Workshop


With the recent abundant availability of data in many economic and scientific contexts, there is a need for scientists who can "make sense of data." Data science, broadly interpreted, touches on statistics, machine learning, and knowledge discovery.

Optimization, on the other end, wants to "suggest best actions" based on data. Tremendous progress has been made recently in fields like integer programming. This workshop aims at bringing together researchers from data science and optimization (or: predictive and prescriptive analytics) to foster an exchange in both directions. We would like to identify avenues on how to attack and contribute to solving global challenges coming from sectors like energy, mobility, scarce resources, ageing societies, and the like.

Invited Speaker

Kristian Kersting
Kristian Kersting is an Associate Professor in the Computer Science Department at the Technical University of Dortmund, Germany. He received his Ph.D. from the University of Freiburg, Germany, in 2006 and moved to the Fraunhofer IAIS and the University of Bonn using a Fraunhofer ATTRACT Fellowship in 2008 after a PostDoc at MIT, USA. Before moving to the TU Dortmund University in 2013, he was appointed Assistant Professor for Spatio-Temporal Patterns in Agriculture at the University of Bonn in 2012. Additionally, he was Adjunct Assistant Professor at the Medical School of the Wake Forest University, USA, in 2012.

Keynote: Democratization of Optimization (Abstract)Democratizing data does not mean dropping a huge spreadsheet on everyone’s desk and saying, “good luck,” it means to make data mining, machine learning and AI methods useable in such a way that people can easily instruct machines to have a „look" at the data and help them to understand and act on it. A promising approach is the declarative “Model + Solver” paradigm that was and is behind many revolutions in computing in general: instead of outlining how a solution should be computed, we specify what the problem is using some modeling language and solve it using highly optimized solvers. Analyzing data, however, involves more than just the optimization of an objective function subject to constraints. Before optimization can take place, a large effort is needed to not only formulate the model but also to put it in the right form. We must often build models before we know what individuals are in the domain and, therefore, before we know what variables and constraints exist. Hence modeling should facilitate the formulation of abstract, general knowledge. This not only concerns the syntactic form of the model but also needs to take into account the abilities of the solvers; the efficiency with which the problem can be solved is to a large extent determined by the way the model is formalized. In this talk, I shall review our recent efforts on relational optimization. It can reveal the rich logical structure underlying many AI and data mining problems both at the formulation as well as the optimization level. Ultimately, it will make optimization several times easier and more powerful than current approaches and is a step towards achieving the grand challenge of automated programming as sketched by Jim Gray in his Turing Award Lecture.

Joint work with Martin Mladenov and Pavel Tokmakov and based on previous joint works together with Babak Ahmadi, Amir Globerson, Martin Grohe, Fabian Hadiji, Marion Neumann, Aziz Erkal Selman, and many more.

Program

Monday, April 4, 2016:
13:00  Lunch buffet
13:45  Welcome
14:00  Keynote: Kristian Kersting
            Democratization of Optimization   
15:00  Coffee & Tea
15:30  Andreas Tillmann
            New applications for sparsity-based learning
            Danilo Bzdok
            Some Optimization Challanges in the
            Imagin
g Neurosciences
17:00  Break
17:15  Ansgar Steland
            On Statistical Inference for High–Dimensional
            Covariance Matrices Related to some
            Optimization Problems

            Patrick De Causmaeker
            Characterization of neighborhood behaviours
            in a multi-neighborhood local search algorithm

18:45  End of scientific program on Monday
20:00  Dinner at Restaurant Elisenbrunnen
 

Tuesday, April 5, 2016:
09:00  Justin Solomon
            Practical Tools for Applied Linear and
            Quadratic Matching

            Christian Beecks
            Gradient-based Multimedia Signatures
10:30  Coffee & Tea
11:00  Discussion
12:30  Closing
12:45  Lunch
            End of workshop

Registration


Please register here!


Location

RWTH Aachen University, Kackertstraße 7, 52062 Aachen, Room B037


Arrival

You can use this form to get your travel information for arriving at Aachen Central Station (Aachen Hbf) via Cologne or this form for arriving at Aachen West via Düsseldorf.

Accomodation

You find a list of hotels and reservation forms here.

Workshop Sponsor

Profile Area Computational Science & Engineering

This workshop is supported by RWTH Aachen University's profile area "Computational Science & Engineering" as part of its strategic roadmap to attract outstanding scientific talent to the university.