Topics

<> machine learning (59)

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations
Visiting student at King Abdullah University of Science and Technology (KAUST).

Racha Zayni

Visiting Researcher, Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Yimeng Chen

Postdoctoral Research Fellow, Computer Science
Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Nanbo Li

Postdoctoral Research Fellow, Computer Science
Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Omar Ahmed

M.S. Student, Bioengineering
Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent
KAUST-CEMSE-CS-VISION-CAIR-Dalal-Aldossary

Dalal E. Aldossary

M.S. Student, Computer Science
Dalal is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

KAUST advances scalable AI through global collaboration

1 min read ·
KAUST brings global experts together to advance scalable AI and tackle the challenges of training large models.

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

-

B4/5 L0 A0215

This thesis introduces a novel framework for asynchronous first-order stochastic optimization centered on heterogeneous worker speeds that remain fast, stable, and even provably optimal.

Zakaria El Kassimi

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Jonathan Klein

Research Scientist, Computer Science
My fields of research interests include computational simulation, plant simulation and modeling, synthetic training data, machine learning architectures, none-line-of-sight imaging, and computational photography.

KAUST Workshop on Distributed Training in the Era of Large Models

-

Auditorium between B4 & 5, L0, R0215

Join leading researchers and innovators to explore how distributed training is reshaping the next generation of large-scale AI models.

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

-

B9 L2 R2325

This talk will discuss how to design asynchronous optimization methods that remain fast, stable, and even provably optimal.

Training Neural Networks at Any Scale

-

B9 L2 R2325

The talk explores a key mathematical ingredient of scaling in tandem with scaling theory: the numerical solution algorithms commonly employed in deep learning, spanning domains from vision to language models.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B1 L3 R3119

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Yibin Wang

Keep Moving forward

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Andrea Vanessa Rocha

Research Specialist, Statistics
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

Hayssam Dahrouj

Senior Research Scientist at KAUST (King Abdullah University of Science and Technology)

Karen Sanchez

Postdoctoral Research Fellow
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.

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Muhammad Esmat

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

MS Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).

Langevin Monte Carlo as an optimization algorithm

-

B9 L2 H1 R2322

Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Less chat leads to more work for machine learning

1 min read ·
Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

Meet KAUST Prospective Student: Igor Sokolov

1 min read ·
Igor Sokolov, 23, is an applied mathematics and physics graduate who will join KAUST from the Moscow Institute of Physics and Technology, Russia. Igor first came to KAUST as a visiting student in 2019 and was highly impressed by the University's living and working facilities. It was this first impression that prompted him to pursue an M.S. at KAUST. Sokolov will join KAUST in the Fall of 2020 as an M.S. candidate under the supervision of Professor Peter Richtarik.

Meet KAUST student: Konstantin Burlachenko

1 min read ·
Konstantin Burlachenko holds a master’s degree in computer science from Bauman Moscow State Technical University (BMSTU), Russia. After graduating from BMSTU, he worked in several prominent Moscow companies, such as Huawei, NVIDIA, and Yandex. He joined KAUST in August 2020 as a Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab.

Meet KAUST prospective student: Reem Alghamdi

1 min read ·
Reem Alghamdi is a computer science graduate from Princess Nourah bint Abdulrahman University, Saudi Arabia. Reem will join KAUST this fall as a M.S. candidate under the supervision of Professor Peter Richtarik.

Meet KAUST student: Grigory Malinovsky

1 min read ·
Grigory Malinovsky joined KAUST this fall as a M.S./Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab. Grigory holds a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Malinovsky first came to KAUST as a visiting student and was instantly impressed by the research facilities and working atmosphere. His outstanding internship experience was a catalyst in choosing KAUST as the next step in his academic career.

KAUST paper wins 2020 Computational Optimization and Applications Best Paper Award

1 min read ·
KAUST Professor of Computer Science Peter Richtárik and his former student Nicolas Loizou, currently a postdoctoral researcher at Mila - Quebec Artificial Intelligence Institute and soon to take up an assistant professorship position at Johns Hopkins University, recently received the 2020 Computational Optimization and Applications (COAP) Best Paper Award.

