Abstract: This talk focuses on two separate techniques: a) a semantic matching technique that is superior to traditional search that uses machine learning approaches to find semantically matched documents; b) clustering techniques to examine search queries, user generated content, skills, job titles, companies etc to create an overarching taxonomy that can be used for disambiguation, knowledge bases, segmentation etc.
Bio: Recently, in Abe's role as an entrepreneur, consultant and executive, he has focused on unlocking value in new emerging fields and untapped market opportunities. He's very passionate about 'digitizing' the vast amount of loosely connected, unstructured data that exists all around, so hidden relationships and inferences between these data elements can be liberated that can lead to the evolution of smarter systems. He's been successful in developing sophisticated machine learning and deep learning algorithms in data science to explore solutions in computer vision, predictive analytics, recommendation systems, NLP, NLG etc. Abe also enjoys building small, nimble, highly motivated teams who collaborate and work well together to bring value to the business and organization.