Meet the Team

data team

Meet our surgeon scientists involved in Artificial Intelligence, Machine Learning and Natural Language Processing in the Surgical Innovation Center

Arman Kilic MD 

Arman Kilic, M.D.

Cardiothoracic Surgeon & Director

Cardiac Surgery Research Group and Projects 

  • Applying AI to heart transplant allocation in the United States.
  • Utilizing AI and ML for clinical decision support, matching recipients and donors for heart transplants in the U.S. 
I Bostock MD 

Ian Bostock, M.D. MS

Thoracic Surgeon

  • Utilizing machine learning algorithms to analyze preoperative PET-CT scans to determine the likelihood of complete pathologic response after neoadjuvant chemoradiation in patients with esophageal cancer remission.
T Brothers MD 

Thomas Brothers, M.D.

Vascular Surgeon

  • Developing risk models and providing clinical decision support in peripheral vascular disease.
Dr Thomas Curran 

Thomas Curran, M.D. MPH 

Colorectal Surgeon

  • Utilizing machine learning for personalized venous thromboembolism (VTE) risk prediction and management recommendations for VTE prophylaxis.
Dr Eriksson 

Evert Eriksson, M.D.

Acute Care Surgeon

  • Applying AI to chest wall injury for reconstruction surgery.
  • Utilizing AI to determine factors associated with unstable chest wall injuries.
Heather Evans MD 

Heather Evans, M.D. MS

Acute Care Surgeon

  • Investigating an AI algorithm for automatic detection of  surgical wound infections
Kevin Hughes MD  

Kevin Hughes, M.D.

Surgical Oncologist

Dr Mukherjee 

Rupak Mukherjee, Ph.D. 

Cardiothoracic Surgery Research

  • Utilizing natural language processing to evaluate applications for surgical residency programs.
Dr. Ozhathil, M.D. 

Deepak Ozhathil, M.D. 

Burn Surgery 

  • Utilizing Next Generation Sequencing (NGS) technology to collect data about the microbiome and metagenomics of the burn wound and establish a data and bio-repository.  
Chris Streck MD 

Chris Streck, M.D.

Pediatric Surgery

  • Utilizing ML and AI to develop clinical prediction tools which will aid providers at the bedside during the initial evaluation and resuscitation of children in the trauma bay.
Dave Taber 

David J. Taber, Pharm.D. MS 

Transplant Surgery

  • Using NLP, ML, and AI to build and validate an automated prediction model to identify patients at-risk for medication non-adherence following transplantation.
Ravi Veeraswamy, MD  

Ravi Veeraswamy, M.D.

Vascular Surgery

  • Applying artificial intelligence for better stroke risk prediction using carotid imaging data.

Meet the Data and AI Research Team

John Del Gaizo, Ph.D., Lead AI Researcher, leads a team of AI researchers that aims to facilitate AI research in the Harvey and Marcia Schiller Surgical Innovation Center. The team offers AI consultation and prototyping, and access to advanced computational hardware for MUSC Department of Surgery researchers and external collaborators. He has a Ph.D. from the Clemson-MUSC biomedical data science program, years of industrial experience, and extensive knowledge of MUSC data infrastructure and IT capabilities, creating a unique opportunity to be able to support and lead data science efforts in the center.

Brett Welch 

Brett Welch, MBA, MHA

Program Manager

Michael Zhang PhD 

Ruoyu (Michael) Zhang, M.S.

Senior Research Associate

Andrew Wright 

Andrew Wright, M.S.

Research Associate

A Alameldin 

Ahmed Alameldin

Data Analyst

Akinwale Victor Famotire 

Akinwale Famotire

Data Analyst

Mr Mathi 

Roshan Mathi, M.S.

Data Analyst

Sheldon Sutton 

Sheldon Sutton

Data Analyst

Filler headshot 

Atsukko Ueharra

Data Analyst

Will Zielke 

Will Zielke

Data Analyst

Learn More About Our Data and AI Scientists 

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 WelchBrett 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.

Michael Zhang PhDRuoyu (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 WrightAndrew 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.

Data Analysts

Mr MathiRoshan 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.

Sheldon SuttonMy 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.

A AlameldinAhmed 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 ZielkeWill 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 Victor FamotireAkinwale 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.