Soumik Sarkar

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Baskar GanapathysubramanianNSF/USDA AI Institute, Iowa State UniversityVerified email at iastate.edu
Arti SinghIowa State UniversityVerified email at iastate.edu
Asheesh K. SinghProfessor, Iowa State University; Co-Director, Iowa Soybean Research CenterVerified email at iastate.edu
Aditya BaluIowa State UniversityVerified email at iastate.edu
Asok RayDistinguished Professor of Mechanical Engineering and MathematicsVerified email at psu.edu
Zhanhong JiangScientist at TrACVerified email at iastate.edu
Chinmay HegdeNew York UniversityVerified email at nyu.edu
Adarsh KrishnamurthyIowa State UniversityVerified email at iastate.edu
Kin Gwn LorePrincipal Research Engineer, Raytheon Technologies Research CenterVerified email at rtx.com
Adedotun AkintayoIntel CorporationVerified email at intel.com
Sambuddha GhosalResearch Data Scientist @ Bayer R&D, MITVerified email at mit.edu
Chao LiuAssociate Professor, Department of Energy and Power Engineering, Tsinghua University, ChinaVerified email at tsinghua.edu.cn
Kushal MukherjeeIBM ResearchVerified email at in.ibm.com
Soumalya SarkarSenior Principal Scientist, Raytheon Technologies Research Center (RTRC)Verified email at utrc.utc.com
Teshale AssefaIowa State UniversityVerified email at iastate.edu
Marcus Naik Hsiang SingIowa State UniversityVerified email at iastate.edu
Jiaoping ZhangNanjing Agricultural UniversityVerified email at njau.edu.cn
Daniel StoeckleinRose-Hulman Institute of TechnologyVerified email at rose-hulman.edu
Abhishek SrivastavSenior Scientist - AI&CV, GE Aerospace ResearchVerified email at ge.com
Xin JinNational Renewable Energy LaboratoryVerified email at nrel.gov

Soumik Sarkar

Director, Translational AI Center, Professor, Iowa State University

Verified email at iastate.edu - Homepage

Machine learning Cyber-physical systems


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
LLNet: A deep autoencoder approach to natural low-light image enhancement KG Lore, A Akintayo, S Sarkar Pattern Recognition 61, 650-662, 2017	1513	2017
Machine learning for high-throughput stress phenotyping in plants A Singh, B Ganapathysubramanian, AK Singh, S Sarkar Trends in plant science 21 (2), 110-124, 2016	1051	2016
Deep learning for plant stress phenotyping: trends and future perspectives AK Singh, B Ganapathysubramanian, S Sarkar, A Singh Trends in plant science 23 (10), 883-898, 2018	564	2018
An explainable deep machine vision framework for plant stress phenotyping S Ghosal, D Blystone, AK Singh, B Ganapathysubramanian, A Singh, ... Proceedings of the National Academy of Sciences 115 (18), 4613-4618, 2018	533	2018
Plant disease identification using explainable 3D deep learning on hyperspectral images K Nagasubramanian, S Jones, AK Singh, S Sarkar, A Singh, ... Plant methods 15, 1-10, 2019	366*	2019
A real-time phenotyping framework using machine learning for plant stress severity rating in soybean HS Naik, J Zhang, A Lofquist, T Assefa, S Sarkar, D Ackerman, A Singh, ... Plant methods 13, 1-12, 2017	205	2017
Collaborative deep learning in fixed topology networks Z Jiang, A Balu, C Hegde, S Sarkar Advances in Neural Information Processing Systems 30, 2017	201	2017
A weakly supervised deep learning framework for sorghum head detection and counting S Ghosal, B Zheng, SC Chapman, AB Potgieter, DR Jordan, X Wang, ... Plant Phenomics, 2019	176	2019
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, AK Singh, A Singh, ... Plant methods 14, 1-13, 2018	171	2018
An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems T Han, C Liu, L Wu, S Sarkar, D Jiang Mechanical Systems and Signal Processing 117, 170-187, 2019	161	2019
Challenges and opportunities in machine-augmented plant stress phenotyping A Singh, S Jones, B Ganapathysubramanian, S Sarkar, D Mueller, ... Trends in Plant Science 26 (1), 53-69, 2021	158	2021
Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns C Rao, A Ray, S Sarkar, M Yasar Signal, Image and Video Processing 3 (2), 101-114, 2009	158	2009
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	150	2021
Traffic congestion detection from camera images using deep convolution neural networks P Chakraborty, YO Adu-Gyamfi, S Poddar, V Ahsani, A Sharma, S Sarkar Transportation Research Record 2672 (45), 222-231, 2018	144	2018
A deep learning framework to discern and count microscopic nematode eggs A Akintayo, GL Tylka, AK Singh, B Ganapathysubramanian, A Singh, ... Scientific reports 8 (1), 9145, 2018	126*	2018
Computer vision and machine learning for robust phenotyping in genome-wide studies J Zhang, HS Naik, T Assefa, S Sarkar, RVC Reddy, A Singh, ... Scientific Reports 7 (1), 44048, 2017	125	2017
Semantic adversarial attacks: Parametric transformations that fool deep classifiers A Joshi, A Mukherjee, S Sarkar, C Hegde Proceedings of the IEEE/CVF international conference on computer vision …, 2019	114	2019
Predicting county-scale maize yields with publicly available data Z Jiang, C Liu, B Ganapathysubramanian, DJ Hayes, S Sarkar Scientific Reports 10 (1), 14957, 2020	111*	2020
Computer vision and machine learning enabled soybean root phenotyping pipeline KG Falk, TZ Jubery, SV Mirnezami, KA Parmley, S Sarkar, A Singh, ... Plant methods 16, 1-19, 2020	108	2020
Occupancy sensing in buildings: A review of data analytics approaches H Saha, AR Florita, GP Henze, S Sarkar Energy and Buildings 188, 278-285, 2019	108	2019

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