Professional Credentials

Intel Corporation

Mar 2024 - Present

As a Deep Learning Engineer, my responsibilities include developing, deploying, and maintaining classification algorithms on Defect Metrology tools. These algorithms are used to identify nanometer-scale surface defects on material in the factory. As a Defect Metrology Tool Owner, I am in charge of budgeting, scheduling, installing, and qualifying Defect Metrology tools to meet the Factory's commit dates.

Intel Corporation

Aug 2020 - Mar 2024

I've worked as a Segment Team Leader, Optimisation Engineer, and Data Scientist in Intel's Fab24 Production Department. I drove output on both the macro and micro scales through evidence-based solutions, pursuing the most optimal outcomes by conducting insightful data analysis. In the process I coordinated with tool owner groups, managing stakeholder expectations, and applying a wide range of skills to deliver impactful results for the Factory.

Medtronic

Jan - Aug 2018

Prior to my work in Intel and Data Science, I have previously been employed as a Manufacturing Engineer in Medtronic Galway, where I designed manufacturing processes, layouts, and equipment operations for optimal efficiency. Adapted designs to fit factory conditions and developed procedures for process control.

Massachusetts Institute of Technology

Distinction

This Applied Data Science Program with Massachusetts Institute of Technology covers a wide range of supervised and unsupervised models, delving into the inner workings of these algorithms. My capstone project for this course involved producing a convolutional neural network architecture for classification of malaria parasites present in images of red blood cells, to assist in the diagnosis of malaria in remote areas.

• Industry projects covering a wide range of relevant problems in data science, from disease detection and estimating house prices to predicting potential customers and recommending new products.
• Elective project on classification of street view housing numbers using Artificial and Convolutional Neural Networks.
• Capstone project involving detection of malaria from images of red blood cells.
• Program ongoing, results available in May 2024.

University College Dublin

Distinction

This specialist certificate focused on topics such as data cleaning and validation, statistical analysis, supervised and unsupervised learning, and deep learning. My final project for this course involved producing a classification model for service callouts in the Chicago Metropolitan area, to reduce uncertainty on whether a car collision required an ambulance or tow truck.

• Imported data from the Chicago data portal for final project, cleaned data and prepared for input to KNN classification model.
• Initial iteration produced a classification model with 93.1% accuracy and 86.4% F1 score.
• Implemented hyperparameter tuning to reduce overfit on data, leading to final model iteration with an accuracy of 78.7% and f1 score of 83%.

National University of Ireland, Galway

1^st Honours

My Master's Degree in Mechanical Engineering had a strong emphasis on innovation, entrepreneurship, technology, and research methods. Topics of study included advanced mechanics of materials, advanced finite elements, and advanced energy systems. My Master's thesis was focused on the design of a multidimensional test rig for the purpose of wind turbine design testing:

• Designed a multidimensional test rig for stress testing of wind turbine components.
• Iterated design of an Arduino control device for test rig control.
• Wrote Arduino test rig control functions in C.
• Extensive computational modelling and theorical design, in part due to logistical challenges posed by the Covid-19 pandemic.

National University of Ireland, Galway

1^st Honours

My Bachelor's Degree in Mechanical Engineering focused on analytical thinking honed through multidisciplinary projects. Through this I gained a wealth of experience in innovation, sustainability, and cutting-edge technology application within the field. My Bachelor's thesis was focused on the topic of dissimilar materials in additive manufacturing:

• Investigated chemical bonds between metal and polymer materials for new additive manufacturing process.
• Produced computational models of interface bonding between metals and polymers using CAE software.
• Stress tested metal-polymer samples to find most optimal bonding methods.