Experience

Project Manager of Agentiiv's MCP Gateway ML project, building a production-grade communication platform for AI agents and MCP servers. Defined project requirements, scope, and timelines, while tracking progress and managing the team using Agile Scrum and Jira. Leading system design and implementation, as well as addressing technical challenges.

Developing AuditMate, an AI-driven web and mobile platform that automates building energy audits by guiding users through assessments and analyzing results with object detection, OCR, and LLMs.

Performing data analysis and image preprocessing. Building and integrating machine learning and deep learning pipelines that support both professional auditors and individual clients, enabling self-serve and guided audit experiences.

Deploying the full cloud-based architecture using AWS EC2 and integrating AI services with Next.js and Flutter frontends to deliver scalable, accessible applications.

Full-stack developer for GenAI Genesis 2026, Canada's largest AI hackathon with 600+ participants in 2025. Building and maintaining the website, participant and judging platforms, workflow tools, and support systems.

Designing and implementing secure REST APIs, database schemas, role-based access control, and user interfaces to ensure scalable and secure data management.

Collaborating with cross-functional teams to deliver a seamless and successful hackathon experience.

Leading development of the SceneClarity ML project, an interpretable deep learning pipeline for autonomous vehicle perception, and managing the project using Jira. Analyzing and augmenting large datasets, and building ML models for object detection (YOLOv11, LaneNet), classification (MobileNetV3, ResNet-50), and unsupervised cause attribution (KMeans, GMM).

Integrating outputs from multiple models and researching scoring and weighting methods to reflect real-world autonomous vehicle safety. Delivering Dockerized REST APIs and a React web app for use by other researchers and developers. Presented the project to stakeholders at various events and conferences, including the EigenAI 2025 Conference.

Developing machine learning pipelines for the Functional Gene Expression Analysis project to identify disease biomarkers and predict autoimmune diseases. Analyzing multi-dataset gene expression data, training and fine-tuning classification models (linear, tree-based, ensemble, gradient boosting, and SVM), and applying SHAP and LIME methods to interpret disease mechanisms and feature importance.

Building and maintaining a Supabase PostgreSQL relational database to store staged and processed pipeline data.

Official contributor and modder of the starblast.io game. Developed the official “Capture the Flag” mod, which has been played over 2 million times, improving performance, balance, and engagement. Developed multiple mods using the Starblast.io API, implementing real-time game logic in JavaScript with WebSockets, and designing custom 3D ships using CoffeeScript for Three.js rendering.

Mentored and led FTC teams 16488 and 22101. Designed and led the development of PID-based motion control and finite state machine architectures for autonomous robot operation. Implemented real-time object detection using TensorFlow models integrated with OpenCV for vision-based decision making. Developed high-fidelity robotic simulations directly interfaced with the FTC SDK to validate control algorithms and model complex robot behaviors. Won 2nd place at the Ontario Provincial Championships, as well as Innovate and Design Awards.

Designed and developed the web platform using the SvelteKit framework. Engineered intuitive database schemas and relationships in the Firestore cloud database to support core app functionality such as user management and course enrollment. Continuously improved the user experience based on user feedback.

Led, trained, and supervised a team of tutors to teach students in programming, STEM, and languages. Adjusted course trajectory based on student feedback. Developed marketing initiatives to expand the organization’s reach, positively impacting 300+ students across 50+ schools in Ontario.

Worked as a part-time tutor at Upper Markham Learning Centre, teaching coding, robotics, competitive programming, and high school STEM subjects including math and physics. Delivered personalized lessons, managed multi-level classrooms, and provided detailed progress feedback to parents while adapting strategies to support individual learning needs.

Led the coding section at the non-profit organization FutureEd 4 Kids, managing mentors and organizing team meetings. Designed new programming courses to expand the coding section in the organization. Taught students in engaging and interactive classes.

Implemented the Vision Transformer (ViT-B/16) architecture from scratch in PyTorch, following the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale." Manually built all core components, including convolutional patch embeddings, class and positional embeddings, Multi-Head Self-Attention (MSA) and MLP blocks with Layer Normalization (LN) and residual connections, as well as the final classification head.

Used the equations and architectural definitions from the original paper to reason about data flow and tensor transformations throughout the model, explicitly tracking tensor shapes step-by-step from input images to output classification in order to ensure correctness and deepen understanding of the model structure.

Validated the implementation end-to-end by training the model from scratch on a 5-class weather image classification dataset sourced from Kaggle. Documented training simplifications relative to the paper and compared the custom implementation with PyTorch’s built-in ViT.

As a senior member of team Schwarzschildren, conducted in-depth theoretical and experimental research on open-ended physics problems, winning a gold medal in 2023 and a bronze medal in 2024. Researched various physics topics including waves, acoustics, electromagnetism, collision theory, beam theory, fluid dynamics, and optics.

Developed simulations, designed experimental setups, and collected experimental measurements; conducted data cleaning and statistical analysis, interpreting results to validate physical models using MATLAB and Python.

Constructed research reports, presented, and defended findings in formal physics debates, demonstrating strong scientific reasoning and communication.

Conducted controlled measurements to research key pendulum relationships: Period vs Angle, Period vs Length, and Q-factor vs Length. Designed and fabricated a custom experimental apparatus to systematically investigate pendulum characteristics.

Developed a Python-based analysis pipeline in Jupyter Notebooks to extract motion data from Tracker and quantitatively compare results with theoretical predictions. Identified and characterized deviations from standard models at large angles and with non-negligible rotational inertia, proposing a refined pendulum model to account for nonlinear and damping effects.

Conducted research to identify optimal matboard box beam bridge designs using a custom Python structural analysis and simulation tool developed in Google Colab.

Simulated loads and computed Factors of Safety (FOS) across design candidates to evaluate and compare structural performance. Validated theoretical predictions by constructing a physical matboard bridge, achieving a maximum load of 640N, exceeding the 400N baseline by 60%.

Produced a 4000-word research paper integrating empirical data and literature to assess autonomous vehicle (AV) societal and regulatory impacts. Researched and analyzed deep learning methods for AV perception and path-planning, evaluating their technical and ethical limitations. Conducted a primary survey on public AV perceptions, generating quantitative insights on safety and ethics.

Researched and developed a mathematical thrust-curve model for black-powder rocket engines that reflected the propagation of 3D flame-front geometry in a two phase burn. Developed a Python program that extracted rocket motor datasheet features, optimized model constants, and batch-processed CSV outputs.

Achieved low median prediction errors for average thrust (13.42%), maximum thrust (15.07%), and tailing thrust (9.31%), while identifying limitations in total impulse and burn-time predictions.

Conducted experimental research on cantilever dynamics, testing the hypothesis that increasing length decreases stiffness and lowers natural frequency (f ∝ 1/L²).

Analyzed sound waveforms in Audacity by applying noise gating, spectrogram analysis, and Fourier transforms, then performed curve-fitting in LoggerPro to validate the predicted inverse-square relationship.

Investigated the relationship between alcohol chain length (methanol to octanol) and soot production, testing the hypothesis that alcohols with higher molar enthalpies of incomplete combustion produce more soot.

Designed a dual-purpose calorimetry and soot-collection experimental setup to measure heat release and capture soot under controlled airflow. Processed data with Vernier Graphical Analysis and Microsoft Excel, observing a strong linear correlation between soot mass per mole and molar enthalpy (R² = 0.986).