جست‌وجوی استاد و پژوهشگر

Markus Perolaمشاهده پروفایل

پژوهشگر

• Biomedicine
• Genetic Epidemiology
• Population Health
• +۵ مورد دیگر

Markus Perola serves as a University Researcher at the University of Helsinki, holding dual roles as Supervisor for the Doctoral Programme in Biomedicine and the Doctoral Programme in Population Health. He maintains an active external position at THL (Finnish Institute for Health and Welfare) since 2001, demonstrating long-term commitment to national public health research. His research spans Biomedicine , Genetic Epidemiology , and Population Health , with particular focus on Genetic determinants of autoimmune diseases and diabetes Chronobiology and socioeconomic interactions Mitochondrial mechanisms in liver disease Large-scale biobank analytics (FinnGen) Electronic health record mining for clinical insights cutting-edge methodologies in precision medicine and population-scale data integration. Analysis of his 367 publications reveals dominant themes in genomic epidemiology and translational biomedicine , with recent work emphasizing AI-driven risk prediction (particularly for COVID-19 outcomes), mitochondrial pathways in chronic diseases, and fine-scale genetic ancestry mapping. His research increasingly integrates multi-omics data with real-world clinical records. His scientific leadership includes major EU-funded initiatives: B1MGplus (€10,420 funding) EU/PROPHET (€66,681 funding) PRIMUS privacy-preserving meta-learning project GoE: Genome of Europe initiative FinnGen biobank consortium Perola actively supervises doctoral candidates through two university programs while contributing to 34 media engagements that translate complex genomic findings for public understanding. His work demonstrates consistent translational impact from basic genetic mechanisms to clinical applications and public health policy.

• Natural Language Processing
• Cross-modal Learning
• Generative AI
• +۷ مورد دیگر

Dr. Yan Gong is a Lecturer in Computer Science at Bournemouth University, Faculty of Science and Technology, Department of Computing and Informatics. He holds a PhD in Computer Science from Loughborough University (2023) and a Master’s degree in Communications and Signal Processing with distinction from Newcastle University (2012). Before academia, he worked over six years as a lead AI engineer in industry. Research Interests: Dr. Gong specializes in cutting-edge areas of artificial intelligence, including Natural Language Processing (NLP), Cross-modal Learning, Generative AI, and AI Agents. His work bridges theoretical advances with real-world applications, particularly in multimodal information retrieval and neural search systems. He is passionate about solving practical problems through AI and actively collaborates with industry partners. Publication Trends: His recent publications (2021–2024) focus on improving cross-modal information retrieval using deep learning, especially Vision Transformers and semantic embedding techniques. Key themes include hard negative mining, relation-focused learning, and neural search engines for artwork and general domains, published in high-impact journals like Pattern Recognition and ACM Transactions on Knowledge Discovery from Data . Scientific Service: Reviewer, Pattern Recognition (Elsevier) Reviewer, ACM MM 2023 Conference Guest Editor, IEEE Journal of Biomedical and Health Informatics Reviewer, Neural Networks , Knowledge and Information Systems , AI Communications Teaching and Advising: Dr. Gong is the unit leader for COMP7076 (Industrial Skills and Professional Issues) in the MSc Human-Centred Artificial Intelligence program. He supervises postgraduate students and welcomes PhD applicants interested in NLP, Generative AI, and multimodal AI. While no specific students are listed, he emphasizes mentorship and real-world research translation. Labs and Research Groups: Though not explicitly named, Dr. Gong is affiliated with AI and computing research activities at Bournemouth University, contributing to the university's research in human-centred AI and intelligent systems. His collaborations with Dr. Georgios Cosma and others suggest active participation in a research team focused on multimodal learning and information retrieval.

James Knierimمشاهده پروفایل

استاد

• Systems Neuroscience
• Cognitive Neuroscience
• Computational Neuroscience
• +۱۳ مورد دیگر

