Dear all,
this is a reminder for Jürgen Giesl's talk on Improving Automatic
Complexity Analysis of Probabilistic and Non-Probabilistic Integer
Programs
<https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Das-Graduiertenkolleg/Aktuel…>
taking place *today at 16:30* in room 5053.2 and on Zoom. Please find
the details below.
> We present an approach for automatic complexity analysis of integer
> programs, based on an alternating modular inference of upper runtime
> and size bounds for program parts. While our approach was originally
> developed for non-probabilistic programs, we show how we extended it
> to also infer upper bounds on the expected runtimes of probabilistic
> integer programs automatically.
>
> Moreover, for the non-probabilistic case, we show how recent
> techniques to improve automated termination analysis of integer
> programs can be integrated into our approach for the inference of
> runtime bounds.
>
> To evaluate its power, we implemented our approach in a new version of
> our tool KoAT.
Part of the programme of the research training group UnRAVeL is a series
of introductory lectures on the topics of "randomness" and "uncertainty"
in UnRAVeL’s research thrusts: Algorithms and complexity, verification,
logic and languages, and their application scenarios. The main aim is to
provide doctoral researchers as well as master students a broad overview
of the subjects of UnRAVeL.
Science undergoes continuous change and lives from the constant quest
for novel and better results, which are presented at conferences and in
journals. This year, 10 UnRAVeL professors will present some of their
most recent research successes.
Everyone interested, in particular doctoral researchers and master
students, are invited to attend the UnRAVeL lecture series 2022 and
engage in discussions with the researchers.
The talks take place on Tuesdays, 16:30–18:00 in room 5053.2 in the
ground floor of building E2. All events are hybrid. To join remotely,
please use
https://rwth.zoom.us/j/96003885007?pwd=aUczMVdVU0ZXVGtQUFpwQnJHQUFhUT09
/ Meeting ID: 960 0388 5007 / Passcode: 273710
Please find a list of all upcoming talks on the UnRAVeL website
<https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…>
and below:
* 03/05/2022 Jürgen Giesl: Improving Automatic Complexity Analysis of
Probabilistic and Non-Probabilistic Integer Programs
* 10/05/2022 Martin Grohe: Graph Representations Based on Homomorphisms
* 17/05/2022 Christina Büsing: A Branch & Bound Algorithm for Robust
Binary Optimization with Budget Uncertainty
* 24/05/2022 Erika Ábrahám: The Challenge of Compositionality for
Stochastic Hybrid Systems
* 21/06/2022 Sebastian Trimpe: Uncertainty Bounds for Gaussian Process
Regression with Applications to Safe Control and Learning
* 28/06/2022 Britta Peis: Stackelberg Network Pricing Games
* 05/07/2022 Gerhard Lakemeyer: Tractable Reasoning in First-Order
Knowledge Bases
We are looking forward to seeing many of you in the UnRAVeL survey
lecture "What's New in UnRAVeL?".
Best regards,
Andreas Klinger, Birgit Willms, and Tim Seppelt
Logo
+**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Montag, 9. Mai 2022, 15:00 Uhr
Zoom:
https://rwth.zoom.us/j/98637141061?pwd=Qkw3blFhWEIrelduWmpPSGNtQnN4dz09
Meeting ID: 986 3714 1061
Passcode: 618965
Referent: Jonathan Hüser, M.Sc.
Lehrstuhl Informatik 12
Thema: Discrete Tangent and Adjoint Sensitivity Analysis for Discontinuous
Solutions of Hyperbolic Conservation Laws
Abstract:
We consider the discrete tangent and adjoint sensitivities computed via
algorithmic differentiation of shock capturing numerical methods for
hyperbolic conservation laws which are widely used for models of fluid
dynamics such as those based on the Euler equations.
For discontinuous solutions the discrete sensitivities do not generally
converge to the correct sensitivities of the analytical solution as the
discretization grid is refined because the analytical sensitivities are
singular at the discontinuities of the solution.
In this thesis we propose a convergent numerical approximation of the
correct sensitivities of shock discontinuities in discontinuous solutions
of hyperbolic conservations laws with respect to the parameters of the
initial data.
We compute the shock sensitivities by approximating the Rankine-Hugoniot
condition taking into consideration the numerical viscosity of shock
capturing numerical methods in a way that can be computed by algorithmic
differentiation tools.
