Arijit Maji did his Ph.D. from Shailesh J. Mehta School of Management, Indian Institute of Technology Bombay.  He completed his Master’s degree in Quality, Reliability & Operations Research from Indian Statistical Institute, Kolkata, India and his Bachelor’s in Power Engineering from the National Power Training Institute (E.R.), Durgapur, India. His broad area of research interests are Industrial Engineering & Operations Management. Specifically, he is working in the interface of Statistical Quality Control (SQC), Machine Learning, and Optimization Techniques for advanced process monitoring, diagnosis & real-time process adjustment. His research contributions are published in the International Journal of Production Research and Quality and Reliability Management. In addition to academia, he has industry experience as a Business Analyst at Axis Bank and AXA Pvt. Ltd.

• Degree with full title, institution, title if thesis, year. Ph.D. , Indian Institute of Technology Bombay, ‘A Study on Distribution-free Approaches for Multivariate Manufacturing Process Monitoring and Diagnosis, 2024
• Degree with full title, institution, year. M.Tech in Quality, Reliability and Operations Research, Indian Statistical Institute, Kolkata, 2017
• Degree with full title, institution, year. B.Tech in Power Engineering, National Power Training Institute Durgapur, 2014.

Business Analytics, Quality Engineering, Machine Learning,  Applied Optimization Techniques

Statistical Learning & Data Mining, Big Data Analytics, Text Mining

• Assistant professor, Symbiosis Statistical Institute Pune
• Business Analyst at Axis Bank Ltd.
• Associate Data Science Consultant, AXA Business Services Pvt Ltd.

Assistant professor, Symbiosis Statistical Institute Pune

Research papers:

1. Maji, A., & Mukherjee, I. (2024). An efficient adaptive r-MD multivariate single control chart for nonlinear multiple quality characteristics. International Journal of Production Research, 62(3), 784-822.
2. Maji, A., & Mukherjee, I. (2023). An unsupervised one-class-classifier support vector machine to simultaneously monitor location and scale of multivariate quality characteristics. International Journal of Quality & Reliability Management, 40(2), 419-454.

Submitted Paper:

1. A distribution-free multivariate control chart for simultaneously monitoring and diagnosis of nonlinearly correlated quality characteristics
2. An Improved integrated Distribution-free Multivariate Control chart Framework for Individual and Subgroup Observations using One-Class Support Vector Machines

Real-time Multivariate Process Monitoring and Adjustment

Conference:

Maji, A., Amed, S., & Sen, T. (2021). Multivariate Missing Value Imputation using Weighted Cluster SoftMax Technique. In Interdisciplinary Research in Technology and Management (pp. 128-133). CRC Press.

Thesis Title: A study on Distribution-free Approaches for multivariate Manufacturing Process Ministering and Diagnosis