Research Positions in the SNAP Group
Winter Quarter 2018-19

Welcome to the application page for research positions in the SNAP group, Winter Quarter 2018-19!

Our group has open positions for Research Assistants and students interested in independent studies and research (CS191, CS195, CS199, CS399). These positions are available for Stanford University students only. Below are some of the possible research projects. All projects are high-impact, allowing participants to perform research and work on real-world problems and data, and leading to research publications or working systems. Positions are often extended over several quarters. We are looking for highly motivated students with any combination of skills: data mining, machine learning, algorithms, social network analysis, and computer systems.

Please apply by filling out and submitting the form below. Apply quickly since the positions usually get filled early in the quarter. Thanks for your interest!

If you have any questions please contact Yesenia Gallegos at ygallegos@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

Project(s)

Please select all the projects that you are interested in. You can find the project descriptions below.

Networks of Human Mobility [description]
Keywords: network analytics, data science, human mobility, computational social science
Deep Learning for Toxicity Prediction [description]
Keywords: deep learning, graph neural networks, drug discovery, chemistry, biology
Recommender Systems for Multimodal Networks [description]
Keywords: deep learning, graph neural networks, recommender systems, network analytics

Position

Please select the position you are interested in. Please select all that apply.

25% RA
50% RA
Independent study (CS399, CS199, CS191)

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

Networks of Human Mobility

Keywords: network analytics, data science, human mobility, computational social science

The purpose of this project is to leverage new, terabyte-scale data about minute-by-minute human movement patterns to study networks of mobility and interaction. This project will require developing computationally efficient techniques to deal with and collect (eg, via web-scraping) large, challenging datasets.

We are looking for students with experience with large datasets and network science (eg, CS246 and CS224W). Working knowledge of Python is required; experience with Hadoop or Spark is a plus.

Go to the application form.

Deep Learning for Toxicity Prediction

Keywords: deep learning, graph neural networks, drug discovery, chemistry, biology

One of the significant reasons for the large cost of developing new drugs is the high rates of attrition in clinical development. In particular, unexpected toxic effects can surface later on during human drug testing, which are difficult to predict accurately using early in vitro and in vivo animal data.

The goal of this project is to develop novel techniques to predict the human level toxic effects of molecules using low level chemical and biological data that can be obtained early in the drug development process. Such a tool will allow us to prioritize drug candidates to advance to clinical development and reduce overall costs. We will explore techniques that can effectively integrate diverse types of chemical and biological data.

We are looking for students with experience in deep learning, statistics, and network science (e.g., CS230, CS224W, CS246, STATS200 and others), and an interest to work at the intersection of computer science, biology and chemistry. Basic understanding of biology and chemistry is a plus, but not required. Working knowledge of Python is required, and applicants should have some experience with a deep learning framework (e.g., Tensorflow, PyTorch).

Go to the application form.

Recommender Systems for Multimodal Networks

Keywords: deep learning, graph neural networks, recommender systems, network analytics

Many real world platforms (Social Networks, Online Retailers, etc.) can be modeled as temporal multimodal networks, encoding the relation of various entities and attributes over time. In this project, we intend to develop a novel recommender system method that captures all the idiosyncrasies of such complex data with a single, holistic model.

Traditional recommender algorithms (such as Matrix Factorization) require a careful combination of terms and biases (often hand-crafted) to encompass all the features of a temporal multimodal network. In contrast, we plan to leverage the latest advances in Deep Learning and Graph Convolutional Networks to train a simpler (but more accurate) model. Among the different benefits, we foresee that our method will help to mainstream more advanced Recommender Systems algorithms.

We are looking for students with experience in deep learning, statistics, and network science (e.g., CS230, CS224W, CS246, STATS200 and others), and an interest to work on recommender systems and network embeddings. Basic understanding of recommender systems and Graph Convolutional Networks is a plus, but not required. Working knowledge of Python is required.

Go to the application form.