Search and Discovery in News and Research


How do you build a search and discovery system for financial news and research? In this talk, we will look at Bloomberg's decade-long investment in three specialized areas in the field of AI: natural language processing (or, the application of machine learning methods to text), information retrieval and search, and core machine learning (including deep learning), and how this is enabling us to apply autocompletion, query understanding, index enrichment (topics, people, sentiment), question answering, summarization, and relevance ranking in order to enable our clients to discover insightful information from the complexity of unstructured data.


Dr. Prabhanjan (Anju) Kambadur is the head of the AI Engineering group at Bloomberg, which consists of 200+ researchers and engineers responsible for building financial solutions using technologies from Machine Learning, Natural Language Processing, Dialog Understanding, Graph Analytics, Time Series Analysis, Information Retrieval, Recommendation Systems, Speech Recognition, Computer Vision, and Optimization. Some of the products the AI Group helps build include News, Research, Communications, and Finance. The members of his group are active in the academic community, where they have published more than 100 peer-reviewed papers in the last three years.

Before Bloomberg, Anju was a research staff member in the Business Analytics and Mathematical Sciences department at IBM Research’s Thomas J. Watson Research Center, where he worked on problems in machine learning, such as matrix completion and sketching, sparse coding, genome-wide association studies, temporal causal modeling, and high performance computing. He received his Ph.D. from Indiana University and has published peer-reviewed articles in the fields of high performance computing, machine learning, and natural language processing.

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