Hi, I'm Ahmad Assaf

My personal space to share my thoughts and ideas on AI, Data and Productivity

A driven AI and Machine Learning (ML) leader with a passion for discovering solutions to create the future of work through my current role as VP of AI and Data @Beamery. As one of the founding engineers, I have built and scaled engineering and data science teams and helped Beamery become one of the latest tech unicorns.

I am a Knowledge Graph and Semantic Web Enthusiast (PhD in Semantic Web and Information Retrieval) with publications on Linked Data, Data Quality and Recommender Systems.

I am currently leading the team working on various exciting AI and Machine Learning technologies, from Natural Language Processing (NLP) methods for text understanding and generation, entity disambiguation and reconciliation, and Large Language Models (LLMs) to Deep Learning (DL) methods such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) for recommender systems and personalization.

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Featured from the blog

An Objective Assessment Framework & Tool for Linked Data Quality

The standardization of Semantic Web technologies and specifications has resulted in a staggering volume of data being published. In this post, I propose an objective assessment framework for Linked Data quality after having surveyed the landscape of Linked Data quality tools to discover that they only cover a subset of the proposed quality indicators

Introduction to Multidimensional Databases

The multidimensional model presents data as facts with associated numerical values called measures or dimensions that characterize the facts and they are “mostly” textual. In this post, we talk about the databases that allow us to store and manipulate complex multidimensional data.

Introduction to Semantic Web

In the same way that Web 1.0 abstracted away the network and physical layers, the Semantic Web abstracts away the document and application layers involved in the exchange of information. The Semantic Web connects facts, so that rather than linking to a specific document or application

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