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Laura EloView profile

Professor

• Computational Biomedicine
• Medical Bioinformatics
• Data Science
• +7 more

Laura Elo serves as Professor of Computational Medicine and Head of the Medical Bioinformatics Centre at the University of Turku, Finland. She concurrently holds the position of Research Director at Turku Bioscience Centre and acts as InFLAMES Flagship Contact, driving interdisciplinary biomedical research initiatives. Her academic foundation includes a PhD in Applied Mathematics (2007) and Adjunct Professorship in Biomathematics (2011), establishing her quantitative expertise before transitioning into biomedical applications. Her research program focuses on transforming molecular and clinical datasets through statistical modeling and advanced machine learning . Key thrusts include robust computational tools for proteome/epigenome analysis, AI-driven digital health diagnostics, and computational systems immunology for immune-mediated diseases. This work directly addresses challenges in reproducibility and scalability of high-throughput biotechnology data. Analysis of her recent publications reveals dominant themes in type 1 diabetes biomarker discovery , multi-omics integration , and immune system modeling , with strong emphasis on clinical translation through collaborations with experimental and medical teams. Her scientific recognition includes: JDRF Career Development Award Professor Elo actively trains MSc/PhD students and postdoctoral fellows while leading major research initiatives including ERC grants. Her teaching portfolio spans Bioinformatics Journal Club, AI in Diagnostics, and Systems Biology courses. The Elo Lab (https://elolab.utu.fi) operates as a hub for computational biomedicine, developing open-source tools like CellRomeR while maintaining close ties with Turku Bioscience Centre's experimental facilities for validating computational predictions in immunology and metabolic disease contexts.

Markus HeinonenView profile

Research Fellow

• Machine Learning
• Probabilistic Modeling
• Bayesian Inference
• +5 more

Markus Heinonen is an Academy Research Fellow at Aalto University's Department of Computer Science within the School of Science. His academic position is tied to Harri Lähdesmäki's Professorship, focusing on probabilistic machine learning. He holds a Doctoral degree in Engineering and Technology from the University of Helsinki (2013). His research integrates probabilistic modeling , deep learning , and differential equations , with applications in computational biology, drug discovery, and biophysics. Key themes include Gaussian processes, Bayesian inference, generative models, and their use in understanding complex biological systems like immune cell behavior (e.g., T cell receptor analysis in aplastic anemia) and molecular design. He leads major projects such as the Deep Learning with Differential Equations initiative (2020–2025), exploring continuous-time models and physics-informed neural networks. His work bridges theory and application, evidenced by collaborations in diffusion models , optimal transport , and single-cell analysis . Publications span over 65 peer-reviewed outputs, with recent emphases on robust neural network training, multi-target molecular prediction, and interpretable drug design frameworks. His research contributes to UN Sustainable Development Goal 3 (Good Health) through advancements in disease modeling and therapeutic development. He has undertaken visiting research roles at the University of California, San Francisco (2017) and Telecom ParisTech (2013–2014). Media highlights include recognition for work on TCR-epitope prediction and AI-driven enzyme engineering.

Pekka MarttinenView profile

Associate Professor

• Machine Learning
• Large Language Models
• Reinforcement Learning
• +8 more

Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.

• Artificial Intelligence
• Data Science
• Computational Creativity
• +2 more

Hannu Toivonen is a Full Professor of Computer Science at the University of Helsinki, affiliated with the Faculty of Science and the Department of Computer Science. He leads the Discovery Research Group and is part of the Helsinki Institute for Information Technology (HIIT) and the Finnish Center for Artificial Intelligence (FCAI). He obtained his PhD in Computer Science from the University of Helsinki in 1996 and has held his professorship since 2002. His research spans Artificial Intelligence , Data Science , Computational Creativity , and Data Mining , with applications in generative art, automated journalism, and bioinformatics. His research focuses on: Developing AI systems for creative tasks (e.g., poetry, music, and art generation) Cross-lingual natural language processing and unsupervised learning Ethical and societal implications of AI in media and labor Analysis of his 15 most recent publications (2021–2025) reveals a strong emphasis on computational creativity and AI-driven content generation , with interdisciplinary applications in digital humanities, journalism, and biology. Trends include ethical AI frameworks, human-AI collaboration, and innovative evaluation methods for generative systems. Scientific Awards & Honors: Knight, First Class, Order of the White Rose of Finland (2019) Best Applied Research Award, IEEE ICDM (1998) Honorary Member of TKO-äly (2010) Member of Finnish Academy of Science and Letters He has supervised over 20 doctoral theses and secured ~7 MEUR in grants (e.g., EU projects: Embeddia , Newseye ). He leads the Discovery Research Group and collaborates with institutions like VUB (Belgium) as a Francqui International Professor (2025).

