ICU mortality rate
Took in ICU data, trained various methods to classify and identify mortality rate from biometric data and written notes.
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Apart Research AIxBio Hackathon - Pandemic Early Warning (Multi-Signal Surveillance Evaluation - Track 2). Brings 3+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Python, pandas, and DeBERTa. Education includes Bachelor of Science, Montana State University (2026). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Evaluation and Rating.
Took in ICU data, trained various methods to classify and identify mortality rate from biometric data and written notes.
Developed and submitted an evaluation paper for an AIxBio Hackathon focused on pandemic early warning using multiple surveillance signal types across the 2021–2024 endemic transition and a 2021–2025 influenza control. Assessed quantitative changes and performed detector evaluation by replicating CDC dashboard logic and testing statistical change-point methods against variant-peak ground truth. The work emphasized measurement validity, privacy-relevant aggregation implications, and rigorous replication of established pipelines. • Evaluated NWSS wastewater, Google Trends, Wikipedia pageviews, and HHS hospital admissions signals. • Analyzed novelty-cycle artifacts (e.g., variance compression) and compared against influenza controls. • Conducted LLM-based prompt surveillance sparsity analysis using a DeBERTa classifier on WildChat. • Implemented and benchmarked CUSUM/control-chart detectors using CDC variant-peak ground truth.
Bachelor of Science, Mathematics
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