Profiles

José Maria earned his Bachelor of Engineering in Electronic Engineering, with a minor in Telecommunications, from Universidad Peruana de Ciencias Aplicadas (UPC) in Lima, Peru, in 2018. During his undergraduate studies, he was awarded a partially funded scholarship in 2013. In 2018, he received funding for a research project through the VII Annual Research Incentive Competition at UPC, which led to the publication of a paper and a presentation at the XXII Symposium on Image, Signal Processing, and Artificial Vision (STSIVA) in 2019.

José Maria began his career with internships at IBM Peru, first as a Computer Specialist and later as a Software Developer. His interest in the Internet of Things (IoT) grew, leading him to become a speaker at various universities, where he taught IoT and Machine Learning. He focused on using single-board computers to connect multiple devices and leverage environmental data. José Maria has worked on several research projects at UPC, including a notable Digital Image Processing project aimed at developing a reliable method for diagnosing health issues in coffee plants using machine learning in Python, providing early diagnosis tools for cultivators.

José Maria is proficient in Python, Java, and machine learning technologies. His research interests lie in leveraging machine learning to enhance communication methods, with a focus on coding and modulation, Smart Grid technologies, and satellite networks. He is passionate about the Internet of Things (IoT) and has shared his knowledge as a speaker at various universities, teaching IoT and Machine Learning. His sessions emphasize the use of single-board computers for data-driven device connectivity, showcasing practical applications of these technologies.

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. 

Karim is a graduate from Bauman Moscow State Technical University. He received a specialist degree in a field of electrical engineering (2014 – 2020), with a focus on radars and wireless communications. He worked at part-time job as embedded systems and DSP engineer for 2.5 years. After that he had internship in summer of 2019 at Huawei Russian Research Institute (RRI) and after graduating from university, he worked at Huawei RRI for 1 year. During his job he improved receiver’s sensitivity applying some convex and non-convex optimization approaches and provide some dimensionality reduction approaches for system identification.

Nowadays Karim works in topics related to Joint Sensing And Communication (JSAC), quantum computing and communications over unlicensed spectrum.

Adaptive algorithms in wireless communications, Radar signal processing and machine learning. Quantum computing.

Khalil obtained a bachelor of science degree in electromechanical engineering (with honor) in 2013 and a master of science in robotics in 2014 from the National Engineering School of Sfax (ENIS), Tunisia.

Khalil's Ph.D. research work is about "Miniaturized Drug Delivery Systems for Biomedical Applications" and his research interests include Drug Delivery Systems, Micropumps, Biomedical Devices, Bioengineering, and MEMS.