Biomedical Informatics Center

Abolfazl Molallo, Ph.D.

mollalo@musc.edu

"I specialize in conducting research focused on the analysis and mapping of spatially referenced data. I routinely engage with diverse datasets, especially those enriched with location information. This entails applying advanced analytical techniques and leveraging cutting-edge geospatial techniques to gain profound insights into the geographic distribution of health-related issues and addressing health disparities.

Students who have a potential interest in mapping, spatial analysis, and health disparities should contact Dr. Mollalo."

George Hanna, Ph.D.

hannag@musc.edu

We specialize in Natural Product Extraction, Isolation, and Characterization from diverse biological (plants, invertebrates, microbial cultures, tissue, etc.) and environmental (water, soil, etc.) sources. Bioassay and chemical analysis (mass spectrometry, NMR, etc.) are utilized to guide the isolation of molecules of interest, which can include environmental toxins, drug leads, indicators of disease or effectors of a wide variety of biological or ecological processes. These efforts can be utilized to characterize novel compounds and produce analytical standards that may not be commercially available—thus facilitating further investigation. The strength of this program is its capacity to support collaboration between natural product chemists and basic scientists, microbiologists, toxicologists, ecologists, or other researchers with diverse interests.

Paul Heider, Ph.D.

heiderp@musc.edu

My lab investigates methods to identify, quantity, and mitigate sources of algorithmic bias and evidence of clinician bias baked into Natural Language Processing (NLP) models derived from Electronic Health Records (EHRs). I'm also interested in the best means to evaluate equitable performance of an algorithm between different institutions and between cohorts at the same institution as part of an effort to re-use models more often than we train new models. I have active projects on using NLP extractions from unstructured clinical notes to reduce documentation inequities, using NLP to monitor and measure pejorative language use in notes, and using NLP to improve the speed and efficiency of clinical trial enrollment. As Head of the NLP Core (a service center), I regularly apply state-of-the-art NLP and Machine Learning methods to extract social determinants of health (SDoH) and other factors from unstructured clinical notes. My work tends to be technical in nature but I am also open to students with expertise instatistical methods, ethical/equity frameworks, or bias reduction.

Post Doctoral Researchers

Dmitry Scherbakov, Ph.D.

scherbak@musc.edu

My areas of interest include data science, machine learning and natural language processing applied to social determinants of health and mental health problems. Currently I am working on extraction of stressful life events, such as becoming evicted from home or going through divorce, from clinical notes and evaluating their impact on health using longitudinal EHR data.

Sari Mayhue, PhD

mayhue@musc.edu

I am a postdoctoral fellow with the Alekseyenko group where my primary focus lies in head and neck cancer research. My interest is in oncogenomics and its impact in cancer therapeutics and disease outcome. Genetic profiling is a critical component in cancer intervention strategizing and has become an exciting field of biomedical exploration