Alvo Aabloo is a Full Professor at the University of Tartu's Institute of Technology, where he leads the Intelligent Materials and Systems Laboratory (IMS Lab). His affiliations include a postdoctoral position at Uppsala University (1995–1996) and ongoing roles at the University of Tartu since 2005. The IMS Lab, accessible via www.ims.ut.ee , specializes in electroactive polymers, biomimetic robotics, and sustainable materials. His research integrates Advanced Materials , Nanotechnology , and Soft Robotics , with emphasis on: Biomimetic actuators (e.g., spider-leg exoskeletons, plant-inspired fluid transport) Ionic polymer-metal composites for precision manipulation Acoustic metamaterials for noise control Green sensors using bacterial cellulose and bio-derived ionic liquids Recent publications (2022–2025) reveal trends in: Robotics education tools (ROS2 web labs, 3D-printable robots) Programmable metamaterials for environmental applications Textile-based encoding and wearable compliance modulation He pioneers sustainable tech, such as all-printed micro-supercapacitors and biodegradable artificial muscles, while collaborating globally on projects spanning Italy, China, and Sweden.
Professor Marek Domański is a distinguished faculty member at Poznań University of Technology, holding the position of Professor at the Institute of Multimedia Telecommunications within the Faculty of Computing and Telecommunications. With over four decades of academic career since completing his dissertation in 1983, he has established himself as a leading researcher in video coding and processing. His research primarily focuses on advanced video coding techniques, particularly Video Coding for Machines (VCM), neural network applications in video processing, immersive video coding, and multiview video compression. Professor Domański has made significant contributions to the field through his extensive publication record and active participation in international standardization efforts, particularly at MPEG meetings. Analysis of his recent publications (2020-2025) reveals a strong emphasis on machine-oriented video coding, with numerous contributions to MPEG standardization activities. His work demonstrates a consistent evolution from traditional video coding toward specialized techniques for machine vision applications, incorporating artificial neural networks to improve coding efficiency and processing capabilities. As an academic supervisor, Professor Domański has guided 27 doctoral students to completion, with recent dissertations focusing on cutting-edge topics in video processing and coding. His research group at PUT maintains active international collaborations, particularly with South Korean institutions like ETRI, reflecting the global relevance of his work. Professor Domański's research has practical applications in virtual reality, free-viewpoint television, and machine vision systems, with numerous technical reports indicating ongoing research projects focused on improving machine vision coding techniques. His work bridges theoretical advances with practical implementations, contributing significantly to both academic knowledge and industry applications in video technology.
Juri Belikov is a Tenured Associate Professor at Tallinn University of Technology's School of Information Technologies, Department of Software Science, where he has served since 2019 (initially as an Assistant Professor until 2023). Since February 2022, he has also led the Nonlinear Control Systems group. His academic journey began with a B.Sc. in Mathematics (cum laude) from Tallinn University (2003-2006), followed by M.Sc. and Ph.D. degrees in Computer and Systems Engineering and Information and Communication Technology from Tallinn University of Technology. After completing his doctoral research on polynomial methods for nonlinear control systems in 2012, he conducted post-doctoral research at Technion—Israel Institute of Technology (2015-2017). Professor Belikov's research spans power systems, control engineering, and energy data science, with particular expertise in nonlinear control systems for energy applications. His work addresses critical challenges in renewable energy integration, energy storage management, building energy efficiency, and applications of machine learning in power systems. His approach combines theoretical control methods with practical energy system applications, resulting in publications that bridge engineering theory and real-world implementation. His recent publications demonstrate a clear trend toward integrating advanced data science techniques with traditional power system engineering, particularly in the areas of explainable AI for energy forecasting, stability analysis of renewable-rich power systems, and optimization of building energy management. This interdisciplinary approach has positioned him at the intersection of computer science, control theory, and energy systems engineering. Best paper award from International Conference on Power, Control, and Computing Technologies (2024) Tallinn University of Technology, Young scientist of the 2020 year (awarded 2021) Best-of-the-best conference paper award from IEEE PES General Meeting (2020) Estonian Cultural Foundation of the President, Young IT-scientist award (2019) As an educator, Professor Belikov teaches 'Energy Data Science' (ITS8080), equipping students with IT knowledge and skills for energy sector solutions using data science tools. His administrative roles include membership on the evaluation committee of the Estonian Research Council (ETAG) since 2023, Senior Member of IEEE since 2019, and full membership in Sigma Xi since 2024. His leadership of the Nonlinear Control Systems group demonstrates his commitment to advancing research in control methodologies for modern energy systems, particularly as they integrate increasing amounts of renewable generation and digital technologies.
