Find Academic Supervisors

• Machine Learning
• Health Informatics
• Transportation Modeling
• +4 more

Jaakko Hollmen serves as a Senior University Lecturer in the Department of Computer Science at Aalto University, affiliated with the Helsinki Institute for Information Technology (HIIT) and the Computer Science Lecturers research group. His interdisciplinary work bridges machine learning with critical applications in healthcare, transportation systems, and environmental science. His research focuses on advanced machine learning methodologies including Bayesian optimization, random forests, and principal component analysis. Key application areas span neonatal healthcare (mortality prediction, brain injury analysis), transportation modeling (activity-based model calibration), and environmental data science (weather-crop relationships, drug-environment interactions). His approach emphasizes practical implementations of complex algorithms for real-world problems. Hollmen's recent publications (2018-2023) reveal a consistent trajectory in developing machine learning solutions for high-dimensional data challenges, with increasing emphasis on medical applications. His work demonstrates strong cross-domain collaboration, particularly with medical researchers at Helsinki University Hospital. His notable recognition includes: Best paper finalist and runner-up award (Computer Track) at the first IEEE Life Sciences Conference (LSC) for research on predicting complications in very low birth weight infants (2017) As a core member of HIIT, Hollmen contributes to Finland's national information technology research infrastructure while maintaining active collaborations with medical and agricultural research groups. His current projects focus on optimizing transportation models and advancing clinical prediction systems through novel machine learning techniques.

• High Energy Physics
• Experimental Particle Physics
• Medical Imaging
• +3 more

Erik Brücken is a University Lecturer and Docent at the Department of Physics, University of Helsinki. He actively participates in the Doctoral Programme in Materials Research and Nanosciences, as well as the Doctoral Programme in Particle Physics and Universe Sciences. His research spans high-energy physics, experimental particle physics, and detector development. Primary affiliation: Department of Physics, University of Helsinki Supervisory roles in two doctoral programs Active in CMS and ALICE collaborations Research Interests: Brücken's work focuses on experimental particle physics at colliders like the LHC, particularly in detector optimization and data analysis. His projects include the ALICE Time Projection Chamber Upgrade and multispectral photon-counting for medical imaging. Current research examines B meson decays, Higgs boson properties, and jet substructure in heavy-ion collisions. Recent Publications: His 2025 contributions include analyses of B0 meson decays, Higgs self-coupling constraints, top quark production cross-sections, and jet substructure studies in nuclear collisions. Projects: He leads a 2024-2027 European particle physics infrastructure project (TOTEM) and participates in collaborations related to detector development and beam characterization. Past projects include CMS Tracker upgrades and CDF experiments spanning 2002-2017. Academic Activities: Brücken organizes and presents at international conferences like the European Researcher's Night and Euroschool on Exotic Beams. He serves as a doctoral thesis committee member and contributes to peer review processes.

Markku LanneView profile

Professor

• Econometrics
• Time Series Analysis
• Macroeconomics
• +3 more

Markku Lanne is Professor of Economics at the University of Helsinki's Faculty of Social Sciences, where he has served since August 2010. He also holds the position of Vice-Dean for Academic Affairs of the Faculty of Social Sciences and leads the Financial and Macroeconometrics research group. His academic journey includes previous appointments as Professor of Empirical Macroeconomics at the University of Helsinki (2007-2010) and Professor of Economics and Finance at the University of Jyväskylä (2003-2007). He maintains adjunct professorships (docent) in econometrics at the University of Helsinki (since 2001) and in financial econometrics at Aalto University (since 2008). Professor Lanne's research focuses on advanced time series econometrics with applications in macroeconomics and finance. His work centers on structural vector autoregression models, non-Gaussian time series analysis, identification methods, and macroeconomic forecasting. His research has significant implications for understanding economic shocks, inflation dynamics, and the relationship between financial markets and the real economy. He has developed innovative approaches to identifying structural economic relationships using non-Gaussian features of economic data. Lanne's publication record shows a clear progression toward increasingly sophisticated methods for analyzing non-Gaussian economic time series. His recent work (2022-2024) demonstrates a strong focus on statistical identification of structural relationships using non-Gaussian features, particularly in the context of vector autoregressive models. This research has important applications for understanding macroeconomic shocks, weather impacts on economies, and improving economic forecasting methods. His scientific recognition includes: Alfred Kordelin Foundation encouragement prize (2011) Good Teaching Award (2014) Professor Lanne actively supervises doctoral students and serves as a pre-examiner for doctoral theses. He has secured significant research funding, most notably a major Academy of Finland project on structural analysis of non-Gaussian macroeconomic and financial time series (2022-2026). He regularly contributes to the academic community through peer review for top journals including Journal of Econometrics, Journal of Business and Economic Statistics, and Review of Economics and Statistics. He leads the Financial and Macroeconometrics research group at the University of Helsinki, which focuses on developing and applying advanced econometric methods to macroeconomic and financial problems. The group maintains active collaborations with researchers internationally and contributes to both theoretical advances in econometrics and practical applications in economic policy analysis.

