network model for force prediction in multi-principal-element alloys
77 | Neural-network model for force prediction in multi-principal-element alloys
R. Singh, P. Singh, A. Sharma, O.R. Bingol, A. Balu, G. Balasubramanian, A. Krishnamurthy, S. Sarkar, Duane D. Johnson
Computational Materials Science, Volume 198, 2021, 110693
76 | Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications
76 | Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications
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
Plant Phenomics, 2021
75 | UAS-Based Plant Phenotyping for Research and Breeding Applications
75 | UAS-Based Plant Phenotyping for Research and Breeding Applications
W. Guo, M. E. Carroll, A. Singh, T. L. Swetnam, N. Merchant, S. Sarkar, A. K. Singh, and B. Ganapathysubramanian
Plant Phenomics, 2021
74 | Crop yield prediction integrating genotype and weather variables using deep learning
74 | Crop yield prediction integrating genotype and weather variables using deep learning
J. Shook, T. Gangopadhyay, L. Wu, B. Ganapathysubramanian, S. Sarkar, A. K. Singh
PLoS ONE 16(6): e0252402, 2021
73 | How useful is active learning for image-based plant phenotyping?
73 | How useful is active learning for image-based plant phenotyping?
K. Nagasubramanian, T. Jubery, F. F. Ardakani, S.V. Mirnezami, A.K. Singh, A. Singh, S. Sarkar, B. Ganapathysubramanian
Plant Phenome Journal. 2021; 4:e20020
72 | Fast inverse design of microstructures via generative invariance networks
72 | Fast inverse design of microstructures via generative invariance networks
X.Y. Lee, J.R. Waite, CH. Yang, B. Pokuri, A. Joshi, A. Balu, C. Hedge, B. Ganapathysubramanian, S. Sarkar
Nature Computational Science, March 2021
71 | 3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems
71 | 3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems
T. Gangopadhyay, V. Ramanan, A. Akintayo, P. K. Boor, S. Sarkar, S. R. Chakravarthy, S. Sarkar
Energy and AI, March 2021
70 | A fast saddle-point dynamical system approach to deep learning
70 | A fast saddle-point dynamical system approach to deep learning
Y. Esfandiari, A. Balu, K. Ebrahimi, U. Vaidya, N. Elia, S. Sarkar
Neural Networks, July 2021
69 | Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems
69 | Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems
2020
2020
68 | Robust Deep Reinforcement Learning for Traffic Signal Control
68 | Robust Deep Reinforcement Learning for Traffic Signal Control
K.L. Tan, A. Sharma, S. Sarkar
Journal of Big Data Analytics in Transportation, December 2020, DOI:https://doi.org/10.1007/s42421-020-00029-6
67 | Predicting county-scale maize yields with publicly available data
67 | Predicting county-scale maize yields with publicly available data
Z. Jiang, C. Liu, B. Ganapathysubramanian, D. J. Hayes, S. Sarkar
Scientific Reports, September 2020, DOI:https://doi.org/10.1038/s41598-020-71898-8
66 | Challenges and Opportunities in Machine-Augmented Plan Stress Phenotyping
66 | Challenges and Opportunities in Machine-Augmented Plan Stress Phenotyping
A. Singh, S. Jones, B. Ganapathysubramanian, S. Sarkar, D. Mueller, K. Sandhu, K. Nagasubramanian
Trends in Plan Science - Cell Press, August 2020, DOI:https://doi.org/10.1016/j.tplants.2020.07.010
65 | Automated trichome counting in soybean using advanced image‐processing techniques
65 | Automated trichome counting in soybean using advanced image‐processing techniques
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
Applications in Plant Sciences, July 2020, DOI:https://doi.org/10.1002/aps3.11375
64 | Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures
64 | Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures
X.Y. Lee, S. Saha, S. Sarkar, B. Giera
Data in Brief, August 2020, DOI:https://doi.org/10.1016/j.dib.2020.106119
63 | Automated detection of part quality during two-photon lithography via deep learning
63 | Automated detection of part quality during two-photon lithography via deep learning
62 | Soybean Root System Architecture Traits Study Through Genotypic, Phenotypic and Shape-based Clusters
62 | Soybean Root System Architecture Traits Study Through Genotypic, Phenotypic and Shape-based Clusters
K. G. Falk, T.Z. Jubery, J. A. O’Rourke, A. Singh, S. Sarkar, B. Ganapathysubramanian, A. K. Singh
Plant Phenomics, June 2020, DOI:https://doi.org/10.34133/2020/1925495
61 | Supervisory Control and Distributed Optimization of Building Energy Systems
61 | Supervisory Control and Distributed Optimization of Building Energy Systems
Z. Jiang, V. Chinde, A. Kohl, A. G. Kelkar, and S. Sarkar
ASME journal of Dynamics, Systems and Control, June 2020, DOI:https://doi.org/10.1115/1.4047448
60 | Data-driven Performance Monitoring of Dynamical Systems Using Granger Causal Graphical Models
60 | Data-driven Performance Monitoring of Dynamical Systems Using Granger Causal Graphical Models
H. Saha, C. Liu, Z. Jiang, and S. Sarkar
ASME journal of Dynamics, Systems and Control, April 2020, DOI:https://doi.org/10.1115/1.4046673
59 | Computer Vision and Machine Learning Enabled Soybean Root Phenotyping Pipeline
59 | Computer Vision and Machine Learning Enabled Soybean Root Phenotyping Pipeline
K. G. Falk, T. Z. Jubery, S. V. Mirnezami, K. A. Parmley, S. Sarkar, A. Singh, B. Ganapathysubramanian, A. K. Singh
Plant Methods, January 2020, DOI:https://doi.org/10.1186/s13007-019-0550-5
2019
2019
58 | A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves
58 | A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves
A. Balu, S. Nallagonda, F. Xu, A. Krishnamurthy, M. C. Hsu, S. Sarkar
Scientific Reports, December 2019, DOI:https://doi.org/10.1038/s41598-019-54707-9
57 | Machine Learning Approach for Prescriptive Plant Breeding
57 | Machine Learning Approach for Prescriptive Plant Breeding
K. Parmley, R. Higgins, B. Ganapathysubramanian, S. Sarkar, A. Singh
Scientific Reports, November 2018, DOI:https://doi.org/10.1038/s41598-019-53451-4
56 | Interpretable deep learning for guided structure-property explorations in photovoltaics
56 | Interpretable deep learning for guided structure-property explorations in photovoltaics
B. S. S. Pokuri, S. Ghosal, A. Kokate, S. Sarkar, B. Ganapathysubramanian
Nature (npj) Computational Materials, October 2019, DOI:https://doi.org/10.1038/s41524-019-0231-y
55 | Plant disease identification using explainable 3D deep learning on hyperspectral images
55 | Plant disease identification using explainable 3D deep learning on hyperspectral images
K. Nagasubramanian, S. Jones, A. K. Singh, S. Sarkar, A. Singh, B. Ganapathysubramanian
Plant Methods, August 2019, DOI:https://doi.org/10.1186/s13007-019-0479-8
54 | A Case Study of Deep Reinforcement Learning for Engineering Design: Application to Microfluidic Devices for Flow Sculpting
54 | A Case Study of Deep Reinforcement Learning for Engineering Design: Application to Microfluidic Devices for Flow Sculpting
X. Y. Lee, A. Balu, D. Stoecklein, B. Ganapathysubramanian, S. Sarkar
ASME Journal of Mechanical Design – Special Issue: Machine Learning for Engineering Design) , November 2019, DOI:https://doi.org/10.1115/1.4044397
53 | Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean
53 | Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean
K. Parmley, K. Nagasubramanian, S. Sarkar, B. Ganapathysubramanian, A. K. Singh
Plant Phenomics, July 2019, DOI:https://doi.org/10.34133/2019/5809404
52 | A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting
52 | A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting
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
Plant Phenomics, June 2019, DOI:https://doi.org/10.34133/2019/1525874
51 | Traffic Dynamics Exploration and Incident Detection Using Spatiotemporal Graphical Modeling
51 | Traffic Dynamics Exploration and Incident Detection Using Spatiotemporal Graphical Modeling
C. Liu, M. Zhao, A. Sharma, S. Sarkar
Journal of Big Data Analytics in Transportation, June 2019, DOI:https://doi.org/10.1007/s42421-019-00003-x
50 | Occupancy sensing in buildings: A review of data analytics approaches
50 | Occupancy sensing in buildings: A review of data analytics approaches
H.Saha, A.R.Florita , G.P.Henze, S.Sarkar
Energy and Buildings, April 2019, DOI:https://doi.org/10.1016/j.enbuild.2019.02.030
49 | An Adaptive Spatiotemporal Feature Learning Approach for Fault Diagnosis in Complex Systems
49 | An Adaptive Spatiotemporal Feature Learning Approach for Fault Diagnosis in Complex Systems
C. Liu, T. Han, L. Wu, S. Sarkar, D. Jiang
Mechanical Systems and Signal Processing, February 2019, DOI:https://doi.org/10.1016/j.ymssp.2018.07.048
2018
2018
48 | A Novel Multirobot System for Plant Phenotyping
48 | A Novel Multirobot System for Plant Phenotyping
T. Gao, H. Emadi, H. Saha, J. Zhang, A. Lofquist, A. Singh, B. Ganapathysubramanian, S. Sarkar, A. Singh, S. Bhattacharya
Robotics, September 2018, DOI:https://doi.org/10.3390/robotics7040061
47 | Linked read technology for assembling large complex and polyploid genomes
47 | Linked read technology for assembling large complex and polyploid genomes
A. Ott, J. C. Schnable, C. Yeh, L. Wu, C. Liu, H. Hu, C. L. Dalgard, S. Sarkar and P. S. Schnable
Robotics, September 2018, DOI:https://doi.org/10.1186/s12864-018-5040-z
46 | Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems
46 | Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems
K. Nagasubramanian, S. Jones, S. Sarkar, A. K. Singh, A. Singh, B. Ganapathysubramanian
Plant Methods, October 2018, DOI:https://doi.org/10.1186/s13007-018-0349-9
45 | Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives
