Published in Machine Learning for Health Symposium, 2024
Recommended citation: Lopez-Martinez, Daniel; Bafna, Abhishek (2024). "Detecting sensitive medical responses in general purpose large language models." Machine Learning for Health Symposium 2024.
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Published in KDD Workshop on Evaluation and Trustworthiness of Generative AI Models, 2024
Recommended citation: Lopez-Martinez, Daniel (2024). "Trustworthiness in medical product question answering by large language models." KDD 2024 Workshop on Evaluation and Trustworthiness of Generative AI Models.
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Published in CVPR Responsible Generative AI Workshop, 2024
Recommended citation: Lopez-Martinez, Daniel. (2024). "Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models." CVPR 2024 Responsible Generative AI Workshop.
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Published in Machine Learning for Health (ML4H), 2022
Recommended citation: Lopez-Martinez, Daniel; Yakubovich, Alex; Seneviratne, Martin; Lelkes, Adam; Tyagi, Akshit; Kemp, Jonas; Steinberg, Ethan; N. Downing, Lance; C. Li, Ron; E. Morse, Keith; H. Shah, Nigam; Chen, Ming-Jun, (2022). "Instability in clinical risk stratification models using deep learning." Machine Learning for Health (ML4H) 2022.
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Published in United States Patent Office, 2025
Describes systems and methods to generate synthetic prompts for red-teaming large language models (LLMs). A first ML model (e.g., a Q-learning model) learns prompt modifications that increase the probability of eliciting responses that violate constraints, while additional models score prompts and responses and generate rationales to guide subsequent prompt generation and improve evaluation coverage.
Recommended citation: Lopez Martinez, Daniel. Systems for Generation of Prompts for Evaluation of Language Models. U.S. Patent Application Publication No. US 2025/0378274 A1, published Dec. 11, 2025 (filed Jun. 7, 2024). Applicant: Amazon Technologies, Inc.
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Published in United States Patent Office (pending), 2025
Describes systems and methods for automated agents to interact with user interfaces on behalf of a user. The system uses multiple ML–based agents (including LLM-based planners) to perceive the state of a webpage, determine and execute actions (e.g., navigation, variant selection, cart operations, checkout), and verify that each action aligns with user intent, constraints, and security guardrails (such as controlled handling of payment information). The approach enables reliable end-to-end task execution across heterogeneous third-party interfaces and supports agentic purchasing workflows such as Buy For Me.
Recommended citation: Lopez Martinez, Daniel; et al. Systems for Automated Interaction with User Interfaces. U.S. Patent Application (pending), filed 2025. Applicant: Amazon Technologies, Inc.