Year 3 Pilots

Company Launch: EC Safety Technologies LLC

About Founded in 2025, EC-Safety was born with a clear purpose: to transform geriatric care through technology designed with empathy, science, and real collaboration with those on the front lines of caregiving. From the beginning, we have worked hand in hand with experts in gerontology, clinical professionals, technologists, and elder care leaders to create a platform that not only digitizes processes but also eases the operational and emotional burden faced by caregivers. This mission has led us to collaborate with prestigious institutions such as the University of Massachusetts Amherst, where researchers use EC-Safety as a technological foundation in a pioneering project supported by the Massachusetts AI & Technology Center for Aging and Alzheimer’s Disease (MassAITC), focused on improving the lives of Alzheimer's patients and their caregivers through artificial intelligence. Our technology was not born in…

Continue ReadingCompany Launch: EC Safety Technologies LLC

Oral Presentation: MIT Club of Northern California – AI in Healthcare at the JP Morgan Healthcare Conference

John Ralston presented on the NEURVESTA device mentioning the MassAITC pilot project work on January 14, 2025 as an featured AI startup at the AI Healthcare Event in association with the JP Morgan Healthcare Conference. Source: Event Post on MIT Club of Northern California Website

Continue ReadingOral Presentation: MIT Club of Northern California – AI in Healthcare at the JP Morgan Healthcare Conference

Funding: Pre18, parallel18’s pre-acceleration program, grant

A new company founded by G. Antonio Sosa-Pascual (PI) to commercialize the technology and AI developed through this pilot project was one of 24 companies selected (out of more than 200 applicants) to join the pre18 accelerator as a recipient of a $25,000 grant. Source: News is my Business News Release

Continue ReadingFunding: Pre18, parallel18’s pre-acceleration program, grant

Oral Presentation: Inference for Longitudinal Data After Adaptive Sampling

This presentation was given on November 20, 2024 by Dr. Susan Murphy as part of the L. Brown Distinguished Lecture series at University of Pennsylvania Abstract: Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects. However, current methods for statistical inference on such data (a) make strong assumptions regarding the environment dynamics, e.g., assume the longitudinal data follows a Markovian process, or (b) require data to be collected with one adaptive sampling algorithm per user, which excludes algorithms that learn to select actions using data…

Continue ReadingOral Presentation: Inference for Longitudinal Data After Adaptive Sampling

Grant Funding: NIH Trailblazer Award

This is an R21 award for $650,000 over a period of 3 years. Their smartphone app prototype capitalizes on the handheld nature of a mobile phone and uses its built-in sensors to gauge grip strength to enhance preoperative screening for potential risks of complications post cardiac surgery. This effort will contribute to the growing portfolio of smartphone-based health monitoring solutions actively being developed by Wang and his research team. Source: https://today.ucsd.edu/story/uc-san-diego-researcher-receives-nih-trailblazer-award

Continue ReadingGrant Funding: NIH Trailblazer Award