Asst. Prof. Prashant Pandey: Assistant professor at Amity University Ranchi Jharkhand, India

Title/Designation: Asst. Prof
Name: Prashant Pandey
Current Role/Designation: Assistant Professor
Organization/Institution: Amity University Ranchi, Jharkhand
Country: India
Subject Track: Mathematics
Award Category: Emerging Researcher Award

Prashant Kumar Pandey is an applied mathematician and researcher with expertise in numerical analysis, scientific computing, and high-order methods for partial differential equations. He specializes in the development of non-oscillatory and entropy-stable numerical schemes for hyperbolic conservation laws, with growing interests in machine-learning-assisted numerical methods. He completed his PhD in Applied Mathematics from SRM Institute of Science and Technology, where his work addressed robustness, accuracy, and stability in advanced computational schemes.

Online Profiles

Google Scholar Profile

Since 2020, Prashant Kumar Pandey’s research work has received 9 total citations, with an h-index of 2, reflecting the growing visibility and scholarly impact of his contributions in applied mathematics, numerical analysis, and entropy-stable computational schemes.

Education

He earned his PhD in Applied Mathematics (2018–2024) from SRM Institute of Science and Technology, Kanchipuram, with a doctoral thesis focused on efficient and robust construction of non-oscillatory and entropy-stable schemes. Prior to this, he completed an MSc in Mathematics from IIT Madras in 2013, gaining strong analytical and computational training, and a BSc (Hons.) in Mathematics from Banaras Hindu University in 2010, establishing a solid foundation in pure and applied mathematics.

Research Focus

His research interests center on high-order finite difference and finite volume methods for hyperbolic conservation laws, including ENO and WENO reconstructions, entropy-stable discretizations, and shock-capturing techniques. He also works on integrating machine learning approaches—such as classification and regression neural networks—into numerical schemes to enhance adaptivity, accuracy, and computational efficiency in simulations involving discontinuities.

Experience

He has accumulated experience in both teaching and research roles, having served as a lecturer in mathematics at Meerut Institute of Engineering and Technology and as a JEE (Main) mathematics trainer at reputed integrated institutions in Karnataka and Tamil Nadu. Alongside teaching, he has extensive research experience gained during his doctoral tenure, involving numerical modeling, algorithm development, scientific programming, and collaborative research in applied mathematics.

Research Timeline & Activities

Since December 2017, he has been actively engaged in doctoral and postdoctoral-level research activities at SRM Institute of Science and Technology. His research timeline includes developing novel numerical schemes, publishing in SCI-indexed journals, presenting research at national and international conferences, participating in workshops on conservation laws and numerical methods, and collaborating with researchers on interdisciplinary projects combining numerical analysis and machine learning.

Awards & Honors

He has demonstrated strong academic performance at the national level by qualifying the IIT-JAM Mathematics examination with an All India Rank of 120 in 2011 and successfully qualifying the GATE examination in Mathematics in 2013. These achievements reflect his solid theoretical background and competitive standing among mathematics graduates in India.

Top Noted Publication

His top noted publications include Q1 and Q2 SCI-indexed journal articles in Applied Mathematics and Computation, Computational and Applied Mathematics, Physica Scripta, and Soft Computing. Among these, his 2023 article in Applied Mathematics and Computation on sign-stable arbitrary high-order reconstructions for entropy-stable schemes stands out for its methodological rigor and impact in the field of numerical methods for conservation laws.

  • Sign stable arbitrary high order reconstructions for constructing non-oscillatory entropy stable schemes
    P. K. Pandey, R. K. Dubey, Applied Mathematics and Computation, Vol. 454, Article 128099, 2023 — Cited by 4

  • High-resolution WENO schemes using local variation-based smoothness indicator
    P. K. Pandey, F. Ismail, R. K. Dubey, Computational and Applied Mathematics, 41(5), 208, 2022 — Cited by 3

  • Learning numerical viscosity using artificial neural regression network
    R. K. Dubey, A. Gupta, V. K. Jayswal, P. K. Pandey, International Conference on Computational Sciences: Modelling, Computing and Simulation, 2020 — Cited by 2

  • An efficient scaling of WENO-JS weights for accuracy preserving and higher resolution schemes
    P. K. Pandey, R. K. Dubey, Physica Scripta, 98(8), 085247, 2023

  • ENO classification and regression neural networks for numerical approximation of discontinuous flow problems
    V. K. Jayswal, P. K. Pandey, Soft Computing, 2024

  • Efficient diffusion for high order non-oscillatory entropy stable schemes
    A. Sahu, P. K. Pandey, R. K. Dubey, Applied Mathematics and Computation, Vol. 518, Article 129901, 2026

Strengths for the Emerging Researcher Award

  1. Strong Research Foundation in High-Impact Areas
    Dr. Prashant Kumar Pandey has developed a solid research foundation in applied mathematics, particularly in numerical analysis of hyperbolic conservation laws. His work on non-oscillatory and entropy-stable schemes addresses fundamental challenges of stability, accuracy, and robustness, making his research highly relevant to both theoretical and computational mathematics.

  2. Publications in Reputed SCI Journals
    He has published multiple research articles in well-regarded Q1 and Q2 SCI-indexed journals such as Applied Mathematics and Computation, Computational and Applied Mathematics, Physica Scripta, and Soft Computing. These publications demonstrate his ability to produce high-quality, peer-reviewed research early in his academic career, a key indicator of an emerging researcher.

  3. Interdisciplinary Integration of Machine Learning and Numerics
    A notable strength of his research profile is the integration of machine learning techniques—such as classification and regression neural networks—into classical numerical schemes. This interdisciplinary approach enhances adaptivity and performance in numerical simulations and reflects innovation at the intersection of applied mathematics and artificial intelligence.

  4. Growing Research Impact and Visibility
    Since 2020, his work has accumulated citations with an h-index of 2, indicating increasing recognition within the research community. His most cited works in Applied Mathematics and Computation and Computational and Applied Mathematics highlight the relevance and applicability of his contributions to ongoing research in numerical methods.

  5. Commitment to Academic Growth and Research Leadership
    As an Assistant Professor at Amity University Ranchi, he actively balances teaching with research, contributing to curriculum delivery while continuing scholarly output. His consistent engagement in conferences, workshops, collaborations, and future-oriented research projects reflects strong potential for long-term academic leadership and recognition as an emerging researcher.

Prashant Pandey, Mathematics, Emerging Researcher Award