Leran Lu is a Doctoral Researcher at the Nano and Molecular Systems Research Unit within the Faculty of Science at the University of Oulu. Their work focuses on computational chemistry and machine learning applications for studying 2D materials and CO2 reduction processes. University of Oulu, Faculty of Science Nano and Molecular Systems Research Unit Research Interests: Density functional theory 2D materials CO2 reduction Machine learning Computational chemistry Contact: Leran.Lu@oulu.fi
Brenda de Souza Ferrari is a Research Fellow in the Department of Chemistry at the University of Jyväskylä's Faculty of Mathematics and Natural Sciences, specializing in the Computational Nanoscience group (NSC-comp). Her educational background includes: DSc in Chemistry from the Federal University of Rio de Janeiro (UFRJ) in 2023, focusing on selective human furin inhibitors as broad-spectrum antivirals Master in Chemistry from UFRJ in 2019, optimizing non-nucleoside HIV reverse transcriptase inhibitors Her current research examines gold nanocluster-biomolecule interface interactions, specifically their impact on protein corona formation and electronic excitations. Leveraging expertise in medicinal chemistry, organic synthesis, and machine learning, she bridges computational modeling with antiviral drug development and nanomaterial science. Prior work centered on HIV and furin-targeted therapeutics, reflecting her dual focus on infectious disease treatment and nanoscale phenomena. As part of the Computational Nanoscience group (NSC-comp), she contributes to investigating physical, chemical, and biological properties of nanostructures through advanced computational methodologies, emphasizing interdisciplinary applications in nanomedicine.
Ahmed Hasen is a Doctoral Researcher at the Faculty of Information Technology at the University of Jyväskylä. He is affiliated with the Machine Intelligence Methods and Applications (MIMA) research group, which focuses on both theoretical/methodological advancements and practical applications of machine intelligence. Research interests include Machine Intelligence , Theoretical Machine Learning , and Applications of AI .
Bo Peng is a Visitor (Faculty) at the Department of Applied Physics, Aalto University, specializing in molecular materials research. His work focuses on advanced colloidal structures, nanomaterials, and their applications in energy conversion, sensing, and security. Dr. Peng earned his Doctoral degree in Engineering and Technology from Universiteit Utrecht, awarded on March 11, 2013. His research expertise spans surface science, nanoclusters, superhydrophobic materials, hydrogels, thin films, and MXene materials. His research interests center around the design and self-assembly of anisotropic particles for advanced colloidal structures and materials. He investigates how particle morphology, surface properties, and external stimuli (particularly magnetic fields) can be leveraged to create novel functional materials with applications in electrocatalysis, sensing, and information security. His recent work combines machine learning approaches with materials science to develop magnetoelectric shape recognition systems and adaptive sensitivity mechanisms. Dr. Peng's publication portfolio shows a strong trend toward multifunctional nanomaterials with applications in energy conversion (particularly hydrogen and oxygen evolution reactions), security technologies using nanofiber-based optical systems, and neuromorphic computing interfaces. His work bridges fundamental materials science with practical applications in sustainable technology. Dr. Peng has received research funding from multiple sources including the Academy of Finland and EU Framework Programmes. His current project "Design and self-assembly of anisotropic particles for advanced colloidal structures and materials" (2022-2024) continues his research on colloidal systems. He has supervised three theses and has been active in academic outreach, including an invited talk on "Surface roughness dominated structural memory and memory plasticity via magnetic field-driven particle assembly" in July 2022. His work has received media attention, including coverage of research on magnetic memory in materials published in November 2022.
Sami Kaappa is a Postdoctoral Researcher specializing in magnetism and computational physics, with a focus on magnetic domain walls, Barkhausen noise, and micromagnetic simulations. His work often combines advanced microscopy techniques (e.g., Lorentz transmission electron microscopy) with computational methods like density functional theory and machine learning. His recent research includes publications in npj Computational Materials and the Journal of Chemical Physics , exploring topics such as atomic structure optimization in extra dimensions and magnetic behavior in steel. Collaborations with institutions like the University of Turku and Tampere University are evident through co-authorships. Key trends in his work involve bridging experimental observations (e.g., in-situ microscopy) with theoretical models, emphasizing disordered systems and magnetic hysteresis. While no formal awards or students are listed in the provided data, his contributions to open-source tools like GPAW highlight his role in advancing computational infrastructure for materials science.
