Jonne Pohjankukka
Doctor of Philosophy
jjepoh@utu.fi ORCID identifier: https://orcid.org/0000-0002-5808-2577 |
Computer science, data analysis, machine learning, statistical methods
Data scientist specialized in machine learning and computer vision. My background is from research and development work via AI solutions, which I have worked on both in the public and private sectors. My main job responsibilities during the last years have revolved around analytical problem solving via machine learning and statistical methods, project management, producing intelligent cloud-based software solutions for institutions and client companies. My work also involves coordination and teaching of machine learning experts and methods, organizing seminars and public presentations.
My Ph.D. studies were focused on the development and application of machine learning techniques for open remotely sensed data sets. I have a passionate interest in all the sciences and technologies related to artificial intelligence, and I actively increase my frame reference on these subjects like the latest software, algorithm and theoretical solutions.
- Top skills -
◼️ Data analysis
◼️ Machine learning
◼️ Computer vision
◼️ Statistical modeling
◼️ Mathematics / Optimization
◼️ Software engineering
◼️ Signal / Digital image processing
◼️ Model validation
My research is focused on the development and application of machine learning techniques for open remotely sensed data sets and deep learning solutions in sensor fusion domains.
I work as an assistant lecturer on courses related to the application and validation of machine learning methods with real-world data sets. My interest areas include deep learning, Gaussian processes and Bayesian methods generally.
- Multistream Convolutional Neural Network Fusion for Pixel-wise Classification of Peatland (2023) 2023 26th International Conference on Information Fusion (FUSION) Farahnakian Fahimeh, Zelioli Luca, Pitkänen Timo, Pohjankukka Jonne, Middleton Maarit, Tuominen Sakari, Nevalainen Paavo, Heikkonen Jukka
(Refereed article in conference proceedings (A4)) - Bayesian Approach for Optimizing Forest Inventory Survey Sampling with Remote Sensing Data (2022)
- Forests
(Refereed journal article or data article (A1)) - Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology (2020)
- Forestry
(Refereed journal article or data article (A1)) - Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization (2019)
- PLoS ONE
(Refereed journal article or data article (A1)) - The spatial leave-pair-out cross-validation method for reliable AUC estimation of spatial classifiers (2019)
- Data Mining and Knowledge Discovery
(Refereed journal article or data article (A1)) - Comparison of estimators and feature selection procedures in forest inventory based on airborne laser scanning and digital aerial imagery (2018)
- Scandinavian Journal of Forest Research
(Refereed journal article or data article (A1)) - Effect of homogenised and pasteurised versus native cows' milk on gastrointestinal symptoms, intestinal pressure and postprandial lipid metabolism (2018)
- International Dairy Journal
(Refereed journal article or data article (A1)) - Machine Learning Approaches for Natural Resource Data (2018) Pohjankukka Jonne
(Doctoral dissertation (article) (G5)) - Estimating the prediction performance of spatial models via spatial k-fold cross validation (2017)
- International Journal of Geographical Information Science
(Refereed journal article or data article (A1)) - Real-Time Swimmer Tracking on Sparse Camera Array (2017) Pattern Recognition Applications and Methods: 5th International Conference, ICPRAM 2016, Rome, Italy, February 24-26, 2016, Revised Selected Papers Paavo Nevalainen, M. Hashem Haghbayan, Antti Kauhanen, Jonne Pohjankukka, Mikko-Jussi Laakso, Jukka Heikkonen
(Refereed article in conference proceedings (A4)) - Triangular Curvature Approximation of Surfaces: Filtering the Spurious Mode (2017) Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017) Paavo Nevalainen, Ivan Jambor, Jonne Pohjankukka, Jukka Heikkonen and Tapio Pahikkala
(Refereed article in conference proceedings (A4)) - Analysing and Modelling the On-chip Traffic of Parallel Applications (2016) 42th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2016 Thomas Xu, Jonne Pohjankukka, Ville Leppänen
(Refereed article in conference proceedings (A4)) - Predictability of boreal forest soil bearing capacity by machine learning (2016)
- Journal of Terramechanics
(Refereed journal article or data article (A1)) - New computational methods for efficient utilisation of public data (2015) Jari Ala-Ilomäki, Juval Cohen, Jyri Heilimo, Eija Hyvönen, Pekka Hänninen, Jaakko Ikonen, Maarit Middleton, Paavo Nevalainen, Tapio Pahikkala, Jonne Pohjankukka, Jouni Pulliainen, Henri Riihimäki, Raimo Sutinen, Sakari Tuominen, Jari Varjo
(Published development or research report or study (D4)) - Parallel Applications and On-chip Traffic Distributions: Observation, Implication and Modelling (2015) Proceedings of the 10th International Conference on Software Engineering and Applications (ICSOFT-EA) Thomas Canhao Xu, Jonne Pohjankukka, Paavo Nevalainen, Ville Leppänen, Tapio Pahikkala
(Refereed article in conference proceedings (A4)) - Arctic soil hydraulic conductivity and soil type recognition based on aerial gamma-ray spectroscopy and topographical data (2014) 22nd International conference on pattern recognition Jonne Pohjankukka, Paavo Nevalainen, Tapio Pahikkala, Pekka Hänninen, Eija Hyvönen, Raimo Sutinen, Jukka Heikkonen
(Refereed article in conference proceedings (A4)) - Predicting water permeability of the soil based on open data (2014)
- IFIP Advances in Information and Communication Technology
(Refereed article in conference proceedings (A4))