Niklas Lavesson

Professor Computer Science
Department of Computing , School of Engineering

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E2424a
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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

García-Martín, E., Bifet, A., Lavesson, N. (2021). Energy modeling of Hoeffding tree ensembles Intelligent Data Analysis, 25(1), 81-104.
García-Martín, E., Lavesson, N., Grahn, H., Casalicchio, E., Boeva, V. (2021). Energy-aware very fast decision tree International Journal of Data Science and Analytics, 11(2), 105-126.
Kusetogullari, H., Yavariabdi, A., Hall, J., Lavesson, N. (2021). DIGITNET: A Deep Handwritten Digit Detection and Recognition Method Using a New Historical Handwritten Digit Dataset Big Data Research, 23.
Green, D., Lavesson, N. (2019). Chaos theory and artificial intelligence may provide insights on disability outcomes Developmental Medicine & Child Neurology, 61(10), 1120.
Westphal, F., Grahn, H., Lavesson, N. (2018). Efficient document image binarization using heterogeneous computing and parameter tuning International Journal on Document Analysis and Recognition, 21(1-2), 41-58.
Shao, B., Lavesson, N., Boeva, V., Shahzad, R. (2016). A mixture-of-experts approach for gene regulatory network inference International Journal of Data Mining and Bioinformatics, 14(3), 258-275.
Martin, E., Lavesson, N., Doroud, M. (2016). Hashtags and followers An experimental study of the online social network Twitter Social Network Analysis and Mining, 6(1).
Unterkalmsteiner, M., Feldt, R., Gorschek, T., Lavesson, N. (2016). Large-scale Information Retrieval in Software Engineering - An Experience Report from Industrial Application Journal of Empirical Software Engineering, 21(6), 2324-2365.
Beyene, A., Welemariam, T., Persson, M., Lavesson, N. (2015). Improved concept drift handling in surgery prediction and other applications Knowledge and Information Systems, 44(1), 177-196.
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.
Lavesson, N., Boeva, V., Elena, T., Davidsson, P. (2014). A method for evaluation of learning components Automated Software Engineering: An International Journal, 21(1), 41-63.
Shahzad, R., Lavesson, N. (2013). Comparative Analysis of Voting Schemes for Ensemble-based Malware Detection Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 4(1), 98-117.
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.
Rezaee, S., Lavesson, N., Johnson, H. (2012). E-Mail Prioritization using Online Social Network Profile Distance International Journal of Computer Science and Applications, 9(1), 70-87.
Lavesson, N., Axelsson, S. (2012). Similarity assessment for removal of noisy end user license agreements Knowledge and Information Systems, 32(1), 167-189.
Lavesson, N., Boldt, M., Davidsson, P., Jacobsson, A. (2011). Learning to detect spyware using end user license agreements Knowledge and Information Systems, 26(2), 285-307.
Lavesson, N. (2010). Learning Machine Learning: A Case Study IEEE Transactions on Education, 53(4), 672-676.
Lavesson, N., Davidsson, P. (2007). Evaluating learning algorithms and classifiers International Journal of Intelligent Information and Database Systems, 1(1), 37-52.

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).
Kostadinova, E., Boeva, V., Lavesson, N. (2011). Clustering of Multiple Microarray Experiments Using Information Integration. In: Böhm, C. (Ed.), Information Technology in Bio- and Medical Informatics (pp. 123 -137).
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