Zhize Li

Research Scientist, Computer Science
Zhize Li is a Research Scientist working with Professor Peter Richtarik in his research group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Zhize Li received his Ph.D. in Computer Science from the Institute for Interdisciplinary Information Sciences of Tsinghua University in 2019 under the supervision of Prof. Jian Li. On the career side, before joining KAUST, he was a visiting scholar at the Computer Science Department of Duke University (Hosted by Prof. Rong Ge) and a visiting scholar at the Industrial and

Xun Qian

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Xun Qian is a Postdoctoral Research Fellow at the Visual Computing Center (VCC), King Abdullah University of Science and Technology (KAUST), working with Professor Peter Richtárik in his research group. Education and Early Career Dr. Qian obtained his B.Sc. degree in Mathematics from Huazhong University of Science and Technology, China, in 2013. In 2017, he has obtained a Ph.D. degree in Mathematics from Hong Kong Baptist University (HKBU), Hong Kong. Research Interest Xun Qian is interested in stochastic methods and algorithms, machine learning and interior-point methods. Selected

Slavomir Hanzely

Slavomir Hanzely is an MS/Ph.D. student in the Applied Mathematics and Computational Science Program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Slavomir earned his Bachelor's Degree in Informatics with a background in both Mathematics and Computer Science from the Faculty of Mathematics, Physics and Informatics University (FMPH UK), Slovakia in 2019. Research Interest He is interested in Machine Learning, Federated Learning, and Stochastic Algorithms. Education

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Mher Safaryan

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Mher Safaryan is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Mher received his Ph.D. in Mathematics from Yerevan State University, Armenia under the supervision of Prof. Grigori Karagulyan. Before joining KAUST, he was a Junior Researcher at the Institute of Mathematics of National Academy of Sciences, Real Analysis Department in Armenia. After that, he joined the Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)

Konstantin Mishchenko

Ph.D. Student, Computer Science
Konstantin Mishchenko is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Konstantin Mishchenko obtained his bachelor degree in Computer Science and Physics from Moscow Institute of Physics and Technology in 2016. After that in 2017, he received his master degree in Machine Learning MASH from Université Paris Dauphine in Paris, France. Konstantin had many internships during his studies. The most recent was with Amazon in

Kai Yi

Ph.D. candidate working on machine learning optimization, LLMs and federated learning.

Igor Sokolov

Igor Sokolov is an M.S. candidate in the KAUST Peter Richtarik Research Group under the supervision of Professor Peter Richtarik. Before joining KAUST, Igor obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Igor's research interests lie in the intersection of optimization and machine learning. He is interested in both the theoretical analysis of optimization algorithms as well as their practically efficient applications.

Grigory Malinovsky

Grigory Malinovsky is a M.S./Ph.D. candidate at the KAUST Optimization and Machine Learning Lab under the supervision of Professor Peter Richtarik. Before joining KAUST, Grigory obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Grigory's research interests include machine learning, optimization, and applied statistics. Specifically, he is interested in the theoretical aspects of optimization algorithms that can be applied to real machine-learning problems. Education Profile B.A., Major in Applied

Grigorii Malinovskii

Ph.D. Student, Computer Science
I am a PhD student in Applied Mathematics and Computational Science at King Abdullah University of Science and Technology, advised by Prof. Peter Richtárik, working on optimization theory and its applications in Machine Learning. My hobbies include drums, fitness, martial arts, football, and basketball.

Filip Hanzely

Filip Hanzely is a recent PhD graduate from the group of Peter Richtarik. His research focuses mostly on various aspects of optimization for machine learning, designing provably efficient algorithms for solving big data problems. Filip is joining Toyota Technological Institute in Chicago (TTIC) as a research assistant professor in December 2020. Education and Early Career Filip Hanzely graduated with honor for his bachelor degree in Economics and Financial Mathematics from Comenius University in Bratislava, Slovakia. Later on in 2017, he received his master in science degree in Mathematics and

Elnur Gasanov

Ph.D. Student, Computer Science

Egor Shulgin

Ph.D. Student, Computer Science
Egor Shulgin is a CS student in the MS/Ph.D. program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Education and Early Career Egor obtained his bachelor's degree in Applied Mathematics, Physics, and Computer Science from the Moscow Institute of Physics and Technology (MIPT) in 2019. He first came to KAUST for a research visit in 2019. Later, he joined Prof. Peter Richtarik's group as a MS/Ph.D. student. In 2021 Egor did a research internship with Distributed AI team at the

Dmitry Kovalev

Ph.D. Student, Computer Science
Education Dmitry obtained a B.S. degree from Moscow Institute of Physics and Technology, Moscow, Russia, in 2018. He is an M.S. in the Computer Science program at the King Abdullah University of Science and Technology as of Fall 2018. Publications Dmitry Kovalev, Samuel Horvath and Peter Richtárik. Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop, arXiv preprint arXiv:1901.08689, 2019. Dmitry Kovalev, Peter Richtárik, Eduard Gorbunov, Elnur Gasanov. Stochastic Spectral and Conjugate Descent Methods . Advances in Neural Information Processing

