Prof. Dr. Tobias Friedrich

Research Seminar (Summer Term 2022)

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COVID-19 and virtual seminars

Due to the current situation we are not hosting seminars at the HPI anymore. Instead, we host virtual seminars via zoom. If you want to join the seminar on short notice or if you do not want to subscribe to the mailing list, please send an email to Katrin.Casel(at)hpi.de or to Andreas.Goebel(at)hpi.de.


A series of talks on current research and interesting topics in algorithm engineering and theoretical computer science. Everyone is welcome to attend talks. 

If you want to give a talk about a topic in algorithm engineering or theoretical computer science, please write an e-mail to Dr. Katrin Casel or to Dr. Andreas Göbel.


For announcements of talks, subscribe to our mailing list.

Talks (click on title to show abstract)

DateRoom SpeakerTitle
01.04. 11:00virtual Janosch Ruff

The talk is about a constructive proof of the Lovász local lemma. A beautiful result on its own, Robin A. Moser presented a remarkable simple algorithm to construct a combinatorial object with properties known to exist thanks to the probabilistic method. The main focus of the talk is an elegant proof technique that tries to compress the incompressible by a Kolmogorov Complexity argument giving an efficient algorithm as a byproduct. This one is definitely from "The Book". You don't have to believe in God, but you should believe in "The Book".

08.04. 11:00virtual Bachelor-Project TomTom 22/21

In this talk we will present theoretical and practical results of our Bachelor Project in the field of (electric vehicle) routing. Therefore we split our talk in two parts: In the first part we will talk about the routing speedup technique Arc-Flags. In particular we will present our approaches and ideas on explaining this algorithms efficiency using the graph parameter Skeleton Dimension. In the second part we will then visually compare electric vehicle routing to conventional routing and highlight the differences regarding travel time, costs and detours based on real world data.

22.04. 11:00virtual Bachelor-Project Valyria 22/21

In our bachelors project, we come up with machine learning models to predict the price of real estate objects. In particular, we are aiming to employ transfer learning to improve the prediction quality on domains where only little training data is available. In our presentation, we will explore four concepts that we have been working with over the past semester. We will cover some simple transfer learning ideas for Deep Neural Networks and, in contrast, two techniques to improve DNN performance in a non-transfer context. Following that, we will present two other machine learning algorithms, k-nearest-neighbour and clustering, and how we adapt them to a transfer learning setting.

16.05. 15:00Belvedere (hybrid)Aneta Neumann
Frank Neumann

In many real-world scenarios, it is necessary to handle uncertainty, and potentially disruptive events that violate constraints in stochastic settings need to be avoided. A lot of evolutionary multi-objective algorithms have recently been analyzed and applied to submodular problems with different types of constraints. This talk will provide you insights on the impact of uncertainty in advanced mine optimisation on the real-word stochastic optimisation problem which allow one to mitigate the uncertainty in the deposit while maintaining high profitability. We will also show the behavior of evolutionary multi-objective algorithms on different submodular chance constrained network problems.

Joint work with Benjamin Doerr, Carola Doerr, Frank Neumann, Simon Ratcliffe, Will Reid, Michael Stimson, Andrew M. Sutton.

Diversity plays a crucial role in the area of evolutionary computation. Traditionally, diversity has been used to enable successful crossover operations and prevent premature convergence. In recent years, computing sets of solutions that have structural or behavioural diversity has gained significant attention under the terms evolutionary diversity optimization and quality diversity. This talk will give an overview on this area of research and point out some recent results. We will cover results in the area of evolutionary diversity optimization for the classical traveling salesperson problem and show how quality diversity approaches can be used to achieve better solutions for the traveling thief problem.

Joint work with Viet Anh Do, Mingyu Guo, Aneta Neumann, Adel Nikfarjam

20.05. 11:00virtual Elazar Goldberg

In many applications one wants to measure distances between a given pair of strings. The simplest way, called Hamming distance, is scanning the strings and comparing the number of characters by which they differ. However, in many cases this is not very useful. Instead, a very common measurement is called edit distance, which counts the minimal number of edit operations needed for converting the first string into the second. Where the set of edit operations consists of character substitution, insertion and deletion. While the edit distance is more applicable, computationally, it seems to be more challenging. A possible method to bridge between the measurements is called embedding: This is a mapping that preserves distances. In this talk I'll show an embedding protocol. The correctness analysis follows by an argument concerning a drunk walk. No prior knowledge is needed. The talk is based on a joint work with Michal Koucky and Diptarka Chakraborty.

27.05. 11:00virtual Stefan Neubert


10.06. 11:00virtualLeon Schiller,
Simon Wietheger

24.06. 11:00virtual Vanja Doskoč