Profiles

Jinane received her Bachelor's Degree in Computer and Communications Engineering from the American University of Beirut (AUB) in 2021, with High Distinction.

From June 2020 to June 2021, she was a visiting student at the Communication and Computing Systems Laboratory (CCSL) at KAUST, in collaboration with the University of California-Irvine (UCI).

Her research interests focus on in-memory computing for deep learning hardware acceleration to enhance the efficiency and performance of neural network computations.

João Vitor Cordeiro de Brito is a MS/PhD student in the Statistics Program at King Abdullah University of Science and Technology (KAUST).

Prior to KAUST, he obtained his degree in Applied Mathematics from the Federal University of Rio de Janeiro (UFRJ), Brazil. During his bachelors, João did undergraduate research on ergodic aspects of problems from number theory, and was awarded with honors/prizes in national and international contests in mathematics.

His research interests lie primarily in the intersection of harmonic analysis, statistical inference, and network science, with applications in neuroscience, among others.

In 2019, Jose Manuel Taboada completed his bachelor's degree in Nanofabrication and Microelectronics at the National Autonomous University of Mexico (UNAM). He then obtained a master's degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST) in 2022, where he is currently pursuing a Ph.D. His doctoral research focuses on the simulation, design, fabrication, and testing of gallium oxide (Ga₂O₃) based diodes.

Jose Manuel Taboada's research interests include advanced semiconductors, Nanometric fabrication, and characterization processes, and data analysis.

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.

Juan M. Marin is a Doctoral Candidate in Electrical Engineering at King Abdullah University of Science and Technology (KAUST). He received his B.S. degree in Electrical Engineering from the National University of Colombia in 2021. He is currently a postgraduate researcher with KAUST Photonics Laboratory.

His research focuses on the development of Integrated Sensing and Communications (ISAC) as a fundamental asset for the next generation of fiber-based telecommunication networks, endowing optical fibers with multi-parameter sensing and pattern recognition capabilities enabled by machine learning algorithms. In addition to this, his work envisions incorporating fiber optics into the Internet of Things (IoT) by enabling simultaneous power delivery and energy harvesting in fiber-optic networks.

Fiber-optic sensors; Optical Networks; Applied Machine learning; Signal Processing

Juexiao Zhou is a PhD candidate at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, under the supervision of Professor Xin Gao. He is also the co-founder and Chief AI Scientist at DermAssure.ai, MOSS.ai, and BeautyX.ai. His research lies at the intersection of computer science and biomedicine, with a primary focus on AI-driven intelligent healthcare, bioinformatics, and ethical and trustworthy AI in healthcare. Juexiao develops cutting-edge deep learning models and large language models (LLMs) to enable disease detection, prognosis, and risk assessment across clinical settings. In the domain of bioinformatics, he builds intelligent computational frameworks to decode gene regulatory networks, predict protein structure and function, and model complex biological systems. His recent research also explores curiosity-driven AI agents as autonomous scientific researchers. He is committed to advancing ethical AI in healthcare, tackling key challenges such as data privacy, bias, fairness, security, toxicity, and the broader implications of emerging Artificial General Intelligence (AGI) in clinical practice. Juexiao has authored over 30 publications in top-tier journals and conferences, including Science Advances, Nature Machine Intelligence, Nature Computational Science, Nature Communications, The Lancet, Genome Research, Trends in Genetics, Bioinformatics, IEEE TMI, and MICCAI. His work has been featured by major media outlets such as Arab News, Radio Television Hong Kong (RTHK), and Inside Precision Medicine. He is an active member of CAAI, APBioNET, and GBD. He serves as a reviewer for leading journals and conferences, including Nature, Nature Methods, Nature Communications, Medical Image Analysis, Genome Biology, Genome Research, NeurIPS, SIGKDD, and MICCAI. He is also an editorial board member of BMC Bioinformatics, a guest editor for Biomedical Informatics, and currently serves as co-chair of the IS-HIS 2025 Symposium at ICCNS 2025 in Varna, Bulgaria.

• IngIntelligent Healthcare: develop machine learning (ML) / deep learning (DL) techniques for healthcare, including disease detection, risk assessment, etc.
• Bioinformatics: develop new methods for bioinformatics tasks, including gene regulation understanding, system biology, etc.
• Privacy and Security
• Artificial General Intelligence (AGI)
• Large Language Model (LLM)

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.

Kerven Durdymyradov is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), supervised by Professor Mikhail Moshkov. Kerven holds a Master’s degree in Artificial Intelligence from the Moscow Institute of Physics and Technology (2022) and a Bachelor’s degree in Applied Mathematics and Information Technology from Magtymguly Turkmen State University (2017). He has received several bronze and silver medals in the well-known International Mathematical Olympiads, including IMO, IMC, BMO, etc.

Kerven's research focuses on relations between decision trees and decision rule systems.

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 is a Ph.D. candidate at the KAUST Optimization and Machine Learning Lab under the supervision of  Professor Peter Richtarik. Before joining KAUST, Konstantin worked in several prominent Moscow companies, such as Huawei, NVIDIA, and Yandex. He holds a master’s degree in computer science from Bauman Moscow State Technical University, Russia. 

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.

His dissertation title is ”Optimization Methods and Software for Federated Learning”. 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.