Hi, I'm Ahmad Assaf

AI and Machine Learning Leader, Mentor and Advisor

This is 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 Posts

Embed/Preview inline links with React/Next.js and Rehype/MDX

Embedding links in your content can be a powerful way to provide additional context or information. In this post, we explore how to create a custom MDX component to embed/preview inline links in your React/Next.js application using Rehype and MDX.

An Introduction to Knowledge Graphs

Knowledge Graphs are a powerful tool for organizing and representing information in a structured way. In this post, we explore the concept of Knowledge Graphs, their applications, and how they are transforming the way we interact with data.

An Objective Assessment Framework and 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

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