Decoding behavioral signifiers for the brain state of vigilance can have far reaching implications for understanding the neural basis for 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 mice during exploration and 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. We are also obtaining neural signal data, which can be aligned with the behavioral signifiers. 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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Neurodevelopmental disorders (NDDs) comprise of a group of disorders associated with abnormal brain development. Rare genetic variants have been shown to play a key role in their development, especially in those NDDs which are severe in nature. During the last decade, genetic testing has emerged as an important etiological diagnostic test for NDDs with a considerable impact on disease management and treatment. Yet, current genetic testing has a diagnostic rate of ~ 50%. Due to technical limitations in modern next-generation sequencing techniques, these techniques fail to asses a large part of the genome (2/3rd), missing critical regions which may have clinical significance. New methods now have emerged that can assess these regions better, can access repetitive regions and identify complex structural genomic events with more accuracy. This project will employ and integrate novel genomic technologies, including optical genome mapping and long read sequencing, to perform a comprehensive investigation of the human genome in parent-child trios which remained genetically unsolved after standard genomic approaches.

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Background: The central dogma of biology stipulates that DNA is transcribed into RNA, which are translated into proteins, which carry out functions around the cell. However, as time passes, we are discovering more and more exceptions to this dogma. One of which are small non-coding RNAs (sRNA): these short fragments of RNA don’t get translated into proteins; instead, they fold into small structures and carry out many key and catalytic functions in the bacterial cell. sRNAs are uniquely versatile, as they are capable of interacting with both protein and nucleic acid targets, are responsible for bacterial responses to environmental stimuli, and can serve as virulence mechanisms.

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Students will design and contribute new features to the AI Model Share MLOps Platform. Projects will allow students first hand experience working with and developing MLOps tools including model deployment, continuous model improvement, and ML analytics. Individualized projects will allow students to 1) integrate advanced deep learning models into our system, 2) work on ML model replication tools, 3) integrate new ML dashboards into our toolkit, and more.

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Research in Lyme Disease shows that it is very hard to identify clinically meaningful improvement for chronic patients whose symptoms tend to wax and wane. Our team developed a diagnostic tool - General Symptom Questionnaire (Fallon et all., 2019, PMID: 31867334) and gathered a lot of data on patients who attended our center for research and/or treatment. The purpose of the proposed study is to analyze the existing data to find clinically meaningful cut-offs on the scale that can inform clinicians on whether the patient improved or not. If you are interested in psychometrics and want to contribute to understanding of how the chronic disease evolves, this is the project for you.

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The aim of this project is to use artificial intelligence (AI) to extract valuable information from unstructured eye movements of highly-skilled domain experts, in particular those of expert clinicians as they perform complex diagnostic decision-making tasks. Such eye-movement data is rich in patterns that can be deciphered using the power of unsupervised machine learning algorithms (such as k-nearest neighbor/hierarchical clustering and principal components analysis) or unsupervised deep learning algorithms (such as deep generative models, autoencoders, and long short-term memory autoencoders for sequence data). Furthermore, as novices transform into experts, patterns embedded in their eye movements (time spent on regions of interest vs. time spent on surgical equipment) may offer a valuable tool for extracting features that pinpoint the critical mechanisms (’eureka moments’) behind expert decision-making. The primary objectives of this project are (1) to collect eye movements of novice and expert ophthalmologists as they view medical images during eye-disease diagnoses using benchtop-based, head-mounted, or Virtual Reality embedded eye trackers (Eyelink 1000, Pupil Labs Core, or HTC Vive Pro, respectively) and (2) to apply unsupervised machine learning/deep learning approaches to extract meaningful information from this data. Features to be extracted from this data include but are not limited to: fixation duration and fixation count in regions of interest, fixation order, saccade velocity, and pupil diameter. This data collection and data analytics project will enable extraction of the most relevant features for task-oriented training of future AI-based disease diagnosis systems. Capturing eye movements, and thereby the underlying visual decision-making mechanisms behind an expert’s knowledge that are not otherwise quantifiable, will allow us to mimic these mechanisms in AI systems, potentially improving their diagnostic accuracy and interpretability for future clinical applications.

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Columbia Data Science Institute (DSI) Scholars Program

The DSI Scholars Program is to engage and support undergraduate and master students in participating data science related research with Columbia faculty. The program’s unique enrichment activities will foster a learning and collaborative community in data science at Columbia.

Columbia University DSI

New York, NY