# Advancing nonparametric regression: from convex multi-index and Bayesian models to BART and neural networks

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · University of California-Berkeley (CA) · $175,000

## Abstract

This project addresses fundamental challenges in statistical modeling,
to develop more accurate and reliable methods for
analyzing complex data. As data becomes increasingly central to
scientific discovery, economic prosperity, and national security, the
need for advanced statistical tools is paramount. This research will
create new techniques in nonparametric regression, a field of
statistics focused on fitting models to data without pre-supposing the
relationship's form. It confronts three recurrent obstacles in
analyzing large datasets -- curse of dimensionality, ad-hoc tuning
choices, and the tension between flexibility and interpretability -- by
developing principled regression and density-estimation tools, thereby
improving our ability to interpret complex information. The work
forges new links between shape-constrained nonparametric methods and
neural networks, adapts ideas from image processing to statistics, and
also unites frequentist and Bayesian thinking through simple,
intuitive priors. The development of these methods will have
wide-ranging benefits in many applied fields. Furthermore, this
project will contribute to the education and training of the next
generation of statisticians and data scientists, ensuring that the
nation remains at the forefront of this critical field.

The investigator will develop a suite of novel approaches to
nonparametric regression. One area of focus is a new shape-constrained
method for multi-index convex re

## Key facts

- **NSF award ID:** 2515470
- **Awardee organization:** University of California-Berkeley (CA)
- **SAM.gov UEI:** GS3YEVSS12N6
- **PI:** Adityanand Guntuboyina
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** STATISTICS
- **Estimated total:** $175,000
- **Funds obligated:** $175,000
- **Transaction type:** Standard Grant
- **Period:** 08/15/2025 → 07/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2515470

## Citation

> US National Science Foundation, Award 2515470, Advancing nonparametric regression: from convex multi-index and Bayesian models to BART and neural networks. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2515470. Licensed CC0.

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