Rate My Professor Uri Keich

Keich's research centers on statistical methods for computational biology, emphasizing false discovery rate control via multiple hypothesis testing. His work focuses on tandem mass spectrometry proteomics, introducing competition-based approaches that parallel the knockoff filter framework and have attracted attention in statistics and machine learning. Additional interests include genomics applications like sequence alignment, motif finding, and DNA replication origins, alongside computationally efficient algorithms for significance estimation. He has authored numerous publications, including 'Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment' (Nature Methods, 2025, with B. Wen et al.), 'Semi-supervised Learning While Controlling the FDR with an Application to Tandem Mass Spectrometry Analysis' (RECOMB 2024), 'Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics' (Journal of Proteome Research, 2015, Best Paper Award at RECOMB 2015), and many others in Journal of Proteome Research, amassing over 2,300 citations. He has developed key software tools such as FDRBench for entrapment experiments, Crema for TDC-based FDR estimation, and CONGA for combining MS/MS search strategies. In education and leadership, he served as Statistics Honours Coordinator from 2013 to 2025, Deputy Head of School from January to May 2025, and currently holds the position of Faculty of Science Academic Lead for AI and Assessment since 2025. His teaching portfolio includes advanced probability and statistical models, statistical thinking with data, and statistical methods in bioinformatics. Awards include the 2024 Faculty of Science Learning & Teaching Award for Teaching and Learning Excellence and the RECOMB 2015 Best Paper Award.