Anas Dorbani
PhD Student in Computer Engineering
A PhD student at Polytechnique Montreal specializing in the intersection of AI and data systems. My research focuses on multimodal data integration, tabular understanding, and enhancing database systems with large language models. I am passionate about building the next generation of intelligent data systems.
Published Papers
Factorized and Vectorized Execution: Optimizing Analytical and Semantic Queries over Relations
Sunny Yasser, Anas Dorbani, Amine Mhedhbi
Education
PhD in Computer Engineering
Polytechnique Montreal & Mila
Montreal, Canada
Focus
Researching multimodal data integration and tabular understanding, with a focus on large language models and database systems.
Advisor
Research Lab
B.Sc. in Computer Science
National School of Computer Science And System Analysis
Rabat, Morocco
Thesis
Developed an automated approach to schema generation and data processing for financial compliance systems using advanced language models, improving metadata consistency and enhancing overall data integration and interpretability.
Industry Collaboration
Oracle Labs
Work Experience
Oracle Labs
Casablanca, Morocco
Research Assistant
Data Integration Team
Automated schema generation for Oracle's Financial Crimes & Compliance systems, enhancing data processing. Fine-tuned 7B models to optimize schema and handle abbreviated column names. Created a framework to evaluate schema generation and data integration accuracy. Improved metadata consistency from 0.4 to 0.6, boosting data interpretability. Optimized output parsing for better data flow and results with 7B models.
Oracle Labs
Casablanca, Morocco
Research Assistant
AutoMLx Team
Enhanced machine learning explainability for the AutoMLx project by optimizing LFI/GFI explainers, reducing their processing time by 80% and improving inference speed. Streamlined memory usage from 20GB to 4GB, lowering operational costs for explanation services. Achieved 83% code coverage to ensure reliability and maintainability of explainability features. Collaborated with cross-functional teams to deliver scalable, high-performance ML explainability solutions within AutoMLx
National University of Rabat
Rabat, Morocco
Research Assistant
Valuation and Transfer Management
Engineered a deep learning model to predict RFID pricing by scraping specifications and market data. Deployed the solution on GCP using Docker for scalable performance and built a Django web application to streamline data collection and real-time model testing.
TAships
INF3710: Files and Databases
Polytechnique Montreal
Introduction to files and databases: needs analysis via the entity-relationship model; relational model and relational algebra; SQL DDL/DML and embedded SQL; concurrency control and transaction management; relational schema design (functional dependencies and normal forms); storage models and file structures; indexing and hashing.
Awards & Grants
VLDB Travel Grant
GrantVery Large Data Base Endowment Inc.
Funding support for students, researchers, and faculty to attend the VLDB 2025 conference in London, covering travel, lodging, and free registration to promote participation in database research.
Projects
FFX
August 2025 - PresentFast Factorized eXecution engine for join-heavy analytical and semantic queries. Built in C++ with factorized intermediates and vectorized execution to optimize performance for modern data workloads.
Flock
September 2024 - PresentDBMS extension integrating LLM and RAG into OLAP systems. Developed FlockMTL from infrastructure design to code implementation and optimization. Designed custom map and reduce functions to integrate advanced workflows into relational database systems. Implemented dynamic batching over tuples to improve query execution efficiency.
OpenHands
Mars 2024 - August 2024Platform for software development agents. As a core maintainer, I helped with reimplementing the SWE agent and fixing its benchmark to improve performance and reliability. Assisted with issue resolution and reviewed pull requests to maintain project quality.
SecureStream
Feb 2024A network security project that employs machine learning and real-time traffic monitoring to detect anomalies in network data. Powered by the CSE-CIC-IDS2018 dataset and cicflowmeter, it enables swift identification of potential threats, enhancing overall network security.