Sicheng Zhu

✉ sczhu[at]umd[dot]edu Google Scholar Twitter

I am a Ph.D. student in the Computer Science Department at the University of Maryland, College Park, advised by Prof. Furong Huang.

My goal is to build trustworthy machine learning models.

When you are sitting in a self-driving car controlled by such a machine learning model, you know it can recognize trucks because it can identify the wheels, body, and cab that make up the truck, and it can recognize the wheels because it notices contours, tire materials, and hubs (interpretability). It can identify trucks at various angles, lighting conditions, and even under adversarial stickers (robustness). It can also recognize a wide variety of trucks, from heavy trailers to Cybertrucks (generalization). Therefore, you can confidently hand over the steering wheel to it and take a nap on your way to work without worrying about it inexplicably crashing into an overturned white truck.

My approach to achieving this goal is by endowing machines with common sense about the physical world, with a current focus on modeling the symmetries of objects under various changes that could potentially reduce the learning complexity. For example, when applying angle transformations, material changes, or some pattern changes on a wheel in an image, it still appears as a wheel to humans and maintains its function. My current work aims at incorporating these symmetries into the model.

Previously, I was a visiting scholar at the University of Virginia where I was fortunate to be advised by Prof. David Evans. I received my M.E. from Institute of Electronics, Chinese Academy of Sciences and B.S. from University of Electronic Science and Technology of China.

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