Machine Learning Engineer2019 - PRESENTCoda Platform
Technologies: Python, Deep Neural Networks, Image Analysis, Image Classification, Natural Language Processing (NLP), Clustering, Logistic Regression, SQL, Metaflow, Pandas, Linux, PyCharm, Convolutional Neural Networks, Applied Mathematics, Scikit-learn, Python 3, TensorFlow, Keras, OpenCV, Statistics, Data Analysis, Deep Learning, Computer Vision, Data Science, Machine Learning, Image Recognition, Object Tracking
- Increased game revenue by implementing user-level optimization of game configurations for cohorts of users through an ML-based configuration and developing an A/B test system.
- Detected upcoming concept trends in the App Store which led to a higher success rate in game development.
- Implemented real-time game categorization and similarity comparison using computer vision and NLP models.
Data Scientist2018 - 2019Visulytix
Technologies: Python, Data Analysis, Deep Learning, Data Science, Image Analysis, Image Classification, Logistic Regression, Pandas, Linux, PyCharm, Applied Mathematics, Scikit-learn, Python 3, TensorFlow, Keras, OpenCV, Statistics, Machine Learning, Convolutional Neural Networks, Deep Neural Networks, Kubernetes, Computer Vision, Object Tracking, Image Recognition
- Optimized the accuracy of disease detection which allowed nuanced visualization of features through the extraction of model parameters. This provided clinical justification for our methods and diagnosis.
- Improved the prediction in rare disease classification by 20% through training on synthetic data augmented via the use of GANs.
- Implemented UNet segmentation and R-CNN object detection for biomarker analyses.
Ph.D. Candidate and Researcher2015 - 2019University of Liverpool
Technologies: Deep Neural Networks, Python, OpenCV, Image Analysis, Computer Vision, Data Science, TensorFlow, Keras, Scikit-learn, Convolutional Neural Networks, PyCharm, Linux, Machine Learning, Data Analysis, Statistics, Object Detection, Image Segmentation
- Worked on deep learning techniques to tackle medical imaging problems; specifically diabetic retinopathy.
- Developed, using mainly OpenCV, a UV image analysis to determine if specific areas of the face are missed during routine sunscreen application and whether the provision of public health information is sufficient to improve coverage.
- Constructed the architecture for a Fourier Convolution Neural Network (FCNN) for medical image classification (ECML); the advantage offered is that there is a significant speedup in training time without loss of effectiveness.
- Created a convolutional neural network approach for first locating vessel junctions and then classifying them as either branchings or crossings which helped with the challenges involved in the quantitative analysis of retinal blood vessels.
- Developed a CNN approach, using digital fundus images, to diagnose and accurately classify the disease severity for diabetic retinopathy.