45 | Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives
A. K. Singh, B. Ganapathysubramanian, S. Sarkar, A. Singh
Plant Methods, August 2018, DOI:https://doi.org/10.1016/j.tplants.2018.07.004
44 | A Deep Learning Framework to Discern and Count Microscopic Nematode Eggs
44 | A Deep Learning Framework to Discern and Count Microscopic Nematode Eggs
A. Akintayo, G. Tylka, A. K. Singh, B. Ganapathysubramanian, A. Singh, S. Sarkar
Scientific Reports, June 2018, DOI:https://doi.org/10.1038/s41598-018-27272-w
43 | Traffic sensor health monitoring using spatiotemporal graphical modeling
43 | Traffic sensor health monitoring using spatiotemporal graphical modeling
L. Wu, C. Liu, T. Huang, A. Sharma, S. Sarkar
International Journal of Prognostics and Health Management, May 2018, DOI:
42 | Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data
42 | Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data
A. Akintayo and S. Sarkar
Signal Processing, October 2018, DOI:https://doi.org/10.1016/j.sigpro.2018.04.025
41 | An Explainable Deep Machine Vision Framework for Plant Stress Phenotyping
41 | An Explainable Deep Machine Vision Framework for Plant Stress Phenotyping
S. Ghosal, D. Blystone, A. K. Singh, B. Ganapathysubramanian, A. Singh and S. Sarkar
Proceedings of the National Academy of Sciences of the United States of America (PNAS), May 2018, DOI:https://doi.org/10.1073/pnas.1716999115
40 | Learning Localized Features in 3D CAD Models for Manufacturability Analysis of Drilled Holes
40 | Learning Localized Features in 3D CAD Models for Manufacturability Analysis of Drilled Holes
S. Ghadai, A. Balu, A. Krishnamurthy and S. Sarkar
Computer Aided Geometric Design Journal, May 2018, DOI:https://doi.org/10.1016/j.cagd.2018.03.024
39 | Traffic Congestion Detection from Camera Images using Deep Convolution Neural Networks
39 | Traffic Congestion Detection from Camera Images using Deep Convolution Neural Networks
P. Chakraborty, Y. A. Gyamfi, S. Poddar, V. Ahsani, A. Sharma and S. Sarkar
Transportation Research Record (TRR), Journal of the Transportation Research Board, June 2018, DOI:https://doi.org/10.1177/0361198118777631
38 | Traffic System Anomaly Detection using Spatiotemporal Pattern Networks
38 | Traffic System Anomaly Detection using Spatiotemporal Pattern Networks
T. Huang, C. Liu, A. Sharma, S. Sarkar
International Journal of Prognostics and Health Management, January 2018, DOI:
37 | Multivariate Exploration of Non-intrusive Load Monitoring via Spatiotemporal Pattern Network
37 | Multivariate Exploration of Non-intrusive Load Monitoring via Spatiotemporal Pattern Network
C. Liu, A. Akintayo, Z. Jiang, G.P. Henze, S. Sarkar
Applied Energy, February 2018, DOI:https://doi.org/10.1016/j.apenergy.2017.12.026
36 | A Deep Learning Framework for Causal Shape Transformation
36 | A Deep Learning Framework for Causal Shape Transformation
K. G. Lore, D. Stoecklein, M. Davies, B. Ganapathysubramanian, S. Sarkar
Neural Networks, February 2018, DOI:https://doi.org/10.1016/j.neunet.2017.12.003
2017
2017
35 | Generalized Gossip-based Subgradient Method for Distributed Optimization
35 | Generalized Gossip-based Subgradient Method for Distributed Optimization
Z. Jiang, K. Mukherjee, S. Sarkar
International Journal of Control, November 2017, DOI:https://doi.org/10.1080/00207179.2017.1387288
34 | An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling
34 | An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling
C. Liu, S. Ghosal, Z. Jiang, S. Sarkar
Cyber-Physical Systems, November 2017, DOI:https://doi.org/10.1080/23335777.2017.1386717
33 | Energy prediction using spatiotemporal pattern networks
33 | Energy prediction using spatiotemporal pattern networks
Z. Jiang, C. Liu, A. Akintayo, G. Henze, S. Sarkar
Applied Energy, November 2017, DOI:https://doi.org/10.1016/j.apenergy.2017.08.225
32 | A Passivity-Based Power-Shaping Control of Building HVAC Systems
32 | A Passivity-Based Power-Shaping Control of Building HVAC Systems
V. Chinde, K. C. Kosaraju, A. Kelkar, R. Pasumarthy, S. Sarkar, N. M. Singh
Journal of Dynamic Systems, July 2017, DOI:https://doi.org/10.1115/1.4036885
31 | Deploying Fourier coefficients to unravel soybean canopy diversity
31 | Deploying Fourier coefficients to unravel soybean canopy diversity
T. Jubery, J. Shook, K. Parmley, J. Zhang, H. Naik, R. Higgins, S. Sarkar, A. Singh, A. Singh, B. Ganapathysubramanian
Frontiers in Plant Science, January 2017, DOI:https://doi.org/10.3389/fpls.2016.02066
30 | Computer vision and machine learning for robust phenotyping in genome-wide studies
30 | Computer vision and machine learning for robust phenotyping in genome-wide studies
J. Zhang, H. Naik, T. Assefa, S. Sarkar, C. Reddy R. V, A. Singh, B. Ganapathysubramanian, A. Singh
Scientific Reports, March 2017, DOI:https://doi.org/10.1038/srep44048
29 | LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
29 | LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
K. G. Lore, A. Akintayo, S. Sarkar
Pattern Recognition, January 2017, DOI:https://doi.org/10.1016/j.patcog.2016.06.008
28 | Bridge damage detection using spatiotemporal patterns extracted from dense sensor network
28 | Bridge damage detection using spatiotemporal patterns extracted from dense sensor network
C. Liu, Y. Gong, S. Laflamme, B. Phares, S. Sarkar
Measurement Science and Technology, January 2017, DOI:https://doi.org/10.1088/1361-6501/28/1/014011
27 | A real-time phenotyping framework using machine learning for plant stress severity rating in soybean
27 | A real-time phenotyping framework using machine learning for plant stress severity rating in soybean
H. Naik, J. Zhang, A. Lofquist, T. Assefa, S. Sarkar, D. Ackerman, A. Singh, A. Singh, B. Ganapathysubramanian
Plant Methods, April 2017,
DOI:https://doi.org/10.1186/s13007-017-0173-7
26 | Deep Learning for Flow Sculpting: Insights into Efficient Learning using Scientific Simulation Data
26 | Deep Learning for Flow Sculpting: Insights into Efficient Learning using Scientific Simulation Data
D. Stoecklein, K. G. Lore, M. Davies, B. Ganapathysubramanian, S. Sarkar
Scientific Reports, April 2017,
DOI:https://doi.org/10.1038/srep46368
2016
2016
25 | Prognostics of Combustion Instabilities from Hi-speed Flame Video using a Deep Convolutional Selective Autoencoder
25 | Prognostics of Combustion Instabilities from Hi-speed Flame Video using a Deep Convolutional Selective Autoencoder
A. Akintayo, K. G. Lore, S. Sarkar, S. Sarkar
International Journal of Prognostics and Health Management, September 2016,
24 | A composite discretization scheme for symbolic identification of complex systems
24 | A composite discretization scheme for symbolic identification of complex systems
S. Sarkar, A. Srivastav
Signal Processing, August 2016,
DOI:https://doi.org/10.1016/j.sigpro.2016.01.018
23 | Machine Learning for High-Throughput Stress Phenotyping in Plants
23 | Machine Learning for High-Throughput Stress Phenotyping in Plants
A. Singh, B. Ganapathysubramanian, A. K. Singh, S. Sarkar
Trends in Plant Sciences, February 2016,
DOI:https://doi.org/10.1016/j.tplants.2015.10.015
22 | Deep Learning for Automated Occlusion Edge Detection in RGB-D Frames
22 | Deep Learning for Automated Occlusion Edge Detection in RGB-D Frames
S. Sarkar, V. Venugopalan, K. Reddy, J. Ryde, N. Jaitly, M. Giering
Journal of Signal Processing Systems, December 2016,
DOI:https://doi.org/10.1007/s11265-016-1209-3
2015
2015
21 | Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns
21 | Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns
K. G. Lore, D. Stoecklein, M. Davies, B. Ganapathysubramanian and S. Sarkar
JMLR: Workshop and Conference Proceedings, December 2015,
20 | Path planning in GPS-denied environments via collective intelligence of distributed sensor networks
20 | Path planning in GPS-denied environments via collective intelligence of distributed sensor networks
D. K. Jha, P. Chattopadhyay, S. Sarkar, A. Ray
International Journal of Control, December 2015,
DOI:https://doi.org/10.1080/00207179.2015.1110754
19 | Fault-tolerant optimal control of a building heating, ventilation, and air conditioning system
19 | Fault-tolerant optimal control of a building heating, ventilation, and air conditioning system
S. Bengea, P. Li, S. Sarkar, S. Vichik, V. Adetola, K. Kang, T. Lovett, F. Leonardi, A. Kelman
Science and Technology for the Built Environment, August 2015,
DOI:https://doi.org/10.1080/23744731.2015.1057085
2014
2014
18 | Event-triggered Decision Propagation in Proximity Networks
18 | Event-triggered Decision Propagation in Proximity Networks
S. Sarkar and K. Mukherjee
IEEE Transactions on Systems, Man, and Cybernetics, December 2014, DOI:https://doi.org/10.3389/frobt.2014.00015
17 | Sensor Fusion for Fault Detection & Classification in Distributed Physical Processes
17 | Sensor Fusion for Fault Detection & Classification in Distributed Physical Processes
S. Sarkar, S. Sarkar, N. Virani, A. Ray, M. Yasar
IEEE Transactions on Systems, Man, and Cybernetics, December 2014, DOI:https://doi.org/10.3389/frobt.2014.00016
2013
2013
16 | Multi-sensor information fusion for fault detection in aircraft gas turbine engines
16 | Multi-sensor information fusion for fault detection in aircraft gas turbine engines
S. Sarkar, S. Sarkar, K. Mukherjee, A. Ray, A. Srivastav
Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, December 2013,
DOI:https://doi.org/10.1177/0954410012468391
15 | Behavior Prediction for Decision & Control in Cognitive Autonomous Systems
15 | Behavior Prediction for Decision & Control in Cognitive Autonomous Systems
A. Ray, S. Phoha, S. Sarkar
