Data Science + Computer Science graduate who bridges statistical thinking with strong CS fundamentals. I build end-to-end systems: from exploratory analysis and modeling to clean, efficient implementations that can ship.
🎯 Core Strengths: Data analysis & modeling, algorithmic problem solving, system design, and writing production-quality code
🔬 Technical Focus: Machine learning + experimentation, software architecture, and algorithmic efficiency
💻 Build Philosophy: Write clean, efficient code; design robust systems; solve problems with elegant algorithmic solutions
- Data Science: turn messy data into signal (EDA, features, evaluation, iteration)
- Computer Science: make it reliable + scalable (data structures, algorithms, systems thinking)
- Result: I can take an idea from notebook → application, without losing rigor or engineering quality
3D Interactive ML Visualization Platform Built an immersive 3D web application that transforms complex word embeddings into navigable space environments. Users can explore semantic relationships between words in real-time. Impact: Bridges the gap between advanced NLP theory and practical understanding
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AI-Powered Mental Health Navigator Developed an intelligent chatbot that connects Victoria residents with appropriate mental health services. Processes natural language queries and matches users with resources based on their specific needs. Impact: Addressing critical healthcare accessibility challenges in the community
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Advanced Strategic AI Agent Engineered a competitive game AI using minimax algorithm with alpha-beta pruning optimizations. The agent demonstrates advanced strategic thinking and consistently outperforms human players. Technical Achievement: Efficient tree search with sophisticated evaluation functions
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Custom Playlist Generator Custom Playlist Generator based off of user context (fed via image), mood and music preference. The system analyzes visual context from user images to understand environment and emotional state. Innovation: Multi-modal AI combining image analysis with music recommendation algorithms
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Advanced Data Structures: Trees (AVL, Red-Black, B-Trees), Graphs, Hash Tables, Heaps, Tries, Segment Trees
Algorithm Design: Dynamic Programming, Greedy Algorithms, Divide & Conquer, Graph Algorithms (Dijkstra, A*, DFS/BFS)
Complexity Analysis: Big O notation, time/space optimization, algorithmic efficiency evaluation
Software Architecture: Modular design, scalable system architecture, API design principles
Object-Oriented Design: SOLID principles, design patterns (Factory, Observer, Strategy, Singleton)
Code Quality: Clean code practices, unit testing, debugging, performance optimization
Mathematical Foundations: Discrete mathematics, linear algebra, probability theory, statistics
Computational Thinking: Breaking down complex problems, pattern recognition, abstraction
Optimization: Algorithm optimization, memory management, performance tuning
Mathematically minded: Strong foundation in statistics, probability, and linear algebra
Data Analysis: EDA, data cleaning, feature engineering, and clear communication through visuals
Modeling: supervised/unsupervised learning, embeddings, dimensionality reduction, model selection
Rigor: metrics-first evaluation, validation strategies, error analysis, and iteration loops
- Advanced Algorithms: Graph algorithms (Dijkstra, A*, Floyd-Warshall), Dynamic programming, Greedy algorithms, Backtracking
- Data Structure Implementation: Custom hash tables, balanced trees, priority queues, graph representations
- System Programming: Memory management, concurrency, parallel processing, low-level optimization
- Software Design: Design patterns, SOLID principles, modular architecture, scalable system design
- Database Systems: SQL optimization, indexing strategies, ACID properties, database design
- Computational Complexity: Algorithm analysis, optimization techniques, performance benchmarking
- Machine Learning Engineering: Algorithm implementation, model optimization, data pipeline design
- AI Systems: Search algorithms, game theory, intelligent agents, decision-making systems
- Full-Stack Development: End-to-end application development, API design, database integration
- Data Processing: ETL pipelines, data structure optimization, real-time processing systems
- Performance Engineering: Code profiling, bottleneck analysis, scalability optimization
Staying current with industry best practices and emerging technologies
- Advanced algorithm design and computational complexity analysis
- Distributed systems architecture and microservices design
- High-performance computing and system optimization
- Machine learning system design and MLOps practices
- Database optimization and large-scale data processing
Actively building and learning:
- Algorithm Visualization Tools - Interactive platforms for understanding complex algorithms
- Performance Benchmarking Suite - Tools for analyzing and optimizing code performance
- Distributed System Prototypes - Exploring scalable architecture patterns
- Open Source Contributions - Contributing to algorithmic libraries and CS education tools
I'm particularly interested in:
- 💻 Software Engineer roles focusing on fullstack development and algorithm implementation
- 🤖 AI/ML Engineer opportunities combining CS fundamentals with intelligent systems
- 🔬 Research & Development roles exploring cutting-edge computational problems
- 🚀 Technical challenges requiring strong algorithmic thinking and system design skills
