Staring at a page of derivatives or integrals can feel like trying to read a foreign language. Your professor moves fast, the ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Text mining and knowledge graphs connect cell-culture parameters to glycosylation for faster bioprocess optimization.

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated.

Engineers have long battled a problem that can cause loud, damaging oscillations inside gas turbines and aircraft engines: ...

Learn how to secure Model Context Protocol (MCP) deployments using Kyber-encapsulated context windows and zero-trust policy enforcement for post-quantum security.

Abstract: Encoding and decoding of Reed-Muller codes have been a major research topic in coding and theoretical computer science communities. Despite of the fact that there have been numerous encoding ...

At the Spinelli Center, we’re happy to meet you where you are in your mathematics journey. Designed to close gaps in experience—whether you never took a core class in high school, haven’t been in the ...

Abstract: In recent years, Graph Neural Networks (GNNs) have achieved significant success in graph-based tasks. However, they still face challenges in complex scenarios, particularly in integrating ...

This project was carried out as part of the "Advanced Algorithms and Complexity" module for the Master 1 in Bioinformatics at USTHB (Year 2023/2024). It is a Python implementation of fundamental graph ...