Kostiantyn Kucher was born in Odessa, Ukraine. He received the Master's degree in Computer Science from Odessa National Polytechnic University in 2012. Currently Kostiantyn is a PhD student in Computer Science specializing in Information Visualization. He conducts his research under the supervision of Prof. Dr. Andreas Kerren with the ISOVIS group at the Computer Science Department of Linnaeus University (Växjö, Sweden).
Kostiantyn's research is focused at several problems that overlap the boundaries of Information Visualization field and domains relevant to the StaViCTA project:
- Text Analytics and Visualization: there is a need for novel visualization and interaction techniques that can make use of computational algorithms focusing on the textual content of various types of documents available on the Internet, as well as connections between documents, document collections, authors and readers – with regard to the scalability aspect for handling massive amounts of information available for analysis (the “Big Data” problem);
- Temporal Data Visualization: in order to analyze developments in the data over time (both from the computational and the end-user perspectives), special representations and techniques should be created and validated to be able to tackle the issues of dynamic (streaming) data, inconsistencies of data availability and granularity, and revealing the latent patterns in the data – once again, with the scalability concern in mind; and
- Visual Analytics applications: besides the basic research on visualization and interaction techniques, the multidisciplinary nature of the StaViCTA project (as well as the field in general) calls for the applied research focused at discovering the best ways to combine the state-of-the-art (as well as the standard and use-proven) methods, the real-world data and the domain specialists' expertise in order to facilitate the data analysis for end-users by providing them with powerful means to explore and manipulate the data.
From the technical aspect, the modern software technologies and tools are used for the research with the concerns for scalability, availability for the end-user and flexibility for the augmentation / extension.
Artikel i tidskrift (Refereegranskat)
- Kucher, K., Paradis, C., Kerren, A. (2018). The State of the Art in Sentiment Visualization. Computer graphics forum (Print). 37. 71-96.
- Simaki, V., Paradis, C., Skeppstedt, M., Sahlgren, M., Kucher, K., et al. (2017). Annotating speaker stance in discourse : the Brexit Blog Corpus. Corpus linguistics and linguistic theory.
- Kucher, K., Paradis, C., Sahlgren, M., Kerren, A. (2017). Active Learning and Visual Analytics for Stance Classification with ALVA. ACM Transactions on Interactive Intelligent Systems (TiiS). 7.
- Kerren, A., Kucher, K., Li, Y., Schreiber, F. (2017). BioVis Explorer : A visual guide for biological data visualization techniques. PLoS ONE. 12.
- Kucher, K., Schamp-Bjerede, T., Kerren, A., Paradis, C., Sahlgren, M. (2016). Visual Analysis of Online Social Media to Open Up the Investigation of Stance Phenomena. Information Visualization. 15. 93-116.
- Skeppstedt, M., Kucher, K., Stede, M., Kerren, A. (2018). Topics2Themes : Computer-Assisted Argument Extraction by Visual Analysis of Important Topics. .
- Kucher, K., Paradis, C., Kerren, A. (2018). DoSVis : Document Stance Visualization. Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '18). 168-175.
- Martins, R.M., Simaki, V., Kucher, K., Paradis, C., Kerren, A. (2017). StanceXplore : Visualization for the Interactive Exploration of Stance in Social Media. .
- Kerren, A., Kucher, K., Li, Y., Schreiber, F. (2017). MDS-based Visual Survey of Biological Data Visualization Techniques. EuroVis 2017 - Posters. 85-87.
- Skeppstedt, M., Kucher, K., Paradis, C., Kerren, A. (2017). Language Processing Components of the StaViCTA Project. Proceedings of the Workshop on Logic and Algorithms in Computational Linguistics 2017 (LACompLing 2017). 137-138.
- Kucher, K., Kerren, A., Paradis, C., Sahlgren, M. (2016). Methodology and Applications of Visual Stance Analysis : An Interactive Demo. International Symposium on Digital Humanities, Växjö 7-8 November 2016 : Book of Abstracts. 56-57.
- Kucher, K., Kerren, A., Paradis, C., Sahlgren, M. (2016). Visual Analysis of Text Annotations for Stance Classification with ALVA. EuroVis Posters 2016. 49-51.
- Kucher, K., Cernea, D., Kerren, A. (2016). Visualizing Excitement of Individuals and Groups. Proceedings of the ACM IUI 2016 Workshop on Emotion and Visualization (EmoVis '16). 15-22.
- Schamp-Bjerede, T., Paradis, C., Kucher, K., Kerren, A., Sahlgren, M. (2015). New Perspectives on Gathering, Vetting and Employing Big Data from Online Social Media : An Interdisciplinary Approach. Abstracts Booklet, ICAME 36 : Words, Words, Words – Corpora and Lexis. 153-155.
- Kucher, K., Kerren, A. (2015). Text Visualization Techniques : Taxonomy, Visual Survey, and Community Insights. Proceedings of the 8th IEEE Pacific Visualization Symposium (PacificVis '15),. 117-121.
- Kucher, K., Kerren, A., Paradis, C., Sahlgren, M. (2014). Visual Analysis of Stance Markers in Online Social Media. Poster Abstracts of IEEE VIS 2014.
- Kucher, K., Kerren, A. (2014). Text Visualization Browser : A Visual Survey of Text Visualization Techniques. Poster Abstracts of IEEE VIS 2014.
- Schamp-Bjerede, T., Paradis, C., Kucher, K., Kerren, A., Sahlgren, M. (2014). The Signifier, Signified and Stance : Happy/Sad Emoticons as Emotionizers. Book of Abstracts, IACS 2014. 219-219.
- Schamp-Bjerede, T., Paradis, C., Kucher, K., Kerren, A., Sahlgren, M. (2014). Turning Face : Emoticons as Reinforcers/Attenuators. .
- Schamp-Bjerede, T., Paradis, C., Kerren, A., Sahlgren, M., Kucher, K., et al. (2014). Hedges and Tweets : Certainty and Uncertainty in Epistemic Markers in Microblog Feeds. Book of abstracts : 47th Annual Meeting of the Societas Linguistica Europaea 11–14 September 2014, Adam Mickiewicz University, Poznań, Poland. 199-199.
- Kucher, K., Weyns, D. (2013). A Self-Adaptive Software System to Support Elderly Care. Proceedings of Modern Information Technology, MIT, 2013.