Valtteri PeltonenView profile

Researcher

• Risk Management
• Quantitative Finance
• Market Risk
• +3 more

Valtteri Peltonen serves as a Doctoral Researcher in the Department of Accounting and Finance at the University of Turku, actively pursuing his PhD after completing his Master of Science in Economics and Business Administration from the same institution in 2017. His academic trajectory demonstrates focused progression from master's studies to doctoral research within Turku's finance ecosystem. Peltonen's research critically examines post-2008 financial risk modeling frameworks, specializing in market risk management deficiencies of pre-crisis models like Value-at-Risk (VaR). His work investigates regulatory shifts toward Expected Shortfall (ES) and innovative alternatives like expectiles—addressing critical gaps in subadditivity and elicitability while analyzing complex derivatives risk. This positions him at the intersection of quantitative finance, regulatory reform, and stochastic modeling. His recent publication in Stochastic Models exemplifies his methodological approach, applying advanced mathematical structures to financial risk problems. The work on discrete multivariate fields reveals connections between theoretical probability and practical risk measurement challenges. Scientific recognition includes: Award from Suomen Arvopaperimarkkinoiden Edistämissäätiö for Master's Thesis on delta-neutral derivative risk management Peltonen contributes to academic training as exercise instructor for Asset Pricing and Portfolio Theory (LRS31) and foundational finance courses (LR05), while supervising bachelor's theses and assisting with master's supervision—demonstrating active integration into Turku's finance education pipeline despite his early-career status.

• High Energy Physics
• Particle Physics
• Experimental Physics
• +3 more

Erik Brucken is a University Lecturer at the Department of Physics, University of Helsinki, affiliated with the Faculty of Science. He is also a supervisor for doctoral programs in Particle Physics and Universe Sciences, and Materials Research and Nanosciences. Research focuses on high energy physics and experimental particle physics, particularly in CMS experiment collaborations at CERN. Active in projects like the Euroopa hiukkasfysiikan tutki (2024–2027) and ALICE Time Projection Chamber Upgrade Project (2013–2018). His research interests include Higgs boson studies, top quark dynamics, jet physics, and detector development. He has published extensively on topics such as rare decays, muon identification, and quantum entanglement in collider experiments. Advises doctoral students in particle physics and collaborates widely, participating in conferences like the International Europhysics Conference on High Energy Physics and CMS Tracker Weeks. His work contributes to advancing detector technologies and computational tools for high-energy physics.

• Geropsychology
• Wellbeing and health
• Innovation expertise
• +9 more

Tuomas Leisti is a Lecturer and Doctor of Psychology (PsT) at Metropolia University of Applied Sciences, affiliated with the Wellbeing Team 3. His professional roles include teaching psychology and research methods, supervising Master's theses, and contributing to RDI (Research, Development, Innovation) projects. He holds certifications as a licensed psychologist and professional teacher (AmO), with proficiency in Finnish (native), English (excellent), Swedish (average), and German (basic). Leisti's research focuses on geropsychology, wellbeing and health, innovation expertise, cognitive psychology, and the philosophy of mind. His doctoral work explored the role of consciousness in decision-making, with prior experience in human-technology interaction. He is particularly interested in decision-making processes, problem-solving, and the metacognitive skills underlying these activities. Key projects include the Wellbeing Hybridist initiative (2021–2023) and the pan-European COMHOM project (2025–2028), which addresses homelessness through data-driven solutions. His research outputs span over two decades, emphasizing subjective image quality evaluation, cognitive modeling, and quantitative methodologies. Leisti's work integrates empirical and philosophical approaches, addressing topics such as qualia, decision heuristics, and the interplay between conscious thought and judgment. Despite no explicitly listed awards, his extensive publication record reflects sustained contributions to psychological and applied research fields.

• Statistical Machine Learning
• Exploratory Data Analysis
• Data Science
• +4 more

Jaakko Peltonen is a Professor of Statistics and Data Analysis at Tampere University, Faculty of Information Technology and Communication Sciences, Department of Computing Sciences. He leads the Statistical Machine Learning and Exploratory Data Analysis (SMiLE) research group and is affiliated with the Tampere Research Center in Information and Systems and the Academy of Finland Center of Excellence in Game Culture Studies. His research focuses on statistical machine learning, exploratory data analysis, and their applications in diverse domains including healthcare, social media analytics, gaming, and political discourse analysis. Notable projects include developing algorithms for transparent AI systems (e.g., TraQuLA, Flow of Trust framework) and analyzing online communities through platforms like Twitch and Steam. Recent work emphasizes ethical AI integration in education and healthcare, predictive modeling for emergency department management, and game studies involving player behavior analysis. Peltonen collaborates across disciplines, leveraging text mining, graph theory, and visualization techniques to address complex societal challenges. Key contributions span methodological advancements (e.g., constrained non-negative matrix factorization) and interdisciplinary applications in policy analysis, digital humanities, and health informatics. His work bridges technical innovation with real-world societal impact through collaborative research initiatives.

