Python

Advanced4+ years experienceProgramming Languages4 internships1 job2 research roles1 teaching role2 certifications1 publication

Proficient with extensive hands-on experience in production environments

My Experience

My go-to language for data science, machine learning, and backend development. Specialized in data processing, conversational AI systems, and environmental data pipelines with extensive experience across research, industry, and academic settings.

Internships

INTERA Incorporated (Data Science)Pivotal Research Inc.INTERA Incorporated (Data Engineering)Momentum Technologies

Jobs

PitchFact

Research Roles

TAMU x Soft Interaction LabAlgoverse

Teaching Roles

CSCE 221 Teaching Assistant

Certifications

Data Science Math Skills (Coursera)Scientific Computing with Python (freeCodeCamp)

Publications

ChatGPT 4o Performance on Mechanical Engineering Concept Inventories

Technical Deep Dive

Core Concepts I'm Proficient In:

• Data Processing & Analytics: Advanced Pandas operations for DataFrame manipulation, Excel file processing, row/column transformations, and complex data aggregation workflows

• Asynchronous Programming: Async/await patterns for full-stack communication, enabling efficient frontend-backend data flow and real-time system interactions

• Type Safety & Data Validation: Type hints implementation combined with Pydantic for robust data validation, especially in LLM training pipelines and API development

• Object-Oriented Design: Strategic use of dataclasses for structured data representation (with performance considerations in mind)

• Data Visualization Ecosystem: Advanced plotting and interactive dashboard creation using Matplotlib, Plotly, and Dash for business intelligence applications

Advanced Libraries & Integration:

• Pydantic: JSON schema validation and data serialization for LLM training pipelines processing 500+ PDFs weekly

• HTTP Communication: Advanced usage of Requests library for API integrations, data collection workflows, and external service communications

• Cloud Integration: AWS S3 operations using boto3 for cloud-based data storage and retrieval

• Web Frameworks: Flask for research applications serving 500+ SANE nurses, FastAPI for high-performance API development

• Database Integration: SQLAlchemy ORM for complex data modeling and large-scale transaction processing

• Automation: Selenium for web scraping and automated data collection from environmental sources

Complex Problem-Solving Examples:

Environmental Data Pipeline Challenge: Architected and implemented a comprehensive carbon footprint tracking system at INTERA Incorporated that automated data collection from multiple company locations using Selenium, processed complex environmental datasets with Pandas, and created interactive visualizations using Plotly and Matplotlib. The system identified key emission sources across different departments and geographical locations, providing actionable insights through automated reporting. This technical solution directly influenced the company founder to launch enterprise-wide carbon reduction initiatives, demonstrating how technical expertise can drive strategic business decisions and improve ESG investment appeal.

High-Performance LLM Pipeline: At PitchFact, designed and built FastAPI-based LLM processing pipelines handling 500+ PDF documents weekly, integrating Pydantic for robust data validation and Pandas for complex startup evaluation analytics. The system required optimization for concurrent processing and reliable data transformation across diverse document formats.

Medical AI Research Application: Developed Flask-based conversational AI tools at TAMU's Soft Interaction Lab serving 500+ SANE nurses, integrating Firebase for real-time data synchronization and implementing sophisticated prompt engineering techniques for medical training applications.

Areas for Continued Growth:

• Performance Optimization: Expanding expertise in Python profiling, multiprocessing, Cython, and advanced performance tuning techniques

• Cloud Architecture: Deepening AWS services knowledge beyond S3, exploring serverless architectures and distributed computing

• Advanced ML Operations: MLOps practices, model deployment, and production ML system optimization