Profiles

Amal Alghamdi is a computational scientist and founder of Impact Alpha, Saudi Arabia. She holds a Ph.D. and M.S. in Computational Science, Engineering, and Mathematics from the University of Texas at Austin, and another M.S. in Computer Science from King Abdullah University of Science and Technology (KAUST). Before founding Impact Alpha, she was a postdoctoral researcher in the Scientific Computing section at the Technical University of Denmark. 

Amal's research focuses on inverse problems, uncertainty quantification, optimization, and high-performance computing, with applications in geophysics and biomedicine, aiming to bridge advanced computing and mathematical modeling for real-world impact. Her M.S. Thesis topic was "Parallelization of a hyperbolic PDE solver in Python".

Ibrahim Abdelaziz is a Senior Research Scientist at IBM Research, where he focuses on enhancing the capabilities of Large Language Models (LLMs), particularly on improving their agentic and reasoning abilities. Prior to this, Ibrahim led several projects in the areas of knowledge graphs, question answering, knowledge representation, and reasoning. He earned his Ph.D. from KAUST, where his research focused on graph analytics and distributed computing, specifically on building distributed systems to efficiently manage, query, and mine large-scale graphs.

Ibrahim Abdelaziz's research interests included Data Mining over large datasets, Distributed Systems, Machine Learning and Pattern Recognition.

Kai Yi is a PhD candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), supervised by Peter Richtarik and working in the Optimization and Machine Learning Lab. He earned his master’s degree in Computer Science at KAUST in 2021 under the supervision of Mohamed Elhoseiny. He completed his Bachelor of Engineering with honors at Xi’an Jiaotong University (XJTU) in 2019.

He has interned at several leading research institutions, including Sony AI, Vector Institute, Tencent AI Lab, CMU Xulab, NUS CVML Group, and SenseTime Research. His primary research focuses on centralized and federated LLM compression. His work is highly interconnected, featuring significant contributions such as the LLM post-training compression algorithms SymWanda and PV-Tuning (NeurIPS Oral); communication-efficient federated learning methods Cohort-Squeeze (NeurIPS-W Oral), FedP3 (ICLR), and EF-BV (NeurIPS); and multimodal language model projects DACZSL (ICCVW), HGR-Net (ECCV), and VisualGPT (CVPR).

He actively serves as a reviewer for leading journals, including TPAMI, IJCV, and TMC, as well as top conferences such as NeurIPS, ICLR, ICML, CVPR, ECCV, and ICCV.

Kai Yi's primary research interest lies in centralized and federated LLM compression. My work is highly interconnected, featuring significant projects such as the LLM post-training compression algorithms SymWanda and PV-Tuning (NeurIPS Oral), with more on the way; communication-efficient federated learning methods CohortSqueeze (NeurIPS-W Oral), FedP3 (ICLR), and EF-BV (NeurIPS); and multimodal language model projects DACZSL (ICCVW), HGR-Net (ECCV), and VisualGPT (CVPR). His research interests include:

• Machine learning optimization in the large-scale data/model era.
• Conceptual-level knowledge transfer learning: theories and applications.

Specifically, he works on machine learning optimization, federated learning, and zero-shot learning. He is particularly interested in accelerated local training methods and personalized federated learning in data and system heterogeneity.