The two DTU Compute Assistant Professors are among the 41 talented research leaders who receive money for their ground-breaking research projects.
Photo credit Claus Lillevang for Independent Research Fund Denmark.
The Sapere Aude research grant is given to young researchers who have performed top-class research. With the grant, the research talents get the opportunity to develop and strengthen both their research ideas and their research management. Of the 41 grant recipients, nine are from DTU, and the two from DTU Compute.
Christian Majenz and Dimitrios Papadopoulos will use the money to research cryptographic algorithms (5.6 Mio. DKK), and to establish deeper human-machine collaboration for computer vision (6.2 Mio. DKK), respectively. The Compute researchers are very grateful for the grant.
"I have started as an Assistant Professor at DTU less than two years ago. Therefore I am in the process of starting a research group for the first time. Many researchers have more good ideas than they can follow up on just by themselves, and I am one of them. The Sapere Aude grant will be a unique opportunity to get some extra helping hands (or, rather, brains) to work on these ideas," says Christian Majenz.
Dimitrios Papadopoulos adds:
"It is a great honor to receive the Sapere Aude grant. The grant will give me a unique opportunity to establish my research group in Denmark and conduct research at the highest level in computer vision. I am looking forward to continuing growing as a researcher and research leader and strengthening my international network. Sapere Aude will enable me to continue my research directions and apply for other Danish and European funding schemes in the future."
Learn more about their projects below.
The text is published in agreement with Independent Research Fund Denmark.
What is your project about?
In this project I will, together with two PhD Students, develop mathematical methods for analyzing cryptographic algorithms in a quantum computing setting. We will then use these methods for writing mathematical proofs for the fact that certain important cryptographic algorithms, like, for example, digital signatures and so-called hash functions, are secure despite being under quantum computing attack. In this way, we contribute to prepare IT security for the quantum computing age.
How did you become interested in your particular field of research?
I have always been interested in mathematics and natural sciences. I was already fascinated by mathematical methods and proof while I was still in high school. While my professional focus was on physics and quantum theory during my undergraduate studies and most of my PhD at Copenhagen University, I developed a side interest in cryptography and IT Security. Since then, I have combined my interests by focussing my research on quantum-secure cryptography.
What are the scientific challenges and perspectives in your project?
In this project, we want to use mathematics that has been known for a long time, but that has not been used for the analysis of quantum-secure cryptography. It is part of what defines research as such that, once we move into new terrain like this, we don't know exactly if, and how, we will reach our goal. I might even happen that we need to develop new results in the mathematical theory we would like to use.
What is your estimate of the impact, which your project may have to society in the long term?
In modern society, we rely on digital tools and services a lot. In many cases, these digital helpers have become essential to an extent where society cannot function anymore without them. Examples include, e.g., card payments and the Danish MitID system. All these services are secured using cryptography. It is therefore essential that secure cryptographic algorithms remain available once the effort of building quantum computers succeeds. We, the cryptographic research community, therefore works hard to develop and analyze quantum-secure algorithms, and this project contributes to that effort.
The text is published in agreement with Independent Research Fund Denmark.
What is your project about?
The incredible rise in computer vision and in artificial intelligence over the past decade has been propelled by the use of deep learning models and the creation of datasets with multi-million annotated images. The achilles' heel of state-of-the-art models is the need for huge volumes of data manually and passively annotated by humans to provide labels for them. This procedure is one of the most important steps in a machine learning pipeline and is tedious, expensive and susceptible to noise. The goal of the ACHiLLES project is to establish a deeper human-machine collaboration for all stages of the learning pipeline: first, humans will assist machines by providing labels iteratively, and second, the machines will assist humans while they annotate images to actively train humans and to prevent human labeling errors.
How did you become interested in your particular field of research?
I grew up loving math and anything related to numbers since I was a young child. My mother as a mathematician probably played a role in that. I also discovered the art of photography very early in my life, which is my father’s profession, and since then it has become one of my main hobbies. Therefore, it was inevitable that I would fall in love with computer vision which is the field that basically combines images and mathematics. I was very fortunate to discover computer vision in the middle of my undergraduate studies when I worked on my very first vision project. I immediately found it very intriguing and interesting because of the amazing applications and their impact in society. This is the reason I decided to pursue a PhD in this field and continue to work in the same research direction until now.
What are the scientific challenges and perspectives in your project?
The main goal of the ACHiLLES project is to establish a deeper human-machine collaboration for computer vision pipelines where humans and machines will collaborate at all steps of the learning pipeline. The main challenge of this project is that besides the methodological approaches that we will propose, we aim to validate them in practice and show their applicability in real world scenarios. To do this, we plan to go beyond the standard evaluation schemes on simulated experiments and conduct crowdsourcing experiments with real human annotators.
What is your estimate of the impact, which your project may have to society in the long term?
We live in an exciting era for computer vision and artificial intelligence in general with great applications and amazing breakthroughs. Nowadays, we build supervised deep learning models that we apply to solve several problems in our society ranging from understanding scenes to building cars that can drive autonomously to having applications in remote sensing that can help us deal with several environmental issues like climate change or recycling. I expect that the ACHiLLES project will go beyond computer vision in the long term and will help solve a major issue of the state-of-the-art models which is the expensive and error-prone labeling procedure.