Scholar
Joseph Gatto
Google Scholar ID: QOCT60gAAAAJ
Dartmouth College
Citations & Impact
Citations
104
H-index
7
i10-index
3
Publications
19
Co-authors
7
list available
Contact
Publications
Resume (English only)
Academic Achievements
- - Publications:
- - "Follow-up Question Generation For Enhanced Patient-Provider Conversations" (ACL 2025)
- - "Document-Level Event-Argument Data Augmentation for Challenging Role Types" (ACL 2025)
- - "Chain-of-Thought Embeddings for Stance Detection on Social Media" (EMNLP Findings 2023)
- - "Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments" (EMNLP 2024)
- - "Text Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual Similarity" (GEM 2023)
- - "Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning–Based Solution" (Journal of Medical Informatics 2022)
- - "In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages" (ML4H Findings 2024)
- - "Scope of Pre-trained Language Models for Detecting Conflicting Health Information" (AAAI ICWSM 2023)
- - "HealthE: Recognizing Health Advice & Entities in Online Health Communities" (AAAI ICWSM 2023)
Research Experience
- - Work Experience:
- - Research Intern at NASA's Jet Propulsion Laboratory from 2018 to 2020, working on a variety of computer vision research projects ranging from Mars Rover technology to subsurface imaging.
- - Research Projects:
- - PortalPal-AI (on Huggingface), focusing on portal messaging.
Education
- - Degree: Bachelor's degree
- - University: Columbia University
- - Year: 2020
- - Major: Computer Science
- - Advisor: Not specifically mentioned
Background
- - Research Interests: LLMs for Clinical NLP, Information Seeking Agents, Synthetic Data Generation, Information Extraction, LLM Evaluation
- - Field: Computer Science
- - Brief Introduction: Currently a senior Ph.D. candidate at Dartmouth College's Department of Computer Science, particularly interested in developing better NLP tools for clinical tasks.
