Jara Uitto is an Assistant Professor in the Department of Computer Science. His research focuses on Massively Parallel Computation, Distributed Computing, and Sublinear Computing, with a particular emphasis on graph algorithms and distributed systems. Research Interests: Massively Parallel Computation (MPC) Distributed Algorithms and Symmetry Breaking Graph Theory and Edge Processing Algorithm Design for Sparse Graphs Approximation and Optimization in Streaming Models Key Project: Massively Parallel Algorithms for Large-Scale Graph Problems (2020-2024) , where he led research on optimizing algorithms for distributed and parallel computing environments. Publications highlight contributions to symmetry breaking, parallel coloring, and approximation algorithms in dynamic streams. His work bridges theoretical foundations with practical distributed computing challenges.
Petteri Kaski is an Associate Professor at the Department of Computer Science, School of Science, Aalto University , and a member of the Helsinki Institute for Information Technology (HIIT) . His research focuses on theoretical computer science, particularly in algorithm design, exact and parameterized algorithms, algebraic algorithms, and combinatorics. Doctoral Degree in Engineering and Technology, Helsinki University of Technology (2005) Licentiate Degree in Engineering and Technology, Helsinki University of Technology (2002) Master's Degree in Engineering and Technology, Helsinki University of Technology (2001) His recent work explores tensor scaling, Johnson-Lindenstrauss transforms, Hamiltonian cycles, and computational complexity, with contributions to polynomial-time algorithms, finite field computations, and combinatorial optimization. Notable awards include the Best Paper Award at ICALP 2017 , an ERC Starting Grant (2014) , and the Kirkman Medal (2007) . He has served on scientific committees for conferences like STACS 2025 and ICALP 2024 , and collaborated with institutions such as the IT University of Copenhagen and Universität Regensburg . Key Research Areas : Theoretical Computer Science, Algorithm Design, Exact Algorithms, Algebraic Computation, Graph Theory, Combinatorics
Pauli Miettinen is a Professor of Data Science at the University of Eastern Finland, affiliated with the School of Computing within the Faculty of Science, Forestry and Technology. His research focuses on data science methodologies, including matrix and tensor decompositions, redescription mining, and social network analysis. Key applications span ecological niche modeling, health data analysis, and parliamentary candidate opinion analysis. He leads the Algorithmic Data Analysis research group and contributed to the Neuro-Innovation project (2021–2026). Recent work includes advancements in differentially private redescription mining and hyperbolic community graph generation. His publications emphasize efficient algorithms for data mining tasks like biclustering and non-negative matrix factorization. Selected achievements include developing the HyGen graph generator and pioneering techniques for interpretable data representation. His research bridges theoretical method development with practical applications in diverse domains.
Antoine Doucet is Docent (equivalent to Associate Professor) in the Department of Computer Science at the University of Helsinki and a researcher at the Helsinki Institute for Information Technology. His research record spans from 2005 to 2022, evidencing sustained scholarly activity in computational linguistics and digital humanities. Research Interests: Doucet's work sits at the intersection of computer science and the humanities. He investigates computational approaches to language , including multi-document summarization, word-association networks, and language-independent methods. A second strand focuses on digital humanities applications , especially large-scale analysis of historical newspapers and creation of open datasets for diachronic linguistics. More recently, he has explored computational humour generation and the semantic mechanisms behind lexical replacement jokes. Across more than 35 refereed publications (journal articles, conference papers, book chapters, and one doctoral thesis), Doucet demonstrates a clear trend toward interdisciplinary collaboration , combining NLP techniques with historical, literary, and library-science perspectives. Press & Dissemination: Invited talk on Sequential pattern mining for robust event detection , covered by media on 4 Oct 2018. Collaboration & Networks: Recent external collaborations span multiple countries, reflecting his active participation in European research consortia around language resources and digital infrastructures.
