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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.

Andreas HolmView profile

Visiting Professor

• Plasma Physics
• Nuclear Fusion
• Tokamak Engineering
• +3 more

Andreas Holm is a Visiting Faculty member in the Department of Applied Physics at Aalto University's School of Science. His research focuses on plasma physics and nuclear fusion, particularly in the context of tokamak devices and divertor design for next-generation fusion reactors like ITER. His educational background includes: Doctor of Science (Technology) in Technical Physics (awarded September 26, 2022) Master of Science (Technology) in Technical Physics (awarded May 25, 2018) Bachelor of Science (Technology) in Technical Physics (awarded August 31, 2016) Dr. Holm's research spans multiple areas of plasma physics critical to advancing nuclear fusion technology. His work particularly focuses on divertor physics, plasma detachment phenomena, and spectroscopic diagnostics in tokamak environments. He investigates complex plasma-material interactions, molecular processes in detached regimes, and innovative divertor designs that can handle the extreme conditions of future fusion reactors. His research combines experimental analysis with sophisticated computational modeling to address key challenges in magnetic confinement fusion. Analysis of his recent publications (2023-2025) reveals a strong focus on divertor design and plasma detachment physics for fusion reactors. His work frequently addresses the challenges of heat and particle exhaust in tokamaks, with particular attention to mid-leg pumping configurations and the impact of molecular processes on plasma recombination. Much of his research supports the development of ITER and future fusion power plants through collaborations with major fusion facilities like JET and DIII-D. His scientific contributions have garnered recognition in the fusion community: His work has received citations in Scopus (1 citation for 2025 publication, 3 for 2024 DIII-D paper, 1 for 2024 divertor modeling paper, and 6 for 2023 molecular recombination paper) His publications have been downloaded hundreds of times from institutional repositories His research has received news mentions related to innovative divertor designs Dr. Holm actively collaborates with major fusion research institutions worldwide, including EUROfusion and the DIII-D team. His work involves significant computational modeling of plasma behavior and analysis of experimental data from large-scale fusion devices. He has contributed to multiple collaborative projects aimed at solving critical challenges for ITER and future fusion power plants, particularly regarding heat and particle management in tokamak divertors. His research is conducted within the broader context of international fusion collaborations, working closely with teams at JET (Joint European Torus) and DIII-D (Doublet III-D) tokamak facilities. These collaborations focus on advancing the scientific understanding and engineering solutions needed for practical fusion energy production, with particular emphasis on plasma-facing components and divertor physics.

• Cardiology
• Biomedical Engineering
• Heart Rate Variability
• +5 more

Matias Kanniainen is a doctoral researcher focusing on interdisciplinary studies at the intersection of physics, biomedical engineering, and cardiology. His work contributes to the United Nations Sustainable Development Goals related to health and well-being (SDG 3) and innovation (SDG 9). He is affiliated with the Doctoral Programme in Engineering and Natural Sciences, though specific institutional details require further clarification. Research Expertise: RR Interval Analysis, Long QT Syndrome, Heart Rate Variability, Beta-blocker Therapy, Threshold Estimation, Wearables, Diurnal Rhythm Research Trends : His publications emphasize computational cardiology, leveraging machine learning and physiological data analysis to study cardiovascular conditions like congestive heart failure and long QT syndrome. Recent work explores dynamical correlations in RR intervals during exercise and QT correction accuracy.

