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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https://volgenau.gmu.edu/profile/view/10248