Research Projects

My research spans computer vision, machine learning, and their applications to science and conservation. See also Google Scholar for a full list of publications.

Fine-grained Recognition and Texture Understanding

A long-standing theme in my work is understanding visual categories that require fine-grained discrimination — species, materials, textures, and scenes. Early work established bilinear CNN models and deep filter banks that capture second-order statistics for texture and fine-grained recognition. More recently we have studied how vision-language models (VLMs) can be adapted for zero-shot and few-shot recognition, and how to improve transductive inference for fine-grained tasks.

3D Shape Understanding and Synthesis

We develop methods for representing, reconstructing, segmenting, and generating 3D shapes. Early work on multi-view CNNs and SPLATNet established strong baselines for 3D shape recognition. Later work explored learning structured shape representations (CSGNet, ParSeNet), unsupervised shape generation from 2D views, and differentiable reconstruction. More recently we have extended this to 3D-aware image editing and novel view synthesis using neural radiance fields.

Few-shot, Meta-learning, and Self-supervised Learning

We study how to learn visual representations and models that generalize from few examples. This includes meta-learning algorithms for few-shot classification, task embedding methods that characterize dataset complexity, self-supervised and semi-supervised learning for fine-grained categories, and analyzing when self-supervision helps few-shot learning.

Ecological Remote Sensing

We develop computer vision and machine learning methods for monitoring the natural environment at scale using weather radar and satellite imagery. In collaboration with ecologists and hydrologists, we have built systems for detecting bird migration patterns and communal roosts in Doppler radar, estimating historical migration phenology, and monitoring rivers from space using high-resolution satellite data.

Biodiversity and Species Understanding

We collaborate with biodiversity informatics researchers to develop machine learning methods for understanding species distributions and identification at scale. This includes learning species range maps from community science observations combined with language, building large-scale sound datasets for bioacoustics monitoring, and benchmarking multimodal models on fine-grained species identification tasks.

Active Learning and Scalable Estimation

We develop statistically efficient methods for estimation and counting in large image collections, motivated by ecological and scientific applications where ground truth labels are expensive. This includes importance sampling approaches for counting objects in aerial surveys, human-in-the-loop visual re-identification for population size estimation, and active measurement methods for estimating statistics of scientific images at scale.

AI for Physical Sciences

We apply computer vision to problems in astronomy and materials science. In astronomy, we have developed deep learning methods for classifying star clusters in Hubble Space Telescope images and built AI-assisted tools for cataloging galaxies at scale. In materials science, we have developed 3D CNNs for predicting adsorption properties of nanoporous zeolites from their molecular structure.