جستجوی استاد راهنمای دانشگاهی

Garg Vikasمشاهده پروفایل

استادیار

• Quantum Computing
• Artificial Intelligence
• Machine Learning
• +۸ مورد دیگر

Professor Garg Vikas holds the position of Assistant Professor in the Department of Computer Science at the School of Science. His research spans quantum computing, artificial intelligence, and machine learning with applications in computational biology, healthcare, and drug design. He leads the HEALED/Garg project focused on human-steered machine learning for drug discovery and collaborates with institutions like MIT and industry partners. He co-founded YaiYai Oy, providing AI/ML solutions to global sectors. Education: PhD from MIT CSAIL under Tommi Jaakkola, with postdoctoral and industry experience at Amazon, Microsoft, and IBM. His work aligns with UN SDGs, particularly in healthcare and sustainable energy. Research interests include graph neural networks, generative models, and quantum AI. Recent projects involve climate modeling via physics-informed neural ODEs and optimizing quantum circuits using graph autoencoders. Key collaborations include MIT’s MLPDS Consortium and the Finnish Center for Artificial Intelligence. He supervises doctoral researchers like Yogesh Verma and postdocs such as Kogkalidis.

• Bioinformatics
• Genomic Epidemiology
• Metagenomics
• +۳ مورد دیگر

Tommi Mäklin is a researcher affiliated with the University of Helsinki, conducting interdisciplinary research at the intersection of bioinformatics, microbiology, and genomic epidemiology. His work focuses on bacterial pathogen analysis, metagenomics, and the application of computational methods to study infectious diseases. Mäklin has contributed to high-impact studies on topics such as colibactin-producing Escherichia coli's link to cancer incidence, hospital-acquired infections during the pandemic, and enhanced metagenomic analysis tools like TRACS and Themisto. Key affiliations include visiting research positions at the University of Oslo (2024) and EMBL European Bioinformatics Institute (2023–2023), where he received the Theory@EMBL Visitor fellowship. His research has been featured in prominent journals like Nature Communications and The Lancet Microbe , with findings highlighted in media such as Yle and HS. Education: Not explicitly stated in provided materials. Research Interests: Genomic epidemiology, bacterial pathogen transmission, metagenomics, antibiotic resistance, and computational methods for microbial analysis. His recent work explores geographical cancer incidence correlations with bacterial exposure, hospital pathogen surveillance, and scalable genomic tools for outbreak analysis. Awards include recognition for methodological advancements in bioinformatics and microbial genomics.

Sanni Kotimäkiمشاهده پروفایل

مدرس

• Life-course research
• Socioeconomic health disparities
• Intergenerational effects
• +۱۰ مورد دیگر

Sanni Kotimäki is a University Lecturer at the INVEST Research Flagship Centre, University of Turku, where she conducts research on life-course dynamics, socioeconomic health disparities, intergenerational effects, and the role of early-life experiences. Her work integrates quantitative methods and applied statistics to analyze complex social and health outcomes. Research Interests: Her research centers on understanding how parental resources, childhood environments, and socioeconomic conditions shape health and wellbeing across the lifespan. She investigates mechanisms behind health inequalities, including the influence of parental education, mental health, and family structure. A key focus is on intergenerational transmission of disadvantage and resilience, particularly through the lens of family policies such as parental leave. Publication Trends: Her recent publications (2020–2024) reflect a consistent focus on population health, using sibling fixed-effects models, administrative data, and mixed-methods approaches. Themes include fatherhood and workplace peer effects on parental leave, birth outcomes and NEET status, prenatal mental health disparities, and the long-term impacts of parental death. These works demonstrate methodological rigor and policy relevance. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no mention of students or advising roles in the available information. While no specific grants are listed, her affiliation with the INVEST Research Flagship Centre suggests involvement in large-scale, funded research initiatives focused on social and health disparities. Labs and Teams: Sanni is actively involved with the INVEST Research Flagship Centre, a multidisciplinary research environment at the University of Turku dedicated to understanding early-life determinants of health and wellbeing. She collaborates closely with researchers such as Satu Helske, Simon Chapman, and Juho Härkönen, contributing to both academic and public-facing outputs including blogs and policy reports.

