Rate My Professor Bala Rajaratnam

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Bala Rajaratnam

University of Sydney

4.60/5 · 5 reviews
5 Star3
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1 Star0
5.08/20/2025

Encourages students to think creatively.

4.05/21/2025

Always clear, engaging, and insightful.

5.03/31/2025

Encourages critical thinking and analysis.

4.02/27/2025

Patient, kind, and always approachable.

5.02/4/2025

Great Professor!

About Bala

Professor Bala Rajaratnam serves as a Visiting Professor of Business Analytics in the Discipline of Business Analytics at the University of Sydney Business School. He is concurrently a Professor in the Department of Statistics at the University of California, Davis. Rajaratnam earned his Ph.D. from Cornell University. His academic career includes prior positions at Stanford University, where he received the DARPA Young Faculty Award in 2011. His research interests encompass machine learning, data science, high-dimensional statistical inference, covariance estimation, graphical models, high-dimensional data analysis, climate modeling, and statistical computing. Rajaratnam has made significant contributions to statistical methodology, particularly in high-dimensional settings and graphical model estimation. He serves on the editorial board of the SIAM Journal on Mathematics of Data Science and has been involved in committees such as the Committee on Probability and Statistics in the Physical Sciences. Notable publications include "A Unified Framework for Correlation Mining in Ultra-High Dimension" (Wei, Rajaratnam, Hero, IEEE Transactions on Information Theory, 2023), "Anthropogenic Aerosols Delay the Emergence of GHGs‐Forced Wetting of South Asian Rainy Seasons Under a Fossil‐Fuel Intensive Pathway" (Singh et al., Geophysical Research Letters, 2023), "Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion" (Wu, Yin, Rajaratnam, Guo, ICLR, 2023), "The Khinchin–Kahane and Lévy Inequalities for Abelian Metric Groups, and Transfer from Normed (Abelian Semi)Groups to Banach Spaces" (Khare, Rajaratnam, Journal of Mathematical Analysis and Applications, 2023), and "Climate Field Completion via Markov Random Fields: Application to the HadCRUT4.6 Temperature Dataset" (Vaccaro et al., Journal of Climate, 2021). Additional works cover sparse Gaussian graphical model estimation (Biometrika, 2017) and influence diagnostics for high-dimensional Lasso regression. Rajaratnam has moderated public events, such as "The Role of AI in the Accounting Profession" at the University of Sydney Business School. His research impacts fields ranging from climate science to machine learning through rigorous statistical frameworks.

Professional Email: bala.rajaratnam@sydney.edu.au

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