Sarah Alnegheimish

I am a Ph.D. student at MIT pursing degree in Electrical Engineering and Computer Science (EECS), advised by Dr. Kalyan Veeramachaneni, and a member of the Data to AI Lab at LIDS. My research interest is in the field of ML & NLP. I work on applying deep learning to solve various challenges, particularly within time series data. I am also interested in open-source development and in building systems that make machine learning usable.

Projects

ML Frameworks for Time Series
Building Sintel ecosystem for time series tasks. Sintel abstracts time series functionalities into primitives and pipelines for classification, forecasting, and anomaly detection tasks. [paper] [code]
Unsupervised Time Series Anomaly Detection
Developing Orion, a machine learning library for unsupervised time series anomaly detection. Given time series data, users select from an array of pipelines, including TadGAN and AER, to identify rare patterns. [paper] [code] [blog] [press]
Biases in Large Language Models
Analyzing and evaluating gender-profession bias in language models by curating a dataset of real-world natural prompts. Comparing template– and natural-based prompts that elicit biased output. [paper] [code]
Predictive Framework for Healthcare Data
Developing Cardea, an ML framework built on top of schemas that support electronic health records (EHR), such as FHIR. It provides an easy-to-use library to develop ML models for numerous problems by applying series of sequential processes. [paper] [code] [press]

Publications

Using Natural Sentence Prompts for Understanding Biases in Language Models
Sarah Alnegheimish, Alicia Guo, Yi Sun 2022 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL).
Sintel: A Machine Learning Framework to Extract Insights from Signals
Sarah Alnegheimish, Dongyu Liu, Carles Sala, Laure Berti-Equille, Kalyan Veeramachaneni 2022 ACM SIGMOD International Conference on Management of Data.
MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series
Dongyu Liu, Sarah Alnegheimish, Alexandra Zytek, Kalyan Veeramachaneni 2022 ACM Conference on Computer-Supported Cooperative Work annd Social Computing (CSCW).
Probabilistic Programming Bots in Intuitive Physics Game Play
Fahad Alhasoun, Sarah Alnegheimish 2021 AAAI Conference on Artificial Intelligence.
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
Alex Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan Veeramachaneni 2020 IEEE International Conference on Big Data.
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
Sarah Alnegheimish, Najat Alrashed, Faisal Aleissa, Shahad Althobaiti, Dongyu Liu, Mansour Alsaleh, Kalyan Veeramachaneni 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA).