الدكتورة هند علي حمد هزيم المريخي

باحث مشارك أول

البريد الإلكتروني

halmerekhi@hbku.edu.qa

الدكتورة هند علي حمد هزيم المريخي

باحث مشارك أول

المؤهلات العلمية

PhD in Computer Science & Engineering

MSc in Computing

الكيان

معهد قطر لبحوث الحوسبة

القسم

تقنيات اللغة العربية

السيرة الذاتية

Hind Al-Merekhi is a Senior Research Associate with a PhD in Computer Science and Engineering from HBKU, specializing in Data Science and Artificial Intelligence. As a senior research associate at QCRI, Hind focuses on leveraging NLP techniques for the Arabic language, exploring applications of large language models (LLMs), and developing AI-driven educational systems. Through this work, Hind aims to bridge advanced AI research with practical applications, driving progress in language processing and intelligent learning technologies.

PhD in Computer Science & Engineering

Hamad Bin Khalifa University

2022

MSc in Computing

Qatar University

2015

BSc in Computer Science

Qatar University

2012

  • Natural Language Processing (NLP) for Arabic Language
  • Applications of Large Language Models (LLMs)
  • AI-Driven educational assistance systems
  • Social computing and online human behavior
  • Analysis and mitigation of online toxicity

Senior Research Associate

Arabic Language Technologies, Hamad Bin Khalifa University

2023 - Present

RDI Program Senior Specialist

Qatar National Research Fund, Qatar Research Development and Innovation Council (QRDI)

2023

Research Associate

Social Computing, Qatar National Research Fund (QNRF) - QCRI

2016 - 2023

Computer Science Lecturer

Computer Science and Engineering, Qatar University

2020 - 2022

Research Assistant

Arabic Language Technologies, Hamad Bin Khalifa University

2012 - 2016

Almerekhi, H., Kwak, H., Salminen, J., & Jansen, B. J. (2022). PROVOKE: Toxicity trigger detection in conversations from the top 100 subreddits. Data and Information Management, 6(4), 100019.

Almerekhi, H., Kwak, H., & Jansen, B. J. (2022). Investigating toxicity changes of cross-community Redditors from 2 billion posts and comments. PeerJ Computer Science, 8, e1059.

Almerekhi, H., Kwak, H., & Jansen, B. (2020). Statistical modeling of harassment against Reddit moderators. Companion Proceedings of the Web Conference 2020, 122–123.

Almerekhi, H., Hasanain, M., & Elsayed, T. (2016). EveTAR: A new test collection for event detection in Arabic tweets. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 689–692.

Almerekhi, H., & Elsayed, T. (2015). Detecting automatically-generated Arabic tweets. In G. Zuccon, S. Geva, H. Joho, F. Scholer, A. Sun, & P. Zhang (Eds.), Information Retrieval Technology (pp. 123–134). Springer International Publishing.

Complete Publication Listing(s): Google Scholar

  • THANAA Award, Qatar Foundation (2014-2016, 2018-2019), for Outstanding Academic Performance
  • Best Poster Award (3rd place), Women in Data Science Conference, 2017, Poster contest
  • Best Poster Award, Information Retrieval Course, Qatar University, 2015 – “Detecting Automatically-Generated Arabic Tweets”
  • Finalist (second place), Microsoft Imagine Cup, Qatar, 2012 – for LingoSnacks m-Learning platform
  • Best Course Project Award, Computer Science and Engineering (CSE) Open Day, Qatar University, 2012 – “Simulation-based study for video transfer over mobile ad hoc networks”