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Daniella Patton
AI and Medical Imaging - Research Fellow at the University of Michigan
Professional Background
Daniella Patton is an accomplished PhD-level biomedical engineer whose expertise lies within the realms of medical imaging, bone biomechanics, and machine learning. With a robust educational foundation and diverse professional experience, Daniella possesses a unique skill set tailored for the dynamic fields of analytics, health data science, and consultancy. Actively seeking full-time opportunities in these areas, she combines her extensive academic study with hands-on experience in the healthcare technology sector.
With over four years dedicated to statistical analysis, research design, and experimental methodologies, Daniella has established herself as a knowledgeable and reliable professional. Her PhD research, conducted between 2015 and 2019 at the prestigious University of Michigan, has had a significant impact in the field of biomedical engineering. Daniella's doctoral work focused on the critical task of predicting cadaveric bone strength in proximal femurs, aiming to create a more accurate and clinically relevant measure of osteoporotic fracture risk. This innovative approach involved applying machine learning techniques and predictive modeling, showcasing her adeptness in leveraging technology to improve health outcomes.
Currently, Daniella serves as a Health Data Science Fellow at Insight Data Science, where she has made remarkable contributions, including the development of an injury predictor for female professional tennis players. This project not only highlights her analytical capabilities but also her commitment to applying her knowledge for the advancement of injury prevention in sports.
Education and Achievements
Daniella boasts an impressive educational background, having earned her Doctor of Philosophy (Ph.D.) in Bioengineering and Biomedical Engineering from the University of Michigan, where she further honed her research skills and technical proficiency. She also holds a Master’s Degree in the same field from the University of Michigan - Rackham Graduate School, further solidifying her deep understanding of bioengineering principles.
Before embarking on her graduate studies, Daniella completed her Bachelor of Science degree in Biophysics at Xavier University. This foundational education provided her with a solid grounding in the physical principles that govern biological systems, framing her future work in biomedical engineering and health data science.
Throughout her career, Daniella has engaged with various organizations to enhance her experience. She has served as a Research Fellow at the University of Michigan, where her contributions expanded the current understanding of biomechanics and health technology. Notably, Daniella has also worked as a Consultant at miLEAD Consulting, providing insights and expertise within the consultative sphere of health data science. Her earlier roles include positions such as Graduate Student Research Assistant and Undergraduate Summer Researcher, further enriching her capabilities and breadth of knowledge.
Daniella's diverse experiences and commitment to her field combined with her technical skills and dedication to research make her a standout candidate in the fields of analytics and health data science.
Achievements
Among her notable achievements, Daniella's research on predicting cadaveric bone strength holds a place of prominence, reflecting her innovation in using machine learning approaches for addressing important health-related issues. Additionally, her current project developing an injury predictor for professional female tennis players underscores her ability to translate complex data into practical applications that can enhance the safety and performance of athletes.
Daniella has built a solid reputation as a thought leader in her field through her innovative approach to problems and her focus on utilizing data science for real-world health applications. Her work is not only pivotal in understanding osteoporotic fracture risks but also in shaping how data is employed within various health-related contexts, making her a valuable asset to any organization looking to leverage health data for better decision-making and improved patient outcomes.
