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Thank you to our 2025 Methods Symposium Planning Commitee 

Michael Mugavero, MD, MHSc

Madeline Gibson, MPH

 Amber Ganoe, MPA

 Tapan Mehta, PhD

 John Osborne, PhD

Rena Patel, MD

Crystal Chapman Lambert, PhD, CRNP, NP‐C, FNP‐BC, AAHIVS, ACRN, FAAN

Bisakha "Pia" Sen, PhD

Agenda 

• 8:00am - 8:55am

  Registration and Breakfast
  UAB School of Nursing
  1st Floor, Room 1054

• 8:55am - 9:00am

  Welcome

• 9:00am - 9:40am

  State of the Field
  Matt Might, PhD
  University of Alabama at Birmingham (UAB)

• 9:40am - 10:20am

  Natural Language Processing
  John Osborne, PhD
  The University of Alabama at Birmingham (UAB)

• 10:20am - 10:50am

  Networking Break

• 10:50am - 11:30am

  Machine Learning
  Sandra Safo, PhD
  University of Minnesota
  

• 11:30am - 12:30pm

  Ethical Deployment
  Meredith Kilgore Endowed Lecturer
  Danielle Bitterman, MD
  Harvard Medical School

• 12:30pm - 1:30pm

  Small group lunches with speakers
  

• 1:30pm - 2:30pm

  Concurrent Demos

  1. How much AI do I need? - Ryan Melvin
  2. How much bias do I really have? Practical strategies for probing & Accessing bias in LLMs & VLMS. - Shan Chen & Kuleen Sasse
• 2:30pm - 3:00pm

  Evaluation and Closing Remarks

Speakers 

• Danielle S. Bitterman, MD
  

  Danielle S. Bitterman, MD

  Harvard Medical School
  

  Dr. Bitterman is a physician-scientist with unique expertise in AI and natural language processing. Her research is dedicated to incorporating technological advances in AI into intelligent electronic health systems to improve data-driven cancer care. Dr. Bitterman's laboratory develops natural language processing methods that process vast amounts of data contained within electronic health records to provide insights individualized to each patient and facilitate more effective communication between patients, caregivers, and clinicians. This work includes automating information extraction from text across multiple clinical and biomedical domains and applying deep learning-based language models to support clinical decision-making and patient education. Dr. Bitterman's clinical translational efforts also include prospective clinical trials regarding the use of AI technologies in cancer care. Dr. Bitterman and her team have published multiple high impact articles on these topics in journals such as Nature Medicine, JAMA Oncology, and Lancet Digital Health, and Nature Reviews Clinical Oncology. Dr. Bitterman received her undergraduate degree at Columbia University, and attended medical school at New York University School of Medicine. She completed an internship in internal medicine at the Brigham and Women's Hospital, and her residency at the Harvard Radiation Oncology Program. She completed her post-doctoral fellowship in natural language processing at the Computational Health Informatics Program at Boston Children's Hospital.

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• Matt Might, PhD
  

  Matt Might, PhD

  University of Alabama at Birmingham

  Since 2017, Matt Might has served as Director of the Hugh Kaul Precision Medicine Institute at the University of Alabama at Birmingham (UAB). At UAB, Matt is the Hugh Kaul Endowed Chair of Precision Medicine, a Professor of Internal Medicine and a Professor of Computer Science. At UAB, Dr. Might's NIH and philanthropically funded research focuses on precision prevention, diagnosis and therapeutics across rare disease, cancer and common/chronic conditions. A principal theme in his research is the use of computer and data science to enhance clinical and academic medicine. At UAB, Dr. Might's NIH and philanthropically funded research focuses on precision prevention, diagnosis and therapeutics across rare disease, cancer and common/chronic conditions. A principal theme in his research is the use of computer and data science to enhance clinical and academic medicine. From 2016 to 2018, Dr. Might was a Strategist in the Executive Office of the President in The White House. At The White House, Dr. Might worked primarily on President Obama's Precision Medicine Initiative with both the NIH and the Department of Veterans Affairs. Prior to this role, Dr. Might was a faculty member in the Department of Biomedical Informatics at the Harvard Medical School. At Harvard, Dr. Might's research focuses on rare disease discovery and diagnosis, and on the development of personalized therapeutics for rare disease. Dr. Might's journey from computer science to medicine has been inspired by his son Bertrand. In 2012, Bertrand became the first patient in the world to be diagnosed with NGLY1 deficiency, and this inspired Dr. Might to use social media to discover other patients and form a community. Through the community's efforts in science, two therapeutics for NGLY1 deficiency have been identified since its discovery, and more are under active development. Dr. Might is co-founder and Chief Scientific Officer of NGLY1.org, a non-profit dedicated to finding treatments for NGLY1 deficiency, and he is a co-founder and Scientific Advisor to Pairnomix, a start-up which identifies potential patient-specific therapies for rare disorders -- and genetic epilepsies in particular.. Prior to medicine, Dr. Might spent nearly a decade as a professor of computer science at the University of Utah. In computer science, Dr. Might's research focuses primarily on cybersecurity; scientific computing; and automated reasoning about programs. At Utah, his research was supported by the Department of Defense (via DARPA), the Department of Energy and the National Science Foundation, including an NSF CAREER Award in 2014. In 2014, he was named one of Utah's first six Presidential Scholars.

