Overview




We are working in collaboration with the Smart City Research Lab at the University of Bamberg, Germany to develop more efficient and secure smart city environments. The University of Bamberg operates a Smart City Living Laboratory throughout the university and the city of Bamberg, Germany that is able to collect information such as noise levels, CO2 levels, and the number of people in a particular area from sensors throughout the city. Smart cities collect information and process it to help decision makers make better decisions with a fuller understanding of the complex interactions of people and services in a city. Some of the information comes from sources that are considered trustworthy such as CCTV cameras deployed by city workers; however, these trustworthy sources are often expensive to set up and maintain. Crowd sourcing the information provides a cheaper and often more efficient way of obtaining information; however, since people can lie or malicious software could lie on their behalf, this information is significantly less trustworthy.

During 2023-2025, we will recruit 3 different cohorts of 4-5 students per year for 10 weeks each to conduct research at the University of Bamberg, Germany during Summer 2023-25. Each cohort will consist of a mix of undergraduate and graduate students. We will assemble a diverse cohort of students each year that have the technical skills to contribute to the project and will further their career goals by participating.

Students who are selected will receive the following:

  • $5000 work stipend.
  • $1500 housing allowance.
  • $1100 flight allowance.
  • $800 meals and incidentals allowance.
  • $150 health insurance allowance.
  • $100 phone service allowance.

Applications are due extended to Dec 1, 2023. Results will be announced on or before Dec 5, depending on how long it takes to get recommendations. Students will have until Dec 8 to accept the offer. At this point, offers will be made to waitlisted students. Please submit the following material to lagesse@uw.edu with the Subject line [Application for International Research Experience Year 2] for the Year 2 position.

  • Resume or CV demonstrating appropriate technical preparation.
  • Unofficial Transcript.
  • Up to 2 letters of recommendation from university faculty (or contact information for faculty) that address your technical preparation and ability to act as a representative for the University in a foreign country.
  • A brief (250 word maximum) description of previous international experiences (personal or academic), if any. Students who have never been abroad are encouraged to apply, so please just state that you have never left the country if this is the case. We want a cohort that mixes both people with and without international experience
  • Essay (500 word maximum) describing your interest in this research experience and how it will contribute to career goals.
  • Essay (500 word maximum) on your contribution to the cohort in terms of both specific academic and life experiences.

People


Faculty
Brent Lagesse
Daniela Nicklas (University of Bamberg)


2023 Cohort
Sarah Asad

Christian Bergh

Anthony Bustamante Suarez
Linkedin Researchgate
My work:
One notable aspect of my work involved spearheading the development of Anomaly Detection Algorithms aimed at preempting injection attacks, including but not limited to SQL injection, Cross-Site Scripting (XSS), and command injection. In pursuit of this objective, I leveraged Machine Learning Algorithms dedicated to anomaly detection, such as the Isolation Forest, One-class Support Vector Machine (SVM), and autoencoders. Note: Word done in conjunction with Sarah Asad.

Additionally, I undertook the creation of a methodology designed to identify anomalies within streaming data. This methodology has the potential to significantly enhance our capability to detect flooding attacks, Denial of Service (DoS) incidents, and Distributed Denial of Service (DDoS) attacks, and a combination of Times Series and Non-Linear problems in general. This proposed approach advocates the utilization of regression algorithms for establishing a threshold value. This threshold is established through pattern recognition techniques, including Alignment Cost and Euclidean Distance, which facilitates the evaluation of incoming network traffic. By analyzing the corresponding Alignment Cost (AC) or Euclidean Distance (EC) and comparing it against expected spectrums, anomalies are promptly identified.

My favorite part:
I am a person who loves running and doing outdoor activities, Germany was so well organized that every week I went running to a new surrounding town in Bamberg, and every time I did so I saw so many people on bicycles doing the same... sometimes I shared some greetings and even lunch one time, that was really fulfilling.
Go to Fussen and to Neuschwanstein Castle, you will need at least 2 days to do so from Bamberg, but it is 1000% worth it... Fussen is next to Austria but if you go there you will have the chance to see the Alps, and once there the view is spectacular, don't pay for agencies that take you there, trains and busses are enough... instead save your money to get some good food, beer and a good hotel...


Christopher Long

Breanna Powell

My work:
I worked on the Ground Truth team, conducting experiments and gathering data to create a framework for future researchers in this field. We looked at serious data quality issues that can lead to overestimation or underestimation of the crowdedness of an area. We were focused on better understanding three aspects: 1) what methods to use for counting people on the street and for validating the data we receive from the sensors 2) whether or not we can use signal strength (RSSI) to understand how close people are to the sensor and perhaps detect movement 3) how to map out the sensor range to see what devices the sensors are picking up and where. To create this framework, we drafted up tables to add to the database to track our different types of experiments. We also wanted to be able to lay the foundation for building machine learning models to analyze the data, so I worked on pulling data about the surrounding places using the OpenStreetMap API that we will be able to plug into an ML model in the future. We mapped out / visualized our 3 types of experiments using the Leaflet Javascript library. The Ground Truth team also counted people on the ground and made bar graphs to check what sort of time window we should use for comparing the data from the database to the real life situation on the street.

My favorite part:
I loved being able to go to so many places. I went to Nuremberg, Fuerth, Bamberg, Forchheim, Erlangen, Rothenburg ob der Tauber (2x), Rüdesheim am Rhein, Frankfurt, Leipzig, Berlin, Pottenstein (outer toboggan area), Regensburg, Coburg, Bayreuth, Kronach, and Munich. I lived in Fuerth (Fürth) next to Nuremberg (Nürnberg) for the whole summer. It became a 2nd home to me. It was great being so close to Nuremberg, because the city hosts tons of festivals throughout the summer. I enjoyed free music on many occasions. At the Fuerth Classic Open Air, I was surprised to hear two phenomenal opera singers. For Bardentreffen, the whole city of Nuremberg immersed itself in music. They closed down their daily market in the square and set up 5 stages throughout the city. On top of the scheduled performances at each of those stages, there were dozens of street musicians. Some streets had as many as three or four musicians in one block. Klassikopenair Nuremberg had two nights of the largest sea of people (and sparklers) I've ever seen in a public park. Brückenfestival was a cool indie sort of experience under a bridge where the sound reverberated from the piers.