Bridging the gap between complex biological systems, advanced computational modeling, and scalable tech infrastructure.
I am an ambitious and results-driven bioengineer currently pursuing an MS in Bioengineering at the University of Maryland. My work sits at the intersection of biological research, computational modeling, and medical device engineering — translating complex scientific problems into rigorous, data-driven solutions.
My research spans CRISPR gene therapy, machine learning for oncology, and medical device prototyping — always with a focus on clinical impact and scientific rigor. I bring the same systematic approach to data analysis, building automated pipelines that transform raw datasets into actionable insights.
I believe the most meaningful advances in medicine come from engineers who can speak both the language of biology and the language of computation.
A multi-disciplinary skill set spanning wet-lab biology, computational analysis, and engineering design.
Selected projects spanning medical device design, gene therapy, and machine learning for clinical applications.
Engineered a multi-filter system supporting blood flow rates of 4–5 L/min to enhance toxin clearance in critical care settings. The system architecture optimizes membrane surface area and pressure differentials for maximum clinical efficacy.
Designed a Vacuum-Assisted Breast Biopsy (VABB) device using SolidWorks, operating at 70–100 kPa. The design improves tissue sample quality while minimizing patient discomfort through precision pressure control.
Designed lentiviral vectors to deliver CRISPR/Cas9 for CCR5 gene knockout, targeting the primary co-receptor used by HIV for cell entry. This approach offers a potential functional cure strategy for HIV infection.
Built machine learning models (Logistic Regression, Random Forest, SVM) to predict tumor histologic grades using Python and MATLAB. Achieved high classification accuracy to support personalized oncology treatment planning.
Whether you're interested in research collaboration, industry opportunities, or just want to discuss bioengineering and data science — I'd love to hear from you.