Pietari Hannikainenمشاهده پروفایل

مدرس

• Religious Pedagogy
• Religious and Ideological Education
• Christian Communities
• +۶ مورد دیگر

Pietari Hannikainen is a University Lecturer in Practical Theology at the Faculty of Theology, University of Helsinki. His work focuses on religious pedagogy, supervision of theses (Bachelor's to doctoral levels), and quantitative research methods. He actively engages in projects analyzing men's religiosity, confirmation education, and urban religious communities. Research Interests: Religious education, young adult religiosity, religious literacy, value polarization, and well-being in faith contexts. Projects: Men, Masculinities and Religion (2024–2028), Confirmation self-evaluation steering (2024–), and Millenniaalien kirkko (2021–2023). Media Contributions: 13 public engagements on worship communities, religious literacy, and confirmation trends.

Jari Elorantaمشاهده پروفایل

استاد

• Economic and Social History
• Cliometrics
• War Economy
• +۳ مورد دیگر

Jari Eloranta is a Professor at the University of Helsinki , affiliated with the Faculty of Social Sciences and the Department of Economic and Social History . His work bridges economic history, cliometrics, and the analysis of warfare and state formation. Eloranta holds a Ph.D. in Economic History and has served in various external roles, including as President of the Economic and Business History Society (2018–2019) and as a member of the International Economic History Association (2014–2021) . He has edited journals like Configurações and book series for Routledge. Research Focus His research spans the economic history of warfare , Nordic economic convergence , and quantitative methods in business history . Notable projects include Nordic Fiscal States from the 16th to the 21st Centuries and Suomen innovaatioiden historia . Publications and Collaborations Eloranta has published extensively, with recent works appearing in The Routledge Economic History of War and Handbook of Cliometrics . His collaborations span universities in Finland, the US, and Europe, focusing on themes like war economies , state capacity , and security policy . Scientific Awards Recipient of the Eino Jutikkala historiapalkinto (2022). Awarded the Jonathan Hughes Prize for Excellence in Teaching Economic History (2021). Supervision and Editorial Roles Eloranta supervises doctoral candidates in the Doctoral Programme in Political, Societal and Regional Change and serves as an editor for research journals and anthologies, including Small and Medium Powers in Global History (2019) and Economic History Review (2009).

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +۴ مورد دیگر

Ti John is a Research Fellow at Aalto University's Department of Computer Science within the School of Science. He is affiliated with Professor Marttinen's research group and the Probabilistic Machine Learning group led by Professor Samuel Kaski. His work connects with the Finnish Center for Artificial Intelligence (FCAI) and the Helsinki Institute for Information Technology (HIIT). Dr. John's research focuses on machine learning, particularly Bayesian optimization, Gaussian processes, and point process models. His work spans theoretical developments in neural processes and practical applications in healthcare analytics and large language models. He has made significant contributions to equivariant neural processes, causal mediation analysis in healthcare, and interpretability of additive models. His publication record shows consistent output with 17 publications between 2021-2024, including multiple papers at top AI conferences like NeurIPS, ICML, and ICLR. His research demonstrates strong interdisciplinary connections between statistical modeling, artificial intelligence, and healthcare applications. Active reviewer for NeurIPS, ICLR, AISTATS Reviewer for Journal of Machine Learning Research Member of Finnish Center for Artificial Intelligence project Dr. John has been actively contributing to the machine learning community through peer review and conference participation, demonstrating expertise across multiple subfields of artificial intelligence and statistical modeling.

