Category: Cloud Computing, Big Data Analytics

Overview

Built a cloud-based analytics environment on GCP to support large-scale e-commerce data processing using containerized tools, distributed storage, and notebook-based analysis workflows.

What I Built

• Provisioned and configured a GCP VM for cloud-based analytics workflows
• Set up Docker and Docker Compose to run the analytics environment
• Deployed Jupyter Notebook for interactive analysis on the cloud instance
• Moved large CSV datasets into the working environment and HDFS for scalable access
• Used PySpark to load, combine, and preprocess product and review datasets
• Performed exploratory analysis on cleaned data, including customer behavior and price-related patterns

Tech Stack

Python, PySpark, Docker, Docker Compose, HDFS, Jupyter Notebook, GCP VM

Outcome

Successfully deployed a working cloud analytics environment for handling large e-commerce datasets and demonstrated practical experience in combining cloud infrastructure, distributed storage, and notebook-based data analysis.

Links

Category: Sports Analytics, Motion Analysis

Overview

Built a machine learning pipeline to detect motion anomalies in table tennis swings and analyze how swing consistency relates to player experience using large-scale wearable sensor data.

What I Built

• Preprocessed and analyzed 97k+ swing motion records
• Implemented anomaly detection using Isolation Forest
• Built experience-level prediction models using Ordinal Logistic Regression and XGBoost
• Performed model tuning and evaluation under class imbalance conditions
• Analyzed relationship between anomaly frequency and player experience

Tech Stack

Python, Scikit-learn, XGBoost, Pandas, NumPy, Matplotlib

Outcome

Models successfully identified anomalous swing patterns and showed that higher-experience players generally exhibit more consistent motion with fewer anomalies.

Links

Category: Finance, Generative-models, Time-series

Overview

Built a deep learning pipeline to generate synthetic Limit Order Book (LOB) sequences using TimeGAN, aiming to reproduce realistic statistical and temporal patterns of high-frequency financial market data for simulation and analysis.

What I Built

• Developed a preprocessing pipeline to convert raw high-frequency LOB snapshots into sequential model-ready training data
• Engineered 43 standardized LOB features from LOBSTER AMZN Level-10 data, including relative prices, log-transformed volumes, spread, mid-price return, and imbalance
• Implemented a full TimeGAN architecture with Embedder, Recovery, Supervisor, Generator, and Discriminator modules using GRU-based recurrent networks
• Trained the model in two phases: supervised reconstruction / latent transition learning followed by adversarial refinement
• Performed hyperparameter tuning with Optuna across 20 trials to identify more stable configurations
• Experimented with an enhanced variant using empirical latent sampling, AR(1) latent correlations, stronger regularization, and extended training
• Evaluated synthetic data quality using KL divergence, SSIM, AR(1) temporal gap, latent divergence, and representative heatmap comparisons

Tech Stack

Python, PyTorch, NumPy, Pandas, Matplotlib, Optuna

Outcome

The project successfully produced a working TimeGAN pipeline for synthetic LOB generation and improved training stability through tuning and enhanced latent modeling. However, the final models did not meet the target realism thresholds: KL divergence and SSIM remained well outside target ranges, and the generated depth maps were still overly smooth compared with real market data. The project highlighted both the feasibility and the practical difficulty of applying GAN-based sequence generation to high-frequency financial microstructure data.

Links

Category: Natural Language Processing, Information Retrieval

Overview

Implemented and evaluated a Retrieval-Augmented Generation (RAG) system for multi-hop factoid question answering across a 609-document corpus and 2,556 queries, comparing retrieval strategies and downstream language models to improve answer relevance and factual consistency.

What I Built

• Implemented a two-stage experimental pipeline covering retrieval evaluation and downstream RAG answer generation
• Compared six retrieval variants including BM25 sparse retrieval, dense bi-encoder retrieval, hybrid sparse-dense fusion, and cross-encoder reranking
• Integrated top-ranked retrieval outputs with four instruction-tuned 7B language models: Falcon, Dolphin, Qwen2, and Mistral
• Evaluated retrieval performance using Hits@10, MAP@10, MRR@10, and NDCG@10
• Evaluated answer generation quality using Precision, Recall, F1, and ROUGE-L
• Analyzed how retrieval quality and LLM behavior interact across inference, comparison, and temporal query types

Tech Stack

Python, BM25, Sentence Transformers, Cross-Encoder Reranking, PyTorch, scikit-learn, Hugging Face Transformers

Outcome

Dense retrieval methods substantially outperformed the BM25 baseline on multi-hop queries, with Rankers E and F achieving the strongest retrieval performance. In the generation stage, the Falcon/F pipeline achieved the best overall balance, reaching F1 = 0.28 and ROUGE-L = 0.29, showing that stronger semantic retrieval and instruction-tuned generation together improved answer quality for multi-hop QA.

