Welcome

Mohamed Ragab

AI Research Lead | Agentic & Industrial AI for Complex Engineering Systems

Time-Series & LLM Intelligence Domain Adaptation Predictive Maintenance Agentic AI Transfer Learning Self-supervised Learning

Senior Researcher at the Technology Innovation Institute (TII), UAE, and Adjunct Researcher at A*STAR, Singapore. Leading AI-driven solutions for complex engineering systems — from agentic AI and LLM intelligence to time-series analysis. 30+ publications in ICML, KDD, IJCAI, IEEE TPAMI, TNNLS, and TKDE. PhD from NTU (QS Top 12).

"Advancing AI to solve real-world challenges in time series and beyond."

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Mohamed Ragab

About Me

Research interests & technical expertise

Domain Adaptation Transfer Learning Self-supervised Learning Predictive Maintenance Privacy-preserving AI Continual Learning Federated Learning Time Series Analysis

Focused on challenges including domain adaptation, transfer learning, self-supervised learning, and privacy-preserving AI for scenarios with scarce labeled data and distribution shifts. Secured competitive research funding as Principal Investigator, including grants totaling over $450K.

Technical Stack

Python PyTorch Keras Scikit-Learn Hugging Face Pandas NumPy C++ MATLAB Docker Git LaTeX Linux GCP W&B HPC

Recent Highlights

Best Paper Award, CCIA 2025

KDD 2025 paper accepted

Promoted to Senior Researcher at TII (Oct 2025)

Latest News

Recent updates and achievements

2026
New Paper accepted in IEEE TKDE 2026: "Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation."
Jun 2025
Co-organized the PHM Conference 2025 in Singapore.
PHM Conference 2025 Singapore
Jun 2025
Invited speaker at Beyond Earth: Space Innovation Forum (TII), presenting on Prognostics & Health Management for future space missions. View post
Beyond Earth Talk - Aviation Systems Beyond Earth Talk - Industrial AI
Oct 2025
Won Best Paper Award at CCIA 2025.
Oct 2025
Promoted to Senior Researcher at Technology Innovation Institute (TII), UAE.
2026
New Paper accepted in IEEE/CAA Journal of Automatica Sinica: "Deep Domain Adaptation for Turbofan Engine RUL Prediction."
May 2025
Paper on Boosting Time-series Domain Adaptation via Time-Frequency Consensus accepted.
Feb 2025
Paper accepted in IEEE Trans. Instrumentation & Measurement: "Evidential Domain Adaptation for RUL Prediction with Incomplete Degradation."
Jan 2025
Paper accepted in Mechanical Systems and Signal Processing: "EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis."
Jan 2025
Paper accepted in IEEE Trans. Automation Science & Engineering: "From Inconsistency to Unity: Benchmarking UDA for RUL."
Jan 2025
Paper accepted at KDD 2025: "Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test-Time Adaptation."
Oct 2024
Paper accepted in IEEE Trans. Instrumentation & Measurement: "Overcoming Negative Transfer by Online Selection for Fault Diagnosis."
Jul 2024
Paper accepted in IEEE TNNLS: "A Virtual-Label-Based Hierarchical Domain Adaptation Method for Time-Series Classification."
Jun 2024
Joined TII Propulsion & Space as Researcher, Abu Dhabi, UAE.
Jun 2024
Survey paper accepted in IEEE Trans. Artificial Intelligence: "Label-Efficient Time Series Representation Learning: A Review."
May 2024
Paper accepted at ICML 2024: "TSLANet: Rethinking Transformers for Time Series Representation Learning."
Jan 2024
Awarded the A*STAR Career Development Award (CDF) with SGD 150K research grant.
Aug 2023
Paper accepted in IEEE TPAMI: "Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification."
Jun 2023
Paper accepted in IEEE Trans. Artificial Intelligence: "Contrastive Domain Adaptation for Time-Series via Temporal Mixup."
May 2023
Paper accepted at KDD 2023: "Source-Free Domain Adaptation with Temporal Imputation for Time Series Data."
Feb 2023
Paper accepted in ACM TKDD: "AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data."
May 2022
Joined A*STAR CFAR & I2R as Research Scientist, Singapore.
Jun 2021
Paper accepted at IJCAI 2021: "Time-Series Representation Learning via Temporal and Contextual Contrasting (TS-TCC)."
Jun 2020
Won Finalist Paper Award at IEEE ICPHM 2020.
Aug 2018
Started PhD at Nanyang Technological University with SINGA Scholarship.

