The ocean significantly mitigates climate change by absorbing fossil fuel carbon from the atmosphere. Cumulatively since the preindustrial times, the ocean has absorbed 40% of emissions. To understand past changes, diagnose ongoing changes, and to predict the future behavior of the ocean carbon sink, we must understand its spatial and temporal variability. However, the ocean is poorly sampled and so we cannot do this directly from in situ measurements.
The REACH OUT study is a multi-institutional collaboration funded by the Health Effects Institute to determine if populations who have been chronically exposed to higher levels of air pollution are at greater risk of severe COVID-19 outcomes. This is a paid position which will provide students with the opportunity to use analytic approaches on public health and environmental data. The selected student will work as part of a research team to analyze publicly available COVID-related and environmental data from the New York City Department of Health. They will also integrate findings with those from harmonized electronic health records and conduct simulations to assess the potential for selection bias in study populations. All analyses will be performed using R/R Studio. Students will be expected to create shareable and commented code as part of this work. Students will also create visualizations to communicate study results in publications and presentations.
We research techniques for improving safety of pedestrians, traffic flow, and smart streetscape applications. We aim to accomplish precise localization and tracking of objects by utilizing infrastructure-installed multimodal sensors such as cameras and lidars to provide a global view of the behavior of smart city traffic participants. We rely significantly on traffic intersections which are particularly suitable locations for the deployment of computing, communications, and AI-based services for smart cities of the future. The abundance of data to be collected and processed, in combination with privacy and security concerns, motivates the use of machine learning and deep learning processing, and to deploy the edge-computing paradigm which aligns well with physical intersections in metropolises.
Type 1 diabetes (T1D) is a chronic autoimmune disease that requires patients to need lifelong insulin therapy. Diabetes technology (insulin pumps and continuous glucose monitors (CGM)) have been shown to reduce the risk of high and low blood sugars and thus reduce long term diabetes complications. Hybrid closed-loop (HCL) insulin pumps integrate CGM technology to automatically adjust insulin delivery from the pump, with the goal of improved blood sugar and better quality of life for the patient. There are two commonly used HCL systems in our diabetes center: the t:slim x2 with Control IQ (TS) and the Omnipod 5 (O5).
Vehicle routing has been extensively studied in optimization problems. With the advance of AI and big data, this project aims to solve vehicle routing problems (VRP) using reinforcement learning.
In this project, we aim to develop GCN-based GAN models to predict spatio-temporal evolution using open human mobility datasets – SafeGraph (https://www.safegraph.com/).
The Data Science Institute is calling for faculty submissions of research projects for the Spring and Summer 2023 sessions of the Data Science Institute (DSI) Scholars Program. The goal of the DSI Scholars Program is to engage undergraduate and master students to work with Columbia faculty, through the creation of data science research internships. Last year, we worked with over 30 projects and received more than 250 unique applications from Columbia Students. The program’s unique enrichment activities foster a learning and collaborative community in data science at Columbia. Apply here.