This project investigates the long-term effects of perinatal serotonin modulation on unlearned fear and anxiety like behaviors. The project will use DeepLabCut (a method for 3D pose estimation based on transfer learning with deep neural networks) to quantify fear and anxiety-related behaviors in animals, in which serotonin has been chemogenetically manipulated early in life.
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.
Using NLP techniques to discover 1) new e-cig products online and 2) proposed health claims that users advocate for.
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.
