Profiles

Dr. Aram Mkrtchyan is currently a Postdoctoral Fellow at the Integrated Photonics Laboratory (IPL) at King Abdullah University of Science and Technology (KAUST), where he works under the supervision of Prof. Yating Wan. He earned his Ph.D. in Photonics from the Skolkovo Institute of Science and Technology (Skoltech), where he specialized in nonlinear optics, ultrafast fiber lasers, and photonic integrated circuits. He received his M.Sc. degrees in parallel from Skoltech and the Moscow Institute of Physics and Technology (MIPT), and holds a B.Sc. degree in Applied Physics and Mathematics from MIPT.

Before joining KAUST in 2025, Dr. Mkrtchyan served as a Senior Research Scientist at Skoltech, contributing to both academic and industry-driven initiatives, including the Huawei Innovation Research Program and Skoltech's Translational Research and Innovation Program. His achievements include the development and commercialization of an ultrafast all-fiber laser at 920 nm, used as a pump source for single-photon quantum emitters in quantum computing systems, advanced studies on carbon nanotube-based photonic devices, and the development of a hybrid microresonator-based frequency comb system. 

In 2024, Dr. Mkrtchyan was nominated for the Skoltech Educational Leadership Excellence Award, honoring for demonstrating leadership and best practices in making Skoltech the top choice for international education in Russia.
Dr. Mkrtchyan has authored over 15 peer-reviewed publications, including articles in Nano Letters, Nanophotonics, Carbon, Journal of Power Sources, Applied Physics Letters, Journal of Lightwave Technology, etc and holds several patents in the fields of ultrafast optics and integrated photonics. His research focuses on nonlinear optics, integrated photonics, ultrafast fiber lasers, Quantum nanomaterials including carbon nanotubes for photonic applications.
 

Dr. Mkrtchyan’s research focuses on nonlinear optics, integrated photonics, ultrafast fiber lasers, Quantum nanomaterials including carbon nanotubes for photonic applications.

Arved Bartuska obtained his bachelor's and master's degrees at the University of Vienna. He received his Ph.D. in 2025 at RWTH Aachen University and is currently a postdoctoral fellow at KAUST.

Arved Bartuska's research interests include applied mathematics, stochastic analysis, Bayesian optimal experimental design, and uncertainty quantification.

Azimkhon Ostonov 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. Azimkhon obtained his Bachelor’s degree in Applied Mathematics and Informatics in 2012 and completed his Master’s degree in Computer Systems and their Software in 2014, both from the National University of Uzbekistan. He has made considerable contributions to the field, with publications including works on Machine Learning and Complexity Analysis.

Before joining KAUST, he worked as a teacher at the National University of Uzbekistan for four years. Prior to that, he began his programming career as a junior programmer at Fido-Biznes in Tashkent.

Azimkhon's research focuses on complexity of decision trees for decision tables.

Binghao Wu is a postdoctoral researcher in the Stochastic Processes Group at King Abdullah University of Science and Technology (KAUST). He received his PhD in Mathematics and Master of Financial Mathematics from Monash University, and his bachelor’s degree in Materials Physics from South China Normal University. His research interests lie in probability theory and stochastic analysis, particularly local times of stochastic processes, Gaussian random fields, stochastic partial differential equations, and stochastic models on metric graphs.

Probability Theory, Stochastic Analysis, Local Times of Stochastic Processes, Gaussian Random Fields, Stochastic Partial Differential Equations, Malliavin Calculus, and Stochastic Processes on Metric Graphs.

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

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).

David Jesus received his Ph.D. from the University of Coimbra in Portugal under the supervision of José Miguel Urbano and Edgard Pimentel in 2023. Then completed 2 years of postdoc in Bologna before joining KAUST in 2025.

Dr. Defan Sun 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). He earned his B.S. degree from the China University of Petroleum (UPC) in 2020 and his Ph.D. degree from the University of Chinese Academy of Sciences (UCAS).

Before joining KAUST, he worked as a Research Assistant at the Institute of Semiconductors, Chinese Academy of Sciences, for four years (2021-2025). During this time, he was a core member of several key research projects, including the National Key R&D Program of China, the National Natural Science Foundation of China (NSFC), and collaborative projects with Huawei Technologies Co., Ltd. His research focuses on the development and application of optical frequency combs based on mode-locked lasers. He has published 11 peer-reviewed articles (7 as first and corresponding authors) such as Photonics Research, Optics Express, and Optics Communications, etc.

He was recognised as the Outstanding Graduate of Shandong Province in 2020 and the Director's Scholarship of the Institute of Semiconductors, CAS in 2025. 

