I am a joint PhD student in Neural Computation and Machine Learning at Carnegie Mellon University. I am advised by Mike Tarr and Leila Wehbe.
I study visual and semantic processing in the human brain. In my work, I collect data using functional Magnetic Resonance Imaging (fMRI) and use models from computer vision and natural language processing to model brain responses of viewing natural images and movies.
Before joining CMU, I received a B.A. in Cognitive Science and Statistics from UC Berkeley.
Other than research, I enjoy rock climbing, mountaineering, cooking, and reading.
Joint Natural Language and Image Pre-training Builds Better Models of Human Higher Visual Cortex [paper] [poster] [talk]
Aria Y. Wang, Kendrick Kay, Thomas Naselaris, Michael J. Tarr, Leila Wehbe
In Review.
Selectivity for Food in Human Ventral Visual Cortex [paper]
Nidhi Jain, Aria Y. Wang, Margaret M Henderson, Ruogu Lin, Jacob S Prince, Michael J. Tarr, Leila Wehbe
Communications Biology, 6(1), 175.
Joint Interpretation of Representations in Neural Network and the Brain [paper]
Aria Y. Wang*, Ruogu Lin*, Michael J. Tarr, Leila Wehbe
ICLR Workshop 2021 - ‘How Can Findings About The Brain Improve AI Systems?’
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity [paper] [poster] [website]
Aria Y. Wang, Michael J. Tarr, Leila Wehbe
Neural Information Processing Systems (NeurIPS) 2019
Learning Intermediate Features of Object Affordances with a Convolutional Neural Network [paper]
Aria Y. Wang, Michael J. Tarr
Conference on Cognitive Computational Neuroscience (CCN) 2018
*Equal contribution
Image Embeddings Informed by Natural Language Improve Predictions and Understanding of Human High-Level Visual Cortex
Aria Y. Wang, Michael J. Tarr, Leila Wehbe
Poster presented at Conference on Cognitive Computational Neuroscience (CCN) 2022
Image Embeddings Informed by Natural Language Improve Predictions and Understanding of Human High-Level Visual Cortex [youtube]
Aria Y. Wang, Michael J. Tarr, Leila Wehbe
Talk presented at the 4th Neuromatch conference 2021
Joint Interpretation of Representations in Neural Network and the Brain
Aria Y. Wang*, Ruogu Lin*, Michael J. Tarr, Leila Wehbe
Talk presented at the ICLR Workshop 2021 - “How Can Findings About The Brain Improve AI Systems?”
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity
Aria Y. Wang, Leila Wehbe, Michael J. Tarr
Poster presented at Conference on Neural Information Processing Systems (NeurIPS) 2019
Expanding Visual Feature Spaces towards a General Encoding Model of Scene Perception
Aria Y. Wang, Michael J. Tarr, Leila Wehbe
Poster presented at Society for Neuroscience (SfN) 2019
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity
Aria Y. Wang, Leila Wehbe, Michael J. Tarr
Poster presented at Algonauts Workshop at MIT
Learning Intermediate Features of Affordances with a Convolutional Neural Network
Aria Y. Wang, Michael J. Tarr
Poster presented at Conference on Cognitive Computational Neuroscience (CCN) 2018
Learning Intermediate Features of Affordances with a Convolutional Neural Network
Aria Y. Wang, Michael J. Tarr
Poster Presented at Annual Meeting of the Vision Science Society (VSS) 2018
*Equal contribution