Dr. Shabbeer Basha is an accomplished professional with expertise in computer vision, machine learning. With a strong academic background and industry experience, he has contributed in building low compute vision models, neural architecture search. His work focuses on neural network compression, deep active learning, multitask learning, and domain adaptation. Passionate about education and innovation, Shabbeer has published research articles in reputed journals and conferences.

Professional Education

Courses Taught

• Machine Learning
• Deep Learning
• Design and Analysis of Algorithms
• Computer Vision

Research Interests

• Computer Vision
• Machine Learning

Core problems:

• Neural Network Compression
• Deep Active Learning,
• Multitask Learning and Domain Adaptation

Selected Publications

• S.H.Shabbeer Basha, Mohammad Farazuddin, Viswanath Pulabaigari, Shiv Ram Dubey, Snehasis Mukherjee, "Deep Model Compression based on the Training History", Neurocomputing (Impact factor: 6), Elsevier, 2024.
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• Sarvani CH, Mrinmoy Ghorai, Shabbeer Basha, "PRF: Deep Neural Network Compression by Systematic Pruning of Redundant Filters", Neural Computing and Applications (Impact Factor: 4.5), Springer, 2024.
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• S.H.Shabbeer Basha, Debapriya Tula, Sravan Kumar Vinakota, Shiv Ram Dubey, Target Aware Architecture Search and Compression for Efficient Knowledge Transfer, Multimedia Systems (Impact factor: 3.9), Springer, 2024.
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• Shiv Ram Dubey, SH Shabbeer Basha, Satish Kumar Singh, and Bidyut Baran Chaudhuri, AdaInject: Injection based adaptive gradient descent optimizers for convolutional neural networks, , IEEE Transactions on Artificial Intelligence, 2023.
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• Shabbeer Basha, Viswanath Pulabaigari, Snehasis Mukherjee, An Information- rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos, Multimedia Tools & Applications, (Impact factor: 3.6), Springer, March 2022, (Springer)
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• Sarvani CH, Mrinmoy Ghorai, Shiv Ram Dubey, Shabbeer Basha, HRel: Filter Pruning based on high relevance between activation maps and class labels,  Neural Networks (Impact facor: 7.8), Elsevier, Jan 2022, (Elsevier)
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• Shabbeer Basha, Sravan Kumar Vinakota, Shiv Ram Dubey, Viswanath Pulabaigari and Snehasis Mukherjee, AutoFCL: Automatically Tuning Fully Connected Layers for Hadnling Small Dataset. Neural Computing and Applications (Impact Factor:6),  Nov 2020. (Springer)
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• SH Shabbeer Basha, Sravan Kumar Vinakota, Viswanath Pulabaigari, Snehasis Mukherjee and Shiv Ram Dubey, AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer Learning. Neural Networks (Impact Factor:7.8), 133:112-122, Jan 2021. (Elsevier)
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• SH Shabbeer Basha, Shiv Ram Dubey, Viswanath Pulabaigari and Snehasis Mukherjee , Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification.. Neurocomputing (Impact Factor: 6), 378:112-119, Feb 2020. (Elsevier)
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Conferences

• SH Shabbeer Basha, Sheethal N Gowda, Jayachandra Dakala A Simple Hybrid Filter Pruning for Efficient Edge Inference, In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3398-3402. IEEE, 2022. (A* conference in signal processing domain)
• SH Shabbeer Basha, Soumen Ghosh, Kancharagunta Kishan Babu, Shiv Ram Dubey, Viswanath Pulabaigari and Snehasis Mukherjee, RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification. Fifteenth International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, Nov 2018. (IEEE) (Core C conference in Robotics). Read More

Patents

• Sheethal Gowda, S.H.Shabbeer Basha, Jayachandra D, "Inclement Weather Detection", United States Patent Application 20240199049, 2024. Read More

Research Projects Undertaken

• Secured a consultancy project from Perforated AI, USA for “Compressing Perforated AI models”
• Secured another consultancy project from VISIST AI Technologies Private Ltd., Bengaluru for “Building Computer Vision Models for Badminton Match Summarization”
• Obtained a consultancy project from VectraTech Global, USA on “Analysis of Breast Cancer Detection using Mammograms”