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Big Data Science Meetup Event

  • May 17, 2014 · 1:30 PM
  • SGI Auditorium (enter through Patio door with red umbrellas)


1:30 P.M. - 2:00 P.M.  Networking


2:00 P.M. - 2:45 P.M.  Session 1

Title: Working With Deep Autoencoders in Cognitive Computing

Speaker:  Adam Gibson

Abstract: Cognitive computing tools like Watson have shown the world their power, speed and accuracy, and question-answering is just one application that has brought artificial intelligence to an inflection point. At the heart of question-answering is a deep-learning algorithm
called a deep autoencoder network, which makes it possible to model topics in a collection of documents. This talk will address how autoencoders can be used for topic modeling, and employed for classification and prediction to advance cognitive computing. While deep learning works equally well with images, sound and time-series
data, we will focus on the different ways it can handle text, citing real world use cases such as sentiment analysis.

Speaker Bio:  Adam is an Adjunct Instructor at Zipfian Academy focused on deep learning, natural language processing, distributed systems, and data engineering. He is the co-founder of, a machine learning and NLP startup that helps public relations professionals to monitor coverage and avoid disasters. Before, Adam did Data Mining at Mashape, ran his own web development consultancy, and was a Dev Ops engineer.

Adam is a sought-after speaker on question answering and information retrieval, including Hadoop Summit, OSCon. He is a dropout of Computer Science and MIS at Michigan Technological University.

2:45 P.M. - 3:00 P.M.  Q/A


3:00 P.M. - 3:45 P.M.  Session 2

Title:  HFE & BCR-ABL: In Search of Links

Speaker: Jack Park

Abstract: The intellectual space occupied by Cancer Genomics is populated with research data, reported findings, and ongoing research. This talk will sketch a candidate intersection of cancer genomic data and research literature, and use that to animate an explanation of the SolrSherlock experiment in cognitive agents as research assistants. SolrSherlock is, fundamentally, a framework for such experimentation. It is comprised of varieties of agents serving processes of machine reading of literature, knowledge organization in terms of actors and their relations, and modeling in support of hypothesis formation and discovery. Specific to this talk, we will focus on aspects of machine reading and knowledge organization; the platform in its current form couples a Link Grammar Parser with a Topic Map for reading, together with a kind of knowledge fabric we call a HyperMembrane, which organizes claims, counter-claims, and evidence from the literature. The system is exploring a collection of context helpers we call Lenses to further refine parse results into higher-order structures such as the topic map and models which take the forms of conceptual graphs and qualitative process models.

Speaker Bio:   Jack Park is a computer scientist working in the fields of artificial and collective intelligence. He created, edited, and co-authored 
the book XML Topic Maps: Creating and Using Topic Maps for the Web, was a Ph.D. student researching the topic of knowledge federation applied to 
hypermedia discourse, and designs and builds software platforms for knowledge gardening. He was a research scientist at SRI International working on their Cognitive Assistant that Learns and Organizes (CALO) project, and authored and co-authored several conference papers on the subjects of topic mapping and semantic desktop applications for collective intelligence. He is an avid player of Jane McGonigal's IBIS card games.

3:45 P.M. - 4:00 P.M.  Q/A


4:00 P.M. - 5:00 P.M.  Networking

Pizza and Soft Drinks will be available.


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