Himanish Shekhar Das

About

Dr. Himanish Shekhar Das, Assistant Professor at the Department of Computer Science & Information Technology, Cotton University, focuses on interdisciplinary applications of artificial intelligence, particularly in healthcare, bioinformatics and natural language processing. His core research interests span speech processing, machine learning, deep learning, natural language processing, bioinformatics, disorder diagnosis and medical image analysis. He has led and collaborated on various impactful projects, including the detection of Parkinson’s disease and breast cancer using deep learning techniques, and air pollution prediction through time series analysis. Currently, he is leading a project titled “Prediction and Grading of Invasive Ductal Carcinoma in Breast Cancer from H&E-stained histopathological microscopic images” under Cotton University’s IHRP Scheme. His recent work includes the development of DOMSCNet, a deep learning model for stomach cancer classification using multi-omics data. His publications in reputed journals like Briefings in Bioinformatics reflect a strong emphasis on precision medicine through computational solutions. His research is further enriched by collaborations with global researchers and contributions to high-impact studies on feature selection in NGS data. His academic journey and research projects underscore a commitment to leveraging AI for socially relevant and medically critical challenges.

Employment

• Cotton University Assistant Professor
  2020 - Present
• Jorhat Engineering College Assistant Professor
  2018 - 2020
• Birla Institute of Technology, Mesra Assistant Professor
  2018 - 2018
• National Institute of Technology Silchar Teaching Assistant
  2015 - 2017

Education

• National Institute Of Technology Silchar Bachelor of Technology (B.Tech)
• National Institute of Technology Durgapur Master of Technology (M.Tech)
• National Institute Of Technology Silchar Doctor of Philosophy (PhD)

Projects & Funding

Projects & funding information is unavailable.

Publications (29)

• Evaluating Imputation Techniques for scRNA-seq Data in Stomach Cancer Analysis
  2026 DOI: 10.1007/978-3-032-14534-5_11
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• Sentiment Analysis in Kokborok: Building Resources and Models for a Low-Resource Language
  2026 DOI: 10.1007/978-3-032-07735-6_11
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• scGImpute: A hybrid BiLayer multi-head graph attention-based imputation framework for zero dropout in single-cell sequencing datasets
  Computational Biology and Chemistry 2026 DOI: 10.1016/j.compbiolchem.2025.108856
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• Ensemble learning approach for detecting breast invasive ductal carcinoma from histopathological images
  Pathology - Research and Practice 2025 DOI: 10.1016/j.prp.2025.156041
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• DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data
  Briefings in Bioinformatics 2025 DOI: 10.1093/bib/bbaf115
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• Assessment of different U-Net backbones in segmenting colorectal adenocarcinoma from H&E histopathology
  Pathology - Research and Practice 2025 DOI: 10.1016/j.prp.2025.155820
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• Landscape of Pattern Learning Applied to Public Health and Social Sciences
  2025 DOI: 10.52305/cyzr4453
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• Auswirkungen des Cuckoo-Suche-Algorithmus auf die Sprachverarbeitung
  Anwendungen des Cuckoo-Suchalgorithmus und seiner Varianten 2024 DOI: 10.1007/978-981-99-7493-1_9
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• Unveiling Molecular Markers and Prediction of Stomach Cancer from Transcriptomic Profile: A Comprehensive Study of Feature Mining and Learning-based Algorithm 
  2024 DOI: 10.21203/rs.3.rs-4572725/v1
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• A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis
  Functional & Integrative Genomics 2024 DOI: 10.1007/s10142-024-01415-x
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