Decoding behavioral signifiers for the brain state of vigilance can have far reaching implications for understanding actions and identifying disease. We are using high resolution video recordings of mice as they navigate a maze, but have access to very few pre-determined behavioral signifiers. Several recent publications implemented computer vision to extract a variety of previously unreachable aspects of behavioral analysis, including animal pose estimation and distinguishable internal states. These descriptions allowed for the identification and characterization of dynamics, which then revealed an unprecedented richness to the behaviors that determine decision making. Applying such computational approaches in our maze in the context of behaviors that have been validated to measure choice and memory can reveal dimensions of behavior that predict or even determine psychological constructs like vigilance. DSI scholars would use pose estimation analysis to evaluate behavioral signifiers for choice and memory and relate it to our real time concurrent measures of neural activity and transmitter release. The students would also have opportunity to examine the effect of disease models known to impair performance on our maze task on any identified signifier.

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We are conducting a study at the Columbia School of Social Work to examine associations between adolescent mental health and physiological data (collected through smart watches), sociability metrics (e.g., number of texts, amount of time on social media platforms) and self-reported daily stressors (collected through mobile surveys) and use this data to inform the development of a just-in-time adaptive intervention (delivered by smartphone) to reduce mental health problems among adolescents. Just-in-time adaptive interventions aim to provide the right type/amount of support, at the right time, by adapting to an individual’s changing internal and contextual state. We are looking for a student(s) to develop a mobile app (Android and iOS compatible) that has the following capabilities:

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Since the industrial revolution the atmosphere has continued to warm due to an accumulation of carbon. Terrestrial ecosystems play a crucial role in quelling the effects of climate change by storing atmospheric carbon in biomass and in the soils. In order to inform carbon reduction policy an accurate quantification of land-air carbon fluxes is necessary. To quantify the terrestrial CO2 exchange, direct monitoring of surface carbon fluxes at few locations across the globe provide valuable observations. However, this data is sparse in both space and time, and is thus unable to provide an estimate of the global spatiotemporal changes, as well as rare extreme conditions (droughts, heatwaves). In this project we will first use synthetic data and sample CO2 fluxes from a simulation of the Earth system at observation locations and then use various machine learning algorithms (neural networks, boosting, GANs) to reconstruct the model’s CO2 flux at all locations. We will then evaluate the performance of each method using a suite of regression metrics. Finally, time permitting, we will apply these methods to real observations. This project provides a way of evaluating the performance of machine learning methods as they are used in Earth science.

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A highly collaborative project is available in Dr. Alison Taylor’s and Dr. Fatemeh Momen-Heravi’s lab. This project aims to identify molecular changes such as mutations and RNA signature of head and neck cancer in Black/African American and Hispanic minority populations with the goal of identifying novel therapies for cancer patients and reduce health disparities. The project entails analysis of DNA and RNA sequencing data. Basic coding skills are necessary and the student will be mentored by both principal investigators. The prospective candidate should be motivated, a fast learner, and be able to work in a highly collaborative team environment.

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Atherosclerosis, a chronic inflammatory disease of the artery wall, is the underlying cause of human coronary heart diseases. Single-cell genomics have catalyzed the revolution in understanding of cellular heterogeneity and dynamics in atherosclerotic vasculature. The goal of the project is to leverage published and our own single-cell genomic data and perform a meta-analysis. Meta-analysis allows integrated analysis of much larger cell numbers and helps resolve the full spectrum of cellular heterogeneity and dynamics in atherosclerotic vessels and facilitate therapeutic translation. The DSI scholar will: (1) use the latest bioinformatic pipeline to integrate the existing scRNA-seq, CITE-seq, and scATAC-seq datasets; (2) analyze the integrated datasets using R/Bioconductor packages (e.g. Seurat); (3) interpret the data using pathway and network analysis. Some relevant workflows are available through the “Resources” page of our lab website at https://hanruizhang.github.io/zhanglab/.

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We will further develop a large scale dataset that evaluates gender biases in sentence-level NLP systems. We will then develop training techniques to encourage models to overcome and mitigate gender-based biases.

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5G cellular networks will use high-frequency millimeter-wave (mmWave) communication, which promises high data rates and ample spectrum availability. Students working on this project will help conduct a mmWave wireless channel measurement campaign around the COSMOS testbed (www.cosmos-lab.org), a wireless networking testbed located at Columbia stretching between 120th and 136th St. In collaboration with Bell Labs students will be able to use unique, state-of-the-art mmWave equipment to conduct these measurements (see pre-pandemic example in https://wimnet.ee.columbia.edu/wp-content/uploads/2019/08/mmNets2019_COSMOS_28GHz.pdf). The measurements will play an important role in the development of network-level control algorithms, which is the other, more analytical side of this research project.

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