# CRII: RI: Uncertainty Estimation and Robustness in Hierarchical Classification

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Illinois Institute of Technology (IL) · $172,913

## Abstract

Hierarchical classification is the task of categorizing items belonging to a structured hierarchy. Examples of this task include tracking of animals for monitoring variability in species and pests in precision agriculture. Reliable automation of hierarchical classification has the potential to accelerate sustainability efforts and boost agricultural productivity. Machine learning is increasingly being used as an automation tool in ecology and agriculture. However, to fully realize the potential benefits of machine learning in hierarchical classification, principled methodologies are needed to ensure the trustworthiness and robustness of the models used in these applications. This research will develop methodologies for machine learning models to accurately report their uncertainty about predictions and to defer to human experts in a principled, resource-aware manner. Data scarcity and low-quality labels often hinder the effectiveness of machine learning systems. To address these challenges, this research will also develop theory and methodologies to overcome the constraints of scarce and low-quality data, both of which are common in hierarchical classification tasks in practice.

The project will develop isotonic regression methods tailored for uncertainty quantification in hierarchical classification. Moreover, it will design efficient algorithms for learning-to-defer in hierarchical classification. It will also develop hierarchical few-shot learning techniques to address 

## Key facts

- **NSF award ID:** 2451714
- **Awardee organization:** Illinois Institute of Technology (IL)
- **SAM.gov UEI:** E2NDENMDUEG8
- **PI:** Yutong Wang
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** ROBUST INTELLIGENCE, CISE Resrch Initiatn Initiatve
- **Estimated total:** $172,913
- **Funds obligated:** $172,913
- **Transaction type:** Standard Grant
- **Period:** 06/15/2025 → 05/31/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2451714

## Citation

> US National Science Foundation, Award 2451714, CRII: RI: Uncertainty Estimation and Robustness in Hierarchical Classification. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2451714. Licensed CC0.

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