James Knierim is a Professor of Neuroscience at the Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, and a researcher at the Zanvyl Krieger Mind/Brain Institute. His laboratory investigates the neurophysiology of memory and spatial navigation in the hippocampal formation using advanced techniques such as multi-electrode recordings, optogenetics, and virtual reality systems like the Dome apparatus. PhD, California Institute of Technology BA, Psychology, Haverford College Dr. Knierim's research focuses on how the brain forms cognitive maps of the environment through the activity of place cells, grid cells, and head direction cells. His work explores the roles of the medial and lateral entorhinal cortex in providing allocentric spatial context and egocentric content representations, respectively, which are integrated in the hippocampus to form episodic memories. He investigates neural computations such as pattern separation and pattern completion across hippocampal subfields (DG, CA3, CA1), and how these processes are affected by aging. His lab also studies path integration and its recalibration through sensory feedback. The recent publications from his lab span topics including neural coding in the dentate gyrus, functional heterogeneity in CA3, aging-related neural changes, multiplexing of spatial and temporal information, and closed-loop control of place cells. These works reflect a strong trend toward integrating behavioral paradigms with computational modeling and advanced neurophysiological techniques to understand memory and navigation at the systems level. Notable scientific contributions include: Discovery of functional differences between CA1 and CA3 place cells supporting attractor network theories Demonstration of non-spatial coding in the lateral entorhinal cortex First physiological demonstration of path integration recalibration in freely moving rodents Identification of distinct firing properties of dentate gyrus mossy cells vs. granule cells Dr. Knierim mentors a team of postdoctoral fellows and graduate students and collaborates with researchers in engineering, psychology, and biomedical sciences. His lab is supported by grants from the NIH and DOD, enabling interdisciplinary projects on neural basis of navigation and memory. He has trained numerous former lab members who have gone on to faculty and research positions worldwide. The Knierim Lab operates within the Zanvyl Krieger Mind/Brain Institute and collaborates closely with the LIMBS Lab in Mechanical Engineering, reflecting a strong interdisciplinary approach to understanding brain function. The lab utilizes cutting-edge tools including Neuropixels probes, miniscopes for calcium imaging, and the Dome virtual reality system for studying freely moving rodents.

• Uncertainty Quantification
• Bayesian Inference
• Scientific Machine Learning
• +۷ مورد دیگر

Nathan Urban serves as Group Leader and Computational Scientist for the Applied Mathematics group at Brookhaven National Laboratory's Computational Science Initiative (CDS), a position held since 2020. Concurrently, he holds adjunct and guest appointments at Stony Brook University's Institute for Advanced Computational Science and Los Alamos National Laboratory's Statistical Sciences group. His interdisciplinary career bridges computational mathematics, statistics, and environmental science with emphasis on decision support under uncertainty. His educational foundation includes multiple degrees from Virginia Tech (1997): B.S. in Physics, Computer Science, and Mathematics, followed by advanced degrees from Penn State University (2006): Ph.D. and M.Ed. in Physics. Ph.D. in Physics, Penn State University (2006) M.Ed. in Physics, Penn State University (2006) B.S. in Physics, Virginia Tech (1997) B.S. in Computer Science, Virginia Tech (1997) B.S. in Mathematics, Virginia Tech (1997) Urban's research centers on uncertainty quantification and Bayesian inference methodologies applied to complex scientific systems. His work develops frameworks for probabilistic prediction, multi-model uncertainty analysis, reduced order modeling, and scientific machine learning. Key application domains include climate science, ice sheet dynamics, and molecular design, where he addresses challenges in decision making under uncertainty and optimal experimental design. His expertise spans computational statistics, hybrid physical-data driven modeling, and in-situ data analysis for large-scale simulations. Recent publications (2021-2024) reveal a pronounced shift toward integrating machine learning with physical modeling, particularly in climate and environmental systems. His work demonstrates innovative applications of active subspaces, Gaussian processes, and deep generative models to quantify epistemic uncertainties in ice sheet projections, molecular design, and extreme weather analysis. A consistent theme is developing scalable methods for uncertainty quantification in high-dimensional systems. Urban received significant recognition through the DOE Office of Science Early Career Research award (2013) in Biological and Environmental Research, supporting his foundational work in climate model uncertainty. Early Career Research award, DOE Office of Science (Biological and Environmental Research), 2013 As Applied Mathematics group leader at Brookhaven, Urban directs research in computational statistics and mathematical modeling for scientific applications. His team develops advanced methods for uncertainty quantification and decision support across multiple DOE mission areas, with strong collaborations in climate science and materials research.

