Computational & Systems Biology Program

The Christina Leslie Lab

Research

Christina Leslie, PhD
Member, Computational & Systems Biology Program

Our lab develops novel computational methods to study cellular biological systems from a global and data-driven perspective. We seek to exploit diverse, high-throughput functional and genomic data to understand the molecular networks underlying fundamental cellular processes, including regulation of transcription, pre-mRNA processing, signaling, and post-transcriptional gene silencing. Our algorithmic methods draw on machine learning, a computational field concerned with learning accurate, predictive models from noisy and high-dimensional data.

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Publications

People

Christina Leslie

Christina Leslie, PhD

Member, Computational & Systems Biology Program

  • Computational biologist Christina Leslie focuses on developing machine learning algorithms for computational and systems biology.
  • PhD, University of California, Berkeley
646-888-2762
Office Phone

Members

Karen Chu
Karen Chu

Graduate Student

Rose  Diloreto
Rose Diloreto

Graduate Student

Vianne Gao
Vianne Gao

Graduate Student

Alireza Karbalaghareh

Research Scholar

Erik Ladewig
Erik Ladewig

Research Fellow

Henri Schmidt
Henri Schmidt

Research Assistant

Zakieh Tayyebi

Graduate Student

Yi Zhong
Yi Zhong

Research Fellow

Lab Alumni
Sagar Chhangawala
Sagar Chhangawala

Graduate Student

Hyunwoo Cho
Hyunwoo Cho

Graduate Student

Lauren Fairchild

Graduate Student

Meghana Kshirsagar

Research Scholar

Yuheng Lu
Yuheng Lu

Graduate Student

Hatice Osmanbeyoglu
Hatice Osmanbeyoglu

Research Associate

Yuri Pritykin
Yuri Pritykin

Research Scholar

Merve Sahin

Research Fellow

Han Yuan
Han Yuan

Graduate Student

Lee Zamparo

Research Scholar

Lab Affiliations

Achievements

  • Introduction of string kernel methodology for SVM classification of biological sequences
  • Development of algorithms for predictive modeling of gene regulation
  • First systems-level analyses of competition between microRNAs and between target transcripts

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