CV

Aali A. Alqarni

Contact Information
Email: alqarni.aali@gmail.com
LinkedIn: aali-alqarni

Education

• Al-Imam Muhammad Ibn Saud Islamic University, Riyadh, SA
  Bachelor of Science in Computer Science
  September 2005 - February 2011

• University of Melbourne, Melbourne, AU
  Master of Data Science
  March 2019 - July 2021

Experience

KACST (King Abdulaziz City for Science and Technology), Riyadh, SA

Senior Researcher (The National Institute for Artificial Intelligence & Robotics)
January 2022 - Present

• Member of the Machine Learning group, conducting research and investigating several ML and Deep Learning models.
• Developed a semantic web tagging tool (TDB) for sentiment labelling of Arabic micro-blogs.
• Provided consultations and presentations to government agencies.
• Conducted experiments on different datasets including Twitter and Reddit. The experiments consist of a number of runs to test different pre-processing techniques, classifiers (MNB, SVM,Deep Learning such as LSTM,CNN,BERT) and feature selection methods on an incremented selection of tokens/words.

Projects:

• Smart Prediction Service (October 2022 - Present)
• Electricity Demand Forecasting (December 2022 - Present)
• KACST/MIT Sports Project (January 2023-Present)

Researcher (Comp. Tech. & Applied Math Centre)
June 2012 - July 2018

• Assisted in developing algorithms, designing, and running scientific experiments.
• Developed a web-based Arabic corpora processing and management system.

Publications

• Abdulmohsen Al-Thubaity, Aali Alqarni, and Ahmad Alnafessah. “Do Words with Certain Part of Speech Tags Improve the Performance of Arabic Text Classification?” In Proceedings of the 2nd International Conference on Information System and Data Mining (ICISDM ’18), ACM, New York, NY, USA, pages 155-161. DOI.
• Alsanoosy, T., Alqarni, A. (2023). YouTube Sentiment Analysis: Performance Model Evaluation. In: Yafooz, W.M.S., Al-Aqrabi, H., Al-Dhaqm, A., Emara, A. (eds) Kids Cybersecurity Using Computational Intelligence Techniques. Studies in Computational Intelligence, vol 1080. Springer, Cham. DOI.
• Alnomay, Ibrahim, Abdullah Alfadhly, and Aali Alqarni. “A Comparative Analysis for GPA Prediction of Undergraduate Students Using Machine and Deep Learning.” International Journal of Information and Education Technology 14.2 (2024). URL

Skills

Programming Languages:
Java, C, C++, R, Python, JavaScript

Databases:
MySQL, Oracle XE, Oracle 10g, Oracle 11g, CouchDB

Web Technology:
HTML, CSS, JavaScript, JSP, Java Servlet, Flask

Operating Systems:
Linux Ubuntu 20.04 LTS, Linux Debian 11

Other Tools:
HTK, CMUSphinx, WEKA, MOA, Rapidminer, GraphML, Anaconda, NodeXL, Gephi, Tableau, NetLogo, Google Cloud, ArcGIS

Training

• Social Network Analysis (Coursera, University of Michigan, September 2012)
• Networked Life (Coursera, University of Pennsylvania, December 2012)
• Computing for Data Analysis (Coursera, Johns Hopkins University, January 2014)
• Neural Networks and Deep Learning (Coursera, Stanford University, November 2019)

Honors and Awards

• FinCausal 2026 (Double Champ),My submission to the FinCausal 2026 Shared Task on detecting causal statements in financial documents achieved the highest score among all participating systems in both evaluation languages (English and Spanish).

The system ranked 1st place overall, outperforming 19 other teams. This result is particularly notable as this was my first participation, while several competing teams had taken part in previous editions.

• Competition page: https://www.lllf.uam.es/wordpress/fincausal-26/

• Workshop: https://wp.lancs.ac.uk/cfie/fnp-2026/

• Arabicthon Competition, June 2022: Top 10 ranking in developing a Poetry Game for children.
• Kaggle Competition ‘Who Tweet?’, September 2019: Top 10 ranking in a large scale Twitter user classification system.
• IBM Build Your Bot Competition, January 2018: Winner for the best bot capable of meaningful conversation in Arabic.