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Fillia Makedon

Fillia Makedon

The University of Texas Arlington

Title: Vocational Computing: A Data Mining Application for the Workplace


Dr. Fillia Makedon is the Jenkins-Garrett Professor at the University of Texas at Arlington (UTA). She received her Ph.D. in Computer Science from Northwestern University in 1982. Between 1991-2006, she was Professor of computer science at Dartmouth College where she founded and directed the Dartmouth Experimental Visualization Laboratory (DEVLAB). Between 2006 and 2014 she served as the department chair of the CSE Department at UTA. Prior to that , in 2005-2006, she was Program Director at the National Science Foundation. Prior to Dartmouth, Prof. Makedon was Assistant and Associate Professor at the Univ. of Texas at Dallas (UTD), where she founded and directed the Computer LEArning Research Center (CLEAR). She supervised over 27 Ph.D. theses and numerous Masters Degree theses. Makedon has received many NSF research awards in the areas of trust management, brain computing, data mining, parallel computing, visualization, knowledge management, cyberphysical systems, major research instrumentation, and cyberhuman systems, to name a few. She has been senior investigator and co-PI of NIH, DOJ and Foundation grants. She received the Dartmouth Senior Research Professor Award, three Fulbright awards, and is author of over 350 peer-reviewed research publications. She is faculty affiliate of the Dartmouth ISTS security institute and currently directs the HERACLEIA Human Centered Laboratory, that develops pervasive technologies for human monitoring. She is member of several journal editorial boards and chair of the international PETRA conference


According to the US Dept. of Labor, thousands of workers die on the job each year because of accidents, or lack of training in using new technologies. Computational methods can be used to provide evidence-based quantitative assessments of worker ability and identify needs for training. Data mining methods can be applied to the analysis of performance multi-sensing interaction data collected while a person performs a certain work task. We describe the iWork smart Service work-assessment system which recommendations for personalized intervention, following multimodal data mining of activity data.  The service assesses mental, cognitive and physical skills of a worker for improved placement and informed decision-making.  The proposed service takes advantage of recent advancements in robotics, sensing technologies, and intelligent communication platforms to enhance human ability to learn by interactive experiences. The service trains assistive workplace robots to provide personalized help to complete difficult cognitive and/or physical tasks in the workplace. A new Machine Learning methodology is described and demonstrated.