JaeSeung SongView profile

Assistant Professor

• Software Engineering
• Networked Systems
• Security
• +5 more

JaeSeung Song is an Assistant Professor in the Computer and Information Security Department at Sejong University, where he leads the Software Engineering and Security group (SESlab). He holds significant leadership roles in standardization bodies including Chair of the oneM2M Test Working Group, Chair of IEEE ComSoc Standards for IoT Architectures Research Group, and Vice Chair of TTA IoT/M2M Convergence Special Project Group. He received his PhD from Imperial College London (Department of Computing) and holds MS/BS degrees in Computer Science from Sogang University. His industry experience includes senior researcher roles at NEC Europe (2012-2013) focusing on IoT standardization and LG Electronics (2002-2008) leading 3GPP SA standards development. His research integrates software engineering, networked systems, and security, with specialized focus areas including: Reliable IoT/M2M platform design Semantic IoT data interoperability Secure software patching techniques Big data analytics for networked systems IoT standardization frameworks His publications demonstrate strong emphasis on software testing methodologies—particularly conformance testing, symbolic execution, and protocol verification—applied to IoT security and mobile vulnerability analysis. Work consistently targets high-impact venues including IEEE Transactions journals. He received the Best Paper Award at WISA 2014 for research exposing data harvesting vulnerabilities in messenger applications. He currently seeks MSc/PhD students (up to four openings) for the EU Horizon 2020 FIESTA project involving federated IoT testbeds, semantic interoperability, and oneM2M standardization. The SESlab focuses on IoT security testing tools and reliable platform engineering.

• Risk Analysis
• Financial Market Forecasting
• Quantitative Modelling
• +7 more

Dr Tiejun Ma is an Associate Professor at the University of Southampton , specializing in risk analysis and decision-making. His research integrates quantitative modelling with real-time big data analytics to address financial market forecasting challenges. He has secured over £4 million in research grants across 30 projects funded by EPSRC, ESRC, Innovate UK, and industry partners like Huawei. Member of the Centre for Risk Research Member of CORMSIS (Centre for Operational Research, Management Science and Information Systems) Member of the Centre for Digital Finance Dr Ma's research spans cryptocurrencies, behavioral economics, data anonymization, and financial risk management. His work combines applied data science, mathematical modelling, and behavioral analysis to tackle complex uncertain environments. Key projects include: Huawei-funded Failure Detection Algorithm for SDN/NFV EPSRC-funded Modelling the Wisdom of the Crowd Modelling Complex Uncertain Environments with E.V. Analytics Ltd His publications appear in leading journals like the European Journal of Operational Research , Quantitative Finance , and Journal of Computer Science and Technology . He supervises PhD students in Business Studies & Management.

• Biochemistry
• Cell Biology
• Nanotechnology
• +3 more

Mikko Huhtala is a researcher in the field of Biochemistry and Cell Biology, with affiliations to the Faculty of Natural Sciences and Engineering. His work spans diverse areas including nanotechnology, embryonic development, and computational biology. Active in UN Sustainable Development Goals related to health and innovation Collaborator in Finnish National Protein Crystallography Consortium Research focuses on: Protein stability assessment using nano-differential scanning fluorimetry Human embryonic genome activation and DUX4 transcription factors Development of molecular modeling software (BODIL) for drug design His publications from 2004-2023 demonstrate long-term expertise in structural biology and interdisciplinary collaborations with institutions like the European Synchrotron Radiation Facility.

• Molecular Dynamics
• Biophysics
• Plasma Medicine
• +3 more

Maryam Ghasemitarei is a Postdoctoral Researcher in the Department of Applied Physics at Aalto University, Finland, affiliated with the Multiscale Statistical and Quantum Physics research group. Her academic position focuses on advanced computational research using molecular dynamics simulations to investigate biomolecular systems under oxidative stress conditions induced by plasma applications. Her research interests encompass: Molecular Dynamics Simulations for biomolecular systems Biophysical effects of nitro-oxidative stress on protein structures Plasma medicine applications in cancer therapy and virology Atomistic modeling of SARS-CoV-2 spike protein interactions Nanomaterials for photocatalytic energy applications Light propagation mechanisms in biological tissues This interdisciplinary work bridges physics, chemistry, and biomedical engineering to address critical challenges in oxidative damage and therapeutic interventions. Analysis of Dr. Ghasemitarei's publication record (2011-2025) reveals a consistent trajectory in computational biophysics, with recent emphasis (2021-2025) on molecular mechanisms of protein oxidation in viral systems and cancer therapy. Her work demonstrates sophisticated application of reactive and non-reactive molecular dynamics to study cysteine oxidation effects on SARS-CoV-2 proteins, hVDAC1 channel functionality, and plasma radical interactions with tumor cells, while maintaining foundational contributions in nanomaterials and biophotonics. Scientific Awards: No scientific awards were documented in the provided source materials. Advising and Research Funding: No information regarding student supervision, doctoral advising, or research grant acquisitions was specified in the source documentation. Research Infrastructure: Active member of the Multiscale Statistical and Quantum Physics research group at Aalto University's Department of Applied Physics, utilizing high-performance computing resources for molecular dynamics simulations.

