CV

Education

Ph.D. in Medical Engineering and Medical Physics, Massachusetts Institute of Technology (Harvard–MIT Program in Health Sciences and Technology), 2014–2019
Graduate Certificate in Healthcare Management, MIT Sloan School of Management, 2018
M.Phil. in Bioscience Enterprise, University of Cambridge, 2013-2014 (intermission)
M.Eng. in Biomedical Engineering, Imperial College London, 2013 (First Class Honours, Medal for Excellence of the Faculty of Engineering)

Work Experience

2023–Present: Senior Applied Scientist, Amazon
- Working on large language models, computer-use models, and agentic AI systems within Amazon Health AI and One Medical.
- Leading research and prototyping for conversational and multimodal AI systems.
- Collaborating cross-functionally with scientists and engineers to deploy LLM-based healthcare applications.
2023: Senior Machine Learning Scientist, Tempus
- Developed deep learning and computer vision models for digital pathology and precision oncology.
- Led model evaluation and integration into production pipelines.
- Collaborated with clinical partners to validate AI-driven diagnostic workflows.
2019–2023: Software Engineer, Google Health
- Worked on natural language processing for electronic health records (EHRs).
- Built scalable data pipelines and machine learning models for medical information extraction.
- Collaborated with clinical and research teams on healthcare AI initiatives.
2014–2019: Graduate Research Assistant, MIT
- Graduate student in the Harvard–MIT Program in Health Sciences and Technology.
- Conducted research in medical AI, signal processing, and computer vision.
- Supervised undergraduate students and collaborated across engineering and clinical labs.
- Published peer-reviewed papers on machine learning, computational medical imaging and biomedical signal analysis.

Awards and Honors

Need to add.

Skills

Artificial Intelligence and Machine Learning
- Large Language Models (LLMs)
- Agentic and Multi-Agent AI Systems
- Model Evaluation, Safety, and Trustworthiness
Natural Language Processing
- Clinical Text Mining and EHR Data Extraction
- Prompt Engineering and Instruction Tuning
- Conversational AI and Dialogue Systems
Computer Vision
- Digital Pathology and Medical Imaging
- Deep Learning for Image Classification and Segmentation
- Multimodal Model Integration
Software Engineering and Research Infrastructure
- Python, PyTorch, TensorFlow, AWS, and Git
- Data Pipelines and Model Deployment
- Experiment Tracking and Evaluation Frameworks
Leadership and Communication
- Cross-Functional Collaboration between Science and Engineering Teams
- Technical Writing, Publications, and Presentations
- Mentorship and Team Guidance

Publications

Detecting sensitive medical responses in general purpose large language models

Lopez-Martinez, Daniel; Bafna, Abhishek (2024). "Detecting sensitive medical responses in general purpose large language models." Machine Learning for Health Symposium 2024.

Trustworthiness in medical product question answering by large language models

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.

Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models

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.

Instability in clinical risk stratiﬁcation models using deep learning

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.