Md. Rezaul Karim, M.Sc.
Contact Information
| Address: |
Lehrstuhl Informatik 5 6014, Ground floor, building: E2 Ahornstr. 55 52056 Aachen DE |
| Phone: | +49 241 80 21501 |
| Fax: | +49 241 80 22321 |
| Email: | rezaul.karim@fit.fraunhofer.de |
I am a researcher, author, and a data science enthusiast with a strong Computer Science background and 10 years of R&D experience. My current research focus is solving emerging bioinformatics research problems and making them well-interpretable. I'm passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI).
I am also a Ph.D. candidate at RWTH Aachen University under the supervision of Prof Dr. Stefan Decker. My PhD dissertation is about developing an explainable diagnosis and prognosis solution for cancer based on neuro-symbolic representation learning. I have MSc and BSc degrees in Computer Science from the Kyung Hee University, Korea, and the University of Dhaka, Bangladesh, respectively. Before joining Fraunhofer FIT, I worked as a Researcher at the Insight Centre for Data Analytics, Ireland. Before that, I worked as a Lead Engineer at Samsung Electronics, Korea. Even before that, I worked as a Research Assistant at Kyung Hee University, Korea.
Research interests:
• Machine learning
• Semantic web and Linked data
• Bioinformatics
• Explainable artificial intelligence (XAI).
Technical skills:
• Programming languages: Java, Python, Scala, R
• Machine learning frameworks: scikit-learn, Spark ML, Keras, TensorFlow, DeepLearning4j, SHAP, LIME
• Big data stack: Spark, Hadoop
• Cloud platforms: Amazon AWS, Google Cloud Platform, Google Colab
• Semantic Web technology: OWL, RDF, SPARQL, Jena, RDF4J, Fuseki, Virtuoso
• Interactive notebook: Jupyter, Zeppelin
• Containerization technology: Docker
• RDBMS and database: Oracle Certified Associate, MySQL, PostgreSQL, MongoDB
• Version control: Git, Perforce.
GitHub: https://github.com/rezacsedu
LinkedIn: https://www.linkedin.com/in/karimanalytics/
Google scholar: https://scholar.google.de/citations?user=LTTNF64AAAAJ&hl=en
ResearchGate: https://www.researchgate.net/profile/Md_Karim34
Current Teaching
Past Teaching
Theses
- Deep Learning-based Knee Osteoarthritis Diagnosis from Radiographs and Magnetic Resonance Images (2019)
- Classification of Cancer with methylation aware motifs (2019)
- An Explainable and Fully Automated Localization and Semantic Segmentation Technique for Biomedical Imaging (2019)
- Implementation of Clinical Decision Support Services to Detect Drug Interactions using Clinical Quality Language (2019)
- Using Deep Neural Networks and Copy Number Variations for Cancer Detection (2018)
- Analysis of Breast Cancer Genomic Data with Multimodal Deep Belief Network (2018)
- Optimizing Mining Maximal Frequent Patterns with MFPAS ()
Publications
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Enabling Ad-hoc Reuse of Private Data Repositories through Schema Extraction
Lars Gleim, Md. Rezaul Karim, Lukas Zimmermann, Oliver Kohlbacher, Holger Stenzhorn, Stefan Decker, Oya Beyan
Published in Journal of Biomedical Semantics volume 11, Article number: 6 (2020)
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Deep learning-based clustering approaches for bioinformatics
Published in Briefings in Bioinformatics
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A Snapshot Neural Ensemble Method for Cancer Type Prediction Based on Copy Number Variations
Md. Rezaul Karim, Md Ashiqur Rahman, Joao Bosco Jares, Stefan Decker, Oya Beyan
Published in Neural Computing and Applications
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Distributed Analytics on Sensitive Medical Data: The Personal Health Train
Oya Beyan, Ananya Choudry, Johan van Soest, Oliver Kohlbacher, Lukas Zimmermann, Holger Stenzhorn, Md. Rezaul Karim, Michel Dumontier, Stefan Decker, Luiz Olavo Bonino da Silva Santos, Andre Dekker
Published in Data Intelligence
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Prognostically Relevant Subtypes and Survival Prediction for Breast Cancer Based on Multimodal Genomics Data
Md. Rezaul Karim, Galih Wicaksono, Ivan G. Costo, Stefan Decker, Oya Beyan
Published in IEEE Access
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Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network
Md. Rezaul Karim, Michael Cochez, Joao Bosco Jares, Mumtaz Uddin, Oya Beyan, Stefan Decker
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OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data
Md. Rezaul Karim, Michael Cochez, Oya Beyan, Stefan Decker, Christoph Lange
Published in 19th IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE 2019)
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A Distributed Analytics Platform to Execute FHIR-based Phenotyping Algorithms
Md. Rezaul Karim, Binh-Phi Nguyen, Lukas Zimmermann, Toralf Kirsten, Matthias Löbe, Frank Meineke, Holger Stenzhorn, Oliver Kohlbacher, Stefan Decker, Oya Beyan
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Schema Extraction for Privacy Preserving Processing of Sensitive Data
Lars Gleim, Md. Rezaul Karim, Lukas Zimmermann, Oliver Kohlbacher, Holger Stenzhorn, Stefan Decker, Oya Beyan
Published in Proceedings of the 2nd Workshop on Semantic Web solutions for large-scale biomedical data analytics (SeWeBMeDA)
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Cancer Risk and Type Prediction Based on Copy Number Variations with LSTM and Deep Belief Networks
Md. Rezaul Karim, Md Ashiqur Rahman, Stefan Decker, Oya Beyan
Best Paper Award at ICWL‘20 