جست‌وجوی استاد و پژوهشگر

Andrea Gannaمشاهده پروفایل

دانشیار

• Biomedicine
• Statistics and Probability
• Genetics
• +۲ مورد دیگر

Andrea Ganna is an Associate Professor at the Helsinki Institute of Life Science (HiLIFE) and an Instructor at Massachusetts General Hospital, Harvard Medical School . Their research spans Biomedicine , Genetics , Developmental Biology , and Biostatistics , with a focus on integrating genetic data and electronic health records for population health applications. Key Research Areas: Genetic risk factors for metabolic and autoimmune diseases Predictive modeling using polygenic scores and EHR data Clonal hematopoiesis in inflammatory conditions Recent Articles (2025): Published in Nature Medicine , Science Advances , and Nature Genetics on topics like weight loss interventions, cleft palate genetics, and predictive analytics for disease. Scientific Awards: Charles J. Epstein Award for Excellence in Human Genetics Research (2017, 2018) Leena Peltonen Prize for Excellence in Human Genetics (2022) ELLIS Member (2022) Fellowship of Excellence for Young Investigators (2017) Projects: Leading multiple funded initiatives, including EU Research and Innovation Actions, Academy Projects from the Research Council of Finland, and grants from Sigrid Jusélius and Jane and Aatos Erkko foundations, focusing on AI-driven diagnostics and public health data. Activities: Regularly invited to speak at conferences like FEBS-IUBMB-ENABLE, Big Data Institute, and Social Sciences and Genetics Conference (2025).

• Sparse Arrays
• Beamforming
• Sensor Arrays
• +۴ مورد دیگر

Robin Rajamäki is a Visiting Professor in the Department of Information and Communications Engineering at Aalto University, Finland. He is affiliated with the Visa Koivunen Group and holds an ORCID ID (0000-0002-5028-6022). His academic credentials include a Doctor of Technology (Tekn. toht.) in Electrical Engineering (2021), a Master's in Engineering and Technology (2016), and a Bachelor's in Telecommunications Engineering (2014), all from Aalto University. Research interests focus on Sparse Arrays , Beamforming , ISAC (Integrated Sensing and Communications) , and Array Configuration models. His work explores optimal array geometries, waveform design, and identifiability guarantees in active sensing systems, with applications to MIMO radar, millimeter-wave communications, and future 6G networks. Key methodologies include statistical signal processing, machine learning, and computational optimization. Recent publications highlight advancements in generative deep synthesis , array geometry optimization , and sensor array applications in ISAC. His research spans 2015–2025, including 5 projects (e.g., FUN-ISAC, INSTINCT) and collaborations with institutions like the University of California, San Diego (2019–2020), Technion (2017–2018), and University of Pennsylvania (2016). Scientific Awards: Best Student Paper Award (3rd place, 2019) Projects include fundamental limits in ISAC, joint sensing-communications systems for immersive connectivity, and sparse antenna array processing for 6G and millimeter-wave applications. His work has been cited in Scopus and supported by the Academy of Finland and EU Horizon grants.

Jing Tangمشاهده پروفایل

استادیار

• Network Pharmacology
• Drug Combination Discovery
• Systems Biology
• +۵ مورد دیگر

Jing Tang is an Assistant Professor at the Department of Biochemistry and Developmental Biology within the Faculty of Medicine at the University of Helsinki . She also holds a Docentship in Biometry and serves as a Supervisor in Doctoral Programmes for Computer Science, Biomedicine, and Integrative Life Science. Education: PhD in Statistics (University of Helsinki, 2016), Docentship in Biometry (2016) Key Research Areas: Network pharmacology for precision medicine, mathematical modeling of drug synergy, systems-level cancer pathway inhibition, computational drug discovery, and biomedical informatics Article Trends : Recent publications focus on network-driven drug response prediction in acute myeloid leukemia, causal inference for essential gene analysis, graph neural networks in drug-target interaction modeling, and seasonal brain-adipose tissue interactions via μ-opioid receptor imaging. Tools developed include SynergyFinder+ and DrugComb platforms. Awards: Academy of Finland Research Fellow (2018), Helsinki Challenge Award (2017), HiLIFE Research Fellow (2017) Supervision: Leads the Tang Lab team, mentoring PhD researchers in method development and software engineering for cancer drug discovery

• Cancer Genetics
• Genomics
• Bioinformatics
• +۴ مورد دیگر

Tatiana Cajuso Pons serves as Senior Researcher at the Helsinki Institute of Life Science (HiLIFE) and holds a Docent title at the Faculty of Medicine, University of Helsinki. Her work bridges biomedical research and computational genomics, with emphasis on translating genetic discoveries into clinical applications. Her research specializes in cancer genetics , particularly colorectal cancer and uterine leiomyomas, employing genomic , bioinformatic , and statistical approaches . Key focus areas include susceptibility gene mapping across diverse populations, somatic mutation analysis, and machine learning applications for clinical diagnostics. Her methodology integrates multi-omic datasets to uncover disease mechanisms and identify therapeutic targets. Recent publication trends reveal consistent high-impact output in top-tier journals (Nature series, American Journal of Human Genetics), with increasing emphasis on ancestry-diverse cohorts and translational applications . Her work frequently involves large international collaborations, reflecting integration into global research networks focused on precision oncology. Dr. Cajuso Pons currently participates in an Academy of Finland-funded project (2024-2028) developing deep learning models for clinical laboratory diagnostics, indicating active grant support and engagement with cutting-edge computational methods. Her research demonstrates strong alignment with HiLIFE's mission to advance life science research through interdisciplinary collaboration.

• Demography
• Fertility Studies
• Population Economics
• +۴ مورد دیگر

Julia Ingrid Sofia Hellstrand is a Postdoctoral Researcher at the Centre for Social Data Science (CSDS) and Helsinki Institute for Demography and Population Health, Faculty of Social Sciences, University of Helsinki. Her research focuses on demographic dynamics with emphasis on fertility trends, human capital investment, and economic sustainability in Nordic contexts. Her primary research interests include: Computational fertility forecasting and scenario modeling Drivers of fertility decline (educational attainment, economic uncertainty, partnership formation) Human capital-fertility interrelationships Nordic comparative demography Long-term socioeconomic implications of demographic change Dr. Hellstrand's recent publications (2023-2025) demonstrate methodological evolution from descriptive cohort analysis to integrated computational frameworks. Her work now combines probabilistic forecasting of Finnish total fertility rate (TFR) with economic sustainability modeling, examining how educational pathways and partnership dynamics influence demographic trajectories. Key contributions include calibrated TFR projections to 2070 and analyses of how specific educational fields affect fertility decisions in Finland. She actively participates in the Academy of Finland-funded FLUX project (2024-2027), collaborating with international researchers from the Max Planck Institute and Nordic universities. Her methodological toolkit spans demographic decomposition, microsimulation, and machine learning applications for population forecasting. As a postdoctoral researcher, Dr. Hellstrand contributes to graduate training through research assistantships and co-supervision within the CSDS framework. Her work provides critical evidence for Nordic population policy debates, particularly regarding family formation support measures and long-term economic planning under low fertility conditions.