Day 1 :
The University of Texas Arlington
Time : 10:00-10:40AM
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.
Institute of Computer Vision and applied Computer Sciences,Germany
Time : 10:40-11:20AM
Petra Perner (IAPR Fellow) is the director of the Institute of Computer Vision and Applied Computer Sciences IBaI. She received her Diploma degree in electrical engineering and her PhD degree in computer science for the work on “Data Reduction Methods for Industrial Robots with Direct Teach-in-Programing”. Her habilitation thesis was about “A Methodology for the Development of Knowledge-Based Image-Interpretation Systems". She has been the principal investigator of various national and international research projects. She received several research awards for her research work and has been awarded with 3 business awards for her work on bringing intelligent image interpretation methods and data mining methods into business. Her research interest is image analysis and interpretation, machine learning, data mining, big data, machine learning, image mining and case-based reasoning.
The ubiquitous availablity of high quality data European industry gathers, allows to optimize manafacturing processes even more and to stay competititve. However, while the data are rich enough to include those elements needed for optimization, the even encreasing volume, veloctiy and variety of the data make mining it effectively increasingly difficult. The paper addresses the special challenges in developing scalable algorithm and infrastructures for creating responsive analytical capabilities that produce timely prediction and monitoring alerts in industrial environments. We will describe a platform that can handle the special needs of the data and has a reach enough tool of data mining techniques. Case-Based Reasoning is used to combine streaminig data of different types ( sensor data, time series, maintenance logs etc.) as well. Special time series algorithm will be developed allowing the efficient analyisis of the machine data. It will be deploded and validated in three industrial cases where data-driven maintenances is expected is expected to have a significant impact: high-tech medical equipment, high-tech manufacturing of hard disks and structural health monitoring.
King Abdullah University of Science and Technology (KAUST), Saudi Arabia
Time : 11:35-12:15PM
Mikhail Moshkov is professor in the CEMSE Division at King Abdullah University of Science and Technology, Saudi Arabia since October 1, 2008. He earned master’s degree from Nizhni Novgorod State University, received his doctorate from Saratov State University, and habilitation from Moscow State University. From 1977 to 2004, Dr. Moshkov was with Nizhni Novgorod State University. Since 2003 he worked in Poland in the Institute of Computer Science, University of Silesia, and since 2006 also in the Katowice Institute of Information Technologies. His main areas of research are complexity of algorithms, combinatorial optimization, and machine learning. Dr. Moshkov is author or coauthor of five research monographs published by Springer.
In the presentation, we consider extensions of dynamic programming approach to the investigation of decision trees as algorithms for problem solving, as a way for knowledge extraction and representation, and as classifiers which, for a new object given by values of conditional attributes, define a value of the decision attribute. These extensions allow us (i) to describe the set of optimal decision trees, (ii) to count the number of these trees, (iii) to make sequential optimization of decision trees relative to different criteria, (iv) to find the set of Pareto optimal points for two criteria, and (v) to describe relationships between two criteria. The applications include the minimization of average depth for decision trees sorting eight elements (this question was open since 1968), improvement of upper bounds on the depth of decision trees for diagnosis of 0-1-faults in read-once combinatorial circuits over monotone basis, existence of totally optimal (with minimum depth and minimum number of nodes) decision trees for Boolean functions, study of time-memory tradeoff for decision trees for corner point detection, study of relationships between number and maximum length of decision rules derived from decision trees, study of accuracy-size tradeoff for decision trees which allows us to construct enough small and accurate decision trees for knowledge representation, and decision trees that, as classifiers, outperform often decision trees constructed by CART. The end of the presentation is devoted to the introduction to KAUST.