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Kaie KubjasView profile

Associate Professor

• Algebraic Statistics
• Nonlinear Algebra
• Matrix and Tensor Decompositions
• +5 more

Kaie Kubjas is an Associate Professor at Aalto University in the Department of Mathematics and Systems Analysis, School of Science. Since 2024, she has held a tenured position, following a tenure-track role from 2017–2024. She earned her PhD in Mathematics at Freie Universität Berlin (2013) under Professors Christian Haase and Klaus Altmann, with postdoctoral research at institutions including the Max Planck Institute and MIT. Her research focuses on applied nonlinear algebra, algebraic statistics, and their applications in biology (e.g., phylogenetics and 3D genome reconstruction), as well as matrix/tensor decompositions. She has organized major events like the European Women in Mathematics General Meeting 2022 and the 2025 workshop on Algebraic Statistics and Multistate Models. Kubjas serves on editorial boards of journals like SIAM Journal on Applied Algebra and Geometry and Annales Fennici Mathematici . Recent work includes advances in log-concave maximum likelihood estimation, 3D genome reconstruction, and structured matrix decompositions. Her students, such as Olga Kuznetsova (Second Place MEGA 2021 Poster Award winner), have contributed to these areas. She regularly contributes to seminars like the Algebra and Discrete Mathematics at Aalto, fostering interdisciplinary collaboration.

Milica TodorovicView profile

Associate Professor

• Computational Materials Science
• Artificial Intelligence for Materials Design
• First-Principles Simulations
• +6 more

Milica Todorovic serves as an Associate Professor in the Department of Materials Science within the Faculty of Science and Engineering at the University of Turku (UTU). She leads the Materials Informatics Laboratory (MIL) and heads the Modern Industrial Materials MSc track. Her institutional roles include Vice-director of the Sustainable Materials and Manufacturing (SUSMAT) UTU profiling area, Vice-chair of COST Action CA22154 DAEMON, Vice-leader of the Human-Centric Artificial Intelligence for Sustainable Future (HAIF) Doctoral Training Network, and Co-lead of Finnish Centre for AI (FCAI) Highlight E. Her research integrates artificial intelligence algorithms with first-principles simulations to optimize functional materials and device performance. The MIL group develops data-driven solutions spanning aerosol research, chemical engineering of bio-based materials, and experimental-computational integration. Current projects focus on battery recycling optimization, magnetic materials design, perovskite engineering, and atmospheric aerosol analysis using Bayesian optimization and active learning techniques. Her work demonstrates significant interdisciplinary reach across computational chemistry, nanotechnology, and sustainable engineering. Scientific contributions include: Development of AI workflows for materials discovery Bayesian optimization frameworks for adsorption structure prediction Question answering systems for materials literature mining Data-efficient methods for molecular property prediction Her teaching portfolio includes advanced courses in Data Visualisation and Analysis, Simulations and New Materials, and Machine Learning for Materials Science. She actively contributes to European research networks through DAEMON and FCAI initiatives, driving collaborative efforts in data-driven materials engineering.

• Statistical signal processing
• Adaptation and learning
• Distributed processing
• +2 more

Stefan Werner is an Adjunct Professor in the Department of Information and Communications Engineering at Aalto University, specializing in statistical signal processing, adaptation and learning, distributed processing, wireless communications, and smart grids. He is an active member of the Risto Wichman Research Group. Werner's research centers on advancing distributed machine learning frameworks and signal processing techniques. His work in federated learning addresses critical challenges including model-poisoning resilience, communication efficiency, and personalized learning with privacy guarantees. In wireless communications , he contributes to massive MIMO channel modeling and consensus algorithms for networked systems. His recent innovations extend to lightweight deep learning for hyperspectral anomaly detection and non-convex optimization methods for quantile regression, with applications spanning smart grids and next-generation wireless networks. Analysis of his 2023-2025 publications reveals a dominant focus on distributed intelligence, with 60% of papers dedicated to federated learning advancements. Key trends include asynchronous online learning protocols, graph-based meta-learning frameworks, and robust optimization techniques. These works consistently bridge theoretical signal processing with practical implementations in wireless communications and remote sensing, demonstrating strong interdisciplinary impact across IEEE's top signal processing and IoT journals. Werner collaborates within the Risto Wichman Research Group at Aalto University, which pioneers signal processing solutions for 6G communications, IoT networks, and energy-efficient wireless systems.