Avetik Karagulyan

Postdoctoral Research Fellow, Optimization and Machine Learning
Avetik Karagulyan have joined the team of Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) as a Postdoctoral fellow in November 2021. Education and Early Career Avetik Karagulyan has defended his thesis at Center of Research in Economics and Statistics (CREST), Paris in June 2021 entitled as "Sampling with the Langevin Monte-Carlo" under the supervision of professor Arnak Dalalyan. In 2018, he received MSc Mathematics, Vision, Learning (MVA) diploma at ENS Paris-Saclay with highest honors. Avetik Karagulyan graduated from

Alibek Sailanbayev

Alibek Sailanbayev is a Ph.D. candidate in Applied Mathematics and Computational Science at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Alibek obtained his bachelor degree Applied Mathematics from Nazarbayev University in Kazakhstan. He joined KAUST in Fall 2017 and received his M.S. degree in Applied Mathematics and Computational Sciences in December 2018. He is now pursuing Ph.D. studies. Alibek took a Summer Internship at Loria/Inria

Adil Salim

Postdoctoral Research Fellow, Computer Science
Adil Salim is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Salim earned his Master degrees in Data Science and Statistical Engineering from ENSAE ParisTech and in Probability Theory and Stochastic Modeling from Paris XI University in France in 2015. He received his Ph.D. in Machine Learning from Télécom ParisTech in France in 2018. Before joining KAUST, Adil worked in many positions starting from an Auditor for banks and insurance companies
<> optimization (31)

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

-

B4/5 L0 A0215

This thesis introduces a novel framework for asynchronous first-order stochastic optimization centered on heterogeneous worker speeds that remain fast, stable, and even provably optimal.

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

-

B9 L2 R2325

This talk will discuss how to design asynchronous optimization methods that remain fast, stable, and even provably optimal.

Training Neural Networks at Any Scale

-

B9 L2 R2325

The talk explores a key mathematical ingredient of scaling in tandem with scaling theory: the numerical solution algorithms commonly employed in deep learning, spanning domains from vision to language models.

BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training

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B9 L2 R2325

We introduce BiCoLoR, the first algorithm to combine local training with bidirectional compression using arbitrary unbiased compressors, achieving accelerated complexity and demonstrating superior empirical performance.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B1 L3 R3119

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

Chaabane Mankai

Surrounded by bright minds and groundbreaking research, KAUST is a great place to learn and grow.

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Langevin Monte Carlo as an optimization algorithm

-

B9 L2 H1 R2322

Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Meet KAUST Prospective Student: Igor Sokolov

1 min read ·
Igor Sokolov, 23, is an applied mathematics and physics graduate who will join KAUST from the Moscow Institute of Physics and Technology, Russia. Igor first came to KAUST as a visiting student in 2019 and was highly impressed by the University's living and working facilities. It was this first impression that prompted him to pursue an M.S. at KAUST. Sokolov will join KAUST in the Fall of 2020 as an M.S. candidate under the supervision of Professor Peter Richtarik.

Meet KAUST student: Grigory Malinovsky

1 min read ·
Grigory Malinovsky joined KAUST this fall as a M.S./Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab. Grigory holds a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Malinovsky first came to KAUST as a visiting student and was instantly impressed by the research facilities and working atmosphere. His outstanding internship experience was a catalyst in choosing KAUST as the next step in his academic career.

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Mher Safaryan

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Mher Safaryan is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Mher received his Ph.D. in Mathematics from Yerevan State University, Armenia under the supervision of Prof. Grigori Karagulyan. Before joining KAUST, he was a Junior Researcher at the Institute of Mathematics of National Academy of Sciences, Real Analysis Department in Armenia. After that, he joined the Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)

Laurent Condat

Senior Research Scientist, Computer Science
I am a senior researcher working in optimization, in the group of Prof. Peter Richtárik.

Konstantin Mishchenko

Ph.D. Student, Computer Science
Konstantin Mishchenko is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Konstantin Mishchenko obtained his bachelor degree in Computer Science and Physics from Moscow Institute of Physics and Technology in 2016. After that in 2017, he received his master degree in Machine Learning MASH from Université Paris Dauphine in Paris, France. Konstantin had many internships during his studies. The most recent was with Amazon in

Kai Yi

Ph.D. candidate working on machine learning optimization, LLMs and federated learning.

Igor Sokolov

Igor Sokolov is an M.S. candidate in the KAUST Peter Richtarik Research Group under the supervision of Professor Peter Richtarik. Before joining KAUST, Igor obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Igor's research interests lie in the intersection of optimization and machine learning. He is interested in both the theoretical analysis of optimization algorithms as well as their practically efficient applications.

