Senior Director of Machine Learning and Artificial Intelligence, Sony Playstation (Sony Interactive Entertainment Inc)
Dr. Melli with 2 decades of experience in the delivery of automated data-driven solutions in a technical lead and management capacity with a passion for introducing semantic capabilities into mission critical processes. Dr. Melli has led over twelve large-scale data-driven initiatives at both enterprises ranging from Sony PlayStation, Microsoft, AT&T, T-Mobile, ICBC, Washington Mutual and WalMart, and at start-ups such as Datasage (acquired by Vignette/Open Text), Meals.com, PredictionWorks, VigLink, and OpenGov. He is currently Senior Director of Machine Learning and Artificial Intelligence at Sony Interactive Entertainment (SIE). He hold a PhD and completed Master of Science from Simon Fraser University and proud alumnus University of British Columbia
Supervised Identification of Concept Mentions and their Linking to an Ontology The 19th ACM International Conference on Information and Knowledge Management (CIKM-2010)
Mapping algorithm with rich relationship expression and higher-order abstraction Prior Art Database (October 31, 2011)
The invention empowers a non-technical business user to easily establish a central master of product relationship rules. It provides flexibility in the specification of relationship functionality and in the rules for membership in a collection. It implements a higher-order specification (“any” source relates to “same” target) and stores the data efficiently with automatic updates.
Motivation: PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles.
This paper describes the design of the SQUASH system, the SFU Question Answering Summary Handler, developed by members of the Natural Language Lab from the SFU School of Computing Science in order to participate in the 2005 Document Understanding Conference (DUC-2005) summarization task.
This paper presents a lazy model-based algorithm, named DBPredictor, for on-line classification tasks. The algorithm proposes a local discretization process to avoid the need for a lengthy preprocess stage. Another advantage of this approach is the ability to implement the algorithm with tightly-coupled SQL relational database queries.
In this paper we present the SDOIrmi text graph-based semi-supervised algorithm for the task for relation mention identification when the underlying concept mentions have already been identified and linked to an ontology. To overcome the lack of annotated data, we propose a labelling heuristic based on information extracted from the ontology. We evaluated the algorithm on the kdd09cma1 dataset
With billions of database-generated pages on the Web where consumers can readily add priced product offerings to their virtual shopping cart, several opportunities will become possible once we can automatically recognize what exactly is being offered for sale on each page.