Scholar
Chengxu Zhuang
Google Scholar ID: 1UvYRp0AAAAJ
AI Research Scientist, Meta
Citations & Impact
Citations
2,119
H-index
15
i10-index
18
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Published 'Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling' at ACL 2024 (Findings)
- Published 'Visual Grounding Helps Learn Word Meanings in Low-Data Regimes' at NAACL 2024 (Oral, Best Paper Award)
- Published 'How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?' at NeurIPS 2022
- Published 'Unsupervised neural network models of the ventral visual stream' in PNAS (2021)
- Published 'Unsupervised Learning from Video with Deep Neural Embeddings' at CVPR 2020
- Published 'Local Aggregation for Unsupervised Learning of Visual Embeddings' at ICCV 2019 (Oral, Best Paper Award Nomination)
- Published 'Flexible Neural Representation for Physics Prediction' at NeurIPS 2018
- Published 'Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System' at NIPS 2017 (Oral)
Background
- Currently an AI Research Scientist at Meta
- Previously worked at OpenAI on ChatGPT Advanced Voice Mode
- Former ICoN Postdoctoral Fellow at MIT, working with Ev Fedorenko and Jacob Andreas
- Research interests include: Natural Language Processing, Language Acquisition, Computer Vision, Computational Neuroscience, Artificial Intelligence, Deep Learning
- Aims to understand brain mechanisms and develop more effective AI models
Miscellany
- Served as teaching assistant for multiple courses at Stanford, including:
- PSYCH252: Statistical Methods for Behavioral and Social Sciences (Winter 2021)
- PSYCH251: Experimental Methods (Autumn 2019)
- PSYCH249 / CS375: Large-Scale Neural Network Models for Neuroscience (Autumn 2018)
- PSYCH253: High-Dimensional Methods for Behavioral and Neural Data (Spring 2018, 2019, 2021)
Co-authors