2025 Amazon Fellows
Congratulations to the 13 Ph.D. students from UCLA Samueli School of Engineering who have been selected by the Science Hub Advisory Group as the 2025 Amazon Fellows. The selection process was difficult with a pool of 47 highly accomplished nominees. We commend all of the nominees for their outstanding accomplishments thus far.
The 2025 Amazon Fellows will present their research during the Lightning Talks event to be held in the Fall of 2025. Their presentation decks will be posted here soon after the event.
Oliver Broadrick
ADVISOR: Guy Van den Broeck.
Computer Science
What (minimal) properties of an AI model enable provable guarantees about its behavior? I study the tradeoff in models between expressive-efficiency (size) and tractability (computational complexity of answering queries about the model), especially focused on probabilistic models tractable for the fundamental query of marginalization.
Kunlin Cai
ADVISOR: Yuan Tian
Electrical and Computer Engineering
My research lies in the security and privacy of emerging technologies, particularly extended reality (XR) and machine learning. Currently, I am developing security tools to support XR development and exploring new attacks to understand the threats that large audio models are facing.
Sunwoong Choi
ADVISOR: Sriram Narasimhan
Mechanical and Aerospace Engineering
My research focuses on the development of infrastructure inspection robots capable of embodying expert inspector skills through deep learning using multimodal egocentric data. Research topics include robot policy generation for inspection through imitation learning/reinforcement learning, gaze-based visual attention level analysis and scene prioritization, and defect quantification combining human decision-making and image-processing technology.
Yihe Deng
ADVISORS: Wei Wang, Kai-Wei Chang
Computer Science
My research focuses on post-training methods for LLMs, including RLHF, synthetic data, and self-improvement. I’m currently exploring multi-modal reasoning and long-horizon agentic tasks to enhance LLM capabilities in complex, dynamic environments.
Qiwei Di
ADVISOR: Quanquan Gu
Computer Science
My research interests center on the theoretical foundations of machine learning, with a particular focus on bandit theory and reinforcement learning. I aim to uncover the underlying theoretical principles of widely used machine learning methods, with the goal of improving existing algorithms or designing new ones.
Yiwen Kou
ADVISOR: Raghu Meka
Computer Science
My primary research interest lies in learning theory, where I explore the mathematical foundations of machine learning, aiming to understand and develop principled methods for efficient learning. I am also interested in probability-related aspects of theoretical computer science, particularly in the role of randomness and probabilistic methods in algorithms, complexity theory, and combinatorics.
Shufan Li
ADVISOR: Aditya Grover
Computer Science
My primary research interest lies on unified modeling for multi-modal understanding and generation tasks. My most recent works put particular emphasis on multi-modal diffusion models. Beyond this, I also work on text-to-image generation, vision-language understanding, and classic visual perception problems.
Natarajan Balaji Shankar
ADVISOR: Abeer Alwan
Electrical and Computer Engineering
My research passion lies at the intersection of machine learning and speech processing, with a focus on developing robust automatic speech recognition (ASR) systems for low-resource domains, such as children’s speech and African-American English.
Che-Yung Shen
ADVISOR: Aydogan Ozcan
Electrical and Computer Engineering
My research focuses on AI-driven computational imaging. I am exploring the potential of optical neural networks and advancing the field of computational imaging, aiming to contribute meaningful innovations in the realm of AI and optics.
Rishi Upadhyay
ADVISOR: Achuta Kadambi
Computer Science
My research interests are centered around developing physics-informed neural networks for computer vision. I focus on embedding and learning physical laws as constraints in these networks with the goal of developing models with stronger guarantees, making them more reliable for applications such as medical imaging and scientific discovery.
Xue Wang
ADVISOR: Yang Zhang
Electrical and Computer Engineering
My research focuses on human-computer interaction in wearable sensing. I am particularly interested in silent speech recognition, activity recognition, and context-aware computing. By developing advanced AI techniques, I aim to build intelligent wearable systems that effectively balance privacy, usability, and functionality.
Xueqing Wu
ADVISORS: Nanyan Peng, Kai-Wei Chang
Computer Science
My research focuses on reasoning over multi-modal information, such as images, videos, and large-scale database. I am particularly interested in complex reasoning behaviors such as self-reflection, symbolic reasoning using code and tools, and reasoning enhanced with grounding.
Yanqiao Zhu
ADVISORS: Wei Wang, Yizhou Sun
Computer Science
My research focuses on developing advanced AI models and exploring their applications across diverse real-world domains, with particular emphasis on graph and geometric deep learning, data-efficient learning approaches including unsupervised and self-supervised methods, and large language models. I apply these techniques to critical areas such as recommender systems and healthcare, while recently expanding into AI for Science with a focus on computational chemistry and autonomous scientific discovery.