The CONCERN project aims to develop models and tools to quantify clinician concern about patient deterioration in the inpatient setting that can be used in early warning scores. We have discovered and validated several measurable ways within the Electronic Health Record (EHR) to measure clinician concern and have demonstrated that our approach identified patients at risk of deterioration earlier than other methods, which focus only on physiological data. One of our approaches is leveraging documentation of certain concepts within narrative text in nursing notes that are consistent with concern about a patient. However, this narrative free text is not easily accessible - it is often mixed together with structured or templated text and varies over note types. The steps to be performed are

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Advances in genomic technologies have led to the identification of many novel disease-gene associations, allowing medical diagnoses to be more precise and tailored to an individual. However, the high number of variants present in each individual represents a significant challenge for the implementation of genomic medicine. The goal of this project is to enable the identification of novel genes associated with recessive disorders.

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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.

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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.

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Air quality is a major crisis globally, leading to about 5 million premature deaths every year. In sub-Saharan Africa, there is little air pollution data available to characterize the problem, and a lack of focus on solutions. Using output from a high spatiotemporal resolution atmospheric chemistry transport model over Africa simulated by Dr. Westervelt and his group, the student will characterize levels of pollution and validate model results by comparing observed data to model output. The student will also analyze results from sensitivity simulations in which sources of air pollution have been artificially “turned off” in the model. Comparison between the two simulations will allow for source attribution of air pollution, which is important for developing satisfactory mitigation strategies to improve air quality.

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The goal of this project is to collect anonymized traces from the Columbia network in order to analyze video traffic characteristics during the work/study-from home period. This information will be used for developing various ML-based tools for Quality of Experience (QoE) measurement. We will perform the feature extraction at the collection time itself and use anonymization techniques (e.g., IP address anonymization), to preserve user privacy. Students will analyze/measure encrypted network traffic to provide ground truth for potential RL/ML algorithms for estimating video QoE and identifying device/application (e.g., the start of a video streaming session). These algorithms can serve as a basis for new video adaptation techniques (see for example - https://wimnet.ee.columbia.edu/wimnet-team-wins-3rd-place-in-the-acm-mmsys20-twitch-grand-challenge/)

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Health care professionals cannot examine every person calling the office with a question nor can they return every call. Therefore, medical offices seeking to improve the speed and efficiency of evaluating and triaging patients must utilize telephone personnel who are often non-clinical staff. These telephone triage personnel may be limited in their knowledge and ability to obtain the necessary details of the patient’s medical symptoms and direct medical care accordingly. Their role is not to make diagnoses by phone, but rather to collect sufficient data related to the patient’s complaints and assign them appropriately in order to get the patient to the right level of care with the right provider in the right place at the right time.

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