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.
Download my CV .
- Deep Learning
- Transfer Learning
- Domain Adaptation
- Time-series data
- Predictive Maintenance
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PhD in Computer Science and Engineering, 2022
Nanyang Technological University
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MSc in Medical Image Processing, 2017
Aswan University
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BSc in Electrical Engineering, 2014
Aswan University
Recent News
[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
Experience
- 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.
- 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.
- 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.
- 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
- Assists with labs or discussion sections.
- Attend weekly course staff meetings.
- Perform occasional other tasks such as mentoring student in the E-learning.
