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A structured web version of my education, research experience, selected projects, and core technical skills.

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[01] Education

Iowa State University

Ames, IA / Expected Graduation: May 2028

Bachelor of Science in Software Engineering
Minors in Artificial Intelligence and Data Science

[02] Experience

Computer Vision Research Intern / AI Institute for Resilient Agriculture (AIIRA)

Jun 2026 - Present / Ames, IA

Building computer vision pipelines for agricultural phenotyping, converting drone- and phone-recorded RGB tassel videos from field data collection into 3D point clouds for maize analysis.
Supporting Structure-from-Motion and NeRF-based reconstruction workflows using COLMAP, Nerfstudio, OpenCV, Open3D, CloudCompare, CUDA, and Jetstream2.
Developing data-processing pipelines for raw video, frame extraction, camera pose estimation, NeRF training, point-cloud export, and individual tassel .ply extraction.

Machine Learning Research Intern / Translational AI Center (TrAC)

Aug 2025 - Present / Ames, IA

Expanded a D-ICL benchmark for tabular foundation models by adding 5 public regression datasets, large synthetic regression tasks, and OpenML Yolanda dataset 42705 to the evaluation pipeline.
Ran TabPFN and TabICL experiments across IID/non-IID partitions, 120k-sample large-regression settings, and paper-aligned seeds, reporting RMSE, MAE, R2, and mean/standard-deviation summaries.
Implemented batched regression inference to resolve CUDA memory limits on large test sets and generated reproducible JSON/CSV summaries supporting a paper currently under review at NeurIPS.

Undergraduate Research Assistant / Iowa State University

Jan 2025 - Jul 2025 / Ames, IA

Improved LPBF spatter-tracking accuracy by approximately 20% over manual methods by building a Python computer-vision pipeline with NumPy, Pandas, SciPy, OpenCV, and scikit-learn.
Applied feature extraction and statistical modeling to analyze spatter velocity, size, and ejection angle across LPBF high-speed imaging experiments.
Visualized 30,000 fps high-speed imaging data with Matplotlib, supporting reproducible large-scale ML experiments.

[03] Projects

RunScope - LPBF Process Monitoring Dashboard

Rust / React / TypeScript / WebSockets / SQLite / Jun 2026

Built a research-inspired Rust and React MVP for LPBF process monitoring with real-time telemetry, recipe sequencing, rule-based anomaly detection, SQLite-backed run history, and process simulations for oxygen, temperature, recoater, laser, and spatter behavior.
Developed an async Rust/Axum/Tokio backend with REST APIs, SQLx, and 300 ms WebSocket updates, plus a React/TypeScript dashboard with live charts, alerts, run controls, experiment history, and automated tests.
Repository: github.com/somtri/run_scope.git

SmartSignal - Stock Movement Forecasting Pipeline

Python / Random Forest / Streamlit / Dec 2025

Built a Random Forest stock-direction pipeline using 26 engineered price, volume, volatility, momentum, and sentiment features with automated yfinance ingestion, preprocessing, and model persistence.
Implemented leakage-aware chronological holdout and expanding-window validation, achieving 63.3% walk-forward accuracy on a deterministic market simulation, with Streamlit dashboards for ROC AUC, equity curves, confidence, and feature importance.
Repository: github.com/somtri/smart_signal.git

[04] Technical Skills

Languages

Software / ML / Research

Python, C++, Java, Rust, TypeScript/JavaScript, SQL, R

Frameworks and tools

Research systems / application development

PyTorch, scikit-learn, NumPy, Pandas, OpenCV, React, Streamlit, Axum, Tokio, SQLx, SQLite, Docker, Git, Linux, CUDA, pytest, GitHub Actions

Core competencies

Methods

Machine Learning, Computer Vision, Time Series Forecasting, Statistical Modeling, Feature Engineering, Regression, Classification, Tabular Foundation Models, REST APIs, WebSockets, Batched Inference, Anomaly Detection, Reproducible ML Experiments