Summary
Sach Mukherjee is an MRC Investigator (Programme Leader) in the Biomedical Machine Learning theme at the MRC Biostatistics Unit, and Principal Investigator and Head of Statistics and Machine Learning at the DZNE in Bonn, Germany.
He earned a DPhil in machine learning at Oxford, was a postdoctoral fellow in statistics at UC Berkeley and has previously held faculty positions in Warwick and Amsterdam. His research interests centre on high-dimensional statistics and machine learning for biomedicine, including in particular methods for high-dimensional and heterogeneous data and causality. He has been a Fulbright Fellow and a recipient of the Wolfson Research Merit Award of the Royal Society.
Selected Publications
- M. F. Eigenmann, S. Mukherjee and M. H. Maathuis. “Evaluation of Causal Structure Learning Algorithms via Risk Estimation”. In Proceedings of Uncertainty in Artificial Intelligence (UAI) 2020.
- F. Dondelinger and S. Mukherjee. “The joint lasso: high-dimensional regression for group structured data”. Biostatistics, 21(2):219-235, 2020
- S. M. Hill, C. J. Oates, D. Blythe and S. Mukherjee. “Causal Learning via Manifold Regularization”. Journal of Machine Learning Research, 20(127):1-32, 2019
- S. M. Hill, N. K. Nesser, K. Johnson-Camacho … J. W. Gray, G. B. Mills, S. Mukherjee and P. T. Spellman. “Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling”. Cell Systems, 4(1):73-83, 2017
- N. Städler and S. Mukherjee. “Two-sample testing in high dimensions”. Journal of the Royal Statistical Society, Series B, 79(1):225-246, 2017
- C. J. Oates, J. Q. Smith and S. Mukherjee. “Estimating Causal Structure Using Conditional DAG Models”. Journal of Machine Learning Research, 17(54):1-23, 2016
- S. M. Hill, L. M. Heiser, T. Cokelaer … G. Stolovitzky, J. Saez-Rodriguez and S. Mukherjee. “Inferring causal molecular networks: empirical assessment through a community-based effort”. Nature Methods, 13:310-318, 2016 (cover article)
- R. J. B .Goudie and S. Mukherjee. “A Gibbs Sampler for Learning DAGs”. Journal of Machine Learning Research, 17(30):1-39, 2016
