Profiles

Mohamed Elhoseiny is an associate professor in the Computer Science (CS) Program at KAUST, a senior member honoree of AAAI and IEEE, and the principal investigator of the KAUST Vision-CAIR Research Group. Before joining KAUST, he was a visiting faculty member at Stanford Computer Science Department, a visiting faculty member at Baidu Research, and a postdoctoral researcher at Facebook AI Research.

Elhoseiny earned his Ph.D. in 2016 from Rutgers University. He received a B.Sc. degree in 2006 and an M.S. degree in 2010, both in computer systems from Ain Shams University.

His work has received numerous recognitions, including third place at the Data+AI Summit hackathon at San Francisco held in May 2024 (200 participants) with a multimodal LLM hack called HomeGPT. He was selected as an MIT 35 under 35 semi-finalist in 2020. He received the Best Paper Award at the 2018 European Conference on Computer Vision (ECCV) Workshop on Fashion, Art, and Design for his research "DesIGN: Design Inspiration from Generative Networks." He also received the Doctoral Consortium Award at the 2016 Conference on Computer Vision and Pattern Recognition (CVPR) and an NSF Fellowship for his "Write-a-Classifier Project" in 2014. His research on creative art generation has been featured in New Scientist Magazine and MIT Technology Review, which also highlighted his work on lifelong learning.

Professor Elhoseiny’s contributions include zero-shot learning, which was featured at the United Nations, and his creative AI work was featured in MIT Technology Review, New Scientist Magazine, Forbes Science, and HBO's Silicon Valley. He has served as an Area Chair at major CV/AI conferences, including CVPR21, ICCV21, IJCAI22, ECCV22, ICLR23, CVPR23, ICCV’23, NeurIPS23, ICLR’24, CVPR’24, ECCV’24, SG Asia’24, and has organized Closing the Loop Between Vision and Language workshops at ICCV’15, ICCV’17, ICCV’19, ICCV’21, ICCV’23.

He has been involved in several pioneering works in affective AI art creation and has authored or co-authored numerous award-winning papers.

His primary research interests are in computer vision, focusing on efficient multimodal learning with limited data in zero- and few-shot learning, and Vision and language. He is also interested in Affective AI and especially in understanding and generating novel visual content, such as art and fashion.

• Digital signal.
• Image processing.
• Machine learning
• Wireless communications

Aldossary research interests include computer vision, machine learning, deep learning, computational neuroscience, and brain–computer interfaces. She is particularly interested in EEG representation and behavior learning, with applications in clinical EEG analysis and brain–computer interfacing.

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. 

He completed his undergraduate studies in Computer Science and Engineering at Sejong University in South Korea. He then pursued a master’s degree in Computer Science at KAUST under the supervision of Prof. Mohamed Elhoseiny, focusing on machine learning and generative models. Building on this foundation, he continued into the PhD program at KAUST, where his research now spans affective vision–language modeling, generative AI, and Neuroscience + AI.

His research focuses on affective vision–language modeling, generative AI, and the integration of Neuroscience with machine learning. He works on multimodal emotion understanding, interpretable generative models, and EEG-based neural signal modeling, aiming to build human-centered AI systems that connect perception, affect, and computational intelligence.

• Digital signal.
• Image processing.
• Machine learning
• Wireless communications

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.