The resulting discrete sensitivities enable for example the gradient-based
parameter optimization of optimization problems constrained by a hyperbolic
conservation law.
Es laden ein: die Dozentinnen und Dozenten der Informatik
Dear all,
this is a reminder for Nils Nießen's talk on Acceptance of Driverless
Trains <https://www.unravel.rwth-aachen.de/go/id/taywq?#aaaaaaaaaatayyk>
taking place *tomorrow at 16:30* in room 5053.2 and on Zoom. Please find
the details below.
> Digitalisation and automation are also making progress in rail
> transportation. In isolated networks, such as metros, trains can
> already run driverless today. The talk will highlight the
> opportunities and risks of driverless driving on rail.
>
> A novel system can only be successfully implemented if it is also
> accepted by the users. One focus of the talk will therefore be the
> analysis of passenger acceptance of driverless rail transport.
>
Part of the programme of the research training group UnRAVeL is a series
of introductory lectures on the topics of "randomness" and "uncertainty"
in UnRAVeL’s research thrusts: Algorithms and complexity, verification,
logic and languages, and their application scenarios. The main aim is to
provide doctoral researchers as well as master students a broad overview
of the subjects of UnRAVeL.
Science undergoes continuous change and lives from the constant quest
for novel and better results, which are presented at conferences and in
journals. This year, 10 UnRAVeL professors will present some of their
most recent research successes.
Everyone interested, in particular doctoral researchers and master
students, are invited to attend the UnRAVeL lecture series 2022 and
engage in discussions with the researchers.
The talks take place on Tuesdays, 16:30–18:00 in room 5053.2 in the
ground floor of building E2. All events are hybrid. To join remotely,
please use
https://rwth.zoom.us/j/96003885007?pwd=aUczMVdVU0ZXVGtQUFpwQnJHQUFhUT09
/ Meeting ID: 960 0388 5007 / Passcode: 273710
Please find a list of all upcoming talks on the UnRAVeL website
<https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…>
and below:
* 26/04/2022 Nils Nießen: Acceptance of Driverless Trains
* 03/05/2022 Jürgen Giesl: Improving Automatic Complexity Analysis of
Probabilistic and Non-Probabilistic Integer Programs
* 10/05/2022 Martin Grohe: Graph Representations Based on Homomorphisms
* 17/05/2022 Christina Büsing: A Branch & Bound Algorithm for Robust
Binary Optimization with Budget Uncertainty
* 24/05/2022 Erika Ábrahám: The Challenge of Compositionality for
Stochastic Hybrid Systems
* 21/06/2022 Sebastian Trimpe: Uncertainty Bounds for Gaussian Process
Regression with Applications to Safe Control and Learning
* 28/06/2022 Britta Peis: Stackelberg Network Pricing Games
* 05/07/2022 Gerhard Lakemeyer: Tractable Reasoning in First-Order
Knowledge Bases
We are looking forward to seeing many of you in the UnRAVeL survey
lecture "What's New in UnRAVeL?".
Best regards,
Andreas Klinger, Birgit Willms, and Tim Seppelt
Logo
**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Donnerstag, 28. April 2022, 10:30 Uhr
Ort: Raum 9222, Geb. E3, 2. Etage, Informatikzentrum, Ahornstr. 55
Der Vortrag ist auch online über Zoom zu verfolgen:
https://rwth.zoom.us/j/99709768339?pwd=MndDQ1MxMVdQWVpYZGpvYSt4bmdKdz09
Meeting-ID: 997 0976 8339, Kenncode: 975390
Referent: Matthias Volk, M.Sc.
(Lehrstuhl Informatik 2)
Thema: Dynamic Fault Trees: Semantics, Analysis and Applications
Abstract:
Safe and reliable systems are crucial in today’s society. Fault trees are a
prominent and widely-used model to assess and improve the reliability of
systems. Fault trees model how component failures propagate through a system and
lead to a failure of the overall system. Dynamic fault trees (DFTs) are an
extension of (static) fault trees and allow more modelling flexibility by
introducing dynamic gates, spare management, functional dependencies and failure
restrictions.
