Open lectures of Björn W. Shuller – professor of Machine Learning from Imperial College London at VGTU

October 24, 2016
Björn W. Schuller, professor of Machine Learning in the Department of Computing at Imperial College London (United Kingdom) will read the lectures and lead for discussions for students, PhD students and lecturers of Vilnius Gediminas Technical University on 24-28 October 2016.
 
Prof. Björn W. Schuller is an elected member of the IEEE Speech and Language Processing Technical Committee, Senior Member of the IEEE, and member of the ACM and ISCA. Before, he was President of the Association for the Advancement of Affective Computing (AAAC, former HUMAINE Association, registered Charity in the UK, 2013-2015), and Honorary Fellow and member of the TUM Institute for Advanced Study (IAS). He (co-)authored 5 books and more than 500 publications in peer reviewed books (>20), journals (>60), and conference proceedings in the field leading to more than 12230 citations (h-index = 53). He was co-founding member and secretary of the steering committee and guest editor, and still serves as associate editor and Editor in Chief of the IEEE Transactions on Affective Computing, associate and repeated guest editor for the Computer Speech and Language, associate editor for the IEEE Signal Processing Letters, IEEE Transactions on Cybernetics and the IEEE Transactions on Neural Networks and Learning Systems, and guest editor for the IEEE Intelligent Systems Magazine, Neural Networks, Speech Communication, Image and Vision Computing, Cognitive Computation, and the EURASIP Journal on Advances in Signal Processing.
 
Professor‘s research fields: Machine Learning, Complex Systems, Audiovisual Signal Processing, Human-Computer/Robot-Interaction, Affective Computing.
 
Schedule of the lectures
 
Schedule of the Lecture Series on Complex Intelligent Interactive & Affective Computing (CI²AC)
Date
Time
Room
Type of the Lecture
Topic
2016.10.24
16:20 – 17.55
SRK-II 602
 
Lecture
Intelligent Audio Analysis and Human-Computer/Robot-Interaction
18:10 – 21:30
Discussion
2016.10.25
16:20 – 17.55
SRL-I 427
 
Lecture
Intelligent Audio Analysis
18:10 – 19:45
Discussion
2016.10.26
16:20 – 17.55
SRL-I 509
 
Lecture
Complex Systems
18:10 – 19:45
Discussion
2016.10.27
16:20 – 17.55
SRL-I 427
 
Lecture
Affective Computing
18:10 – 19:45
Discussion
2016.10.28
8:30 – 10:05
SRL-I 509
Lecture
Machine Learning
 
 
Language of the lectures: English.

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New doctoral dissertation
New doctoral dissertation
VILNIUS TECH Library invites you to follow the published new dissertations. The dissertation „Twin transition impact assessment model for agricultural performance in the context of sustainability“ prepared at VILNIUS TECH by Kristina Šermukšnytė-Alešiūnienė. The dissertation was prepared in 2020–2026. Scientific consultant – Dr Rasa Melnikienė. The dissertation was defended at the public meeting of the Dissertation Defence Council of the Scientific Field of Economics in the Aula Doctoralis Meeting Hall of Vilnius Gediminas Technical University at 10 a. m. on 22 September 2026. The twin transition in agriculture, combining the digital and green transitions, is one of the key directions of the European Union’s strategy for developing a more sustainable, resilient, and competitive agricultural sector. This dissertation addresses the problem of how to assess the impact of digital and green transitions on agricultural performance in the context of sustainability. The dissertation aims to develop and empirically substantiate the Twin Transition Impact Assessment Model. The model integrates three assessment levels: farm-level adoption and economic performance; the bioeconomy and value-chain level; and the national and regional context level. Structural asymmetries and enabling mechanisms are included as cross-cutting components. A mixed-methods research design was applied, combining scientific literature analysis, conceptual synthesis, case-study analysis, structured survey analysis, comparative assessment, correlation and regression analysis, sensitivity analysis, semi-structured interviews, document review, and the synthesis of empirical findings. The empirical basis consists of five interconnected studies covering small-farm digitalisation, digital technology adoption in sustainable agriculture and the bioeconomy, the twin transition in Lithuania and Romania, food supply-chain digitalisation, and structural asymmetries in Lithuania’s bioeconomy transition. The empirical results show that the twin transition improves agricultural performance when digital and green solutions are integrated across the farm, value chain, and regional and national levels. Digitalisation supports labour efficiency, planning, customer relations, investment decisions, loss reduction, traceability, product quality, and supply-chain visibility, while also contributing to resource efficiency, renewable energy use, water-use efficiency, emissions reduction, carbon sequestration, and climate resilience. The empirical evidence indicates that the effects of digital and green transitions depend on infrastructure, investment capacity, policy support, digital skills and institutional conditions. The results also show structural differences in transition outcomes: higher investment intensity does not automatically produce stronger economic results, and digital adoption may occur separately from ecological production orientation. On this basis, the dissertation concludes that digital and green transitions can strengthen agricultural performance and sustainability when they are integrated, context-specific and supported by enabling mechanisms. The proposed model provides a scientific and practical framework for assessing agricultural performance under the twin transition across farm-level, bioeconomy and agri-food value-chain, regional-national and structural dimensions. Doctoral dissertation readers can search via VILNIUS TECH Virtual Library.
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