معرفی
Ann Maharaj is an Adjunct Associate Professor in the Department of Econometrics and Business Statistics at Monash University's Caulfield Campus, within the Faculty of Business and Economics. She is an active researcher and educator with expertise in statistical computing and time series analysis.
- Department: Econometrics and Business Statistics
- Role: Adjunct Associate Professor
- Institution: Monash University
- Campus: Caulfield
Her research focuses on advanced statistical methodologies, particularly in time series classification, wavelet analysis, fuzzy classification, and interval time series analysis. These methods are applied in diverse domains such as finance, environmental science, climatology, and human mobility. She has co-authored a book on time series clustering and classification and has published extensively in top-tier journals.
The recent trend in her publications (2020–2024) shows a strong emphasis on clustering and classification of complex time series data using wavelet, cepstral, and fuzzy techniques. Her work integrates statistical theory with practical applications, particularly in financial and environmental datasets, contributing to sustainable development goals through data-driven insights.
She has received recognition for her teaching excellence:
- Monash Business School Award for Teaching Excellence (2017)
Ann Maharaj is actively involved in academic service and professional communities. She has supervised research students and contributed to statistical consulting and workshops. Her professional affiliations include:
- Elected member of the International Statistical Institute (ISI)
- Member of the International Association of Statistical Computing (IASC), serving on its Council (2013–2017) and Executive (2015–2017)
- Accredited statistician with the Statistical Society of Australia (SSA)
- Former Secretary and Academic Vice-President of the Monash Branch of the NTEU (2000–2014)
She led a research project funded by the Collier Charitable Fund in 2005 on computational infrastructure, indicating early engagement with data-intensive research. Her ongoing scholarly output demonstrates sustained research activity and collaboration with international scholars in statistics and data science.
She is associated with research groups and networks focused on statistical computing and time series analysis, contributing to both methodological advancement and real-world application through interdisciplinary collaboration.
