Xuanzhou Chen
New York University. +1 470-278-4892
255 Schermerhorn Street
New York, NY 11201
This is Xuanzhou Chen. I am currently a MS student at NYU and also a 2023 Phd applicant. My research vision is to bridge the gap between theoreties and applications in deep learning, specifically interested in building efficient/robust/generalized deep learning algorithms to be applied in multiple fields such as NLP and medical imaging. Overall, my research interests lie in the intersection of :
- Machine Learning Privacy & Security
- Information Theory
- Machine Learning Theory
- Deep Learning Algorithm
- Data Analysis
Overall, I am eagerly exploring some research problems as following:
- How to make deep neural network models more robust against adversarial perturbations? -> R-D theory?
- How to generalize a deep learning model (such as a transformer-based model) by investigating inner topological strcture in neural networks and high dimensional data?
- How to make the training process more computationally efficient? How to remove “redundant” information while training DL models?
- How to mitigate the inherent bias in pretrained models and train them fairly in downstream tasks?
Currently, I am self-learning Topological Structures in Neural Networks, Topological Data Analysis, and Multi-task Learning.
2023 New Year Aspirations: Stay curious, read more, learn more, experiement more, socialize more, and start to draft my research proposal!
News
May 17, 2023 | Graduation: M.S. in Computer Engineering, New York University |
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Dec 13, 2020 | Graduation: B.S. in Mathematics, University of Wisconsin - Madison |