Pietro Perona
Allen E. Puckett Professor of Electrical Engineering
Research interests: machine vision, visual psychophysics, machine learning, signal processing
Overview
Professor Perona's research focusses on vision: how do we see and how can we build machines that see.
Professor Perona is interested in visual recognition, more specifically visual categorization. In collaboration with his students, he develops algorithms to enable machines to learn to recognize frogs, cars, faces and trees with minimal human supervision, and to enable machines to learn from human experts. His project `Visipedia' has produced two smart device apps (iNaturalist and Merlin Bird ID) that anyone can download to their smart device and use to recognize the species of plants and animals from a photograph.
In collaboration with Professors Anderson and Dickinson, professor Perona is building vision systems and statistical techniques for measuring actions and activities in fruit flies and mice. This enables geneticists and neuroethologists to investigate the relationship between genes, brains and behavior.
Professor Perona is also interested in studying how humans perform visual tasks, such as searching and recognizing image content. One of his recent projects studies how to harness the visual ability of thousands of people on the web to crowdsource the annotation of images.
Professor Perona is committed to developing responsible artificial intelligence (AI) algorithms. He works on developing experimental methods for assessing algorithmic accuracy and bias in face recognition and other applications of computer vision.
Related News
Read more newsPublications
- Israel, Uriah;Marks, Markus et al. (2024) A Foundation Model for Cell SegmentationbioRvix
- Zhang, Tony;Rosenberg, Matthew et al. (2024) Endotaxis: A neuromorphic algorithm for mapping, goal-learning, navigation, and patrollingeLife
- Liang, Hao;Perona, Pietro et al. (2023) Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluation
- Sun, Jennifer J.;Karashchuk, Pierre et al. (2022) BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos
- Sun, Jennifer J.;Ulmer, Andrew et al. (2022) The MABe22 Benchmarks for Representation Learning of Multi-Agent Behavior
- Kondapaneni, Neehar;Perona, Pietro et al. (2022) Visual Knowledge Tracing
- Cole, Elijah;Wilber, Kimberly et al. (2022) On Label Granularity and Object Localization
- Kay, Justin;Kulits, Peter et al. (2022) The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting
- Feng, Qianli;Shah, Viraj et al. (2022) Near Perfect GAN Inversion
- Kittenplon, Yair;Lavi, Inbal et al. (2022) Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer