Approach
I work by matching method to question: combining mechanistic experimentation, computational and structure-informed analysis, and clinically anchored human data when the problem requires it. The goal is not methodological breadth for its own sake, but the ability to choose the level of analysis that yields the most informative, biologically meaningful, and clinically relevant answer.
Experimental and mechanistic biology
My background is rooted in molecular medicine, medical biochemistry, molecular pathology, and mechanistic disease biology. Experimental work has included cell and molecular biology, cell culture, microscopy, immune-based assays, flow cytometry, RT-PCR-based methods, genomics and transcriptomics, CRISPR-Cas9, and related approaches for studying disease-relevant molecular and cellular processes. More recent work also includes lentiviral and other perturbation-based strategies where controlled experimental systems are needed to test mechanistic hypotheses.
Computational, structural, and bioinformatic analysis
I use computational and integrative approaches to support biological interpretation rather than to replace it. This includes image analysis, pathway analysis, sequencing-related workflows, statistical analysis, bioinformatics, and data analytics, as well as structure-informed reasoning where useful. I am particularly interested in analytical approaches that can connect sequence, structure, and function to disease relevance, including the use of structural modeling and AI-enabled tools such as AlphaFold.
Cohorts, epidemiology, and real-world clinical data
A central part of my current profile is the use of patient-linked cohorts, epidemiological reasoning, electronic health records, and real-world evidence to test whether biological questions remain meaningful in human disease. This includes work with clinical datasets, cohort-linked interpretation, and analytical frameworks that preserve rigor and interpretability rather than treating real-world data as a substitute for careful study design.
Scientific design and execution
Beyond specific methods, I place high value on scientific design: identifying the real question, choosing the level at which it becomes tractable, and building a coherent path between mechanism, analysis, and disease. This includes project conceptualization, development, implementation, and cross-disciplinary integration, as well as communication and mentoring within research settings where clarity and execution matter as much as technical competence.
Selected capabilities
Selected capabilities include molecular and cellular biology, biochemistry, mechanistic experimental design, lentiviral workflows, bioinformatics, statistical and pathway analysis, structure-informed reasoning, AI-enabled analysis, and cohort- and EHR-linked translational data interpretation.