Russell W. F. LaiView profile

Assistant Professor

• Cryptography
• Security
• Privacy
• +2 more

Russell W. F. Lai is an Assistant Professor at the Department of Computer Science , Aalto University , focusing on the algebraic aspects of public-key cryptography and their applications to security and privacy. His research intersects mathematical foundations with practical cryptographic protocols . Research Areas: Cryptography Security Privacy Public-Key Cryptography Algebraic Cryptography Recent work includes advancements in lattice-based cryptography , with applications to updatable encryption , threshold signatures , traitor tracing , and verifiable delay functions . Key techniques involve Learning with Errors (LWE) and Short Integer Solution (SIS) problems. Scientific Awards: The Andreas Pfitzmann - PETS Best Student Paper Award Runner Up (2022) for "On Defeating Graph Analysis of Anonymous Transactions"

• Network Science
• Temporal Networks
• Multilayer Networks
• +5 more

Arash Badie-Modiri is a Visiting Professor at Aalto University's School of Science, Department of Computer Science. His research focuses on temporal networks, multilayer networks, and computational modeling of complex systems. Institution: Aalto University School: School of Science Department: Department of Computer Science Rank: Visiting Professor Email: ext-arash.badie-modiri@aalto.fi His work spans network science, statistical physics, and software development, with particular emphasis on temporal network analysis , multilayer network modeling , and dynamic connectivity estimation . Recent publications highlight contributions to epidemic modeling, crowdsourcing methodological improvements, and open-source software tools like the pymnet library. Collaborations with researchers such as Kimmo Kaski, Jari Saramäki, and Mikko Kivelä reflect his integration into multidisciplinary academic networks. Research outputs demonstrate expertise in temporal network theory , weighted event graphs , and percolation dynamics applied to complex systems.

• large scale learning
• extreme multi-label classification
• deep learning
• +2 more

Rohit Babbar serves as an Assistant Professor in the Department of Computer Science at Aalto University, Finland, leading a research group dedicated to advancing large-scale machine learning methodologies. His team specializes in tackling computational challenges inherent in extreme classification problems with massive output spaces while ensuring model robustness. His primary research domains encompass large-scale learning systems, extreme multi-label classification architectures, deep learning integration, sequential data processing, and robustness engineering. This work directly addresses industry pain points like computational inefficiency in massive label spaces and model vulnerability to distribution shifts, with applications spanning natural language processing, information retrieval, and recommendation systems. Publication trends reveal a strategic focus on algorithmic innovation for extreme classification, featuring breakthroughs in dynamic sparsity techniques, large language model integration for zero-shot scenarios, and calibration of extreme classifiers. Recent work demonstrates consistent emphasis on computational efficiency through optimized negative sampling, lightweight frameworks like InceptionXML, and specialized metrics for long-tail performance evaluation. Scientific recognition includes: Outstanding Reviewer Award at ACL 2021 Conference (July 2021) for exceptional contributions to computer science peer review As research group leader, Babbar directs collaborative efforts on next-generation classification systems while mentoring emerging scholars in machine learning. His team maintains active partnerships with industry leaders in search and recommendation technologies. The research group operates at the intersection of theoretical machine learning and practical deployment, developing frameworks that balance computational feasibility with predictive accuracy in extreme-scale environments. Current initiatives focus on integrating foundation models with specialized classification architectures while addressing real-world challenges like data sparsity and concept drift.

• Neuroscience
• Digital Phenotyping
• Functional Brain Connectivity
• +4 more

Ana Triana Hoyos is a Researcher in the Department of Computer Science at Aalto University, Finland, holding a Contingent Worker position (T313) while serving as a Visitor (Faculty) within Professor Jari Saramäki's research group. Her work bridges computational methods with clinical neuroscience through advanced digital phenotyping and neuroimaging techniques. Her primary research investigates functional brain connectivity using fMRI combined with real-world behavioral monitoring, focusing on mental health disorders including depression, ADHD, and psychosis. She pioneers digital phenotyping methodologies through mobile sensing and develops analytical tools like the Niimpy behavioral data analysis toolbox, enabling longitudinal assessment of environmental and lifestyle impacts on neural function. Analysis of her 2019-2025 publications reveals consistent innovation in integrating neuroimaging with real-world behavioral data, particularly through multimodal longitudinal designs. Key contributions include establishing digital biomarkers for mood disorders, mapping connectivity alterations in psychiatric conditions, and developing rigorous methodological frameworks for fMRI preprocessing and behavioral data analysis. Scientific Awards: No awards were documented in the source materials. Advising and Grants: No student advisement records or grant funding details were specified in the available information. She actively contributes to Professor Saramäki's research ecosystem at Aalto University, collaborating with multidisciplinary teams spanning computer science, clinical psychiatry, and neuroscience to advance computational approaches in mental health research.

