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
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
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
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
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.
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
Michael Kleinman
Advisor: Jonathan Kao
My research interests span the areas of representation learning, computational neuroscience, machine learning, and information theory. Contact me
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
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.
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
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
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
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