Akshay Paruchuri
I'm a student researcher at Google and a CS PhD student at UNC Chapel Hill, advised by Professor Henry Fuchs. My research interests are at the intersection of computer vision, computer graphics, and machine learning. I work on research that typically involves applications in healthcare and/or augmented reality. Currently, I'm working toward a future where wearable, spatial computing devices, such as augmented reality eyeglasses capable of all-day use, are contextually aware and personalized to the benefit of users and their goals (e.g., human memory enhancement, becoming healthier).
Please visit my website for the latest information about me: https://akshayparuchuri.com/
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The Anatomy of a Personal Health Agent
Ahmed Metwally
Ken Gu
Jiening Zhan
Kumar Ayush
Hong Yu
Amy Lee
Qian He
Zhihan Zhang
Isaac Galatzer-Levy
Xavi Prieto
Andrew Barakat
Ben Graef
Yuzhe Yang
Daniel McDuff
Brent Winslow
Shwetak Patel
Girish Narayanswamy
Conor Heneghan
Max Xu
Jacqueline Shreibati
Mark Malhotra
Orson Xu
Tim Althoff
Tony Faranesh
Nova Hammerquist
Vidya Srinivas
arXiv (2025)
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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. However, the solution to fulfill diverse needs from individuals in daily non-clinical settings is underexplored. In this work, we aim to build a comprehensive personal health assistant that is able to reason about multimodal data from everyday consumer devices and personal health records. To understand end users’ needs when interacting with such an assistant, we conducted an in-depth analysis of query data from users, alongside qualitative insights from users and experts gathered through a user-centered design process. Based on these findings, we identified three major categories of consumer health needs, each of which is supported by a specialist subagent: (1) a data science agent that analyzes both personal and population-level time-series wearable and health record data to provide numerical health insights, (2) a health domain expert agent that integrates users’ health and contextual data to generate accurate, personalized insights based on medical and contextual user knowledge, and (3) a health coach agent that synthesizes data insights, drives multi-turn user interactions and interactive goal setting, guiding users using a specified psychological strategy and tracking users’ progress. Furthermore, we propose and develop a multi-agent framework, Personal Health Insight Agent Team (PHIAT), that enables dynamic, personalized interactions to address individual health needs. To evaluate these individual agents and the multi-agent system, we develop a set of N benchmark tasks and conduct both automated and human evaluations, involving 100’s of hours of evaluation from health experts, and 100’s of hours of evaluation from end-users. Our work establishes a strong foundation towards the vision of a personal health assistant accessible to everyone in the future and represents the most comprehensive evaluation of a consumer AI health agent to date.
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What Are The Odds? Language Models are Capable of Probabilistic Reasoning
Shun Liao
Jake Sunshine
Tim Althoff
Daniel McDuff
arXiv (2024)
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Language models (LM) are capable of remarkably complex linguistic tasks; however, numerical reasoning is an area in which they frequently struggle. An important but rarely evaluated form of reasoning is understanding probability distributions. In this paper we focus on evaluating the probabilistic reasoning capabilities of LMs using idealized and real-world statistical distributions. We perform a systematic evaluation of state-of-the-art LMs on three tasks: estimating percentiles, drawing samples, and calculating probabilities. We find that zero-shot performance varies dramatically across different families of distributions and that performance can be improved significantly by using anchoring examples (shots) from within a distribution, or to a lesser extent across distributions within the same family. For real-world distributions, the absence of in-context examples can be substituted with context from which the LM can retrieve some statistics. Finally, we show that simply providing the mean and standard deviation of real-world distributions improves performance. To conduct this work, we developed a comprehensive benchmark distribution dataset with associated question-answer pairs that we release publicly, including questions about population health, climate, and finance.
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