Structured Prediction: Data
Analytics Meets Applications
Wednesday, November 8th, 2017 at
1:00pm
Johnson Center, Room E
Dr. Huzefa Rangwala
Department of Computer Science,
George Mason University
Today we are in the “data” age.
Data-driven science and engineering are at the forefront of new
discoveries and unbounded positive societal impact. Meaningful
discovery and actionable insights require extracting useful
information from large, heterogeneous and complex datasets,
ubiquitous across several domains. Complexity within these
datasets arises due to heterogeneity, incompleteness, missing
information, noisy nature and inter-dependencies between the input
and output domains. Structured prediction is a framework for
solving classification and regression problems, in which the
output/input variables are mutually dependent or constrained.
Examples of dependencies and constraints include sequential,
combinatorial or spatial structure in the problem domain and
capturing these interactions leads to better prediction models.
Several real world applications have dependencies between output
labels (multi-label, hierarchical classification), or have an
internal structure that is described by inter-dependent components
(e.g., sequences, trees, networks, dyadic relationships).
In this talk, I will provide an
overview of my contributions related to the development of
structured prediction algorithms and their applications. I will
present a sample of my work across multiple inter-disciplinary
applications in (i) educational mining, (ii) genome analysis,
(iii) social forecasting and (iv) cyber-physical systems.
Biography: Huzefa Rangwala is an
Associate Professor at the Department of Computer Science, George
Mason University. He was a Visiting Faculty Member at Department
of Computer Science, Virginia Tech in 2015-2016. His research
interests include data mining, learning analytics, bioinformatics
and high performance computing. He is the recipient of the NSF
Early Faculty Career Award in 2013, the 2014 GMU Teaching
Excellence Award, the 2014 Mason Emerging Researcher Creator and
Scholar Award, the 2013 Volgenau Outstanding Teaching Faculty
Award, 2012 Computer Science Department Outstanding Teaching
Faculty Award and 2011 Computer Science Department Outstanding
Junior Researcher Award. His research is funded by NSF, DHS, NIH,
NRL, DARPA, USDA and nVidia Corporation.
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Stephen G. Nash
Senior Associate Dean
Volgenau School of Engineering
George Mason University
Nguyen Engineering Building, Room 2500
Mailstop 5C8
Fairfax, VA 22030
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Phone: (703) 993-1505
Fax: (703) 993-1633
https://volgenau.gmu.edu/profile/view/10248