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Tryambak Gangopadhyay
Tryambak Gangopadhyay
Applied Scientist II, Machine Learning at Amazon AWS
Verified email at amazon.com - Homepage
Title
Cited by
Cited by
Year
Crop yield prediction integrating genotype and weather variables using deep learning
J Shook, T Gangopadhyay, L Wu, B Ganapathysubramanian, S Sarkar, ...
Plos one 16 (6), e0252402, 2021
1152021
Spatiotemporal attention for multivariate time series prediction and interpretation
T Gangopadhyay, SY Tan, Z Jiang, R Meng, S Sarkar
ICASSP 2021-2021 IEEE international conference on acoustics, speech and …, 2021
472021
Dynamic characterization of a ducted inverse diffusion flame using recurrence analysis
U Sen, T Gangopadhyay, C Bhattacharya, A Mukhopadhyay, S Sen
Combustion Science and Technology 190 (1), 32-56, 2018
322018
3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems
T Gangopadhyay, V Ramanan, A Akintayo, PK Boor, S Sarkar, ...
Energy and AI, 100067, 2021
242021
Temporal attention and stacked lstms for multivariate time series prediction
T Gangopadhyay, SY Tan, G Huang, S Sarkar
NeurIPS Workshop on Modeling and Decision-Making in the Spatiotemporal Domain, 2018
222018
Deep learning algorithms for detecting combustion instabilities
T Gangopadhyay, A Locurto, JB Michael, S Sarkar
Dynamics and Control of Energy Systems, 283-300, 2020
192020
Investigation of ducted inverse nonpremixed flame using dynamic systems approach
U Sen, T Gangopadhyay, C Bhattacharya, A Misra, S Karmakar, ...
Turbo Expo: Power for Land, Sea, and Air 49767, V04BT04A059, 2016
122016
Characterizing combustion instability using deep convolutional neural network
T Gangopadhyay, A Locurto, P Boor, JB Michael, S Sarkar
Dynamic Systems and Control Conference 51890, V001T13A004, 2018
112018
Integrating genotype and weather variables for soybean yield prediction using deep learning
J Shook, L Wu, T Gangopadhyay, B Ganapathysubramanian, S Sarkar, ...
bioRxiv, 331561, 2018
112018
Deep Time Series Attention Models for Crop Yield Prediction and Insights
T Gangopadhyay, J Shook, AK Singh, S Sarkar
NeurIPS Workshop on Machine Learning and the Physical Sciences, 2019
92019
Interpretable deep learning for monitoring combustion instability
T Gangopadhyay, SY Tan, A LoCurto, JB Michael, S Sarkar
IFAC-PapersOnLine 53 (2), 832-837, 2020
72020
Interpretable deep attention model for multivariate time series prediction in building energy systems
T Gangopadhyay, SY Tan, Z Jiang, S Sarkar
Dynamic Data Driven Applications Systems: Third International Conference …, 2020
72020
An Explainable Framework using Deep Attention Models for Sequential Data in Combustion Systems
T Gangopadhyay, SY Tan, A Locurto, JB Michael, S Sarkar
NeurIPS Workshop on Machine Learning and the Physical Sciences, 2019
62019
Deep learning for monitoring cyber-physical systems
T Gangopadhyay
Iowa State University, 2019
42019
Interpreting the Impact of Weather on Crop Yield Using Attention
T Gangopadhyay, J Shook, AK Singh, S Sarkar
NeurIPS Workshop on AI for Earth Sciences, 2020
32020
Mode decomposition and convolutional neural network analysis of thermoacoustic instabilities in a rijke tube
A LoCurto, T Gangopadhyay, P Boor, S Sarkar, JB Michael
32018
A Deep Learning Approach to Detect Lean Blowout in Combustion Systems
T Gangopadhyay, S De, Q Liu, A Mukhopadhyay, S Sen, S Sarkar
arXiv preprint arXiv:2208.01871, 2022
22022
Cross-Modal Virtual Sensing for Combustion Instability Monitoring
T Gangopadhyay, V Ramanan, SR Chakravarthy, S Sarkar
arXiv preprint arXiv:2110.01659, 2021
22021
Comparison of thermoacoustic characteristics of a ducted non-premixed flame and a ducted inverse diffusion flame
S Panja, U Sen, T Gangopadhyay, R Roy, S Sen, A Mukhopadhyay
Proceedings of the Forty Second National Conference on Fluid Mechanics and …, 2015
22015
Spray characterisation of diesel using a hybrid atomizer
A Garai, T Gangopadhyay, A Mukhopadhyay, S Sen
Sādhanā 48 (3), 150, 2023
12023
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