The UCLA Science Hub for Humanity and Artificial Intelligence has announced the winners of its inaugural set of awards, recognizing recipients who fulfill the hub’s mission of researching the societal impact of artificial intelligence (AI). The awards include six gift-funded research projects and twelve Amazon fellowships.
Funded by Amazon and housed at the UCLA Samueli School of Engineering, the UCLA Science Hub aims to facilitate collaboration between academic researchers and Amazon scientists.
Amazon and UCLA launched the Science Hub in October 2021 and concurrently announced an open call for research proposals from faculty across the UCLA campus, as well as nominations for Amazon fellowships. The hub’s advisory group chose the six gift award recipients from among fifty-five submissions and selected the twelve Amazon Fellows from among twenty-five nominations.
The award winners’ research connects to the Hub’s central theme of applications of AI for humanity and society at large.
The fellowships provide selected doctoral graduate students at UCLA Samueli with up to two quarters of funding during the academic year to pursue independent research projects. The Amazon Fellows study within the departments of computer science, electrical and computer engineering, and mechanical and aerospace engineering. In addition to project funding, they will be invited to apply to intern at Amazon.
What follows is the list of fellows, their areas of research, and their UCLA faculty advisors:
- Xiangning Chen, automated and efficient machine learning, such as automatically identifying high-performance neural architectures and developing optimizers to accelerate large-scale pre-training. Advisor: Cho-Jui Hsieh
- Ruchao Fan, children’s automatic speech recognition (ASR) in a low-resource perspective and non-autoregressive end-to-end ASR models. Advisor: Abeer Alwan
- Antonious Girgis, communication-efficient and privacy-preserving machine learning as well as the trade-off between privacy and utility in statistical machine learning. Advisor: Suhas Diggavi
- Ziniu Hu, more efficient graph neural networks to model large-scale and complex graphs and differentiable symbolic reasoning with graph neural networks. Advisor: Yizhou Sun
- Zijie Huang, deep learning for reasoning over graph-structured dynamic data and modeling spatiotemporal data and knowledge graphs. Advisors: Yizhou Sun and Wei Wang
- Michael Kleinman, representation learning, computational neuroscience, machine learning, and information theory, including usable information and evolution of optimal representations during training. Advisor: Jonathan Kao
- Liunian Harold Li, learning vision-language alignment from unaligned data and using vision-language data to facilitate computer vision models. Advisor: Kai-Wei Chang
- Tao Meng, constrained inference for bridging the distributional gap in natural language processing and machine learning. Advisor: Kai-Wei Chang
- Yifan Qiao, democratization of large-scale machine learning training with full-stack systems and developing full-stack solutions covering ML algorithms and systems, operating systems, and cloud infrastructures. Advisors: Harry Xu and Miryung Kim
- Akash Deep Singh, bridging the gap between radio-frequency sensing hardware and machine learning frameworks in mobile systems and the Internet of Things. Advisor: Mani Srivastava
- Weitong Zhang, optimization and machine learning, especially explainable and accountable reinforcement learning systems, and designing a provably data-efficient algorithm with application toward real-world problems. Advisor: Quanquan Gu
- Huajing Zhao, developing novel, multisensory-driven robotic systems with visual and tactile perception that enable dexterous manipulation control and decision-making for safe human-robot collaboration. Advisor: Veronica Santos
The recipients of gift research awards receive funding for exploratory projects in AI and machine learning. Four of the projects relate to potential applications of AI in addressing public health challenges:
- “Knowledge Graph Representation Learning and Applications in Biomedicine,” Wei Wang
- “Deep Learning for Biological Discovery with Application to Cardiometabolic Disease,” Sriram Sankararaman, Paivi Pajukanta, and Hsiai Tzung
- “Prediction of Perinatal Depression Using EHR-Derived Phenotypes and Genetic Risk Scores” Loes Olde Loohuis and Jeffrey Chiang
- “Scalable Sequencing Approaches for Detection of Novel Pathogens and Evolving Viral Variants,” Valerie Arboleda, Joshua Bloom, Eleazar Eskin, Chongyuan Luo, and Leonid Kruglyak
The other two awarded projects propose the application of AI and machine learning to address other societal needs:
- “Fighting Wildfires with AI: Enabling High-Fidelity Wildfire Simulation Using Probabilistic Geospatial Deep Learning,” Ertugrul Taciroglu and Mohamad Alipour
- “Coresets for Efficient and Robust Machine Learning,” Baharan Mirzasoleiman
