Anshul Verma

About

Anshul Verma received his M.Tech. and Ph.D. degrees in Computer Science and Engineering from ABV–Indian Institute of Information Technology and Management, Gwalior, India. He completed his postdoctoral research at the Indian Institute of Technology, Kharagpur. He is currently an Assistant Professor in the Department of Computer Science, Banaras Hindu University, Varanasi, India, and is presently on study leave, serving as a Visiting Academician at the School of Computing and Information Systems, University of Melbourne, Australia. He has previously served at MNNIT Allahabad and NIT Jamshedpur, India. His research interests include Cloud Computing, Distributed Systems, Mobile Ad Hoc Networks, and Formal Verification.

Employment

• The University of Melbourne Visiting Researcher
  2025 - Present
• Banaras Hindu University Assistant Professor
  2017 - Present
• Indian Institute of Technology Kharagpur Postdoctoral Fellow
  2017 - 2017
• National Institute of Technology Jamshedpur Assistant Professor (Ad-hoc)
  2016 - 2017
• Motilal Nehru National Institute of Technology Assistant Professor (Ad-hoc)
  2015 - 2016

Education

• Indian Institute of Technology Kharagpur Postdoc
  2017 - 2017
• Atal Bihari Vajpayee Indian Institute of Information Technology and Management Ph.D.
  2011 - 2016
• Atal Bihari Vajpayee Indian Institute of Information Technology and Management M.Tech.
  2009 - 2011

Projects & Funding

Projects & funding information is unavailable.

Publications (47)

• TinyCNN-Based Lightweight Framework for Automated Wheat Leaf Disease Detection
  2027 DOI: 10.1007/978-3-032-27120-4_29
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• Enhancing Interpretability for Automated Human Monkeypox Skin Lesions Detection Using Pre-trained Transfer Learning Networks
  Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 2026 DOI: 10.1007/s40010-026-01197-w
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• Non-Destructive Fruit Quality Evaluation via Multisensor Fusion and Agglomerative Clustering
  2026 DOI: 10.1007/978-3-032-26352-0_22
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• Modern Kubernetes: From Core Concepts to Intelligent Autoscaling for Cloud Applications
  2026 DOI: 10.1007/978-3-032-12972-7
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• L2-Regularized Deep Neural Network Model for Robust Multi-class Fetal Health Risk Prediction
  2026 DOI: 10.1007/978-3-032-22908-3_20
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• Optimizing Patient Admission and Room Allocation in Cloud-Enabled Healthcare Systems
  IETE Technical Review 2026 DOI: 10.1080/02564602.2026.2688070
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• IHFD: an improved heartbeat-based failure detector of $$\Diamond \,P$$ class for partially synchronous hierarchical distributed systems
  Complex & Intelligent Systems 2026 DOI: 10.1007/s40747-026-02423-z
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• Critical insights into runtime scheduling, image, storage, and networking challenges in modern Kubernetes environments
  Computer Science Review 2026 DOI: 10.1016/j.cosrev.2025.100851
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• A multivariate transformer-based monitor-analyze-plan-execute (MAPE) autoscaling framework for dynamic resource allocation in cloud environment
  Computing 2025 DOI: 10.1007/S00607-025-01426-X
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• Statistical Analysis and Performance Evaluation of a Routing Protocol of Opportunistic Networks Using Design of Experiments Methodology
  Wireless Personal Communications 2025 DOI: 10.1007/S11277-025-11842-Z
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