Profiles

Haoyang Li is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST) supervised by Prof. Xin Gao. His research focuses on developing AI-driven methods to integrate multimodal biomedical data, spanning spatial multi-omics, histopathology, and single-cell genomics, with the goal of decoding spatial heterogeneity in diseases and advancing diagnosis, prognosis, and biomarker discovery for personalized therapies. His research has been published in prestigious journals and conferences such as Nature Machine Intelligence, Nature Communications, Science Advances, Bioinformatics, IEEE TMI, and MICCAI. One of his first-authored papers in Nature Communications is recognized as Editor’s Highlight and the 50 best papers in 'Biotechnology and methods'. In addition, Haoyang had two internships at Yale University and Stanford University as visiting student researcher in 2023 and 2024. He has served as a teaching assistant and guest lecturer for the Ministry of Interior in Saudi Arabia, Saudi Aramco, and KAUST. Currently, he serves as an editorial board member and a guest editor for Molecular & Cellular Biomechanics and Biomedical Informatics.

Haoyang has a broad interest in developing AI models to address biological and healthcare problems. Recently, his research has focused on developing AI models for spatial biology, including spatial multi-omics, spatiotemporal omics, and pathological imaging, aimed at translational and clinical research.

He received his M.S. degree in Computer Science from Shenzhen University in 2022, where he was honored with the China National Scholarship and Outstanding Graduate Award. In 2022, he interned at Jarvis Lab, Tencent, and was a visiting student at Norwegian Biometrics Laboratory (NBL), Norwegian University of Science and Technology (NTNU). In 2024, he joined Meta AI as a research scientist internship. He has first-authored more than ten papers in top-tier journals and conferences. His work was recognized as the best paper in the NeurIPS 2023 workshop. He is the reviewer for CVPR, ICCV, ECCV, ICML, AAAI, and MICCAI. His recent research interests primarily focus on generative models, particularly their efficiency and security aspects. His long-term goal lies in making the learnable systems reliable, responsible, and explainable..

Deep Generative Models, XAI, Efficient AI and Responsible AI.

Started his academic path at Specialized Educational Scientific Center of NSU, Russia in 2015, then finished B.S. in Automation of Physical and Technical Research at Novosibirsk State University from 2017–2021, and now doing research in King Abdullah University of Science and Technology, Saudi Arabia while MS and PhD.

Besides his academic work he is an author of "Vectozavr" Youtube channel where he presented his research and some fun projects.

In 2021 he created an online school - vectozavr.ru of physics and math for game developers.

Jichen Lu is currently a Ph.D. student at Network Lab. He earned a Bachelor's degree in Computer Science from the Southern University of Science and Technology (SUSTech). He joined King Abdullah University of Science and Technology (KAUST) in 2021 and completed his Master's degree in 2023. His current research interests include optimization in computer networks, machine learning methods for networks, and AI applications on edge computing.

Jichen's research involves designing optimization algorithms for computer networks and distributed devices, considering multiple KPIs like energy consumption, latency, throughput, etc. He is also focusing on machine learning algorithms for networks and artificial intelligence applications on edge devices.

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. 

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.

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)

Manal A. Alshehri is a Doctoral Candidate in Computer Science at King Abdullah University of Science and Technology (KAUST). She holds a B.S. and M.S. degree in Computer Science from King Abdulaziz University, where she also serves as a lecturer. Her research has been published in leading international conferences, including IEEE Big Data and CIKM.

Her research spans a broad range of artificial intelligence applications, with a focus on enhancing recommendation systems and advancing text mining techniques. She employs cutting-edge AI methodologies to address key challenges such as cold-start problems, user privacy, diversity, and filter bubbles. She is also interested in analyzing user behavior across digital platforms and in leveraging generative large language models to create realistic simulations and automate labor-intensive tasks.

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.

security, distributed systems