I’m completing the Master of Science in Artificial Intelligence in Business at ASU’s W. P. Carey School of Business, graduating in December 2026. ASU launched it in 2024 as the first AI graduate degree program from a business school in the United States, with an applied curriculum built around real business problems.
Every applied AI project from my coursework and research, the methods behind it and the question it answered. The case studies and research below go deeper on the strongest ones.
| Project | Methods | What it answered |
|---|---|---|
| Underwriting risk model | Regression | How to price risk more accurately |
| Credit risk and churn scoring | Decision tree, random forest, AdaBoost, logistic regression; GridSearchCV; selection by AUC | Which customers are likely to default or leave |
| Loan eligibility app | Classification, deployed with Streamlit | A live web app non-technical users can run themselves |
| Cross-sell propensity | Pruned decision tree; confusion matrix and AUC | Which customers will accept a loan offer |
| Agentic model development | Claude Code, agentic code review, documentation in Linear, Obsidian and GitHub | How to go from a business question to a tested, documented model in one session |
| Language models for gene editing | Evaluation of small language models; research papers | How language models can help design gene-editing enzymes |
| Wearable health patterns | Random forests and neural networks on fitness-tracker data | Which health patterns wearables can reveal for chronic illness management |
| Portfolio segmentation | K-means and hierarchical clustering | How to turn a portfolio into personas executives can act on |
| Customer review sentiment | NLP with VADER, text visualization | What customers are saying, at scale |
| Leadership communication sentiment | NLP with custom metrics for clarity, emotional intelligence and persuasiveness | How effective a team’s written business communication is |
| Political discourse analysis | NLP and sentiment analysis with Voyant | How coverage of an issue shifts across regions and 16 years of debates |
| Interactive dashboards | Tableau, with parameters | How to make data explorable for decision-makers |
| Economic resilience | Data analysis and visualization of GDP, tourism and disaster data | How an economy responds to natural disasters |
| AI tool evaluation | Evaluation framework and presentation | How to judge a new AI tool before adopting it |
| Data governance | NIST and CIA-triad frameworks | How to keep data secure and trustworthy |
From my MS in AI in Business. How I’d govern AI across a company, then six machine learning models, each starting from a business decision, setting its success measure before training, comparing models against a simple baseline and naming its limits.
The platforms, languages and software I use day to day, grouped by what they’re for.
Public, open-source and private models across every kind of work: commercial models and AI assistants including Claude, ChatGPT, Gemini and Microsoft Copilot, with Claude as my main platform for agents, skills and development; small and open-source language models evaluated for gene-editing research; private models deployed on proprietary company data; voice AI models with ElevenLabs; and the machine learning models I build myself, including regression, decision trees, random forests, bagging, AdaBoost, clustering, neural networks and NLP sentiment models.
Claude Code, Claude skills, MCP, prompt engineering, Make, Superblocks, Lovable, Supabase, ElevenLabs
Python, pandas, scikit-learn, TensorFlow, R, MySQL, Jupyter, Google Colab
Tableau, Streamlit, Vercel, interactive data visualization, AI interface design, anime.js
Linear, Wrike, Jira, Obsidian, GitHub, Excel, AI intake and scoring, portfolio tracking, monthly leadership reporting
NIST AI Risk Management Framework, NIST Generative AI Profile, ISO/IEC 42001, confidentiality, integrity and availability for data, AI risk tiering, vendor AI and data disclosure reviews, AI-agent code review
Executive presentations in PowerPoint, live product demos, change management, AI training and curriculum design
Applied machine learning and data research, including research on language models for gene editing that led into my work at Neoclease.
Researched and wrote papers on how language models can be used for gene editing, analyzing the methods and their applications across biotech and pharmaceutical research.
Built an NLP model that evaluates written business communication, such as emails and meeting transcripts, with custom metrics for clarity, emotional intelligence, persuasiveness, adaptability and professionalism, to give actionable feedback for more effective communication.
Used random forests and neural networks on data from wearable fitness trackers to identify meaningful health patterns, exploring how wearables could support chronic illness management.
Applied NLP and sentiment analysis to presidential debate transcripts and newspaper headlines from 2008 to 2024, comparing how coverage of immigration differed between border-region and other cities over 16 years.
Analyzed and visualized decades of data on GDP, household consumption, tourism and natural disasters to show how Costa Rica’s economy, and especially its tourism sector, responds to earthquakes, floods and storms.
Master of Science in Artificial Intelligence in Business
BA in Digital Computational Studies · BA in Sociology