Grigory Malinovsky

Grigory Malinovsky is a M.S./Ph.D. candidate at the KAUST Optimization and Machine Learning Lab under the supervision of Professor Peter Richtarik. Before joining KAUST, Grigory obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Grigory's research interests include machine learning, optimization, and applied statistics. Specifically, he is interested in the theoretical aspects of optimization algorithms that can be applied to real machine-learning problems. Education Profile B.A., Major in Applied

Filip Hanzely

Filip Hanzely is a recent PhD graduate from the group of Peter Richtarik. His research focuses mostly on various aspects of optimization for machine learning, designing provably efficient algorithms for solving big data problems. Filip is joining Toyota Technological Institute in Chicago (TTIC) as a research assistant professor in December 2020. Education and Early Career Filip Hanzely graduated with honor for his bachelor degree in Economics and Financial Mathematics from Comenius University in Bratislava, Slovakia. Later on in 2017, he received his master in science degree in Mathematics and

Elnur Gasanov

Ph.D. Student, Computer Science

Egor Shulgin

Ph.D. Student, Computer Science
Egor Shulgin is a CS student in the MS/Ph.D. program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Education and Early Career Egor obtained his bachelor's degree in Applied Mathematics, Physics, and Computer Science from the Moscow Institute of Physics and Technology (MIPT) in 2019. He first came to KAUST for a research visit in 2019. Later, he joined Prof. Peter Richtarik's group as a MS/Ph.D. student. In 2021 Egor did a research internship with Distributed AI team at the

Dmitry Kovalev

Ph.D. Student, Computer Science
Education Dmitry obtained a B.S. degree from Moscow Institute of Physics and Technology, Moscow, Russia, in 2018. He is an M.S. in the Computer Science program at the King Abdullah University of Science and Technology as of Fall 2018. Publications Dmitry Kovalev, Samuel Horvath and Peter Richtárik. Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop, arXiv preprint arXiv:1901.08689, 2019. Dmitry Kovalev, Peter Richtárik, Eduard Gorbunov, Elnur Gasanov. Stochastic Spectral and Conjugate Descent Methods . Advances in Neural Information Processing

Avetik Karagulyan

Postdoctoral Research Fellow, Optimization and Machine Learning
Avetik Karagulyan have joined the team of Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) as a Postdoctoral fellow in November 2021. Education and Early Career Avetik Karagulyan has defended his thesis at Center of Research in Economics and Statistics (CREST), Paris in June 2021 entitled as "Sampling with the Langevin Monte-Carlo" under the supervision of professor Arnak Dalalyan. In 2018, he received MSc Mathematics, Vision, Learning (MVA) diploma at ENS Paris-Saclay with highest honors. Avetik Karagulyan graduated from

Alibek Sailanbayev

Alibek Sailanbayev is a Ph.D. candidate in Applied Mathematics and Computational Science at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Alibek obtained his bachelor degree Applied Mathematics from Nazarbayev University in Kazakhstan. He joined KAUST in Fall 2017 and received his M.S. degree in Applied Mathematics and Computational Sciences in December 2018. He is now pursuing Ph.D. studies. Alibek took a Summer Internship at Loria/Inria

Adil Salim

Postdoctoral Research Fellow, Computer Science
Adil Salim is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Salim earned his Master degrees in Data Science and Statistical Engineering from ENSAE ParisTech and in Probability Theory and Stochastic Modeling from Paris XI University in France in 2015. He received his Ph.D. in Machine Learning from Télécom ParisTech in France in 2018. Before joining KAUST, Adil worked in many positions starting from an Auditor for banks and insurance companies
<> deep learning (12)

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations
Visiting student at King Abdullah University of Science and Technology (KAUST).

Yimeng Chen

Postdoctoral Research Fellow, Computer Science
Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Zakaria El Kassimi

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Training Neural Networks at Any Scale

-

B9 L2 R2325

The talk explores a key mathematical ingredient of scaling in tandem with scaling theory: the numerical solution algorithms commonly employed in deep learning, spanning domains from vision to language models.

Adonai Vera

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Juan Pablo Muñoz Díaz

Karen Sanchez

Postdoctoral Research Fellow
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.

Mhd. Emad Alolaby

Sixiu Liu

Ph.D. degree in Earth science and engineering with the King Abdullah University of Science and Technology (KAUST)

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)
<> artificial intelligence (11)

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Omar Ahmed

M.S. Student, Bioengineering
Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent
Salem Alsaqaf

Salem Al-saqaf

Salem Al-Saqaf is an Electrical and Computer Engineer specializing in semiconductors, electronics and AI. He combines strong academic training with practical industry experience in AI-driven solutions, data science, and system optimization. His work spans applied research and real-world innovation, aiming to advance intelligent systems, enhance semiconductor applications, and develop technologies that bridge academic discovery with industrial impact.
Najmeddine Dhieb

Najmeddine Dhieb

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

Yasmeen Alsaedy

M.S. Student, Computer Science
She is an AI researcher specializing in generative AI and deep learning. With a strong academic foundation and hands-on experience in machine learning, data science and artificial intelligence, she is passionate about advancing AI applications in real-world scenarios. She has contributed to cutting-edge projects, published research, and collaborated on innovative solutions that bridge the gap between AI theory and practice. Her expertise includes designing and implementing advanced data models, AI-driven visual/text computing, and deep learning architectures.

