This book discusses the topics presented at the 2nd Annual QEDS Conference on Advances in Applied and Computational Mathematics: Theory, Methods, and Applications in Finance and Data Science (AACM 2025), organized by the Centre for Quantitative Economics and Data Science, Birla Institute of Technology Mesra, Ranchi, Jharkhand, India, from February 1315, 2025. This book focuses on the convergence of applied and computational mathematics with finance, data science, and data-driven public policy. It highlights recent research contributions in areas such as mathematical optimization, simulation techniques, statistical modelling, and algorithmic developments, emphasizing their growing relevance in engineering, finance, and data science.
Chapters in the book reflect both theoretical advancements and practical methodologies that address contemporary challenges in an increasingly data-centric world. This book is carefully curated for a broad readership, including researchers, industry professionals, and policy experts from diverse academic and professional backgrounds. It offers valuable insights for those seeking to deepen their understanding and remain current in rapidly evolving technological and analytical domains. For the academic community, this book serves as an essential resource for students, educators, and researchers in mathematics, statistics, data science, finance, and related fields.

Emphasizes mathematical foundations on graph theory, combinatorics, coding theory, numerical analysis, and optimization Focuses on the development of statistical methodologies for complex data scenarios Presents a comprehensive treatment of stochastic and queueing models

Autorentext

Mrinal Jana is an assistant professor (Grade I) at the Centre for Quantitative Economics and Data Science, Birla Institute of Technology Mesra, Ranchi, Jharkhand, India. An academician specializing in applied mathematics, quantitative economics, and data-driven decision sciences, he holds M.Sc. and a Ph.D. degrees in mathematics from the Indian Institute of Technology Kharagpur, West Bengal, India, respectively, in 2011 and 2016. His research interests include mathematical optimization under interval uncertainty, multi-objective programming, dynamic and risk-adjusted portfolio allocation, and optimization-based applications in finance. He published his research in reputed peer-reviewed international journals and conference proceedings, reflecting his sustained engagement in both theoretical and applied quantitative research.
Pushkal Kumar is an assistant professor in the Centre for Quantitative Economics and Data Science, Birla Institute of Technology Mesra, Ranchi, India. He earned his Ph.D. from the National Institute of Technology Rourkela, India, in 2018. His research interests include applied probability, statistical inference, statistical information theory, statistical decision theory, and order statistics. He has published several research papers in international journals and conference proceedings.
Somesh Kumar is a professor in the Department of Mathematics, IIT Kharagpur, India. He did his M.Sc. in statistics followed by a Ph.D. from the IIT Kanpur, respectively, in 1984 and 1990. He has published more than 130 research papers in international journals and book chapters and supervised 15 Ph.D. scholars. His major research interests are in statistical decision theory and inference, in particular on estimation after selection, quantile estimation, estimation of parameters under constraints, two-stage and sequential estimation, the problem of classification, inference on directional distributions, limit theorems for dependent models, estimating measures of reliability and entropy, stochastic ordering and bootstrap tests. His research has found applications in diverse fields, including agriculture, medicine, mechanical engineering, electronics, environmental science, and ecological studies.
Jitendra Kumar is a professor in the Department of Mathematics at IIT Ropar, Punjab, India. Prior to joining the IIT Ropar, he served at the IIT Kharagpur from 2009 to 2022. He completed his M.Sc. in industrial mathematics from the IIT Roorkee, followed by a second M.Sc. from the Technical University of Kaiserslautern, Germany. He was awarded a doctorate in 2006 from Otto-von-Guericke University Magdeburg, Germany. After completing his Ph.D., he was offered a lectureship at the Institute for Analysis and Numerical Mathematics, Otto-von-Guericke University Magdeburg, Germany, where he served from 2006 to 2008. He is the recipient of several recognized awards and fellowships, including the Alexander von Humboldt fellowship and DAAD & DFG scholarships. He has published more than 100 articles in international journals of high repute and has supervised 15 Ph.D. students.
Arup Kumar Sahoo is a Hatter postdoctoral researcher in the Autonomous Navigation and Sensor Fusion Laboratory (ANSFL), Charney School of Marine Sciences, University of Haifa, Israel. His research interests encompass mobile robots, inertial navigation, applied mathematics, and scientific machine learning. He is currently working on a project integrating physics-informed neural networks with advanced navigation systems for maneuvers of autonomous robots (quadrotors, wheeled robots and AUVs). He has authored and edited 1 book and 16 research papers in journals and conferences. Currently, he is serving as an editor for a special issue of the International Journal of Data Mining and Bioinformatics journal.

Inhalt

A New Paradigm for Graph-Based Learning Using Lyndon Partial Words.- Distributed Data Feature Retrieval using Linear Codes with Prescribed Hull Dimension.- EduSpectra: An Image dataset for understanding student's dropout behaviour.- Levenberg-Marquardt Neural Network Approach for Solving Complex Fuzzy Linear Systems of Equations.- Strong matching preclusion number on binary de Bruijn graphs.