Meet the Team

John Del Gaizo, Ph.D., Lead AI Researcher, has years of industrial experience and extensive knowledge of the MUSC data infrastructure and IT capabilities, creating a unique opportunity to support and lead data science efforts in the center.

Brett Welch, MBA, MHA is the program manager for the Harvey and Marcia Schiller Surgical Innovation Center, managing more than 50 projects and facilitating the research from start to finish, including the QI process. Mr. Welch is a data scientist with experience in managing healthcare projects, operational management, and analytics.

Ruoyu (Michael) Zhang, MS is a Senior Research Associate at the Medical University of South Carolina. He earned his Master's degree in Healthcare Analytics from Carnegie Mellon University, where he developed an interest in applying AI and ML to the healthcare sector. At the Surgical Innovation Center, Ruoyu leads various projects focusing on natural language processing, large language models, and ETL pipeline design to improve health

Andrew Wright is a Ph.D. student in the Clemson-MUSC joint Biomedical Data Science and Informatics program. He obtained a B.S. in Mathematics from Anderson University in Anderson, SC, and a M.S. in Mathematical Sciences from the College of Charleston in Charleston, SC. He began working at MUSC in 2021 in Information Solutions where he has gained valuable experience dealing with healthcare data. With a background in applied mathematics, as well as experience in healthcare technology, he is currently researching applications of Artificial Intelligence and Machine Learning in the surgical and transplant domains.

Roshan Mathi, MS is an AI Researcher with experience in ML and AI. After receiving a master's degree and working at a startup healthcare company, he joined to work on novel approaches to predict healthcare outcomes and to create diagnostic tools for healthcare providers. Over the past year, he had an abstract accepted by the American Heart Association and presented work at national and regional conferences. He is currently working on anomaly detection with time series data and predicting surgical outcomes after organ transplants.

My research program focuses primarily on development, evaluation, and dissemination of culturally tailored, technology-enhanced behavioral health interventions and resources for vulnerable and clinically underserved populations in the context of trauma with a goal of increasing access to services and improving outcomes. This includes first responders, survivors of traumatic injuries and burns, and disaster survivors. I have developed and am currently directing the Burn Behavioral Health program housed within MUSC’s comprehensive Burn Center to support mental, physical, and social rehabilitation of burn patients to improve well-being and quality of life post-burn. Research opportunities include assisting with a randomized control trial of an mHealth application for burn patients and their families, quality improvement initiatives specific to evaluation and dissemination of stepped-care services for burn patients, optimizing inpatient and outpatient patient screening across multiple burn centers, and increasing patient satisfaction with care.

Ahmed Alameldin is an AI Researcher who is completing his Master's in Biomedical Data Science at Clemson University. He is a Fulbright Scholar from Egypt with a demonstrated history of working in health wellness who is skilled in R, Python, Machine Learning, and Deep Learning. His interest focuses on Natural Language Processing and its applications in medical text classification, AI Explainability and Fairness of some deep learning models, and Convolutional Neural Networks for medical image classification.

Will Zielke is a lab assistant at the Medical University of South Carolina in Charleston, SC. He is a Sophomore at Emory University studying Computer Science and Finance. Before joining MUSC, Will garnered experience with natural language processing, LLMs, TensorFlow, and statistical analysis through his involvement in Emory's Comparative Political Advertising project, where he analyzed the sentiment of extensive datasets of political campaign advertisements. At MUSC's AI Innovation Center, Will is applying his expertise to critically evaluate the accuracy of various predictive models; he is statistically testing multiple models –predicting topics such as patient mortality or organ donation status– and comparing them to derive the most accurate one given a medical outcome. 

Akinwale Famotire received his bachelor's degree in Industrial Mathematics from the Federal University of Technology Akure (FUTA), Nigeria and is currently pursuing a Master's degree in Mathematics with a concentration in Bioinformatics at Georgia State University (GSU). His academic journey has involved analytical work, focusing on predictive analytics encompassing forecast and regression models. Current research endeavors revolve around a project that entails computational modeling of retinal pigment epithelium cells in a 3D environment, utilizing Compucell3D to understand the dynamics and structure of the cells and investigate conditions such as  Age-related Macular Degeneration (AMD). Research interests are the application of Artificial Intelligence (AI) in predictive analytics, particularly in the realm of medical imaging.

Atsuko Ueharra bio to be updated soon.