Bashir Mohammed, PhD.

Networking and Edge Group (NEX)

Office of the Chief Technology Officer(OCTO)

Intel Corporation.
2200 Mission College Blvd,
Santa Clara,
CA 95054, USA.
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About me

I am a Senior Staff AI Architect at Intel’s Network and Edge group, leading groundbreaking innovations in AI at the Edge. My work focuses on the development and deployment of Large Language Models (LLMs), Large Vision Models(LVMs), Small Language Models(SLMs) and Multi-Agentic Workflows to drive transformative solutions across various industries.

I hold a Ph.D. in Computer Science and bring a wealth of research experience from my previous role at Lawrence Berkeley National Laboratory , where I specialized in developing AI and ML applications for intelligent networks, automatic control systems, quantum communication networks, and data provenance in high-performance computing and distributed systems. Before then I was part of the Quantum Application Network Testbed for Novel Entanglement Technology(QUANT_NET) and Berkeley Lab's Cybersecurity R&D for Science and Energy in the Integrated Data Frameworks(IDF) group under the Scientific data Division.

In the past I was a post-doctoral research scholar in the Computational Research Division(CRD) working on the Deep Learning and Artificial Intelligence High-Performance Networks(DAPHNE) project under the supervision of Mariam Kiran of Energy Sciences Network(Esnet) and John Wu at Berkeley Lab. I received my (MSc) in Control Systems Engineering under the supervision of Professor Peter J Fleming from the University of Sheffield, and a PhD in Computer Science from the University of Bradford in the United Kingdom under the supervision of Professor Irfan Awan and Professor Hassan Ugail. My work focused on developing AI and Machine Learning algorithms to control high-speed networks such as to avoid network traffic congestion, degraded network performance and network downtime for important Science Bigfile Transfers and experiments. My method led to minimizing network downtime, saving US department of Energy (DOE) costs, and freeing engineers to work on science experiments. Before then, during my Ph.D., my research contributed to the core network research and operations with my strong background in HPC and cloud, where I developed and built these infrastructures during my PhD (Bradstack).

I am a Berkeley Lab research SLAM 2019 award winner. In 2019 I was among six postdoctoral fellows from US National Labs invited to Washington DC to speak about their research to the Capitol Hill audience. I am a passionate science communicator.

Research Interest

I am passionate about developing and deploying Large Language Models (LLMs), Large Vision Models (LVMs), Small Language Models (SLMs), and Multi-Agentic Workflows to drive transformative solutions across diverse industries. In addition, my research focuses on advancing AI/ML in networking, systems control, quantum networks, data provenance, and scientific data management within high-performance computing (HPC) and distributed systems. Previously, I am working on building robust networks through the use of machine‐learning‐based approaches, cloud computing, and software‐defined networks (SDN). This couples deep learning methods with SDN for predicting real‐time network behaviour, avoiding data traffic congestion and degraded network performance. My research interest lies at the intersection between network systems, control systems, and machine learning..

My current research interest

  • Large Language Models (LLMs), Large Vision Models(LVMs), Small Language Models(SLMs) and Multi-Agentic Workflows

  • Real-time Machine Learning Control and Reinforcement Learning in High Speed Networks

  • Quantum Communication Networks

  • Cybersecurity R&D for Science and HPC workflows

  • Developing Machine Learning algorithm for large-scale distributed networks

  • Failure Prediction and Performance related issues on Cloud and HPC systems

  • Cloud Platform implementation (Chameleon Cloud, GENI (Global Environment for Network Innovations), Openstack, AWS, Microsoft Azure, Google Cloud Platform)

  • Automatic Control systems

Honors and Awards

Professional Activities

Teaching

2021
University of California, Davis, California USA.

ECS 289I - 001 Winter 2021- Application of Machine Learning in Computer Networks

2020
Berkeley Lab Teaching Scholar Program , Berkeley, California USA.

2020 Berkeley Lab Teaching Scholar Candidates

2018
Leeds Trinity University, UK

Module leader for the Higher Education Funding Council of England (HEFCE) course on introduction to programming.

Contributing to the academic development, course management, teaching and research development.

Teaching and mentoring Second year students.

Teaching Introduction to Programming, Human Computer Interaction (HCI).

Introduction to Coding using Python and Java.

Introduction to JavaScript, Change Management and Version Control using tools such as Visual Studio, Bracket, Code Academy, Code Pen, GitHub, and Glitch.

2017
University of Bradford, UK

2016-7 ACYR - Cisco Routing and Switching Labs, Computer Communication and Networks (CCN) For 2nd Year Undergraduate Students.

2016
University of Bradford, UK

COS5010-B- Computer Communication and Networks (CCN) For 2nd Year Undergraduate Students.

2016
University of Bradford, UK

CM-1066D- Networks and Protocols For MSc Students in Network and Cyber Security

2015
University of Bradford, UK

CM-0228L - Software Engineering with Group Projects (SEGP)- For 2nd Year Undergraduate Students.

Professional Training and Certification

Media Coverage - Featured