I am a computational scientist and quantitative analyst with experience building reproducible data workflows, stochastic models, and scientific analyses across biology, biophysics, and systems-oriented modeling. My work combines computational reasoning, statistical analysis, and domain expertise to study complex systems and communicate findings clearly.
My projects span:
- genomic and transcriptomic analysis in cancer biology
- stochastic modeling of molecular and cellular systems
- probabilistic reasoning and simulation-based evaluation
- scientific communication through reproducible workflows and analysis reports
The work in this portfolio reflects a consistent interest in translating messy real-world systems into tractable computational models, then testing, visualizing, and interpreting the results with rigor and reproducibility.
Focus: Variant analysis in BRCA1 using public sequencing data
This project examines BRCA1 variant patterns across public SRA datasets and emphasizes heterozygous variant discovery, summarization, and interpretation. The workflow includes data acquisition, alignment, variant calling, and filtering using command-line bioinformatics tools.
Key skills demonstrated:
- Bash scripting and workflow automation
- command-line bioinformatics pipelines
- read alignment and variant calling
- VCF filtering and genomic interpretation
Relevant files:
BRCA1_Analysis/total_pipeline.shBRCA1_Analysis/get_files.shBRCA1_Analysis/BRCA1_analysis.pdf
Focus: Stochastic modeling of microtubule assembly and instability
This project models microtubule growth using a stochastic lattice framework to study how GTP/GDP state, local interactions, and hydrolysis influence protofilament behavior over time. The simulation is designed to test mechanistic hypotheses about growth dynamics and instability.
Key skills demonstrated:
- Python-based scientific computing
- stochastic modeling and Monte Carlo simulation
- numerical simulation and algorithm design
- biological interpretation of dynamic systems
- visualization and scientific reporting
Relevant files:
Microtubule_Dynamics_Simulation/Microtubule_Assembly.ipynbMicrotubule_Dynamics_Simulation/Microtubule_Assembly_Stochastic_Model.pdf
Focus: Simulation of validator overlap and shard compromise risk
This project applies probabilistic modeling and Monte Carlo analysis to study how validator overlap across shards changes system risk. It is a strong example of simulation-based systems reasoning and quantitative risk assessment outside the biological domain.
Key skills demonstrated:
- probabilistic modeling and simulation
- Monte Carlo analysis
- distributed systems reasoning
- security-risk tradeoff analysis
- data-driven experimentation and visualization
Relevant files:
Probabilistic_Cross-Shard_Validator/project_code.ipynbProbabilistic_Cross-Shard_Validator/Probabilistic_Cross_Shard_Validator.pdf
Focus: Protein-ligand interaction analysis in sensory receptor biology
This project explores the structural and mechanistic basis of T1R receptor binding and integrates computational analysis with biological interpretation. It emphasizes how quantitative analysis can support mechanistic hypotheses in molecular biology.
Key skills demonstrated:
- molecular interaction analysis
- biological modeling and hypothesis testing
- notebook-based exploratory analysis
- scientific communication of mechanistic findings
Relevant files:
T1R_Binding_Mechanism/project.ipynbT1R_Binding_Mechanism/T1R_proteins.pdf
- Python and Jupyter workflows
- Bash and shell scripting for reproducible pipelines
- statistical analysis and probabilistic reasoning
- stochastic simulation and Monte Carlo methods
- bioinformatics and genomic data processing
- scientific writing, visualization, and reporting
- computational modeling across biological and systems domains
I enjoy working at the intersection of scientific inquiry, computation, and decision-making. My approach is grounded in reproducibility, quantitative reasoning, and clear communication: I build models, test assumptions, interpret outcomes, and present results in a way that is both technically rigorous and accessible.
This portfolio reflects a broad computational toolkit and a strong interest in using data and simulation to understand complex systems—whether in biology, biophysics, or quantitative analysis more generally.
I am particularly interested in roles where scientific rigor, computational problem-solving, and data-driven insight are valued. I bring a strong foundation in biological modeling, probabilistic analysis, and reproducible research workflows, with the ability to apply those skills across interdisciplinary technical problems.