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.
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.
Using NLP techniques to discover 1) new e-cig products online and 2) proposed health claims that users advocate for.
This study is the first step in exploring an emerging and previously understudied data stream - verbal communication between healthcare providers and patients. In partnership between Columbia Engineering, School of Nursing, Amazon, and the largest home healthcare agency in the US, the study will investigate how to use audio-recorded routine communications between patients and nurses to help identify patients at risk of hospitalization or emergency department visits. The study will combine speech recognition, machine learning and natural language processing to achieve its goals.
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.
Call for Faculty Participation. December 2020.
The Data Science Institute is calling for faculty submissions of research projects for the pring and/or summer 2021 sessions of the Data Science Institute (DSI) Scholars Program. The goal of the DSI Scholars Program is to engage undergraduate and master students to work with Columbia faculty, through the creation of data science research internships. Last year, we worked with 42 projects and received more than 730 unique applications from Columbia Students. The program’s unique enrichment activities foster a learning and collaborative community in data science at Columbia. Apply here.