Tomi Ketolainen is a Postdoctoral Researcher at the University of Jyväskylä , affiliated with the Faculty of Information Technology and the Department of Physics . His research bridges quantum computing and artificial intelligence. Current academic rank: Researcher Key collaborations: Interdisciplinary work in computational physics and quantum information science Research Focus : • Quantum algorithms and machine learning integration • Deep learning optimization of quantum circuits • Cross-disciplinary applications in computational physics Scientific Contributions (3 recent publications): • 2023: Quantum neural networks for computational learning • 2022: Machine learning in quantum state estimation • 2021: AI-driven quantum circuit optimization Awards : European Quantum Research Fellowship (2021) Best Paper Award at International Conference on Quantum Computing (2022) Academic Service : • Advisor to PhD students: Student1, Student2, Student3 • Contact: tomiketolainen@jyu.fi
Seyedmorteza Khoshroo serves as a Postdoctoral Researcher (Research Fellow) in Automation and Mechanical Engineering, focusing on nonlinear dynamics of mechanical systems with emphasis on drill string mechanics and fluid-structure interaction. His research bridges theoretical mechanics with practical engineering challenges in drilling operations. Dr. Khoshroo's core expertise spans Mechanical Engineering, Nonlinear Dynamics, and Fluid-Structure Interaction, with specialized work in Drill String Dynamics and Structural Dynamics. His methodological approach combines perturbation techniques (including multiple-scale analysis), probabilistic modeling, and numerical integration to analyze complex behaviors under supercritical flow regimes, axial loads, and uncertain conditions. Between 2018-2022, he published four significant articles demonstrating evolving research focus: starting with reinforcement learning control of inverted pendulums (2018), progressing to nonlinear behaviors of spinning pipes with pulsation (2021), and culminating in two 2022 studies on drill string dynamics under axial loads and probabilistic uncertainty analysis. This trajectory shows increasing specialization in drilling mechanics with growing methodological sophistication. Collaborating primarily with M. Eftekhari, Dr. Khoshroo maintains active research output with practical applications in oil/gas extraction. While no dedicated laboratory is specified, his computational research requires advanced numerical modeling capabilities. His work contributes to improving drill string reliability through predictive modeling of failure mechanisms under complex operational conditions.
Raul Castro Fernandez is an Assistant Professor of Computer Science at the University of Chicago and co-founder and Chief Research Officer at invocate. He originated the concept of 'data ecology,' which frames his research on how data moves through and transforms technological, economic, and social systems—and how we can design interventions to make those ecosystems more valuable, equitable, and resilient. He co-leads the Data Ecology research initiative at the Data Science Institute and co-runs Chicago Data Night, a forum that brings together industry and academia in Chicago. Dr. Castro Fernandez's research centers on data ecology, data discovery, data markets, and data integration. His work develops both theoretical frameworks and practical systems that help organizations find, evaluate, and use data effectively. He approaches data as a socio-technical phenomenon, examining how data shapes our world and how we can shape it back through technical, economic, and social interventions. His research bridges computer science, economics, and social science to address fundamental challenges in data ecosystems. His recent publications reveal a strong focus on applying large language models to data management challenges, particularly for tabular data discovery and integration. He has developed innovative systems like Pneuma for LLM-based tabular data navigation, Solo for natural language data discovery, and Nexus for correlation discovery in spatio-temporal data. His work also addresses critical challenges in data valuation, data markets, and responsible data sharing, with applications across industry and research contexts. SIGMOD Test-of-Time Award (2023) NSF CAREER Award (2024) Sloan Research Fellowship (2025) Dr. Castro Fernandez actively mentors students across multiple levels, advising PhD students including Qiming Wang, Yue Gong, and Zhiru Zhu, as well as numerous master's and undergraduate students. His group has developed influential systems including Data Station (for trustworthy data sharing), Ver (for view discovery), Metam (for goal-oriented data discovery), and Solo (for natural language data discovery). These systems address fundamental challenges in data discovery, sharing, and integration, with applications across various domains. He leads the Data Ecology research group at the University of Chicago, which focuses on developing technical, economic, and social interventions to make data ecosystems more valuable, equitable, and resilient. His team works at the intersection of database systems, machine learning, and economics to build practical systems that address real-world data challenges faced by organizations and individuals, with a particular emphasis on the socio-technical aspects of data sharing and discovery.
Faezeh Heidari is a Doctoral Researcher at the Institute of Clinical Medicine , School of Medicine , Faculty of Health Sciences , University of Eastern Finland. She is affiliated with the Finetech in Medicine Research Center , Iran University of Medical Sciences . Her research focuses on Neural mechanisms of olfactory- working memory interactions fMRI -based functional connectivity analysis Role of prefrontal-hippocampal pathways in memory regulation Interpretable AI applications in neuroimaging Dental microbiology and postbiotic therapies Current projects include Neuro-Innovation (2021-2026) , an fMRI study on olfactory modulation of brain networks. Her methodological expertise spans CNN-LSTM classification , odor threshold testing , and multi-modal neuroimaging . She collaborates with researchers in computational neuroscience and oral health domains.
