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We are happy to introduce you to the SBW 2019 keynotes. 

Lior Pachter

Bren Professor of Computational Biology and Computing and Mathematical Sciences, California Institute of Technology, USA.

Lior Pachter received a PhD in Applied Mathematics from MIT in 1999. He then moved to the University of California at Berkeley where he was a postdoctoral researcher (1999-2001), assistant professor (2001-2005), associate professor (2005-2009), and until 2018 the Raymond and Beverly Sackler professor of computational biology and professor of mathematics and molecular and cellular biology with a joint appointment in computer science. Since January 2017 he has been the Bren professor of computational biology at Caltech.

Lior Pachter’s research interests span the mathematical and biological sciences, and he has authored over 100 research articles in the areas of algorithms, combinatorics, comparative genomics, algebraic statistics, molecular biology and evolution. His lab develops computational and experimental methods for genomics, and is currently focused on the development of single-cell genomics technologies and their application to RNA biology. The computational challenges addressed in his group involve the analysis of high-dimensional data.

Christina Leslie

Associate Member, Computational Biology Program, Sloan Kettering Institute, USA.

Christina Leslie received a PhD in Mathematics from the University of California, Berkeley, and did her postdoctoral training in the Mathematics Department at Columbia University in 1999-2000. She then joined the faculty of the Computer Science Department and later the Center for Computational Learning Systems at Columbia University, where she became the principal investigator leading the Computational Biology Group. In 2007, she moved her lab to the Computational Biology program of Memorial Sloan Kettering Cancer Center, where she is currently an Associate Member.

Chrstina Leslie’s research group uses computational methods to study the regulation of gene expression in mammalian cells and the dysregulation of expression programs in cancer. She is well known for developing machine learning approaches for analysis of high-throughput biological data, particularly from next-generation sequencing. Focus areas in the lab include dissecting transcriptional and epigenetic programs in differentiation, microRNA-mediated gene regulation, alternative cleavage and polyadenylation, and integrative analysis of tumor data sets.

Mihaela Zavolan

Professor in Computational Biology/Genomics, Biozentrum, University of Basel, Switzerland


Mihaela Zavolan received a PhD in Computer Science from the University of New Mexico in Albuquerque. Between 1993 and 2003 she conducted research at the Santa Fe Institute in Santa Fe, the Los Alamos National Laboratory in Los Alamos, as well as at the Rockefeller University in New York. In 2003, Mihaela Zavolan was appointed Professor of Computational and Systems Biology at the Biozentrum of the University of Basel. She is also a group leader in the Swiss Institute of Bioinformatics (SIB).

The primary research focus in Mihaela Zavolan’s group is on microRNAs (miRNAs), which regulate the expression of protein coding genes to control cell differentiation, metabolism, and immune responses. Through the development of high-throughput experimental methods and computational analyses, she has contributed to the discovery of miRNAs in various organisms ranging from viruses to humans. Her group has developed algorithms to predict miRNA genes and miRNA targets, and has worked on the development of the CLIP method (cross-linking and immunoprecipitation) for mapping the binding sites of RNA-binding proteins in RNAs.


Page Manager: Sanna Abrahamsson|Last update: 8/7/2019

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