محمد أحمد ياسين الطباخ

عالم رئيسي

البريد الإلكتروني

meltabakh@hbku.edu.qa

موقع المكتب

B1-1126

محمد أحمد ياسين الطباخ

عالم رئيسي

المؤهلات العلمية

PhD in Computer Science

MSc in Computer Science

الكيان

معهد قطر لبحوث الحوسبة

السيرة الذاتية

Dr. Eltabakh is a Principal Scientist at Qatar Computer Research Institute (QCRI). He received his Ph.D. degree in Computer Science from Purdue University in 2010. He was a faculty member in the Computer Science Department at Worcester Polytechnic Institute (WPI), MA, USA, between 2011 and 2022. He has extensive industry-related experience working at Google, Microsoft, IBM, and Teradata (in the USA) for more than 8 years combined.   Dr. Mohamed Eltabakh’s research expertise spans the broad areas of big data management, scalable data analytics, AI and large language models, and distributed systems. Dr. Eltabakh is leading the Fanar project, an Arabic-centric GenAI platform developed in Qatar. Dr. Eltabakh’s research is funded by several entities, including the National Science Foundation (NSF), USA, the Ministry of Transportation (MoT), Qatar, and the Ministry of Interior (MoI), Qatar. 

PhD in Computer Science

Purdue University, USA

2010

MSc in Computer Science

Purdue University, USA

2005

MSc in Computer Science

Alexandria University, Egypt

2002

B.S. in Computer Science

Alexandria University, Egypt

1999

  • AI and scalable analytics
  • Databases and data management
  • Scalable time series analytics
  • Big data and distributed systems

Principal Scientist

Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University

2022 - Present

Consultant and Technical Lead

Query Optimization Group, Teradata Inc.

2017 – 2022

Associate Professor

Computer Science Dept, Worcester Polytechnic Institute (WPI)

2017 - 2022

Assistant Professor

Computer Science Dept, Worcester Polytechnic Institute (WPI)

2011 - 2017

Eltabakh, M. Y., Kunjir, M., Elmagarmid, A. K., & Ahmed, M. S. (2023). Cross modal data discovery over structured and unstructured data lakes. Proceedings of the VLDB Endowment, 16(11), 2959–2971.

Zhang, L., Eltabakh, M. Y., Rundensteiner, E., & Alnuaim, K. (2023). CLIMBER: Pivot-based approximate similarity search over big data series. 2023 IEEE 40th International Conference on Data Engineering (ICDE) (pp. 3933–3946). IEEE.

Zhang, L., Alghamdi, N., Zhang, H., Eltabakh, M. Y., & Rundensteiner, E. A. (2022). PARROT: Pattern-based correlation exploitation in big partitioned data series. The VLDB Journal, 31, 1–24.

Alghamdi, N., Zhang, L., Rundensteiner, E., & Eltabakh, M. Y. (2022). Scalable time series compound infrastructure. Proceedings of the ACM SIGMOD International Conference on Management of Data.

Eltabakh, M. Y., Subramanian, A., Alomari, A., Al-Kateb, M., Nair, S., Hasan, M., Cabrera, W., Zhang, C., Kishore, A., & Prasad, S. (2021). Not black-box anymore! Enabling analytics-aware optimizations in Teradata Vantage. Proceedings of the VLDB Endowment, 14(12), 2959–2971.

  • Ministry of Transportation (MoT-Qatar) Fund, Qatar, “Enterprise Q&A System for Transportation Applications Using Large Language Models” , [QAR 2,000,000], 05/01/2025 – 04/30/2027 (Role: PI)
  • Ministry of Interior (MoI-Qatar) Fund, Qatar, “Business Intelligence and Visual Analytics on MOI Telecommunication Data”, [QAR 72,000], 04/01/2023 – 12/30/2023 (Role: PI)
  • Kasparian Endowed Fund for Neuroscience Research, USA, “Understanding the Neural Mechanisms of Dissociation and Suicidality Towards Advanced Treatment for Mental Illness,” [$500,00], 7/01/2019-6/30/2022, (Role: CO-PI), PI: Jean King.
  • WPI Faculty Achievement Recognition, 2017.