Links

Category: Backend Systems, Cloud Platform

Overview

Built the backend platform for FrostFlow’s Proof-of-Concept, focused on ingesting device telemetry and wearable health data, normalizing them into a unified model, and exposing APIs for internal Web UI and system validation.

What I Built

• Implemented serverless REST APIs for telemetry, device state, command dispatch, and health data access
• Designed and applied a canonical health data model to normalize wearable metrics across integrations
• Built backend ingestion flows for Fitbit data using OAuth2 authorization and server-side token exchange
• Stored telemetry and health samples in DynamoDB using time-series oriented access patterns
• Developed a lightweight internal Web UI to validate end-to-end device → cloud → UI behavior and support command testing

Tech Stack

AWS Lambda, API Gateway, DynamoDB, TypeScript, Node.js, Fitbit Web API, OAuth2, Internal Web UI

Outcome

Validated the FrostFlow backend architecture for ingesting telemetry and wearable health data into a unified cloud platform. The PoC established a working serverless API layer, Fitbit integration flow, normalized health data model, and internal Web UI for end-to-end system verification.

Links

• Codebase: Private company repository

Category: Embedded Systems, Edge Computing

Overview

Developed an end-to-end IoT device emulator stack that reproduces how an embedded device communicates with cloud services through a layered gateway architecture. The system enabled full device-to-cloud testing without requiring physical hardware.

What I Built

• Implemented MCU firmware simulation for telemetry generation and command handling
• Built gateway services to bridge MCU-side communication and cloud connectivity
• Implemented MQTT-based telemetry and command exchange with AWS IoT Core
• Added telemetry buffering to reduce data loss during network interruptions
• Built end-to-end command routing from cloud services to the emulated device
• Implemented an OTA update agent to support device software update workflows
• Added service supervision and containerized process management for runtime reliability
• Designed the stack to support integration with a Rust + Slint desktop GUI for monitoring and control

Tech Stack

C, Python, MQTT, AWS IoT Core, SQLite, Docker, systemd, Unix sockets, Rust, Slint

Outcome

Created a modular IoT emulator stack that enabled rapid validation of telemetry, command routing, and OTA update flows without physical hardware. The architecture provided a practical foundation for device-to-cloud testing and future evolution toward Rust-based edge services.

Links

• Codebase: Private company repository

Category:

Full-Stack / Backend / Cloud Deployment

Overview

Shiroku is a post-payment monitoring and anomaly detection platform designed for solo founders and small business owners. The product focuses on helping merchants detect unusual refund spikes, transaction anomalies, and other post-payment issues without needing enterprise-scale tooling.

I worked on Shiroku as a full-stack product and engineering project, covering backend development, deployment, CI/CD, release workflow design, and production environment setup. The platform includes a FastAPI backend, a React frontend, Docker-based local development, and production deployment across Vercel and Railway. The application also includes a waitlist-first landing page integrated with branded domain email onboarding.

What I Built

I built and refined the backend and deployment foundation for Shiroku, with particular focus on release quality, developer workflow, and production readiness.

On the backend side, I worked with FastAPI, SQLAlchemy, Alembic, and PostgreSQL to support a structured application foundation for uploads, transaction analysis, authentication-related flows, and future anomaly detection services. I also worked within a Docker-based local setup so the application could be developed and run consistently across environments.

On the product delivery side, I implemented and hardened the CI/CD pipeline. This included:

• separating automated test execution from image publishing
• making Docker image publishing depend on successful test completion
• creating a manual dev to main release workflow
• hardening the release workflow with preflight checks for stale release branches, tags, and partial reruns
• aligning main back into dev after release so versioning remained consistent for future releases

I also helped establish the production deployment flow using:

• Vercel for the frontend
• Railway for the backend and database
• GHCR for Docker image publishing
• custom domains including shiroku.app and api.shiroku.app

A major part of the work was not just shipping code, but designing a release process that was safe enough to use repeatedly as the project matured.

Tech Stack

• Frontend: React, Vite, Tailwind CSS
• Backend: Python, FastAPI
• Database: PostgreSQL
• ORM / Migrations: SQLAlchemy, Alembic
• Data Processing: Polars
• Email / Waitlist: Resend
• Deployment: Vercel, Railway
• Containers / Infra: Docker, GitHub Container Registry (GHCR)
• CI/CD: GitHub Actions

Outcome

The result was a working production setup with a cleaner engineering workflow behind it.

By the end of this phase, Shiroku had:

• a live branded frontend at shiroku.app
• a live branded backend API at api.shiroku.app
• a test-gated CI pipeline so broken dev changes would not publish images
• a manual but structured release workflow from dev to main
• a follow-up alignment process to keep dev and main on the same release baseline
• a stronger foundation for future beta and product expansion

This project was especially valuable because it forced me to move beyond just writing features and into real release engineering decisions: versioning, environment setup, deployment safety, branch strategy, workflow recovery, and production consistency.

Links

• Shiroku