Research
Niklas Lavesson’s research is focused towards the practical application of machine learning tools and techniques in collaboration with partners from the private and public sector. In particular, Lavesson explores real-world problems in various domains and tries to model, prototype, and evaluate the performance of data-intensive solutions, based on theory and methods from artificial intelligence, data mining & knowledge discovery, and machine learning.
Biography
Niklas Lavesson is Professor of Computer Science at the School of Engineering, Jönköping University since November 1, 2017. Lavesson received his MSc in software engineering and PhD in computer science from Blekinge Institute of Technology in 2003 and 2008, respectively. He has previously held appointments as Assistant Professor of Computer Science (2009-2011) and Associate Professor of Computer Science (2011-2015) at Blekinge Institute of Technology. Lavesson was appointed Professor of Computer Science at his alma mater in May 2015 and still maintains this position part-time.
Lavesson has always nurtured a strong interest in public outreach. He has appeared on Swedish television to discuss the potential consequences of widespread AI adoption in society, and discussed a variety of topics concerning AI and machine learning on Swedish radio. The most recent radio interviews, all at P4 Blekinge, concerned machine learning to handle revenge porn in social media (November 9, 2017), digitalization and smart homes (October 12, 2017), artificial intelligence (May 16, 2017), and digitalization and current AI trends (Jan 5, 2017). Lavesson is a frequent panelist and speaker at various events concerning computer science, digitalization, artificial intelligence, and machine learning at organizations, schools, and in the private and public sector.
Lavesson started his teaching career as a teaching assistant (2003) during his MSc studies at Blekinge Institute of Technology, and has since held various teaching and managing roles in more than twenty courses. In parallel to working as a lecturer, examiner, as well as course and program responsible for a variety of courses and programs in computer science and software engineering, he has held various commissions of trust related to academic rights and higher education administration and development. He was chairman of the Doctoral Student Council in 2007-2008 and Coordinator of Doctoral Education at Blekinge Institute of Technology, appointed by the Faculty board, 2009-2013.
Lavesson maintains a broad network in the scientific community as well as in the private and public sector. He is a frequent reviewer of several reputable scientific journals, including: Data & Knowledge Engineering, Empirical Software Engineering, Information Sciences, Information & Software Technology, Knowledge & Information Systems, Machine Learning, Machine Learning Research, Neurocomputing, and Transactions on Internet Technology.
Lavesson is a frequent reviewer or member of the program committee for top ranked conferences. Recent assignments include: AISTATS 2018, ICLR 2018, AAAI 2017, ECML/PKDD 2017, IJCAI 2017, and NIPS 2017.
Niklas Lavesson currently supervises six PhD students, four as main supervisor and two as co-supervisor (all enrolled at Blekinge Institute of Technology). He is also a member of more than ten external PhD student support or evaluation committees. Previously he has supervised two completed PhD theses and more than 25 completed MSc and BSc theses. Lavesson has been opponent, or served in the grading committee, of 12 PhD students.
Lavesson has published over 60 peer-reviewed journal articles, conference papers, monographs, and book chapters. For a complete list, visit his Google Scholar profile.
Article
Borg, A., Boldt, M., Lavesson, N., Melander, U., Boeva, V.
(2014).
Detecting serial residential burglaries using clustering Expert systems with applications, 41(11), 5252-5266.
Kazemi, S., Abghari, S., Lavesson, N., Johnson, H., Ryman, P.
(2013).
Open Data for Anomaly Detection in Maritime Surveillance Expert systems with applications, 40(14), 5719-5729.
Book chapter
Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P., Lavesson, N.
(2019).
An Expertise Recommender System based on Data from an Institutional Repository (DiVA).
In:
Leslie Chan & Pierre Mounier
(Ed.),
Connecting the Knowledge Common from Projects to sustainable Infrastructure:
The 22nd International conference on Electronic Publishing - Revised Selected Papers
(pp. 135 -149).
García Martín, E., Lavesson, N., Grahn, H.
(2017).
Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm.
In:
Rokia Missaoui, Talel Abdessalem, Matthieu Latapy
(Ed.),
Trends in Social Network Analysis:
Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment
(pp. 229 -252).
Cham, Switzerland: Springer
Johnson, H., Lavesson, N., Zhao, H., Wu, S.
(2011).
On the Concept of Trust in Online Social Networks.
In:
Salgarelli, Luca; Bianchi, Giuseppe; Blefari-Melazzi, Nicola
(Ed.),
Trustworthy Internet
(pp. 143 -157).
Lavesson, N., Davidsson, P., Boldt, M., Jacobsson, A.
(2008).
Spyware Prevention by Classifying End User License Agreements.
In:
Nguyen, Ngoc Thanh; Katarzyniak, Radoslaw
(Ed.),
New Challenges in Applied Intelligence Technologies
(pp. 373 -382).
Berlin / Heidelberg: Springer
Collections
Conference paper
Annavarjula, V., Mbiydzenyu, G., Riveiro, M., Lavesson, N.
