Master's Student in Computer Science @ UC Davis | Full-Stack Developer | ML Engineer

Currently building intelligent systems that solve real-world problems. Passionate about fintech, algorithmic trading, and scalable web applications.

Statistical arbitrage strategy using autoencoders and ensemble ML models.
Result: 646% return on 14-year backtest of S&P 500 stocks

Tech: Python • Scikit-Learn • XGBoost • PostgreSQL
Highlights:

• ✨ Cointegration analysis for pair selection
• 🎯 PSO optimization achieving 0.93 ROC-AUC
• 📈 42% Sharpe ratio improvement over baseline

Production Flask application with OAuth 2.0 and serverless deployment.
Impact: Automated workflows for thousands of playlists

Tech: Flask • REST APIs • Google Cloud Functions • OAuth 2.0
Highlights:

• 🔐 Secure authentication flow with token management
• ☁️ CI/CD pipeline with serverless architecture
• ⚡ Automated data processing with error handling

Full-stack React application with real-time data visualization.
Impact: Serving 500+ students, 30% engagement increase

Tech: React • Flask/Django • Chart.js • D3.js • PostgreSQL
Highlights:

• 📱 Cross-platform mobile app using JavaScript Bridge
• 📈 Real-time data visualization for complex datasets
• 🎓 Faculty recognition for improving learning outcomes

Languages:

ML & Data:

Web & APIs:

Databases:

Cloud & DevOps:

Finance & Analytics:
Statistical arbitrage • Algorithmic trading • Time-series forecasting • Risk modeling

Data Engineering & Analytics Student Assistant @ UC Davis (Nov 2025 - Present)

• Building enterprise data aggregation systems integrating Salesforce, Banner APIs
• Designing Power BI dashboards for real-time financial analytics
• Implementing ETL pipelines with dimensional modeling (Snowflake schema)

Research Engineer Intern @ JobRobo (Jul 2023 - Mar 2024)

• Developed ML algorithms for startup investment analysis (25% accuracy improvement)
• Built data pipelines processing 1,000+ records for business intelligence
• Created quantitative algorithms for TAM evaluation and market analysis

Post-Quantum Cryptography Security Analysis (Apr 2023 - Jun 2024)
Evaluated NIST-shortlisted PQC algorithms (Falcon, Kyber, Dilithium, Sphincs) for quantum-resistant systems. Analyzed computational complexity across lattice-based, hash-based, and code-based cryptographic schemes.

Current Research: Exploring ML optimization techniques for algorithmic trading efficiency and sublinear algorithm design for large-scale data processing.

Courses:

• Sublinear Algorithms - Designing efficient algorithms for massive datasets; focusing on optimization problems and computational complexity
• Software Engineering - Building production-grade applications with emphasis on system design, testing, and deployment

Goals:

• Master algorithm optimization for real-time trading systems
• Gain hands-on experience building scalable, production-ready software
• Apply sublinear techniques to financial data processing at scale

🔹 Distributed System Design - Exploring Kubernetes and microservices architecture for scalable fintech applications
🔹 Algorithm Research - Applying sublinear algorithm concepts to high-frequency data streams

📚 Learning: Spring Boot • Kubernetes • Sublinear algorithms • Advanced quantitative finance
🎯 Seeking: Summer 2026 SWE Internships in Fintech, Quant Trading, or Full-Stack roles
🌟 Research Interests: Algorithmic trading optimization • High-frequency systems • Post-quantum cryptography • Distributed computing
💼 Career Focus: Building high-performance, scalable financial systems that combine ML with algorithmic efficiency

🎓 4.0 GPA in Master's program at UC Davis
📈 646% return on algorithmic trading strategy backtest
👥 500+ users served by production web applications
🏅 30% engagement increase through interactive platform design

📧 hverma@ucdavis.edu
🔗 LinkedIn: linkedin.com/in/hima-verma

✅ Software Engineering Internships (Summer 2026)
✅ Quantitative Trading & Algorithmic Finance
✅ Full-Stack Development
✅ ML Engineering & Data Science

Available: June - September 2026 | Location: Open to relocation (SF Bay Area, NYC, Remote)