Ryan King

Ryan King

Ph.D. student in Computer Science at Texas A&M University, developing multimodal foundation models for healthcare applications. Research focuses on contrastive pretraining, transfer learning, and continual learning for EHR data.

About

I’m Ryan King, a Ph.D. candidate in Computer Science at Texas A&M University, developing multimodal foundation models for healthcare applications. My research focuses on contrastive pretraining, transfer learning, and continual learning for Electronic Health Records (EHR) data.

Education

Ph.D. in Computer Science - Texas A&M University (Expected 2026)
Advisors: Dr. Bobak Mortazavi and Dr. Tianbao Yang
Engineering Graduate Merit Fellowship

B.S. in Computer Science - Texas A&M University (2019-2021)
Summa Cum Laude, Research Honors

B.S. in Civil Engineering - Virginia Military Institute (2011-2015)
Minor in Applied Mathematics

Professional Experience

Graduate Research Assistant, Clinical AI Lab

Texas A&M University | Aug 2021 – Present

  • Pioneered multimodal medical LLM research developing foundation models that unify clinical text, structured EHR data, and medical imaging. Designed novel architectures for bi-modal pretraining that enable zero-shot clinical reasoning and cross-modal knowledge transfer.
  • Led breakthrough work in medical LLM pretraining, creating models that can simultaneously process clinical notes, lab values, vital signs, and imaging data. Resulted in multiple first-author publications demonstrating superior performance on downstream clinical tasks.
  • Directed a multidisciplinary team of graduate and undergraduate researchers on cutting-edge projects involving medical LLM fine-tuning, multimodal clinical reasoning, and incorporation of clinical interventions for causal inference in pretraining.
  • Designed lab onboarding infrastructure and maintain two high-performance compute servers; created and published a new student setup guide that improved onboarding efficiency and research productivity.
  • Co-authored two federal research proposals (NSF Smart & Connected Health and NIH NLM R01), contributing sections on multimodal medical LLM pretraining, clinical data infrastructure, and evaluation methodology for healthcare AI systems.

Undergraduate Research Assistant, Bioinformatics Lab

Texas A&M University | Dec 2019 – May 2021

  • Developed and evaluated a domain adaptation technique that reduced batch effects via Maximum Mean Discrepancy minimization
  • Ported deep generative models by translating a TensorFlow Zero-Inflated Negative Binomial Autoencoder (ZINB-AE) into PyTorch

Infantry Officer, United States Army (Active Duty)

1st Armored Division, Fort Bliss | May 2015 – July 2019

  • Led operations and logistics for a 120-soldier infantry company, deploying $80M in equipment across Syria, Iraq, and Kuwait
  • Engineered a custom logistics tracking system integrated with the Army SAP platform, facilitating recovery of $300M in inventory
  • Planned and executed adaptive supply chains supporting 4 platoons under dynamic mission objectives

Teaching Experience

Instructor of Record, CSCE 421: Machine Learning - Texas A&M University

  • Selected to teach a class of 100 undergraduate students
  • Designed comprehensive curriculum covering Logistic Regression, SVMs, Decision Trees, CNNs, RNNs, and Auto-Encoders
  • Developed lectures, quizzes, homework assignments, projects, and exams

Service & Leadership

  • Young Professionals (YP) Chair for IEEE-EMBS BHI 2025
  • Organized COVID-19 Data Competition for four consecutive years
  • Mentored 25+ junior researchers, including undergraduates and master’s students
  • Reviewer for NeurIPS, ICML, ICLR, ML4H, BHI, JBHI, and Neurocomputing

Open Source Contributions

Honors & Awards

  • Engineering Graduate Merit Fellowship - Texas A&M University
  • University Nomination for Google PhD Fellowship - 2025 Google PhD Fellowship in Health Research
  • Best Reviewer Award - 2024 IEEE EMBS International Conference on Biomedical and Health Informatics
  • Undergraduate Research Honors - Texas A&M University
  • Travel Awards - Multiple conference travel awards

Skills

Programming Languages: Python, SQL, C, C++, Java, JavaScript, C#, VBA, Scheme

Packages & Frameworks: PyTorch, TensorFlow, PySpark, Lightning, Hydra, Numpy, Pandas, Matplotlib, Sklearn