Niklas Lavesson

Professor Datateknik
Avdelningen för datateknik och informatik , Tekniska Högskolan i Jönköping AB

Forskning

Niklas Lavessons forskning är fokuserad mot praktisk tillämpning av verktyg och tekniker från maskininlärning i samarbete med partners från den offentliga och privata sektorn. Mer specifikt undersöker Lavesson reella problem i olika domäner och försöker modellera, utveckla, samt utvärdera prestandan hos, dataintensiva lösningar, baserat på teori och metoder från artificiell intelligens, informationsutvinning och maskininlärning.

Biografi

Niklas Lavesson är sedan den 1 november 2017 professor i datateknik vid Tekniska Högskolan i Jönköping AB. Lavesson erhöll teknologie magisterexamen i programvaruteknik (2003) samt teknologie doktorsexamen i datavetenskap (2008) från Blekinge Tekniska Högskola. Han har tidigare varit anställd som universitetslektor i datavetenskap (2009-2015) och blev utnämnd till oavlönad docent i datavetenskap (2011) vid samma lärosäte. Lavesson blev befordrad till professor i datavetenskap vid Blekinge Tekniska Högskola i maj 2015 och innehar fortfarande denna tjänst på deltid.

Lavesson har alltid närt ett starkt intresse för tredje uppgiften. Han har medverkat i Sveriges Television för att diskutera de potentiella samhälleliga konsekvenserna av AI och han har även medverkat vid flera tillfällen i Sveriges Radio för att diskutera olika ämnen. De senaste radiointervjuerna med P4 Blekinge behandlar exempelvis: maskininlärning för att minska spridningen av så kallad hämndporr i sociala medier (9 nov 2017), digitalisering och smarta hem (12 okt 2017), artificiell intelligens (16 maj 2017) samt digitalisering och AI-trender (5 jan 2017). Lavesson är flitigt anlitad i paneler samt som talare vid olika arrangemang som rör digitalisering, artificiell intelligens och maskininlärning för organisationer, stiftelser, företag, skolor och myndigheter.

Lavesson påbörjade sin karriär inom undervisning som amanuens (2003) vid Blekinge Tekniska Högskola och har sedan dess innehaft olika undervisning och ledningsroller inom ramen för över 20 kurser på högskolenivå. Parallellt med arbetet som lärare, examinator, kursansvarig och programansvarig för olika kurser och program inom datavetenskap och programvaruteknik så har Lavesson innehaft flertalet förtroendeuppdrag relaterade till akademiska rättigheter, administration och utveckling. Han var ordförande för doktorandsektionen vid Blekinge Tekniska Högskola (2007-2008) och koordinator för forskarutbildningen vid samma lärosäte, utsedd av fakultetsnämnden (2009-2013).

Lavesson underhåller ett brett nätverk inom akademin så väl som i den offentliga och privata sektorn. Han är en flitigt anlitad granskare av artiklar inskickade till välrenommerade tidskrifter, exempelvis: Data & Knowledge Engineering, Empirical Software Engineering, Information Sciences, Information & Software Technology, Knowledge & Information Systems, Machine Learning, Machine Learning Research, Neurocomputing, och Transactions on Internet Technology.

Lavesson är återkommande granskare eller medlem i programkommittén till ett större antal topprankade vetenskapliga konferenser. De senaste exemplen är: AISTATS 2018, ICLR 2018, AAAI 2017, ECML/PKDD 2017, IJCAI 2017, och NIPS 2017.

Niklas Lavesson handleder för närvarande sex doktorander, fyra som huvudhandledare och två som bihandledare (samtliga inskrivna vid Blekinge Tekniska Högskola). Han är dessutom medlem eller extern granskare i tiotalet stöd- eller utvecklingskommittéer för andra doktorander. Han har tidigare agerat som handledare för två doktorsavhandlingar och mer än 25 master-, magister- eller kandidatuppsatser. Lavesson har agerat opponent eller medverkat i betygsnämnden för 12 doktorander.

Lavesson har publicerat mer än 60 fackgranskade tidskriftsartiklar, konferensartiklar, monografier och bokkapitel. För en komplett förteckning, besök hans Google Scholar-profil.

Antologibidrag

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

Artikel

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.

Doktorsavhandling

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

Konferensbidrag

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.
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.
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.
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.

Licentiatavhandling

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

Samlingsverk

Övrigt

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