• Materials Science
• Nanotechnology
• Physics
• +۳ مورد دیگر

Ville Jantunen is a Researcher and Supervisor in the Doctoral Programme in Materials Research and Nanosciences. His research focuses on atomistic simulations of materials under extreme conditions, including ion irradiation effects, defect evolution in fusion materials, and nanoparticle dynamics. He is actively involved in the SPATEC project (2022–2026), funded by the Academy of Finland, which explores time and spatial dependence of cascade damage in materials under pulsed ion beams. Key research areas include computational materials science, radiation effects in nanomaterials, and predictive modeling of electronic/atomic phenomena. His work spans both fundamental and applied aspects, with contributions to quantum technology through spin-qubit array studies and fusion energy via tungsten defect analysis. Collaborations include international teams on nanoparticle shape transformation mechanisms and kinetic Monte Carlo simulations. He has organized educational outreach activities like the LEGO lab workshop at Helsinki Natural Science Lyceum (2018), demonstrating engagement in science communication. Publications emphasize interdisciplinary approaches, combining computational methods with experimental insights to address challenges in nanotechnology, fusion materials, and radiation physics.

Kaius Sinnemäkiمشاهده پروفایل

استاد

• Linguistics
• Language Typology
• Comparative Linguistics
• +۹ مورد دیگر

Kaius Sinnemäki is a Professor of Quantitative and Comparative Linguistics at the University of Helsinki, affiliated with the Department of Languages under the Faculty of Arts. Since 2025, he has directed the strategic profiling action Diversity in Society and Life (DIVSOL) , funded by the Research Council of Finland, and previously led the ERC Starting Grant project Linguistic Adaptation (2019-2024). His research focuses on large-scale comparative linguistics, integrating language typology with sociolinguistics. Key areas include core argument marking, statistical methods in typology, linguistic complexity, language contact, and the interplay between language and religion. He teaches courses spanning language typology, evolution, sociolinguistics, and quantitative methods. Recent publications address replication in linguistic research, adaptation in contact ecologies, and complexity measures across phonological, morphosyntactic, and syntactic domains. His work often bridges theoretical linguistics with empirical data analysis, utilizing open datasets and computational tools. Scientific Awards: Prize for the Humanities (2021) He actively supervises academic theses, serves on editorial boards (e.g., Linguistic Typology ), and participates in international collaborations, peer reviews, and media engagements on linguistic diversity and secularism.

Jarno Vanhataloمشاهده پروفایل

استاد

• Statistics
• Probability
• Ecology
• +۶ مورد دیگر

Professor Jarno Vanhatalo is a Professor of Statistics at the University of Helsinki, serving as vice director of the Research Center for Ecological Change. He is affiliated with both the Faculty of Biological and Environmental Sciences and the Faculty of Science, where he leads the Environmental and Ecological Statistics Group. His work bridges advanced statistical methodology with pressing ecological and environmental challenges. His research focuses on the development and application of statistical models for ecological and environmental data. Key areas include: Bayesian statistics and hierarchical modeling Gaussian processes and spatial statistics Species distribution modeling Ecological risk assessment Climate change impact analysis Biodiversity monitoring and conservation Professor Vanhatalo's publication record shows a consistent focus on integrating sophisticated statistical methods with ecological applications. His recent work demonstrates increasing emphasis on climate change impacts on biodiversity, spatially explicit modeling of ecological processes, and the development of Bayesian methods for uncertainty quantification in environmental predictions. His research spans terrestrial and marine ecosystems, with particular attention to Nordic and Baltic regions. Among his notable contributions are advancements in: Joint species distribution modeling Bayesian calibration of environmental models Spatiotemporal survey design Model-based variance partitioning in ecological studies Statistical approaches to Arctic risk management Professor Vanhatalo has received significant research funding, including an ERC grant for "Predective Understanding of the effects" (2024-2029) and leadership roles in multiple Academy of Finland and EU-funded projects focused on biodiversity, ecological change, and risk management in polar waters. He actively contributes to the academic community through: Supervising doctoral students in Wildlife Biology and Mathematics and Statistics programs Serving on editorial boards for journals including Conservation Biology and Ecology Letters Peer reviewing for numerous statistical and ecological journals Organizing workshops on statistical methods in ecology

