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Giomara Lárraga MaldonadoView profile

Researcher

• Computer Science
• Interactive Multiobjective Optimization
• Evolutionary Computation
• +3 more

Giomara Lárraga Maldonado is a Postdoctoral Researcher at the Faculty of Information Technology within the University of Jyväskylä , Finland. She contributes to the Multiobjective Optimization Group and is affiliated with the Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO) thematic research area. Research Focus: Interactive Multiobjective Optimization, Evolutionary Computation, Explainable AI Key Areas: Preference integration, Decomposition-based methods, Human-Computer Interaction for decision support Her recent work explores explainability frameworks (e.g., LIME integration), phase-specific algorithm configuration, and semantic distance studies for visualization. She collaborates with researchers like Kaisa Miettinen and Giovanni Misitano. She has contributed to conferences such as GECCO, PPSN, and AAMAS, with publications emphasizing open-access availability. The R-XIMO framework (2022) highlights her work on explainable systems.

Mario Di FrancescoView profile

Professor

• Wireless Networking
• Mobile and Ubiquitous Computing
• Internet of Things
• +4 more

Mario Di Francesco is a Professor at the Department of Computer Science, Aalto University. He holds the position of Professor in Computing Systems (ComputingSystems) Research Area and leads multiple research projects in networking and IoT. Previously, he was a research associate and adjunct faculty at the University of Texas at Arlington (2012–2014), and completed his PhD in Information Engineering at the University of Pisa under Prof. Giuseppe Anastasi. He also served as a research fellow at Scuola Superiore Sant'Anna in 2009. Research Interests : His work focuses on wireless networking, mobile and ubiquitous computing, and the Internet of Things (IoT). Key areas include edge computing, fog networks, virtual/augmented reality, and AI-driven resource management. His research contributes to UN Sustainable Development Goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), and industry/infrastructure development (SDG 9). Publications : His recent work spans topics like private virtual reality conversations, cloud-based network measurement frameworks (CLAIM), microservice connectivity analysis, and edge-DNN optimization. These publications reflect a trend toward integrating AI with distributed systems for real-time, low-latency applications in IoT and edge computing. Awards : Recipient of the Google Internet of Things Technology Research Award (2016) and MobileHCI Honorable Mention (2020). Grants & Projects : CRUISE (2023–2025): Cross-system architecture for autonomous wireless networks using lifelong machine learning. MeXICO (2020–2024): Mobile cross-reality through immersive computing. ASPECT (2016–2018): Spectrum utilization for mobile video. Labs & Teams : Leads research groups focused on edge computing, IoT systems, and immersive technologies. Collaborates with international partners on projects like fog radio networks and autonomous system design.

Josias Láng-RitterView profile

Researcher

• Flood Risk Management
• Water Resources Management
• Environmental Engineering
• +4 more

Josias Láng-Ritter is a Postdoctoral Researcher at Aalto University's Department of Built Environment and Water and Environmental Engineering. He holds a PhD in Environmental Engineering and focuses on hydrometeorological extremes, water resources management, and socio-economic interactions in a changing climate. His work aligns with UN Sustainable Development Goals, particularly addressing flood risk, rural population dynamics, and sustainable food systems. Research Interests: His primary areas include flood early warning systems, global streamflow alterations, and community-centric disaster risk reduction. He explores interdisciplinary solutions for climate adaptation and resource management, emphasizing the intersection of technology and socio-economic resilience. Publications: Recent work highlights include studies on rural population underrepresentation in global datasets, flood impact forecasting systems, and sustainable food pathways. His research often integrates mixed methods and geospatial tools to enhance disaster preparedness and policy-making. Awards: He received the Allianz Climate Risk Research Award (2018) for innovative climate risk analysis. He collaborates internationally, contributing to projects in Nepal and Europe. Labs/Teams: Active in interdisciplinary teams at Aalto University, focusing on environmental engineering and disaster resilience. His work leverages open-source tools like ReAFFINE for real-time flood impact modeling.

Bhupinder SainiView profile

Researcher

• Multiobjective Optimization
• Evolutionary Algorithms
• Interactive Decision Support Systems
• +4 more

Bhupinder Saini is a Postdoctoral Researcher at the Faculty of Information Technology, University of Jyväskylä, Finland, affiliated with the Multiobjective Optimization Group and the DEMO project (Decision Analytics utilizing Causal Models and Multiobjective Optimization). His research focuses on: Interactive multiobjective optimization methods Preference incorporation in evolutionary algorithms Data visualization for high-dimensional decision spaces Framework development (DESDEO) Industrial applications in materials engineering Cost-effective optimization for computationally expensive problems His 2021-2025 publications reveal consistent innovation in interactive optimization, particularly through SCORE Band visualizations for outcome comparison and adaptive reference vector techniques. His work bridges theoretical advances with practical implementations, notably in metallurgical property optimization using the DESDEO framework. Collaborations with leading researchers like Kaisa Miettinen demonstrate integration within the global optimization community. As part of the Multiobjective Optimization Group, he contributes to advancing decision-making methodologies where conflicting objectives require sophisticated resolution strategies.