• Bioinformatics
• Computer Science
• Algorithms
• +3 more

Dr. Juha Kärkkäinen is a University Lecturer and Principal Investigator at the Department of Computer Science, University of Helsinki, specializing in Algorithmic Bioinformatics. He supervises PhD students in the Doctoral Programme in Computer Science and actively contributes to research outputs and academic events. Institution: University of Helsinki Department: Department of Computer Science Rank: Lecturer His research focuses on string processing , data structures , and combinatorial pattern matching , with recent work on bijective Burrows-Wheeler transforms, LCP arrays, and efficient string indexing. Google Scholar lists publications spanning 2024 to 1999, highlighting his expertise in algorithm design and compression techniques . Notably, he has participated in and organized events like the International Workshop on Combinatorial Algorithms and delivered a keynote speech at the 1st Summer School on Bioinformatics Algorithms. While no explicit awards are detailed, his editorial roles in scientific collections and conferences underscore his academic influence.

Simon PuglisiView profile

Professor

• Algorithms
• Bioinformatics
• Data Compression
• +3 more

Simon Puglisi is a Professor at the University of Helsinki's Department of Computer Science, within the Faculty of Science. His research focuses on algorithms, bioinformatics, data compression, and string processing, with notable contributions to genomic data analysis and efficient indexing techniques. He holds the Alberto Apostolico Best Paper Award (2021) and leads the WILL # CHAIR # BOSSA project (2025–2029). His work includes scalable k-mer indexing tools like Themisto and advancements in Lempel-Ziv compression and suffix tree algorithms. He frequently collaborates internationally, participates in editorial roles for journals like the ACM Journal of Experimental Algorithmics , and contributes to conferences such as the International Symposium on Combinatorial Pattern Matching. Research Interests: Algorithm design for string processing and bioinformatics Efficient data structures for genomic data Compression techniques (e.g., Lempel-Ziv, Burrows-Wheeler) Dynamic and space-efficient algorithms Grants & Projects: WILL # CHAIR # BOSSA (2025–2029) Ongoing collaborations with institutions like the University of Melbourne and King's College London

• Bioinformatics
• Genomic Epidemiology
• Metagenomics
• +3 more

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.

Lu ChengView profile

Visiting Professor

• Genomics
• Bioinformatics
• Microbial Genetics
• +3 more

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.

• Microbial Physiology
• Systems Biology
• Machine Learning
• +3 more

Chunhao Gu is a Doctoral Researcher at Aalto University's Department of Bioproducts and Biosystems. His work intersects Microbial Physiology , Systems Biology , and Machine Learning , focusing on bacterial metabolism under antibiotic stress and computational approaches to biological systems. The 2021 article highlights his integration of metabolic network modeling and machine learning to study bacterial stress responses, while the 2012 work demonstrates expertise in distributed computing for image retrieval systems using Hadoop and Lucene. His research spans interdisciplinary domains, combining bioinformatics with computational methods to address both biological and data-intensive challenges.

• Alzheimer's Disease
• Neurodegeneration
• Molecular Genetics
• +3 more

Mikko Hiltunen is a Professor of Tissue and Cell Biology at the Institute of Biomedicine, School of Medicine, University of Eastern Finland. His research focuses on molecular mechanisms of neurodegenerative disorders, particularly Alzheimer's disease and idiopathic normal pressure hydrocephalus (iNPH). He leads several research groups including the Molecular Genetics of Alzheimer's Disease (Hiltunen Lab) and participates in the Brain Research Unit and Genome Center of Eastern Finland. Research interests span Alzheimer's pathogenesis, neuroinflammation, genetic risk factors, biomarker discovery, and therapeutic development. His work integrates molecular biology, genetics, and clinical neuroscience to investigate microglial function, amyloid pathology, and cerebrospinal fluid biomarkers. Key areas include APOE genetics, PLCγ2-mediated neuroprotection, and machine learning applications in neurodegeneration. His recent publications demonstrate a strong focus on cerebrospinal fluid biomarkers, genetic association studies, neuroinflammation mechanisms, and therapeutic interventions. Article themes consistently emphasize microglial biology, genetic risk modifiers, and translational approaches for Alzheimer's and iNPH. Collaborative projects include Neuro-Innovation (2021-2026) and NOVEL MSCA Postdoctoral Programme (2024-2029). Hiltunen leads the Clinical Alzheimer Research group and co-directs the UEF Brain Research Unit. His team utilizes advanced models including iPSC-derived microglia and pericyte systems to study neurovascular interactions. Current work explores phospho-tau immunotherapy, lysosomal dysfunction, and polygenic risk score applications across diverse populations.

