Built by people who've spent careers at the edge of wellness science.

Dr. Schadlu grew up in Germany, where he attended medical school at Heinrich-Heine-University in Düsseldorf. In his first year, he was introduced to the concepts of integrative and preventive wellness, as well as the importance of empowering individuals to optimize their own well-being through habit optimization — a philosophy that has guided his career ever since.

After pursuing a surgical residency and becoming Chief Resident at the renowned Washington University School of Medicine in St. Louis, he was selected into one of the most competitive surgical retina fellowships in the nation at the Barnes Retina Institute. Having published and authored numerous scientific articles, he joined the faculty at the University of Pennsylvania Scheie Eye Institute before establishing a highly successful private practice in 2009.

His passion for wellness and empowering individuals to make good daily choices ultimately led to Preventa Wellness. His vision is to use artificial intelligence not only to assess where individuals currently stand but how to optimize their day-to-day habits with a focus on longevity, optimized physical and mental performance. Having been immersed in the study of wellness for the last 30 years, Dr. Schadlu is uniquely qualified to lead an evidence-based approach to performance optimization.

Eric is the Chief Technology Officer and Co-Founder of Preventa Wellness, bringing over 20 years of software engineering expertise and 15+ years at the forefront of artificial intelligence and machine learning. He architects and leads all technical operations — from AI model development to full-stack platform engineering — driving the technology that powers the Preventa Score.

He earned his Master of Science in Electrical & Computer Engineering from the University of Iowa, where he specialized in machine learning for biomedical research at the Carver College of Medicine, developing algorithms to classify retinal degeneration. This work produced multiple co-authored publications with Dr. Stephen Stasheff, currently a Staff Clinician at The National Eye Institute.

At MIT Lincoln Laboratory, he contributed to research that earned both MIT's Best Invention Award (2014) and Best Paper Award (2018) for the Offshore Precipitation Capability. This research culminated in the R&D 100 Award — recognizing the top 100 innovations in research and development globally.

In 2020, Eric was awarded the National Science Foundation SBIR Phase I grant as Principal Investigator. He is also CTO and Co-Founder of iLoopes, a vision-assisted device that won the "Winning Pitch Challenge" at the Annual Meeting of the American Society of Retina Specialists. He has authored or co-authored numerous peer-reviewed publications in engineering and technology.

Dr. Fowers is a biomedical informatician and applied data scientist whose research focuses on understanding, measuring, and predicting long-term health behavior maintenance using large-scale longitudinal data. He earned his Ph.D. in Biomedical Informatics from Arizona State University, where his dissertation examined computational approaches for detecting behavioral patterns associated with sustained healthy behaviors. His training spans econometrics, advanced biostatistics, machine learning, and database systems.

His research integrates methods from machine learning, causal inference, and health informatics to study habit formation, mindfulness, physical activity, and mental health outcomes — particularly using mobile and wearable health data. He has authored and co-authored peer-reviewed publications in leading journals including the Journal of the American Medical Informatics Association, Journal of Medical Internet Research, Mindfulness, Behavioral Sciences, Brain Imaging and Behavior, and BMJ Open. His work has contributed novel objective metrics of behavioral consistency, temporal patterning, and adherence that advance the measurement of sustained engagement in digital wellness interventions.

Dr. Fowers has held applied research and data science positions across academic, clinical, and industry settings, including Arizona State University, Brigham Young University, Mayo Clinic, and Select Health — where he has developed and deployed machine learning models for clinical imaging, health economics, and operations. He also has substantial teaching and mentorship experience, and maintains an online educational platform with over 10,000 subscribers focused on Python programming and machine learning.