Research Positions in the SNAP Group
Winter Quarter 2016-17

Welcome to the application page for research positions in the SNAP group, Winter Quarter 2016-17!

Our group has an open position for a student interested in independent studies or research (CS191, CS199, CS399) in computational biology and network analysis. This position is available for Stanford University students only. A description of the research project is below. The project will lead to research publications or working systems. We are looking for a highly motivated student with a combination of the following skills: computational biology, data mining, machine learning, algorithms, and network analysis.

Please apply by filling out and submitting the form below. Thanks for your interest!

If you have any questions please contact Prof. Leskovec at jure@cs.stanford.edu.

Application form

First and Last Name

SUNetID

SUNetID is your Stanford CS login name and contact email address, <your_SUNetID>@cs.stanford.edu. If you don't have a SUNetID, use <your_last_name>_<your_first_name>, so if your last name is Smith and your first name is John, use smith_john.

Email

Department

Student Status

Statement of Purpose

Briefly explain why you would like to participate in this project, why you think you are qualified to work on it, and how you would like to contribute.

Your Resume

Your Transcript

Click on the button below to Submit


Projects

Large Scale Networks of Biological Information

Keywords: computational biology, network analysis

Recent decades have resulted in a deluge of biological information, coming from a broad range of sources and with widely varying characteristics. New valuable biomedical insights can be gained by understanding not only individual pieces of information, but the relationships among them. For example, existing drugs could be applied to cure additional diseases (drug repurposing) or drugs can be customized to a specific patient (personalized medicine). One challenge is how to integrate heterogeneous biological data from many sources in a unified network representation. The second challenge is how to perform a scalable analysis over this network representation in order to uncover new biological discoveries. The goal of this project is to build large scale networks of biological information, consisting of information such as genes, proteins, diseases, and drugs, and then apply advanced network analysis techniques to generate new biomedical hypotheses. Examples of hypotheses are identifying new drug candidates or improving the process of diagnosing genetic diseases. You will be utilizing our SNAP network analysis platform and our large-memory servers, the largest having 12TB RAM and 288 cores.

We are looking for students that have knowledge of basic biology and interest in working with large scale biological datasets. Experience with Python or an equivalent programming language is required. Knowledge of network analysis (CS224W) is a plus.

Go to the application form.