New Mathematics and Natural Computation, November 2013,
DOI:https://doi.org/10.1142/S1793005713400061
14 | Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines
14 | Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines
S. Sarkar, K. Mukherjee, S. Sarkar, A. Ray
Journal of Dynamic Systems, Measurement, and Control, January 2013,
DOI:https://doi.org/10.1115/1.4007699
13 | Distributed Decision Propagation in Mobile-agent Proximity Networks
13 | Distributed Decision Propagation in Mobile-agent Proximity Networks
S. Sarkar, K. Mukherjee, A. Ray
International Journal of Control, June 2013,
DOI:https://doi.org/10.1080/00207179.2013.782511
2012
2012
12 | Equilibrium Thermodynamics for Heterogeneous Packet Transmission in Communication Networks
12 | Equilibrium Thermodynamics for Heterogeneous Packet Transmission in Communication Networks
S. Sarkar, K. Mukherjee, A. Ray, A. Srivastav, and T. Wettergren
IEEE Transactions on Systems, Man, and Cybernetics, March 2012, DOI:https://doi.org/10.1109/TSMCB.2012.2186611
11 | Optimization of Symbolic Feature Extraction for Pattern Classification
11 | Optimization of Symbolic Feature Extraction for Pattern Classification
S. Sarkar, K. Mukherjee, X. Jin, and A. Ray
Signal Processing, March 2012, DOI:https://doi.org/10.1016/j.sigpro.2011.08.013
10 | Symbolic Identification for Fault Detection in Aircraft Gas Turbine Engines
10 | Symbolic Identification for Fault Detection in Aircraft Gas Turbine Engines
S. Chakraborty, S. Sarkar, and A. Ray
Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, November 2011, DOI:https://doi.org/10.1177/0954410011409980
2011
2011
9 | Data-driven Fault Detection in Aircraft Engines with Noisy Sensor Measurements
9 | Data-driven Fault Detection in Aircraft Engines with Noisy Sensor Measurements
S. Sarkar, X. Jin, and A. Ray
Journal of Engineering for Gas Turbines and Power, August 2011, DOI:https://doi.org/10.1115/1.4002877
8 | Anomaly Detection in Nuclear Power Plants via Symbolic Dynamic Filtering
8 | Anomaly Detection in Nuclear Power Plants via Symbolic Dynamic Filtering
X. Jin, Y. Guo, S. Sarkar, A. Ray, R.M. Edwards
IEEE Transactions on Nuclear Science, Feb 2011, DOI:https://doi.org/10.1109/TNS.2010.2088138
2009
2009
7 | Statistical Estimation of Multiple Faults in Aircraft Gas Turbine Engines
7 | Statistical Estimation of Multiple Faults in Aircraft Gas Turbine Engines
S. Sarkar, C. Rao and A. Ray
Proceedings of the I Mech E Part A: Journal of Power and Energy, April 2009, DOI:https://doi.org/10.1243/09544100JAERO481
6 | Generalization of Hilbert Transform for Symbolic Analysis of Noisy Signals
6 | Generalization of Hilbert Transform for Symbolic Analysis of Noisy Signals
C. Rao, K. Mukherjee, S. Sarkar and A. Ray
Signal Processing, June 2009, DOI:https://doi.org/10.1016/j.sigpro.2008.12.009
5 | Statistical Estimation of Multiple Parameters via Symbolic Dynamic Filtering
5 | Statistical Estimation of Multiple Parameters via Symbolic Dynamic Filtering
C. Rao, K. Mukherjee, S. Sarkar and A. Ray
Signal Processing, June 2009, DOI:https://doi.org/10.1016/j.sigpro.2008.11.018
4 | Review and Comparative Evaluation of Symbolic Dynamic Filtering for Detection of Anomaly Patterns
4 | Review and Comparative Evaluation of Symbolic Dynamic Filtering for Detection of Anomaly Patterns
C. Rao, K. Mukherjee, S. Sarkar and A. Ray
Signal Processing, June 2009, DOI:https://doi.org/10.1007/s11760-008-0061-8
2008
2008
3 | Damage Monitoring of Refractory Wall in a Generic Entrained-Bed Slagging Gasification System
3 | Damage Monitoring of Refractory Wall in a Generic Entrained-Bed Slagging Gasification System
S. Chakraborty, S. Sarkar, S. Gupta and A. Ray
Proceedings of the I Mech E Part A: Journal of Power and Energy, November 2008, DOI:https://doi.org/10.1243/09576509JPE638
2 | Fault Detection and Isolation in Aircraft Gas Turbine Engines: Part II Validation on a Simulation Test Bed
2 | Fault Detection and Isolation in Aircraft Gas Turbine Engines: Part II Validation on a Simulation Test Bed
S. Sarkar, M. Yasar, S. Gupta, A. Ray and K. Mukherjee
Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, June 2008, DOI:https://doi.org/10.1243/09544100JAERO312
1 | Fault Detection and Isolation in Aircraft Gas Turbine Engines: Part I Underlying Concept
1 | Fault Detection and Isolation in Aircraft Gas Turbine Engines: Part I Underlying Concept
S. Gupta, A. Ray, S. Sarkar and M. Yasar
Proceedings of the I Mech E Part G: Journal of Aerospace Engineering, June 2008, DOI:https://doi.org/10.1243/09544100JAERO311
Book Chapters
Book Chapters
9 | Multi-modal AI for Ultra-Precision Agriculture
9 | Multi-modal AI for Ultra-Precision Agriculture
T. T. Ayanlade, S. E. Jones, L. Van der Laan, S. Chattopadhyay, D. Elango, J. Raigne, A. Saxena, A. Singh, B. Ganapathysubramanian, A. K. Singh, S. Sarkar
Harnessing Data Science for Sustainable Agriculture and Natural Resource Management. Studies in Big Data, vol 161. Springer, Singapore
8 | High-Throughput Phenotyping in Soybean
8 | High-Throughput Phenotyping in Soybean
A. K. Singh, A. Singh, S. Sarkar, B. Ganapathysubramainan et. al
High-throughput Crop Phenotyping (part of a book series on Advanced Concepts and strategies in Plant Sciences (ACSPS)), Springer-Nature, 2021
7 | Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems
7 | Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems
T. Gangopadhyay, S. Y. Tan, Z. Jiang, S. Sarkar
Dynamic Data Driven Application Systems (pp. 93-101), Springer, 2020.
6 | Deep Learning Algorithms for Detecting Combustion Instabilities
6 | Deep Learning Algorithms for Detecting Combustion Instabilities
T. Gangopadhyay, A. Locurto, J. B. Michael, S. Sarkar
Dynamics and Control of Energy Systems (pp. 283-300), Springer, 2020
5 | A Machine Learning Framework for Decision Support in Design and Manufacturing
5 | A Machine Learning Framework for Decision Support in Design and Manufacturing
A. Balu, S. Ghadai, G. Young, S. Sarkar, A. Krishnamurthy
ASME press, 2019
4 | Deep Learning for Engineering Big Data analytics
4 | Deep Learning for Engineering Big Data analytics
K. G. Lore, D. Stoecklein, M. Davies, B. Ganapathysubramanian, S. Sarkar
Big Data Analytics: From Planning to Performance, CRC Press, Taylor & Francis Group, USA 2017
3 | Probabilistic Graphical Modeling of Distributed Cyber-Physical Systems
3 | Probabilistic Graphical Modeling of Distributed Cyber-Physical Systems
S. Sarkar, Z. Jiang, A. Akintayo, S. Krishnamurthy, A. Tewari
Cyber-Physical Systems: Foundations, Principles and Applications, Elsevier, 2016
2 | Data-enabled Health Management of Complex Industrial Systems
2 | Data-enabled Health Management of Complex Industrial Systems
S. Sarkar, S. Sarkar, A. Ray
Fault Detection: Classification, Techniques and Role in Industrial Systems, Nova Science Publishers, December 2013
1 | Localized Uncertainty Quantification for Baseline Building Energy Modeling
1 | Localized Uncertainty Quantification for Baseline Building Energy Modeling
A. Srivastav, A. Tewari, B. Dong, S. Sarkar, M. Gorbounov
Automated Diagnostics for Facility Equipment, Systems, and Whole Buildings, Fairmont Press, 2014
Articles
Articles
3 | From Smart Homes to Green Cities: Role of Intelligent Diagnostics and Control in Energy Efficient Buildings
3 | From Smart Homes to Green Cities: Role of Intelligent Diagnostics and Control in Energy Efficient Buildings
S. Sarkar, D. Vrabie, M. Krucinski, L. Bertuccelli, T. Lovett, S. Mijanovic
Dynamic Systems & Control Magazine, December 2013
Patents
Patents
3 | Automated Functional Tests for Diagnostics and Control
3 | Automated Functional Tests for Diagnostics and Control
S. Bengea, V. Adetola, M. Krucinski, S. Sarkar, A. Srivastav, T. Lovett, K. Mukherjee, A. Ghosh, M. Chen, P. Li
US Patent Application Serial No. 62/078,735, November 2014
2 | Diagnosis and Estimation of Multiple Faults in Aircraft Gas Turbine Engines
2 | Diagnosis and Estimation of Multiple Faults in Aircraft Gas Turbine Engines
S. Sarkar, A. Ray
PSU Invention Disclosure No. 2009-3602
1 | Method and System for Monitoring Refractory Walls In Slagging Gasification Systems
1 | Method and System for Monitoring Refractory Walls In Slagging Gasification Systems
S. Chakraborty, S. Sarkar, S. Gupta, A. Ray
PSU Invention Disclosure No. 2009-3597
U.S. Patent Application Serial No. 61/265,272, December 2009
Conference
Conference
2025
2025
155 | Latent Safety-Constrained Policy Approach For Safe Offline Reinforcement Learning
155 | Latent Safety-Constrained Policy Approach For Safe Offline Reinforcement Learning
P. Koirala, Z. Jiang, S. Sarkar, C. Fleming
International Conference on Learning Representations (ICLR) 2025, Apr 24 – Apr 28th, 2025, Singapore
DOI
154 | RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception
154 | RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception
J. R. Waite, Md. Z. Hasan, Q. Liu, Z. Jiang, C. Hegde, S. Sarkar
ACM/IEEE 16th International Conference on Cyber-Physical Systems (ICCPS), Irvine, CA, USA, 2025
DOI
153 | Leveraging Vision Language Models for Specialized Agricultural Tasks
153 | Leveraging Vision Language Models for Specialized Agricultural Tasks
M. A. Arshad, T. Z. Jubery, T. Roy, R. Nassiri, A. K. Singh, A. Singh, C. Hegde, B. Ganapathysubramanian, A. Balu. A. Krishnamurthy, S. Sarkar
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), February 2025
DOI
2024
2024
152 | A Unified Convergence Theory for Large Language Model Efficient Fine-tuning
152 | A Unified Convergence Theory for Large Language Model Efficient Fine-tuning
Z. Jiang, N. Saadati, A. Balu, M. Pham, J. R. Waite, N. Saleem, C. Hegde, S. Sarkar
