We are doing research on graph technology. We build knowledge models that are collection of interlinked descriptions of concepts, entities, relationships and events. Knowledge graphs put data in context via linking and semantic metadata and this way provide a framework for data integration, unification, analytics and sharing. Also, we are researching automated KG construction pipeline and knowledge lifecycle management system, as well as application technologies such as recommendation and reasoning using the knowledge graph.

We are actively developing models and algorithms for optimizing Ads and other market-based products. Research items include but not limited to graph neural network for supporting scalable integrated data analysis of heterogeneous data sources enabling better user targeting, graph MLs for inferring hidden relations of user/devices, domain adaptive /transferrable/interoperable modeling for rapidly increasing cold-start ads campaigns and reinforcement learning for optimizing multiple business KPIs in harmony.

Moreover, we are conducting wellness data research such as data imputation and stream reasoning technology to process time-series health sensor data, and developing recommendation system to provide personalized well-being care services in the fields of exercise, diet, sleep, or mindfulness.

Data-driven decision intelligence is a research topic that has recently emerged to innovate business processes, such as supply chain, logistics, marketing and service operation. To this end, we are focusing on advanced data representation and ML algorithm researches to predict future actions or situations and to make optimized decisions.

A top-class AI development environment needs an integrated and advanced system for data acquisition, processing, searching and sharing. Samsung Research is thus developing solutions for high-quality data acquisition, massive data discovery, storage and processing. Building on the latest cloud technology, we are researching technologies for autonomous and scalable deep learning. Ultimately, our goal is to spread our AI platform to all developers and build an ecosystem that will enable them to collaborate and share experiences that they can grow themselves.