Find Academic Supervisors

Marina WaldenView profile

Senior Lecturer

• Formal Methods
• Software Engineering
• Autonomous Systems
• +7 more

Marina Walden serves as a Senior Lecturer in Information Technology at Åbo Akademi University's Faculty of Natural Sciences and Engineering. Her research focuses on formal methods and software engineering with applications to autonomous systems, particularly in maritime contexts. She leads significant research projects including the Cyber security training cooperation network (2023-2025) and previously served as Principal Investigator for ADVICeS and Digihybrid projects. Dr. Walden's research interests span formal verification, Event-B modeling, autonomous ship navigation, distributed systems, and safety-critical systems development. Her work demonstrates a strong focus on applying rigorous formal methods to real-world engineering challenges, particularly in maritime autonomous systems where safety and compliance with international regulations (COLREGs) are critical. She has developed expertise in multi-vessel navigation systems, role-based workflow modeling, and agile development methods. Her publication record shows consistent output with 125 publications spanning from 1988 to 2024, with recent work focusing on autonomous vessels, formal verification techniques, and workflow modeling. The research trends indicate a progression from foundational work in formal methods toward increasingly complex applications in maritime autonomous systems and cyber security. Dr. Walden actively supervises research, with Yevheniia Yehorova being one documented advisee with whom she co-authored a 2024 publication. She has secured significant research funding through Academy of Finland projects and collaborations with government agencies. Her current research activities include hosting academic visitors and leading the active Cyber security training cooperation network project that runs through 2025, demonstrating continued engagement in both research and academic community building.

Ella PeltonenView profile

Assistant Professor

• Ubiquitous Computing
• Large-scale Data Analysis
• Applied Machine Learning
• +5 more

Ella Peltonen is an Assistant Professor at the M3S research unit, University of Oulu, Finland. She joined the Ubicomp Oulu research centre and 6Genesis research programme in November 2018. Prior to this position, she was a postdoctoral researcher at the Insight Centre for Data Analytics in Cork, Ireland. She completed her PhD in the Nodes group at the University of Helsinki, Finland, working on the Carat project of collaborative energy diagnostics for mobile devices. Her educational background includes: PhD in Computer Science, University of Helsinki, Finland (Carat project on collaborative energy diagnostics) Ella Peltonen's research focuses on ubiquitous computing, large-scale data analysis, and applied machine learning. Her work particularly emphasizes everyday sensing and mobile and wearable devices. She aims to apply machine learning algorithms to large, complex data in real-time systems, with a focus on distributed machine learning and data analysis of smart devices. Her research spans various applications including energy consumption monitoring of mobile devices, wearable technology for measuring physiological signals, and exploring future sensing technologies. Peltonen has expressed interest in how future devices might sense human states, become smarter, and provide greater benefits, potentially through innovations like augmented reality glasses or subcutaneous chips. Analysis of her recent publications shows a strong focus on edge computing, vehicular networks, and sustainable computing systems. Her work bridges the gap between theoretical machine learning approaches and practical applications in transportation, healthcare, and environmental monitoring. Many of her papers address challenges in distributed systems, real-time data processing, and privacy-preserving techniques for edge intelligence. Her notable scientific awards include: Nominated to the list of 10 Rising Stars in Networking and Communications by N2 Women 2017 Selected as one of 50 Finnish Researchers by the Finnish Union of University Researchers and Teachers Nokia Scholarship 2015 and 2016 Jorma Ollila Grant 2018 Young Teacher of the Year 2012 Young Researcher of the Year 2015 Peltonen is actively involved in teaching and mentoring, with a teaching philosophy focused on supporting students' independent learning rather than lecturing from above. She enjoys guiding small groups where she can discuss topics together with students and get to know them personally. As a researcher, she describes herself as precise, detail-oriented, and committed to verifying the correctness of her work carefully. She values the combination of mathematical work with experimental work and creativity in technology, noting that research tasks are diverse and can apply different types of methodology. She is part of international research collaborations with several major universities worldwide, as required by Finnish Academy funding. Peltonen is also an advocate for diversity in technology fields, noting that technology is used by all kinds of people from various backgrounds, yet the producers of technology lack diversity. She has highlighted the importance of encouraging more women to pursue technology careers from an early age.

Jukka SuomelaView profile

Associate Professor

• Computer Science
• Algorithms
• Computational Complexity
• +3 more

Jukka Suomela is an Associate Professor in the Department of Computer Science at Aalto University's School of Science. Specializes in theoretical computer science and algorithm design Research focuses on fundamental limitations of computation His work examines both hardware constraints governed by physical laws and software constraints governed by mathematical principles, exploring the boundary between computationally easy and hard problems. Professor Suomela presented his research in Aalto University's Tenured Professors' Installation Talks series in January 2021 with a presentation discussing the capabilities and limitations of computational systems.

Emy GuilbaultView profile

Researcher

• Statistics and probability
• Ecology, evolutionary biology

Dr. Emy Guilbault is a current Postdoctoral Researcher at the University of Helsinki , affiliated with the Faculty of Biological and Environmental Sciences and the Organismal and Evolutionary Biology Research Programme . Her research bridges statistical methodology with ecological applications, focusing on species distribution modeling, biodiversity analysis, and ecological data uncertainty. Education: Ph.D. in Statistical Ecology (2021, University of Newcastle); M.Sc. in Advanced Agricultural Science (SupAgro/University of Montpellier); B.Sc. in Agronomy Her research interests center on statistical ecology , particularly in developing methodologies for species distribution modeling and addressing data uncertainty in ecological datasets. Recent work examines macro-moth community responses to climate and habitat changes, with applications in biodiversity conservation. Key publication trends show specialization in ecological informatics , statistical modeling , and conservation biology . She contributes to software development for species distribution modeling while addressing observer bias in ecological datasets. Scientific Awards: JB Douglas Award 2nd place (2019) Guilbault actively participates in academic activities including conference organization (e.g., HMSC Starting guidelines 2025, Viikki Postdoctoral Association 2023) and scientific seminars. Her collaborative network spans Finland, Australia, France, and international institutions.

