Best Times to Meditate Each Day

Brian Richmond

After establishing the successful people analytics team at the international workspace provider WeWork, data scientist Brian Richmond joined the wellness-focused technology firm Aura Health. As a senior data scientist for the San Francisco-based company, Brian Richmond analyzes how users benefit from meditation to inform product improvements and develop meditation sessions that are suitable for different times of the day, and publishes article
and blogs.

While meditating at any time can be beneficial, sessions have specific advantages when scheduled during the following times of the day:

Start of the Day – The clear-headedness and energy associated with the first hours of the day make it an ideal time to schedule a routine meditation. Beginners can start with a short five-minute session and increase the duration over time.

Break Time – Breaking up the day with a meditation session can be ideal for employees, especially those working at firms that offer on-site quiet rooms. By focusing on breathing and any immediate sensations, the trip to and from the workplace can act as a form of walking meditation. As little as 3 minutes of meditation can lead to hours of improved productivity.

When Overwhelmed – Since the deep breathing associated with meditation can reduce some of the physical symptoms of anxiety, a quick session during a stressful period can help reduce tension and promote relaxation. With enough practice, meditation can train the body and brain to relax when a person encounters overwhelming situations.

The Concept Drift Phenomenon in Machine Learning Models

With a doctoral degree in anthropological sciences, Brian Richmond, Ph.D.
serves as a senior data scientist at Aura Health. Dr. Brian Richmond oversees the product intelligence team at the company and builds machine learning models to improve the product.

A recent Forbes article entitled, Why Machine Learning (ML) Models Crash And Burn In Production brought attention to a unique aspect of ML models. The article argued that unlike software, machine learning models’ accuracy tends to degrade from the moment they are placed into production. Known as “concept drift,” this phenomenon reflects the uncertain nature of the future and the fact that even the best predictive models based on real-time inputs can still change over time in unforeseen ways.

An example given in the article centers on a predictive readmission model at hospitals that began degrading significantly after a few months. Changing simple data to update the electronic health records would often require modification of codes, which would result in interface errors and inaccurate results. Updating models with new data is critical to almost any predictive problem, from hospital readmissions to fraud detection to predicting employee turnover in companies.

To avoid running into problems with concept drifts in ML models, best practices include the article recommends continuous monitoring and measurement of predictive outcomes. When input data unexpectedly changes online, it must trigger an alert that would allow engineers and data scientists to delve into the issue and come up with solutions. Dr. Brian Richmond and his team update their ML models multiple times each day to ensure that customers get personalized recommendations for the best experience possible.