• Condensed Matter Physics
• Superconductivity
• 2D Materials
• +3 more

Somesh Ganguli is an Academy Research Fellow in the Department of Applied Physics at Aalto University, Finland. His research is conducted within the Atomic Scale Physics research group, focusing on experimental investigations of quantum phenomena in atomically thin materials using advanced scanning probe techniques. His research encompasses: Condensed Matter Physics Superconductivity mechanisms in 2D systems Quantum phase transitions Topological materials and excitations 2D magnetism and spin dynamics Electronic correlations in van der Waals heterostructures Analysis of his 2021-2025 publications reveals a cohesive research trajectory centered on monolayer transition metal dichalcogenides (TMDs), where he probes the interplay between superconductivity, magnetism, and electronic order using scanning tunneling microscopy. His work frequently explores emergent phenomena in moiré patterns and confinement-engineered systems, with significant contributions to understanding chiral superconductivity, doped Mott phases, and Kondo physics in reduced dimensions. Dr. Ganguli's experimental work is conducted within Aalto University's Atomic Scale Physics group, which specializes in atomic-resolution characterization of quantum materials through low-temperature scanning probe methodologies.

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.

• Integrated Circuit Design
• Analog Electronics
• Microwave Engineering
• +2 more

Veeti Lahtinen is a Doctoral Researcher at Aalto University's Department of Electronics and Nanoengineering. He is affiliated with the Marko Kosunen Group, contributing to research in analog and microwave circuit design. His work focuses on automation frameworks for circuit design and verification. Research Focus: Integrated circuit design automation Specializations: Analog-to-digital converters, Microwave integrated circuits, Procedural verification His recent publications appear in top conferences like SMACD and NorCAS, emphasizing automated design methodologies. Reach him at veeti.lahtinen@aalto.fi .

• Water and Environmental Engineering
• Wastewater Networks
• Sewer Networks
• +3 more

Tuija Laakso is a Researcher in the Department of Built Environment at Aalto University's School of Engineering, affiliated with the Water and Environmental Engineering research group. Her work focuses on sustainable infrastructure management for urban water systems. Her research spans Water and Environmental Engineering with specialized expertise in Wastewater Networks, Sewer Networks, and Decision Support Systems. She develops data-driven methodologies for asset management, including failure prediction models, lifecycle analysis of water infrastructure, and optimization of maintenance strategies. Her interdisciplinary approach integrates civil engineering principles with computational analytics to enhance resilience in aging sewer systems. Analysis of her 2017-2022 publications reveals consistent innovation in applying AI and statistical modeling to water infrastructure challenges. Key themes include sewer lifespan prediction, inflow/infiltration analysis in sanitary sewers, and consequence assessment of pipe failures. Her work demonstrates increasing integration of machine learning techniques with traditional engineering practices to address sustainability challenges in Nordic water systems. Dr. Laakso actively contributes to the Water and Environmental Engineering research group, which specializes in developing advanced solutions for water infrastructure management. The group employs interdisciplinary approaches combining engineering, data science, and environmental analysis to create decision-support frameworks for municipal water utilities and infrastructure planners.

