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.
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.
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.
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.
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/)
We have a position open for a student(s) who is/are interested in working on systems biology projects in bladder and prostate cancer. Specifically, we are looking for students who are well versed in statistical analysis, basic understanding of standard statistical techniques (appied to biology is a plus) and knowledge of R is required. The position will entail supporting post-doctoral members of the lab with computational analyses of different types of biological data in a wide range of projects.
The goal of this project is to study the molecular background of various congenital disorders affecting the cranial nerves, which are important in senses (hearing, vision, smell), facial muscle movements and more. Abnormal cranial nerve development can cause hearing loss, eye-movement disorders, facial weakness, loss of smell, and difficulties with respiration and swallowing. Some individuals may also have other motor, sensory, intellectual, behavioral and social disabilities. These disorders cause significant disability and are caused by genetic variants, often novel variation or de novo. Unfortunately, disorders affecting the 8th cranial nerve or vestibulocochlear nerve (CN VIII), important in hearing and balance, have been largely understudied. As various cranial nerves can be affected together, such as in Moebius syndrome, and as the vestibulocochlear nerve (CN VIII) and facial nerve (CN VII) also share a path in the internal auditory canal, it is likely that these disorders share underlying genes or closely interacting genes. To investigate the genetic architecture of cranial nerve abnormalities we suggest to molecularly investigate an in-house CN VIII cohort and other cranial dysinnervation cohorts. We will study rare genomic variants (both small variant as structural variants) to identify shared molecular pathways and genes amongst individuals with cranial dysinnervation disorders.