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

KAUST CS student recognized with AAAI Outstanding Program Committee Award

1 min read ·
Konstantin Mishchenko, a Ph.D. student under the supervision of Professor Peter Richtárik, has been selected as one of the Association for the Advancement of Artificial Intelligence’s (AAAI) 12 Outstanding Program Committee Members for 2020. Mishchenko was presented with a certificate in recognition of his outstanding service at AAAI-20 held in New York from February 7-12.

Meet KAUST student: Konstantin Burlachenko

1 min read ·
Konstantin Burlachenko holds a master’s degree in computer science from Bauman Moscow State Technical University (BMSTU), Russia. After graduating from BMSTU, he worked in several prominent Moscow companies, such as Huawei, NVIDIA, and Yandex. He joined KAUST in August 2020 as a Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab.
<> Computer Vision (11)

Nanbo Li

Postdoctoral Research Fellow, Computer Science
Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Omar Ahmed

M.S. Student, Bioengineering
Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent
KAUST-CEMSE-CS-VISION-CAIR-Dalal-Aldossary

Dalal E. Aldossary

M.S. Student, Computer Science
Dalal is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

Adonai Vera

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Abdelrahman Eldesokey

Postdoctoral Research Fellow
Abdelrahman Eldesokey is a Postdoctoral Fellow at KAUST, working on Generative AI and Computer Vision. His research focuses on diffusion models, multimodal large language models, and vision foundation models, aiming to make generative systems more controllable, interpretable, and reliable.
Najmeddine Dhieb

Najmeddine Dhieb

Karen Sanchez

Postdoctoral Research Fellow
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.

Aznaur Aliev

M.S. Student

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Kai Yi

Ph.D. candidate working on machine learning optimization, LLMs and federated learning.
<> Signal processing (10)

Racha Zayni

Visiting Researcher, Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Reem Arnaout

Visiting student at King Abdullah University of Science and Technology (KAUST).

Serine Medjber

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Mohammed Amine Labbas

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology
Almutasem Bellah Enad

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon
Ahmad Dkhan

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Alaa Alameer Ahmad

Research visit to communication theory lab in computer, electrical and mathematical sciences and engineering. KAUST (King Abdullah University of Science and Technology) ,Worked with research groups supervised by Prof. Mohamed-Slim Alouini and Prof. Tareq AL-Naffouri.

Abdullatif Al-Rabah

MS Degree, Computer, Electrical and Mathematical Sciences and Engineering at King Abdullah University of Science and Technology (KAUST).
<> AI (8)

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations
Visiting student at King Abdullah University of Science and Technology (KAUST).

Nanbo Li

Postdoctoral Research Fellow, Computer Science
Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B1 L3 R3119

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Aznaur Aliev

M.S. Student

Mhd. Emad Alolaby

Mohammed A Shukri

Technical Consultant at KAUST (King Abdullah University of Science and Technology).
<> algorithms (8)

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B1 L3 R3119

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Konstantin Mishchenko

Ph.D. Student, Computer Science
Konstantin Mishchenko is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Konstantin Mishchenko obtained his bachelor degree in Computer Science and Physics from Moscow Institute of Physics and Technology in 2016. After that in 2017, he received his master degree in Machine Learning MASH from Université Paris Dauphine in Paris, France. Konstantin had many internships during his studies. The most recent was with Amazon in

Elnur Gasanov

Ph.D. Student, Computer Science

Alibek Sailanbayev

Alibek Sailanbayev is a Ph.D. candidate in Applied Mathematics and Computational Science at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Alibek obtained his bachelor degree Applied Mathematics from Nazarbayev University in Kazakhstan. He joined KAUST in Fall 2017 and received his M.S. degree in Applied Mathematics and Computational Sciences in December 2018. He is now pursuing Ph.D. studies. Alibek took a Summer Internship at Loria/Inria
<> cybersecurity (8) <> Federated learning (8)

Reem Arnaout

Visiting student at King Abdullah University of Science and Technology (KAUST).

KAUST advances scalable AI through global collaboration

1 min read ·
KAUST brings global experts together to advance scalable AI and tackle the challenges of training large models.