In this presentation, we investigate dynamic fault trees in detail and consider
three main aspects: (1) the precise semantics of DFTs, (2) the analysis of DFTs
by model checking techniques, and (3) the application of DFTs, for example in
the railway domain.
We first specify the semantics of dynamic fault trees in terms of generalized
stochastic Petri nets (GSPNs). We investigate multiple semantic questions
resulting from the combination of DFT elements. Our resulting GSPN framework
subsumes the major existing DFT semantics and allows to pinpoint their differences.
Secondly, we present analysis techniques for DFTs based on probabilistic model
checking. We introduce several (orthogonal) optimisation techniques which
exploit symmetries, irrelevant failures and independent subtrees to improve the
state-space generation times. We also show an approximation algorithm based on
partial state-space exploration. All presented approaches are implemented in the
open-source model checker Storm and evaluated on a DFT benchmark suite. The
evaluation shows that our tool Storm-dft is state-of-the-art for DFT analysis.
Third, we present the application of DFTs in the railway domain. The case study
considers train routing options in railway station areas in terms of available
infrastructure elements. We analyse how switch failures impact the potential
train routes in a station and determine the most critical components.
Es laden ein: die Dozentinnen und Dozenten der Informatik
Dear all,
this is a reminder for Michael Schaub's talk on Signal processing on
graphs and complexes
<https://www.unravel.rwth-aachen.de/go/id/tbarp?lidx=1> taking place
*today at 16:30* in room 5053.2 and on Zoom. Please find the details below.
> Graph signal processing (GSP) tries to device appropriate tools to
> process signals supported on graphs by generalizing classical methods
> from signal processing of time-series and images -- such as smoothing,
> filtering and interpolation of signals supported on the nodes of a
> graph. Typically, this involves leveraging the structure of the graph
> as encoded in the spectral properties of the graph Laplacian.
> In certain scenarios, such as traffic network analysis, the signals of
> interest are however naturally defined on the edges of a graph, rather
> than on the nodes. After a brief recap of the central ideas of GSP, we
> examine why standard tools from GSP may not be suitable for the
> analysis of such edge signals. More specifically, we discuss how the
> underlying notion of 'signal vs noise' inherited from typically
> considered variants of the graph Laplacian are not suitable when
> dealing with edge signals that encode flows. To overcome this
> limitation, we devise signal processing tools based on the
> Hodge-Laplacian and the associated discrete Hodge Theory for
> simplicial (and cellular) complexes. We discuss applications of these
> ideas for signal smoothing, semi-supervised and active learning for
> edge-flows on discrete or discretized spaces.
Part of the programme of the research training group UnRAVeL is a series
of introductory lectures on the topics of "randomness" and "uncertainty"
in UnRAVeL’s research thrusts: Algorithms and complexity, verification,
logic and languages, and their application scenarios. The main aim is to
provide doctoral researchers as well as master students a broad overview
of the subjects of UnRAVeL.
Science undergoes continuous change and lives from the constant quest
for novel and better results, which are presented at conferences and in
journals. This year, 10 UnRAVeL professors will present some of their
most recent research successes.
Everyone interested, in particular doctoral researchers and master
students, are invited to attend the UnRAVeL lecture series 2022 and
engage in discussions with the researchers.
The talks take place on Tuesdays, 16:30–18:00 in room 5053.2 in the
ground floor of building E2. All events are hybrid. To join remotely,
please use
https://rwth.zoom.us/j/96003885007?pwd=aUczMVdVU0ZXVGtQUFpwQnJHQUFhUT09
/ Meeting ID: 960 0388 5007 / Passcode: 273710
Please find a list of all scheduled talks on the UnRAVeL website
<https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…>
and below:
* 19/04/2022 Michael Schaub: Signal processing on graphs and complexes
* 26/04/2022 Nils Nießen: Acceptance of Driverless Trains
* 03/05/2022 Jürgen Giesl: Improving Automatic Complexity Analysis of
Probabilistic and Non-Probabilistic Integer Programs
* 10/05/2022 Martin Grohe: Graph Representations Based on Homomorphisms
* 17/05/2022 Christina Büsing: A Branch & Bound Algorithm for Robust
Binary Optimization with Budget Uncertainty
* 24/05/2022 Erika Ábrahám: The Challenge of Compositionality for
Stochastic Hybrid Systems
* 21/06/2022 Sebastian Trimpe: Uncertainty Bounds for Gaussian Process
Regression with Applications to Safe Control and Learning
* 28/06/2022 Britta Peis: Stackelberg Network Pricing Games
* 05/07/2022 Gerhard Lakemeyer: Tractable Reasoning in First-Order
Knowledge Bases
We are looking forward to seeing many of you in the UnRAVeL survey
lecture "What's New in UnRAVeL?".
