CTS 2013 various panel and poster sessions [multiple abstracts]
2013
Have you wondered why there is so much data and why it is growing so fast?Companies have been collecting data for years in transaction systems.With the emergence of social media, user-generated content grew very fast and is now at about 60% of all data, according to the IDC.The adoption of mobile phones and the expansion of the Internet are leading to every machine becoming a node on the Web, relentlessly generating data!We might not know the new merging sources of data, but rest assured, we will continue to focus on deriving insight and determining what it is telling us.This panel's members have been engulfed in the Big Data conversation before it hits the mainstream, and they will discuss what's coming next for Big Data, and what sort of technology and approach is needed to keep businesses engaged and aware of the information within their organizations.The moderator will engage a panel of data scientists and researchers into a discussion on the best tools and approaches for studying social media data and understanding the difference between "tons of data" and "meaningful information derived from data".Panelists will share their insights and knowledge on how new technologies are helping to drive decision-making, uncover patterns and reveal relationships that never before could have been obtained.Furthermore, this panel will discuss opportunities and challenges around deriving insight from big data and provide thought leadership into the following questions:1. What are the types of challenges companies face in the area of data exploration?2. What is the biggest challenge related to big data problems that today's technologies cannot resolve? 3. What is the most fascinating use case of big data technology that has solved a problem at your company? 4. Are there any tools or approaches that are most suited for mining social media data? 5.There is a new area of Machine Learning research called Deep Learning, which uses deep neuronal networks to find patterns and extract information, in similar ways to how sensorial areas of the brain functions.How can this "deep learning" technology be applied to business applications?PANELISTS SHORT BIOS: Aditya Sehgal is Director of Research and New Products at Parity Computing, a leading provider of intelligent automation solutions for Science, Technology, and Medicine (STM) publishing and healthcare organizations.Since joining Parity in 2007, Dr. Sehgal has been a key contributor to Parity's industry-leading products for high-accuracy semantic tagging, linking and profiling of STM content.He led the development of Parity's system for recommending articles and journals to STM scientists.Additionally, his innovations were critical to the success of Parity's NIH-funded initiative for automatically matching clinical trial outcomes to medical records for individual patients, and to the Clinical Vigilance™ decision support system for hospitals.Prior to joining Parity, at the University of Iowa, Dr. Sehgal co-developed Manjal, a web-based system for discovering novel relationships between biomedical topics, and Genedocs, a system that provides ranked lists of relevant documents in MEDLINE for over 16,000 human genes.As a visiting researcher at U.C. San Diego, Dr. Sehgal developed machine learning based techniques to automatically update a specialized biomedical database by identifying relevant records from MEDLINE and from specialized protein databases such as Swiss-Prot and TrEMBL.Dr. Sehgal holds a PhD in Computer Science from the University of Iowa.His areas of scientific and technical expertise include semantic analysis, information retrieval, natural language processing, and machine learning.He can be reached at a.sehgal