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Academic Experts

Shweta Sharma

Biography

Shweta Sharma is an Assistant Professor in the Department of Computer Applications (DCA) with industry experience in software development and emerging technologies. She holds a Master's degree in Computer Applications and is currently pursuing a Ph.D. in the field of Artificial Intelligence and Machine Learning, with a research focus on emotional volatility prediction using machine learning and explainable AI techniques.

Prior to joining academia, she worked as a Software Developer and gained hands-on experience in full-stack web development, particularly using modern JavaScript technologies such as React.js and the MERN Stack (MongoDB, Express.js, React.js, and Node.js). Her industry experience has enabled her to bridge the gap between theoretical concepts and practical applications, helping students develop industry-relevant skills.

Her research interests span Artificial Intelligence, Machine Learning and Explainable AI (XAI). She has contributed to research activities focused on developing interpretable machine learning solutions for real-world problems.

Shweta is committed to fostering an engaging learning environment that encourages innovation, critical thinking, and lifelong learning while preparing students to meet the evolving demands of the technology industry. 

Research Highlights

My research interests lie at the intersection of Artificial Intelligence (AI), Machine Learning (ML) and Explainable AI (XAI). My current doctoral research focuses on the development of machine learning models for predicting emotional volatility using self-reported mood data and interpretable AI techniques. 

The objective is to understand patterns of emotional fluctuations and develop predictive frameworks that can support mental well-being through data-driven insights. The work emphasizes model transparency and interpretability through Explainable AI methods, enabling a better understanding of the factors influencing predictions. The research aims to contribute to the growing field of AI-driven behavioral analytics by developing practical and interpretable predictive models. 

Areas of Interest
  • Artificial Intelligence
  • Machine Learning
  • Explainable AI (XAI)
  • Behavioral Data Analytics
Publications

S. Sharma and A. Jain, “A Narrative Review on Digital Phenotyping, Emotional Volatility and Machine Learning Techniques,” in 2025 International Conference on Digital Innovations for Sustainable Solutions (ICDISS), Faridabad, India, 2025, pp. 1–5, doi: 10.1109/ICDISS68238.2025.11320615.

S. Sharma and V. Sharma, “Navigating the Data Deluge: A Comprehensive Examination of Recommendation Systems in Real-World Applications,” in 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2024, pp. 195–198, doi: 10.1109/Confluence60223.2024.10463318.