Mohamed Trabelsi

ABOUT

I am a Machine Learning Researcher at Nokia Bell Labs. I obtained my PhD in Computer Science from Lehigh University, USA, in 2022. I obtained my Master of Science in Computer Science from University of Louisville, USA, in 2018. I obtained my Bachelor of Science in Computer Science from Tunisia Polytechnic School, Tunisia, in 2016. My research areas include Machine learning, Information Retrieval, and Natural Language Processing. Here is my CV. If you would like to contact me, reach out to me here or through my LinkedIn profile.

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Industry Experience

• Nokia Bell Labs, Murray Hill, NJ, USA
  • Position: Machine Learning Researcher (06/2022-present)
  • Manager: Huseyin Uzunalioglu
• Nokia Bell Labs, Murray Hill, NJ, USA
  • Position: Research Intern in Statistics and Data Science Research Group (06/2021-12/2021)
  • Responsibility: Proposed a new domain adaptation-based method that transfers knowledge from multiple source domains to a target domain in entity matching task.
  • Mentors/Managers: Jin Cao, Huseyin Uzunalioglu
• Nokia Bell Labs, Murray Hill, NJ, USA
  • Position: Data science intern in Statistics and Data Science Research Group (06/2020-08/2020)
  • Responsibility: Proposed a new context-aware semantic labeling method using both the column values and context, which is based on the pre-trained contextualized language model, BERT.
  • Mentor: Jin Cao

Education

• Lehigh University, Bethlehem, PA, USA
  • Ph.D in Computer Science (2018-2022)
  • GPA: 4.0/4.0
  • Advisor: Jeff Heflin
  • Dissertation: Leveraging Dataset Content in Neural Models for Search and Curation. [PDF]
• University of Louisville, Louisville, KY, USA
  • Masters in Computer Science (2016-2018)
  • GPA: 3.938/4.0
  • Advisor: Hichem Frigui
  • Thesis: Robust fuzzy clustering for multiple instance regression. [PDF]
• Tunisia Polytechnic School, Marsa, Tunisia
  • Bachelor in Computer Science (2013-2016)
  • General Rank: 1/45

Publications

StruBERT: Structure-aware BERT for Table Search and Matching. Mohamed Trabelsi, Zhiyu Chen, Shuo Zhang, Brian Davison, and Jeff Heflin. The ACM Web Conference (WWW ’22), 2022. [PDF]

DAME: Domain Adaptation for Matching Entities. Mohamed Trabelsi, Jin Cao, and Jeff Heflin. Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM '22), 2022. [PDF]

MGNETS: Multi-Graph Neural Networks for Table Search. Zhiyu Chen, Mohamed Trabelsi, Jeff Heflin, Dawei Yin, and Brian D Davison. Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), 2021. [PDF]

Neural ranking models for document retrieval. Mohamed Trabelsi, Zhiyu Chen, Brian D Davison, and Jeff Heflin. Information Retrieval Journal, 2021. [PDF]

SeLaB: Semantic Labeling with BERT. Mohamed Trabelsi, Jin Cao, and Jeff Heflin. International Joint Conference on Neural Networks (IJCNN), 2021.

A Hybrid Deep Model for Learning to Rank Data Tables. Mohamed Trabelsi, Zhiyu Chen, Brian D Davison, and Jeff Heflin. IEEE International Conference on Big Data (Big Data), 2020. [PDF]

Relational Graph Embeddings for Table Retrieval. Mohamed Trabelsi, Zhiyu Chen, Brian D Davison, and Jeff Heflin. IEEE International Conference on Big Data (Big Data), 2020. [PDF]

Semantic Labeling Using a Deep Contextualized Language Model. Mohamed Trabelsi, Jin Cao, Jeff Heflin. arXiv preprint arXiv:2010.16037, 2020. [PDF]

Table Search Using a Deep Contextualized Language Model. Zhiyu Chen, Mohamed Trabelsi, Jeff Heflin, Yinan Xu, and Brian D Davison. Proceedings of 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020. [PDF]

Towards Knowledge Acquisition of Metadata on AI Progress. Zhiyu Chen, Mohamed Trabelsi, Brian D. Davison, and Jeff Heflin. ISWC (Posters & Demos), 2020. [PDF]

Improved table retrieval using multiple context embeddings for attributes. Mohamed Trabelsi, Brian D Davison, and Jeff Heflin. IEEE International Conference on Big Data (Big Data), 2019. [PDF]

Robust fuzzy clustering for multiple instance regression. Mohamed Trabelsi, and Hichem Frigui. Pattern Recognition, 2019. [PDF]

Fuzzy and possibilistic clustering for multiple instance linear regression. Mohamed Trabelsi, and Hichem Frigui. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018. [PDF]

Comparison of several single and multiple instance learning methods for detecting buried explosive objects using GPR data. Andrew Karem, Mohamed Trabelsi, Mahdi Moalla, and Hichem Frigui. Detection and sensing of mines, explosive objects, and obscured targets XXIII, 2018.

About

Contact

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• GitHub

Elements

i = 0;

while (!deck.isInOrder()) {
    print 'Iteration ' + i;
    deck.shuffle();
    i++;
}

print 'It took ' + i + ' iterations to sort the deck.';