Laboratory for Interactive Optimization and Learning

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Data-driven Insights

and Decisions

Fusing Machine Learning and Analytics with Optimization

New wearables and low-cost sensors allow for the collection of a plethora of health data. This data can be leveraged in medical diagnostics and health monitoring allowing for early detection of changes in health condition.

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The Laboratory for Interactive Optimization and Learning (IOL) is an experimental setup exploring, both, the fusion of advanced optimization, machine learning, and analytics into data-driven decision support systems, as well as their deployment in various real-world application contexts.

Ever-increasing competitive pressures require constant improvements to global supply chains. The coherent integration of real-time data and decision making is the next step in tomorrow's logistics landscape.

Big Data is one of the key enablers for businesses. The efficient extraction of information from large-scale data requires machine learning and optimization approaches.

Cyber-physical systems combine sensors, computing, and machines to form intelligent machines. Specific frameworks include industry 4.0, the industrial internet, and internet of things.

Key application areas include:

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News and Updates.

> March/April 2017. Jeff Pavelka, Aurko Roy, and Daniel Zink successfully defended their theses.

> March 2017. NSF EAGER funding for "EAGER: SC2: PHY-Layer-Integrated Collaborative Collaborative Learning" (CNS-1737842)

> December 2016. Passed the first hurdle of the DARPA SC2 challenge [project page]

> June 2016. Official launch of Machine Learning @ GT Interdisciplinary Research Center