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Natural language processing and its application in transit
11: Urban Studies and Planning
Peyman Noursalehi, email@example.com
Despite rapid advances in automated text processing, many related tasks in transit and other transportation agencies are still performed manually. For example, incident management reports are of- ten manually processed and subsequently stored in a standardized format for later use. In this project, we aim to develop a comprehensive, pragmatic automated framework for analyzing rail incident reports to support a wide range of applications and functions, depending on the constraints of the available data. The objectives are twofold: a) extract information that is required in the standard report forms (automation), and b) extract other useful content and insights from the unstructured text in the original report that would have otherwise been lost/ignored (knowledge discovery). We are looking for a candidate who is interested in applications at the intersection of machine learning methods and transportation. The ideal candidate should have taken the introduction to machine learning class (6.036), knows some NLP techniques (e.g., knows about tokenization, named entity recognition) and is proficient in basic data analysis skills using Python. The task will involve data annotation, and then running and extending the state-of-the art methods deep learning methods on the annotated data. If interested, please send your resume and a short description of why you would be a strong candidate to Peyman Noursalehi at firstname.lastname@example.org, and Jinhua Zhao at email@example.com.
Python (Pandas, numpy, scikit-learn), some familiarity with PyTorch/TensorFlow, and basic NLP techniques