# Super Greedy Trees

> **NIH NIH R35** · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · 2024 · $422,125

## Abstract

Project Summary/Abstract
We identify critical weaknesses with Classification and Regression Trees (CART), a widely used base learner
for machine learning of big omic-data analysis, and propose to replace these with a fundamentally different type
of base learner we call super greedy trees (SGT's). SGT's cut the space in a fundamentally different manner,
resulting in a richer partition structure with provable consistency and superior empirical performance. The project
will develop a unified SGT framework for big data analysis using machine learning including the treatment of time
varying covariate survival analysis, unsupervised learning, highly imbalanced data and multivariate regression.
The SGT framework will be deployed within scalable and extensible open source software that will allow NIGMS
researchers to deploy them to deal with their challenging big data problems.

## Key facts

- **NIH application ID:** 10851747
- **Project number:** 5R35GM139659-04
- **Recipient organization:** UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
- **Principal Investigator:** Hemant Ishwaran
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $422,125
- **Award type:** 5
- **Project period:** 2021-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10851747

## Citation

> US National Institutes of Health, RePORTER application 10851747, Super Greedy Trees (5R35GM139659-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10851747. Licensed CC0.

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