Education & Degrees

Academic background and qualifications

PhD in Computer Science & Engineering

2018 - 2022

Nanyang Technological University (NTU), Singapore

GPA: 4.88 / 5.0. Thesis: Towards Realistic Data-driven Predictive Maintenance.

SINGA Scholarship

MSc in Electrical Engineering

2015 - 2017

Aswan University, Egypt

GPA: 3.62 / 4.0.

Best Master's Thesis Award

BSc in Electrical Engineering

2009 - 2014

Aswan University, Egypt

GPA: 3.88 / 4.0.

First Class Honours

Professional Experience

Career timeline and key accomplishments

Senior Researcher

Oct 2025 - Present

TII Propulsion & Space, Abu Dhabi, UAE

Leading AI research for jet engine health monitoring. Principal Investigator on 2 projects, and Co-PI on a USD 300K funded project. Driving innovation in anomaly detection, fault diagnostics, and remaining useful life estimation using multimodal AI approaches.
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Researcher

Jun 2024 - Oct 2025

TII Propulsion & Space, Abu Dhabi, UAE

Advanced AI for predictive maintenance of aerospace engines. Published in top venues including KDD 2025, IEEE TASE, IEEE TIM, and MSSP. Developed novel domain adaptation and uncertainty-aware methods for fault diagnosis and prognosis.
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Research Scientist

Dec 2022 - Jun 2024

CFAR, A*STAR, Singapore

Led research on privacy-preserving domain adaptation and continual AI. Principal Investigator on A*STAR Career Development Fund grant (SGD 150K). Published in ICML 2024, IEEE TPAMI, KDD 2023, and multiple IEEE Transactions journals.
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Research Scholar

Aug 2018 - Dec 2022

I2R, A*STAR / NTU, Singapore

Conducted PhD research on domain adaptation and self-supervised learning for time series. Published in IJCAI, IEEE TNNLS, IEEE TII, and other premier venues. Developed foundational methods for contrastive domain adaptation and representation learning.
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AI/ML Intern

Sep 2020 - Dec 2020

ST Engineering Aerospace, Singapore

Developed anomaly detection systems for aircraft engines using deep learning. Applied domain adaptation techniques to real-world aerospace maintenance data.
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Assistant Lecturer

Dec 2017 - Jul 2018

Aswan University, Egypt

Delivered lectures and supervised undergraduate projects in the Department of Electrical Engineering. Mentored students on signal processing and machine learning fundamentals.
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Teaching Assistant

Feb 2015 - Nov 2017

Aswan University, Egypt

Assisted in teaching courses in electrical engineering. Supported laboratory sessions and graded assignments. Pursued MSc research concurrently.
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Research Projects

Selected projects and funded research

AI-Based PHM for Jet Engines

Co-PI | USD 300K | TII, 2025–2026

Anomaly detection, fault diagnostics, and remaining useful life estimation using multimodal AI and LLM integration for aerospace engine health monitoring.

AI-Based IR Radiation Prediction for Engines

PI | TII, 2025–2026

Physics-informed deep learning for infrared radiation prediction in propulsion systems, enabling advanced thermal analysis and engine performance optimization.

Label-Efficient Federated Learning

PI | SGD 150K | A*STAR CDF, 2024–2025

Privacy-preserving federated transfer learning with limited labeled data for time series applications. Received A*STAR Career Development Award funding.