Conference on Neural Information Processing Systems (NeurIPS) 2024 OPT Workshop
DOI
151 | 3D Multiphase Heterogeneous Microstructure Generation Using Conditional Latent Diffusion Models
151 | 3D Multiphase Heterogeneous Microstructure Generation Using Conditional Latent Diffusion Models
N. Baishnab, E. Herron, A. Balu, S. Sarkar, A. Krishnamurthy, B. Ganapathysubramanian
Conference on Neural Information Processing Systems (NeurIPS) 2024 AI4Mat Workshop Spotlight
DOI
150 | Assisted Few-Shot Learning for Vision-Language Models in Agricultural Stress Phenotype Identification
150 | Assisted Few-Shot Learning for Vision-Language Models in Agricultural Stress Phenotype Identification
M. A. Arshad, T. Z. Jubery, A. K Singh, A. Singh, C. Hegde, B. Ganapathysubramanian, A. Balu, A. Krishnamurthy, S. Sarkar
Conference on Neural Information Processing Systems (NeurIPS) 2024 AFM Workshop
DOI
149 | BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity
149 | BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity
C.-H. Yang, B. Feuer, T. Z. Jubery, Z. K. Deng, A. Nakkab, Md Z. Hasan, S. Chiranjeevi, K. O. Marshall, N. Baishnab, A. K Singh, A. Singh, S. Sarkar, N. Merchant, C. Hegde, B. Ganapathysubramanian
Conference on Neural Information Processing Systems (NeurIPS) 2024, Datasets and Benchmarks Track Spotlight
Note: This paper is also listed with a different title: Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity
DOI
148 | GENESIS-RL: GEnerating Natural Edge-cases with Systematic Integration of Safety considerations and Reinforcement Learning
148 | GENESIS-RL: GEnerating Natural Edge-cases with Systematic Integration of Safety considerations and Reinforcement Learning
H.-J. Yang, J. Beck, Md Z. Hasan, E. Beyazit, S. Chakraborty, T. Wongpiromsarn, S. Sarkar
IEEE International Automated Vehicle Validation Conference (IAVVC), Pittsburgh, PA, USA, Oct. 21-23, 2024
147 | SDFConnect: Neural Implicit Surface Reconstruction of a Sparse Point Cloud with Topological Constraints
147 | SDFConnect: Neural Implicit Surface Reconstruction of a Sparse Point Cloud with Topological Constraints
A. Jignasu, A. Balu, S. Sarkar, C. Hegde, B. Ganapathysubramanian, A. Krishnamurthy
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5271-5279
DOI
146 | DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models
146 | DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models
N. Saadati, M. Pham, N. Saleem, J. R. Waite, A. Balu, Z. Jiang, C. Hegde, S. Sarkar
The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), Seattle, WA, USA, Jun 17-21, 2024
DOI
View Publication
145 | Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing
145 | Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing
A. K. Nirala, A. Joshi, S. Sarkar, C. Hegde
IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), Totonto, Canada, April 9-11, 2024
2023
2023
144 | A Predicting Clipping Asynchronous Stochastic Gradient Descent Method in Distributed Learning
144 | A Predicting Clipping Asynchronous Stochastic Gradient Descent Method in Distributed Learning
H. Wang, Z. Jiang, C. Liu, S. Sarkar, D. Jiang, Y. M. Lee
15th Annual Workshop on Optimization for Machine Learning at NeurIPS 2023, New Orleans, LA, Dec 10-16, 2023
143 | Roadway weather challenges illuminate real-world driving biomarkers of dementia risk
143 | Roadway weather challenges illuminate real-world driving biomarkers of dementia risk
Md Z. Hasan, G. Basulto, K.L. Tan, J. Chang, S. Sarkar, A. Sharma, S. Hallmark, M. Rizzo, J. Merickel
Alzheimer’s Association International Conference (AAIC) 2023 Annual Meeting, Amsterdam, Netherlands, July 16-20, 2023
View Publication
142 | Out-of-distribution algorithms for robust insect classification
142 | Out-of-distribution algorithms for robust insect classification
M. Saadati, S. Chiranjeevi, A. Balu, T. Z. Jubery, A. K Singh, S. Sarkar, A. Singh, B. Ganapathysubramanian
AAAI Workshop on AI for Agriculture and Food Systems, Washington D.C., Feb 14, 2023
141 | Optimized Class-specific Data Augmentation for Plant Stress Classification
141 | Optimized Class-specific Data Augmentation for Plant Stress Classification
N. Saleem, B. Ganapathysubramanian, A. Balu, T. Z. Jubery, S. Sarkar, A. Singh, A. K Singh
AAAI Workshop on AI for Agriculture and Food Systems, Washington D.C., Feb 14, 2023
140 | Plant Geometry Reconstruction From Field Data Using Neural Radiance Fields
140 | Plant Geometry Reconstruction From Field Data Using Neural Radiance Fields
A. Jignasu, E. Herron, T. Z. Jubery, J. Afful, A. Balu, B. Ganapathysubramanian, S. Sarkar, A. Krishnamurthy
AAAI Workshop on AI for Agriculture and Food Systems, Washington D.C., Feb 14, 2023
139 | Data driven ensemble learning for soybean yield prediction
139 | Data driven ensemble learning for soybean yield prediction
S. Chattopadhyay, M. E. Carroll, B. Ganapathysubramanian, A. K Singh, S. Sarkar
AAAI Workshop on AI for Agriculture and Food Systems, Washington D.C., Feb 14, 2023
138 | Zero-Shot Insect Detection via Weak Language Supervision
138 | Zero-Shot Insect Detection via Weak Language Supervision
B. Feuer, A. Joshi, M. Cho, K. Jani, S. Chiranjeevi, Z. K. Deng, A. Balu, A. K Singh, S. Sarkar, N. Merchant, A. Singh, B. Ganapathysubramanian, C. Hegde
AAAI Workshop on AI for Agriculture and Food Systems, Washington D.C., Feb 14, 2023
137 | Deep Reinforcement Learning Exploration in Continuous Latent Space for Molecular Design
137 | Deep Reinforcement Learning Exploration in Continuous Latent Space for Molecular Design
H.-J. Yang, C.-H. Yang, P. Sornberger, R. Duke, C. Risko, B. Ganapathysubramanian
AAAI Workshop on AI to Accelerate Science and Engineering, Washington D.C., Feb 13, 2023
View Publication
2022
2022
136 | A study of natural robustness of deep reinforcement learning algorithm towards adversarial perturbations
136 | A study of natural robustness of deep reinforcement learning algorithm towards adversarial perturbations
Q. Liu, X. Y. Lee, S. Sarkar
Deep Reinforcement Learning Workshop at NeurIPS 2022, Virtual, Dec 9, 2022
135 | Communication-efficient Decentralized Deep Learning
135 | Communication-efficient Decentralized Deep Learning
F. Fotouhi, A. Balu, Z. Jiang, Y. Esfandiari, S. Jahani, S. Sarkar
Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications Workshop at NeurIPS 2022, New Orleans, LA, Dec 3, 2022
134 | Enhancing System-level Safety in Autonomous Driving via Feedback Learning
134 | Enhancing System-level Safety in Autonomous Driving via Feedback Learning
S. Y. Tan, W. Fan, Q. Liu, T. Wongpiromsarn, S. Sarkar
Machine Learning for Autonomous Driving Workshop at NeurIPS 2022 , New Orleans, LA, Dec 3, 2022
133 | DriveCLIP: Zero-shot transfer for distracted driving activity understanding using CLIP
133 | DriveCLIP: Zero-shot transfer for distracted driving activity understanding using CLIP
Z. Hasan, A. Joshi, M. Rahman, A. Venkatachalapathy, A. Sharma, C. Hegde, S. Sarkar
Machine Learning for Autonomous Driving Workshop at NeurIPS 2022 , New Orleans, LA, Dec 3, 2022
132 | Generative Design of Material Microstructures for Organic Solar Cells using Diffusion Models
132 | Generative Design of Material Microstructures for Organic Solar Cells using Diffusion Models
E. Herron, X.Y. Lee, A. Balu, B. S. S. Pokuri, B. Ganapathysubramanian, S. Sarkar, A. Krishnamurthy
AI for Accelerated Materials Design Workshop at NeurIPS 2022 , New Orleans, LA, Dec 2, 2022
131 | A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems
131 | A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems
X. Y. Lee, S. Sarkar, Y. Wang
Domain-Aware Scientific Reinforcement Learning minisymposium at SIAM Conference on Mathematics of Data Science (MDS22), San Diego, CA, September 26 - 30, 2022
View Publication
130 | Distributed Online Non-convex Optimization with Composite Regret
130 | Distributed Online Non-convex Optimization with Composite Regret
Z. Jiang, A. Balu, X. Y. Lee, Y. M. Lee, C. Hegde, S. Sarkar
58th ALLERTON Conference on Communication, Control, and Computing, Champaign, IL, Sept 27-30, 2022
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129 | Deep Reinforcement Learning for Robotic Control with Multi-Fidelity Models
129 | Deep Reinforcement Learning for Robotic Control with Multi-Fidelity Models
D. Leguizamo, H-J. Yang, X.Y. Lee, S. Sarkar
Modeling, Estimation and Control Conference (MECC), Jersey City, NJ, October 2 - 5, 2022
View Publication
128 | Stochastic Conservative Contextual Linear Bandits
128 | Stochastic Conservative Contextual Linear Bandits
J. Lin, X. Y. Lee, T. Jubery, S. Moothedath, S. Sarkar, B. Ganapathysubramanian
61st IEEE Conference on Decision and Control (CDC), Cancún, Mexico, Dec. 6-9, 2022
127 | MDPGT: Momentum-based Decentralized Policy Gradient Tracking
127 | MDPGT: Momentum-based Decentralized Policy Gradient Tracking
Z Jiang, X. Y. Lee, S.Y. Tan, K.L. Tan, A. Balu, Y.M. Lee, C Hegde, S. Sarkar
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence(AAAI 2022), Virtual
126 | A Conservative Stochastic Contextual Bandit Based Framework for Farming Recommender Systems
126 | A Conservative Stochastic Contextual Bandit Based Framework for Farming Recommender Systems
S. Moothedath, X. Y. Lee, Z. T. Jubery, B. Ganapathysubramanian, S. Sarkar
AI for Agriculture and Food Systems Workshop in Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2022), Virtual
View Publication
125 | Privacy-Preserving Deep Models for Plant Stress Phenotyping
125 | Privacy-Preserving Deep Models for Plant Stress Phenotyping
M. Cho, K. Nagasubramanian, A. K. Singh, A. Singh, B. Ganapathysubramanian, S. Sarkar, C. Hegde