• Data Mining
• Database Systems
• Machine Learning
• +۷ مورد دیگر

Jixue Liu is an Associate Professor at the School of Information Technology and Mathematical Sciences, University of South Australia, with a research focus spanning data mining, database systems, machine learning, and privacy-preserving technologies. His work bridges theoretical advancements with practical applications in remote sensing, social media analysis, and ethical AI. Education: PhD in Computer Science (Databases), 2001, University of South Australia Master by Research in Mining Engineering, 1987, Beijing University of Science and Technology Bachelor's in Mining Engineering, 1982, Xian University of Architecture and Technology Research interests center on causal inference (e.g., causal decision trees, front-door criterion), privacy-preserving data publishing (e.g., k-anonymity, composition attack mitigation), and machine learning for social media (e.g., adverse drug reaction detection, protest event prediction). Recent work explores algorithmic fairness and XML schema transformations. Article trends reveal expertise in causal modeling (2022-2024), with methodologies like variational autoencoders and instrumental variables, alongside privacy-preserving analytics and event detection systems . His interdisciplinary approach integrates computer science, statistics, and domain-specific applications in forestry and healthcare. Scientific awards include the Best Paper Award at ADC 2014. Major grants from ARC and CRC programs (2005-2023) highlight sustained funding for projects on entity resolution, causal rule mining, and fair predictive modeling. He supervises research degrees and teaches database management, XML databases, and data mining.

• Microgrid Control and Optimization
• Networked Control Systems
• Distributed Filtering and Sensing
• +۴ مورد دیگر

Professor Fuwen Yang is a leading academic at Griffith University's School of Engineering and Built Environment, specializing in Electrical and Electronic Engineering. With expertise in microgrid control, networked control systems, and renewable energy integration, he leads the Smart Energy Systems Group at the Institute for Intelligent and Integrated Systems. His research focuses on optimizing distributed energy resources and enhancing grid resilience through advanced control strategies. Current research aligns with Australian net-zero emissions goals Secured significant grants including ARC Discovery and Linkage projects Supervises 23 PhD and 35 Master's students Recent publications highlight innovations in virtual inertia control, digital twin frameworks, and data-driven predictive control. His work has earned global recognition, including the Stanford Top 2% Researchers list and Fellowships from Engineers Australia. Professor Yang's editorial roles include Associate Editor for IEEE Transactions on Industrial Informatics and other prominent journals. Acted as Chief Investigator on 8 major funded projects Contributed to Sustainable Development Goals 7 (Affordable Energy) and 9 (Infrastructure Innovation)

Ouri Wolfsonمشاهده پروفایل

استاد

• Database Systems
• Distributed Systems
• Mobile Computing
• +۴ مورد دیگر

Ouri Wolfson is the Richard and Loan Hill Professor of Computer Science at the University of Illinois at Chicago (UIC), with a joint appointment at the University of Illinois at Urbana-Champaign (UIUC). He earned his Ph.D. in Computer Science from NYU's Courant Institute in 1984 and has previously held faculty positions at Columbia University and Technion. His research focuses on database systems, distributed systems, mobile/pervasive computing, and computational transportation science. His work bridges theoretical foundations with practical applications in intelligent transportation, urban computing, and mobile data management. Wolfson has authored over 200 publications spanning databases, transportation systems, and computational neuroscience. His recent work demonstrates strong focus on: Spatio-temporal algorithms for transportation networks Intelligent urban mobility solutions Computational neuroscience applications Resource management in distributed environments Honors include: ACM Fellow AAAS Fellow IEEE Fellow University of Illinois Scholar (2009) ACM Distinguished Lecturer (2001-2003) He founded two technology companies (Mobitrac, Pirouette Software) and has secured significant research funding from NSF, DARPA, NASA, and others, including a $3.1M NSF grant establishing a Ph.D. program in Computational Transportation Science.

• Econometrics
• Econophysics
• Financial Markets
• +۳ مورد دیگر

Andreia Teixeira Marques Dionísio is an Associate Professor with Habilitation at Universidade de Évora and serves as Vice-Director of CEFAGE (Centro de Estudos e Formação Avançada em Gestão e Economia), a prominent research center focused on management and economics. Her academic career demonstrates deep engagement in quantitative financial analysis, econophysics, and market dynamics research. She holds a Ph.D. in Management from ISCTE (2006), an M.Sc. in Organization and Information Systems from Universidade de Évora (2001), and a B.A. in Business Management from Universidade de Évora (1997). Her educational background provided the foundation for her interdisciplinary research approach that bridges traditional economics with physics-inspired methodologies. Dionísio's research primarily focuses on Econometrics, Econophysics, and Financial Markets , with significant contributions to understanding cryptocurrency dynamics, energy market relationships, and nonlinear dependencies in global financial systems. Her work frequently applies innovative methodologies including entropy analysis, mutual information, and detrended fluctuation analysis to address complex financial phenomena. Recent research has explored the intersection of geopolitical events with financial markets, cryptocurrency volatility patterns, and the application of statistical physics to financial time series analysis. Her publication record shows a clear evolution toward increasingly sophisticated analyses of financial market interconnections, with recent work emphasizing cryptocurrency markets, energy-finance linkages, and the application of econophysics approaches to traditional financial questions. The research demonstrates methodological diversity while maintaining focus on understanding market efficiency, risk transmission, and nonlinear dependencies. Dionísio has successfully supervised numerous graduate students through their academic journeys, demonstrating commitment to academic mentorship and knowledge transfer. Her research projects, including 'Grampcity: Inovstone 4.0' and 'Exploration of Entropy on Decision Models,' reflect practical applications of theoretical frameworks to real-world economic challenges. She actively participates in research teams and collaborates on interdisciplinary projects that bridge economics, physics, and data science methodologies. Her work with CEFAGE positions her at the forefront of innovative economic research in Portugal, contributing to both theoretical advancements and practical applications in financial market analysis.