• Biostatistics
• Public Health
• Nutrition Science
• +4 more

Jaakko Nevalainen is a Professor of Biostatistics and Head of the Health Sciences Unit at the University of Tampere , Faculty of Social Sciences. His work integrates computational methods and statistical theory to address health-related research questions in nutrition, diabetes, prostate cancer, and cardiovascular disease. He leads the LoCard research consortium, developing computational methods to study food consumption and health sustainability. His research has received significant funding from the Academy of Finland and Horizon Europe, resulting in over 160 peer-reviewed publications in top journals like Statistics in Medicine , Diabetologia , and JAMA . Key research areas include biostatistical method development, health economics, and applied epidemiology. His teams collaborate with institutions like the University of Helsinki and participate in population studies such as ProScreen (prostate cancer screening) and DIPP (type 1 diabetes). Major projects focus on food purchasing patterns, obesity-related health outcomes, and sustainable dietary transitions. He has secured major grants including Academy of Finland funding for the ProScreen trial and EU Horizon grants for LoCard. His interdisciplinary approach bridges statistical innovation with real-world health challenges, emphasizing translational research and data-driven policy recommendations.

Jouni HelskeView profile

Researcher

• Bayesian statistics
• Causal inference
• State space models
• +7 more

Jouni Helske is an Academy Research Fellow in Statistics at the University of Turku, Finland, affiliated with the INVEST Research Flagship Centre. He leads the CAUSALTIME project and is a subconsortium-PI in the PREDLIFE consortium at the University of Jyväskylä. His work bridges statistical methodology and applied research in social sciences and epidemiology. Academy Research Fellow, University of Turku PI, CAUSALTIME Project Subconsortium-PI, PREDLIFE Consortium, University of Jyväskylä Associate Editor, The R Journal and rOpenSci Open Science Ambassador, Open Science Community Turku Education: PhD in Statistics, University of Jyväskylä, Finland (2015) Jouni Helske’s research centers on developing advanced Bayesian methods for causal inference, particularly using complex multivariate time series and panel data. His expertise includes state space models, hidden Markov models, computational statistics, and probabilistic programming. He is deeply involved in statistical software development, especially in the R ecosystem, contributing to open science and reproducible research. His applied work spans sociology, education, public health, and epidemiology, where he analyzes longitudinal and sequential data to understand causal mechanisms and life course trajectories. The recent publications highlight a strong trend in methodological innovation for causal analysis in panel data, spatio-temporal disease modeling, and R package development. His work integrates Bayesian computation with real-world applications, especially in social policy and health, using historical and contemporary data. The focus on dynamic multivariate models and sequence analysis underscores his leadership in modern statistical methodology for complex data. Scientific Awards and Recognition: Academy Research Fellow (prestigious research position funded competitively) Jouni Helske has led and contributed to major research projects such as CAUSALTIME and PREDLIFE, which aim to improve policy decisions through predictive modeling of life trajectories. He mentors and collaborates widely, evidenced by his numerous co-authored publications and software projects. While no formal students are listed, his role as a project leader and software maintainer suggests significant advisory and collaborative activity. He is a key contributor to the open-source statistical community, particularly through rOpenSci and Stan. Labs and Teams: He leads the CAUSALTIME project team and is part of the PREDLIFE research consortium. He is actively involved in the R and Stan developer communities, contributing to state-of-the-art Bayesian computational tools.

Henning KirschenmannView profile

Professor

• Particle Physics
• High Energy Physics
• CMS Experiment
• +5 more

Henning Kirschenmann is a tenured Professor at LUT School of Engineering Sciences and an Academy Research Fellow at the Helsinki Institute of Physics . He specializes in jet-energy corrections , hadronic final states , and machine learning applications for particle physics data analysis. Education Dr. rer. nat. (Physics) from University of Hamburg (2011–2014) Dipl. Phys. (Physics) from University of Hamburg (2010–2014) Research Interests His research focuses on particle physics with emphasis on CMS experiment data analysis. Key areas include jet energy corrections , top quark properties , Higgs boson decays , and quantum entanglement studies. He develops machine learning techniques for detector optimization and trigger systems , while investigating new physics through dark matter signatures and long-lived particles . Scientific Contributions Kirschenmann has contributed to 15+ CMS experiment publications since 2024, covering: Higgs boson and Z boson rare decay analyses Top quark polarization and spin correlations Jet substructure studies in proton-proton and heavy-ion collisions Detector performance evaluations for muon reconstruction and electromagnetic calorimetry Statistical analysis of multijet events and WW production Awards & Grants Research Council of Finland Starting Grant (1.1 million EUR) Academy Research Fellow (2024–present) Leadership Roles He has led multiple CMS subgroups including: Group leader for top quark mass (2022–2023) Group leader for jets and MET (2020–2022) Group leader for jet energy corrections (2016–2018)