Dr. Ranjeet Kumar Singh
Dr. Singh is an Assistant Professor and Ph.D. in Statistics specializing in Statistical Process Control (SPC) from Banaras Hindu University in 2021. His expertise lies in Statistical Inference, Quality Control and Process Improvement, Hypothesis Testing, and Probability Theory.
Through his research, he contributes to the advancement of Statistical Process Control that involved creating mathematical development in SPC techniques to detect process variations, reduce defects, and enhance manufacturing processes.
As an Assistant Professor, he is dedicated to inspiring students in the field of Statistics and Mathematics, and Operations Research in UPES. He teaches Undergraduate and Post-graduate courses on Statistics and Operations Research with practical skills and enables them to apply Statistical and Operations Research techniques using tools like TORA and Excel.
He actively collaborates with industry partners to bridge academia and real-world applications in the respective domain. His research focuses on developing advanced techniques for real-time process monitoring, analysing multivariate data, and optimizing process control strategies for enhanced quality and efficiency.
As an Assistant Professor at Business School and Ph.D. in Statistics, he is committed to driving continuous improvement and preparing students for successful careers in data-driven decision-making. His expertise, analytical skills, and passion for academic excellence enable him to make a significant impact in quality management and process optimization.
Dr. Singh, previously an Assistant Professor at the School of Advance Science, VIT-AP University, and School of Basic and Applied Science, Adamas University, possesses extensive experience teaching Data Science and Statistics to students. With a focus on curriculum development, he designed comprehensive courses and instructional materials. Dr. Singh excelled in delivering engaging lectures, facilitating interactive sessions, and providing valuable mentorship to students. Furthermore, he fostered industry collaborations to bridge academia and industry. Dr. Singh's work experience highlights his expertise in Data Science and Statistics, dedication to student growth, and commitment to staying updated with advancements in the field.
Statistical Process Control | Statistical Inference | Time Series Analysis | Data Analysis
Dr. Singh’s teaching philosophy prioritizes fostering a supportive learning environment. He employs a "flipped classroom" approach, where students complete pre-reading assignments and quizzes before class. In-class sessions focus on real data and problem-solving, promoting a shift from novice to expert thinking. By incorporating authentic problems, Dr. Singh empowers students to engage actively and develop a deeper understanding of the subject matter.
Dr. Singh instructs the elective course on Statistics, Mathematics, and Operations Research. His research delves into how individuals' backgrounds and incentives influence the formation and evolution of relationships in these fields. By examining these factors, Dr. Singh gains insights into the dynamics that shape these relationships and their progression over time.
Awards and Grants
- UGC-JRF (Junior Research Fellowship) recipient for her outstanding academic performance and research potential, which enabled her to pursue her Ph.D.
- UGC-SRF (Senior Research Fellowship) awardee in recognition of her exceptional contributions to research in the field of Statistics.
- Published numerous research papers in renowned peer-reviewed journals, contributing to the advancement of statistical theory and methodology.
- Presented research findings at national and international conferences, fostering collaborations and exchanging knowledge with fellow statisticians and researchers.
- Served as a reviewer for prestigious statistical journals, ensuring the quality and rigor of research in the field.
- Developed and maintained a strong professional network by participating in professional associations and attending workshops and seminars.