DNA sequence reads from a community of microbial genomes are currently processed without considering sequence variants. The project involves building a processing pipeline of such billions of short reads, identifying closest strains they might belong to, assembling them into specific clones, calling their variants, and analyzing the dynamic nature of these bacterial strains along sampling points.
Recently Columbia University, Cornell, and NewYork-Presbyterian have agreed to integrate their clinical (healthcare) and business IT systems onto one shared platform called Epic. The motivating factors to move to Epic are to enhance the patient experience, improve and integrate care, and give our physicians an integrated technology platform that supports the mission of an academic medical center. The intern will assist with developing the “operational” analytics capabilities of Columbia University Medical Center including financial, healthcare operations and healthcare quality analytics.
Microelectrode array recordings from patients undergoing surgical evaluation have captured typical clinical seizures. Because of the extreme pathological conditions at these times, identifying single units from extracellular data is a particular challenge. Our group has developed techniques for tracking neurons through the ictal transition. We are applying them to newly acquired data and addressing fundamental questions about the activity of different cell classes at seizure initiation.
The quality of biomedical evidence can affect research sustainability, patient safety, and the public’s trust in biomedical research. However, often the quality of biomedical evidence remains opaque to the public. It is imperative to improve the transparency of evidence quality. This project aims to leverage the public data sources, including but not limited to The ClinicalTrials.gov, The PubMed database for biomedical literature, The National Health and Nutrition Examination Survey (NHANES) database, and so on, to develop and apply novel data mining and visualization methods for appraising the biomedical research evidence, uncovering implicit biases in clinical research designs at different levels, and presenting this information intuitively to the public. Students on this project will acquire or hone their skills in data mining, results presentation, and user interface designs and evaluation.
We are collecting and analyzing survey data asking people about the political attitudes and other characteristics of their family, friends, and others in their social circles. Some of this work is described here and we are also doing polling relevant to the 2018 midterm elections.
Robotic grasp planning based on raw sensory data is difficult due to occlusion and incomplete scene geometry. Often one sensory modality does not provide enough context to enable reliable planning. A single depth sensor image cannot provide information about occluded regions of an object, and tactile information is incredibly sparse spatially. We are building a Deep Learning CNN that combines both 3D vision and tactile information to perform shape completion of an object seen from a single view only, and plan stable grasps on these completed models.
The ubiquity of current smart and IoT devices has the potential to transform healthcare. For example, current devices can measure continuously activity levels, heart rate, blood oxygen levels, and electrocardiogram. Our lab is developing new devices which can measure additional streams of health data which are currently not possible. The summer project will involve visualization of this entire set of data, machine learning, and multiparametric data analysis to extract trends that match health outcomes.
