Combining emotional intelligence with advanced analytics to drive meaningful connections and data-driven insights that transform business outcomes.
I'm a Master of Science in Business Analytics graduate from SC Johnson School of Business who enjoys turning complex data into clear, actionable insights. My leadership experience comes from coordinating cross-functional teams, and my Lean Six Sigma Black Belt and Champion credentials aren't just fancy letters after my name; they're my toolkit for creating order from chaos and turning "good enough" into "absolutely exceptional."
At Cornell, I found my sweet spot balancing technical innovation with community building. As Co-Chair and Head of DeFi and Blockchain for our MSBA Quantitative Finance Group, I connected academic research with practical financial applications. As Co-Chair for the Event Management Committee, I created meaningful experiences for our cohort and alumni, because strong relationships are just as valuable as strong analytical models.
My expertise includes financial analytics, business intelligence, product-focused development, and data-driven decision making, supported by hands-on experience with programming languages and analytical tooling. I am highly engaged in the work I do and enjoy collaborating with stakeholders to translate complex data into clear, actionable insights. I design and build analytical products with a focus on efficiency, scalability, and usability, ensuring solutions are practical, maintainable, and aligned with real business needs. I combine analytical rigor with strategic thinking to develop platforms, financial models, and community tools that bridge innovation with real business value.
Academic foundation in business analytics and finance from top-tier institutions.
A showcase of my work in Machine Learning/Artificial Intelligence, Analytics, and UI/UX Designs.
Capstone research modeling indicators that distinguish successful startups from failing ones, validated through IPO outcomes
Capstone Project Summary – As part of my graduate capstone, our team designed and built an end-to-end analytics dashboard for a simulated investment firm, Big Red Capital. The project focused on translating business strategy into measurable execution by combining project management, performance analytics, and financial insight. We developed a dynamic dashboard that tracked weekly tasks, milestones, KPIs, and risk status using a RAG framework, with all data entered through the UI and persisted across sessions. The solution emphasized clarity, accountability, and decision support, enabling leadership to monitor progress, identify bottlenecks, and adjust priorities in real time.
The project was selected by faculty for final-day presentation at Cornell Tech’s Bloomberg Center, presented to students and leadership from the SC Johnson College of Business.
Machine Learning-driven mobile application for personalized running performance optimization
Cornell Machine Learning Project - AI and machine learning-driven mobile application designed to improve running performance through personalized music recommendations. Integrates Apple HealthKit and Spotify APIs to dynamically adapt playlists in real-time based on runner's physiological state. Features supervised learning models, predictive analytics for song recommendations, and personalized performance optimization algorithms. Developed in partnership with Maya Andrews using SwiftUI and advanced ML techniques.
Cracking the Billboard Hot 100 - Advanced Analytics Competition
Top 5 - Advanced machine learning analysis of Billboard Hot 100 data combined with Spotify audio features. Built predictive models using Random Forest, XGBoost, and Neural Networks achieving 81% accuracy in predicting song rankings. Analyzed 50+ years of music data to identify key patterns in successful songs across genres and decades.
Professional dashboard for comprehensive stock market analysis and investment insights
Interactive Financial Dashboard - Comprehensive stock market analysis across 7 major companies (Tesla, Meta, Microsoft, NVIDIA, Visa, Walmart, UnitedHealth). Features advanced price trend analysis, volume patterns, moving averages, volatility tracking, and quarterly performance metrics spanning 50+ years of historical market data. Built with professional Tableau development and business intelligence techniques.
AI-powered DeFi solutions with integrated smart contract optimization
AI-powered DeFi ecosystem with smart contract optimization and machine learning analytics. Features gas optimization algorithms, predictive yield farming models, and automated risk assessment protocols developed in Remix IDE with comprehensive testnet validation.
Advanced AI-driven analytics platform with machine learning models for DeFi risk assessment and predictive market analysis. Features real-time optimization algorithms, automated trading signals, and intelligent portfolio rebalancing strategies.
AI-enhanced blockchain supply chain solution with smart contract automation and machine learning optimization. Features intelligent logistics routing, predictive demand forecasting, and automated compliance monitoring with gas-optimized contracts.
Comprehensive event management platform for Cornell SC Johnson cohort and alumni events. Features event creation, RSVP management, and community engagement tools designed to foster inclusion and strengthen social connections across the program.
Professional platform showcasing our Quantitative Finance Group speakers and events with integrated registration system. Features speaker profiles, event calendar, and streamlined sign-up process for finance professionals and students.
Innovative platform for the Cornell MSBA program enabling external contributions and rewards. Features hackathon organization, analytics job board, and contribution tracking system to expand the program's reach beyond traditional boundaries.
Comprehensive real estate investment analysis platform with renovation ROI calculator, property appraisal analyzer, and data-driven investment decision tools. Features walkability scoring, market multipliers, and interactive property location mapping.
Momentum-based AMZN strategy applying custom-coded RSI and MACD indicators. Showcases full trading logic and entry/exit conditions in Python.
Advanced .NET 9.0 algorithmic trading engine designed for high-performance order matching, market data processing, and real-time trade execution. Foundation for sophisticated trading systems with order simulation capabilities.
AI-powered token sniping bot with machine learning rug pull detection and intelligent trading algorithms. Features real-time pattern recognition, optimized gas strategies, and predictive market analysis with Solidity smart contract integration.
Advanced machine learning model for SPY stock price prediction using neural networks, feature engineering, and comprehensive backtesting framework with risk management protocols.
Comprehensive deliberation on governance and accountability for Solana-based crypto trading bots. Academic research project examining ethical AI principles, decision-making transparency, and responsible automation in DeFi environments. Features detailed analysis of bot accountability frameworks and user trust mechanisms.
Interested in discussing opportunities? Send me a message and I'll get back to you within 24 hours.