Computer Science student focused on systems analysis, process improvement, and designing efficient technical solutions.
- 🎓 Studying Computer Science at The University of Toronto
- 💼 Previously a Software Engineer at Wholeviz & Research Data Analyst at Modality.ai
- 🔭 Currently working on Agentic Project Mentor
- 🌱 Focusing on combining software development with systems analysis to deliver reliable, well-designed technical solutions
- ☁️ I am an AWS Certified Cloud Practitioner with a focus on architecting scalable, cloud-native AI systems
Software Engineer @ Wholeviz (Sep 2025 – Dec 2025)
- Architected a granular Role-Based Access Control (RBAC) system using a Django backend and Next.js frontend, enforcing permissions across datasets, visualizations, and workspace actions
- Integrated guided workflows that reduced manual data preparation time by 60%, allowing users to resolve complex inconsistencies through an intuitive, step-by-step UI
- Developed an AI-driven recommendation engine that enabled users to generate data visualizations 3x faster, utilizing automated dataset profiling to suggest optimal chart types and layout transformations
Research Data Analyst @ Modality.ai (May 2024 – Aug 2024)
- Architected an end-to-end data pipeline leveraging OpenAI’s Whisper to automate the transcription and linguistic analysis of patient audio, reducing manual processing time by 85%
- Engineered a suite of high-precision NLP functions using spaCy and NLTK to extract 25+ high-dimensional linguistic biomarkers from clinical assessments, achieving a 92% reliability rate
- Conducted rigorous analytical validation and comparative statistical modeling to isolate diagnostic signals between patient and control cohorts, culminating in a published research paper on linguistic biomarkers for neurological disorders
| Category | Skills & Technologies |
|---|---|
| Primary Languages | |
| Systems & Low-Level | |
| Specialized & Legacy | |
| Frontend | |
| Backend & Tools | |
| Cloud/DevOps |  |
| Analysis & Systems |
An enterprise-grade multi-agent system designed to automate and scale software development planning.
- Multi-Agent Orchestration: Architected a robust multi-agent backend using LangChain and FastAPI, leveraging LangGraph for the stateful orchestration layer to automate complex software development planning
- Intelligent Export Engine: Engineered an autonomous Exporter Agent that synthesizes raw multi-agent memory into interactive architecture maps and project documentation
- Dynamic Canvas UI: Spearheaded a highly responsive Next.js and Tailwind conversational UI, allowing users to visualize real-time system architectures and interactively refine agentic outputs
An AI-driven digital whiteboard focused on zero-friction ideation via voice-commanded canvas manipulation.
- Voice-First Interaction: Developed an AI-powered whiteboard that enables users to create and modify canvas elements using natural language processing and voice commands
- Session Persistence: Engineered a cloud-sync service using an Express-based gateway and Firebase Firestore to manage complex, voice-generated canvas states
- Real-Time Bridge: Implemented an asynchronous client-side communication layer in Next.js to bridge the Konva interface with the centralized Node.js backend
Predicting societal disturbances 30 days in advance using 10 years of longitudinal socioeconomic data.
- Predictive Modeling: Engineered a Random Forest-based forecasting model utilizing a decade of longitudinal data to capture shifting socioeconomic trends and cyclical patterns, accurately predicting regional unrest with a 30-day lead time
- Feature Engineering: Developed a pipeline for temporal signal processing, utilizing lagged feature transformations to extract predictive signals from high-dimensional socioeconomic datasets
- Model Interpretability: Applied SHAP and feature importance techniques to isolate key drivers of unrest, providing data-driven recommendations for policy analysis