Name of Speaker: Dr. M. Shahriar Hossain
Place: CS Conference Room, 755 in 25 PP Building
Time: 11:00 am – noon on Thursday, February 16
Title: Storyboarding to Explore and Analyze Evolving Data
Seventy-five percent of the data created each day, as estimated by IBM, is unstructured and
coming from heterogeneous sources including text, image, and relevant metadata. These
unstructured data collections are growing everyday resulting in a massive archive for many
disciplines. This advent of wide-variety of available data-feeds has strengthen the potential to
analyze lineage of information from interdisciplinary viewpoints, contribute to the growth of
domain-specific knowledge, and develop a sense of the future-states of specific concepts.
However, with great possibility comes great challenges – How can we identify the contextual
snippets from heterogeneous sources that will constitute a specific type of lineage? Can we
model the influence structure between conceptual phenomena over time? How will we
meander through multitude of combinations to discover an implicit genealogical graph?
In this presentation, we will discuss different representations of unstructured text; alignment
procedures to map heterogeneous snippets such as images, metadata, and entities detected
within text content; mechanisms to extract different types of context and how those contexts
evolve over time for topics of interest; and methods to capture eventual and conceptual
lineage for social and scientific unstructured data. In the social science domain, we will discuss
how subevents from different times can be connected as a diffusion of context over a timeline.
Our discussions on the scientific domains will focus on how scientific concepts influence one
another and evolve over time.
The methodologies I will describe fall under a wide set of analytical algorithms named
Storyboarding. The presentation will outline how storyboarding fosters interdisciplinary
research by incorporating objective functions that consider domain knowledge, and by
emphasizing on experts’ feedback to provide a better understanding of the evolution of today’s
About the speaker:
Dr. Mahmud Shahriar Hossain has been an Assistant Professor of the Department of Computer
Science at the University of Texas at El Paso (UTEP) since 2013. He received his PhD from the
Department of Computer Science at Virginia Tech in 2012. His research interests lie in the
areas of Big Data Analytics including data mining and machine learning. Along with the
theoretical aspects of algorithm design for Big Data Analytics, Dr. Hossain is interested in
building prototypes to solve data mining problems in different disciplines. Dr. Hossain
collaborates with researchers from many disciplines of the academia, along with scientists
from the industry and government sectors. His research spans diverse application areas
including intelligence analysis, sustainability, mechanical engineering, social network analysis,
software engineering, and biomedical science.