Stenhager, E., Lavesson, N. (2021). Hit Detection in Sports Pistol Shooting. 33rd Annual Workshop of the Swedish Artificial Intelligence Society, SAIS 2021, 14 June 2021 through 15 June 2021.
Peretz-Andersson, E., Lavesson, N., Bifet, A., Mikalef, P. (2021). AI Transformation in the Public Sector: Ongoing Research. 33rd Annual Workshop of the Swedish Artificial Intelligence Society, SAIS 2021, 14 June 2021 through 15 June 2021.
Westphal, F., Grahn, H., Lavesson, N. (2020). Representative image selection for data efficient word spotting. 14th IAPR International Workshop on Document Analysis Systems, DAS 2020; Wuhan; China; 26 July 2020 through 29 July 2020.
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.
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., 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.
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.
Westphal, F., Lavesson, N., Grahn, H. (2019). Learning character recognition with graph-based privileged information. 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019, Sydney, Australia, 20 - 25 September 2019.
Westphal, F., Grahn, H., Lavesson, N. (2018). User Feedback and Uncertainty in User Guided Binarization. 18th IEEE International Conference on Data Mining Workshops, ICDMW, Singapore; Singapore; 17 November 2018 through 20 November.
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.
Westphal, F., Lavesson, N., Grahn, H. (2018). Document Image Binarization Using Recurrent Neural Networks. 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), vienna.
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.
Kusetogullari, H., Grahn, H., Lavesson, N. (2017). Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering. 2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016, Limassol.
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.
García-Martín, E., Lavesson, N. (2017). Is it ethical to avoid error analysis?. 2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2017).
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.
Isaksson, O., Bertoni, M., Hallstedt, S., Lavesson, N. (2015). Model Based Decision Support for Value and Sustainability in Product Development. 20th International Conference on Engineering Design (ICED),Milan.
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.
García Martín, E., Lavesson, N., Grahn, H. (2015). Energy Efficiency in Data Stream Mining. Int’l Symp. on Foundations and Applications of Big Data Analytics (FAB 2015), Paris.
Borg, A., Lavesson, N., Boeva, V. (2013). Comparison of clustering approaches for gene expression data. Scandinavian Conference on Artificial Intelligence SCAI 2013.
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.
Borg, A., Lavesson, N. (2012). E-mail Classification using Social Network Information. Prague, Czech Republic: IEEE, Seventh International Conference on Availability, Reliability and Security.
Shahzad, R., Lavesson, N. (2012). Veto-based Malware Detection. Seventh International Conference on Availability, Reliability and Security.
Allahyari, H., Lavesson, N. (2011). User-oriented Assessment of Classification Model Understandability. Trondheim: IOS Press, 11th Scandinavian Conference on Artificial Intelligence.
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.
Borg, A., Boldt, M., Lavesson, N. (2011). Informed Software Installation through License Agreement Categorization. Johannesburg: IEEE Press, Information Security for South Africa.
Lavesson, N., Johnson, H. (2011). Measuring Profile Distance in Online Social Networks. Sogndal: ACM Press, International Conference on Web Intelligence, Mining and Semantics.
Shahzad, R., Lavesson, N. (2011). Extended Abstract: Detecting Scareware by Mining Variable Length Instruction Sequences. Trondheim: IOS Press, 11th Scandinavian Conference on Artificial Intelligence.
Grahn, H., Lavesson, N., Lapajne, M., Slat, D. (2011). CudaRF: A CUDA-based Implementation of Random Forests. Sharm El-Sheikh, Egypt: IEEE, 9th ACS/IEEE Int'l Conference on Computer Systems And Applications (AICCSA 2011).
Shahzad, R., Lavesson, N., Johnson, H. (2011). Accurate Adware Detection using Opcode Sequence Extraction. Vienna: IEEE Press, Sixth International Conference on Availability, Reliability and Security.
Shahzad, R., Lavesson, N. (2011). Detecting Scareware by Mining Variable Length Instruction Sequences. Johannesburg: IEEE Press, Information Security for South Africa.
Boeva, V., Ivanova, P., Lavesson, N. (2010). A Hybrid Computational Method for the Identification of Cell Cycle-regulated Genes. London: IEEE Press, IEEE Intelligent Systems.
Grahn, H., Lavesson, N., Lapajne, M., Slat, D. (2010). A CUDA Implementation of Random Forests: Early Results. Göteborg: Chalmers Institute of Technology, Third Swedish Workshop on Multi-core Computing.
Shahzad, R., Haider, S., Lavesson, N. (2010). Detection of Spyware by Mining Executable Files. Krakow: IEEE Computer Society, The Fifth International Conference on Availability, Reliability and Security (ARES 2010).
Lavesson, N., Davidsson, P. (2010). APPrOVE: Application-oriented Validation and Evaluation of Supervised Learners. London: IEEE Press, IEEE Intelligent Systems.
Lavesson, N. (2010). Predicting the Risk of Future Hospitalization. Bilbao: IEEE Press, Second International Workshop on Database Technology for Data Management in Life Sciences and Medicine.
Persson, M., Lavesson, N. (2009). Identification of Surgery Indicators by Mining Hospital Data: A Preliminary Study. Linz, Austria: IEEE Press, 20th International Workshop on Database and Expert Systems Applications.
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.
Boldt, M., Jacobsson, A., Lavesson, N., Davidsson, P. (2008). Automated Spyware Detection Using End User License Agreements. Busan, Korea: IEEE, 2nd International Conference on Information Security and Assurance.
Lavesson, N., Davidsson, P. (2008). Towards Application-specific Evaluation Metrics. Helsinki The 3rd workshop on Evaluation Methods for Machine Learning.
Lavesson, N., Davidsson, P. (2008). Generic Methods for Multi-criteria Evaluation. Atlanta, Georgia, USA: SIAM Press, SIAM International Conference on Data Mining.
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.
Lavesson, N., Davidsson, P. (2007). Analysis of Multi-Criteria Methods for Classifier and Algorithm Evaluation. 24th Annual Workshop of the Swedish Artificial Intelligence Society.
Lavesson, N., Davidsson, P. (2006). Quantifying the Impact of Learning Algorithm Parameter Tuning. Boston, USA The Twenty-First National Conference on Artificial Intelligence (AAAI-06).
Lavesson, N., Davidsson, P. (2005). Quantifying the Impact of Learning Algorithm Parameter Tuning (short version). Västerås: Mälardalen University, 3rd Joint SAIS/SLSS Workshop.
Lavesson, N., Davidsson, P. (2004). A Multi-dimensional Measure Function for Classifier Performance. Varna, Bulgaria: IEEE, 2nd IEEE International Conference on Intelligent Systems.

Doctoral thesis

Lavesson, N. (2008). On the Metric-based Approach to Supervised Concept Learning (Doctoral thesis, Ronneby: Blekinge Institute of Technology).

Licentiate thesis

Lavesson, N. (2006). Evaluation and Analysis of Supervised Learning Algorithms and Classifiers (Licentiate thesis, Karlskrona: Blekinge Institute of Technology).

Other publications

García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E., Boeva, V. . Hoeffding Trees with nmin adaptation.