68 citations · 79 across the 9 of their papers we have counts for
11 papers
Towards a Science of Scaling Agent Systems
Yubin Kim, Ken Gu, Chanwoo Park +17
Agents, language model-based systems capable of reasoning, planning, and acting are widely adopted in real-world tasks, yet how their performance changes as these systems scale acr…
The Anatomy of a Personal Health Agent
A. Ali Heydari, Ken Gu, Vidya Srinivas +35
Health is a fundamental pillar of human wellness, and the rapid advancements in large language models (LLMs) have driven the development of a new generation of health agents. Howev…
SensorLM: Learning the Language of Wearable Sensors
Yuwei Zhang, Kumar Ayush, Siyuan Qiao +17
We present SensorLM, a family of sensor-language foundation models that enable wearable sensor data understanding with natural language. Despite its pervasive nature, aligning and…
LSM-2: Learning from Incomplete Wearable Sensor Data
Maxwell A. Xu, Girish Narayanswamy, Kumar Ayush +22
Foundation models, a cornerstone of recent advancements in machine learning, have predominantly thrived on complete and well-structured data. Wearable sensor data frequently suffer…
RADAR: Benchmarking Language Models on Imperfect Tabular Data
Ken Gu, Zhihan Zhang, Kate Lin +18
Language models (LMs) are increasingly being deployed to perform autonomous data analyses. However, their data awareness -- the ability to recognize, reason over, and appropriately…
Insulin Resistance Prediction From Wearables and Routine Blood Biomarkers
Ahmed A. Metwally, A. Ali Heydari, Daniel McDuff +9
Insulin resistance, a precursor to type 2 diabetes, is characterized by impaired insulin action in tissues. Current methods for measuring insulin resistance, while effective, are e…
A Scalable Framework for Evaluating Health Language Models
Neil Mallinar, A. Ali Heydari, Xin Liu +10
Large language models (LLMs) have emerged as powerful tools for analyzing complex datasets. Recent studies demonstrate their potential to generate useful, personalized responses wh…
Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel Representation Learning
A. Ali Heydari, Naghmeh Rezaei, Javier L. Prieto +2
Blood biomarkers are an essential tool for healthcare providers to diagnose, monitor, and treat a wide range of medical conditions. Current reference values and recommended ranges…
No Pairs Left Behind: Improving Metric Learning with Regularized Triplet Objective
A. Ali Heydari, Naghmeh Rezaei, Daniel J. McDuff +1
We propose a novel formulation of the triplet objective function that improves metric learning without additional sample mining or overhead costs. Our approach aims to explicitly r…
A Recommendation System to Enhance Midwives' Capacities in Low-Income Countries
Anna Guitart, Afsaneh Heydari, Eniola Olaleye +4
Maternal and child mortality is a public health problem that disproportionately affects low- and middle-income countries. Every day, 800 women and 6,700 newborns die from complicat…
SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions
A. Ali Heydari, Craig A. Thompson, Asif Mehmood
Adaptive loss function formulation is an active area of research and has gained a great deal of popularity in recent years, following the success of deep learning. However, existin…