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +۳ مورد دیگر

Daolang Huang is a Doctoral Researcher and Student in the Department of Computer Science at the School of Science, affiliated with Professor Samuel Kaski's group. He holds a Bachelor's degree in Engineering and Technology from Jinan University (2020). His research focuses on advanced machine learning techniques, including Bayesian inference, robust statistical modeling, and simulation-based methods. Key areas include experimental design optimization, neural processes, and equivariance in deep learning. Recent work emphasizes decision-aware algorithms and cost-effective simulation frameworks. Huang has collaborated internationally, with publications in top venues like NeurIPS. Despite no listed awards, his work demonstrates significant contributions to probabilistic modeling and optimization. Education : Bachelor's degree in Engineering and Technology, Jinan University (2020) Research Interests : Bayesian methods and amortized inference Robust statistics under model misspecification Continuous control and neural process architectures Optimization algorithms with decision-theoretic foundations Recent Research Trends : His articles (2020–2025) emphasize Bayesian experimental design, preference-based optimization, and equivariant neural networks. Themes include balancing statistical rigor with computational efficiency, particularly in high-dimensional decision-making contexts. Labs/Teams : Active member of Samuel Kaski’s research group, focusing on interdisciplinary applications of machine learning.

Kati Launisمشاهده پروفایل

استاد

• Digital Literary Studies
• 19th Century Literary History
• Women Writers
• +۴ مورد دیگر

Kati Launis is a Professor of Literature (Literary Culture) at the University of Eastern Finland, specializing in digital literary studies and 19th-century women writers . She works within the School of Humanities and leads the Digital History for Literature in Finland project (2022-2026) funded by SA. University affiliation: University of Eastern Finland Academic rank: Professor Research group: SUVEF - Gender Studies Network Research Interests Her work focuses on digitized literary history, gendered authorship, and the application of computational methods to analyze historical reading patterns. Key areas include: Digital humanities approaches 19th-century Finnish literary culture Women's writing in Finland Library loan data analysis Corpus stylistics Cultural exchange mapping Recent Article Trends Her publications emphasize digital methodologies (2023-2025) for analyzing historical reading habits , gender roles , and cultural metadata , with a focus on Finnish and Nordic contexts. Collaborations Works frequently with interdisciplinary teams on projects like: Co-Loan Analysis LibDat Digital metadata archives

Steven Collinsمشاهده پروفایل

دانشگاهی

• Timber Engineering
• Structural Reliability
• Sustainability
• +۷ مورد دیگر

Steven Collins is a Professor of Practice in Civil Engineering at Aalto University , specializing in industrial wood construction. His work bridges scientific research and industry applications to enhance timber's role in sustainable structural engineering. Research Interests : Species-specific timber behavior (particularly birch), focusing on modeling, testing, and variance analysis. Non-destructive testing techniques like laser scattering, grain orientation analysis, and digital image correlation. Statistical and Bayesian approaches for probabilistic modeling of material properties and uncertainty quantification. Timber finger joint mechanics and wood processing techniques (e.g., densification, chemical treatment). Scientific Contributions : His recent publications (2022–2025) emphasize mechanical behavior of timber , predictive modeling , and innovative processing methods . Key trends include optimizing birch timber for structural applications, advancing Bayesian statistical models for wood mechanics, and improving non-destructive testing protocols using laser technologies and digital image correlation. Laboratory & Collaborations : Actively involved in experimental testing (destructive and non-destructive) and statistical analysis of wood properties. Collaborates with researchers like Gerhard Fink , Lauri Rautkari , and Farid Vafadar on timber mechanics and reliability studies. Part of the Structures – Structural Engineering, Mechanics and Computation research group at Aalto University.