• Distributed Algorithms
• Quantum Computing
• Graph Theory
• +2 more

Henrik Lievonen is a Doctoral Researcher at Aalto University's Department of Computer Science. His work focuses on distributed algorithms and their connections to mathematics, computer science theory, and quantum computing. University: Aalto University Role: Researcher in distributed algorithms and quantum computing Research interests center on distributed systems, quantum advantage analysis, and algorithmic complexity in graph problems. Key areas include LOCAL model constraints, quantum derandomization, and theoretical limits of distributed quantum computing. Scientific Awards: Nokia Scholarship 2024 (Nokia Foundation)

• Machine Learning
• Robotics
• Autonomous Driving
• +3 more

Jatan Shrestha is a Doctoral Researcher at Aalto University, affiliated with the Department of Electrical Engineering and Automation. His research focuses on applying machine learning techniques to solve complex problems in robotics and autonomous systems. Department: Electrical Engineering and Automation Email: jatan.shrestha@aalto.fi Research Interests: Jatan's work spans machine learning, robotics, and optimization. Key areas include autonomous driving algorithms, multi-agent trajectory planning, visibility-aware navigation, and GPU-accelerated computing for real-time robotic applications. Publication Trends: His research output demonstrates expertise in applying machine learning to hardware optimization (2024), advancing autonomous driving algorithms in dense traffic scenarios (2023), and developing GPU-based optimization frameworks for robotic navigation (2021-2022).

• Machine Learning
• Human-Computer Interaction
• Network Science
• +3 more

Hasti Nariman Zadeh is a Researcher at the Department of Computer Science , Aalto University . Her work spans interdisciplinary domains, combining computational methods with applications in social sciences and agronomy. Research Interests Network science and community detection algorithms Crowdsourcing methodologies with bias mitigation Machine learning for high-dimensional data representation Data-driven approaches in agricultural informatics and phenology modeling Analysis of social media dynamics and cross-domain engagement patterns Publications Her research output demonstrates expertise in network science, crowdsourcing, and computational modeling. Key themes include: modularity optimization for graph partitioning, social behavior analysis during global crises, and uncertainty quantification in crop simulations. Collaborative Networks She has collaborated extensively with international research groups across agronomy, computational social sciences, and machine learning domains, as evidenced by co-authorships on interdisciplinary publications.

• Formal Methods
• Model Checking
• Satisfiability (SAT) Solving
• +4 more

Tommi Junttila serves as a Senior University Lecturer in the Department of Computer Science at Aalto University, Finland, where he conducts cutting-edge research at the intersection of formal methods and computational logic. His academic profile demonstrates sustained contributions to theoretical computer science with practical applications in system verification and blockchain technology. His research program centers on advancing formal verification techniques, with core expertise in: Symmetry reduction algorithms for state space explosion SAT/SMT solving with specialized parity and XOR reasoning Bounded model checking of timed and asynchronous systems Blockchain protocol verification (notably DeFi lending pools) Canonical labeling tools for graph automorphism detection Recent work shows increasing focus on decentralized finance applications while maintaining foundational contributions to solver technology. Analysis of his publication trajectory (2011-2022) reveals consistent innovation in SAT solving methodologies, with symmetry reduction and parity reasoning forming persistent research threads. His 2022 work on DeFi lending pools represents a strategic expansion into blockchain verification, leveraging established formal methods expertise for emerging financial technologies. Tool development (bliss, PySMT) demonstrates commitment to practical research impact. No scientific awards were documented in the source materials. Similarly, no information regarding student supervision, grant funding, or laboratory affiliations was present in the provided text. His independent tool development (including bliss for graph canonical labeling and PySMT for SMT solver interfaces) indicates significant technical leadership within the formal methods community.

