Kalyanasundaram V

About

I am a research-oriented software engineer and analyst with a strong focus on applied artificial intelligence, deep learning, computer vision, and IoT-enabled systems. My research portfolio encompasses both theoretical and practical investigations across AI-driven applications in biomedical engineering, explainable deep learning, stock market forecasting, and human–machine interaction technologies. I have contributed to multiple peer-reviewed publications with Springer Nature and IEEE, and am an inventor on several patent applications addressing wearable gesture-recognition systems and other innovative engineering solutions.

Currently, I serve as an Analyst Intern at HSBC, where I apply analytical frameworks and data-driven decision methodology within enterprise environments. I have also been recognized as a Microsoft Learn Student Ambassador and supported by Google Cloud as a program facilitator, reflecting a commitment to both research engagement and technological leadership in cloud and AI ecosystems. My academic foundation was formed at Vellore Institute of Technology (VIT), where I collaborated with cross-disciplinary teams to publish impactful research and develop award-winning outcomes in both academic and real-world problem spaces.

My research interests include but are not limited to:
• Deep Learning & Explainable AI
• Computer Vision & Pattern Recognition
• IoT & Wearable System Design
• Predictive Analytics & Time Series Forecasting
• Human–Computer Interaction
• Biomedical Signal Processing
• Cloud-based Intelligent Systems

Employment

Employment history is unavailable.

Education

• Vellore Institute of Technology University MTech Integrated
  2021 - 2026

Projects & Funding

Projects & funding information is unavailable.

Publications (33)

• AI in Autonomous Systems
  Responsible AI 2026 DOI: 10.1002/9781394355471.ch12
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• Systematic Migration of on-Premise Databases to Azure SQL: Overcoming Challenges and Maximizing Cloud Benefits
  7th International Conference on Mobile Computing and Sustainable Informatics Icmcsi 2026 2026 DOI: 10.1109/ICMCSI67283.2026.11412475
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• Real-Time Stock Forecasting and User Verification Using Azure AI Services
  2026 DOI: 10.1007/978-3-032-06688-6_14
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• Real-Time Analysis of Athlete Movements Using Deep Learning-Based Pose Detection Models
  2026 DOI: 10.2139/ssrn.7157978
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• Prohibition of Illegal Movement of Sandalwood from Reserve Forests through Retracing Supply Chain on a Permissioned Blockchain
  Harvesting Data Blockchain AI and Advanced Innovations in Agriculture 2026 DOI: 10.1002/9781394310630.ch10
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• Prepartum Prediction of Cephalopelvic Disproportion Based on Maternal Anthropometry, Classification of Shape of Pelvic Bone and Head Circumference of the Foetus
  2026 DOI: 10.1007/978-981-96-7140-3_17
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• Marine Animal Classification with EfficientNetB7 and Grad-CAM: A Novel Deep Learning Approach Leveraging Explainable AI
  2026 DOI: 10.1007/978-981-95-0701-6_34
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• Intrusion Detection Model Using Fused Quantum NN–LeNet With Improved Elliptic Curve Cryptography Based Data Security
  IEEE Access 2026 DOI: 10.1109/ACCESS.2026.3686106
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• Explainable AI and Deep Learning for Brain Tumor Classification: A Comprehensive Approach Using Grad-CAM Visualization
  2026 DOI: 10.1007/978-3-031-93694-4_12
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• Effective Visualization Approaches for Distributed Denial of Service Threat Prediction in Large-Scale Networks Using Interpretable Machine Learning
  2026 DOI: 10.1007/978-981-95-7134-5_28
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