I'm interested in machine learning and data science. I've worked on machine learning projects in the domains of computer vision and computational genomics. I've graduated from the Masters in Computational Biology program at Carnegie Mellon University and I'm working as a Deep Learning Engineer in the biotech and robotics space.
- Developed a novel GAN using the CIFAR-10 dataset to defend a target classifier against adversarial attacks.
- Benchmarked against a custom dataset to record an improvement in model accuracy by up to 14% from a defenceless target.
- Implemented an attention-based sequence-to-sequence model from literature to predict text from speech utterances.
- Tuned network hyperparameters to achieve an average Levenshtein distance of 26 from the ground truth.
- Developed a pipeline using Shell scripting to extract sequences for protein binding from eCLIP assays.
- Developed an API to enable end-users to apply our pipeline to their own data.
- Developed a tutorial on object detection using Pytorch to inform data science students.
- Integrated Faster R-CNN model and OpenCV into a pipeline to perform inference on the COCO dataset
- Computational Genomics: SpliceAI
- Computer Vision : Generative Adversarial Networks (GANs), ResNet-34, Faster R-CNN
- NLP: attention-based sequence-to-sequence model
- Python, MySQL, Shell scripting
- Pytorch, TensorFlow, Keras
- NumPy, scikit-learn, Pandas, Matplotlib, seaborn, OpenCV, sci-kit image
- Pyspark, AWS, Databricks