Research Staff Member
San Jose , California
April 30, 2017
We are looking for candidates who are interested in exploring and prototyping emerging architectures and designs for information management systems and their application to specific domains such as health care and finance. We are researching scalable machine learning architectures that data scientists use to experiment with novel machine learning and deep learning algorithms, and run on scale-up, multi-core, large memory machines as well as scale-out, data parallel platforms. These architectures consider trends in multi-core processors, persistent and non-volatile memory (NVRAM), and HW acceleration (GPUs, FPGAs) for compute-intensive algorithms as used in Watson for services such as image/video processing, NLP, and IoT as well as IBM cognitive solutions. We are also building new data enrichment and management platforms for supporting Watson, focusing on ontology-based entity extraction, resolution, integration, and querying of knowledge graphs. We are building cognitive querying systems that utilize ontologies, and natural language interfaces.  We are investigating a wide variety of "big data" research problems associated with cognitive and data-intensive analytics using data-parallel systems such as Spark, large-scale relational database systems, and other types of advanced data management systems. This includes information systems deployed in the cloud, particularly in the context of massive interactions with mobile applications and sensors deployed for smart phone use, connected cars, health care, and several other cognitive solutions. We are interested in many related technologies, including support for unstructured, semi-structured, and structured data and analytics, cognitive computing, column-store techniques, large scale graph analytics, semantic querying of knowledge graphs, and new query languages and query processing methods. We are actively prototyping next-generation online transaction processing (OLTP) systems that concurrently support online analytical processing (OLAP) queries, and more flexible data modeling and scripting capabilities.

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