2022 Amazon Fellows

The Science Hub advisory group selected 12 Amazon Fellows for 2022 out of 25 nominations. The fellows are PhD students in the departments of computer science, electrical and computer engineering, and mechanical and aerospace engineering in the UCLA Samueli School of Engineering.

Xiangning Chen

Xiangning Chen

Advisor: Cho-Jui Hsieh

My research interests lie in automated and efficient machine learning, such as automatically identifying high-performance neural architectures and developing optimizers to accelerate large-scale pre-training. Contact me

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Ruchao Fan

Ruchao Fan

Advisor: Abeer Alwan

I am interested in speech processing and machine learning. Currently, I am focusing on children’s automatic speech recognition (ASR) in a low-resource perspective and non-autoregressive end-to-end ASR models. Contact me

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Antonious Girgis

Antonious Girgis

Advisor: Suhas Diggavi

My research interest lies broadly in machine learning, information theory, and privacy. Specifically, I am interested in studying the trade-off between privacy and utility in statistical machine learning. Contact me

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Ziniu Hu

Ziniu Hu

Advisor: Yizhou Sun

My research goal is to develop more efficient Graph Neural Networks (GNNs) to model large-scale and complex graph, and also explore whether GNNs can help tasks that require complex symbolic reasoning.

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Zijie Huang

Zijie Huang

Advisors: Wei Wang and Yizhou Sun

​​My research interest lies in graph learning, deep learning and machine learning in general. I am particularly interested in modeling spatiotemporal data as well as knowledge graphs. Contact me

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Michael Kleinman

Michael Kleinman

Advisor: Jonathan Kao

My research interests span the areas of representation learning, computational neuroscience, machine learning, and information theory. Contact me

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Liunian Harold Li

Liunian Harold Li

Advisor: Kai-Wei Chang

I am interested in learning aligned representation between vision and language from natural supervision. Recently, I have been working on learning vision-language alignment from unaligned data and using vision-language data to facilitate computer vision models. Contact me

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Tao Meng

Tao Meng

Advisors: Kai-Wei Chang

My research interest lies in constrained inference for bridging the distributional gap in natural language processing and machine learning.

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Yifan Qiao

Yifan Qiao

Advisors: Harry Xu and Miryung Kim

My research aims to democratize machine learning with higher efficiency and lower costs. I am developing full-stack solutions covering ML algorithms and systems, operating systems, and cloud infrastructures. Contact me

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Akash Deep Singh

Akash Deep Singh

Advisor: Mani Srivastava

I design and build hardware and software frameworks that allow machines to better perceive their environments. My Ph.D. research aims to combine radio-frequency (RF) sensing with vision to create richer, robust and ubiquitous sensing paradigms. Through my thesis, I aim to bridge the gap between RF sensing hardware and machine learning frameworks in mobile systems and, more recently, the Internet of Things. Contact me

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Weitong Zhang

Weitong Zhang

Advisor: Quanquan Gu

My research interests are optimization and machine learning, especially reinforcement learning. I am interested in discovering the theory of reinforcement learning and designing the provably data-efficient algorithm with application towards the real-world problem. Contact me

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Huajing Zhao

Huajing Zhao

Advisor: Veronica Santos

I am interested in developing novel, multisensory-driven robotic systems with visual and tactile perception that enable dexterous manipulation control and decision-making for safe human-robot collaboration. Contact me

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