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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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