Structural variants (SVs) are large genomic alterations which can be implicated in disease. This project will focus on using novel genomic techniques to identify structural variants in genomic cold cases with neurological disorders. These “cold” cases which have previously remained unsolved with standard genomic approaches. We will use optical genome mapping and long read sequencing, together with novel bioinformatic techniques to detect and analyze structural variants.
LEAP will offer research experiences for undergraduate students as summer research internships as part of LEAP’s research through synergistic innovations in data science and climate science. This includes, but is not limited to, physics-informed and causally-informed machine learning, novel ML-based subgrid parameterizations for Earth System Models, global parameter inference, and new ML- based diagnostics and metrics for evaluating these models, with a focus on the Community Earth System Model (CESM). The center is committed to building a diverse research community at the intersection of geosciences and data sciences with the objective to build a LEAP community on par with the US population in terms of gender and race diversity.
Asset management companies have been among the largest investors in the financial market. With $89 trillion in assets under management, this industry has been experiencing rapid changes. For example, many firms started enlarging the technological capabilities to improve decision-making, data management, and client experience.
Road traffic crashes involving child passengers, child pedestrians, and child bicyclists are the leading cause of death for people aged 5 to 15 years in the USA. A total of 10,344 children died on US roads in the decade from 2010-2019; a further 4.2 million were hospitalized. Urban design—meaning the overall physical form of cities—is a modifiable environmental feature that can be changed to reduce the immense burden due to child road traffic injuries. Altering the overall configuration of a city’s transportation network affects the way children and other road users routinely travel through urban space, thereby altering children’s risks for being injured or killed in a road traffic crash.
Future wireless networks will use high-frequency millimeter-wave (mmWave) links for transmitting and receiving information with high throughput. A key difference between mmWave links and conventional sub-6GHz links is that mmWave links are severely affected by weather conditions. Students working on this project will use a state-of-the-art mmWave radar to assess the impact of wind speed, temperature, humidity, and other factors on the high-frequency link. The end goal of the project is to develop a classifier that can infer weather conditions based on the signal received from the mmWave radar. In this project, students are expected to learn how the mmWave radar works, design experiments to obtain labeled data, perform measurements, and develop the classifier.
