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Academic Experts
Dr. Parul Agarwal

Biography

Dr. Parul Agarwal is an Assistant Professor (Senior Grade) in the Department of Computer Science & Information Technology at Jaypee Institute of Information Technology (JIIT), Noida. She hold a Ph.D. in Computer Science from JIIT (2019), where her research focused on "Novel Variants of Nature Inspired Algorithms for Clustering High Dimensional Data." She completed my M.Tech. in Computer Science and Engineering with a specialization in Distributed Systems from Banasthali University in 2012. With over 12 years of academic experience, she is deeply committed to advancing knowledge through innovative research and fostering a dynamic learning environment that encourages critical thinking, curiosity, and lifelong learning. She have taught a wide range of core and advanced subjects including Data Structures, Object-Oriented Systems and Programming, Database Systems and Web Technologies, Machine Learning, Big Data, Soft Computing, Fuzzy Logic, Neural Networks, Meta-heuristic Modeling and Optimization, and Computational Intelligence. She have supervised more than 100 undergraduate and postgraduate student projects and theses, primarily in the fields of computational intelligence, machine learning, nature-inspired algorithms, and soft computing. Her scholarly contributions include research articles published in reputed SCI and Scopus-indexed journals, conferences, and book chapters. She actively participate in academic events such as conferences, workshops, and faculty development programs (FDPs), and have also delivered expert talks in FDPs. Additionally, she serve as a reviewer for multiple international journals and have been part of the Publication Committee for the International Conference on Contemporary Computing (IC3) since 2014. Presently, she is guiding 3 Ph.d. scholars in field of machine learning, deep learning and computational intelligence.

Educational Qualifications

Ph.D. (Thesis Title: Novel Variants of Nature Inspired Algorithms for Clustering High Dimensional Data) from Jaypee Institute of Information Technology in June 2019., M.Tech. (Computer Science and Engineering with Specialization in Distributed Systems), July 2010– May 2012 from Banasthali University, Jaipur, Rajasthan., B.Tech. (Computer Science and Engineering), Aug 2006 - May 2010 from Uttar Pradesh Technical University, Lucknow.

Research Highlights

Dr. Parul Agarwal’s research is centered around the development and application of intelligent computational models for solving complex real-world problems. Her work primarily focuses on nature-inspired algorithms, soft computing techniques, and hybrid intelligent models that address challenges in high-dimensional data clustering, feature selection, and optimization. She has explored the synergy between evolutionary computation and machine learning to enhance classification and clustering performance. Her research also extends into deep learning and natural language processing (NLP), where she investigates novel frameworks for pattern recognition and data-driven decisionmaking. Dr. Agarwal has published her findings in several reputed SCI and Scopus-indexed journals, conferences, and edited volumes. Her current research work involve applying hybrid models for biomedical data analysis, interpretable AI, and multimodal learning. She actively collaborates across disciplines and mentors postgraduate and doctoral students in these emerging areas of intelligent systems.

Areas of Interest
  • Nature-Inspired Algorithms and Meta-heuristic Optimization
  • Machine Learning and Soft Computing
  • High-Dimensional Data Clustering and Feature Selection
  • Deep Learning and Natural Language Processing (NLP)
  • Hybrid Intelligent Techniques and Data Mining
Publications
  • P. Agarwal, R. K. Agrawal, and B. Kaur, “Multi-objective particle swarm optimization with guided exploration for multimodal problems,” Applied Soft Computing, vol. 120, p. 108684, 2022, doi: 10.1016/j.asoc.2022.108684 (SCI indexed).
  • P. Agarwal, S. Mehta, and A. Abraham, “A meta-heuristic density-based subspace clustering algorithm for high-dimensional data,” Soft Computing, doi: 10.1007/s00500-021-05973-1. (SCI indexed)
  • P. Agarwal and S. Mehta, "Empirical analysis of five nature-inspired algorithms on real parameter optimization problems." Artificial Intelligence Review pp. 1-57, 2017 (SCI Indexed)
  • P. Agarwal and S. Mehta, “ABC_DE_FP: a novel hybrid algorithm for complex continuous optimisation problems,” International Journal Bio-Inspired Computations, Vol.14, no. 1, 2019, pp. 46-61. (SCI Indexed)
  • R. K. Agrawal, B. Kaur, and P. Agarwal, P. “Quantum inspired Particle Swarm Optimization with guided exploration for function optimization”, Applied Soft Computing, 102, 10712, 2021 (SCI indexed)