Error Feedback for Communication-Efficient First and Second-Order Distributed Optimization: Theory and Practical Implementation

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B9 L2 R2325

This seminar will discuss advancements in Federated Learning, including theoretical improvements to the Error Feedback method (EF21) for communication-efficient distributed training and the development of significantly more practical and efficient implementations of the Federated Newton Learn (FedNL) algorithm.

Better Methods and Theory for Federated Learning: Compression, Client Selection, and Heterogeneity

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B5 L5 R5209

Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., mobile phone devices, with an incentive to collaborate in solving a machine learning problem coordinated by a central server. FL was proposed in 2016 by Konecny et al. and McMahan et al. as a viable privacy-preserving alternative to traditional centralized machine learning since, by construction, the training data points are decentralized and never transferred by the clients to a central server. Therefore, to a certain degree, FL mitigates the privacy risks associated with centralized data collection. Unfortunately, optimization for FL faces several specific issues that centralized optimization usually does not need to handle. In this thesis, we identify several of these challenges and propose new methods and algorithms to address them, with the ultimate goal of enabling practical FL solutions supported with mathematically rigorous guarantees.

Laurent Condat

Senior Research Scientist, Computer Science
I am a senior researcher working in optimization, in the group of Prof. Peter Richtárik.

Konstantin Burlachenko

Ph.D. Student, Computer Science
Konstantin's research focuses on large-scale mathematical optimization systems for machine learning and artificial intelligence, especially those requiring high-performance computing approaches.

Grigorii Malinovskii

Ph.D. Student, Computer Science
I am a PhD student in Applied Mathematics and Computational Science at King Abdullah University of Science and Technology, advised by Prof. Peter Richtárik, working on optimization theory and its applications in Machine Learning. My hobbies include drums, fitness, martial arts, football, and basketball.

Egor Shulgin

Ph.D. Student, Computer Science
Egor Shulgin is a CS student in the MS/Ph.D. program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Education and Early Career Egor obtained his bachelor's degree in Applied Mathematics, Physics, and Computer Science from the Moscow Institute of Physics and Technology (MIPT) in 2019. He first came to KAUST for a research visit in 2019. Later, he joined Prof. Peter Richtarik's group as a MS/Ph.D. student. In 2021 Egor did a research internship with Distributed AI team at the
<> IoT (7)
Salem Alsaqaf

Salem Al-saqaf

Salem Al-Saqaf is an Electrical and Computer Engineer specializing in semiconductors, electronics and AI. He combines strong academic training with practical industry experience in AI-driven solutions, data science, and system optimization. His work spans applied research and real-world innovation, aiming to advance intelligent systems, enhance semiconductor applications, and develop technologies that bridge academic discovery with industrial impact.

Serine Medjber

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Mohammed Amine Labbas

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri
Ruochen Zhu

Ruochen Zhu

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).
<> Photonics and optoelectronics (7)
polina.morozova photo

Polina Morozova

M.S. Student, Photonics Laboratory
md alshahriar shakil photo

Md Al Shahriar Shakil

M.S. Student, Photonics Laboratory
Md Al Shahriar Shakil was awarded his Bachelor of Science degree in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology (BUET), where his undergraduate research focused on quantum computing and quantum photonics. After completing his bachelor’s degree, he worked as a Research Assistant at BUET, where he gained experience in spin-based quantum qubits for quantum computing. He is currently an MS/PhD student in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), where his research focuses on
Hanqi Li profile picture

Hanqi Li

M.S. Student, Photonics Laboratory
Hanqi Li received her Bachelor of Science degree in Electronic Science and Technology from Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2025. She was an intern student at the Institute of Semiconductors, Chinese Academy of Sciences, during 2024 and 2025, where she worked on topics related to optoelectronics and silicon photonics. Hanqi is currently an MS/PhD student in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, and a member of the Photonics Laboratory.
Mohammed Alotaibi

Mohammed Obidallah Alotaibi

Ph.D. Student, Photonics Laboratory
I am Mohammed Obidallah Alotaibi, joining the KAUST Photonics Lab. I hold an M.S. in Electrical Engineering from the University of Central Florida and a B.S. from Umm Al-Qura University. Previously, I served as an Instrument Engineer at Aramco and currently lecture at Taibah University. I am passionate about advancing photonic technologies in energy and sustainability applications, and I look forward to contributing to the lab’s research in optoelectronics and integrated photonic systems. Previous Work Experience: Instrument Engineer, Saudi Aramco (2010–2012) Teaching Assistant & Lecturer

