Quick intro

Michael Beck

I studied mathematics and computer science at the University of Kaiserslautern in Germany. Now I am a post-doc at the University of Winnipeg with the TerraByte project. In 2020 I co-founded the start-up Particuleye Technologies Inc. together with students from the University of Manitoba and the University of Toronto.

Full CV

2005 to 2010

Master Degree

Mathematics
University of Kaiserslautern
Major: Probability Theory
Minor: Computer Science

2012 to 2016

Doctorate Degree

summa cum laude
Computer Science University of Kaiserslautern
Thesis: Advances in Theory and Applicability of Stochastic Network Calculus

2016 to 2018

Previous Post-docs

City University of Hong Kong
University of Manitoba

Since 2019

Post-doc

University of Winnipeg. TerraByte Project
.

Research Interests

Plant data acquisition for and with machine learning

The recent rise in popularity for machine learning algorithms, specifically the field of convolutional neural networks created a large demand for labelled image data. An image is labelled if there exists accompanying information of what can be seen on the image and where these objects are located within the image. In many fields of "common knowledge" the task of labelling can be outsourced to the crowd (for example with Captcha-challenges to prove oneself as not being a robot), in other areas this is however not possible. Plant classification is one of these tasks where expert knowledge is required to differentiate between similarly looking species, for example Oats and Wild Oats. To overcome this bottleneck, one can use robotic systems to image known plants from many different angles to build a database of labelled plant data. This is exactly one of the things we do at the TeraByte project.

Digital agriculture

Agriculture currently undergoes a digital revolution with many new technologies making their way into all aspects of the industry. In the Digital technologies in agriculture and rural areas briefing paper of the United Nations Food and Agriculture Organization we can read: "In the agriculture and food sector, the spread of mobile technologies, remote-sensing services and distributed computing are already improving smallholders’ access to information, inputs, market, finance and training. [...] Digitalization will change every part of the agrifood chain." The agriculture of tomorrow could look vastly different to what we are used to today. Autonomous agents equipped with intelligent cameras and sensors could evaluate crops and weeds on a plant-by-plant basis and take action not unsimilar to how we take care of our plants in our gardens. These are challenging tasks for today's robotic systems and machine learning models but solving them promises a reduction in resources used while improving yields and preserving our environment.

Automated analysis of microplastics and particles in fluids

Microplastics are plastic particles in the size range from 5 mm down to a few micrometers. They pose a global environmental challenge as their occurrences appear to be omnipresent in all water bodies investigated, including arctic ice and the deep sea. These particles enter the food chain and can impact the health of animals and humans, as they often release toxic additives. The pathways and sources of microplastics need to be better understood to to tackle this type of pollution. For this the research community is in dire need of automation when it comes to microplastic analysis. New technologies in the fields of deep neural networks and embedded systems are a promising candidate to fulfill this need.

Stochastic network calculus

Queueing networks in their most simple form consists of servers, their buffers, and jobs that need to be processed by service elements. The world is full of such systems: the check-outs at the supermarket are a simple one, the world wide web with millions of service elements in the form of switches and routers is another queueing system. In many situations we are interested in the worst-case scenario, rather than the average performance of a queueing network. For example, consider the exit times of visitors to a stadium in case of an evacuation: Here, the average time is a poor metric, and we are rather interested in the time needed to get even the last person out of danger. Another example are SLAs (Service Level Agreements) between a service provider and a client or the maximum transmission delay in a car that breaks-by-wire. Stochastic Network Calculus is a mathematical theory that provides bounds on these worst-case scenarios, excluding events with negligible likelihoods. This methodology is used in designing and modelling queueing networks. It has the advantages that complex queueing systems can be simplified to easier scenarios and thus provides us with an end-to-end analysis.

Teaching

A first course in stochastic network calculus

This First Course in Stochastic Network Calculus builds on the lecture Performance Modeling of Distributed Systems at the University of Kaiserslautern. The concepts of stochastic network calculus parallels those of deterministic network calculus. Therefore, I reference to the lecture of 2011 at several points to stress these connections. This document however also works as a stand-alone course and hence a study of the above-mentioned lecture should not be necessary.

This course contains a rather large probability primer to ensure the student can really grasp the expressions that appear in stochastic network calculus. A student familiar with probability theory might skip this first chapter and delve directly into stochastic network calculus. For each topic exercises are given, which can (and should) be used to strengthen the understanding of the presented definitions and theory.

SNC Course

Supervised Theses

  • Yinhua Xu: Achieving Robustness in MSN by scheduling the measurements
  • Simon Birnback and Sebastian Henningsen: Applying Stochastic Network Calculus in Scnearios with Incomplet Knowledge
  • Ahmed Alsaedi: Optimization Methods for Stochastic Network Calculus Performance Bounds

Courses

As main lecturer:
  • Computer Networks (ACS-3911), University of Winnipeg, 2022
  • Advanced Internet Programming (ACS-3909), University of Winnipeg, 2020
Teaching assistant and substitute lecturer:
  • Stochastic Analysis of Distributed Systems, University of Kaiserslautern, 2015/16
  • Worst-Case Analysis of Distributed Systems, University of Kaiserslautern, 2014/15
  • Quantitative Aspects of Distributed Systems, University of Kaiserslautern, 2014, 2015
  • Performance Modeling of Distributed Systems, University of Kaiserslautern, 2013/14
  • Communication Systems, University of Kaiserslautern, 2011, 2012
  • Security in Distributed Systems, University of Kaiserslautern, 2010/11, 2011/12

Publications

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011