Profiles

Roberto earned a B.Sc. degree in Mechanical Engineering from University of Padova in 2014. Then he obtained a M.Sc. in Applied Mathematics at Politecnico di Torino in 2017. Subsequently he earned a Ph.D. in pure and applied mathematics at Politencnico di Torino under a Marie Curie Horizon 2020 program in 2021.

Roberto's research involves numerical analysis of high-order algorithms for solving partial, ordinary, fractional, and integral equations. He is also interested in approximation theory in the context of numerical methods for partial differential equations. Furthermore, he also works on multiphysics models, coupled algorithms, and randomized linear algebra.

• GNSS Localization.
• Attitude Determination.

Yang Liu is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). His primary research interests involve uncertainty quantification, Monte Carlo methods, and finite element methods. He obtained a Master's Degree in Applied Mathematics and Computational Science from KAUST in 2019.

Abdullah Alshehri is a Ph.D. candidate in the Electrical and Computer Engineering (ECE) department at the Integrated Circuit and System (ICS) Research Group at King Abdullah University of Science and Technology (KAUST). 

Alshehri received his Bachelor of Science degree in Electrical and Electronics Engineering from the University of Brighton in the United Kingdom in 2014. In 2016, he earned his master's degree in Electrical Engineering (Electro-physics) from KAUST in Saudi Arabia.

Alshehri's research focuses on designing low-power, high-speed CMOS interface circuits for sensors. He has published over 10 papers and obtained a patent. Throughout his academic and professional journey, Alshehri has gained valuable experience from positions at companies such as STC, Digital Network, ARAMCO, SEMC, Umm Al Qura University, and KAUST.

Abdullah's research interests focus on the design of low-power, high-performance interface circuits for advanced applications. Specific areas of focus include:

1. Low-power, high-speed CMOS interface circuit design:
   • Developing energy-efficient amplifiers, filters, and other analog building blocks
   • Optimizing circuit topologies and techniques to improve power, speed, and linearity
2. High-performance analog comparators:
   • Designing low-offset, low-noise comparator circuits
   • Enhancing comparator speed and resolution for precision applications
3. Energy-efficient digital logic circuits:
   • Implementing compact, low-power digital logic blocks and controllers
   • Exploring circuit techniques to minimize dynamic and static power consumption
4. High-speed, low-power analog-to-digital converters (ADCs):
   • Advancing ADC architectures and circuit designs to improve power consumption and conversion rates
   • Reducing the design complexity of high-performance ADC

First-order mean-field games, linear and quasi-linear elliptic equations, the transport equation, and free boundary problems.  

• Developing Biosensors to detect biomarkers for cancer and other ailments.
• Studying electrochemical characteristics of various materials, polymers and films.
• Designing wearable, point-of-care, biotech medical devices.
• Utilizing machine learning to aid in sensing and data interpretation.

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

• Neuromorphic engineering
• spiking neural network

Computer Vision, Edge Machine Learning, Generative Modeling.

Ahmed Abdali graduated from the University of Bahrain with a BSc in Mathematics in 2022.

Nonlinear PDEs and applications.

Deep Learning , Near-Field Communication, Array Signal Processing

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.