Self-Aware Continuously Learning Models

Member | AI Singapore, 2022–2025

Continual test-time adaptation and self-supervised learning for time series. Key outputs: TSLANet (ICML 2024), EverAdapt (MSSP 2025), KDD 2025.

MAPU_SFDA_TS

Learning with Less Data

Member | A*STAR Programmatic Fund, 2021–2024

Self-supervised and label-efficient representation learning for time series. Key outputs: TS-TCC (IJCAI 2021, TPAMI 2023), AdaTime benchmark (TKDD).

SLARDA

XAI for Multi-modal PHM of Jet Engines

Member | IAPF, 2019–2021

Explainable AI for multi-modal sensing in engine health monitoring, integrating physics-informed models with deep learning for interpretable fault detection.

Attention-Seq2Seq-RUL

Selected Publications

30+ publications in top-tier venues — Full list on Google Scholar

KDD 2025

Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test-Time Adaptation

Mohamed Ragab, Emadeldeen Eldele, Min Wu, Xiaoli Li

Preprint 2025

UniFault: A Fault Diagnosis Foundation Model from Bearing Data

Emadeldeen Eldele, Mohamed Ragab, Xu Qing, Edward, Zhenghua Chen, Min Wu, Xiaoli Li, Jay Lee

New
2025

Boosting Time-Series Domain Adaptation via A Time-Frequency Consensus Framework

Mohamed Ragab et al.

New
ICML 2024

TSLANet: Rethinking Transformers for Time Series Representation Learning

Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Xiaoli Li

KDD 2023

Source-Free Domain Adaptation with Temporal Imputation for Time Series Data

Mohamed Ragab, Emadeldeen Eldele, Min Wu, Chuan-Sheng Foo, Xiaoli Li, Zhenghua Chen

ECML PKDD 2023

Diverse and Consistent Multi-view Networks for Semi-supervised Regression

Mohamed Ragab et al.

ICPR 2022

Domain Generalization via Selective Consistency Regularization for Time Series Classification

Mohamed Ragab et al.

IJCAI 2021

Time-Series Representation Learning via Temporal and Contextual Contrasting

Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, Cuntai Guan

ICASSP 2021

Robust Domain-Free Domain Generalization with Class-Aware Alignment

Mohamed Ragab et al.

ICPHM 2020

Adversarial Transfer Learning for Machine Remaining Useful Life Prediction

Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li

Finalist Paper Award
ProvSec 2020

Secure Transfer Learning for Machine Fault Diagnosis Under Different Operating Conditions

Mohamed Ragab et al.

IEEE TKDE 2026

Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation

Mohamed Ragab, Peiliang Gong, Emadeldeen Eldele, Wenyu Zhang, Min Wu, Chuan-Sheng Foo

New
IEEE/CAA JAS 2026

Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends

Yucheng Wang*, Mohamed Ragab*, Yubo Hou, Zhenghua Chen, Min Wu, Xiaoli Li

New
MSSP 2025

EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis Environments

Mohamed Ragab et al.

IEEE TIM 2025

Evidential Domain Adaptation for Remaining Useful Life Prediction with Incomplete Degradation

Mohamed Ragab et al.

IEEE TASE 2025

From Inconsistency to Unity: Benchmarking Deep Learning-Based Unsupervised Domain Adaptation for RUL

Mohamed Ragab et al.

IEEE TNNLS 2024

A Virtual-Label-Based Hierarchical Domain Adaptation Method for Time-Series Classification

Mohamed Ragab et al.

IEEE TIM 2024

Overcoming Negative Transfer by Online Selection: Distant Domain Adaptation for Fault Diagnosis

Mohamed Ragab et al.

IEEE TAI 2024

Label-Efficient Time Series Representation Learning: A Review

Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Xiaoli Li

IEEE TPAMI 2023

Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification

Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, Cuntai Guan

IEEE TAI 2023

Contrastive Domain Adaptation for Time-Series via Temporal Mixup

Mohamed Ragab et al.

IEEE TNSRE 2023

Self-supervised Learning for Label-Efficient Sleep Stage Classification

Emadeldeen Eldele, Mohamed Ragab et al.

ACM TKDD 2023

AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data

Mohamed Ragab, Emadeldeen Eldele et al.

IEEE TNNLS 2022

Self-supervised Autoregressive Domain Adaptation for Time Series Data

Mohamed Ragab, Emadeldeen Eldele, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li

IEEE TETCI 2022

ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training

Emadeldeen Eldele, Mohamed Ragab et al.

IEEE TIM 2021

Conditional Contrastive Domain Generalization for Fault Diagnosis

Mohamed Ragab et al.

Neurocomputing 2021

Attention-Based Sequence to Sequence Model for Remaining Useful Life Prediction

Mohamed Ragab et al.

Neurocomputing 2021

Contrastive Adversarial Knowledge Distillation for Deep Remote Sensing Image Hashing

Mohamed Ragab et al.

IEEE TII 2020

Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction

Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li

IEEE TIM 2020

Adversarial Multiple-Target Domain Adaptation for Fault Classification

Mohamed Ragab et al.

Teaching & Service

Mentorship, teaching, and academic community engagement

Supervised Students

Mariam Mostafa

PhD, URV Spain | Aug 2022 - Present

Domain Adaptation for Breast Cancer Detection

Hou Yubo

PhD, NTU Singapore | Jan 2023 - Present

Adaptation for Fault Diagnosis

Soohyeon Choi

Exchange PhD, UCF USA | Jun 2023 - Jun 2024

LLMs for Code Authorship

Peiliang Gong

Exchange PhD, Nanjing U China | Oct 2023 - Oct 2024

Source-Free Domain Adaptation

Haodong Wang

Exchange PhD, HKUST | Jun 2023 - Dec 2023

Continual Test-Time Adaptation

Edward

MSc, NTU | Mar 2023 - Sep 2023

Continual DA for Fault Diagnosis

Yang Sizhe

MSc, NUS | Jan 2023 - Jun 2023

Robust Uncertainty Quantification for Time Series

Ahmed Ali

UG Exchange, E-JUST Egypt | Jan 2024 - Jun 2024

PINNs for Nanophotonics

Guest Lectures

  • Generative AI — Aalborg University, 2025
  • Neural Networks & Deep Learning — Aalborg University, 2024
  • Transfer Learning — URV, 2022

Co-Teaching

  • AI for Industrial Applications — NTU, 2024
  • Ensemble Models — NTU, 2023
  • Deep Learning for Time Series — NTU, 2022

Professional Service

PC Member

NeurIPS ICML ICLR KDD IJCAI AAAI

Journal Reviewer

IEEE TPAMI IEEE TNNLS IEEE TII IEEE TIM IEEE Sensors IEEE TAI Neural Networks MSSP

Media & Press

Research highlights and media coverage

Calculating the End of Machine Life Flexibly

A*STAR Research Highlights | May 2022

A*STAR researchers developed a computational platform that forecasts when machines will reach the end of their operational life, featuring Mohamed Ragab's CADA algorithm for transfer learning-based predictive maintenance.

A*STAR Researcher Profile

A*STAR Research Portal

Featured researcher profile on A*STAR's official research portal, highlighting work on deep learning for condition monitoring and predictive maintenance.

NTU Digital Repository — TS-TCC

NTU Singapore | 2021

Time-Series Representation Learning via Temporal and Contextual Contrasting (IJCAI 2021), featured in NTU's official digital research repository.

Awards & Honors

Recognitions and achievements

Best Paper Award, CCIA 2025

October 2025

A*STAR Career Development Award, SGD 150K

January 2024

Finalist Paper Award, IEEE ICPHM 2020

July 2020

SINGA PhD Scholarship

August 2018

Best Master's Thesis Award, Aswan University

August 2017

First Class Honours, Aswan University

July 2014