Timothy Cribbinمشاهده پروفایل

مدرس ارشد

• Information visualisation
• Information retrieval
• Text mining
• +۷ مورد دیگر

Dr. Timothy Cribbin is a Senior Lecturer in the Department of Computer Science within the College of Engineering, Design and Physical Sciences at Brunel University London. He has been with the university since 2001, initially joining as a lecturer and advancing to his current position. His academic home is firmly rooted in the intersection of information science, human-computer interaction, and data analytics. His educational background includes: PGCert Learning and Teaching in Higher Education, Brunel University (2007) PhD Information Science, Brunel University (2005) for research exploring spatial-semantic interfaces for exploratory document search MSc Industrial Psychology, University of Hull (1996), where he was awarded the Tom Hoyes Memorial Prize BSc (Hons) Psychology, University of Portsmouth (1994) Dr. Cribbin's research focuses on information visualization, interactive search interfaces, and text analytics, with particular expertise in processing and modeling large text collections to uncover meaningful insights. His work spans the design and evaluation of algorithms, interaction models, and end-user tools that support search, navigation, exploration, and sense-making within connected information spaces like scholarly publications and social media platforms. Early in his career, he pioneered work on interactive visualization using distance-similarity and spatial-semantic metaphors, making key contributions through the application of geodesic distance and second-order similarity transformations. More recently, his research has centered on citation-enhanced information retrieval and social media analytics, including the development of the Chorus Twitter analytics project. His scholarly output reveals a consistent trajectory from foundational work in information visualization to increasingly applied research in social media analytics and text mining. Throughout his career, Dr. Cribbin has maintained a strong focus on human-centered approaches to information processing, with particular attention to how users interact with and make sense of complex information spaces. His recent work demonstrates growing interest in psychological aspects of information processing, author classification, and the analysis of linguistic patterns in online radicalization. Dr. Cribbin has received notable recognition including: Tom Hoyes Memorial Prize for his MSc in Industrial Psychology Fellowship of the Higher Education Academy (FHEA) He has secured research funding for projects including "Predicting online radicalisation" and "Facilitating social media research in social sciences." Dr. Cribbin serves as a Deputy Senior Tutor (Academic Misconduct) and provides supervisory duties for final year undergraduate and Masters dissertation projects. He regularly acts as a reviewer for conferences and journals in information science, social media analytics, and information visualization. Dr. Cribbin is a key contributor to the User Centred Design research group and is the founder and lead programmer of the Chorus Twitter analytics project. His work bridges theoretical research with practical applications, particularly in the areas of social media analytics and text mining.

• Gene regulation
• Bioinformatics
• RNA Biology
• +۶ مورد دیگر

Albin Gustav Sandelin is a Professor in the Department of Biology at the University of Copenhagen, specializing in bioinformatics and RNA biology. With a PhD in Functional Genomics from Karolinska Institute (2004) and an MSc in Molecular Biology from Stockholm University (2000), his career spans roles at institutions including RIKEN Yokohama Institute and Tokyo Medical and Dental University. His research focuses on Gene regulation mechanisms Transcription start site dynamics RNA sequencing methodologies Promoter architecture analysis Cancer microenvironment interactions . Recent publications highlight his work in pancreatic cancer adaptation to acidic environments (2024-2025), TLDR-seq for RNA analysis, and JASPAR database development. He supervises PhD students including Eivind Valen and Eric Man-Hung, with a history of teaching bioinformatics courses at Karolinska Institute and Copenhagen University. Key collaborations involve institutions in Japan, Canada, and Sweden, with ongoing contributions to open-access genomic resources like JASPAR.