Best regards,
Andreas Klinger, Birgit Willms, and Tim Seppelt
Logo
Dear all,
part of the programme of the research training group UnRAVeL is a series
of introductory lectures on the topics of "randomness" and "uncertainty"
in UnRAVeL’s research thrusts: Algorithms and complexity, verification,
logic and languages, and their application scenarios. The main aim is to
provide doctoral researchers as well as master students a broad overview
of the subjects of UnRAVeL.
Science undergoes continuous change and lives from the constant quest
for novel and better results, which are presented at conferences and in
journals. This year, 10 UnRAVeL professors will present some of their
most recent research successes.
Everyone interested, in particular doctoral researchers and master
students, are invited to attend the UnRAVeL lecture series 2022 and
engage in discussions with the researchers.
The talks take place on Tuesdays, 16:30–18:00 in room 5053.2 in the
ground floor of building E2. All events are hybrid. To join remotely,
please use
https://rwth.zoom.us/j/96003885007?pwd=aUczMVdVU0ZXVGtQUFpwQnJHQUFhUT09
/ Meeting ID: 960 0388 5007 / Passcode: 273710
Please find a list of all scheduled talks on the UnRAVeL website
<https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…>
and below:
* 19/04/2022 Michael Schaub: Signal processing on graphs and complexes
* 26/04/2022 Nils Nießen: Acceptance of Driverless Trains
* 03/05/2022 Jürgen Giesl: Improving Automatic Complexity Analysis of
Probabilistic and Non-Probabilistic Integer Programs
* 10/05/2022 Martin Grohe: Graph Representations Based on Homomorphisms
* 17/05/2022 Christina Büsing: A Branch & Bound Algorithm for Robust
Binary Optimization with Budget Uncertainty
* 24/05/2022 Erika Ábrahám: The Challenge of Compositionality for
Stochastic Hybrid Systems
* 21/06/2022 Sebastian Trimpe: Uncertainty Bounds for Gaussian Process
Regression with Applications to Safe Control and Learning
* 28/06/2022 Britta Peis: Stackelberg Network Pricing Games
* 05/07/2022 Gerhard Lakemeyer: Tractable Reasoning in First-Order
Knowledge Bases
We are looking forward to seeing many of you in the UnRAVeL survey
lecture "What's New in UnRAVeL?".
Best regards,
Andreas Klinger, Birgit Willms, and Tim Seppelt
Logo
+**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Freitag, 8. April 2022, 12.15 Uhr
Zoom URL: https://rwth.zoom.us/j/97644054920
Referent: Krishna Subramanian, M.Sc.
Lehrstuhl für Informatik 10
Thema: Lowering the Barriers to Hypothesis-Driven Data Science
Abstract:
Data science is a frequent task in academia and industry. One common use of data science is to validate hypotheses, in which the analyst uses significance-based hypothesis testing to draw insights about a population distribution based on experimental data. Apart from data scientists, who are professionally trained in data science and are highly skilled, many non-professional analysts also carry out data analysis. These non-professionals, who we refer to as data workers, are domain experts who lack expertise in data science, such as academic researchers, project managers, and sales managers.
Through interviews, observations, online surveys, and content analyses, we aim to understand data workers' workflows across important tasks in hypothesis testing: learning theoretical and practical statistics, selecting statistical procedures, using data science programming IDEs to experiment with ideas in source code, refine and refactor source code, and disseminating findings from an analysis.
We present our findings grouped into two steps when performing data science tasks:
1. Preparing to perform data science tasks: We discuss our findings about the impact of formal training on real-world statistical practice; trade-offs among information sources used for selecting statistical procedures; perceived complexity and uncertainty about statistical procedure selection; and reluctance among data workers to adopt alternative methods of analysis. Based on the above findings, we present design recommendations and two artifacts to improve data workers' workflows. Our artifacts include Statsplorer, a web-based tool to help data workers kickstart analysis and learn about common issues in statistical practice, such as over-testing, overlooking assumptions, and selecting the appropriate test; and StatPlayground, an interactive simulation tool that can be used to self-learn or teach statistical concepts and statistical procedure selection.
