Research Interest

        • Ph.D. from Gautam Buddha University, Greater Noida in 2014.
        • M.Tech. in Computer Science and Engineering from YMCAIE, Faridabad with 1st Division.
        • IBM Certified Database Associate (Candidate\Testing ID: PR1326592)
        • Honours Diploma from NIIT Technologies in Network-Centered Computing.
        • “Object-oriented analysis and design using with essentials of Rational software architect” Certified.
        • Qualified GATE with AIR Ranking 348.

      • Research Interests

        My research focuses on building intelligent systems that can learn adapt and make decisions with minimal human intervention. The central goal is to enable computers to perform complex tasks efficiently while discovering meaningful patterns hidden within large volumes of data. My work lies at the intersection of Expert Systems Machine Learning and Soft Computing where computational models are designed to learn from experience improve their performance over time and assist in knowledge discovery.

        In today’s data driven environment a major challenge is information overload. Large datasets often contain valuable insights but extracting useful knowledge from them requires advanced computational methods. My research aims to develop algorithms and intelligent frameworks that can automatically identify significant patterns distinguish genuine regularities from random occurrences and utilize prior knowledge to enhance learning outcomes.

        A key aspect of this research is designing systems that not only learn effectively but also produce results that are interpretable and understandable to humans. This includes exploring how machines can autonomously determine suitable knowledge representations learn under limited computational resources and combine multiple learning models to improve reliability accuracy and robustness.

        Current and Recent Research Areas

        My ongoing and recent research work includes the following areas:

        • Natural Language Processing with focus on automatic text summarization and information extraction.
        • Deepfake Detection and Analysis using advanced machine learning and deep learning models.
        • Supervised and Unsupervised Machine Learning Techniques for knowledge discovery and predictive modeling.
        • Rule Based Learning Systems for representing and learning concepts using structured rule sets.
        • Probabilistic Learning Models to handle uncertainty and incomplete information in data.
        • Automatic Representation Learning for selecting optimal knowledge representations during the learning process.
        • Ensemble Learning Methods where multiple models are combined to improve prediction accuracy and stability.
        • Model Evaluation and Selection Techniques to prevent overfitting and ensure generalizable learning outcomes.
        • Explainable and Interpretable Machine Learning so that learned models remain understandable to human users.
        • Knowledge Guided Learning where pre existing domain knowledge is integrated to improve learning efficiency.
        • Scalable Learning Approaches using subsampling and efficient data processing techniques for large datasets.
        • Cost Sensitive Decision Algorithms that incorporate decision costs into the learning and prediction process.

        Through these research directions my work aims to contribute toward the development of intelligent systems that are efficient adaptive interpretable and capable of supporting real world decision making across diverse application domains.