Data is found everywhere today in our society. This applies to everything from our smartphones and GPS navigation in cars to the sensors mounted on wind turbines. And by analyzing these huge amounts of data you can detect anomalies, which can contribute to improve our state of health and optimize companies’ production.

The concept is called anomaly detection and the 31-year-old Tung Kieu has plunged into this topic.

Can you tell us a bit about your background and how you ended up working with big data and anomaly detection?

I have a Master’s degree in computer science from Vietnam National University, and I have been in Denmark for about five years. I came to Denmark because I received a PhD scholarship in the research group Daisy – Center for Data-Intensive Systems at Aalborg University, which is led by Professor Christian S. Jensen. When I finished my PhD, I became a research assistant, and after a few months, I got a position as Assistant Professor.

Aalborg has a great reputation in computer science and engineering and Christian S. Jensen is furthermore recognized for his outstanding research in databases and data mining. In Vietnam, my supervisor was affiliated with Christian S. Jensen and, in this way, I got in touch with him and received the scholarship.

In what way is research in Denmark different to research where you come from?

– The environment in Denmark is very good and, furthermore, Aalborg is the happiest city in EU, according to a study from EU-Commission. We have a very good work-life balance, where we focus more on the efficiency than the working time. Aalborg University is a young yet very active university. The university ranks very high compared to other universities, and our lab – Center for Data-Intensive Systems (DAISY) ranks 2nd best among all research groups in Europe. It’s great to be part of that.

Can you tell us about your research?

I work with databases and data mining and, more specifically, the area called anomaly detection in data. Due to the extensive digitization of processes, new data are constantly created, and by being able to analyze and utilize data, we can optimize our everyday lives.

However, there are a number of challenges. We produce such large amounts of data all the time that very efficient algorithms are required to analyze for anomalies. In addition, data quality is a challenge because much sensor data is subject to noise and potentially contains incorrect values. This means that you have to clean data to achieve the required quality. But this is also what makes the area interesting.

What do you expect to get out of your research and how can your research make a difference for companies and communities?

It may be easier to understand if I give a few examples. Anomaly detection can be used in many different places. For example, supermarkets collect data about their customers, and we can analyze these data and get an overview of people’s shopping patterns. The supermarkets can use this to customize their purchases so that they do not end up with a lot of products that they cannot sell.

Another example are data collected from sensors installed on wind turbines. Here we can use the algorithms to detect anomalies and thus predict if components in a wind turbine are about to collapse, which is of great benefit to the wind turbine manufacturers.

Today, smartphones are very common and people use them to measure their health and how much exercise they get. We can use these data to analyze people’s health state. When smartphone users record data about their heart rate, we can actually analyze when people will potentially get a heart attack. The possibilities are endless, which makes the research area interesting.

Read more about Tung Kieu

Christian Schilling from Germany is interested in formal verification and modeling and has come to Aalborg University to be part of the DEIS group. He is also part of the DIREC project Verifiable and safe AI for Autonomous Systems and explains how research in cyber-physical systems makes a difference for companies and society.

Can you tell a bit about your background and why you ended up in Denmark as a computer scientist?

I did my PhD at a German university (Freiburg) and was a postdoc at an Austrian research institute (IST Austria). Now I am a tenure-track Assistant Professor at Aalborg University. The DEIS group at Aalborg University has an international reputation and is a great fit for my interests. It is productive to work with people who “speak my language.” At the same time I can develop my own independent research directions.

What are you researching and what do you expect to get out of your research?

Broadly speaking, I am interested in the algorithmic analysis of systems. More precisely, I work on cyber-physical systems, which are systems consisting of a mix of digital (cyber) and analog (physical) components. Nowadays these systems are everywhere, from thermostats to aircraft. I want to answer the fundamental question of safety: Can a system end up in an error? My analysis is based on mathematical models, and I also work on the construction of such models from observational data.

We look at models of systems and then we try to find behaviors of that system and it might not be what you want. Or if you don’t find any errors you can get a mathematical proof that your model is correct. Of course you could make mistakes with the wiring when you implement the models in a practical system, we cannot cover that. That’s why there are still more practical aspects of our work.

What are the scientific challenges and perspectives in your project?

One of the grand challenges is to find approaches that scale to industrial systems, which are often large and complex. In full generality this goal cannot be achieved, so researchers focus on identifying a structure in practical systems that still allows us to analyze the system. The challenge is to find that structure and develop techniques that exploit this challenge.

Another recent relevant trend is the rise of artificial intelligence and how it can be safely integrated into systems without causing problems. Think about autonomous systems like vacuum cleaners, lawn mowers, and of course self-driving cars in the near future. 

It is certainly a challenge to analyze and verify systems that involve AI, because the way AI is used these days is really more like a black box where nobody understands what happens. It is very difficult to say that a self-driving car under no circumstance will kill a person. 

To make this kind of analysis you need a model, and of course you could say that an engineer could build this model, but at a certain size it becomes too complex and very difficult to do. So you want an automatic technique to do that. 

Another challenge is to go from academic models to real world systems, because usually you do some simplifications which you have to take into consideration and solve when you implement the models. 

How can your research make a difference for companies and communities?