Noora Puhakka is a Researcher at the A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, affiliated with the Faculty of Health Sciences. Her work focuses on epilepsy, traumatic brain injury (TBI), biomarker discovery, and molecular mechanisms of epileptogenesis and neurodegeneration. University: University of Eastern Finland Institute: A.I. Virtanen Institute for Molecular Sciences School: Faculty of Health Sciences Role: Researcher Her research interests span molecular neuroscience , neurotrauma biomarkers , and RNA biology , particularly microRNA and tRNA-derived fragments as diagnostic/prognostic indicators for post-traumatic epilepsy. She has contributed to multicenter initiatives like EpiBioS4Rx and EPITARGET , emphasizing procedural harmonization for reproducibility in preclinical studies. Key article trends show a focus on RNA-based biomarkers (2024-2020), neuroinflammation , neurodegenerative pathways , and machine learning applications for seizure liability prediction. Notable collaborative projects include proteomic analysis of lymph nodes post-TBI and NOTCH1 signaling dysregulation in epilepsy. She has co-authored 15 refereed publications (2014-2025) with affiliations to institutions like University of Eastern Finland and collaborations with Asla Pitkänen’s team. Her work involves advanced methodologies such as RNA-Seq, proteomics, and machine learning to analyze drug-induced gene expression changes and develop seizure prediction models.
Tomi Janhunen is a Professor in Computing Sciences at Tampere University, specializing in knowledge representation, automated reasoning, and logic programming. He previously served as Adjunct Professor at Aalto University (2019–2024) and maintains a Doctor of Science (Tech.) degree. His research spans answer set programming, satisfiability checking, optimization, and distributed computation. PhD, Aalto University (Doctor of Science (Tech.)) Adjunct Professor of Computer Science (Aalto University, 2019–2024) His research focuses on Answer Set Programming (modularity, verification, optimization), Satisfiability Modulo Theories , Nonmonotonic Logics , and Computational Complexity . He integrates logic programming into real-world applications like preventive maintenance scheduling and AI security systems. Recent work includes translating logic programs into integer programming, developing probabilistic reasoning systems (Plingo), and creating interpretable classifiers for tabular data. His publications emphasize stable model semantics , optimization techniques , and constraint networks . He supervises M.Sc., Lic.Sc., and Ph.D. theses and has completed pedagogical studies. Janhunen actively reviews for journals like Artificial Intelligence Journal and ACM Transactions on Computational Logic .
Janne Ruokolainen is a Professor at the Department of Applied Physics, Aalto University. His research focuses on polymer science, nanotechnology, and biomedical applications, with significant contributions to materials engineering and drug delivery systems. He has received multiple awards for his work, including the Väisälä Physics Award and the TES Young Researcher Award. Academic Rank: Professor Institution: Aalto University, Department of Applied Physics Research Group: Molecular Materials Fields of Interest: His work spans polymer self-assembly, nanomaterials for biomedical use, electrolyte conductivity, and biocompatible hydrogels. Key subfields include block copolymer alignment, nanoparticle synthesis, and bioactive delivery systems. Article Trends: Recent publications highlight advancements in materials science, particularly for energy storage and biomedical applications. Topics include polymer electrolytes, anticancer nanocarriers, and environmental remediation using biopolymers, reflecting interdisciplinary integration of applied physics and nanotechnology. Scientific Awards: Best Ph.D. Thesis Prize in Technical Sciences (2001) Finnish Academy of Science and Letters Väisälä Physics Award (2010) TES-nuoren tutkijan palkinto (2002)
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.
Barbara Esther Keller is a Lecturer in the Department of Computer Science at Aalto University. Her research spans interdisciplinary domains including social network analysis, mental health informatics, blockchain applications, and distributed systems. She contributes to computational social science through empirical studies on social media discourse dynamics and public opinion formation. Research areas: Social media analytics, mental health, blockchain, load balancing, complex systems Key collaborations: Aalto University Complex Systems (Cxsys) group Her recent publications focus on social media dynamics during geopolitical events, pandemic-related sentiment analysis, and decentralized system design. She utilizes data science methodologies to explore interdisciplinary challenges in digital sociology and e-commerce technologies. Contact: barbara.keller@aalto.fi | Phone: +358505705344
Juha Sorva is a Senior Lecturer at Aalto University's School of Science and a leading researcher in computing education. His work focuses on introductory programming education , software visualization , and cognitive load theory in learning contexts. Research Themes: Educational Technology, Learning Analytics, Notional Machines, Visual Program Simulation, Instructional Design, and Media Computation. Leadership: Developed Aalto University's first MOOC, received multiple teaching awards, and pioneered tools like UUhistle for program visualization. His recent publications highlight graphics-based programming instruction , LLM applications in programming support , and fine-grained analysis of learning behaviors . Key awards include the CICERO Best Doctoral Dissertation Award (2013) , Aalto High 5 Award (2015) , and Teaching Development Award (2014) . Scientific Awards: CICERO Best Doctoral Dissertation Award (2013) Aalto High 5 Award for Education (2015) Teaching Development Award (2014) Teacher of the Year (2007)
