My research and teaching operate at the interface of biology, medicine, and computation. I possess a diverse skill set that spans experimental laboratory techniques, deep biological domain knowledge, and advanced computational and data science methodologies. This unique combination allows me to effectively design, execute, and interpret complex biomedical research projects.
Cancer Biology: Specializing in Breast Cancer mechanisms, microenvironment, and hormone signaling pathways.
Molecular & Cellular Biology: Core principles of gene expression, protein function, cell signaling, and molecular interactions.
Microbiology: Host-pathogen interactions, bacterial virulence factors, oral microbiome studies (Fusobacterium nucleatum, Enterococcus faecalis).
Genetics & Genomics: SNP analysis, genotyping techniques, understanding genetic variation related to disease susceptibility (e.g., CYP19A1).
Medical Laboratory Science: Foundational understanding of clinical diagnostics, laboratory procedures, quality control, and the interpretation of clinical laboratory data (hematology, biochemistry, microbiology, clinical pathology).
Immunology: Basic principles relevant to host response, inflammation, and the tumor microenvironment.
Pharmacology Principles: Understanding drug action, particularly related to enzyme inhibition (Aromatase, 17β-HSD).
Microbiological Techniques: Bacterial culture, isolation, identification, virulence factor assays.
Molecular Biology Techniques: DNA/RNA extraction, PCR, qPCR (Real-Time PCR), Gel Electrophoresis.
Genotyping: Experience with SNP genotyping methodologies.
Biochemical Assays: Principles of enzyme kinetics and inhibition assays.
General Laboratory Procedures: Media preparation, sterilization techniques, sample handling and processing (including clinical samples).
Programming & Scripting:
R: Extensive experience for statistical analysis, data manipulation (tidyverse), visualization (ggplot2)
SQL: Database querying.
Version Control: Git / GitHub for code management and collaboration.
Shell Scripting: Linux command line proficiency.
(Also familiar with: .Net, Visual Basic, JavaScript, Google Go, Google Apps Script, FoxPro)
Data Analysis, Statistics & Machine Learning:
Statistical modeling and hypothesis testing.
Machine Learning: Classification, Regression, Clustering, Feature Selection.
Data Cleaning, Wrangling, and Preprocessing.
Advanced Data Visualization techniques.
Experience with platforms like Kaggle.
Computer-Aided Drug Design (CADD):
In silico screening methodologies.
Molecular docking and binding affinity prediction.
Understanding principles of molecular dynamics simulations.
Tools, Platforms & Environments:
Software: RStudio, VS Code.
Operating Systems: Linux, Windows.
Cloud: Google Cloud Platform (Fundamentals, Cloud Shell, Colab).
Web Development: Google Sites, HTML/CSS/JavaScript fundamentals.
Software Development:
Developed a functional Stock Management Software using Microsoft Visual Studio (.Net/VB).