Profiles

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.

Khusrav Yorov joined Visual Computing Center (VCC)  as a Ph.D. student in August 2021 under the supervision of Prof. Helmut Pottmann. Since he was a kid, Khusrav has been involved in the beautiful math world.  Because of it almost all his life is somehow connected to math.  Khusrav has several other hobbies, too, like teaching, biking, travelling, playing football and table tennis.

Khusrav is interested in Geometry and its application in real life, especially Differential Geometry, Discrete Differential Geometry, and Isotropic Geometry.

Konstantin Burlachenko obtained an M.S. degree in Computer Science and Control Systems from the Bauman Moscow State University in 2009. 

After his graduation, he worked as a Senior Engineer for Acronis ,Yandex ,NVIDIA, and as a Principal Engineer for HUAWEI. Konstantin attended in Non-Degree Opportunity program at Stanford between 2015 and 2019 and obtained:

• Data, Models and Optimization Graduate Certificate
• Artificial Intelligence Graduate Certificate

One of his sports achievements is the title of candidate Master of Sport in Chess.

Current research interest is mainly focused is on various aspects of Distributed Stochastic Optimization and Federated Learning. The venues that accepted Konstantin’s works include:

• International Conference on Machine Learning (ICML)
• International Conference on Learning Representations (ICLR)
• Transactions on Machine Learning Research (TMLR)
• SIAM Journal on Mathematics of Data Science (SIAM SIMODS)
• ACM International Workshop on Distributed Machine Learning (ACM CoNext)

Li Zhang received the B.S. degree in microelectronic science and engineering from University of Electronic Science and Technology of China, China, 2018 and the M.S. degree in electrical engineering from King Abdullah University of Science and Technology, Saudi Arabia, 2019.

Li Zhang is interested in quantized neural networks, neural network accelerator and software/hardware co-design.

Luca is currently a PhD candidate in the Applied Mathematics and Computational Sciences department at KAUST. Additionally, he is pursuing a dual PhD degree in Aeronautical Engineering at Politecnico di Milano. He obtained his M.Sc. in Aeronautical Engineering and his B.Sc. in Aerospace Engineering from Politecnico di Milano.

Luca's research focuses on developing computational techniques for aeroacoustics and aerodynamics. In particular, he investigates high order methods for the noise prediction of Urban Air Mobility vehicles in complex scenarios.

Lucas graduated in Electrical Engineering at the Federal University of Rio Grande do Norte (UFRN), Natal, Brazil, on December 2019. From January to July 2019, he was a visiting student at the Information Systems Lab/KAUST, when he worked on indoor localization systems using acoustic waves. In the following year, he joined KAUST as a MSc. student under the supervision of Dr. Tareq Al-Naffouri. He successfully obtained his MSc. degree in December 2021. His Master's thesis is titled A Bayesian Approach to D2D Proximity Estimation using Radio CSI Measurements, and the continuation of this work led to a journal publication at the IEEE Open Journal of the Communications Society. He is currently a Ph.D. student at the Distributed Systems and Autonomy Group/KAUST, under the supervision of Dr. Shinkyu Park.

Lucas' research interests are multi-agent systems, robotics, and deep learning (especially Multi-Agent Reinforcement Learning).

Luis Vazquez obtained a Bachelor Degree on Robotics and Telecommunication from Universidad de las Americas Puebla, his final thesis work was a review on autonomous control and Natural Language Processing for the Robotics human-machine collaboration and interconnection between digital and physical components.

My research is focused on the effects of physical attacks to a robot sensor in the digital processing of the autonomous process more focused on Autonomous control algorithms for 2D vehicles and drones.

Małgorzata Forystek is a Statistics Master's student at the Bayesian Computational Statistics & Modeling research group  (BAYESCOMP) under the supervision of Professor Håvard Rue. She received her B.Sc. degree in Applied Mathematics from AGH University of Science and Technology in Kraków, Poland, in 2023.

Her research interest is mainly in Bayesian and computational Statistics and Data Science.

Michał Forystek received his B.Sc. degree in Information and Communication Technology from AGH University of Science and Technology in Kraków, Poland, in 2023.

He has a 2 years of experience as a Java Developer working for IG Group in Kraków.

Currently he is a Computer Science Master's student at the Secure Next Generation Resilient Systems Lab (SENTRY) under the supervision of Professor Charalambos Konstantinou.

Michał's research involves using Load Altering Attacks to exploit the Load Frequency Control in Power Systems and developing the appropriate countermeasures.