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.

Joud Almuqati is an M.S. student in Materials Science and Engineering at KAUST, conducting her thesis research in the Integrated Interfaces (I²) Lab under Professor Wedyan Babatain. She holds a B.S. in Physics from King Abdulaziz University (2025), where she published a first-author study on novel hybrid WSSe monolayers.

Almuqati’s thesis research investigates architected LIG polymer interfaces for electronic skin, establishing how laser processing conditions, polymer characteristics, and surface geometry govern LIG microstructure, conductivity, and electromechanical response. She then integrates these structures with compliant elastomers to build conformal sensing interfaces. Her broader interests include flexible sensors, biosensors, wearable diagnostics, and printed electronics, with a long-term interest in sensing technologies for microgravity and space applications.

His research interests lie in Bayesian and computational statistics, with a particular focus on the development and application of methods in R-INLA.

Juan Pablo Muñoz Díaz is a Ph.D. candidate under the supervision of Prof. Jesper Tegnér (KAUST), co-supervised by Dr. Narsis Kiani (karolinska institutet). His research merges machine learning and bifurcation theory to uncover hidden variables in nonlinear biological systems. He has conducted collaborative research at the University of Cambridge and presented at leading international conferences, including the International Conference on Systems Biology and the SIAM Conference on Dynamical Systems.

Juan Pablo is interested in learning Generative Causal Models from Sparse Temporal Observations during Cellular Reprogramming. His research focuses on identifying efficient algorithms for generation of ensembles of dynamical models and using supervised deep learning algorithms for pattern discovery in large-scale simulation data-sets. These steps aiming to and motivated both by our quest of asking fundamental questions of relevance to human nature and discovery of transformative intelligent technologies inspired from nature.

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.

Khalid Kanaan received his bachelor’s degree in Electrical Engineering from Alfaisal University, Saudi Arabia, in 2022, graduating with First Honors. He obtained his M.S. degree in Electrical and Computer Engineering (ECE) from King Abdullah University of Science and Technology (KAUST), Saudi Arabia. During his undergraduate studies, he was awarded first place in the 4th Boeing Engineering Student Competition for his capstone project. He is currently pursuing a Ph.D. degree in ECE at KAUST.

Khalid’s research interests include multimodal sensing, machine learning for wireless communication, millimeter-wave communication, and massive MIMO systems.

Khusrav Yorov is a Ph.D. candidate in Applied Mathematics and Computational Science (AMCS) at King Abdullah University of Science and Technology (KAUST), specializing in discrete differential geometry and applied mathematics. He earned his bachelor’s degree from Tajik National University and completed his master’s studies at the Moscow Institute of Physics and Technology (MIPT).

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.

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)

Kuilian Yang received the B.S. degree in Integrated Circuits and Systems from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2019, and the M.S. degree in Electrical and Computer Engineering from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2021. He is currently pursuing the Ph.D. degree in Electrical and Computer Engineering at KAUST.

Kuilian's research focuses on neuromorphic computing and efficient hardware acceleration of Spiking Neural Networks (SNNs), including exploiting sparsity in streaming SNN accelerators and designing high-throughput, low-latency architectures for real-time edge intelligence applications.

Lama studied Information technology at King Saud University (KSU) in Riyadh and graduated with a Bachelor degree in 2011. After her bachelor degree, she joined KSU as a Teaching Assistant. Later on, she continued her studies and earned her Master degree in Computer Science from the School of Computing Science at the University of Glasgow, United Kingdom.

Lama first joined KAUST as an Intern with Professor Jeff Shamma until she was enrolled for the Ph.D. program in Computer Science working in Professor Bernard Ghanem research group Image and Video Understanding Lab (IVUL).

Leo’s expertise spans semiconductor device fabrication, material characterization, electrical measurements, and device modelling, with hands-on experience in photolithography, electron-beam lithography, direct laser writing, atomic layer deposition, sputtering, electron-beam evaporation, and dry and wet etching. 

His characterization experience includes scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, atomic force microscopy, profilometry, and electrical device analysis. 

His research interests include wide- and ultrawide-bandgap oxide semiconductors, vertical thin-film transistors, back-end-of-line-compatible processing, monolithic three-dimensional integration, contact and interface engineering, high-k gate dielectrics, nanoscale fabrication, and emerging electronic applications.

