Hasham Ul Haq is a Data Scientist at John Snow Labs, and an AI scholar and researcher at PI School of AI. During his carrier, he has worked on numerous projects across various sectors, including healthcare. At John Snow Labs, his primary focus is to build scalable and pragmatic systems for NLP, that are both, production-ready, and give SOTA performance. In particular, he has been working on Natural Language Inference, disambiguation, Named Entity Recognition, and a lot more! Hasham also has an active research profile with a publications in NeurIPS, AAAI, and multiple scholarship grants and affiliations.
Prior to John Snow Labs, he was leading search engine and knowledge base development at one of Europe’s largest telecom providers. He has also been mentoring startups in computer vision by providing trainings and designing ML architectures.
Zero-Shot Learning (ZSL) is a new paradigm that has gained massive popularity recently due to its potential of reducing data annotations and high generalisability. In the pursuit of bringing product-ready...
The surging amount of biomedical literature & digital clinical records presents a growing need for text mining techniques that can not only identify but also semantically relate entities in unstructured...
Adverse drug reactions / events (ADR/ADE) have a major impact on patient health and health care costs. Detecting ADR’s as early as possible and sharing them with regulators, pharma companies,...
Easy to use, scalable NLP framework that can leverage Spark. Introduction of BERT based Relation Extraction models. State-of-the-art performance on Named Entity Recognition and Relation Extraction. Reported SOTA performance of...
Recognizing entities is a fundamental step towards understanding a piece of text – but entities alone only tell half the story. The other half comes from explaining the relationships between...
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