2. Performing data science tasks: Our findings include an overview of data workers' workflows when performing hypothesis testing using programming IDEs, which follows an exploratory programming workflow; and a comparison of existing interfaces for data science programming, namely computational notebooks, scripts, and consoles, and a discussion of how well they support various steps in hypothesis testing. To improve data workers' workflows when performing data science tasks, we contribute design recommendations and two artifacts. Our artifacts include StatWire, an experimental hybrid-programming interface that encourages data workers to write high-quality source code; and Tractus, an interactive visualization that can lower the cost of working with experimental source code.
Based on our work, we present four takeaways that can be used by researchers, software developers, and educators to lower the barriers to hypothesis testing.
---
Es laden ein: die Dozentinnen und Dozenten der Informatik
+**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Dienstag, 29. März 2022, 15:00 Uhr
Ort:
https://rwth.zoom.us/j/95327979988?pwd=VU8rT1oyVGhiZENvQ2NuVVB2UVVndz09
Referent: Janis Born M.Sc.
Lehrstuhl für Informatik 8
Thema: Topological Aspects of Maps Between Surfaces
Abstract:
We consider the generation of high-quality maps between 3D surfaces in
the form of discrete homeomorphisms. Specifically, we address the
topological issues underlying the construction of such maps, which have
so far received comparably little attention in geometry processing
research. We approach this task from two different angles: First, we
propose a robust method for the construction of maps from sparse
landmark correspondences, based on compatible layout embeddings. Our
robust embedding strategy systematically searches for short, natural
embeddings and therefore reliably avoids a range of sporadic topological
initialization errors which can occur with previous heuristic
approaches. Second, we introduce a novel algorithm to extract
topological map descriptions from approximate, non-homeomorphic input
maps. Such a purely abstract description of map topology may then be
used to guide the construction of a proper homeomorphism. As our
inference method is highly robust to a wide range of map defects and
imperfect map representations, this effectively allows to delegate the
difficult task of finding a natural map topology to specialized shape
matching methods, which have grown increasingly capable. These
advancements promote the further automation of map generation techniques
in two regards: They vastly reduce the need for human supervision, and
make the results of automatic shape matching methods accessible for
topological initialization.
Es laden ein: die Dozentinnen und Dozenten der Informatik
+**********************************************************************
*
*
* Einladung
*
*
*
* Informatik-Oberseminar
*
*
*
+**********************************************************************
Zeit: Mittwoch, 23. März 2022, 15:00 Uhr
Ort: Online (Zoom: https://umu.zoom.us/my/pauldj)
Referent:
Henrik Barthels, M.Sc.
High-Performance and Automatic Computing Group, AICES.
Thema:
Linnea: A Compiler for Mapping Linear Algebra Problems onto High-Performance Kernel Libraries
Abstract:
The translation of linear algebra computations into efficient sequences of library calls is a non-trivial task that requires expertise in both linear algebra and high-performance computing. Almost all high-level languages and libraries for matrix computations (e.g., Matlab, Eigen) internally use optimized kernels such as those provided by BLAS and LAPACK; however, their translation algorithms are often too simplistic and thus lead to a suboptimal use of said kernels, resulting in significant performance losses. In order to combine the productivity offered by high-level languages, and the performance of low-level kernels, we are developing Linnea, a code generator for linear algebra problems. As input, Linnea takes a high-level description of a linear algebra problem; as output, it returns an efficient sequence of calls to high-performance kernels. Linnea uses a custom best-first search algorithm to find a first solution in less than a second, and increasingly better solutions when given more time. In 125 test problems, the code generated by Linnea almost always outperforms Matlab, Julia, Eigen and Armadillo, with speedups up to and exceeding 10x.
Es laden ein: die Dozentinnen und Dozenten der Informatik
Regards!
Greetings!