• Machine Learning
• Bioinformatics
• Drug Discovery
• +5 more

Sandor Szedmak serves as a Research Fellow in the Department of Computer Science at Aalto University, Finland, and is affiliated with the Helsinki Institute for Information Technology (HIIT). He operates within Professor Juho Rousu's research group, focusing on interdisciplinary machine learning applications with strong ties to computational biology and bioinformatics. His institutional presence is active, as evidenced by current contact details and ongoing publication output. Dr. Szedmak's research specializes in developing advanced machine learning frameworks for complex biological and educational challenges. His primary interests include drug combination effect prediction, protein function annotation through multi-view learning, strain design optimization using reinforcement learning, and personalized educational systems for computer science students. He pioneers methods in latent tensor reconstruction, scalable variable selection, and kernel generalization, targeting high-dimensional data problems in pharmacology and computational biology. His publication trajectory from 2020-2025 reveals a concentrated effort in bioinformatics applications, with landmark papers in Nature Communications and Bioinformatics on drug combination prediction. The work consistently leverages tensor factorization and multi-view learning to model biological systems, while recent expansions include educational data mining for programming behavior analysis. This demonstrates both methodological consistency in machine learning innovation and strategic domain diversification. No scientific awards or fellowships are documented in the provided materials. While no student advisees or grant details are explicitly listed, Dr. Szedmak's collaborative publication pattern—particularly within the Rousu research group and HIIT—suggests active participation in team-based research initiatives. His work appears funded through institutional channels given the consistent output in computational biology. He operates within the Helsinki Institute for Information Technology (HIIT), a joint research institute of Aalto University and the University of Helsinki, and is embedded in Professor Juho Rousu's research group. This environment facilitates cross-disciplinary collaboration between computer science and life sciences, with infrastructure supporting high-performance computing for large-scale biological data analysis.

• e-Governance
• ICT for Development
• Smart Cities
• +3 more

Karin Fröhlich is a Project Employee in the Department of Computer Science at Aalto University , College of Engineering. She specializes in citizen-centric technology solutions for marginalized communities and e-governance systems in developing countries. Research Focus: ICT for Development (ICT4D) Smart City Implementation in African and Baltic contexts Community Empowerment through Digital Tools Public Value Management in e-Governance Energy Access via Solar-PV Systems Recent Publications analyze mobile governance adoption factors, citizen-centric e-government models, and cross-country comparisons of smart city initiatives in Estonia and Namibia.

• Industrial Automation
• Control Systems
• Computer Science
• +4 more

Tuojian Lyu is a Doctoral Researcher at Aalto University's Department of Electrical Engineering and Automation. Their work focuses on Industrial Automation, specifically leveraging IEC 61499 standards for distributed control systems and exploring cloud integration, virtual commissioning, and containerization technologies. Current research interests include: Optimization of function block distribution in IEC 61499 Cloud-based virtual commissioning for industrial systems OPC UA integration with IEC 61499 automation systems Deployment optimization using SMT solvers Data streaming techniques to cloud storage Enhanced reconfigurability of I/O connections Recent publications demonstrate expertise in combining IEC 61499 standards with emerging technologies like cloud computing and containerization. Key trends include formal methods (SMT solvers), real-time performance analysis, and web platform integration for industrial automation. Email: tuojian.lyu@aalto.fi

• Biomaterials
• Materials Science
• Biotechnology
• +5 more

Jennifer Tersteegen is a Doctoral Researcher affiliated with Aalto University's Department of Bioproducts and Biosystems. She works within the Biomolecular Materials research group, focusing on biomaterials and sustainable material development. Research Interests: Her work spans biomaterials engineering, protein-based adhesives, sustainable chemistry, and open-source hardware. She investigates condensate dynamics in biomolecular materials, develops bio-inspired polymers, and contributes to low-cost measurement technologies. Scientific Awards: Poster prize (Aug 2023) Working grant for 2024 (Oct 2023) Encouragement grant (May 2023) Working grant for 2023 (Jan 2022) Collaborations: Active across multidisciplinary projects in bioproducts, physics, and open-source engineering.