Marinka Zitnik

Postdoctoral Research Fellow
Computer Science
Stanford University
{ machine learning, network science, data fusion, genomics, medicine }

I am a postdoctoral research fellow in Computer Science at Stanford University where I work with Jure Leskovec and collaborate closely with major biomedical research departments worldwide. I am also a Chan Zuckerberg Biohub postdoctoral researcher.

My research investigates machine learning for biomedical sciences, focusing on large networks of interactions between biomedical entities--e.g., proteins, drugs, diseases, and patients. I leverage these networks at the scale of billions of interactions among millions of entities and develop new methods blending machine learning with statistical methods and network science.

I use my methods to answer burning scientific questions, such as how Darwinian evolution changes molecular networks, and how data-driven algorithms can accelerate scientific discovery.

I use my methods to solve high-impact problems that serve as a first step to bridging the divide between basic science and patient data, such as what drugs and combinations of drugs are safe for patients, what molecules will treat what diseases, and how newborns are transferred between hospitals and how these transfers influence outcomes.

I received a Ph.D. in Computer Science from University of Ljubljana in 2015 while also researching at Imperial College London, University of Toronto, Baylor College of Medicine, and Stanford University. I obtained a B.Sc. in Computer Science and Mathematics in 2012.

I am excited to be named a Rising Star in EECS by MIT! I am honored to be one of the Next Generation in Biomedicine by The Broad Institute of Harvard and MIT!

Starting in December 2019, I will be a tenure-track Assistant Professor at Harvard University, and my laboratory at Harvard Medical School will focus on Machine Learning for Science and Medicine.

I am looking for outstanding students and postdoctoral fellows who would like to join me in transforming science and medicine to data-driven and computationally enabled disciplines.

The research focus is on new data science and machine learning methods for learning and reasoning over rich interaction data and on translation of these methods into solutions for biomedical problems. This scientific approach not only opens up new avenues for understanding nature, analyzing health, and developing new medicines to help people but can impact on the way predictive modeling is performed today at the fundamental level. Among others, possible research projects include:

  • Representation learning for biomedicine in an effort to set sights on new frontiers in genomics, drug discovery, and diseases beyond classic applications of neural networks on image and sequence data.
  • Network embedding methods in an effort to bridge the divide between basic science and patient data.
  • Fusion of diverse data into knowledge graphs in an effort to combine biomedical data in their broadest sense to reduce redundancy and uncertainty and make them amenable to analyses.
  • Next-generation algorithms for networks, focusing on large networks of interactions between biomedical entities and their applications to network biology and medicine.
  • Contextually adaptive AI in an effort to advance algorithms to train more with less data and reason about never-before-seen phenomena as algorithms encounter new patients, diseases, or cell types.

If you are excited about problems in machine learning and/or applications in genomics, medicine, and health and would like to work with me at Harvard, please contact me with a brief description of your research interests and your CV.

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