Runqian (Ray) Wang

I am a PhD student at UC Berkeley, advised by Prof. Alexei Efros. I graduated from MIT with double major in AI and Math. I am fortunate to have worked with Prof. Kaiming He, Prof. Yilun Du, and Dr. Zhirong Wu. I also interned as a student researcher at Microsoft Research Asia and MIT-IBM Watson AI Lab.

I hope to work on general problems in deep learning through the lens of computer vision. I am most interested in simple yet effective methods that can generalize and scale.

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For a full list of publications, see Google Scholar page.

New generative model that directly learns equilibrium dynamics using an implicit energy-based formulation. Exceeds Flow Matching in performance, supports optimization-based sampling, and naturally performs multiple downstream tasks.

Plug-and-play representation regularizer for generative modeling that brings consistent improvement across different settings. Follow-up works also show promising adoptation in robotics and NLP.

Reframes ARC-AGI dataset as an image-to-image translation problem, achieving competitive performance with those of leading LLMs.