About

Anika Tabassum is a highly skilled software engineer with over 12 years of relevant experience. Currently working at OpenText as a Software Developer for Python and DevOps, Anika is responsible for developing the Document Relation Engine (DRE) which ranks documents on relatedness. This engine is utilized by OpenText Insight, a document review and search engine for legal matters. Anika has introduced NLTK to extract phrases from documents during graph builds, implemented JSONlines format for command-line graph builds, and optimized deletion of expected judgements and marking documents as expected in a project. She has also worked on ad-hoc data analysis and experiments such as improving the scoring function. Anika has enhanced and written technical documentation, including instructions for graph builds, system design and abstract requirements, onboarding, and Windows-specific documentation. Anika has previously worked as a Mentor at Udacity, where she reviewed over 1000 career projects for Udacity nanodegrees. She has also worked as a Research Assistant for Deep Learning at Ryerson University, where she developed a system to classify ECG signals into heartbeat abnormality-based categories by transforming time series to images. Anika has written a paper on developing a new evaluation criterion for the confidence level of mammography image classification. Anika holds a Bachelor's Degree in Computer Science from McGill University and a Master of Science in Data Science and Analytics from Ryerson University. She is proficient in various technologies such as Python, DevOps, Web, Deep Learning, Search, Research Scientist, and Javascript. Anika is passionate about building worlds with software and words and is a mentor at Udacity.