Abdul Wahid

SFI Confirm Centre for Smart Manufacturing fully funded PhD Scholarship.

• Leader of Students Union, Data Science Institute, University of Galway, 2022-2023

Full merit CBNU Scholarship.

GPA: 4/4

• I am an Industry Research Fellow in collaboration with FCAT, Fidelity Investments, funded through the Research Ireland Academic-Industry Postdoctoral Fellowship 2026.
• My research aim is to develop a knowledge graph-based representation of prediction markets to model agents, signals, events, incentives, and information flows, enabling structured reasoning over market behaviour.
• Leverages Large Language Models (LLMs) for market summarisation, narrative extraction, and risk scoring, integrating Graph-RAG pipelines for explainable and verifiable analysis. -

• Design and deliver an undergraduate course on Artificial Intelligence and Machine Learning. -
• Coordinate and support students through project-based learning, ensuring engagement with real datasets and industry-relevant tools.
• Integrate blended learning techniques and digital platforms (e.g., Moodle, interactive assessments) to enhance accessibility and student success.

As a postdoctoral researcher, I am involved in developing ontologies and knowledge graphs using large language models (LLMs) to enhance virtual worlds. My work extends to investigating the legal, ethical, and governance models essential for the sustainable and responsible development of these virtual environments.

I overlook the development and co-creation of the Advanced Computer Aided Phytotherapy (aCAP) system, which represents a significant leap in applying AI to identify and develop plant-based therapeutics for the animal industry.

• Designed and delivered a graduate-level course on autonomous vehicle sensor technologies and system architectures.
• Created original content, including lectures, case studies (Tesla, Waymo, Cruise), and hands-on assignments on Camera, LiDAR, Radar, IMU, GPS & V2X systems.
• Covered emerging trends such as sensor fusion, AI-based perception, cybersecurity, edge computing, and ethical deployment of AV technologies.

• Developed predictive analytics solutions for clients (Cinch UK).
• Applied machine learning to analyse client data and develop predictive models for identifying which car types are likely to sell fastest, based on market dynamics, customer behaviors, and historical sales trends

• Reinforced student learning of skills and materials through daily and weekly check-ins, informal assessments, and observations
• Monitor students for carrying out experiments and address the problems they face
• Created individual homework assignments for students based on their progress in specific sections
• Engage in critical reflection on practice as a basis for improving student knowledge
• Assisting the professor in marking the assignments and scheduling the exams
• Provided timely and frequent feedback to students, fostering an environment of open communication and interest in discovery.

• I was a part of the cloud core network (CNN) team where my focus was on Digital Twin technology
• My research focused on modelling digital twin technology for cloud networks
• Investigated the strategies to overcome the limitations of current modelling languages
• AI-based code generation engines for modelling DT technology for telecommunication networks
• Produced thorough research reports on a range of digital twin technologies, assessing suitability for telecommunication networks.

• Developed a semantic segmentation model using Deep learning for an autonomous vehicle
• Analyzed data to spot patterns, trends and outliers and formulated solid conclusions
• Semantic labelling and instance segmentation using weakly supervised convolutional neural network
• Estimating the mass of the Pig from its 2D image using deep convolutional neural networks
• Image Denoising Method Based on Directional Total Variation Filtering
• Disease Detection in hyperspectral images of apples using Deep convolution Neural Network
• Deep learning-based simple end-to-end architecture capable of extracting contextual information from the images
• Pedestrian recognition and detection using transfer learning
• Korean celebrity face recognition system-based YOLO.

• Supported research projects, aiding academic progression and growth in line with demands
• Taught courses like:
• Digital Signal Processing
• Image Processing and Computer Vision
• Visual basics (VB.NET)