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Pier Francesco Palamara (PI)

I am an associate professor at the Department of Statistics in the University of Oxford. I am broadly interested in developing new methods to solve problems in population and medical genetics. Before coming to Oxford I spent three and a half years working on statistical and population genetics as a postdoctoral fellow at the Harvard Chan School of Public Health and at the Broad Institute of MIT and Harvard. I received my PhD in computer science from Columbia University. Prior to that, I obtained a bachelor’s and a master’s degree from Rome's Sapienza University, and a master’s degree from Columbia University, all in computer science with a focus on artificial intelligence, machine learning, and cognitive robotics.




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Juba Nait Saada (DPhil student)

My current research is focused on identical-by-descent (IBD) segment detection in large genomic data sets. I received a bachelor’s and master’s degree in Data Science and Engineering from Télécom ParisTech in 2016 and 2017 respectively. I am interested in artificial intelligence and its application to solve public health issues.




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Brian Zhang (DPhil student)

I am currently working on machine learning algorithms for population and medical genetics. Before coming to Oxford, I worked for two years as a research engineer at DeepMind in London. I graduated from Harvard with a bachelor’s degree in physics in 2015. I also occasionally blog about statistics and other things I am learning in my DPhil.




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Sinan Shi (DPhil student)

(Joint with Simon Myers). I am currently working on developing a genetic inference platform for the Genomics England 100,000 genome project and on a second project on coalescent HMM algorithms. I have a scientific computing background and before joining the lab I worked as research assistant/research engineer in several fields. I am enthusiastic about developing open source scientific software.




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Georgios (Yiorgos) Kalantzis (DPhil rotation)

I am a DPhil rotation student in the SABS DTC, working on a project on rare variant association in collaboration with UCB.