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Hi! I am a PhD student at the University of Washington, where I am very fortunate to be advised by Ranjay Krishna and Pang Wei Koh. I am broadly interested in computer vision and machine learning. My doctoral work is supported by an NSF Graduate Research Fellowship. Previously, I graduated with a BA in Math and Computer Science from Columbia University, where I was lucky to be introduced to research by Carl Vondrick and Junfeng Yang. Email / CV / Google Scholar / GitHub |
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Scott Geng*, Revant Teotia*, Purva Tendulkar, Sachit Menon, Carl Vondrick In submission arXiv / project page We introduce a video framework for modeling goal-conditioned interactions between verbal and non-verbal communication in dyadic conversations. To study this problem, we also introduce the RealTalk video dataset, which contains 100+ hours of unscripted in-the-wild conversations. |
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Chengzhi Mao*, Scott Geng*, Junfeng Yang, Xin Wang, Carl Vondrick ICLR, 2023 arXiv We identify the novel problem of zero-shot adversarial robustness and propose a new text-grounded adversarial training objective that can help make CLIP robust while preserving its ability to generalize. |
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Kexin Pei, Dongdong She*, Michael Wang*, Scott Geng*, Zhou Xuan, Yaniv David, Junfeng Yang, Suman Jana, Baishakhi Ray ESEC/FSE, 2022 arXiv / code Unlike in natural text, the semantic meaning of code is explictly measureable as the CPU's runtime memory values. Inferring execution traces gives a natural self-supervised task, which we leverage to learn good representations of binary code. |
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Shi-Bing Wong, Yi-Mei Wang, Chih-Chun Lin, Scott Geng, Nora Vanegas-Arroyave, Seth Pullman, Sheng-Han Kuo, Ming-Kai Pan The Cerebellum, 2021 paper Low-frequency brain waves are correlated with symptom severity in sporadic essential tremor but not familial (i.e. genetic). Suggests difference in mechanism. |
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At Columbia. Course Assistant (Spring 2021, Fall 2021): COMS 4771 Machine Learning |
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