Daniel Lopez-Martinez

Daniel Lopez-Martinez

Senior Applied Scientist, Amazon
ex-Google, ex-Tempus
PhD from Harvard/MIT

Sitemap

A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

portfolio

Buy For Me — Agentic Shopping System

A browser-based agentic system that helps customers complete purchases across third-party websites using multimodal perception, reasoning, and tool-enabled actions.

Injection Study

We investigated to use electrodermal activity (EDA), heart rate variability (HRV), and facial expression analysis as potential endpoints to determine quantitative pain scores.

SNAPSHOT

The SNAPSHOT study seeks to measure Sleep, Networks, Affect, Performance, Stress, and Health using Objective Techniques.

publications

Instability in clinical risk stratification models using deep learning

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.
Download Paper

talks

Detection limits of automated MRI morphometry for phenotyping in the rodent brains for applications in neurological disorders

Published:

I presented a poster at the Amgen Scholars European Symposium 2010 at the University of Cambridge, describing research conducted during my summer internship at the Wolfson Brain Imaging Centre under the supervision of Adrian Carpenter and Steve Sawiak. The symposium brought together undergraduate researchers from institutions across Europe to share summer projects and participate in a series of academic talks and poster sessions.

Signal Quality Indices and Data Fusion for Determining Acceptability of Electrocardiograms

Published:

Gari Clifford (University of Oxford) and I gave an oral presentation at the Computing in Cardiology (CinC) Conference 2011 in Hangzhou, China, presenting our joint work conducted at the Oxford Institute of Biomedical Engineering (IBME). The talk covered our algorithm for assessing the diagnostic acceptability of electrocardiograms (ECGs) collected in noisy or low-resource ambulatory environments.

Patient-Centered Symptom and Vital Sign Tracking for Lyme Disease Care

Published:

I presented our team’s work at Lyme Innovation, the first-ever Lyme-disease–focused hackathon, held at the Microsoft NERD Center in Cambridge and organized by Spaulding Rehabilitation’s Dean Center for Tick-Borne Illness, the Veterans Affairs Center for Innovation, MIT Hacking Medicine, UC Berkeley, and Harvard Medical School. The three-day event brought together clinicians, scientists, engineers, entrepreneurs, and patients to develop new solutions for Lyme disease. Our project was selected as one of the finalists and received a $5,000 award.

LymeDot: Using Open Data and Mobile AI for Symptom Tracking in Lyme Disease

Published:

I presented work at the White House Open Data Innovation Summit in Washington, D.C., where our team was invited to present our project developed during a Boston-based health hackathon. Our project, LymeDot, explored how mobile technology and open data could help patients with Lyme disease track symptoms over time, support clinical decision-making, and empower individuals managing complex, chronic conditions.

ZenAuto: Emotionally Intelligent Transport

Published:

I presented our startup concept, ZenAuto, at the Lee Kuan Yew Global Business Plan Competition (LKYGBPC) in Singapore, one of Asia’s leading deep-tech entrepreneurship challenges. The competition brings together next-generation founders from around the world to showcase innovations with the potential to reshape cities, industries, and society. Our work was selected for presentation on the competition stage alongside teams from top global universities.

Detecting Real World Driving Induced Affective State Using Physiological Signals

Published:

I delivered an oral presentation at the International Conference on Affective Computing and Intelligent Interaction (ACII 2019) in Cambridge, UK, during the International Workshop on Social and Emotion AI for Industry (SEAIxI). The presentation summarized our work on detecting real world, driving induced affective states using physiological signals, based on our paper presented at the conference.

Panel Discussion: Careers in Academia and Industry

Published:

I was invited to participate as a panelist in the Careers in Academia and Industry session at MICCAI 2022. This flagship event brought together researchers, innovators, and industry leaders to discuss professional pathways, career development, and the evolving relationship between academic research and real world applications.

Instability in Clinical Risk Stratification Models Using Deep Learning

Published:

I presented a poster at the Machine Learning for Health (ML4H) Symposium 2022 in New Orleans, based on research conducted at Google Health. The work investigates how randomness in training deep learning models, despite identical data, architecture, and hyperparameters, can lead to meaningfully different patient-level predictions in clinical risk stratification tasks.

Trustworthiness in Medical Product Question Answering by Large Language Models

Published:

I presented a poster at the KDD 2024 Workshop on GenAI Evaluation in Barcelona, corresponding to the paper “Trustworthiness in medical product question answering by large language models”. The work introduces a claim-level evaluation framework to assess whether large language models provide medically accurate and label-consistent answers when responding to questions about prescription drugs and medical products.

Pioneering Agentic Systems: From Shopping to Health

Published:

I delivered an invited talk for the Amazon North America Stores GenAI Learning Series, presenting a deep dive into the design, architecture, evaluation, and deployment of large-scale agentic systems across Amazon. The talk bridged my work across Shopping Conversations Foundations and Amazon Health AI / One Medical, highlighting the development of agentic LLM systems from consumer shopping experiences (specifically BuyForMe) to clinical and healthcare workflows.

teaching