Abdul Wahid

• Python.
• C / C++.
• MATLAB.
• Docker.

• TensorFlow.
• Keras.
• PyTorch.
• Linux / Windows.
• MS Office / Latex (Overleaf) / SQL.

• Soccer.
• Billiards.
• Travelling.
• Reading.
• Music.

• Received a fully-funded PhD Fellowship from Science Foundation Ireland (SFI)
• Awarded fully funded scholarship for a research-based Master's study in Engineering by Chonbuk National University, South Korea.
• Elected as a student representative for postgraduate students at the Data Science Institute (DSI), University of Galway.
• Invited as speaker for BioTechX health congress in 2022.

University of Galway

Teaching Fellow Feb 2020 - Sep 2022

• 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.

Data Science Institute, University of Galway.

Student Representative May 2022 - present

• Advocating for the interests and needs of PhD students. Represent the concerns, opinions, and ideas of my fellow PhD students to the faculty, administration, and other relevant parties.
• Responsible for planning and organizing academic, social, or professional events and activities for PhD students.
• Offering, guidance, advice and support to other PhD students experiencing academic or personal difficulties.
• Engaging in meetings and committees where decisions affecting PhD students are made.
• Effectively communicating, and disseminating important information to PhD students promptly and appropriately.
• Encourage collaboration among PhD students, both within and across departments, and help to build a sense of community among them.

Software Eng. Lab, Chonbuk National University

Project Lead:

Autonomous Vehicle and Knowledge Extraction from 2D Images, March 2017 - June 2019

• As project leader defined the scope and goals of the project. This involved understanding the business requirements, identifying the use cases, and defining the performance metrics. The project scope and goals were communicated clearly to the team, stakeholders, and clients.
• As a project lead, evaluated different algorithms, such as deep learning, reinforcement learning, or decision trees, and selected the ones that best suited the problem at hand.
• Identified the sources of data, ensured proper and meaningful data collection, and prepared it for training. This involved data cleaning, data transformation, and data augmentation.
• Select the appropriate model architecture and tuned its hyperparameters to optimize performance. This involved selecting the right loss function, optimizer, and regularization techniques. Also experimented with different model architectures, such as convolutional neural networks, recurrent neural networks, or gradient-boosting machines.
• As a project lead, designed experiments to validate the performance of the models and compared them against baselines. This involved running simulations, real-world data collection, and conducting A/B testing.
• As project lead I defined the roles and responsibilities of team members. This involved identifying the skills required for the project, defining the job descriptions, and assigning tasks based on individual strengths and weaknesses.
• Effectively communicated to ensure that all team members understand the project goals, scope, and timelines. Established regular communication channels, such as weekly team meetings, and ensured that everyone is kept informed of project updates.
• Built a motivated and cohesive team. Fostered a culture of collaboration and innovation by organizing team-building activities, providing feedback and recognition, and encouraging knowledge sharing.
• Managed the resources required for the project, such as budget, equipment, and software licenses. And ensuring that the resources are allocated efficiently and that any issues with resource availability are addressed promptly.
• As a project lead, I identified and solved problems quickly and effectively by working closely with the team to understand the issues and identify potential solutions.
• As a project lead, I was responsible for managing the performance of team members. This involved setting performance expectations, providing feedback and coaching, and conducting performance reviews. I address any performance issues proactively and provided opportunities for professional development.

Data Science Institute, University of Galway

PhD Researcher June 2019 - present

• I am a part of the Social Semantic group supervised by Prof John G Breslin
• My research focuses on using machine learning and deep learning models in smart manufacturing.
• Developed algorithms that predict the remaining useful life of complex mechanical systems in smart manufacturing.
• Part of Confirm smart manufacturing project with a research focus on Data Analytics: AI, Predictive Modelling, and Decision Analytics.
• Used different tools and techniques to perform robust primary and secondary research.
• Planned research activities to generate valuable findings and comply with the project brief.
• Stayed abreast of new and updated protocols in the research department
• Wrote annual reports to summarise data and implications of results.

