Akshaya Shanmugam, a UMass Amherst alumna and CEO and co-founder of Lumme Inc., an Amherst-based spinoff company, has been recognized in Forbes’s list of 30 under 30 for Healthcare.
Shanmugam, who earned her master’s degree and doctorate in electrical and computer engineering at UMass Amherst in 2012 and 2016, respectively, has developed software for smokers who want to quit. Lumme’s current focus is the development of an effective program for smokers to quit by combining wearable sensors, data analytics, and behavioral psychology. It is currently conducting extensive clinical trials in collaboration with the Yale Medical School.
The company is also focused on preventing other addiction problems such as drinking and obesity. Lumme is funded by the National Cancer Institute.
The technology developed at Lumme continuously analyzes sensor data from a wristband and a mobile phone to detect smoking and high-risk triggers for smoking lapses. The company says this allows them to automatically detect triggers associated with each user’s smoking pattern, predict when the user is most likely to experience cravings and prevent a relapse by offering tailored, personalized intervention.
Shanmugam’s doctoral research focused on developing an array of low-cost disease screening and health monitoring systems. She is an expert in data analytics, health monitoring, and in system integration and testing. While at UMass Amherst, she was the recipient of the Hluchyj fellowship, Special Tang award, David Wolf prize, Glass family demonstration award, and garnered business scholar award, the Eugene M. Isenberg scholar award for two consecutive years. At Lumme, she combines her research experience in working with health monitoring systems and her entrepreneurial experiences to develop solutions that help people lead healthier lives.
DeepakGanesan, company president, is a professor at the College of Computer and Information Sciences at UMass Amherst. He has more than a decade of experience in the use of mobile health sensors including detection of behavioral targets such as cocaine use and smoking, understanding interactions between multiple behaviors through multi-modal sensing, prediction of future behavioral context using advanced machine learning, design of novel ultra-low power behavioral sensing platforms such as computational eyeglass for visual context sensing, and incentive strategies for mobile health.