Xin (Eric) Wang
Assistant Professor
ericxwang@ucsb.edu
Personal Website
Education
Ph.D. in Computer Science, UC Santa Barbara
B.Eng. in Computer Science, Zhejiang University
Off-Campus Affiliations
Head of Research, Simular AI
External Publications
Google Scholar
Awards
Best Paper Award, ICLR Agentic AI for Science, 2025
eBay Faculty Award, 2024
Cisco Research Award, 2024
eBay Faculty Award, 2023
UCSC Innovator of the Year Award, Finalist, 2023
Amazon Alexa Prize Award, TaskBot Finalist (Top 5), 2023
Amazon Alexa Prize Award, SocialBot Second Place, 2023
Amazon Alexa Prize Award, SimBot Third Place, 2022
Google Faculty Research Award, 2022
AAII Interdisciplinary Award, 2022
CVPR Best Student Paper Award, 2019
Research Areas
Machine Learning and Data Mining
Visual Computing and Interaction
Bio

Xin (Eric) Wang is an Assistant Professor in The Computer Science Department at UC Santa Barbara and the Head of Research at Simular. His research interests include Natural Language Processing, Computer Vision, and Machine Learning, with an emphasis on Multimodal and Embodied AI Agents. Previously, he was a faculty at UC Santa Cruz and also spent time at Google Research, Meta FAIR, Microsoft Research, and Adobe Research. 

Eric has served as (Senior) Area Chair for conferences such as ACL, NAACL, EMNLP, ICLR, and NeurIPS, and organized workshops and tutorials at those venues. He has received several awards and recognitions for his work, including Best Paper Awards from CVPR and ICLRW, Google Research Faculty Award, Amazon Alexa Prize Awards, Cisco Research Award, eBay Research Awards, and various gift awards from Adobe, Snap, Microsoft, Cybever, etc.

Research

Prof. Wang’s research aims to build intelligent multimodal AI agents that can understand the world, collaborate with humans, and perform real-world tasks—from everyday activities to high-stakes missions. His work spans multimodal representation learning, embodied AI for human-agent collaboration, and the ethical design of trustworthy AI systems. Drawing on methodologies from machine learning, computer vision, natural language processing, and robotics—with insights from cognitive science and neuroscience—his research develops generalizable, efficient, and socially responsible agents that perceive, communicate, and act in complex environments.