Click on each project title to find a description of the lab and any preferred background. Many of these labs are interdisciplinary, so please look through projects that are housed in other departments.
We will be adding more labs during the weeks, so please come back to check out any additional labs that have been posted.
Faculty: Mayank Kejriwal
Project Description: AI, Networks and Society is a multi-year collection of projects in our group that seeks to use various sources of data to learn more about society using data-driven tools and frameworks, including machine learning and AI. By drawing on an empirically rigorous methodology, we seek to study complex systems in domains such as elections, social media, health and finance. Individual projects are often published in top-tier journals and conferences, and some have received widespread press coverage, including in Popular Science and San Francisco Times.
Related Background: A course in probability & statistics is preferred, and some background in AI or machine learning (whether applied or through coursework) is preferred as well.
Prerequisites: Introductory programming courses, including data structures
Faculty: Sze-Chuan Suen
Project Description: We have recently developed and validated a model to predict need for massive transfusion (MT) using modern machine learning (ML) methods. MT may be needed when an injured patient enters the trauma center, and MT protocols require blood for transfusion to be supplied from inventory. Accurate prediction of the need for MT may reduce delays and unnecessary inventory requests. We are now working on building a user-friendly online interface to help physicians use our ML method in emergency settings. This may require simplification of the model to fewer variables, as well as developing methods to increase usability of the interface (layout design, etc.).
Related Background: Experience with usability studies, design of websites/interfaces
Prerequisites: Experience working with data (machine learning experience welcomed)
Faculty: Meisam Razaviyayn
Project Description: The goal of this project is to train neural networks using measures of performance other than accuracy in the presence of content-shifts. This is particularly important in applications such as classification of hateful/misinformation posts on social media platforms. In this application, the number of positive samples (sample posts containing misinformation) is small compared to the entire number of samples. Hence non-decomposable measures of performance, such as AUROC or accuracy at the top, are needed for auto-enforcement of the integrity-related policies on social media platforms. However, these measures of performance are vulnerable to content shifts. Our goal is to develop scalable algorithms for training neural networks based on measures of performance related to accuracy at the top. Furthermore, the resulting model needs to be robust against content-shifts. This is because the topic of misinformation changes over social media platforms over time.
Related Background: Basic knowledge of machine learning and neural networks. Being familiar with PyTorch and TensorFlow.
Prerequisites: Machine Learning, PyTorch, TensorFlow
Published on December 8th, 2021
Last updated on December 9th, 2021