• Computational Geometry
• Hyperbolic Space
• Data Structures
• +6 more

Geert van Wordragen is a doctoral researcher at the Department of Computer Science, School of Science, Aalto University. His work focuses on computational geometry, hyperbolic space, and algorithmic complexity. University: Aalto University School: School of Science Department: Department of Computer Science Rank: Researcher Geert’s research explores hyperbolic space, data structures, and fine-grained complexity. He has contributed to discrete Voronoi games and nearest neighbor algorithms, with publications from 2023 to 2025. His recent work includes: 2025: Structure and Independence in Hyperbolic Uniform Disk Graphs 2024: A Quadtree, a Steiner Spanner, and Approximate Nearest Neighbours in Hyperbolic Space 2024: Fine-Grained Complexity of Earth Mover’s Distance Under Translation 2023: Improved Bounds for Discrete Voronoi Games Conference Activities Member of the Helsinki Algorithms & Theory Days committee (2024)

Jaakko PeltonenView profile

Visiting Professor

• Machine Learning
• Information Retrieval
• Dimensionality Reduction
• +3 more

Jaakko Peltonen is a Visiting Professor at the Department of Computer Science within Aalto University , affiliated with the Probabilistic Machine Learning group and the Helsinki Institute for Information Technology (HIIT) . His research focuses on machine learning, information retrieval, and interactive visualization, with significant contributions to probabilistic modeling and data analysis. Tekniikan tohtori (Doctor of Technology), Helsinki University of Technology (2004) Diplomi-insinööri (Master of Science in Technology), Helsinki University of Technology (2001) His research spans machine learning applications in computational biology (e.g., analyzing cellular processes with regression planes), probabilistic matrix factorization for recommendation systems, and hierarchical Dirichlet processes for topic modeling in text mining. He has pioneered methods for exploratory search with visual interactive intent modeling and graph-based priors for scalable ML algorithms. Notable activities include international collaborations (e.g., visiting researcher roles in 2016), organizing Eurovis 2016 workshops, and conference presentations across Spain, United States, and other countries. His publications (85+ total) demonstrate expertise in probabilistic models, dimensionality reduction, and interactive visualization techniques.

Milad MalekzadehView profile

Research Fellow

• Geosciences
• Environmental Sciences
• Computer and Information Sciences
• +7 more

Milad Malekzadeh is a Postdoctoral Researcher at the University of Helsinki , affiliated with the Faculty of Science , Department of Geosciences and Geography , and research organizations Helsinki Institute of Sustainability Science and Helsinki Institute of Urban and Regional Studies (Urbaria) . His work bridges Geosciences , Environmental Sciences , and Computer Science , focusing on urban mobility, spatial analysis, and AI applications. Projects : WinWin4WorkLife (2024–2027), BORDERSPACE (2023–2025), MOBI-TWIN (2023–2026), GREENTRAVEL (2023–2027). Research Trends : His recent articles examine greenery assessment inconsistencies, mobility deviation metrics, AI-driven urban attractiveness analysis, and cross-border student mobility patterns. Methodologies include semantic segmentation, geospatial modeling, and big data analytics. Collaborations : Active in peer review for journals like Landscape and Urban Planning and International Journal of Geographical Information Science . Participates in conferences such as 33rd Annual GIS Research UK Conference and 19th International Conference on Location Based Services . Outreach : Maintains a personal website , Twitter @MiladMzdh , and LinkedIn profile .

Jingyi LiaoView profile

Researcher

• Ambient Backscatter Communication
• Cellular Communication Systems
• Internet of Things (IoT)
• +3 more

Jingyi Liao is a doctoral researcher at Aalto University's School of Electrical Engineering, where they are affiliated with the Department of Information and Communications Engineering. Their research focuses on Ambient Backscatter Communication systems, leveraging LTE and cellular infrastructure for energy-efficient IoT and wireless communication. Education: Master of Engineering in Communication Engineering (2018), University of Electronic Science and Technology of China Bachelor of Engineering in Electronic Engineering (2014–2018), University of Electronic Science and Technology of China Research Interests: Jingyi's work centers on integrating backscatter communication with cellular networks, particularly exploring LTE pilots and ambient signals for low-power IoT devices. Their key contributions include real-time backscatter communication systems, energy-autonomous devices for 6G, and receiver design leveraging commercial cellular infrastructure. Techniques such as coded modulation and channel estimation are critical to their work, aiming to enhance efficiency and scalability in wireless communication systems. Recent Trends in Publications: Jingyi's articles emphasize practical implementations, including low-cost multi-antenna receivers and open-source prototyping. Their 2025 work on Ambient IoT integration with cellular systems highlights advancements in system design and network interoperability. Earlier research (2020–2021) explored radar signal detection, showcasing interdisciplinary skills in signal processing. Awards: HPY Research Foundation 2025 grants (2025) Most downloaded paper award (2024) Activities: Jingyi's 2024–2025 visit to the University of Houston advanced collaboration on 6G network applications. Their work has been recognized through citations (e.g., 7 citations for their 2023 IoT demo paper) and open-access dissemination.