I have completed my Ph.D. in Computer Science at the
University of Illinois Chicago,
where I worked with my advisor, Professor
Cornelia Caragea.
My research areas generally lie in Machine Learning, Data Science, and natural language processing.
More specifically, I am focused on NLP for low-resource domains, few-shot learning, prompt-based fine-tuning,
semi-supervised learning, and data augmentation. I have also done several projects in security & privacy,
recommender systems, affective computing, and big data analysis.
I received my undergraduate degree in Computer Engineering from
Shahid Beheshti University, Tehran, Iran.
Publications
Semi-Supervised Domain Adaptation for Emotion-Related Tasks
Mahshid Hosseini,
Cornelia Caragea,
in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL)-Findings, 2023
Feature Normalization and Cartography-based Demonstrations for Prompt-based Fine-tuning on Emotion-related Task
Mahshid Hosseini,
Cornelia Caragea,
in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023
Calibrating Student Models for Emotion-related Tasks
Mahshid Hosseini,
Cornelia Caragea,
in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022
Distilling Knowledge for Empathy Detection
Mahshid Hosseini,
Cornelia Caragea,
in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
It Takes Two to Empathize: One to Seek and One to Provide
Mahshid Hosseini,
Cornelia Caragea,
in Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021
CREPE: A Privacy-Enhanced Crash Reporting System
Kiavash Satvat, Maliheh Shirvanian,
Mahshid Hosseini,
Nitesh Saxena, in Proceedings of the Tenth ACM Conference on Data and Application Security and Privacy, 2020
Brain Hemorrhage: When Brainwaves Leak Sensitive Medical Conditions and Personal Information
Ajaya Neupane, Kiavash Satvat,
Mahshid Hosseini,
Nitesh Saxena, in proceedings of the International Conference on Privacy, Security and Trust (PST), 2019
PurgeMEM: Towards Building A Memory Safe Cloud
Yasser Karim, Kiavash Satvat,
Mahshid Hosseini,
Ragib Hasan, in proceedings of IEEE SoutheastCon, 2019
Camouflaged with Size: A Case Study of Espionage using Acquirable Single-Board Computers
Kiavash Satvat,
Mahshid Hosseini,
Maliheh Shirvanian, in proceedings of the International Conference on Networks & Communications, 2018
A robust SIFT-based descriptor for video classification
Raziyeh Salarifard,
Mahshid Hosseini,
Mahmood Karimian, Shohreh Kasaei, in proceedings of the International Conference on Machine Vision (ICMV), 2014
Work Experience
Bloomberg, New York, NY, USA
AI Research Intern - CTO Office
Utilizing large language models (LLMs) to assist with annotations
Relativity-TextIQ, Chicago, IL, USA
Research and Development Intern
Unconscious Bias Detection
Spotify, New York, NY, USA
Machine Learning Intern
Ad Spend Optimization
Sony Interactive Entertainment, San Diego, CA, USA
Data Science Intern
Whale Modeling
Discover Financial Services, Riverwoods, IL, USA
Data Science Intern
Customer Behavior Credit Score Modeling
University of Illinois Chicago, Chicago, IL, USA
Teaching and Research Assistant
Natural Language Processing-Computational Social Science
Teaching Experience
Teaching Assistant for Introduction to Data Science , UIC, Fall 2019, Spring 2021 and 2022.
Teaching Assistant for Program Design , UIC, Fall 2022.
Teaching Assistant for Data Structures , UIC, Fall 2021.
Teaching Assistant for Mathematical Foundations of Computing , UIC, Spring 2020.
Teaching Assistant for Machine Learning , UAB, Fall 2018.
Teaching Assistant for Software Design and Integration , UAB, Spring 2017.