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 15+ years of software engineering expertise, with machine learning and AI running through nearly all of it. 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.

At MIT Lincoln Laboratory he was part of the team behind the Offshore Precipitation Capability, which won the MIT Lincoln Laboratory Best Invention Award (2014), the MIT Lincoln Laboratory Best Paper Award (2018) for "Creating Synthetic Radar Imagery Using Convolutional Neural Networks", and an R&D 100 Award (2016) — recognizing the top 100 innovations in research and development globally.

Beyond Preventa Wellness, Eric is a founder of numerous startups, including Preventa Medical Corporation, iLoopes, and IN2Labs. Preventa Medical Corporation — a separate company — was awarded a National Science Foundation SBIR Phase I grant in December 2019 with Eric as Principal Investigator; the work began in January 2020. iLoopes, a vision-assisted device, won the "Winning Pitch Challenge" at the Annual Meeting of the American Society of Retina Specialists. His two peer-reviewed publications are "Creating Synthetic Radar Imagery Using Convolutional Neural Networks" (Veillette, Hassey, Mattioli, Iskenderian & Lamey, 2018, Journal of Atmospheric and Oceanic Technology 35(12):2323, doi:10.1175/JTECH-D-18-0010.1) and the conference paper "Learning Airspace Flow Rates through Fast-time Simulation" (Jones, DeLaura, Glina & Hassey, 2018, AIAA Aviation Technology, Integration, and Operations Conference, doi:10.2514/6.2018-2881). He is first author of the 2016 conference presentation "Uncertainty in Radar Performance Due to Evaporation Ducting" (AMS 21st Conference on Satellite Meteorology), a co-author of the MIT Lincoln Laboratory technical report "The Offshore Precipitation Capability" (ATC-430, 2016), and a co-author of a 2015 conference abstract on retinal ganglion cell stimulus-response relationships for the categorization and staging of retinal degeneration (Investigative Ophthalmology & Visual Science 56(7), ARVO Annual Meeting).

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.