• Multiobjective Optimization
• Decision Analytics
• Causal Modeling
• +3 more

MaoMao Liang is a Doctoral Researcher at the Faculty of Information Technology , University of Jyväskylä, Finland. Their work focuses on multiobjective optimization and decision analytics, particularly in interactive evolutionary methods. Affiliation: Multiobjective Optimization Group Project: Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO) Research interests include improving decision-making in scenarios with conflicting objectives. Their 2024 publication on a modified hypervolume indicator reflects expertise in computational intelligence and preference-based optimization. Contact: maomao.m.liang@jyu.fi

Ville MustonenView profile

Professor

• Computer and information sciences
• Bioinformatics
• Ecology
• +8 more

Ville Mustonen is a Professor at the University of Helsinki , affiliated with the Faculty of Biological and Environmental Sciences and the Organismal and Evolutionary Biology Research Programme . His research bridges computational biology, genetics, and evolutionary theory with applications in cancer genomics and antimicrobial resistance. Research Interests: Computational modeling of evolutionary dynamics Resilience in biological systems Mutational signature analysis Optimal control in cellular growth Antimicrobial resistance evolution Recent Publications (2023-2025) demonstrate expertise in: Predictive evolutionary modeling Cancer genome interpretation Microbial community dynamics Formalin-induced artifact correction Time-dependent fitness landscapes Multiobjective treatment optimization He leads the AMR CoE (Antimicrobial Resistance Centre of Excellence) funded by the Academy of Finland (2024-2026) and collaborates internationally on microbial ecology and cancer evolution projects.

• Multiobjective Optimization
• Multi-criteria Decision Making
• Mathematical Modeling
• +4 more

Babooshka Shavazipour is an Academy Research Fellow at the University of Jyväskylä's Faculty of Information Technology, where she leads the Multiobjective Optimization Group. She holds a PhD from the University of Cape Town (2018) and specializes in developing computational frameworks for complex decision-making scenarios. Her research integrates: Multiobjective optimization under uncertainty Interactive decision support systems Robustness and risk analysis methodologies Machine learning applications in healthcare and materials science Scenario planning for sustainable resource management Her publications (2021-2026) demonstrate strong cross-disciplinary applications, including: Healthcare cost prediction and therapy optimization Forest landscape planning under uncertainty Metallurgical property optimization Evolutionary algorithm enhancements These works consistently feature interactive frameworks that bridge theoretical optimization with practical decision-making needs. She leads several key initiatives: HYDRA project on anti-fragile decision-making Collaboration with Skogforsk on forestry decision support University's DEMO profiling area in decision analytics Her Multiobjective Optimization Group develops methodologies to resolve conflicting objectives in real-world applications, emphasizing stakeholder-centered computational approaches.

Johanna SilvennoinenView profile

Senior Lecturer

• Human-Computer Interaction
• Cognitive Science
• Affective Computing
• +4 more

Johanna Silvennoinen serves as Senior Lecturer at the Faculty of Information Technology, University of Jyväskylä, within the Department of Educational Technology and Cognitive Science. Her academic work bridges cognitive science and human-technology interaction, with active supervision of master's and PhD students. Her research centers on cognitive and affective processes in human-computer interaction, specifically investigating how cognitive information processing fluency, perceived visual usability, and aesthetic appraisal contribute to experience design predictability. Additional focus areas include multiobjective decision-making dynamics and the psychology of aesthetics in technological contexts, with recent expansion into place-belongingness in urban environments. Analysis of her 2024-2025 publications reveals strong interdisciplinary convergence between human-computer interaction, cognitive science, and operations research. Key emerging themes include place-belongingness applications in smart cities (6 publications), human-AI co-creation methodologies, and color perception studies. Her work predominantly employs experimental and systematic review approaches across art, transportation safety, and citizen science domains. As a research supervisor, Silvennoinen mentors graduate students while contributing to the DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization) profiling area at University of Jyväskylä. Her grant-related activities focus on multiobjective optimization frameworks and decision analytics. She actively participates in the Multiobjective Optimization Group, which develops methodologies for complex decision scenarios, and the Cognitive Science research group where she explores neural and cognitive foundations of human-technology interaction.