Sunday, October 19, 2014

On the Road to your PhD

In the blog Between a rock and a hard place, James Hickey posted a valuable list of tipps for succeeding in your PhD:

1. Learn Latex
2. Use Bibtex
3. Keep your papers organised
4. Keep a formatted list of your own publications and conference abstracts as you go along
5. Always give conference abstracts different titles
6. Keep on top of your emails
7. Manage time
8. Hypothesis testing
9. Keep detailed notes
10. Avoid perfectionism
11. Always give deadlines when you want feedback
12. Source additional funding
13. Write as you go
14. Don’t be scared of your supervisors
15. Log out of Facebook
16. Keep an eye on your budget
17. Diversify yourself
18. Music
19. Get your workstation set up
20. Take notes in meetings
21. Read around your subject
22. Write a literature review
23. Socialise!
24. Sport and/or hobbies
25. Go to conferences and workshops
26. Network
27. Establish a routine
28. Take the lead
29. Practice presenting your work
30. Be prepared for the worse
31. Back up, and back up again
32. Small steps to success
33. Keep on top of admin

Being somebody who already has his PhD, I find the list very useful, the title "Things I wish I knew when I started my PhD..." definitely holds some truth, although I accidently did many of the good things mentioned there. See the original post for a detailed explanation of every item!

Sunday, October 12, 2014

Self-organizing Processes in Physical Geography

In this talk, Marco van der Wiel presents some ideas on self-organization and self-organized criticality, and how these relate to physical geography and (explanatory and exploratory) modelling in physical geography.

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The talk is in english, you might want to skip the german introduction until 1:40. Video by AAU Campus TV/Egmont Sparouz.

Tuesday, August 19, 2014

DarwinTunes - Evolution of Music by Public Choice
The natural world – creatures, plants, infections – is the result of Darwinian evolution by natural selection, i.e., the gradual process of (biological) traits become either more or less common in a population based on the success of the organism carrying these traits. This process, repeated for two billion years, has created the vast diversity of life on earth.
The same process can be also observed in human society where cultural artifacts – words, songs, images, ideas – are constantly remixed and reinterpreted by people. A reinterpretation is an imperfect copy and therefore a “mutation”. Thus, the variety of our culture is the result of a cultural evolution.
In order to examine the underlying mechanism of cultural evolution, Robert M. MacCalluma, Matthias Mauch, Austin Burta, and Armand M. Leroia constructed a Darwinian music engine consisting of a population of short audio loops that sexually reproduce and mutate.
The selection is based on human feedback via a webpage that implements the remixing of tunes as a game. By remixing your tune with others, the other parent gets a score point. Your goal is to make an attractive tune - the more often you get remixed, the more points you have.

In 2010, researchers from the Alpen-Adria-Universität Klagenfurt released a similar system where people could vote for recombinations of music tunes.  


Sunday, August 10, 2014

Tenure Track Research Professor Position in Big Data at UNAM

The Computer Science Department of the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) of the Universidad Nacional Autónoma de México (UNAM) has a open call for a tenure-track research professor in big data.

Located in the heart of the UNAM's Ciudad Universitaria, a UNESCO World Heritage site, the IIMAS has been the leader in computer science in Mexico since the first computer in the country was acquired by UNAM. Researchers at UNAM have a privileged position for several reasons. UNAM is the highest ranked spanish speaking higher education institution in the world and produces half of the research in Mexico and is the largest in the continent (300K+ students). Professors in faculties do more teaching than research, while researchers in institutes (such as IIMAS) do more research than teaching (about 48 hours per year, usually to the best graduate students in the country. Groups of more than five students get a teaching assistant). Students in most graduate programs at UNAM receive automatically a scholarship, and there is travel budget for researchers, minimizing the grant writing load. There are several grant calls with high acceptance rates. There are also two postdoctoral fellowship calls per year internal to UNAM. High performing researchers can reach tenure in less than five years.

Requirements for this call are:

  1. To be less than 39 (women) or 37 (men) years old.
  2. To have a PhD degree in computer science, statistics, or related areas from a renowned institution.
  3. To have published high quality research papers related to big data.
  4. To have teaching skills at undergraduate and graduate levels.
  5. To have abilities to direct undergraduate and graduate theses.
  6. To be able to collaborate in multidisciplinary research projects.
  7. To fulfill the norms described at
Interested candidates should send the following documentation to Dr. Ricardo Berlanga (berlanga "at", Academic Secretary of IIMAS before October 7th, 2014:
  1. Application request (in Spanish).
  2. Updated Curriculum Vitae, including publication list (in Spanish).
  3. Copy of PhD degree.
  4. Copy of publications.
  5. At least two references with email included.
  6. A work plan which includes research and teaching prospects for the next three years (in Spanish.
Selected candidates will be invited to give an open talk or videoconference at IIMAS. An ad hoc commission will make a final decision.
More information can be requested to:
Dr. Carlos Gershenson
Head of Computer Science Department, IIMAS, UNAM
cgg "at"

