A showcase of AI and data science projects that demonstrate real-world impact and technical excellence across various domains.

Personal Project • Mar 2026
Built a traveler-facing web app that combines checkpoint wait data, live flight boards, nearby air traffic, route-level delay risk, and expiring traveler reports in a single airport view.
Impact:
Helps travelers make faster departure decisions by consolidating fragmented airport signals into one live interface.
Tech Stack:

Personal Project • Feb 2026 - Present
Built a customer-only news intelligence portal with hourly RSS ingest, PostgreSQL-backed search, token-gated APIs, and filtered article access by section, source, country, and date.
Impact:
Moves high-volume news monitoring from static exports to authenticated, scalable, customer-specific access.
Tech Stack:

Deloitte • 2024 - Present
Led the data pipeline and audit-risk labeling workflow for global news intelligence.
Impact:
Keeps audit-risk data structured, current, and reliable for downstream RiskSensing, API Platform, and Omnia teams.
Tech Stack:
Personal Project • Dec 2025 - Present
Anonymous voice support app for people preparing for jobs or major exams, or pushing through burnout and depression. No follows, no networking, no influencer dynamics.
Impact:
Creates a safe, low-pressure space where encouragement feels human and sincere.
Tech Stack:
Personal Project • Aug 2025 - Oct 2025
Mobile invoicing for field technicians, dog walkers, and private tutors. Create invoices on-site with voice input or templates, attach proof photos, and share with clients immediately.
Impact:
Replaces paper forms with faster billing, clearer documentation, and more professional client communication.
Tech Stack:

Personal Project • Jul 2025 - Present
Built a cross-platform CLI for Claude Code, OpenAI Codex, and Gemini CLI that sends desktop, sound, and voice notifications when tasks complete or user input is needed.
Impact:
Reduces context switching for AI-assisted development with native notifications, project-specific settings, and simple Homebrew installation across macOS, Linux, and Windows.
Tech Stack:

GWU Research Lab • 2023-2024
Led the end-to-end development of an intelligent nursing diagnostic system. I designed and implemented a Retrieval-Augmented Generation (RAG) system that references a knowledge base of 80+ documented nursing scenarios. When new patient data is entered, the system retrieves the top 3 most similar scenarios to inform its diagnostic suggestions.
Impact:
Crucially, I architected a Human-in-the-Loop (HITL) feedback mechanism. Nurses can provide feedback on the AI's suggestions, which is then vectorized and stored in our Deeplake (Vector DB). This creates a self-improving system where accuracy and relevance continuously increase with each interaction.
Tech Stack:

GWU Research Lab • 2023-2024
I was responsible for the entire audio processing pipeline. My primary role was to extract and analyze audio from raw video footage, tackling the significant challenge of low-quality audio in Korean. I developed a noise reduction process using spectral subtraction and a filtering logic to isolate the child's voice from background noise and parental speech, significantly improving the quality of data for the model.
Impact:
This work was critical for enabling the analysis of 'in-the-wild' videos, a key goal of our research. By successfully processing the audio data, I helped create a system that provides objective, data-driven insights to support clinicians, making behavioral analysis more efficient and accessible.
Tech Stack:

Atos Zdata • 2023
Developed a private LLM with RAG using LangChain and vector databases (FAISS, Qdrant) to support Q&A, summarization, and enterprise document retrieval. Built an auto-updating vector index that detects document changes in real time and compared LLMs (Llama-2, Falcon, GPT4ALL) for accuracy and latency.
Impact:
Enabled automated draft responses for RFP/RFI/SoW workflows and faster retrieval across internal knowledge bases.
Tech Stack:

GWU Research Lab • 2023
Developed a multimodal AI system enabling the Pepper robot to navigate autonomously. The robot uses Microsoft HoloLens for real-time environment scanning, obstacle detection, and spatial mapping.
Impact:
This research aimed to give Pepper spatial awareness for free movement in new environments, with future goals of recognizing and remembering individuals. LLM was used for conversational interaction.
Tech Stack:

GWU Research Lab • 2023
I took over a stalled project that used a traditional NLP model and a Unity 3D avatar. I completely redesigned the system by integrating the GPT API for fluid conversation and OpenAI's Whisper API for robust speech-to-text and text-to-speech capabilities. The virtual avatar was replaced with a physical Pepper robot for tangible user interaction.
Impact:
This overhaul transformed a non-interactive prototype into a successful project. The new system was not only presented at a university poster session but was also significant enough for my supervising professor to present at an academic conference.
Tech Stack:

Bauman Moscow State Technical University (Bachelor's Thesis) • 2022
Developed a novel methodology combining Finite Element Analysis (FEA) with Machine Learning to predict structural integrity in aerospace components.
Impact:
Achieved 95-97% predictive accuracy by generating a proprietary dataset from scratch via complex ANSYS simulations.
Tech Stack: