Profiles

Computer Vision, Machine Learning, Computational Imaging, Multidimensional Signal Processing, and Image/Video Understanding.

Charalampos Antoniadis received a Diploma degree (Hons.) in Computer and Communication Engineering, an M.S. degree in Computer Science, and a Ph.D. in Electrical and Computer Engineering from the University of Thessaly, Volos, Greece, in 2011, 2014, and 2019, respectively. He is currently a Postdoctoral Fellow in the Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. Before joining KAUST, he served as an Adjunct Lecturer at the University of Thessaly for one year.

Dr. Antoniadis was awarded an M.Sc. study fellowship by the University of Thessaly and the Hellenic Foundation for Research and Innovation (HFRI) fellowship for his Ph.D. studies. Additionally, he led a student team that won the First Place Award in the ACM TAU 2020 and TAU2 2021 timing analysis contests.

Charalampos's research focuses on the following areas:

• Efficient AI
• Generative AI and LLMs
• Smart city applications
• Electronic Design Automation

Dr. Daria Sushnikova is a Postdoctoral Research Fellow at KAUST specializing in fast algorithms for large-scale scientific computing. She earned her Ph.D. in Mathematical Modeling and Numerical Methods at the Institute of Numerical Mathematics, Russian Academy of Sciences, under Prof. Ivan Oseledets. Her research spans numerical linear algebra, hierarchical matrices, and high-performance computing, with contributions such as the FMM-LU solver, Compress-and-Eliminate factorization, and H2-MG algorithm. She received the Rising Stars in Computational & Data Sciences Award (2020).

I am a PostDoc at King Abdullah University of Science and Technology (KAUST) in Stochastic Processes and Mathematical Statistics Research Group.

I am interested in mathematical statistics, Markov chains, Monte Carlo methods, and stochastic analysis. My research is focused on theoretical and applied problems associated with stochastic differential equations (SDEs) and stochastic partial differential equations (SPDEs). The goal is to propose and establish methods that are well-studied theoretically and provide numerical implementations that corroborate the proven theoretical findings and their efficacy.

Markov Chain Monte Carlo, Particle Methods, Stochastic Control, Machine Learning, Stochastic Partial Differential Equations

Dr. Emmanuel Ambriz is a Ph.D. in Statistics whose research has focused on frontier challenges in copula theory, particularly in multivariate vine copula models, and their relevance to other branches of modern statistics.

Dr. Ambriz has recently joined the CEMSE Division as a postdoctoral fellow. He obtained his M.S. and Ph.D. degrees from the Centro de Investigación en Matemáticas (CIMAT), Mexico, in 2016 and 2024, respectively. From 2017 to 2022, he has worked as a Professor and Researcher at the Universidad Regional Amazónica Ikiam in Ecuador, where he has been involved in several research projects related to conservation and water resource challenges in the Ecuadorian Amazon.

In addition to his research career, Dr. Ambriz has actively collaborated as a statistical consultant with various industries, public institutions, and NGOs in Mexico and Ecuador, applying statistical methods to support data-driven decision-making across diverse sectors.

Emmanuel's research focuses on developing novel interpretable ordering methods for multivariate functional data, enabling improved distributional analysis and flexible nonlinear functional quantile regression.

I completed my Ph.D. at the University of Amsterdam, advised by Prof. Cees Snoek. My area of interest is Video Understanding, with my PhD thesis focusing on Video-Efficient Foundation Models. I am particularly interested in training foundation models via self-supervised learning from multiple modalities of the video data.

Computer Vision, Video Understanding, Self-supervised Learning, Video Foundation Models.

Georgios Grekas is a Postdoctoral Research Fellow at the Applied PDE group of the Applied Mathematics and Computational Science program (AMCS) of CEMSE division at the King Abdullah University of Science and Technology, Saudi Arabia. He received his Ph.D degree from the department of Applied Mathematics from the University of Crete in 2019. He held a postdoctoral associate position at the department of Aerospace Engineering and Mechanics, in University of Minnesota (2019-2022). Then he moved as a postdoctoral researcher at the Institute of Applied and Computational Mathematics in FORTH, Greece. He joined KAUST and the Applied PDE group in March 2024.

His research interests include solid mechanics, biomechanics, numerical analysis and scientific computing. 

Dr. Hongyan Yu is a Postdoctoral Fellow at the Integrated Photonics Laboratory (IPL) under the supervision of Prof. Yating Wan at King Abdullah University of Science and Technology (KAUST). Before joining KAUST, he received his B. S. degree in Material Physics from Harbin University of Science and Technology, in 2015, and M. Sc. degree in Optical Engineering from Beijing University of Technology, in 2019. He earned his Ph.D degree in Optical Engineering from Zhejiang University - Westlake University joint program, in 2023.
Prior to joining IPL, Dr. Yu was a postdoctoral scholar at Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences (2023-2025). He has published over 15 peer-reviewed articles (10 first and corresponding authors), including Nature Communications, ACS Photonics, APL Photonics, Journal of Lightwave Technology, etc. 

Dr. Yu’s research interests include heterogeneous integrated optical chip, high-speed electro-optic modulator, optical computing.

Inês is a Postdoctoral Research Fellow at the CyberSaR.

Before coming to KAUST, she worked as a Research Scientist at Intel Labs (Germany), where she explored safety features in the realms of open-source hardware and chiplets. In 2022, she obtained her Ph.D. from the University of Luxembourg where, being part of the CritiX group of the Interdisciplinary Center for Security, Reliability, and Trust (SnT), she researched architectural support for hypervisor-level intrusion tolerance in multiprocessor systems-on-chip (MPSoCs). In the same year, she briefly worked as a Research Associate in the same group, looking into NoC security and FPGA-based matrix accelerators.

Her Bachelor's and Master's studies were completed at the University of Lisbon, where she also worked as a Junior Researcher in the LaSIGE research unit (Navigators group).

Her research interests include fault- and intrusion-tolerant resilient systems, computer architecture, hardware design, FPGA security, FPGA partial reconfiguration, hardware description languages (HDLs) and Multi-Processor Systems-on-Chip (MPSoCs).

Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy. She earned her PhD in Engineering, MSc in Electronic Engineering, and a Bachelor’s degree in Energy Engineering in Colombia.

Her research interests include video understanding, domain adaptation, generative AI, and preserving patient privacy, with a focus on deep learning, machine learning, and artificial intelligence for healthcare applications.

Kerven Durdymyradov is currently a Postdoctoral Research Fellow at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. He received his PhD from KAUST in May 2025, specializing in Computer Science under the supervision of 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.

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.

Lisa Gaedke-Merzhäuser is a Postdoctoral Fellow in the BayesComp group at KAUST, led by Prof. Håvard Rue. She holds a PhD in Computational Science from Università della Svizzera italiana (USI) in Lugano, Switzerland. Her research interests lie in fusing statistical learning techniques with methods from high-performance computing. She has been developing INLA_DIST, a distributed memory GPU-accelerated version of INLA for large-scale spatio-temporal models.

Combining statistical learning techniques with methods from high-performance computing, large-scale Bayesian modeling, computationally efficient methods for INLA.