Friday, July 4, 2014

On Evolving Self-organizing Technical Systems

Moofushi Kandu fish (Image by Bruno
de Giusti under CC-BY-SA-2.5)
Individual swarm fish behave according to simple rules, which make the overall swarm an efficient entitiy for hunting and avoiding predators. Despite the simple local rules, a school of fish is a working, intelligent system. István Fehervari examined in his doctoral thesis how this behavior can be transferred to technical systems. The result is a tool making it easier to develop self-organizing systems.
Sometimes, it is possible to mimic natural self-organizing behavior for a technical system. At other times, you might not have access to such a template. "If there is no natural system, which we can copy, we have to develop it on our own," says István Fehérvári from the Institute of Networked and Embedded Systems at Alpen-Adria-Universität Klagenfurt. Fehérvári further explains: "This is very difficult because the behavior of a complex system is difficult to predict and the definition of the proper interaction behaviors is hard. Any change in the system creates an effect, often with unwanted consequences. This is why we apply an artificial evolution approach to evolve the local interaction rules,"

To make this approach feasible, the FREVO software has been developed, a tool that helps to apply the evolutionary approach in a unified way to different problem statements and settings. "FREVO provides a one-stop shop, with all the necessary steps for designing a self-organized algorithm for a given problem." says Fehérvári who devoted a major part of his thesis work to design and implement FREVO.  Now the tool is available to other researchers for further experiments and investigations. FREVO is an open-source program in Java that can be freely downloaded at

Further readings and downloads:

Wednesday, June 11, 2014

Simulating the Soccer World Cup 2014

You cannot wait for the soccer World Cup to start? We proudly present a peek preview of the World Cup 2014 - played by teams created with evolutionary algorithms. Using our evolutionary tool FREVO for designing self-organizing systems we have evolved neural networks that make robots playing soccer. During the evolution phase, a fitness function combines different aspects of gameplay like zone defense, man-marking, passing, shots, and goals. By tweaking the weights for these parameters we can influence the playing style of a team while the overall gameplay is still generated automatically by the evolutionary process. Thus we can simulate playing styles of different national teams and then match them against each other.

The following video shows a simulation of Brazil versus Croatia, the opening game of the world cup. The commentary is from Toni Polster, a legendary Austrian soccer player.

While the result is credible, we have not done this to exactly predict the outcome of the games - this would spoil the whole tournament! Furthermore, our approach is not meant for prediction but a system to train a distributed agent-based system to achieve an emergent cooperative behavior in a self-organized way. Setting up this work helped us in improving our understanding how we can create and guide self-organizing systems. We have chosen the soccer simulation as a demonstration because in soccer the global goal (no pun intended) can be achieved in so many different ways , for example with a defensive, offensive, kick-and-rush, pass-intensive, etc. style. And it is nice to watch - who said good science can't be fun!

Further readings:

Thursday, May 22, 2014

Self-Organization for the Smart Grid

In my keynote "AI Techniques for Smart Grids" at the 2014 IEEE Innovative Smart Grid Technologies - Asia conference, I discussed the role and potential of self-organization in the smart grid. Since power systems are critical infrastructure, applying a distributed, bottom-up control structure is a delicate step. On the one hand, self-organizing systems provide very attractive properties like scalability, adaptability and robustness. On the other hand, due to non-linear interactions in the system, small changes that appear insignificant can have severe consequences. To ensure secure operation, this is not the most-wanted property. However, sometimes one gets such systems without a choice. An example is the effect of the unbundling process of power generation and system operation to create a free energy market - a decision that was taken to support distributed on-site power generation with renewable energy sources. At the UCTE grid, the largest synchronous grid in the world in terms of generation capacity, the decreased the scope of control for the Transmission System Operators (TSOs) due to the liberalization of the power market has caused the TSOs to develop a new way of understanding of the system in order to carefully and knowledgeable interact with the system. So we got a self-organization system without explicitly designing the system for being one. Another possibility where self-organization can explicitly support the smart grid is the utilization of smart meters in a way that supports the utilities while still keeping control and private information locally at the customer's side.

To enable self-organization for the smart grid, it is necessary to provide models, proofs, case studies, etc. showing that self-organizing approaches work. For finding self-organizing algorithms we provide our part: with the open-source software tool FREVO, we can evolve agent behavior for self-organizing systems using an automated process based on artificial evolution.