AI for Agriculture and Food Systems Workshop in Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2022), Virtual
View Publication
124 | Self-Supervised Learning Improves Agricultural Pest Classification
124 | Self-Supervised Learning Improves Agricultural Pest Classification
S. Kar, K. Nagasubramanian, D. Elango, A. Nair, D. S. Mueller, M. E. O’Neal, A. K. Singh, S. Sarkar, B. Ganapathysubramanian, A. Singh
AI for Agriculture and Food Systems Workshop in Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2022), Virtual
View Publication
123 | Exploring the use of 3D point cloud data for improved plant stress rating
123 | Exploring the use of 3D point cloud data for improved plant stress rating
S. Chiranjeevi, T. Young, T. Z.Jubery, K. Nagasubramanian, S. Sarkar, A. K. Singh, A. Singh, B. Ganapathysubramanian
AI for Agriculture and Food Systems Workshop in Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2022), Virtual
View Publication
122 | Inverse Design of Microstructures via Generative Networks for Organic Solar Cells
122 | Inverse Design of Microstructures via Generative Networks for Organic Solar Cells
X. Y. Lee, A. Balu, B. S. S. Pokuri, A. Krishnamurthy, S. Sarkar, B. Ganapathysubramanian
AI for Design and Manufacturing Workshop in Thirty-Fifth AAAI Conference on Artificial Intelligence(AAAI 2022), Virtual
121 | Fast Unsupervised Generative Design for Structural Topology Optimization
121 | Fast Unsupervised Generative Design for Structural Topology Optimization
E. Herron, A. Jignasu, J. Rade, X. Y. Lee, A.Balu, A. Krishnamurthy, S. Sarkar
AI for Design and Manufacturing Workshop in Thirty-Fifth AAAI Conference on Artificial Intelligence(AAAI 2022), Virtual
120 | Multigrid Distributed Deep CNNs for Structural Topology Optimization
120 | Multigrid Distributed Deep CNNs for Structural Topology Optimization
J. Rade, A. Balu, E.Herron, A. Jignasu, S. Botelho, S. Adavani, S. Sarkar, B. Ganapathysubramanian, A. Krishnamurthy
AI for Design and Manufacturing Workshop in Thirty-Fifth AAAI Conference on Artificial Intelligence(AAAI 2022), Virtual
2021
2021
119 | Cross-Modal Virtual Sensing for Combustion Instability Monitoring
119 | Cross-Modal Virtual Sensing for Combustion Instability Monitoring
T. Gangopadhyay, V. Ramanan, S. R. Chakravarthy, S. Sarkar,
Machine Learning and the Physical Sciences Workshop in Neural Information Processing Systems (NeurIPS 2021), Virtual
118 | Distributed Deep Learning for Persistent Monitoring of Agricultural Fields
118 | Distributed Deep Learning for Persistent Monitoring of Agricultural Fields
Y. Esfandiari, K. Nagasubramanian, F. Fotouhi, P. S. Schnable, B. Ganapathysubramanian, S. Sarkar
AI for Science Workshop in Neural Information Processing Systems (NeurIPS 2021), Virtual
117 | Differentiable Spline Approximations
117 | Differentiable Spline Approximations
M. Cho, A. Balu, A. Joshi, A. D. Prasad, B. Khara, S. Sarkar, B. Ganapathysubramanian, A. Krishnamurthy, C. Hegde
NeurIPS, 2021.
116 | A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems
116 | A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems
X.Y. Lee, S. Sarkar, Y. Wang
Deep Reinforcement Learning Workshop in Neural Information Processing Systems (NeurIPS 2021), Virtual
115 | Distributed Multigrid Neural Solvers on Megavoxel Domains
115 | Distributed Multigrid Neural Solvers on Megavoxel Domains
Balu, A., Botelho, S., Khara, B., Rao, V., Hegde, C., Sarkar, S., Adavani, S, Krishnamurthy, A. Ganapathysubramanian, B.
Supercomputing, 2021
114 | AI Guided Measurement of Live Cells Using AFM
114 | AI Guided Measurement of Live Cells Using AFM
J. Rade, S. Sarkar, A. Krishnamurthy, J. Ren, A. Sarkar
Modeling, Estimation and Control Conference (MECC), (Austin, TX), October 24-27, 2021.
113 | Multi-resolution 3D CNN for Learning Multi-scale Spatial Features in CAD Models
113 | Multi-resolution 3D CNN for Learning Multi-scale Spatial Features in CAD Models
S. Ghadai, X. Y. Lee, A. Balu, S. Sarkar, A. Krishnamurthy
SIAM Conference on Geometric and Physical Modeling (GD/SPM21), Virtual, Sept 27-29, 2021.
112 | Cross-Gradient Aggregation for Decentralized Learning from Non-IID data
112 | Cross-Gradient Aggregation for Decentralized Learning from Non-IID data
Y. Esfandiari, S. Y. Tan, Z. Jiang, A. Balu, E.D. Herron, C. Hegde, S. Sarkar
International Conference on Machine Learning (ICML), Virtual, July 18-24, 2021.
111 | Battery-Free Camera Occupancy Detection System
111 | Battery-Free Camera Occupancy Detection System
A. Saffari, S. Y. Tan, M. Katanbaf, H. Saha, J. Smith, S. Sarkar,
5th International Workshop on Embedded and Mobile Deep Learning (EMDL), Virtual, June 24-25, 2021.
110 | Deep learning for fast Atomic Force Microscopy data analytics
110 | Deep learning for fast Atomic Force Microscopy data analytics
A. Sarkar, J. Waite and S. Sarkar
65th Biophysical Society Annual Meeting, Feb 22-26, 2021, Virtual.
109 | Decentralized Deep Learning Using Momentum-Accelerated Consensus
109 | Decentralized Deep Learning Using Momentum-Accelerated Consensus
A. Balu, Z. Jiang, SY. Tan, C. Hedge, Y. M. Lee, and S. Sarkar
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (Toronto, Canada), 2021
108 | Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation
108 | Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation
T. Gangopadhyay, S. Y. Tan, Z. Jiang, R. Meng, S. Sarkar
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (Toronto, Canada), 2021
107 | Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents
107 | Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents
X.Y. Lee, Y. Esfandiari, K.L. Tan, S. Sarkar
International Conference on Cyber-Physical Systems (ICCPS), (Nashville, TN), 2021
2020
2020
106 | Interpreting the Impact of Weather on Crop Yield Using Attention
106 | Interpreting the Impact of Weather on Crop Yield Using Attention
T. Gangopadhyay, J. Shook, A. K. Singh, S. Sarkar
NeurIPS Workshop on AI for Earth Sciences, 2020, Virtual
105 | Local Gradient Aggregation for Decentralized Learning from Non-IID data
105 | Local Gradient Aggregation for Decentralized Learning from Non-IID data
Y. Esfandiari, S.Y. Tan, Z.H. Jiang, A. Balu, C. Hegde, S. Sarkar
Optimization for Machine Learning Workshop, Neural Information Processing Systems (NeurIPS 2020), Virtual
104 | Differentiable Programming for Piecewise Polynomial Functions
104 | Differentiable Programming for Piecewise Polynomial Functions
M. Cho, A. Joshi, X.Y. Lee, A. Balu, B. Ganapathysubramanian, S. Sarkar, C. Hegde
Learning Meets Combinatorial Algorithms Workshop, Neural Information Processing Systems (NeurIPS 2020), Virtual
103 | Adaptive Gradient Tracking In Stochastic Optimization
103 | Adaptive Gradient Tracking In Stochastic Optimization
Z. Jiang, X.Y. Lee, S.Y Tan, A. Balu, Y.M. Lee, C. Hegde, S. Sarkar
Optimization for Machine Learning Workshop, Neural Information Processing Systems (NeurIPS 2020), Virtual
102 | Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents
102 | Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents
X.Y. Lee, Y. Esfandiari, K.L. Tan, S.Sarkar
Deep Reinforcement Learning Workshop in Neural Information Processing Systems (NeurIPS 2020), Virtual
101 | Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems
101 | Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems
T. Gangopadhyay, S. Y. Tan, Z. Jiang, S. Sarkar
International Conference on Dynamic Data Driven Application Systems (DDDAS), (Boston, MA), 2020
100 | Automated Detection of Part Quality During Two Photon Lithography via Deep Learning
100 | Automated Detection of Part Quality During Two Photon Lithography via Deep Learning
B. Giera, X. Lee, S. Saha, S. Sarkar
Annual International Solid Freeform Fabrication Symposium (SFF Symp 2020), Austin, TX, August 17-19, 2020
99 | Solving Linear PDEs with Generative Models
99 | Solving Linear PDEs with Generative Models
A. Joshi, B. Khara, S. Sarkar, B. Ganapathysubramanian, C. Hedge
ASILOMAR 2020 Conference on Signals, Systems, and Computers, Pacific Grove, CA, 2020
98 | Orthogonal Distance Fields Representation For Machine-Learning Based Manufacturability Analysis
98 | Orthogonal Distance Fields Representation For Machine-Learning Based Manufacturability Analysis
A. Balu, S. Ghadai, S. Sarkar, A. Krishnamurthy
ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE), St. Louis, MO, 2020
97 | Automatic Lane Detection Algorithm For Noisy Naturalistic Driving Data
97 | Automatic Lane Detection Algorithm For Noisy Naturalistic Driving Data
L. Riera, K. Ozcan, J. Merickel, M. Rizzo, S. Sarkar, A. Sharma
IEEE Intelligent Vehicle Symposium (IV), (Las Vegas), 2020
96 | Granger Causality-based Hierarchical Time Series Clustering for State Estimation
96 | Granger Causality-based Hierarchical Time Series Clustering for State Estimation
S.Y. Tan, H. Saha, M. Jacoby, A.R. Florita, G.P. Henze, S. Sarkar
IFAC World Congress, (Berlin, Germany), 2020
95 | Interpretable Deep Learning for Monitoring Combustion Instability
95 | Interpretable Deep Learning for Monitoring Combustion Instability
T. Gangopadhyay, S. Y. Tan, A. Locurto, J. B. Michael, S. Sarkar
IFAC World Congress, (Berlin, Germany), 2020
94 | Robustifying Reinforcement Learning Agents via Action Space Adversarial Training
94 | Robustifying Reinforcement Learning Agents via Action Space Adversarial Training
K.L. Tan, Y. Esfandiari, X.Y. Lee, Aakanksha, S. Sarkar
Proceedings of American Control Conference (ACC 2020), Denver, CO
93 | A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning
93 | A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning
Y. Esfandiari, A. Balu, K. Ebrahimi, U. Vaidya, N. Elia, S. Sarkar
Artificial Intelligence Safety (SafeAI) workshop in Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), New York, NY, 2020
92 | Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
92 | Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
X.Y. Lee, S. Ghadai, K.L. Tan, C. Hedge, S. Sarkar
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), New York, NY, 2020
91 | InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models
91 | InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models
A. Joshi, M. Cho, V. Shah, B. Pokuri, S. Sarkar, B. Ganapathysubramanian, C. Hedge
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), New York, NY, 2020
2019
2019
90 | Learning State Switching for Multi-sensor Integration
90 | Learning State Switching for Multi-sensor Integration
89 | An Explainable Framework using Deep Attention Models for Sequential Data in Combustion Systems
89 | An Explainable Framework using Deep Attention Models for Sequential Data in Combustion Systems
T. Gangopadhyay, S. Y. Tan, A. Locurto, J. B. Michael, S. Sarkar
NeurIPS Workshop on Machine Learning and the Physical Sciences, (Vancouver, Canada), 2019
88 | On Higher-order Moments in Adam
88 | On Higher-order Moments in Adam
Z. Jiang, A. Balu, S.Y. Tan, Y.M. Lee, C. Hedge, S. Sarkar
Beyond First Order Methods in Machine Learning workshop in 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
87 | Semantic Domain Adaptation for Deep Classifiers via GAN-based Data Augmentation
87 | Semantic Domain Adaptation for Deep Classifiers via GAN-based Data Augmentation
A. Mukherjee, A. Joshi, S. Sarkar, C. Hegde
Machine Learning for Autonomous Driving workshop at Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
86 | Generative Models for Solving Nonlinear Partial Differential Equations
86 | Generative Models for Solving Nonlinear Partial Differential Equations
A. Joshi, V. Shah, S. Ghosal, B. Pokuri, S. Sarkar, B. Ganapathysubramanian, C. Hegde
Machine Learning and the Physical Sciences Workshop at Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
85 | A perspective on multi-agent communication for information fusion
85 | A perspective on multi-agent communication for information fusion
H. Saha, V. Venkataraman, A. Speranzon, S. Sarkar
Visually Grounded Interaction and Language at Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
84 | Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
84 | Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
X.Y. Lee, S. Ghadai, K.L. Tan, C. Hedge, S. Sarkar
Deep Reinforcement Learning Workshop in Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
83 | Deep Time Series Attention Models for Crop Yield Prediction and Insights
83 | Deep Time Series Attention Models for Crop Yield Prediction and Insights
T. Gangopadhyay, J. Shook, A. K. Singh, S. Sarkar
NeurIPS Workshop on Machine Learning and the Physical Sciences, (Vancouver, Canada), 2019
82 | Deep Learning for Dynamic Deformation Simulation of Bioprosthetic Heart Valves
82 | Deep Learning for Dynamic Deformation Simulation of Bioprosthetic Heart Valves
Aditya Balu, K.L. Tan, Michael C.H. Wu, Ming-Chen Hsu, Soumik Sarkar, Adarsh Krishnamurthy
USNCCM 15, Austin, TX, 2019
81 | Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers
81 | Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers
A. Joshi, A. Mukherjee, S. Sarkar, C. Hegde
International Conference on Computer Vision (ICCV), Seoul, South Korea, Oct 27 – Nov 2, 2019
80 | Linking age-related decline to driver behavior at signalized intersections
80 | Linking age-related decline to driver behavior at signalized intersections
Barnwal, A., Merickel, J., Riera-Garcia, L., Ozcan, K., Sarkar, S., Sharma, A., Rizzo, M.
Association for the Advancement of Automotive Medicine’s (AAAM) 63rd Annual Scientific Conference, Madrid, Spain, October 2019.
79 | Deep Reinforcement Learning for Adaptive Traffic Signal Control
79 | Deep Reinforcement Learning for Adaptive Traffic Signal Control
K.L. Tan, S. Poddar, S. Sarkar, A. Sharma
Proceedings of ASME 2019 Dynamic Systems and Control Conference (DSCC), (Park City, Utah), 2019
78 | Attribute-Controlled Traffic Data Augmentation Using Conditional Generative Models
78 | Attribute-Controlled Traffic Data Augmentation Using Conditional Generative Models
Mukherjee A., Joshi A. , SarkarS., Hegde C.
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 | Multi-level 3D CNN for Learning Multi-scale Spatial Features
77 | Multi-level 3D CNN for Learning Multi-scale Spatial Features
Ghadai, S., Lee, X. Y., Balu, A., Sarkar, S., Krishnmaurthy, A.
Deep Learning for Geometric Shape Understanding workshop at IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Long Beach, CA), 2019 (Invited Talk)
76 | A flexible framework for building occupancy detection using spatiotemporal pattern networks
76 | A flexible framework for building occupancy detection using spatiotemporal pattern networks
S.Y. Tan, H. Saha, A.R. Florita, G.P. Henze, S. Sarkar
Proceedings of American Control Conference, (Philadelphia, PA), 2019
75 | A Deep-Learning Framework for Diagnostics and Design of Bioprosthetic Heart Valves
75 | A Deep-Learning Framework for Diagnostics and Design of Bioprosthetic Heart Valves
A. Balu, M. Hsu, S. Sarkar, A. Krishnamurthy
BMES/FDA conference on Frontiers in Medical Devices : The Role of Digital Evidence to Support Personalized Patient Healthcare, (Washington, DC Metropolitan Area), 2019
2018
2018
74 | 3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys
74 | 3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys
R. Singh, A. Sharma, O. Bingol, A. Balu, G. Balasubramanian, D. D. Johnson and S. Sarkar
Workshop on Machine Learning for Molecules and Materials (MLMM) at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018
73 | Interpretable deep learning for guided structure-property explorations in photovoltaics
73 | Interpretable deep learning for guided structure-property explorations in photovoltaics
B.S.S. Pokuri, S. Ghosal, A. Kokate, B. Ganapathysubramanian, S. Sarkar
Workshop on Machine Learning for Molecules and Materials (MLMM) at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018
72 | Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning
72 | Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning
X. Lee, A. Balu, D. Stoecklein, B. Ganapathysubramanian, S. Sarkar
Deep Reinforcement Learning Workshop at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018
71 | Physics-aware Deep Generative Models for Creating Synthetic Microstructures
71 | Physics-aware Deep Generative Models for Creating Synthetic Microstructures
R. Singh, V. Shah, B. Pokuri, S. Sarkar, B. Ganapathysubramanian, C. Hegde
Workshop on Machine Learning for Molecules and Materials (MLMM) at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018
70 | Temporal Attention and Stacked LSTMs for Multivariate Time Series Prediction
70 | Temporal Attention and Stacked LSTMs for Multivariate Time Series Prediction
T. Gangopadhyay, S. Y. Tan, G. Huang, S. Sarkar
Workshop on Modeling and decision-making in the spatiotemporal domain at Proceedings of Advances in Neural Information Processing Systems (NeurIPS), (Montreal, Canada), 2018
69 | Machine Learning for Diagnostics and Patient-Specific Design of Bioprosthetic Heart Valves
69 | Machine Learning for Diagnostics and Patient-Specific Design of Bioprosthetic Heart Valves
A. Balu, S. Nallagonda, F. Xu, A. Krishnamurthy, M. Hsu, S. Sarkar
Integrating Design and Analysis (IGA), (Austin, TX), 2018
68 | Online Robust Policy Learning in the Presence of Unknown Adversaries
68 | Online Robust Policy Learning in the Presence of Unknown Adversaries
A. Havens, Z. Jiang, S. Sarkar
Proceedings of Advances in Neural Information Processing Systems (NIPS), (Montreal, Canada), 2018
67 | Exploring Granger causality in dynamical systems modeling and performance monitoring
67 | Exploring Granger causality in dynamical systems modeling and performance monitoring
H. Saha, C. Liu, Z. Jiang, S. Sarkar
5th International High Performance Buildings Conference, (West Lafayette, IN), 2018
66 | A Data-driven Approach towards Integration of Microclimate Conditions for Predicting Building Energy Performance
66 | A Data-driven Approach towards Integration of Microclimate Conditions for Predicting Building Energy Performance
L. Wu, V. Chinde, H. Sharma, U. Passe, S. Sarkar
5th International High Performance Buildings Conference, (West Lafayette, IN), 2018
65 | Incremental Consensus based Collaborative Deep Learning
65 | Incremental Consensus based Collaborative Deep Learning
Z. Jiang, A. Balu, C. Hedge, S. Sarkar
Workshop on Modern Trends in Nonconvex Optimization for Machine Learning at International Conference on Machine Learning (ICML), (Stockholm, Sweden), 2018 (Spotlight Presentation)
64 | Characterizing Combustion Instability Using Deep Convolutional Neural Network
64 | Characterizing Combustion Instability Using Deep Convolutional Neural Network
T. Gangopadhyay, A. Locurto, P. Boor, J. B. Michael, S. Sarkar
Proceedings of ASME 2018 Dynamic Systems and Control Conference (DSCC), (Atlanta, Georgia), 2018
63 | Mode decomposition and convolutional neural network analysis of thermoacoustic instabilities in a Rijke tube
63 | Mode decomposition and convolutional neural network analysis of thermoacoustic instabilities in a Rijke tube
A. Locurto, T. Gangopadhyay, P. Boor, S. Sarkar, J. B. Michael
Transportation Research Board 97th Annual Meeting, (Washington, D.C.), 2018
