SCCS Kolloquium

The SCCS Colloquium is a forum giving students, guests, and members of the chair the opportunity to present their research insights, results, and challenges. Do you need ideas for your thesis topic? Do you want to meet your potential supervisor? Do you want to discuss your research with a diverse group of researchers, rehearse your conference talk, or simply cheer for your colleagues? Then this is the right place for you (and you are also welcome to bring your friends along).

Upcoming talks

Paul Wiessner: Anomaly Detection from Stream Data under Uncertainty

SCCS Colloquium |

Internet of Things (IoT) is a rising topic and finds the way into various domains, changing the way we interact with technology and gather data. For instance, in the healthcare sector, IoT-enabled devices such as wearable fitness trackers and remote patient monitoring systems have transformed the landscape by providing real-time health data, enhancing patient care, and enabling proactive interventions.

While networks of IoT devices represent complex structures, they produce significant amounts of time series data, which may be prone to anomalies, unexpected patterns or deviations from the norm within the data. Anomalies in IoT data can cause potential issues, security threats, or operational irregularities, making anomaly detection a critical aspect of ensuring the reliability and security of IoT ecosystems. Nevertheless, the inherent resource constraints of IoT devices raise concerns regarding the precision of their emitted observations. This impreciseness in measurements can be characterized as noise.

In statistics or machine learning, this noise is referred to as data (aleatoric) uncertainty. In addition, the model itself introduces another layer of uncertainty, known as model (epistemic) uncertainty, which becomes particularly interesting in anomaly detection scenarios where differing anomalies from normal patterns requires a nuanced understanding of the model's uncertainty.
Addressing both types of uncertainties is necessary, as neglecting either could overlook a significant aspect of overall uncertainty and impair robustness of anomaly detection systems.

Current approaches to uncertainty in time series anomaly detection mostly focus on quantifying epistemic uncertainty and ignore data dependent uncertainty. However, consideration of noise in data is important as it may have the potential to lead to more robust detection of anomalies and a better capability of distinguishing between real anomalies and anomalous patterns provoked by noise.

In this work, we propose an extended version of a LSTM Autoencoder for anomaly detection which is able to quantify both aleatoric and epistemic uncertainty in time series separately.
We show that implemented mechanisms are effectively recognising different levels of noise. Further, we investigate the impact of anomalies on uncertainty. Finally, we evaluate effectiveness of the approach by evaluating it on real-world datasets.

Master's thesis presentation. Paul is advised by Sana Sellami , and Prof. Dr. Hans-Joachim Bungartz.

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Contribute a talk

To register and schedule a talk, you should fill the form Colloquium Registration at least two weeks before the earliest preferred date. Keep in mind that we only have limited slots, so please plan your presentation early. In special cases, contact

Colloquium sessions are now on-campus. We have booked room MI 00.13.054 for WS2024. You can either bring your own laptop or send us the slides as a PDF ahead of time. The projector only has an HDMI connection, so please bring your own adapters if necessary.

Do you want to attend but cannot make it in person? We now have a hybrid option. Simply join us through this BBB room:

We invite students doing their Bachelor's or Master's thesis, as well as IDP, Guided Research, or similar projects at SCCS to give one 20min presentation to discuss their results and potential future work. The time for this is typically after submitting your final text. Check also with your study program regarding any requirements for a final presentation of your project work.

New: In regular times, we will now have slots for presenting early stage projects (talk time 2-10min). This is an optional opportunity for getting additional feedback early and there is no strict timeline.

Apart from students, we also welcome doctoral candidates and guests to present their projects.

During the colloquium, things usually go as follows:

  • 10min before the colloquium starts, the speakers setup their equipment with the help of the moderator. The moderator currently is Shuo Sun. Make sure to be using an easily identifiable name in the online session's waiting room.
  • The colloquium starts with an introduction to the agenda and the moderator asks the speaker's advisor/host to put the talk into context.
  • Your talk starts. The scheduled time for your talk is normally 20min with additional 5-10min for discussion.
  • The moderator keeps track of the time and will signal 5 min before the end of time.
  • During the discussion session, the audience can ask questions, which are meant for clarification or for putting the talk into context. The audience can also ask questions in the chat.
  • Congratulations! Your talk is over and it's now time to celebrate! Have you already tried the parabolic slides that bring you from the third floor to the Magistrale?

Do you remember a talk that made you feel very happy for attending? Do you also remember a talk that confused you? What made these two experiences different?

Here are a few things to check if you want to improve your presentation:

  • What is the main idea that you want people to remember after your presentation? Do you make it crystal-clear? How quickly are you arriving to it?
  • Which aspects of your work can you cover in the given time frame, with a reasonable pace and good depth?
  • What can you leave out (but maybe have as back-up slides) to not confuse or overwhelm the audience?
  • How are you investing the crucial first two minutes of your presentation?
  • How much content do you have on your slides? Is all of it important? Will the audience know which part of a slide to look at? Will somebody from the last row be able to read the content? Will somebody with limited experience in your field have time to understand what is going on?
  • Are the figures clear? Are you explaining the axes or any other features clearly?

In any case, make sure to start preparing your talk early enough so that you can potentially discuss it, rehearse it, and improve it.

Here are a few good videos to find out more:

Did you know that the TUM English Writing Center can also help you with writing good slides?

Work with us!

Do your thesis/student project in Informatics / Mathematics / Physics: Student Projects at the SCCS.