Interdisciplinary Algorithms Lab, Spring Semester 2018

Instructors:

Prof. Dr. Angelika Steger
Prof. Dr. David Steurer
Dr. Johannes Lengler

First meeting:

The first meeting is postponed to Tuesday, February 27, at 17:15 in CAB G 15.2. Unfortunately, the time slot on Tuesday cannot be used during the semester. Therefore, on the first meeting, we will agree on a time slot for the rest of the semester.

Concept

In this course students will develop solutions for algorithmic problems posed by researchers from other fields. The course addresses master students in computer science, and is suited both for students with a theoretical and with a more applied focus.

The main goal is to learn how to solve algorithmic problems in an interdisciplinary or applied context. The key is to combine (i) a solid understanding of algorithmic methodology with (ii) insights into the problem at hand to judge which side constraints are essential and which can be loosened. Or, phrased differently, real world problems are often ill posed. And a major step toward a solution is to find an appropriate formalization of the problem.

Since we are dealing with real data, performance will be evaluated experimentally. For this evaluation, students need to implement algorithms, preferably by using and combining available tools. The course will be run during the term. The course is 5CP and we thus expect that students work for the lab a full day each week.

Topic

The lab will be in cooperation with researchers from the INI (Institute of Neuroinformatics, University of Zurich). They measure how calcium concentration in the brain changes over time, and they want to deduce at which time points neurons send a signal (action potential or spike) to their neighbors. In the first phase of the project, we will work with generic data from an online contest. In the second half we will work with the actual data from the INI.

Tentative Schedule

In the first week we will introduce the problem and provide access to the data. In the second week we will discuss possible modelling approaches and performance measures. During the next 4-5 weeks students will develop solutions for the mid term competition in teams (2-3 students per team). Each team also has to hand in a written description of their solution (5-10 pages). The aim of this description is to make the approach available for all other student teams. After the midterm competition, depending on the results we will switch to the new data set. In particular this means that students need to avoid overfitting in the first half of the project, since the data for the second half will be generated by sightly different recording techniques.

In the second half of term, each team will improve the solutions for the final competition in the last week of term, using all building blocks that were presented in mid-term. With this setup students should learn that while, naturally, the quality of a solution should be as good as possible, a proper documentation is also essential. The success of a team will thus be viewed as a combination of the quality of the final solution and whether the building blocks presented at mid-term are used by other teams.

Cooperation

The lab aims at bringing together students with different skill sets. For example, a theoretically oriented student may enjoy reading papers about different approaches, while a practically oriented student may have better programming skills, or may come up with efficient heuristics. The teams should be formed in such a way that the students complement each other with their skills and interests.