Pasi Hyytiäinen is a Postdoc researcher and Grant-funded researcher at the Faculty of Theology , focusing on textual criticism and digital humanities. His work integrates computational methods with biblical scholarship, particularly analyzing New Testament manuscripts. Current affiliation: Faculty of Theology Active projects: EXPRECCE, The Spread of Early Christianity in Cultural Evolutionary Perspective Research interests include: Textual Criticism: quantifying relationships between manuscripts Network Analysis: mapping co-occurrence of theological concepts Cultural Evolution: studying adaptive changes in early Christian texts Software Development: creating tools like Relate for textual scholarship Article trends highlight interdisciplinary approaches combining theology, linguistics, and computational modeling. His work emphasizes quantitative methods, manuscript classification, and semantic drift in biblical texts. Key projects involve cultural evolutionary perspectives on early Christianity, analyzing martyr narratives, and outgroup hostility. He has presented extensively on textual drift and software tools at international conferences. Collaborations include partnerships with researchers like Nikki, Luomanen, and Söderholm, contributing to teams exploring manuscript evolution and cultural dynamics in religious traditions.
Jukka Suomela is an Associate Professor in the Department of Computer Science at Aalto University, Finland. His research focuses on the theoretical foundations of distributed and parallel computing, with a strong emphasis on locality and algorithmic complexity. Current roles: Associate Professor, Aalto University Research group: Distributed Algorithms Key conferences: PC Chair for DISC 2019, SIROCCO 2016, Local Chair for ALGO 2018 Steering Committee: Vice Chair, DISC Research Interests include distributed algorithms, local algorithms, parallel computing, graph algorithms, and computational complexity. His work explores fundamental limits of distributed systems and the role of locality in algorithm design. Notable Scientific Awards : FOCS 2019 Best Paper Award DISC 2012 & 2017 Best Paper Awards MSc Thesis Award 2006, University of Helsinki Best Junior Researcher 2008, University of Helsinki Nokia Scholarship 2008 Teaching includes Programming Parallel Computers (freely available), CS-E4510 Distributed Algorithms, and other courses in the Finnish Computer Science major/minor programs. His publications span distributed systems theory, graph algorithms, and interdisciplinary projects in linguistics and cultural analytics.
Pavel Zemcik serves as a Visiting Professor in the Computational Engineering department at the School of Engineering Sciences, Lappeenranta University of Technology (LUT). His academic work spans multiple domains within computer science with a strong emphasis on visual computing technologies. Dr. Zemcik's research interests encompass a broad spectrum of visual computing disciplines, including computer graphics, computer vision, machine vision, and image processing. His work particularly focuses on light field rendering techniques, 3D display technologies, and advanced wavelet transform applications. He has made significant contributions to GPU acceleration methods for real-time visual processing and has explored applications in both industrial settings and medical imaging. Analysis of his recent publication trends reveals a concentrated research trajectory in light field technologies and 3D display systems over the past five years. His work demonstrates increasing sophistication in handling visual quality metrics, focus management, and compression techniques specifically tailored for 3D displays. The research shows strong interdisciplinary connections between computer graphics, signal processing, and human perception studies. His scholarly output demonstrates consistent productivity across multiple high-impact venues in computer graphics and computer vision. While specific grant information isn't detailed in the available materials, his publication pattern suggests sustained research funding supporting his work in visual computing technologies.
Gustavo de Almeida is a Researcher at the Department of Energy Technology , School of Energy Systems , Lappeenranta-Lahti University of Technology (LUT). He holds a verified email at LUT and has over 15 years of experience as a professor in Brazil before joining LUT in 2022. Education: M.Sc. in Chemical Engineering, Federal University of Minas Gerais (2003) Ph.D. in Chemical Engineering, University of São Paulo (2006) His research focuses on data analysis applications in the process industry and AI in digital learning , with specific interests in Industrial AI, Process Monitoring, Model Interpretability, and Data-Centric AI. Recent work includes online course development and applications of machine learning in chemical engineering processes. Key publication trends: Spanning 2025 to 2010, his 15 most recent articles emphasize Industrial AI, fault detection, process optimization, and bioenergy technologies. Topics include explainable AI for energy systems, stochastic boiler optimization, and SO2 emission monitoring in industrial processes.
