Professor of Data Science Konstantinos Stefanidis studies the management and analysis of vast amounts of data, commonly referred to as big data.
“My goal is to facilitate effective and equitable access to information in an era of data-driven decision-making,” Stefanidis says.
In today’s fast-paced world, the ability to understand and leverage data is indispensable. Harnessing the power of data leads not only to informed decisions but also to innovative solutions and sustainable practices that improve quality of life for individuals and communities.
Stefanidis’s Recommender Systems (RecSys) research group develops new algorithms, tools and methodologies to enhance data analysis.
“We engage in interdisciplinary collaboration, for example, to analyse customer behaviour and predict market trends. Through our research, we address pressing global issues, such as the development of healthcare,” Stefanidis says.
Socially responsible recommendations for data users
Stefanidis is particularly interested in data personalisation, fairness and efficient data retrieval. As the name suggests, the Recommender Systems research group develops recommender systems which, for example, guide users of online stores and digital media to content they are likely to find interesting.
According to Stefanidis, the main challenge with recommender systems is ensuring the quality of recommendations. Another area in AI that requires further development is ethics.
“All forms of bias should be minimised to ensure equitable user experiences.”
The RecSys group also explores scalable methods for processing big data to make sure that recommendations are both accurate and socially responsible. In addition, the group develops sequential recommendations that require an in-depth understanding of user behaviour over time, aiming to deliver more contextually relevant and timely recommendations.
“By optimising recommender systems for long-term outcomes, we can capture evolving user preferences and provide recommendations that adapt to users’ changing needs and interests.”
The Data Science Research Centre engages in broad, interdisciplinary projects
Stefanidis leads the Data Science Research Centre at Tampere University, which is dedicated to advancing data science across disciplinary boundaries. The Centre’s research spans multiple fields, including machine learning, AI, big data, computational methods and ethics.
The Data Science Research Centre conducts fundamental research and develops high-impact, real-world applications.
Stefanidis participates in extensive research collaborations through the Data Science Research Centre.
“The Centre facilitates investments in more large-scale academic and commercial projects, driving data science innovation in Finland and beyond,” he says.
Mentoring the next generation of data scientists
Konstantinos Stefanidis joined Tampere University as an Associate Professor in 2016 and has since advanced to a full professorship. He was initially drawn to data science because it addresses complex challenges with data-driven solutions.
“Data science combines computational techniques with analytical precision and has the potential to make a real impact on the world,” he says.
According to Stefanidis, his full professorship plays a significant role in advancing his research.
“Being a Full Professor, I have access to a network of collaborators, funding opportunities and institutional support, enabling me to lead impactful large-scale research projects,” he says.
Stefanidis highlights the role of professors as mentors for early-career researchers.
“My professorship allows me to mentor the next generation of data scientists and help them navigate their careers.”
Towards transparent AI?
Stefanidis is eager to continue furthering his research on fairness in AI and recommender systems, with a strong emphasis on interdisciplinary collaboration.
As there is still much to be done to foster ethical decision-making in automated systems, Stefanidis is also hoping to develop tools that enhance algorithmic transparency.
“Explainable algorithms that address the black box problem of AI would boost user trust.”
He is also looking to expand his research group’s international collaborations.
“We are committed to tackling global challenges and driving innovations that benefit society,” he concludes.
Konstantinos Stefanidis
- Professor of Data Science at Tampere University. He originally joined the University as an Associate Professor in 2016.
- Graduated with a PhD in Computer Science from the University of Ioannina, Greece.
- Has acquired post-doctoral research experience all around the world, including in Hong Kong, China; Trondheim, Norway; and Heraklion, Greece.
- Enjoys travelling and exploring different cultures and cuisines. Hobbies also include cooking and experimenting with new dishes.