(2020).
Implicit user data in fashion recommendation systems.
14th International FLINS Conference (FLINS 2020), Cologne, Germany, 18–21 August 2020.
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E., Boeva, V.
(2019).
How to Measure Energy Consumption in Machine Learning Algorithms.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018; Dublin; Ireland; 10 September 2018 through 14 September 2018.
Abghari, S., Boeva, V., Brage, J., Johansson, C., Grahn, H., Lavesson, N.
(2019).
Higher order mining for monitoring district heating substations.
6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Washington, United States, 5 - 8 October, 2019.
Westphal, F., Lavesson, N., Grahn, H.
(2019).
A case for guided machine learning.
Cham:
Springer, International IFIP Cross Domain (CD) Conference for Machine Learning & Knowledge Extraction (MAKE) CD-MAKE 2019, Canterbury, UK, August 26–29, 2019.
Abghari, S., Boeva, V., Lavesson, N., Grahn, H., Ickin, S., Gustafsson, J.
(2018).
A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences.
IEEE International Conference on Machine Learning and Applications, ICMLA, Orlando.
Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P., Lavesson, N.
(2018).
An Expertise Recommender SystemBased on Data from an Institutional Repository (DiVA).
22nd edition of the International Conference on ELectronic PUBlishing - Connecting the Knowledge Commons: From Projects to Sustainable Infrastructure, Toronto.
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E., Boeva, V.
(2018).
Hoeffding Trees with nmin adaptation.
IEEE 5th International Conference on Data Science and Advanced Analytics, 1–4 October 2018, Turin.
Boeva, V., Angelova, M., Lavesson, N., Rosander, O., Tsiporkova, E.
(2018).
Evolutionary clustering techniques for expertise mining scenarios.
10th International Conference on Agents and Artificial Intelligence, ICAART, Funchal, Madeira.
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E., Boeva, V.
(2018).
How to Measure Energy Consumption in Machine Learning Algorithms.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Dublin.
Abghari, S., Boeva, V., Lavesson, N., Grahn, H., Gustafsson, J., Shaikh, J.
(2018).
Outlier Detection for Video Session Data Using Sequential Pattern Mining.
ACM SIGKDD Workshop On Outlier Detection De-constructed, London,.
Johansson, C., Bergkvist, M., Geysen, D., De Somer, O., Lavesson, N., Vanhoudt, D.
(2017).
Operational Demand Forecasting In District Heating Systems Using Ensembles Of Online Machine Learning Algorithms.
15th International Symposium on District Heating and Cooling (DHC), Seoul.
García Martín, E., Lavesson, N., Grahn, H.
(2017).
Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree.
Cham, Switzerland:
Springer, GPC 2017 : The 12th International Conference on Green, Pervasive and Cloud Computing, Cetara, Amalfi Coast, Italy.
Abghari, S., García Martín, E., Johansson, C., Lavesson, N., Grahn, H.
(2017).
Trend analysis to automatically identify heat program changes.
15th International Symposium on District Heating and Cooling (DHC2016), Seoul.
Dasari, S., Lavesson, N., Andersson, P., Persson, M.
(2015).
Tree-Based Response Surface Analysis.
The International Workshop on Machine learning, Optimization and big Data (MOD 2015), Taormina - Sicily, Italy.
Shahzad, R., Mehwish, F., Lavesson, N., Boldt, M.
(2015).
Consensus decision making in random forests.
International Workshop on Machine learning, Optimization and big Data, Taormina, Sicily.
Davidsson, P., Gustafsson Friberger, M., Lavesson, N., Persson, J.
(2013).
Towards a Prediction Model for People Movements in Urban Areas.
MASS2013, 1st International Workshop on Multiagent-based Societal Systems, Saint Paul, Minnesota, USA, 7th May, 2013.
Bhattacharyya, P., Rowe, J., Wu, F., Haigh, K., Lavesson, N., Johnson, H.
(2011).
Your Best might not be Good enough: Ranking in Collaborative Social Search Engines.
Orlando:
IEEE Press, Seventh International Conference on Collaborative Computing: Networking, Applications and Worksharing.
Muhammad, A., Lavesson, N., Davidsson, P., Nilsson, M.
(2009).
Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images.
Munster:
Springer, 13th International Conference on Computer Analysis of Images and Patterns Munster, GERMANY, SEP 02-04, 2009.
Lavesson, N., Halling, A., Freitag, M., Odeberg, J., Odeberg, H., Davidsson, P.
(2009).
Classifying the Severity of an Acute Coronary Syndrome by Mining Patient Data.
Linköping:
Linköping University Electronic Press, 25th Annual Workshop of the Swedish Artificial Intelligence Society.
Lavesson, N., Davidsson, P., Boldt, M., Jacobsson, A.
(2008).
Spyware Prevention by Classifying End User License Agreements.
Wroclaw, Poland:
Springer, 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems.
Doctoral thesis
Licentiate thesis
Other publications