Cover Image for Using the ENU HPC Cluster for GenAI and Cybersecurity Research
Cover Image for Using the ENU HPC Cluster for GenAI and Cybersecurity Research

Using the ENU HPC Cluster for GenAI and Cybersecurity Research

Hosted by Bill Buchanan OBE FRSE, James Ball & Madjid
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About Event

​GPresenters:

Mark Tehrani, James Ball and Bill Buchanan

​Outline:

At the core of GenAI is the integration of GPUs within a high-performance computing (HPC) infrastructure. With ENU, we have an HPC cluster, and which can be used to run a wide range of GenAI models. This online seminar will demonstrate practical examples of using GenAI models with the HPC infrastructure, including the use of small LLMs, such as gemma4:31b, glm-4.7-flash:q4 K M, glm-4.7-flash:q8 0, and mistral-small3.2:24b. It will provide hands-on advice in running models and which will be useful for active researchers and students undertaking student projects. The experience of this work comes from running a range of advanced models for cybersecurity on the HPC.

Speakers:

Mark Tehrani is an AI/Cybersecurity researcher within the Centre for Cybersecurity, IoT and Cyberphysical, and has published a range of papers related to GenAI and Cybersecurity. His current research focus is Superintelligence in Cryptanalysis and in the application of Quantum Computing.

LinkedIn: madjid-tehrani
Google Scholar: https://scholar.google.co.uk/citations?hl=en&user=mdyZssQAAAAJ

James Ball is a Visiting Academic within the Centre for Cybersecurity, IoT and Cyberphysical, and has extensive experience in AI projects and Digital Assets.

LinkedIn: jamesedwardball

Bill Buchanan is a Professor of Applied Cryptography and is currently the Director of the Scottish Centre of Excellence in Digital Trust and DLT, and leads the Blockpass ID Lab.

LinkedIn: billatnapier

Creating an account:

If you are a Napier student/researcher, you can sign up here for an account (and which may be useful to follow along): https://napier.unidesk.ac.uk/tas/public/ssp/content/serviceflow?unid=b8e6dc4b6bd74cc1bafa72336fd61df9