62 | Traffic Congestion Detection from Camera Images using Deep Convolution Neural Networks
62 | Traffic Congestion Detection from Camera Images using Deep Convolution Neural Networks
P. Chakraborty, Y. A. Gyamfi, S. Poddar, V. Ahsani, A. Sharma, S. Sarkar
Transportation Research Board 97th Annual Meeting, (Washington, D.C.), 2018
61 | Comparison of Machine Learning Algorithms to Determine Traffic Congestion from Camera Images
61 | Comparison of Machine Learning Algorithms to Determine Traffic Congestion from Camera Images
S. Poddar, K. Ozcan, P. Chakraborty, V. Ahsani, A. Sharma and S. Sarkar
Transportation Research Board 97th Annual Meeting, (Washington, D.C.), 2018
60 | On Consensus-Disagreement Tradeoff in Distributed Optimization
60 | On Consensus-Disagreement Tradeoff in Distributed Optimization
Z. Jiang, K. Mukherjee, S. Sarkar
Proceedings of American Control Conference, (Milwaukee, WI), 2018
59 | Building Energy Disaggregation using Spatiotemporal Pattern Network
59 | Building Energy Disaggregation using Spatiotemporal Pattern Network
C. Liu, Z. Jiang, A. Akintayo, G. P. Henze, S. Sarkar
Proceedings of American Control Conference, (Milwaukee, WI), 2018
58 | Hierarchical Optimization for Building Energy Systems
58 | Hierarchical Optimization for Building Energy Systems
Z. Jiang, T. Wilkie, S. Sarkar
Proceedings of American Control Conference, (Milwaukee, WI), 2018
2017
2017
57 | Collaborative Deep Learning in Fixed Topology Networks
57 | Collaborative Deep Learning in Fixed Topology Networks
Z. Jiang, A. Balu, C. Hegde, S. Sarkar
Proceedings of Advances in Neural Information Processing Systems (NIPS), (Long Beach, CA), 2017
56 | A Forward-Backward Approach for Visualizing Information Flow in Deep Networks
56 | A Forward-Backward Approach for Visualizing Information Flow in Deep Networks
A. Balu, T. V. Nguyen, A. Kokate, C. Hegde, S. Sarkar
Symposium on Interpretable Machine Learning at NIPS 2017
55 | Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps
55 | Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps
K. Nagasubramanian, S. Jones, A. K. Singh, A. Singh, B. Ganapathysubramanian, S. Sarkar
Workshop on Intrepreting, Explaining and Visualizing Deep Learning...now what?, NIPS 2017
54 | Interpretable Deep Learning applied to Plant Stress Phenotyping
54 | Interpretable Deep Learning applied to Plant Stress Phenotyping
S. Ghosal, D. Blystone, A. K. Singh, B. Ganapathysubramanian, A. Singh, S. Sarkar
Symposium on Interpretable Machine Learning at NIPS 2017
53 | Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
53 | Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
S. Ghadai, A. Balu, A. Krishnamurthy, S. Sarkar
Symposium on Interpretable Machine Learning at NIPS 2017
52 | High Speed Video-based health monitoring using 3D Deep Learning
52 | High Speed Video-based health monitoring using 3D Deep Learning
S. Ghosal, A. Akintayo, P. K. Boor, S. Sarkar
Proceedings of the Dynamic Data Driven Application Systems (DDDAS), (Cambridge, MA), 2017
51 | Convergence and noise effect analysis for generalized gossip-based distributed optimization
51 | Convergence and noise effect analysis for generalized gossip-based distributed optimization
Z. Jiang, K. Mukherjee, S. Sarkar
Proceedings of the 2nd ACM SIGKDD Workshop on Machine Learning for Prognostics & Health Management. (Halifax, NS, Canada), 2017
50 | Traffic sensor health monitoring using spatiotemporal graphical modeling
50 | Traffic sensor health monitoring using spatiotemporal graphical modeling
L. Wu, C. Liu, T. Huang, A. Sharma, S. Sarkar
Proceedings of the 2nd ACM SIGKDD Workshop on Machine Learning for Prognostics & Health Management. (Halifax, NS, Canada), 2017
49 | Traffic System Anomaly Detection using Spatiotemporal Pattern Networks
49 | Traffic System Anomaly Detection using Spatiotemporal Pattern Networks
T. Huang, C. Liu, A. Sharma, S. Sarkar
Proceedings of the 2nd ACM SIGKDD Workshop on Machine Learning for Prognostics & Health Management. (Halifax, NS, Canada), 2017
48 | Autonomous Mobile Sensing Platform for Spatio-Temporal Plant Phenotyping
48 | Autonomous Mobile Sensing Platform for Spatio-Temporal Plant Phenotyping
H. Saha, T. Gao, H. Emadi, Z. Jiang, A. Singh, B. Ganapathysubramanian, S. Sarkar, A. Singh, S. Bhattacharya
Proceedings of ASME 2017 Dynamic Systems and Control Conference (DSCC) (Tysons, VA), 2017
47 | Data-driven root-cause analysis for distributed system anomalies
47 | Data-driven root-cause analysis for distributed system anomalies
C. Liu, K. G. Lore, S. Sarkar
Proceedings of IEEE Conference on Decision and Control, (Melbourne, Australia), 2017
2016
2016
46 | Data driven exploration of traffic network system dynamics using high resolution probe data
46 | Data driven exploration of traffic network system dynamics using high resolution probe data
C. Liu, B. Huang, M. Zhao, S. Sarkar, U. Vaidya, A. Sharma
Proceedings of IEEE Conference on Decision and Control, (Las Vegas, NV), 2016
45 | Early Detection of Combustion Instabilities using Deep Convolutional Selective Autoencoders on Hi-speed Flame Video
45 | Early Detection of Combustion Instabilities using Deep Convolutional Selective Autoencoders on Hi-speed Flame Video
A. Akintayo, K. G. Lore, S. Sarkar, S. Sarkar
Proceedings of the 22nd ACM SIGKDD Workshop on Machine Learning for Prognostics & Health Management. (San Francisco, CA). 2016
44 | An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection
44 | An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection
A. Akintayo, N. Lee, V. Chawla, M. Mullaney, C. Marett, A. Singh, A. Singh, G. Tylka, B. Ganapathysubramanian & S. Sarkar
Proceedings of the 22nd ACM SIGKDD Workshop on Data Science for Food, Energy and Water. (San Francisco, CA). 2016
43 | A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert knowledge
43 | A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert knowledge
V. Chawla, M.H. Hsiang, A. Akintayo, D. Hayes, P. Schnable, B. Ganapathysubramanian, S. Sarkar
Proceedings of the 22nd ACM SIGKDD Workshop on Data Science for Food, Energy and Water. (San Francisco, CA). 2016
42 | Detection and Analysis of Combustion Instability From Hi-Speed Flame Images Using Dynamic Mode Decomposition
42 | Detection and Analysis of Combustion Instability From Hi-Speed Flame Images Using Dynamic Mode Decomposition
S. Ghosal, V. Ramanan, S. Sarkar, S. R. Chakravarthy, S. Sarkar
Proceedings of ASME 2016 Dynamic Systems and Control Conference (DSCC). (Minneapolis, MN), 2016
41 | Damage Detection of Bridge Network With Spatiotemporal Pattern Network
41 | Damage Detection of Bridge Network With Spatiotemporal Pattern Network
C. Liu, Y. Gong, S. Laflamme, B. Phares, S. Sarkar
Proceedings of ASME 2016 Dynamic Systems and Control Conference (DSCC). (Minneapolis, MN), 2016
40 | A VOLTTRON based implementation of Supervisory Control using Generalized Gossip for Building Energy Systems
40 | A VOLTTRON based implementation of Supervisory Control using Generalized Gossip for Building Energy Systems
V. Chinde, A. Kohl, Z. Jiang, A. Kelkar, S. Sarkar
Proceedings in the 4th International High Performance Buildings Conference, (West Lafayette, IN), 2016
39 | Scalable Supervisory Control of Building Energy Systems using Generalized Gossip
39 | Scalable Supervisory Control of Building Energy Systems using Generalized Gossip
Z. Jiang, V. Chinde, A. Kohl, S. Sarkar, A. Kelkar
Proceedings of the American Control Conference, (Boston, MA), 2016
38 | Building HVAC Systems Control Using Power Shaping Approach
38 | Building HVAC Systems Control Using Power Shaping Approach
V. Chinde, K. C. Kosaraju, A. Kelkar, R. Pasumarthy, S. Sarkar and N. M. Singh
Proceedings of the American Control Conference, (Boston, MA), 2016
37 | Multimodal spatiotemporal information fusion using neural-symbolic modeling for early detection of combustion instabilities
37 | Multimodal spatiotemporal information fusion using neural-symbolic modeling for early detection of combustion instabilities
S. Sarkar, D. K. Jha, K. G. Lore, A, Ray, S. Sarkar
Proceedings of the American Control Conference, (Boston, MA), 2016
36 | Topology Control in Mobile Sensor Networks using Information Space Feedback
36 | Topology Control in Mobile Sensor Networks using Information Space Feedback
K. G. Lore, S. Sarkar, D. K. Jha
Proceedings of the American Control Conference, (Boston, MA), 2016
35 | Deep Value of Information Estimators for Collaborative Human-Machine Information Gathering
35 | Deep Value of Information Estimators for Collaborative Human-Machine Information Gathering
K. G. Lore, N. Sweet, K. Kumar, N. Ahmed, S. Sarkar
Proceedings of the International Conference of Cyber-Physical Systems, (Vienna, Austria), 2016
34 | An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPS
34 | An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPS
C. Liu, S. Ghosal, Z. Jiang , S. Sarkar
Proceedings of the International Conference on Cyber-physical Systems (ICCPS 2016)
33 | Data-driven persistent monitoring of Indoor Air Systems
33 | Data-driven persistent monitoring of Indoor Air Systems
S. Ghosal, C. Liu, U. Passe, S. He, S. Sarkar
Proceedings of the ASHRAE IAQ 2016 Defining Indoor Air Quality: Policy, Standards and Best Practices, (Alexandria, VA), 2016
2015
2015
32 | Early Detection of Combustion Instability by Neural-Symbolic Analysis on Hi-Speed Video
32 | Early Detection of Combustion Instability by Neural-Symbolic Analysis on Hi-Speed Video
S. Sarkar, K. G. Lore and S. Sarkar
Proceedings of the NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches; 29th Annual Conference on Neural Information Processing Systems (NIPS 2015).