• Recommender Systems
• Multimodal Learning
• Music Emotion Recognition
• +۳ مورد دیگر

Marta Moscati works at the Institute of Computational Perception at Johannes Kepler University Linz , focusing on advanced recommendation systems and multimodal learning. Her research spans emotion-based music recommendation, privacy-preserving machine learning, and graph neural networks. Recent work includes: Developing multimodal single-branch architectures for cold-start scenarios Creating preference obfuscation techniques in implicit feedback systems Advancing music emotion recognition with semi-supervised graph networks Contributing to the FAME Challenge for multilingual face-voice association She has published extensively in top AI venues while maintaining technical expertise in both deep learning and theoretical physics , with early work on lepton universality violation. At JKU, she contributes to: Recommendation algorithms development Multimodal representation learning research Musical affective computing applications Privacy-preserving AI frameworks

Ji Hyun (Julia) Yangمشاهده پروفایل

پژوهشگر ارشد

• Neuroimaging
• Neurodegenerative Disorders
• Cognitive Neuroscience
• +۵ مورد دیگر

Dr. Ji Hyun (Julia) Yang is a Postdoctoral Research Fellow at the University of Queensland Centre for Clinical Research under the Faculty of Health, Medicine and Behavioural Sciences . She completed her PhD in 2020 at the same university. Research Interests : Neuroimaging biomarkers for cognitive decline in Parkinson's disease and dementia, anxiety neurobiology in aging, drug repurposing for neurodegenerative disorders Current Funding : 2023-2027 NHMRC MRFF Dementia, Ageing and Aged Care Mission grant Her recent work explores neuroimaging techniques to identify mild cognitive impairment subtypes in Parkinson's disease through resting-state connectivity analysis and attention network testing. She investigates anxiety's role in cognitive decline and participates in clinical trials for therapeutic interventions like telehealth CBT and levetiracetam repurposing. As an associate advisor , she supervised a 2023 PhD thesis on Cognitive subtypes in Parkinson's disease . Her publications span journals such as The Clinical Neuropsychologist , International Psychogeriatrics , and Brain and Behavior .

• Data Mining
• Machine Learning
• Temporal Networks
• +۳ مورد دیگر

Nikolaj Tatti is a Visiting Professor at Aalto University's Department of Computer Science , affiliated with the Helsinki Institute for Information Technology (HIIT) and the Adj. Prof. Gionis Aris research group . His work focuses on algorithmic analysis of temporal networks and graph theory. Research Interests: Temporal network analysis Graph decomposition and density Segmentation algorithms Pattern discovery in time-series data Interactive data analysis frameworks Publication Trends (2018-2021): Research spans Temporal Networks (dynamic hierarchies, cascade reconstruction), Graph Theory (density, decomposition), and Data Mining (periodic patterns, event detection). Collaborative work with Aris Gionis and Polina Rozenshtein dominates his publications. Technical Collaborations : Works with multidisciplinary teams including researchers from ACM Transactions on Knowledge Discovery and IEEE conferences.

• Bioinformatics
• Computational Biology
• Epigenetics
• +۳ مورد دیگر

Maia Aisha Malonzo is a Researcher at the Computational Biology Research Group in the Department of Computer Science at Aalto University . She specializes in Bioinformatics and Epigenetics , with a focus on DNA methylation analysis, transcriptome profiling, and computational modeling of biological systems. Key collaborations with Harri Lähdesmäki 's research team Active in Statistical Genetics and Genomic Data Analysis Her research involves: Developing computational methods for epigenetic profiling (LuxHMM, LuxRep) Investigating DNA methylation changes in disease contexts like Alzheimer's and asthma Elucidating regulatory mechanisms in human pluripotent stem cells

Huanmei Wuمشاهده پروفایل

استاد

• Precision Healthcare
• Clinical Data Integration
• Machine Learning in Healthcare
• +۵ مورد دیگر

Huanmei Wu is currently Professor and Chair of the Department of Health Services Administration and Policy at Temple University's College of Public Health, where she also serves as Assistant Dean for Global Engagement. She previously held leadership roles at Indiana University's School of Informatics and Computing. Education: PhD in Computer and Information Science, Northeastern University BS in Chemistry, Tsinghua University Dr. Wu is a multidisciplinary researcher working at the intersection of data science and healthcare. Her work focuses on clinical data integration, machine learning applications in disease modeling, and predictive analytics for chronic conditions including diabetes, cardiovascular diseases, and neurodegenerative disorders. She emphasizes precision healthcare approaches and collaborates with diverse stakeholders across academia, healthcare institutions, and industry. Her recent publications demonstrate expertise in applying advanced computational methods to public health challenges, including wearable device analysis for geriatric populations, CRISPR screening for cancer targets, and predictive models for radiation therapy outcomes. Research themes include multi-omics integration, biomarker discovery, and health equity considerations. Labs: Lab of Integrative Health Solutions