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• John D. Osborne, PhD
  

  John D. Osborne, PhD

  University of Alabama at Birmingham

  Dr. John Osborne has a secondary appointment in the Department of Computer Science where he teaches Natural Language Processing (NLP) with a focus on biomedical text. Dr. Osborne came to UAB by way of the Robert H. Lurie Cancer Center at Northwestern University where he worked to develop the Disease Ontology while participating in various caBIG related projects. He has worked in microbial bioinformatics both at the Centers for Disease Control in Atlanta and at UAB’s Viral Bioinformatics Resource Center. His current research focus is on Natural Language Processing (NLP), particularly biomedical concept normalization with applications to clinical phenotyping and drug repurposing. Dr. Osborne and his lab develop and maintain NLP integrated registry systems at UAB to identify patients of interest to researchers and clinical care providers. Current phenotypes of interest include reportable cancers, multiple myeloma, delirium, fragility and opiate use disorder..

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• Sandra Safo, PhD
  

  Sandra Safo, PhD

  University of Minnesota

  Dr. Safo is an Associate Professor in the Division of Biostatistics and Health Data Science and a Graduate Faculty of Data Science in the College of Science and Engineering at the University of Minnesota. She is interested in developing robust, explainable, and usable statistical and machine learning methods (e.g. classification, discriminant analysis, clustering, dimension reduction, sample size estimation, deep learning) and algorithms for high dimensional data to advance clinical translational research and precision medicine. Some current interests include functional data analysis and social determinants of health. Her methods research is currently supported by multiple PI grants including: Maximizing Investigators Research Award (MIRA, R35 from NIGMS) for early stage investigators, Administrative Supplements from NIA and the Office of Data Science and Strategy, and a UMN internal grant.

   

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Concurrent Demo Leaders 

• Shan Chen, MS
  

  Shan Chen, MS

  Shan Chen, MS, is a PhD student in the AI in Medicine program at Harvard-MGB and Maastricht University, specializing in interpretable AI for healthcare. A recipient of the 2024 Google PhD Fellowship in Natural Language Processing, Shan's research investigates how large language models can bridge different modalities to improve patient communication and safety

• Ryan L. Melvin, PhD
  

  Ryan L. Melvin, PhD

  Dr. Ryan L. Melvin is the Endowed Faculty Scholar for Data Science, Artificial Intelligence, and Machine Learning and an Associate Professor in the Department of Anesthesiology and Perioperative Medicine at the University of Alabama at Birmingham. His multidisciplinary publication record spans Artificial Intelligence, Biology, Chemistry, Physics, and Statistics. Dr. Melvin has delivered numerous trainings, lectures, and seminars on AI across the globe. His innovative algorithms are actively utilized by academic, private, and governmental institutions. Dr. Melvin holds a Ph.D. in Physics and a Master’s in Statistics from Wake Forest University. His work has been highlighted in an Intel Spotlight video and featured in journals such as British Journal of Anesthesia, Proceesings of the National Academy of Sciences, and Physiology.

• Kuleen Sasse
  

  Kuleen Sasse

  Kuleen Sasse is an undergraduate student majoring in Computer Science and Applied Mathematics at Johns Hopkins University. His research focuses on three key objectives: building trustworthy NLP models that adapt to dynamic environments, expanding NLP capabilities to new contexts and modalities, and applying these advancements to domains such as clinical scenarios and social media analysis.

Congratulations to our 2025 Travel Award Winners

• Chih-Ting (Tim) Lai - Auburn University
• Amer Al Khalifa - Auburn University
• Jiafeng Song - Duke University
• Wasiuddin Najam, PharmD - Indiana University Bloomington
• Dorcas Adom, MPH - Louisiana State University, Health Sciences Center 
• Douglas Gyamfi, RN - University of Maryland
• Chathurika Samudani Dhanasekara, MBBS, MS, PhD - Texas Tech University, Health Science Center
• Chenglin Hong, PhD, MSW, MPH - University of Connecticut
• Ishfaq Rashid, PhD - University of Utah
• Zhenglin Yuan, MPH - Johns Hopkins Bloomberg, School of Public Health
• Emmanuel Essam, MD, MPH - University of Nebraska Medical Center