2024 Amazon Fellows
Congratulations to the 14 graduate students from UCLA Samueli School of Engineering who have been selected by the Science Hub Advisory Group as the 2024 Amazon Fellows. The selection process was not an easy one with a pool of 24 highly accomplished nominees. Although an award could not be made to all, we commend all of the nominees for their outstanding accomplishments thus far.
The 2024 Amazon Fellows will present their research during the Lightning Talks event to be held on December 5, 2024. Their presentation decks will be posted here soon after the event.
Eray Eren
ADVISOR: Abeer Alwan
Electrical and Computer Engineering
My research interests include expressive neural zero-shot text-to-speech synthesis (NTTS), and neural automatic speaker verification (NASV) with network uncertainty. I am particularly interested in low resource and robust speech processing.
Poorva Garg
ADVISORS: Guy Van den Broek, Todd Millstein
Computer Science
My research lies at the intersection of artificial intelligence and programming languages. Specifically, my work focuses on designing and implementing probabilistic programming languages (PPLs). I develop novel generalizable probabilistic inference algorithms for PPLs to make probabilistic modeling fast and accessible.
Evan Harber
ADVISOR: Veronica Santos
Mechanical and Aerospace Engineering
My research focuses on understanding how combining visual and force/tactile feedback can be used together to improve robotic manipulation. Recently, my methods have focused on learning manipulation policies in simulation, then fine-tuning them using real world data.
Siddharth Joshi
ADVISOR: Baharan Mirzasoleiman
Computer Science
My research focuses on furthering the efficiency, performance, and robustness of learning with limited supervision by improving the quality of data. In particular, I design theoretically rigorous algorithms for selecting subsets of data to efficiently train performant large models with limited supervision. Additionally, I investigate the data properties that enhance the robustness of these learning algorithms. My work has been recognized at prestigious conferences such as ICML, ICLR, and AISTATS, amongst others.
Yuzhu Li
ADVISORS: Aydocan Ozcan
Electrical and Computer Engineering
My research interest lies in leveraging advanced AI and deep learning tools to perform better optical sensing and imaging and exploring AI’s transformative potential to enhance biomedical applications through improved optical imaging techniques.
Haofan Lu
ADVISOR: Omid Abari
Computer Science
My research aims to enhance future wireless communication and sensing systems with machine learning and AI. I am developing systems and algorithms to help people understand the distribution of wireless signal power across space, to improve wireless networks’ quality and coverage.
Mingyu Derek Ma
ADVISOR: Wei Wang
Computer Science
I’m highly interested in the architecture, training, and agentic use of generative language models inspired by and applied to clinical, medical, and scientific scenarios. I’m especially working on equipping the language models with the intuition and knowledge of domain experts, such as clinicians or scientists, and utilizing them as assistants for scientific discovery.
Kaan Ozkara
ADVISORS: Suhas Diggavi
Electrical and Computer Engineering
I am broadly interested in large scale machine learning and challenges revolving around it. I’m invested in several areas, including personalized privacy-preserving machine learning, optimization techniques for LLMs, and model compression. Recently, I have been working on personalized Gen AI and efficient training of large models.
Tanmay Parekh
ADVISOR: Nanyun Peng and Kai-Wei Chang
Computer Science
My research lies in the intersection of artificial intelligence and applications. Specifically, I’m working towards utilizing large language models (LLMs) to build generalizable information extraction systems for a wide range of domains, languages, and applications.
Zhenghao Peng
ADVISOR: Bolei Zhou
Computer Science
My key focus is on modeling human behavior in autonomous driving to improve scene understanding and on human-in-the-loop agent learning in robotics. Both areas are critical for enhancing the safety, learning efficiency, and human-compatibility of embodied AI systems.
Arjun Subramonian
ADVISORS: Yizhou Sun
Computer Science
My research focuses on the theoretical foundations of social unfairness in graph neural networks. I am particularly interested in leveraging spectral graph theory and statistics to characterize how structural properties of graphs contribute to algorithmic unfairness. I also research how AI impacts LGBTQIA+ communities.
Yaoxuan Wu
ADVISOR: Miryung Kim
Computer Science
My research aims to help developers automatically detect and fix bugs, with a particular focus on software testing for big data analytics and symbolic execution for general programs.
Heyang Zhao
ADVISOR: Quanquan Gu
Computer Science
My research interest lies in the theoretical foundation of machine learning, especially in the foundation of decision-making problems, including bandits and reinforcement learning. I am also interested in the application of these models in real world problems.
Siyan Zhao
ADVISORS: Aditya Grover
Computer Science
My research focuses on enhancing generative models’ reasoning and planning capabilities while aligning them with human values. I aim to improve the effectiveness and robustness of generative agents’ sequential decision-making processes, while optimizing for sample efficiency and inference speed.