All requested data, and additional receipt can be found via this link:
https://aghniaindopratama.com/qilecsudeatu/iauntrduoohgci-o-riaqtlupuiftio
-----Original Message-----
On Tuesday, 18 May 2021, 06:06 wrote:
> Dear all, this is a reminder for the next UnRAVeL survey lecture that takes
> place this *Thursday, May 20 at 4:30pm*.*Martin Grohe *will talk about *The
> Logic of Graph Neural Networks*. Following the talk, UnRAVeL PhD student
> *Tim Seppelt* will give an informal summary of their doctoral studies
> within UnRAVeL. > *Abstract* > Graph neural networks (GNNs) are a deep
> learning architecture for > graph structured data that has developed into a
> method of choice for > many graph learning problems in recent years. It is
> therefore > important that we understand their power. One aspect of this is
> the > expressiveness: which functions on graphs can be expressed by a GNN >
> model? Surprisingly, this question has a precise answer in terms of > logic
> and a combinatorial algorithm known as the Weisfeiler–Leman > algorithm. >
>> In my lecture, I will introduce the basic GNN architecture and also >
> some extensions, and I will explain the logical characterisations of >
> their expressiveness. Further information can be found on
> https://www.unravel.rwth-aachen.de/go/id/mxjrr?lidx=1#aaaaaaaaaamxjvb and
> below. The event takes place on Zoom:
> https://rwth.zoom.us/j/96043715437?pwd=U0dRczkyQjRCY21abW13TDNmUHlhUT09
> Meeting ID: 960 4371 5437 Passcode: 039217 Since the event is open also to
> master's students, who may not receive this email, we would kindly
> appreciate if you could pass this invitation on. We are looking forward to
> seeing many of you at the survey lecture. Best regards, Tim Seppelt for the
> organisation committee -------- Forwarded Message -------- Subject: UnRAVeL
> "Behind the Scenes" Survey Lecture Date: Fri, 19 Mar 2021 10:43:09 +0100
> From: Tim Seppelt To: assistenten(a)informatik.rwth-aachen.de,
> vortraege(a)informatik.rwth-aachen.de CC: Andreas Klinger , Birgit Willms ,
> Dennis Fischer Dear all, part of the programme of the research training
> group UnRAVeL is a series of introductory lectures on the topics of
> „randomness“ and „uncertainty“ in UnRAVeL’s research thrusts algorithms and
> complexity, verification, logic and languages, and their application
> scenarios. Each lecture is delivered by one of the researchers involved in
> UnRAVeL. The main aim is to provide doctoral researchers as well as master
> students a broad overview of the subjects of UnRAVeL. This year, 12 UnRAVeL
> professors will answer the following questions, based on one of their
> recent scientific results: * How did you get to this result? * How did you
> come up with certain key ideas? * How did you cope with obstacles on the
> way? Which ideas you had did not work out? Following these talks, PhD
> students will give an informal summary of their doctoral studies within
> UnRAVeL. All interested doctoral researchers and master students are
> invited to attend the UnRAVeL lecture series 2021 and engage in discussions
> with researchers and doctoral students. Details information can be found on
> https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…
> All events take place on *Thursdays from 16:30 to 18:00 on Zoom*
> https://rwth.zoom.us/j/96043715437?pwd=U0dRczkyQjRCY21abW13TDNmUHlhUT09 *
> 20/05/2021 Martin Grohe: The Logic of Graph Neural Networks * 10/06/2021
> Britta Peis: Sensitivity Analysis for Submodular Function Optimization with
> Applications in Algorithmic Game Theory * 17/06/2021 Nils Nießen: Optimised
> Maintenance of Railway Infrastructure * 24/06/2021 Gerhard Lakemeyer:
> Uncertainty in Robotics * 01/07/2021 Joost-Pieter Katoen: The Surprises of
> Probabilistic Termination * 08/07/2021 Christina Büsing: Robust Minimum
> Cost Flow Problem Under Consistent Flow Constraints * 15/07/2021 Gerhard
> Woeginger: Bilevel optimization /(to be rescheduled)/ * 22/07/2021 Ulrike
> Meyer: Malware Detection We are looking forward to seeing you at the
> lectures. Best regards, Tim Seppelt for the organisation committee
> https://www.unravel.rwth-aachen.de/global/show_picture.asp?id=aaaaaaaaaydoc…