Baton Rouge, Louisiana, United States
I’m a PhD student in Applied Mathematics at Louisiana State University with a background in Computer Science. My research interests lie in optimization, applied stochastic analysis, and machine learning. I have always had a deep curiosity for data patterns, how numbers tell their own stories and guide impactful decision-making. Motivated and energetic, with an innate drive to take initiative, I have built a strong foundation in data science and machine learning, combining analytical thinking and programming skills with a business-focused mindset and a desire to dive into real-world challenges. I'm always happy to connect here on LinkedIn! As someone who’s constantly learning, I truly value career conversations and insights from others in the field. Whether it's sharing career journeys or chatting about shared interests like data science, math, tech, finance, or life beyond the screen, I genuinely enjoy meeting new people and learning from their experiences. Feel free to reach out anytime! Fun fact: I enjoy playing piano (enthusiastically, albeit not expertly!), chess, going to the gym, and swimming whenever I get the chance. My name is Ngoc (not “Knock” or "Knob"), but you can call me Nora! 😀 One of my favorite quotes is: “Where there’s a will, there’s a way.”
I designed and deployed secure, real‑time IoT data pipelines using Python, InfluxDB (NoSQL time‑series database), Grafana, Mosquitto MQTT broker, and Atsign’s SSH NoPorts™ technology. My work focused on ELT automation, time‑series data engineering, real‑time monitoring, and GenAI applications for IoT devices. • Built an ELT pipeline using MQTT, Telegraf, and InfluxDB to ingest 300K+ time-series sensor records and support sub-second real-time monitoring through Grafana dashboards. • Reduced device setup time by 90% by automating SSH NoPorts™ configuration and MQTT publishing with Python scripts, systemd services on Linux, and launchd agents on macOS. • Automated data cleansing for 8.6K+ daily sensor datapoints by configuring Telegraf to parse JSON payloads, normalize RFC3339 timestamps, and tag device metadata. • Secured IoT communication by integrating Atsign’s NoPorts™ technology for encrypted outbound-only SSH connections, eliminating the need to expose open inbound ports for remote device access. • Developed a local Model Context Protocol server using Python FastMCP and InfluxQL query tools, enabling local LLMs such as Qwen 2.5 through Ollama to retrieve, analyze, and summarize IoT sensor data through natural-language interaction.
The Louisiana Aerospace Catalyst Experience for Students (LaACES) project was funded by the National Aeronautics and Space Administration (NASA) to study the upper atmosphere and light intensity using a specialized payload that flew up to 100,000 feet on a high-altitude balloon. I was selected for the student research team, where my responsibility involved handling data acquisition, processing, and analysis of time-series measurements including altitude, atmospheric pressure, temperature, and light intensity. During development, I led the data handling effort by designing and implementing an end-to-end ETL data pipeline for daily analysis. My contributions include: • Led data science for a 6-person team project, automating GPS atmospheric, workflows and building time-series visualizations. • Automated NMAE log extraction using C++ for 70K+ GPS time-series records, including atmospheric pressure, altitude, and environmental variables, reducing manual setup time by 90% • Developed data cleaning, preprocessing workflows, and Excel export using Python to handle missing values, filter noise, and normalize GPS atmospheric data, ensuring accuracy and consistency across multiple flight datasets. • Created time-series plots and altitude-based visualizations to support post-flight analysis and team reporting. • Conducted anomaly detection and trend prediction using post-flight data to identify unusual environmental patterns and long-term changes. • Enabled stakeholders to explore environmental trends through clear visualizations and reliable post-flight datasets. Our team successfully completed flight operations and presented our results at NASA’s Columbia Scientific Balloon Facility in Palestine, TX.
• Tutored over 200 students in one-to-one and group settings in a variety of mathematics and programming courses • Advised students on test materials and provided weekly feedback on coding assignments and problem sets.
• Conducted research include CFD for fluid modeling using 3D application (Blender) to demonstrate the predictions of fluid-flow on the local area • Laveraged Python API to extract data from the fluid simulation
• Communicated with managers to set up campus computers used on Prometric testing services • Checked-in and monitored test takers’ information during examination process • Maintained computers, testing equipment, and electronic reports