Biography

Mohamed Ragab is a research scientist at both the Institute for Infocomm Research and the Center for Frontier AI Research (CFAR), A*STAR. He earned his Ph.D. in Computer Science and Engineering from Nanyang Technological University (NTU), Singapore, in 2022, with a research emphasis on resilient and transferable AI solutions for dynamic predictive maintenance scenarios. His research interests include self-supervised learning, transfer learning, and robustness for time series applications.

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Interests
  • Deep Learning
  • Transfer Learning
  • Domain Adaptation
  • Time-series data
  • Predictive Maintenance
Education
  • PhD in Computer Science and Engineering, 2022

    Nanyang Technological University

  • MSc in Medical Image Processing, 2017

    Aswan University

  • BSc in Electrical Engineering, 2014

    Aswan University

Recent News

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[Apr-2023] Our paper entitled Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias has been accepted in Conference on Computer Vision and Pattern Recognition (CVPR).

[Jan-2024] Received the Competitive Career Development Fund (CDF) from A*STAR for the project titled Label-Efficient and Resilient Federated Learning Approach for Time Series Applications

[Aug-2023] Our paper entitled Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification has been accepted in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

[Jun-2023] Our paper entitled Contrastive Domain Adaptation for Time-Series via Temporal Mixup has been accepted in IEEE Transactions on Artificial Intelligence (TAI).

[May-2023] Our paper entitled Source-Free Domain Adaptation with Temporal Imputation for Time Series Data has been accepted in The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023).

[Feb-2023] Our paper entitled AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data has been accepted in ACM Transactions on Knowledge Discovery from Data (TKDD).

[Feb-2023] Our paper entitled Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation has been accepted in IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE).

Awards

[Jul-2020] Finalist Paper Award at (ICPHM2020).

[Jun-2018] Singapore International Graduate Award.

[Dec-2017] Best Master’s Thesis Award, Aswan University.

[Jul-2014] Bachelor’s Degree with The First-class Honours, Aswan University.

[Jan-2014] Exemplary Student Award, Aswan University

Skills

Python
Time Series Data Analysis
Deep Learning Frameworks

Experience

 
 
 
 
 
Center for Frontier AI Research, A*STAR
Scientist I
Center for Frontier AI Research, A*STAR
Jun 2022 – Present Singapore
  • Privacy-preserving domain adaptation algorithms for time series data
  • Continual and Sustainible AI for time series applications
  • Robustness and Uncertainty Quantification for time seris data.
 
 
 
 
 
ST Engineering Aerospace
Machine Learning Intern
ST Engineering Aerospace
Sep 2020 – Dec 2020 Singapore
  • Anomaly detection using LSTM, CNN and Autoencoder techniques. I have provided an improved arsenal to tackle future component Predictive Maintenance projects.
  • In advance detection of failure of various air-crafts engines using automatic feature extraction.
 
 
 
 
 
Institute for Infocomm Research (I2R), A*STAR
Research Scholar
Institute for Infocomm Research (I2R), A*STAR
Aug 2018 – May 2022 Singapore
  • Implement end-to-end data science pipeline from data collection to machine learning model deployment for predictive maintenance tasks such as Anomaly detection, Fault Diagnosis, and Fault Prognosis
  • Design Advanced deep learning algorithms for time series data.
  • Develop Transfer Learning and Domain Adaptation techniques to address the challenges of real-world predictive maintenance.
 
 
 
 
 
Aswan University
Assistant Lecturer
Aswan University
Dec 2017 – Jul 2018 Singapore
  • Assist head faculty member with classroom instruction material, exams, and record keeping
  • Guide the development and training of the new graduate assistants
  • Lead, supervise, and plan undergraduate laboratory experience
 
 
 
 
 
Aswan University
Teaching Assistant
Aswan University
Feb 2015 – Nov 2017 Singapore
  • Assists with labs or discussion sections.
  • Attend weekly course staff meetings.
  • Perform occasional other tasks such as mentoring student in the E-learning.

Publications

(2023). Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

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(2023). Source-Free Domain Adaptation with Temporal Imputation for Time Series Data. Knowledge Discovery and Data Mining (KDD).

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(2023). AdaTime:A Benchmarking Suite for Domain Adaptation on Time Series Data. ACM Transactions on Knowledge Discovery from Data (TKDD).

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(2023). Contrastive Domain Adaptation for Time-Series via Temporal Mixup. IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE (TAI).

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(2023). Self-Supervised Learning for LabelEfficient Sleep Stage Classification:A Comprehensive Evaluation. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (TNSRE).

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