Yan Wang

Ph.D. Student, Photonics Laboratory
M.S./Ph.D. in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, Aug. 2024- present BEng. in Communication Engineering at University of Electronic Science and Technology of China (UESTC), Chengdu, China, Sept. 2019 - Jun. 2024​
Jiahoa Wei profile picture

Jiahao Wei

M.S. Student, Photonics Laboratory
Education Jan 2025 - present, M.S./Ph.D. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia. Sep 2020 - Jun 2024, B.S. in Optoelectronic Information Science and Engineering at Southern University of Science and Technology (SUSTech), Shenzhen, China.
Zinan Xiao profile picture

Zinan Xiao

Ph.D. Student, Photonics Laboratory
Education Aug 2024 - present, Ph.D. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia. Oct 2020 - Oct 2021, M.S. in Optics and photonics at Imperial College London, London, UK. Sep 2016 - Jun 2020, B.S. in Optoelectronic information engineering at Chongqing University, Chongqing, China.
<> LLM (6)

KAUST advances scalable AI through global collaboration

1 min read ·
KAUST brings global experts together to advance scalable AI and tackle the challenges of training large models.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B1 L3 R3119

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Ahmar Husain

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

-

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Muhammad Samiullah

Cyber Security, FPGA security, AI/ML security
<> big data (5)

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.
<> Cyber Security (5) <> integrated photonics (5)

Naiqin Bu

KAUST-CEMSE-ECE-IPL-Nikita-Kondratyev

Nikita Kondratyev

Mikhail Mishevskii

Mikhail received the B.S. degree in applied mathematics and physics from Novosibirsk State, Novosibirsk, Russia and received a master's degree with honors from the Skolkovo Institute of Science and Technology (Skoltech). His research interests include: nonlinear optics, integrated photonics, ultrafast lasers, frequency comb generation. During his time at Skoltech participated in the Skoltech Translational Research and Innovation Program industrial project to create a pulsed submicron laser for pumping a single-photon quantum light source. Mikhail awarded with Scholarship of the President of

Aleksandr Movsisian

Aleksandr received his bachelor’s degree in 2023 in Physics from Moscow State University (MSU, ranked first in Russia) and completed a master’s degree in Physics with honors at the same institution. He has over two years of research experience in quantum optics, optomechanics, and quantum measurement theory as part of the LIGO group at MSU. His work has focused on squeezed states of light, measurement topologies, and methods to overcome the Standard Quantum Limit, resulting in two first-author publications in JOSA B and Optics Letters. Aleksandr also gained hands-on experimental experience at

Aram Mkrtchyan

Postdoctoral Research Fellow, Electrical and Computer Engineering
<> ISAC (5)
kaust-cemse-ece-ccsl-changhao-he

Changhao He

kaust-cemse-ece-ccsl-yousef-awad

Yousef Awad

kaust-cemse-ece-ccsl-bumin-kagan-yildirim

Bumin Kagan Yildirim

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Bang Huang works as a postdoctoral fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Bang Huang received the M.S. degree (with honors) in electrical engineering and Ph.D degree in information and communication engineering from the School of Information and Communication Engineering (SICE), University of Electronic Science and Technology of China (UESTC), Chengdu, China, in

Xiaowei Pang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Xiaowei Pang is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Xiaowei Pang received her B.S. degree in Telecommunication Engineering from Northwestern Polytechnical University, Xi'an, China, in 2018, and her Ph.D. degree in Information and Communication Engineering from Dalian University of Technology, Dalian, China, in 2024. From 2021 to 2022
<> Distributed algorithms (4) <> Python (4) <> Reinforcement Learning (4)

Training Neural Networks at Any Scale

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B9 L2 R2325

The talk explores a key mathematical ingredient of scaling in tandem with scaling theory: the numerical solution algorithms commonly employed in deep learning, spanning domains from vision to language models.

Kaiqiang Lin

Postdoctoral Research Fellow, Electrical and Computer Engineering
Kaiqiang Lin is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Kaiqiang Lin received his B.Eng. degree in Electronic and Information Engineering from Fujian Agriculture and Forestry University, Fuzhou, China, in 2018, and his Ph.D. degree with the College of Surveying and Geo-Informatics Tongji University, Shanghai, China, in 2024. From 2021 to

Mohammed Alwagait

Mohammed is a PhD Student in the Electrical and Computer Engineering program (ECE) at KAUST. He focuses on Reinforcement Learning Based Energy Saving Path Planning and Control for UAVs.

Aznaur Aliev

M.S. Student
<> statistics (4)

Yimeng Chen

Postdoctoral Research Fellow, Computer Science
Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).
Juan Jose Galeano Arenas

Juan José Galeano Arenas

Ph.D. Student, Statistics
Juan José Galeano Arenas is a MS/PhD student at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group.