Laura Langohr is a Postdoctoral Researcher at the Finnish Institute of Molecular Medicine (FIMM), University of Helsinki, specializing in computational approaches to biomedical research. Her work bridges computer science and molecular medicine through advanced data analysis techniques. Her core research domains include: Data Mining for pattern extraction in complex datasets Graph Theory applied to biological networks Computational Biology for genetic and physiological modeling Network Analysis of weighted and probabilistic systems Genetics-focused algorithm development Langohr's publication history reveals consistent innovation in subgroup discovery and network algorithms, with emphasis on identifying non-redundant information and representative nodes in biological contexts. Her methodologies directly support molecular medicine applications through computational rigor. Current research funding includes: MetaStem: Academy of Finland Center of Excellence in Stem Cell Metabolism (2023-2025) New insights into leukemia development (Academy of Finland, 2022-2026) iCAN: Digital personalized cancer medicine flagship (Academy of Finland, 2022-2026) Organ transport mechanisms in stem cells (Academy of Finland, 2024-2028) No scientific awards or fellowships are documented in available sources. As a research-focused academic, her contributions center on collaborative project execution rather than formal student supervision. She operates within FIMM's interdisciplinary ecosystem, connecting computational theory with experimental biomedical research.
Professor Johan Lilius is a Full Professor of Embedded Systems at Åbo Akademi University's Faculty of Science and Engineering, Department of Information Technology. He has held this position since 2001 and currently serves as Head of Research. Prof. Lilius has demonstrated extensive leadership throughout his career, having served as director of the Turku Center for Computer Science (TUCS) and multiple terms as Head of the Department of Information Technology, where he led significant departmental restructuring and educational reform efforts through two major university reorganizations. He is currently a member of the steering group for Digivisio2030, a national initiative involving all Finnish higher education institutions aimed at building a new educational ecosystem. Prof. Lilius's research focuses on energy-efficient software, safety in autonomous systems, and neuro-symbolic computing. His work contributes to the UN Sustainable Development Goals, particularly in technology and environmental sustainability. His research interests span autonomous navigation, data-parallel computing, energy-aware systems, and maritime technology. His work bridges theoretical computer science with practical applications in embedded systems, particularly in maritime contexts. Analysis of his recent publications shows a strong trend toward autonomous systems, particularly in maritime applications, with significant work on energy efficiency in embedded architectures. His research increasingly integrates AI and machine learning approaches with traditional software engineering, focusing on safety-critical systems where reliability is paramount. The fingerprint analysis of his work reveals strong connections to convolutional neural networks, autonomous navigation algorithms, and scenario-based testing for complex systems. Ten-Year Most Influential Paper Award at the ACM/IEEE Conference on Model Driven Engineering Languages and Systems Several Best Paper Awards 2015 Gadd Prize for Research Excellence at Åbo Akademi Prof. Lilius has demonstrated exceptional commitment to doctoral education, having supervised over 10 completed doctoral theses and currently mentoring several PhD students. He leads the TUCS Graduate program and has co-organized numerous academic workshops, summer schools, and conferences. His research is supported by significant projects including EDISS (Engineering of Data-intensive Intelligent Software Systems), IoT Reboot Factory, DECATRIP (Decarbonizing Transport Corridors), SMARTER (Sea4Value Smart Terminals), and AutoMare EduNet (Autonomous Maritime Education Network), funded by the European Commission, Business Finland, and other organizations. Prof. Lilius collaborates extensively through the Turku Center for Computer Science (TUCS) and participates in national initiatives like Digivisio2030. His work often involves interdisciplinary teams focusing on the intersection of software engineering, AI, and practical maritime applications, with strong industry partnerships that ensure real-world impact of his research.