Dr.Sun’s research focuses on the development and application of optical frequency combs based on mode-locked lasers, specifically covering Optical Frequency Comb Generation based on Mode-Locked Lasers, Photonic Integrated Circuits, and Silicon-based Heterogeneous Integrated Photonic Chips. 

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 in the Applied PDE Group of the Applied Mathematics and Computational Science (AMCS) program, within the CEMSE Division at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. He received his Ph.D. degree in Applied Mathematics from the University of Crete in 2019. Part of his Ph.D. studies took place at the University of Sussex from March 2016 to March 2018. From 2019 to 2022, he held a postdoctoral position at the Department of Aerospace Engineering and Mechanics at the University of Minnesota. He then worked as a postdoctoral researcher at the Institute of Applied and Computational Mathematics (IACM) of FORTH, Greece. He joined KAUST and the Applied PDE Group in March 2024.

Gergo received his BSc from the Budapest University of Technology and Economics in 2017, his MRes from the University of Manchester in 2018 and his PhD from KAUST in 2023 in Chemical Engineering. He is a postdoctoral fellow in KAUST since 2023 focusing on the intersection of machine learning and material science.

Gergo's research has centered on data-centric membrane science and sustainable separations. He is the co-founder of the OSN Database, hosting the largest datasets for separation applications. Currently, he focuses on Self-driving laboratories and large-scale molecular learning for different industrial applications.

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.

Igor Getmanov is a Ph.D. candidate in Electrical and Computer Engineering (ECE) at King Abdullah University of Science and Technology (KAUST), advised by Prof. Atif Shamim. He received his B.S. degree in Radiophysics and his M.S. degree in Physics from the Saint Petersburg State University (SPbU), in 2018 and 2020, respectively. He has published his work as first or co-first author in journals including Nanoscale Advances, IEEE Antennas and Propagation Magazine, and Advanced Electronic Materials.

Igor's research bridges the gap between traditional radio-frequency (RF) methodologies and the optical domain, enabling the quantitative characterization and systematic design of integrated plasmonic devices. He develops high-precision frameworks for understanding light-matter interactions at the nanoscale.

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).

Jianwei Shi is a Postdoctoral Fellow in the Spatio-Temporal Statistics and Data Science group at King Abdullah University of Science and Technologuy (KAUST), led by Prof. Marc G. Genton. He holds a Ph.D. in Statistics from Fudan University in Shanghai, China.

Jianwei's research focuses on distributed and scalable statistical methods, particularly for spatial data.

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.

Kelvin J. R. Almeida-Sousa is a Brazilian mathematician and postdoctoral fellow in the Stochastic Processes and Mathematical Statistics group at King Abdullah University of Science and Technology (KAUST). Since December 2022, he has been working under the supervision of Professor David Bolin, with research focused on non-Gaussian stochastic partial differential equations and random fields on complex domains, combining tools from numerical analysis, spectral theory, and mathematical statistics.

Kelvin obtained his PhD in Mathematics from the Federal University of Paraíba, Brazil, under the supervision of Professor Alexandre de Bustamante Simas. His doctoral research addressed fractional and measure-theoretic elliptic operators and their applications to deterministic and stochastic partial differential equations, leading to results in regularity theory and spectral analysis. He earned his MSc in Mathematics from the Federal University of Piauí, where his work focused on nonlinear thermoelastic systems with boundary damping.

His current research interests include non-Gaussian SPDE models, finite volume and lumped mass discretization methods, spatial statistics on complex domains such as metric graphs and surfaces, and the theoretical foundations connecting stochastic processes with generalized Sobolev-type spaces.

Kelvin’s research interests are structured along two complementary directions, a theoretical and an applied one. On the theoretical side, his work is rooted in probability theory and partial differential equations, with particular emphasis on stochastic processes and stochastic partial differential equations driven by generalized differential operators. He is especially interested in differential equations involving measure-theoretic and fractional operators, for which the associated solutions may exhibit jumps or singular behavior. His interests also encompass the general theory of stochastic processes, ranging from stationary processes to random fields, with direct and indirect connections to the theory of random measures and their analytical and probabilistic foundations.

On the applied side, Kelvin focuses on problems in mathematical statistics on complex domains, including Euclidean domains, metric graphs, and surface manifolds, with a strong emphasis on Bayesian modeling. A central theme of his applied research is the development of theoretical foundations for non-Gaussian latent models arising from fractional stochastic partial differential equations, such as Whittle–Matérn-type models. These developments are achieved through the use of tools from classical numerical analysis, including finite volume and lumped mass discretization methods, bridging rigorous analysis with scalable statistical inference for spatial and spatio-temporal data on complex geometries.

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.