Mathematics & Computational Modeling

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Areas of Interest:

• - Statistical Modeling in Genomics
• - Probability Theory & Its Applications
• - Linear Algebra (The backbone of computational biology)
• - Machine Learning Mathematics
• - Network Theory & Graph Analysis

Current Applications:

• - Statistical analysis of RNA-seq data
• - Probabilistic modeling of gene regulation
• - Matrix operations for sequence analysis
• - Graph theory for regulatory networks
• - Dimensional reduction techniques

Mathematical Tools:

• - R for statistical computing
• - Python's NumPy/SciPy stack
• - Bioconductor packages
• - Mathematical modeling software
• - Custom algorithm implementation

Focus Areas:

• - Bayesian statistics in genomics
• - Time series analysis
• - Differential equations in biological systems
• - Pattern recognition in sequence data
• - Clustering algorithms

Favorite Equations:

$P(A|B) = \frac{P(B|A)P(A)}{P(B)}$ (Bayes' Theorem - Because biology is all about conditional probability)

$H = -\sum_{i=1}^{n} p_i \log_2(p_i)$ (Shannon Entropy - Information theory in genomics)

Always exploring the intersection of mathematics and biology. Because let's face it - biology is just applied math with more coffee breaks.