Profiles

Before joining KAUST, she was trained in supercomputing at IBM's Thomas J Watson Research Center in Yorktown Heights, NY. She provided support to the Shaheen user community at the KAUST Supercomputing Laboratory (KSL) and is currently focusing on HPC research.

I am interested in the broad area of high-performance computing. My current research interests are Fast Fourier Transform (FFT) library algorithms, benchmarks and its implementations; Scalable Performance Tools; Parallel Hardware Benchmarking; and application performance analysis.

I hold a PhD in Computer Vision and Deep Learning from Linköping University, Sweden, specializing in uncertainty estimation for sparse visual data. My PhD spanned a wide range of topics, gained through both academic research and industrial collaborations, including object detection and tracking, video object segmentation, optical flow estimation, and the application of object detection and tracking techniques in thermal imagery.

My current research focuses on various topics within Generative AI, including image generation and editing with diffusion models, employing foundation models to computer vision and graphics tasks, and working with multi-modal language models and retrieval-augmented generation (RAG) systems.

Sheikh received his PhD in Computer Engineering from King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, KSA in 2016. Before coming to KAUST, he was serving as an Assistant Professor in the Department of Electrical & Computer Engineering, Air University, Islamabad, Pakistan.

His research interests lie in designing fault tolerant digital circuits, hardware security and computer architecture. At RC3 he will extend his knowledge in investigating and designing of secure and resilient hardware platforms.

Inês is a Postdoctoral Research Fellow at the Cyber Resilience Research Group - CybeResil - part of the CriSys Community.

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).

• Digital signal.
• Image processing.
• Machine learning
• Wireless communications

Aleksandar Cvejic obtained his bachelor with honors in electrical and computer engineering from University of Novi Sad in 2019. After which, he continued to pursue master’s degree at the same university while specializing in artificial intelligence and obtaining the degree in 2021. During his master studies, he also worked as a TA for several courses, including Algorithms and data structures, Fundamental of information systems and software engineering, Xml and Web Services, Numerical algorithms and numerical software, basics of computing.

Aleksandar Cvejic is interested in topics related to deep learning, machine learning and computer vision.

Azimkhon Ostonov is a Ph.D. candidate at King Abdullah University of Science and Technology in Saudi Arabia, 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. Azimkhon has made considerable contributions to the field, with publications including works on Machine Learning and Compexity Analysis.

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

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

https://sites.google.com/view/chlwr

Machine Unlearning

Elnur Gasanov is a PhD candidate in the Optimization and Machine Learning Lab at the Center of Excellence for Generative AI (GenAI) at KAUST, where he is advised by Professor Peter Richtárik. His research focuses on distributed machine learning, stochastic optimization, and randomized linear algebra. Elnur holds a Master of Science in Computer Science from KAUST and a Bachelor's degree in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology.

Optimization and machine learning.