Prior to joining the technology firm Aura Health as a senior data scientist, Brian Richmond served as the creator and head of people analytics for the New York City-based company WeWork. Outside of his professional achievements, Brian Richmond is a self-described nature lover and has a strong interest in California’s indigenous plants.California contains some of the most biodiverse ecosystems in the United States. It is home to more varieties of plant species than any other state. Some of the state’s most unique indigenous plants include:Desert agave - The agave is well-adapted to the dry desert climates of Southern California. It produces yellow, funnel-shaped flowers only once over its 20-year lifespan. The plant was an important source of food and fiber for local Native American tribes. Sliverpuff - A member of the aster family, silverpuffs originated in the American southwest and are found across California. The yellow and white blossoms transform into wispy seed heads during May and June. Giant Sequoia - These incredible trees are only found at a specific elevation in the Sierra Nevada mountains. They can grow to more than 35 feet in diameter and up to 300 feet tall. With an average life span of 3,000 years, California’s giant sequoias are some of the oldest trees on Earth.
With a doctorate in anthropological sciences, Brian Richmond, Ph.D. serves as a senior data scientist at Aura Health in San Francisco, California. Dr. Brian Richmond, who has built the company’s first data infrastructure and published articles and blogs, utilizes machine learning models to improve and optimize product offering and performance.
A recent analytical study published in the New England Journal of Medicine brought focus to the potential of machine learning in transforming the healthcare industry. Machine learning algorithms can distinguish chromatic complex patterns and anticipate responses to specific treatments. In cases of rare and puzzling ailments, all available knowledge resources are corralled, with the patient’s case adding vital information to an expanding database.One significant contribution of machine learning involves curated data that informs patient-provider interactions and enables evaluations and recommendations backed by the latest evidence-based methodologies. Machine learning can aid physicians in identifying health conditions quicker and more accurately by developing models that can recommend tests and health inquiries based on the patient’s data collected over time. Machine learning models can also sort through patients and automatically identify subpopulations who are eligible for clinical trials or new treatment procedures.At Aura Health, Brian Richmond and his team use Python and R to build machine learning models that drive Aura’s mobile app by learning people’s preferences and delivering meditations and other content to help people feel less stressed or anxious, sleep better, and be happier and more focused.
