Dr. Vijay Pratap Singh works in the fields of mathematical modelling, reliability engineering, and optimization. He earned his Ph.D. in Mathematics from the Indian Institute of Technology (IIT) Roorkee in 2025 under the supervision of Prof. Madhu Jain. He also completed his M.Sc. in Mathematics at the same institute. He has qualified several national-level competitive examinations, including CSIR-UGC NET with Junior Research Fellowship (JRF), the Graduate Aptitude Test in Engineering (GATE), the Joint Admission Test for Masters (IIT-JAM), and IIT-JEE (Main). His teaching interests include Linear Algebra, Calculus, Ordinary and Partial Differential Equations, Real Analysis, Complex Analysis, Numerical Analysis, Optimization, Operations Research, Queueing Theory, Fuzzy Theory, and Applied Mathematics. His research focuses on mathematical modelling and reliability engineering, particularly the performance analysis and optimization of fault-tolerant machining systems (FTMS). Dr. Singh has published several research articles in national and international journals indexed in SCI/SCIE, ESCI, and Scopus. His research combines analytical modelling, computational techniques, and soft computing methods to address problems in reliability engineering and optimization of FTMS. He uses stochastic modelling and queueing theory to analyze system behavior and assess performance, availability, and maintainability of the machining system. He further applies soft computing and optimization techniques to improve system performance and reduce costs, while exploring the use of artificial intelligence and machine learning to predict and analyze the performance of FTMS. He has experience in teaching undergraduate and postgraduate mathematics courses and has participated in national and international academic conferences and workshops. He has also gained professional experience in AI training and mathematical evaluation through his work with Turing and Mercor. His work involved developing mathematics problems at the postgraduate and doctoral levels, reviewing mathematical solutions, and evaluating AI-generated responses to help improve the accuracy and problem-solving abilities of AI models.