Jenny Faucheu is a Full Professor at Mines Saint-Étienne specializing in Materials for Design and the Creative Industries. With 53 publications and over 35,000 reads on ResearchGate, she has established herself as a leading researcher at the intersection of materials science, tactile perception, and design. Her work bridges engineering principles with human sensory experience to inform better product design. Professor Faucheu's research focuses on understanding how humans perceive and emotionally respond to textures through touch. Her expertise spans tactile aesthetics, smart materials, polymer nanocomposites, and graphene-based materials. She investigates the relationship between physical surface properties and subjective emotional responses, particularly examining how textures elicit 'liking' versus 'beauty' judgments. Her recent work explores multimodal perception, studying how visual information influences tactile experience and how cognitive factors like working memory affect texture evaluation. Analysis of her 15 most recent publications reveals a consistent focus on the psychophysical aspects of texture perception, with increasing attention to crossmodal interactions and individual differences in sensory processing. Her research has evolved from fundamental studies of friction-induced vibrations to more complex investigations of emotional responses and cognitive mechanisms underlying tactile aesthetics. The work demonstrates strong connections between materials engineering and human-centered design principles. Professor Faucheu's approach emphasizes the importance of interdisciplinary collaboration, combining expertise from mechanical engineering, psychology, and design to create a comprehensive understanding of material perception. Her educational work focuses on developing tools to teach design students about smart materials and their dynamic behaviors, reflecting her commitment to advancing design education through materials knowledge. Her research on VO2-polymer nanocomposites for thermochromic windows represents an important application area, with studies examining both technical performance and environmental impact through life cycle assessments. This work demonstrates her ability to connect fundamental materials research with practical, sustainable applications for energy-efficient building technologies.
Janina Hesse serves as Junior Professor at the University Medical Center of Johannes Gutenberg University Mainz and leads the Computational Resilience Research group at the Leibniz Institute for Resilience Research (LIR) in Mainz. Her interdisciplinary work bridges mathematical modeling, machine learning, and clinical medicine to investigate resilience mechanisms across biological scales. Her educational background includes: PhD in Computational Neurosciences/Theoretical Biology (2011-2017), Humboldt-University Berlin Diploma in Physics & Cognitive Sciences, École Normale Supérieure Paris (2007-2011) Master of Science in Theoretical Physics of Complex Systems, University Pierre-et-Marie-Curie Paris (2007-2011) Bachelor of Science in Physics, École Normale Supérieure Paris (2007-2011) Vordiplom in Mathematics, Goethe University Frankfurt (2005-2007) Her research centers on computational resilience dynamics , employing dynamical systems theory to model phase transitions in neuronal networks, circadian impacts on cancer therapy, and stress-response mechanisms. The group develops integrative frameworks combining machine learning with experimental data to identify resilience biomarkers and optimize treatment timing, particularly in oncology and neuroscience contexts. Publication trends (2014-2023) reveal a consistent focus on nonlinear dynamics in biological systems , with increasing clinical translation. Early work established theoretical foundations in neural criticality and bifurcation theory, while recent studies apply these principles to colorectal cancer chronotherapy and temperature-dependent neural encoding, demonstrating strong interdisciplinary collaboration with medical institutions. Key recognitions include: Portuguese Pneumology Society First Prize (2020) Berlin Neuroscience Forum Poster Prize (2012) Michael-Loulakis Study Achievement Award (2007) She mentors four doctoral students and a Master's candidate in her LIR research group, with funding primarily from the Boehringer Ingelheim Foundation. Her advisory scope extends to co-initiating ScientistsForFuture (2019) and serving as neuroscience specialist for Germany's Federal Agency for Civic Education. Current collaborations span MSH Hamburg, Charité Berlin, and Oldenburg's Helmholtz Institute. The Computational Resilience Research group operates through six core projects examining neuronal phase transitions, mouse behavioral resilience, cancer gene expression dynamics, and seasonal stress variations. This work integrates theoretical modeling with experimental validation through close partnerships with clinical and basic science teams at Johannes Gutenberg University Mainz, emphasizing translational applications for stress-related disorders.