Engineers design and build systems. Typically, they first develop a model and analyze that model. My research directly addresses this phase and helps engineers learn about the behavior only given a model. This means that they do not need to build a prototype to understand the system. This saves cost in the design phase, as changing a model is cheap but changing a prototype is expensive. On the level of a model you can actually have mathematical correctness guarantees. This is something you cannot achieve in the real world.

The DEIS group has a lot of industry collaboration, but so far I’ve been working with academic modeling. With these verification models you can make sure that intelligent traffic systems work as they should.

The national research centre DIREC has just granted DKK 5.1 million Dkr for 12 new research projects in digital technologies, eight of which are led and run by young, promising researchers. To a great extent, young researchers can contribute with creativity and energy that provide new research perspectives, and DIREC hopes that the grants will boost these researchers to drive their research even further forward.

One of the young researchers is Sophia Yakoubov, who is assistant professor at the Department of Computer Science at Aarhus University. Her project focuses on how to use technologies such as blockchain and multi-party computation (computing) to calculate data without providing personal data.

“There are many use cases in which there is a need for being able to computation on data without compromising data privacy. This applies not least to the healthcare sector, and here technologies such as multi-party computation is an efficient tool that helps us move forward in these research areas,” says Sophia Yakoubov and points out that it is important to provide young researchers with project management.

“I believe it is important to give young researchers the opportunity to learn to lead and project manage their own projects. I myself am a relatively new professor, and this is my first major grant, so I am very much looking forward to the exciting collaboration between different partners, which is provided by this project.”

Great impact on further career
Another grant recipient is Maja Hanne Kirkeby, who is Assistant Professor in computer science at the Department of Humanities and Technology at Roskilde University. She has received funding for a project to investigate energy consumption and performance when implementing algorithms in hardware and software. It is also her first research project as a project manager, and she is also looking forward to expanding her experience in the role of a project manager. As something new, she has chosen to hire both bachelor and master students for the project.

“I believe it is important to give students the opportunity to get in touch with research projects, because usually professors and PhD students participate in research projects, and that is it. This means that we have three levels involved, and I have not seen that before – and I have been involved in a couple of EU projects.”

According to Maja Hanne Kirkeby, it is of great importance for the students’ further careers to participate in research projects, whether they choose an academic or industrial career.

Facts about the eight starter projects:

Hardware/Software Trade-off for the Reduction of Energy Consumption
The project works with the problem of chip implementation of software algorithms. Can we save power and energy by executing programs on an FPGA instead of on general-purpose computers? This project will explore classic sorting and path finding algorithms to see how much energy can be saved by implementing them directly on an FPGA.
Project manager: Assistant Professor Maja Hanne Kirkeby, Roskilde University

Ergonomic & Practical Effect Systems
The project works with effect systems, which are an extension of type systems in programming languages. Power systems can be of great value in programming languages but are too complicated and slow to use. Therefore, the project wants to optimize them.
Project manager: Associate Professor Magnus Madsen, Aarhus University

Understanding Biases and Diversity of Big Data Used for Mobility Analysis
The project will investigate biases in large data sets and try to “debias” data with statistical approaches. The project already has access to large data sets and will in collaboration with UNICEF work on issues in connection with disease detection.
Project manager: Assistant Professor Vedran Sekara, IT University of Copenhagen

Automated Verification of Sensitivity Properties for Probabilistic ProgramsThe overall objective is to explore how automated verification of sensitivity properties of probabilistic programs can support developers in increasing the trust in their software through formal assurances. The project continues to work on research in the field and will among others solve problems with previous approaches and develop a tool to support this. Sensitivity is a key property for checking whether AI solutions and security solutions work properly, which becomes important when AI solutions are implemented widely.
Project manager: Post doc Alejandro Aguirre, Aarhus University

Accountable Privacy Preserving Computation via Blockchain
The project will investigate how to combine multi party computation and blockchain to ensure correct calculations of data categorized as personal data.
Project manager: Assistant Professor Sophia Yakoubov, Aarhus University

Methodologies for scheduling and routing droplets in digital microfluidic biochips
In this project, a series of droplets are guided around a biochip using small electric charges. The droplets form a kind of minicomputer where the droplets can be routed and combined based on a program. At present, it can be complicated to program the chip as a number of factors like topological constraints, the surface on which the droplet is moving must be taken into account. The project will investigate whether it can be made easier to program the chip by making algorithms that can help.
Project manager: Assistant Professor Luca Pezzarossa, Technical University of Denmark

Certifiable Controller Synthesis for Cyber-Physical Systems
Control systems for cyber-physical systems can in some cases be autogenerated, but how do you ensure that autogenerated control systems behave correctly and how can you certify them? The project will investigate this in relation to indoor climate control, adaptive cruise control, floods and floor heating.
Project manager: Post doc. Martijn Goorden, Aalborg University

Algorithms education via animation videos
The project will produce a series of interactive educational videos that explain in new and interesting ways how complex algorithms work. With these videos, the project seeks to make it easier for the students to acquire knowledge within the difficult technical disciplines.
Project manager: Assistant Professor Radu-Cristian Curticapean, IT University of Copenhagen