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Avetik Karagulyan

Postdoctoral Research Fellow, Optimization and Machine Learning
Avetik Karagulyan have joined the team of Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) as a Postdoctoral fellow in November 2021. Education and Early Career Avetik Karagulyan has defended his thesis at Center of Research in Economics and Statistics (CREST), Paris in June 2021 entitled as "Sampling with the Langevin Monte-Carlo" under the supervision of professor Arnak Dalalyan. In 2018, he received MSc Mathematics, Vision, Learning (MVA) diploma at ENS Paris-Saclay with highest honors. Avetik Karagulyan graduated from
<> algorithm (3)

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Zhize Li

Research Scientist, Computer Science
Zhize Li is a Research Scientist working with Professor Peter Richtarik in his research group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Zhize Li received his Ph.D. in Computer Science from the Institute for Interdisciplinary Information Sciences of Tsinghua University in 2019 under the supervision of Prof. Jian Li. On the career side, before joining KAUST, he was a visiting scholar at the Computer Science Department of Duke University (Hosted by Prof. Rong Ge) and a visiting scholar at the Industrial and

Xun Qian

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Xun Qian is a Postdoctoral Research Fellow at the Visual Computing Center (VCC), King Abdullah University of Science and Technology (KAUST), working with Professor Peter Richtárik in his research group. Education and Early Career Dr. Qian obtained his B.Sc. degree in Mathematics from Huazhong University of Science and Technology, China, in 2013. In 2017, he has obtained a Ph.D. degree in Mathematics from Hong Kong Baptist University (HKBU), Hong Kong. Research Interest Xun Qian is interested in stochastic methods and algorithms, machine learning and interior-point methods. Selected
<> embedded systems (3) <> haps (3)

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Bang Huang works as a postdoctoral fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Bang Huang received the M.S. degree (with honors) in electrical engineering and Ph.D degree in information and communication engineering from the School of Information and Communication Engineering (SICE), University of Electronic Science and Technology of China (UESTC), Chengdu, China, in

Xiaowei Pang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Xiaowei Pang is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Xiaowei Pang received her B.S. degree in Telecommunication Engineering from Northwestern Polytechnical University, Xi'an, China, in 2018, and her Ph.D. degree in Information and Communication Engineering from Dalian University of Technology, Dalian, China, in 2024. From 2021 to 2022
H

Hussein Al-Shatri

I chose KAUST because of its reputation for excellence in research and its recent emphasis on applied research. With my extensive background in both academic research and industrial development, I am excited to contribute my experience and ambition. My goal is to foster new ideas and develop innovative prototypes in the field of wireless communications, which will undoubtedly enhance people's lives.
<> LiDAR (3) <> MATLAB (3) <> Nonlinear Optics (3) <> wireless communication (3) <> Wireless Communications (3) <> 6G (2) <> Advanced Perception (2) <> AI4Science (2) <> applied mathematics (2) <> asynchronous algorithms (2) <> Automous Vehicles (2) <> biomedical devices (2) <> bioscience (2) <> communication (2) <> communication efficiency (2) <> Computational Neuroscience (2) <> Computer science (2) <> data analysis (2) <> Deep generative models (2) <> Digital signal processing (2) <> Dynamical Systems (2) <> Electroencephalograms (2) <> FPGA (2) <> Free-space optical communications (2) <> Generative AI and LLMs (2) <> healthcare (2) <> HPC (2) <> Large Language Models (2) <> Localization (2) <> machine learning for wireless communication (2) <> ML (2) <> multimodal sensing (2) <> nanomaterials (2) <> Network Design (2) <> Non-Terrestrial Networks (2) <> numerical analysis (2) <> optical computing (2) <> Optimization for Machine Learning (2)

Better Methods and Theory for Federated Learning: Compression, Client Selection, and Heterogeneity

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B5 L5 R5209

Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., mobile phone devices, with an incentive to collaborate in solving a machine learning problem coordinated by a central server. FL was proposed in 2016 by Konecny et al. and McMahan et al. as a viable privacy-preserving alternative to traditional centralized machine learning since, by construction, the training data points are decentralized and never transferred by the clients to a central server. Therefore, to a certain degree, FL mitigates the privacy risks associated with centralized data collection. Unfortunately, optimization for FL faces several specific issues that centralized optimization usually does not need to handle. In this thesis, we identify several of these challenges and propose new methods and algorithms to address them, with the ultimate goal of enabling practical FL solutions supported with mathematically rigorous guarantees.

Konstantin Burlachenko

Ph.D. Student, Computer Science
Konstantin's research focuses on large-scale mathematical optimization systems for machine learning and artificial intelligence, especially those requiring high-performance computing approaches.
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