Li Zhang is a Ph.D. candidate in the Sensors Lab of the Electrical Engineering Department, CEMSE Division, King Abdullah University of Science and Technology (KAUST). He earned his B.S. in Microelectronic Science and Engineering from the University of Electronic Science and Technology of China in 2018, and his M.S. in Electrical Engineering from KAUST in 2019. His research interests include quantized neural networks, neural-network accelerator architectures, and software–hardware co-design. He has technical expertise in optimization algorithms, machine-learning methods, FPGA-based digital circuit design, and experimental measurement techniques.

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

• Generative Models: Advancing and applying generative models, such as diffusion models, flow matching, and autoregressive models, with a focus on improving their architectures and exploring innovative applications.
• Unified Models: Developing models that bridge generative and understanding tasks, with an emphasis on leveraging understanding capabilities to enhance generative performance.

Liangyu Wang is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), advised by Professor Di Wang. His work has appeared at venues including COLM, NeurIPS, and ACL, with projects such as ZO2, DistZO2, FlashDP, and Canzona. During his Ph.D., he has also worked on large-scale LLM pretraining, including an internship at Aramco and research with the Alibaba Qwen Team. Before joining KAUST, he received his master's degree from The Chinese University of Hong Kong, where he worked on multimodal machine learning.

Liangyu Wang's research interests include optimizing distributed training and inference of LLMs, improving multi-threaded and multi-stream scheduling, and enhancing privacy-preserving methods for LLMs. Currently, I am working on:

• Efficient reinforcement learning (RL) for LLMs reasoning
• Distributed training and inference of LLMs
• Efficient algorithm and infrastructure design for LLMs
• Efficient privacy-preserving methods

Lijie Hu is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST), with a Master’s degree in Mathematics from Renmin University of China. Her research focuses on responsible AI, particularly in explainable AI (XAI) and privacy-preserving machine learning. Lijie’s recent research emphasizes making XAI more accessible and practical. Her work centers on developing Usable XAI-as-a-Service systems (Usable XAI) and Useful Explainable AI toolkits (Useful XAI), bridging the gap between theoretical innovation and real-world application. Her research was recognized as “Best of PODS 2022”. She has received several prestigious honors, including the KAUST Dean’s List Award in 2022, 2024, and 2025, and was recognized as a Top Reviewer at AISTATS 2023. Beyond her research, Lijie actively contributes to the academic community as a member of the AAAI Student Committee.

Lingqi Gao received the B.E. and M.S. degrees in applied geophysics from Jilin University, Changchun, China in 2019 and 2022. Since 2022, he has been a Ph.D. student in Electrical and Computer Engineering at King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, under the supervision of Prof. Hakan Bagci.

Lingqi's research focuses on electromagnetic imaging and inversion algorithms, and their applications.

Luca Galimberti is a Ph.D. candidate in the Applied Mathematics and Computational Sciences (AMCS) at King Abdullah University of Science and Technology (KAUST). He is concurrently pursuing a dual Ph.D. degree in Aeronautical Engineering at Politecnico di Milano, where he also earned his M.Sc. in Aeronautical Engineering and his B.Sc. in Aerospace Engineering.

Luca's research focuses on the development of 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. His work sits at the intersection of computational fluid dynamics, aeroacoustics and aerodynamics, aimed at aeronautical applications.

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.

Luyao Yang is a Ph.D. candidate in Computer Science at the King Abdullah University of Science and Technology (KAUST) working in the Networking Research Lab (NETLAB) under Prof. Basem Shihada. She obtained her Bachelor's degree in Software Engineering at the University of Electronic Science and Technology of China and her Master's degree in Computer Science at King Abdullah University of Science and Technology.

Luyao's research interests focus on the intersection of emerging technologies, focusing on areas such as wearable computing in healthcare and sports.

Madi Makin (Graduate Student Member, IEEE) received both his B.Sc. and M.Sc. degrees in Electrical and Computer Engineering from Nazarbayev University, Astana, Kazakhstan. He is currently pursuing a Ph.D. degree in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.

His research interests include wireless communication systems, with a particular focus on reconfigurable intelligent surfaces (RIS), non-orthogonal multiple-access (NOMA) networks, and physical-layer security.

Madi's research interests include wireless communication systems, with a particular focus on reconfigurable intelligent surfaces, non-orthogonal multiple-access (NOMA) networks, and physical-layer security.