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An experienced Computer Vision, Machine Learning Engineer with success in leading and implementing
projects on re-training and deploying deep learning models. Applies problem solving and innovation to adopt
the latest quantization and sparsity techniques to optimise model speed and power consumption, while
preserving state of the art inference accuracy and presented results to key stakeholders. Re-trained models
for classification, object detection and instance segmentation using Tensorflow and Keras with strong
attention to detail in underfitting and overfitting.
TECHNICAL SKILLS
Python
SQL
Keras
OpenCV
MATLAB
Bash
Tensorflow
Detectron
Data Cleaning
Office 365
PROFESSIONAL EXPERIENCE
Computer Vision / Machine Learning Engineer, Company X
2016 - Present
Created and deployed Deep Learning models on our specially designed hardware, aimed at inference on the
edge.
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Created quantized models, 8-bit or 4-bit activations and weights, which were 75% smaller than FP32
models, thus saving memory. Achieved 1000 Frames Per Second, with only a loss of 1% in accuracy on
the Imagenet dataset, consumed less power during inference, still yielding high test accuracy.
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Led a team to compete in the IEEE LPIRC challenge. Organised project using Gantt Charts and Kanban
Boards which allowed reallocation of resources to the critical path based on the slack time of other
tasks. Overcame all obstacles with effective team communication and tackled early signs of failure.
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Re-trained object detection models including ResNet-SSD and MobileNet-SSD using Tensorflow on
newly created datasets. Utilised Detectron2 for instance segmentation with Mask-RCNN models.
Created fewer layer classification models on Keras to avoid underfitting on smaller specific datasets.
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Explored the trade-offs with image classification models in terms of accuracy and performance.
Compiled metric data in databases - queried using SQL - and presented results to management and
customers to verify product specifications.
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Contributed to the Intel Incubator Program, aimed at helping startups to re-train and deploy deep
learning models. Maintained regular contact throughout the program to help the companies meet
their goals and objectives, gave advice on the direction for the product to progress.
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Coordinated department’s internship program. Liaised with university programs, obtained funding for
hiring and conducted interviews. Created and supervised projects on computer vision and IoT and
mentored students.
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EDUCATION
ME in Electronic and Computer Engineering - First Class Honours, University Name - 2006
Research:

Optimised algorithms for Electrostatic Kinetic Energy Harvesters (EKEH). Created mathematical
models of the behavior of capacitors using MATLAB and processed simulated data of power
generation and energy harvesting.
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Performed data analysis to obtain critical points in the system to maximize and optimise the
harnessed electricity output.
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Published a research paper (below) to an IEEE conference and collaborated with a research team in
France to verify findings with physical devices.
B. Sc. in Electronic and Computer Engineering - Honours 2.1, University Name
PUBLICATIONS
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