Eric Xing

I am a Ph.D. student in the Multimodal Vision Research Lab, part of the McKelvey School of Engineering at Washington University in St. Louis, where I work on computer vision and multimodal learing. I am advised by Nathan Jacobs.

I received my B.S. in Computer Science with a minor in mathematics from Western Kentucky University. I also worked with Dongwon Lee as part of an REU at Penn State University.

Email  /  CV  /  LinkedIn  /  Scholar  /  GitHub

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Research

My research broadly lies in computer vision and multimodal learning. I am interested in learning representations from multimodal data for powerful and versatile AI systems.

PSM_ACMMM.png PSM: Learning Probabilistic Embeddings for Multi-scale Zero-Shot Soundscape Mapping
Subash Khanal, Eric Xing, Srikumar Sastry, Aayush Dhakal, Zhexiao Xiong, Adeel Ahmad, Nathan Jacobs,
ACM Multimedia, 2024
paper / arXiv / code / bibtex

We develop a probabilistic, multi-scale, and metadata-aware embedding space that connects audio, text, and overhead imagery.

ALISON_png ALISON: Fast and Effective Stylometric Authorship Obfuscation
Eric Xing, Saranya Venkatraman, Thai Le, Dongwon Lee
AAAI Conference on Artificial Intelligence (AAAI), 2024
paper / arXiv / code / bibtex

An authorship obfuscation method that demonstrates a ~10x speedup over previous methods while outperforming them in terms of attack success, semantic preservation, and fluency.

ICPR_Uncertainty_png Neural Network Decision-Making Criteria Consistency Analysis via Inputs Sensitivity
Eric Xing, Liangliang Liu, Xin Xing, Yunni Qu, Nathan Jacobs, Gongbo Liang
International Conference on Pattern Recognition (ICPR), 2022
paper / bibtex

Quantification and analysis of neural network decision-making criteria inconsistency and three algorithms to mitigate this inconsistency with minimal performance sacrifice.

EMBC_Generation_Uncertainty_png Beware the Black-Box of Medical Image Generation: an Uncertainty Analysis by the Learned Feature Space
Yunni Qu, David Yan, Eric Xing, Fengbo Zheng, Jie Zhang, Liangliang Liu, Gongbo Liang
International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022
paper / bibtex

Quantitative and clustering-based analyses of learned features spaces of U-Net architechtures for medical image generation.

SIGCSE_Toolkit_png A Toolkit for Assessments in Introductory Programming Courses
Eric Xing, Guangming Xing
ACM Technical Symposium on Computer Science Education (SIGCSE), 2022
paper / bibtex

A versatile online exam toolkit with plagarism and cheating detection as part of the vLab learning management system.

Motorcycle_ICTD_png Motorcycle Safety Investigation in Kentucky Using Machine and Deep Learning Techniques
Eric Xing, Kirolos Haleem
International Conference on Transportation and Development, 2022
paper / bibtex

Interpretability over a new state-of-the-art pipeline for motorcycle crash severity predicition for the analysis of factors contributing to severe motorcycle crashes.


Thank you to Jon Barron for the site template.