• Computer and information sciences

Altti Ilari Maarala is a Researcher specializing in computational genomics and bioinformatics, with active participation in multiple Academy of Finland-funded cancer research initiatives. His work bridges computer science and genomics through scalable computational methods. Research Focus Altti develops distributed computing solutions for genomic data challenges, including: Pan-genome indexing and compressed data structures for sequence alignment Spark-based frameworks for genome assembly and genotype imputation High-throughput sequencing analytics in population genomics Visualization tools for tumor evolution dynamics Active Projects Key collaborative efforts: DYNAMITE (2025-2028): Targeting transcription factor dynamics in ovarian cancer therapy MULTISTANC (2025-2027): Multi-modal data integration to overcome chemotherapy resistance iCAN Digital Precision Cancer Medicine (2022-2026): Flagship program for data-driven oncology Publication Trends Altti's recent work emphasizes scalable cloud-based genomics, with publications focusing on distributed algorithms for genome assembly (Spark), compressed pan-genome indexing, population-scale data analytics, and cancer evolution visualization. His research consistently integrates high-performance computing with biological data challenges.

Aurélie DavrancheView profile

Associate Professor

• Wetland ecology
• Biodiversity
• Remote sensing
• +5 more

Aurélie Davranche serves as an Associate Professor in Ecology and Spatial Analysis at the University of Angers' Department of Biology since 2011, while maintaining an active international research presence as a Visiting Researcher at the Lammi Biological Station, University of Helsinki since September 2024. Her academic career spans multiple European institutions with significant research contributions in wetland ecology and remote sensing applications. Dr. Davranche's research focuses on nature-society interactions with particular emphasis on developing remote sensing tools for wetland monitoring across diverse landscapes from boreal forests to semi-arid zones. Her multidisciplinary approach combines ecology, geosciences, biogeography and mathematics to create robust models for automated mapping of biophysical parameters and species requirements. She specializes in extracting multispectral and multitemporal data from high-resolution satellite and drone images combined with field surveys to monitor landscapes while minimizing sampling costs. Recent work has expanded into social ecology with art-based protocols to understand social cognition of wetlands and developing pedagogical kits that integrate game, art and science to improve public perception of wetland biodiversity. Analysis of her publication record reveals a consistent trajectory in wetland monitoring using remote sensing technologies, with increasing interdisciplinary integration over time. Her early work focused on technical aspects of remote sensing applications for wetland mapping, while recent publications demonstrate expansion into broader ecological concerns including freshwater browning, artificial light pollution impacts, and social-ecological approaches to wetland management. The work shows strong practical application orientation with direct connections to wetland management needs. Dr. Davranche has secured substantial research funding including the Kone Foundation grant for the POOL project (2022-2025), ANR funding for WETLANDSPACE (2017), and multiple CNRS projects. She leads significant collaborative efforts across European institutions with particular strength in French-Finnish research partnerships. As a research supervisor, Davranche has directed numerous students across multiple projects including 19 Master's students, 1 PhD student, and 1 post-doc for the POOL project alone. Her research group structure typically involves interdisciplinary teams of students, technicians, and researchers from multiple institutions working on complex wetland management questions. Current research activities center around the Lammi Biological Station at the University of Helsinki and the Department of Biology at the University of Angers, with extensive international collaboration networks spanning Europe, Africa, and beyond.

• Energy Systems
• Smart Grids
• Demand Response
• +3 more

Mikko Nykyri is a Post-doctoral researcher at the Department of Electrical Engineering within the School of Energy Systems at Lappeenranta-Lahti University of Technology (LUT). His research focuses on energy systems with particular emphasis on smart grids, energy communities, and demand response mechanisms. His work bridges theoretical approaches with practical applications in the energy sector, aligning with LUT's strategic focus on energy transition. Dr. Nykyri's research interests center around the intersection of energy systems and digital technologies. He investigates how machine learning, particularly deep reinforcement learning, can optimize energy efficiency in buildings and industrial processes. His work on energy communities explores economic models, blockchain-based settlement systems, and educational approaches through serious games. He also maintains research interests in power electronics, including inverter technologies and semiconductor device characterization. His research demonstrates strong interdisciplinary connections across electrical engineering, computer science, and energy economics. An analysis of Dr. Nykyri's publication history reveals a clear evolution from fundamental power electronics research toward increasingly complex system-level applications. His early work focused on specific technical challenges in power conversion and semiconductor devices, while his recent publications address holistic energy system challenges including community-scale economics, AI-driven optimization, and educational frameworks. This progression reflects the broader energy transition from component-level improvements to integrated system solutions, with particular emphasis on the role of digitalization in enabling new energy paradigms. Dr. Nykyri has established a robust collaborative network, frequently working with researchers including Tommi J. Kärkkäinen, Saku Levikari, and Pertti Silventoinen. His peer review activity for Applied Energy demonstrates his recognized expertise in the field. His research maintains a practical orientation, with multiple studies involving real-world industrial applications and educational demonstrators that bridge academic research with industry implementation needs.