• Bayesian Statistics
• Reliability Engineering
• Risk Analysis
• +۴ مورد دیگر

Ahmad BahooToroody is an Academy Research Fellow at Aalto University’s Department of Energy and Mechanical Engineering, specializing in Bayesian statistics, reliability engineering, and risk analysis for autonomous maritime systems. He is actively involved with the Marine and Arctic Technology research group and leads projects that integrate machine learning with safety-critical applications in the maritime and offshore sectors. Research Interests Bayesian statistical methods for reliability and risk modeling Machine learning and deep learning for anomaly detection in autonomous systems Safety assessment of offshore installations and maritime operations Prognostic health management of marine renewable energy systems Human factors and expert judgment in sociotechnical maritime systems His work often incorporates advanced techniques such as Gaussian processes, LSTM-based neural networks, and dynamic Bayesian networks to address uncertainties in complex engineering systems operating in harsh marine environments. Publication Trends Across 2022–2025, BahooToroody’s publications reveal a strong trajectory toward integrating data-driven models with physics-based simulations. Dominant themes include real-time risk monitoring of autonomous ships, failure prognosis for unattended machinery, and safety assessment frameworks for offshore structures. His collaborative outputs span high-impact journals such as Reliability Engineering & System Safety and Safety Science , underscoring his leadership in maritime safety analytics. Research Groups & Labs Marine and Arctic Technology Research Group, Aalto University Academy Research Fellow network within the Department of Energy and Mechanical Engineering Doctoral Supervision & Grants While specific grant details are not listed, his role as Doctoral Candidate Supervisor since 2020 indicates active mentorship of PhD researchers. He is also the Principal Investigator on projects funded under the Academy of Finland Fellowship scheme, supporting next-generation maritime risk analytics.

Lu Chengمشاهده پروفایل

استاد مهمان

• Genomics
• Bioinformatics
• Microbial Genetics
• +۳ مورد دیگر

Lu Cheng is a Visiting Professor in the Department of Computer Science at the University of Helsinki, affiliated with the Vehtari Aki Professorship. He holds a Doctor of Philosophy in Natural Sciences from the University of Helsinki (2013). His research focuses on computational genomics, bioinformatics, and microbial genetics, with emphasis on DNA sequence analysis, nanopore sequencing technologies, and systems biology. He leads projects on alternative splicing in cancer and the impact of microbiota on human health. Notable contributions include the NanoBaseLib benchmark dataset and methods for RNA modification analysis. His work addresses UN Sustainable Development Goals related to good health and innovations in data science. Education: Doctor of Philosophy in Natural Sciences (2013), University of Helsinki; Doctoral degree in Natural Sciences (2013), University of Helsinki. Research Interests: Genomics, computational biology, microbial ecology, RNA sequencing technologies, and bioinformatics tool development. His projects explore bacterial population dynamics, host-pathogen interactions, and applications of machine learning in genomics. Advising: Supervises doctoral researchers including Guangzhao Cheng and Chengbo Fu. Active in grants such as the Academy of Finland Research Fellowship (2023-2025). Labs/Teams: Leads research groups focused on single-cell genomics and computational methods for biological systems.

Lasse Heikkinenمشاهده پروفایل

مدرس ارشد

• Physics
• Applied Physics
• Process Tomography
• +۵ مورد دیگر

Lasse Heikkinen is a Senior Lecturer at the Department of Technical Physics, Faculty of Science, Forestry and Technology, University of Eastern Finland. His work spans applied physics, process tomography, and educational technology, with a focus on innovative teaching methods like flipped classrooms during the pandemic. Current Role: Deputy Head of Department, Senior University Lecturer Research Themes: Electrical impedance tomography for industrial processes, flipped teaching frameworks, and physics education adaptation to remote learning. Recent publications highlight his dual expertise in process tomography (gas-solid flows, pharmaceutical monitoring) and pedagogical innovation (learning analytics, flipped classrooms). He has contributed to educational manuals and toolkits for teacher training. Key Projects: Technology education infrastructure development (2015–2024), pandemic-era teaching adjustments (2022). Collaborations: Active in international process tomography conferences and multidisciplinary teams like the Ameba project.