• Bioinformatics
• Computational Genomics
• Protein Structure Analysis
• +3 more

Liisa Holm is a Professor at the Institute of Biotechnology, University of Helsinki, and a supervisor in the Doctoral Programme in Integrative Life Science. Her research focuses on computational genomics, structural bioinformatics, and protein function prediction. Research Interests : Computational biology, protein structure analysis, machine learning applications in genomics Key Projects : HiLIFE Grand Challenge (antimicrobial resistance), burn wound infection metagenomics, protein structural aging studies Recent Trends : Holm's work spans protein structure comparison (DALI algorithm), pan-genome analysis of protein crops (faba bean), and aging-related structural changes in proteins. She also contributes to AI-driven advancements in structural biology. Scientific Awards : 2024 Nobel Prize in Chemistry (for AI-driven protein research) Leadership Roles : Project leader for Academy of Finland grants, member of international scientific committees

• Nanotechnology
• Computational Chemistry
• Surface Chemistry
• +3 more

Nian Wu is a Postdoctoral Researcher at the Department of Applied Physics, Aalto University. Their work bridges computational chemistry and nanoscale surface science. Research Focus: Surfaces and Interfaces at the Nanoscale Email: nian.wu@aalto.fi Research Interests: Nanotechnology for molecular adsorption analysis Deep learning applications in scanning probe microscopy Protein-ligand interaction modeling Publications (2023-2024): Recent work includes studies on lignocellulosic molecule adsorption and protein-ligand interactions, combining machine learning with surface analysis techniques. Collaborations: Works with researchers across multiple institutions, including Joakim Jestilä, Adam Foster, and Peter Liljeroth.

• Emerging Technologies in Society
• Anthropology of Technology
• Science and Technology Studies
• +3 more

Minna Ruckenstein is a Professor of Emerging Technologies in Society at the Consumer Society Research Centre, University of Helsinki. She directs The Datafied Life Collaboratory, focusing on digitalization/datafication processes with emphasis on emotional, social, political, and economic dimensions of algorithmic practices. Her work integrates anthropology of technology, science and technology studies, media/communication studies, and consumer economics. Current research projects include public values in algorithmic futures, re-humanizing automated decision-making, and everyday engagements with digital technologies in Helsinki, Shanghai, and Hangzhou. Notable outputs include the co-edited book *Everyday Automation* and her monograph *The Feel of Algorithms*. Key projects: Brokering Algorithmic Futures (2025–2026), REPAIR (2022–2025), and Citizen Mindscapes (2016–2018). Active in academic networks, she has organized over 180 events and served as a doctoral dissertation opponent.

• Celiac Disease
• Type 1 Diabetes
• Autoimmune Disorders
• +7 more

Kalle Kurppa is a Professor in Pediatrics with affiliations in Children's and Women's Health, Diagnostic Medicine, and Psychiatry. His research is centered on autoimmune and gastrointestinal disorders in children, particularly celiac disease and type 1 diabetes. Professor, Pediatrics Children's and Women's Health Diagnostic Medicine Psychiatry His research expertise spans celiac disease, type 1 diabetes, autoantibodies, gluten sensitivity, and environmental triggers of autoimmune diseases. He has made significant contributions to the clinical and diagnostic understanding of these conditions, particularly in pediatric populations. His work also extends to liver enzyme abnormalities in children, metabolic disorders, and population-level dietary behaviors. The trends in his recent publications show a strong focus on clinical diagnostics, longitudinal cohort studies (e.g., TEDDY), and immunogenetic research. He frequently publishes in high-impact journals in gastroenterology, immunology, and endocrinology, with an emphasis on peer-reviewed articles and review papers. His scientific awards include: Best of Best Digestive Disease Week, Chicago, USA (2011) Coeliac Disease Researcher of the Year (2010) Helena ja Niilo Hallman Prize (2018) UEG Rising Star (2015) Kalle Kurppa is actively involved in academic mentoring, having supervised doctoral candidates, licentiate students, and master’s students. He regularly presents at national and international conferences and participates in public and invited lectures. His research includes collaborations across institutions and countries, particularly in large-scale studies on autoimmune diseases. He has contributed datasets to public repositories, demonstrating commitment to open science. He is associated with research activities in pediatric gastroenterology, autoimmune disease biomarkers, and population health, and continues to lead impactful research projects in Finland.

• Bioinformatics
• Computational Biology
• Algorithms
• +4 more

Jarno Alanko is a Postdoctoral Researcher at the University of Helsinki within the Department of Computer Science . Specializing in algorithmic bioinformatics and computational genomics, he is affiliated with the Genome-scale Algorithmics research group led by Professor Veli Mäkinen. Research Interests: Bioinformatics algorithms String processing Genomic data structures Graph-based sequence representation Metagenomic analysis Space-efficient computing Notable Research Contributions: His work focuses on optimizing k-mer-based analyses through novel data structures like Finimizers and Eulertigs, improving sequence alignment efficiency, and developing graph indexing methods beyond Wheeler graphs. Recent publications in IEEE/ACM Transactions on Computational Biology and Bioinformatics and Algorithmica highlight his contributions. External Collaborations: Alanko has collaborated with institutions such as the Max Planck Institute for Molecular Cell Biology and Genetics during his 2017 academic visit. Contact: jarno.alanko@helsinki.fi | ORCID: 0000-0002-8003-9225