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Peter Radchenko

University of Sydney

Sydney NSW, Australia
4.40/5 · 5 reviews

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4.008/20/2025

Helps students build confidence and skills.

4.005/21/2025

Always fair, constructive, and supportive.

5.003/31/2025

Challenges students to grow and excel.

4.002/27/2025

Inspires students to love their studies.

5.002/4/2025

Great Professor!

About Peter

Peter Radchenko is a Professor of Statistics in the Discipline of Business Analytics at the University of Sydney Business School, where he has been since 2017. Prior to this appointment, he served as Assistant Professor in the Department of Data Sciences and Operations at the Marshall School of Business, University of Southern California, starting in 2008 after holding Visiting Assistant Professor positions at USC from 2006 to 2008 and at the Department of Statistics, University of Chicago, from 2004 to 2006. He completed his PhD and MA in Statistics at Yale University in 2004, with David Pollard as his advisor, and earned a degree in Mathematics and Applied Mathematics, equivalent to an MS in the US, from Lomonosov Moscow State University in 1999.

Radchenko's academic interests center on statistical machine learning and methodologies for high-dimensional and complex data, including sparse linear modeling, high-dimensional single index models, convex clustering via L1 fusion penalization, feature screening in cluster analysis, discrete optimization for variable selection, functional additive regression, and forecast reconciliation. His influential publications feature in leading journals such as the Annals of Statistics, Journal of the American Statistical Association, Journal of the Royal Statistical Society Series B, Journal of Multivariate Analysis, and Operations Research. Key works include 'High Dimensional Single Index Models' (2015), 'Functional Additive Regression' with Yingying Fan and Gareth James (2015), 'Convex Clustering via L1 Fusion Penalization' with Gourab Mukherjee, 'The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization' with Rahul Mazumder, 'Predicting Census Survey Response Rates with Parsimonious Additive Models and Structured Interactions' (2025), and 'Subset Selection with Shrinkage: Sparse Linear Modeling When the Signal is Correlated' (2023). He has earned the ICS Prize for Computing (2022-2026) for breakthroughs in scalable sparse estimators, Unit of Study Survey Awards for Teaching at the University of Sydney (2018, 2020, 2023, 2024), and election to the International Statistical Institute (2024). Previous recognitions encompass a National Science Foundation grant (2012-2015) and the Dean's Award for Research Excellence at USC (2011). Radchenko reviews for top statistical journals and delivers seminars internationally.

Professional Email: peter.radchenko@sydney.edu.au

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