31 | Understanding Wind Turbine Interactions Using Spatiotemporal Pattern Network
31 | Understanding Wind Turbine Interactions Using Spatiotemporal Pattern Network
Z. Jiang, S. Sarkar
Proceedings of ASME Dynamics Systems and Control Conference, (Columbus, OH), 2015
30 | On distributed optimization using generalized gossip
30 | On distributed optimization using generalized gossip
Z. Jiang, S. Sarkar, K. Mukherjee
Proceedings of IEEE Conference on Decision and Control, (Osaka, Japan), 2015
29 | Early Detection of Combustion Instability from Hi-speed Flame Images via Deep Learning and Symbolic Time Series Analysis
29 | Early Detection of Combustion Instability from Hi-speed Flame Images via Deep Learning and Symbolic Time Series Analysis
S. Sarkar, K. G. Lore, S. Sarkar, V. Ramanan, S. Chakravarthy, A. Ray
Proceedings of Annual Conference of the Prognostics and Health Management Society, (San Diego, CA), 2015
28 | Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks
28 | Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks
S. Sarkar, V. Venugopalan, K. Reddy, J. Rayde, M. Giering, N. Jaitly
Proceedings of IEEE High Performance Extreme Computing Conference, (Waltham, MA), 2015
27 | Comparative Evaluation of Control-Oriented Zone Temperature Prediction Modeling Strategies in Buildings
27 | Comparative Evaluation of Control-Oriented Zone Temperature Prediction Modeling Strategies in Buildings
V. Chinde, J. C. Heylmun, A. Kohl, Z. Jiang, S. Sarkar, A. Kelkar
Proceedings of ASME Dynamics Systems and Control Conference, (Columbus, OH), 2015
26 | Scalable human-in-the-loop decision support
26 | Scalable human-in-the-loop decision support
R. Georgescu, K. Reddy, N. Trcka, M. Chen, P. Qumiby, P. O'Neil, T. Khawaja, L. Bertuccelli, D. Hestand, S. Sarkar, O. Erdinc, M. Giering
IEEE Aerospace Conference, Big Sky, MT, March 2015
25 | Deep Learning for Flow sculpting in Microfluidic platforms
25 | Deep Learning for Flow sculpting in Microfluidic platforms
K. G. Lore, M. Davies, D. Stoecklein, B. Ganapathysubramanian, S. Sarkar
NVIDIA GPU Technology Conference, Silicon Valley, 2015
24 | RGBD Occlusion Detection via Deep Convolutional Neural Networks
24 | RGBD Occlusion Detection via Deep Convolutional Neural Networks
S. Sarkar, V. Venugopalan, K. Reddy, J. Ryde, M. Giering, N. Jaitly
NVIDIA GPU Technology Conference, Silicon Valley, 2015
23 | Path planning in GPS-denied environments: A collective intelligence approach
23 | Path planning in GPS-denied environments: A collective intelligence approach
P. Chattopadhyay, D. K. Jha, S. Sarkar, A. Ray
Proceedings of ASME Dynamical Systems and Control Conference, (San Antonio, TX), 2014
22 | A Symbolic Dynamic Filtering approach to unsupervised hierarchical feature extraction from time-series data
22 | A Symbolic Dynamic Filtering approach to unsupervised hierarchical feature extraction from time-series data
A. Akintayo, S. Sarkar
Proceedings of American Control Conference, (Chicago, IL), 2015
2014
2014
21 | Scalable Anomaly Detection and Isolation in Cyber-Physical Systems Using Bayesian Networks
21 | Scalable Anomaly Detection and Isolation in Cyber-Physical Systems Using Bayesian Networks
S. Krishnamurthy, S. Sarkar, A. Tewari
Proceedings of ASME Dynamical Systems and Control Conference, (San Antonio, TX), 2014
20 | A Novel Human Machine Interface for Advanced Building Controls and Diagnostics
20 | A Novel Human Machine Interface for Advanced Building Controls and Diagnostics
R. Khire, F. Leonardi, P. Quimby, S. Sarkar
3rd International High Performance Buildings Conference at Purdue, 2014
19 | Model Predictive Control and Fault Detection and Diagnostics of a Building Heating, Ventilation, and Air Conditioning System
19 | Model Predictive Control and Fault Detection and Diagnostics of a Building Heating, Ventilation, and Air Conditioning System
S. Bengea, P. Li, S. Sarkar, S. Vichik, V. Adetola, K. Kang, T. Lovett, F. Leonardi, A. Kelman
3rd International High Performance Buildings Conference at Purdue, 2014
2013
2013
18 | Spatiotemporal Information Fusion for Fault Detection in Shipboard Auxiliary Systems
18 | Spatiotemporal Information Fusion for Fault Detection in Shipboard Auxiliary Systems
S. Sarkar, N. Virani, M. Yasar, A. Ray, S. Sarkar
Proceedings of American Control Conference, (Washington, D.C.), 2013 (Best Session Paper Award)
17 | Maximally Bijective Discretization for Data-driven Modeling of Complex Systems
17 | Maximally Bijective Discretization for Data-driven Modeling of Complex Systems
S. Sarkar, A. Srivastav, M. Shashanka
Proceedings of American Control Conference, (Washington, D.C.), 2013 (Best Session Paper Award)
2012
2012
16 | Symbolic Transient Time-series Analysis for Fault Detection in Aircraft Gas Turbine Engines
16 | Symbolic Transient Time-series Analysis for Fault Detection in Aircraft Gas Turbine Engines
S. Sarkar, K. Mukherjee, S. Sarkar, A. Ray
Proceedings of American Control Conference, (Montreal, Canada), 2012 (Best Session Paper Award)
15 | Distributed Decision Propagation in Mobile Agent Networks
15 | Distributed Decision Propagation in Mobile Agent Networks
S. Sarkar, K. Mukherjee, A. Ray
Proceedings of American Control Conference, (Montreal, Canada), 2012 (Best Session Paper Award)
2011
2011
14 | Semantic Sensor Fusion for Fault Diagnosis in Aircraft Gas Turbine Engines
14 | Semantic Sensor Fusion for Fault Diagnosis in Aircraft Gas Turbine Engines
S. Sarkar, D. S. Singh, A. Srivastav, A. Ray
Proceedings of American Control Conference, (San Francisco, CA), 2011
13 | Optimal Partitioning of Ultrasonic Data for Fatigue Damage Detection
13 | Optimal Partitioning of Ultrasonic Data for Fatigue Damage Detection
D. S. Singh, S. Sarkar, S. Gupta, A. Ray
Proceedings of American Control Conference, (San Francisco, CA), 2011
2010
2010
12 | Distributed Decision Propagation in Mobile Agent Network
12 | Distributed Decision Propagation in Mobile Agent Network
S. Sarkar, K. Mukherjee, A. Srivastav, A. Ray
Proceedings of Conference on Decision and Control, (Atlanta, GA) 2010
11 | Optimization of Time-series data Partitioning for Parameter Identification
11 | Optimization of Time-series data Partitioning for Parameter Identification
S. Sarkar, K. Mukherjee, X. Jin, A. Ray
Proceedings of ASME Dynamic Systems and Control Conference, (Cambridge, MA), 2010
10 | Critical Phenomena and Finite-size Scaling in Communication Networks
10 | Critical Phenomena and Finite-size Scaling in Communication Networks
S. Sarkar, K. Mukherjee, A. Srivastav, A. Ray
Proceedings of American Control Conference, (Baltimore, MD), 2010.
9 | Symbolic Identification for Anomaly Detection in Aircraft Gas Turbine Engines
9 | Symbolic Identification for Anomaly Detection in Aircraft Gas Turbine Engines
S. Chakraborty, S. Sarkar, A. Ray, S. Phoha
Proceedings of American Control Conference, (Baltimore, MD), 2010.
2009
2009
8 | Suboptimal Partitioning of Timeseries Data for Anomaly Detection
8 | Suboptimal Partitioning of Timeseries Data for Anomaly Detection
X. Jin, S.Sarkar, K. Mukherjee, A. Ray
Proceedings of Conference on Decision and Control, (Shanghai, China), 2009.
7 | Understanding phase transition in communication networks to enable robust and resilient control
7 | Understanding phase transition in communication networks to enable robust and resilient control
S. Sarkar, K. Mukherjee, A. Srivastav, A. Ray
Proceedings of American Control Conference, (St. Louis, MO), 2009. (Best Session Paper Award)
6 | Symbolic Analysis of Time Series Signals Using Generalized Hilbert Transform
6 | Symbolic Analysis of Time Series Signals Using Generalized Hilbert Transform
S. Sarkar, K. Mukherjee, A. Ray
Proceedings of American Control Conference, (St. Louis, MO), 2009.
5 | Estimation of Multiple Faults in Aircraft Gasturbine Engines
5 | Estimation of Multiple Faults in Aircraft Gasturbine Engines
S. Sarkar, C. Rao, A. Ray
Proceedings of American Control Conference, (St. Louis, MO), 2009. (Best Session Paper Award)
2008
2008
4 | Comparative Evaluation of Symbolic Dynamic Filtering for Detection of Anomaly Patterns
4 | Comparative Evaluation of Symbolic Dynamic Filtering for Detection of Anomaly Patterns
C. Rao, S. Sarkar, A. Ray, M. Yasar
Proceedings of American Control Conference, (Seattle, WA), 2008
3 | Estimation of Multiple Parameters in Dynamical Systems
3 | Estimation of Multiple Parameters in Dynamical Systems
C. Rao, K. Mukherjee, S. Sarkar, A. Ray
Proceedings of American Control Conference, (Seattle, WA), 2008
2 | Fault Diagnosis and Isolation in Aircraft Gas Turbine Engines
2 | Fault Diagnosis and Isolation in Aircraft Gas Turbine Engines
S. Sarkar, K. Mukherjee , A. Ray, M. Yasar
Proceedings of American Control Conference, (Seattle, WA), 2008
1 | Symbolic Identification and Anomaly Detection in Complex Dynamical Systems
1 | Symbolic Identification and Anomaly Detection in Complex Dynamical Systems
S. Chakraborty, S. Sarkar, A. Ray
Proceedings of American Control Conference, (Seattle, WA), 2008
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