I am a Computer Science and Biology student at Brandeis University with a strong interest in machine learning, scientific computing, and data-intensive research systems. My work sits at the intersection of software engineering and computational science, where I enjoy turning large, messy datasets into reliable pipelines, usable APIs, and faster analysis workflows.

I have worked on computational neuroscience, computational biology, and machine learning engineering projects across research labs and industry. Recently, I have been building high-performance APIs for 100GB+ 2-photon microscopy data, optimizing ETL workflows for single-cell RNA sequencing data, and developing tools for benchmarking deep learning models on high-dimensional sequence tasks.

Interests

• Machine learning and scientific computing
• Research software engineering for large-scale data
• Data pipelines, numerical optimization, and performance tuning
• Applied AI for biology and neuroscience

Selected Experience

• Computational Neuroscience Research, Van Hooser Lab
  Designed high-performance scientific computing APIs for large microscopy datasets, improving ROI extraction speed and reducing end-to-end latency.
• Machine Learning and Data Engineering Intern, Noah AI
  Built ETL pipelines for high-dimensional scRNA-sequencing data and optimized sparse matrix operations to reduce memory usage and accelerate downstream workflows.
• Computational Biology Research, Kadener Lab
  Implemented gene regulatory network inference and clustering workflows in R using SCENIC and Seurat for single-cell analysis.

Technical Strengths

• Languages: Python, Java, C, C#, R, MATLAB, Bash
• ML and data: PyTorch, NumPy, Pandas, scikit-learn, SciPy
• Tools: Git, Linux, shell scripting

You can find a fuller summary of my experience on the CV page and selected work on the Portfolio page.