Jukka Mikael Kohonen is a University Lecturer in the Department of Mathematics and Systems Analysis at Aalto University, Finland. He is affiliated with the Mathematical Statistics and Data Science, as well as Algebra and Discrete Mathematics research groups. His research interests span lattice theory and combinatorics, with recent work focusing on modular lattice reduction and enumeration techniques. He has contributed to algorithmic optimization and outlier correlation detection, collaborating across disciplines such as biomedical signal processing and computational mathematics. In his publications since 2017, Kohonen has explored topics ranging from symmetry reduction algorithms to additive number theory, with a notable emphasis on computational methods in discrete mathematics. Recent work (2025) advances techniques for simplifying modular lattices through elimination of irreducible elements. Research groups: Mathematical Statistics and Data Science, Algebra and Discrete Mathematics Email: jukka.kohonen@aalto.fi
Sándor Kisfaludi-Bak is an Assistant Professor in the Department of Computer Science at Aalto University, specializing in theoretical computer science with a focus on computational geometry. He develops algorithms for geometric problems involving points, curves, shapes, and spatial networks. His research interests include Algorithm design for geometric optimization Computational geometry fundamentals Spatial network analysis Hyperbolic and planar graph algorithms Parameterized complexity in geometric contexts Recent publications demonstrate expertise in Traveling Salesman Problem optimization, Steiner network construction, and hyperbolic graph analysis. Key research trends span computational geometry, graph theory, and algorithmic complexity in spatial domains. He has no listed scientific awards in the provided materials. No information about student advising or organizational affiliations beyond Aalto University was found.
Ivy K. Y. Woo is a Doctoral Researcher (academic rank: Researcher) in the Department of Mathematics and Systems Analysis at Aalto University, Finland. She is an active member of the Algebra and Discrete Mathematics research group, collaborating with principal investigators including Russell W.F. Lai and Chris Brzuska on advanced cryptographic protocols. Her research concentrates on Cryptography and Privacy with specialized expertise in lattice-based constructions, anonymous transaction systems, and steganography. Key focus areas include traitor tracing mechanisms, functional encryption schemes, Learning With Errors (LWE) assumptions, and privacy-preserving technologies for blockchain applications. Her methodology integrates theoretical cryptography with practical security implementations, particularly addressing leakage resilience and sustainable anonymity systems. Analysis of her 10 recent publications (2022-2025) reveals dominant research trajectories in lattice-based cryptography (70% of output), with significant contributions to traitor tracing, attribute-based encryption, and obfuscation techniques. Parallel streams explore blockchain privacy (20%) through transaction graph analysis and sustainable ring signatures, plus steganographic methods (10%) for natural data hiding. Her work consistently targets high-impact venues including ASIACRYPT, CRYPTO, and Privacy Enhancing Technologies Symposium. Scientific recognition includes: The Andreas Pfitzmann - PETS Best Student Paper Award Runner Up 2022 for "On Defeating Graph Analysis of Anonymous Transactions" Woo maintains active research partnerships within Aalto's cryptography ecosystem, particularly through the Algebra and Discrete Mathematics group. Her current projects involve lattice-based solutions for multi-authority attribute encryption and leakage-resilient cryptographic primitives, with several 2025 publications indicating ongoing high-productivity collaboration with international research teams.
Rongzhen Zhao is a Doctoral Researcher at Aalto University's Department of Electrical Engineering and Automation, affiliated with the Robot Learning research group. Their work focuses on machine learning and object-centric learning, with recent publications in top-tier conferences like ECML and ICLR. Research interests include: Object-Centric Learning Slot Attention Mechanisms Spatiotemporal Processing Neural Network Architecture Design Brain-inspired AI IoT Middleware Development Recent trends in their publications highlight advancements in attention mechanisms, discrete representations for object recognition, and multi-scale fusion techniques. They also explore bio-inspired approaches for temporal data analysis and IoT systems. Research groups: Robot Learning Contact: Email: rongzhen.zhao@aalto.fi
Nikolaj Tatti is a Visiting Professor at Aalto University's Department of Computer Science , affiliated with the Helsinki Institute for Information Technology (HIIT) and the Adj. Prof. Gionis Aris research group . His work focuses on algorithmic analysis of temporal networks and graph theory. Research Interests: Temporal network analysis Graph decomposition and density Segmentation algorithms Pattern discovery in time-series data Interactive data analysis frameworks Publication Trends (2018-2021): Research spans Temporal Networks (dynamic hierarchies, cascade reconstruction), Graph Theory (density, decomposition), and Data Mining (periodic patterns, event detection). Collaborative work with Aris Gionis and Polina Rozenshtein dominates his publications. Technical Collaborations : Works with multidisciplinary teams including researchers from ACM Transactions on Knowledge Discovery and IEEE conferences.
