Yitao Xu (徐一涛)

Welcome to my homepage! I am currently a 1st year PhD student at École polytechnique fédérale de Lausanne (EPFL) in the EDIC program. Before that, I was a master's student at KTH Royal Institute of Technology, majoring in Machine Learning. In 2022 Fall and 2023 Spring, I was an exchange master's student in Computer Science at EPFL.

I got my B.Eng degree in Computer Science and Technology at Beihang University (BUAA). I worked as a research student at State Key Laboratory of Software Development Environment (NLSDE), advised by Prof.Xianglong Liu, and I also finished my bachelor's thesis project focusing on rectifying texture bias exposed in deep neural network there. After graduating from BUAA, I became a Machine Learning Intern at DiDi AI Labs, where I was led by Dr. Zhengping Che and Prof.Jian Tang. Beginning in 12.2020, I spent a wonderful winter at Tsinghua Laboratory of Brain and Intelligence (THBI) under the supervision of Prof.Jia Liu, studying intuitive physics engine and stability inference with convolutional neural networks.

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I'm interested in Computer Vision and Machine Learning. Particularly, I focus on:

• Neural cellular automata and self-organizing systems
• Interpretability of adversarial attacks and adversarial robustness
• Different cognition biases between humans and deep-learning based computer vision models

We propose Dynamic Neural Cellular Automata (DyNCA), a framework for real-time and controllable dynamic texture synthesis. Play with our demo!

We believe that adversarial noises are amplified and propagated through the critical attacking route, identified by the proposed algorithm.

We take the first step to study adversarial attacks for embodied agents. We generate spatiotemporal perturbations to form 3D adversarial examples, exploiting the interaction history in both the temporal and spatial dimensions.

We explain adversarial robustness for deep models from a new perspective of neuron sensitivity which is measured by neuron behavior variation intensity against benign and adversarial examples.

Design