Science Foundation Ireland, Confirm Center for Smart Manufacturing

Researcher June 2019 - present

• Strong background in building novel algorithms using TensorFlow, Keras, PyTorch, and Scikit-learn.
• Skilled in efficiently enriching big data for ML jobs at a scale of 50+ million records using tools such as Streaming Bulk, psycopg2, concurrent futures, ThreadPoolExecutor, and future queues
• Expertise in live chunk-by-chunk copied data validation, multi-stage exception handling, and retrying.
• Extensive knowledge in ELK index template/pattern/management, stack monitoring, Eland, Elasticsearch DSL
• Proficient in creating Spark jobs that run on Elastic Storage System (ESS) Hadoop Yarn Cluster via Jupyter Notebook, including the creation of executables, conda env & conda-pack, tarball, bootstrapper, and spark-submit command with driver-memory, executor-memory/cores, and num-executor.
• Deploying apps to read/write dynamic configs/files in shared storage using AWS SDK (Boto3), environment variables, and PersistentVolumeClaim (PVC).
• Expertise in data analysis techniques such as exploratory data analysis (EDA), principal component analysis (PCA), pandas-profiling, D-Tale, UMAP, and t-SNE.
• Data processing techniques such as epoch, RegEx, lambda function, nested dictionary, lists, dictionaries lists, geo line strings, key-value pairs, pandas, numpy, polygeohasher, geopandas, and converting features and metadata from Dict to Proto data structure
• ML software profiling and stress testing - setting custom metrics, analysis under the idle mode, normal data flow, worst case scenarios - using cProfile, psutil, VizTracer. Thread-safe monitors - messages sent, elapsed time, throughput rate, etc.

Software Eng. Lab, Chonbuk National University

Research Scientist March 2017 - June 2019

• Developed a semantic segmentation model using Deep learning for an autonomous vehicle.
• Analysed 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 DenoisingMethod Base don 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.

Chonbuk National University, South Korea Jeonju

Masters Researcher March 2014 - April 2016

• I was a part of the Media Communication and Signal Processing Lab.
• Modelled a system for Mobile Indoor localization using Kalman Filter and trilateration Technique.
• Proposed an RSSI-based indoor localization model based on Adaptive Neural Fuzzy Inference System.
• Designed a system for the simultaneous detection and segmentation of humans based on the mean Shift Algorithm (MSA).
• Interpreted data to offer potential explanations for trends, patterns and causal links.
• Followed established methodologies and reporting processes in line with the scientific method.

• Adaptive Mobile Localization Method for Indoor Navigation (Journal of Computational &Theoretical Nanoscience’s 2017).
• Image Denoising Method Based on Directional Total Variation Filtering (IEEE, ICTC 2017).
• Analogy of Face Recognition Algorithms & their Subsequent Impact (KIEE).
• Mobile Indoor Localization based on RSSI using Kalman Filter and Trilateration Technique (Proceedings of the SPIE).
• Detection of Pedestrians in Motion with Rotation and Scale Variation (Scinetifc.net).
• Semantic labelling and Instance Segmentation Using Weakly Supervised Convolutional Neural Network (IPCV 2018).
• Caption Generation Model for Images Using Convolutional Neural Networks (KCC 2018).
• Deconvolution Network based Semantic Segmentation (ISITC 2018).
• Deconvolutional Pixel layer model for road segmentation without human assistance (AICS 2019).
• Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework (Applied Sciences, Journal of Artificial Intelligence & Computing 2022).
• Self-Attention Transformer-Based Architecture for Remaining Useful Life Estimation of Complex Machines (ISM 2022).
• Digital Twins: Modelling Languages Comparison (International Conference on Machine Learning and Data Science 2022).

• Soccer.
• Billiards.
• Travelling.
• Reading.
• Music.

• Adaptive Mobile Localization Method for Indoor Navigation (Journal of Computational &Theoretical Nanoscience’s 2017).
• Image Denoising Method Based on Directional Total Variation Filtering (IEEE, ICTC 2017).
• Analogy of Face Recognition Algorithms & their Subsequent Impact (KIEE).
• Mobile Indoor Localization based on RSSI using Kalman Filter and Trilateration Technique (Proceedings of the SPIE).
• Detection of Pedestrians in Motion with Rotation and Scale Variation (Scinetifc.net).
• Semantic labelling and Instance Segmentation Using Weakly Supervised Convolutional Neural Network (IPCV 2018).
• Caption Generation Model for Images Using Convolutional Neural Networks (KCC 2018).
• Deconvolution Network based Semantic Segmentation (ISITC 2018).
• Deconvolutional Pixel layer model for road segmentation without human assistance (AICS 2019).
• Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework (Applied Sciences, Journal of Artificial Intelligence & Computing 2022).
• Self-Attention Transformer-Based Architecture for Remaining Useful Life Estimation of Complex Machines (ISM 2022).
• Digital Twins: Modelling Languages Comparison (International Conference on Machine Learning and Data Science 2022).

• Python.
• C/C++.
• MATLAB
• Docker.

• TensorFlow.
• Keras.
• PyTorch.
• Linux/Windows.
• MS Office/Latex (Overleaf) / SQL.

• Soccer.
• Billiards.
• Travelling.
• Reading.
• Music.