جست‌وجوی استاد و پژوهشگر

Alexander Pachaمشاهده پروفایل

مدرس

• Optical Music Recognition
• Deep Learning
• Music Information Retrieval
• +۳ مورد دیگر

Dr. Alexander Pacha is an External Lecturer at TU Wien, specializing in Optical Music Recognition (OMR) and deep learning applications. His work focuses on advancing OMR systems through graph neural networks, dataset development (e.g., DeepScoresV2), and music score analysis. He leads projects like DeepOMR and contributes to the Mixed Reality Lab and Virtual Meeting Room initiatives. Key research areas include music symbol detection, staff/measure segmentation, and cross-document alignment in music scores. Research interests span computer vision, artificial intelligence, and software tools for music information retrieval. His work bridges theoretical advancements with practical applications in digital libraries and music technology. Notable contributions include self-learning OMR frameworks and benchmarking tools for music object detection. Labs & Teams: Mixed Reality Lab (exploring AR/VR in music contexts), Virtual Meeting Room (collaborative digital environments).

• Systems Engineering
• Internet of Things (IoT)
• Cyber-Physical Systems
• +۳ مورد دیگر

Amirali Amiri is a PostDoc Researcher at TU Wien's Department of Automation Systems, part of the Faculty of Informatics. His work focuses on systems engineering, IoT integration, and cyber-physical systems. He teaches courses like Fundamentals of Digital Systems and Computer Engineering Project . His research emphasizes supply chain optimization, IoT performance, and IT/OT convergence security. Notable contributions include model-based approaches for IIoT lifecycle management and self-adaptive systems. Recent studies explore MQTT broker deployment architectures, semantic annotation for runtime models, and graph-based safety analysis. His work bridges theoretical frameworks with empirical validation in industrial automation contexts. Amiri has contributed to conferences such as IEEE ICM, ICPS, and ETFA, addressing challenges in cloud-based routing, cybersecurity, and DevOps integration.

• Worst-Case Execution Time Analysis
• Real-Time Systems
• Testing of Embedded Systems
• +۲ مورد دیگر

Raimund Kirner is an academic at the Institute of Computer Engineering (E191-01) at Technische Universität Wien , specializing in real-time and embedded systems. His career spans research in Worst-Case Execution Time (WCET) Analysis , Time-Triggered Communication , and Automated Testing for safety-critical systems. He has contributed to projects such as ALL-TIMES, COSTA, and HINT, focusing on timing predictability and composability in real-time systems. Key Research Areas : WCET Analysis, Real-Time Systems, Embedded Testing, Time-Predictable Computing Notable Collaborations : Peter Puschner, Andreas Prantl, Martina Zolda Recent Publications analyze cybersecurity in automotive networks, quantitative interface evaluation for time-triggered systems, and synchronization trade-offs. His work bridges theoretical timing models with practical compiler and hardware solutions for deterministic execution. Awards : Recipient of the Mobilitätsstipendium der Creditanstalt AG (2003) for outstanding dissertation. Advising includes supervising 11 theses from 2002–2014 on topics like Automated Load Balancing , WCET Estimation , and Compiler Timing Models .

• Bioinformatics
• Proteomics
• Genomics
• +۴ مورد دیگر

Rolf Apweiler serves as Director of the European Bioinformatics Institute (EMBL-EBI), part of the European Molecular Biology Laboratory, a position he has held since 2015. Previously, he was Associate Director (2012-2015) and led multiple critical protein resources including UniProt and InterPro. His leadership extends to directing Open Targets since 2018 and spearheading EMBL-EBI's contribution to the European COVID-19 Data Platform. Apweiler's research spans Bioinformatics, Proteomics, and Genomics , with significant contributions to protein annotation methods, proteomics data standards, and major biological databases. His work enables comprehensive analysis of proteome sets across entire organisms and has established foundational resources for the global scientific community. His publication portfolio demonstrates consistent leadership in developing critical bioinformatics infrastructure, with major contributions including UniProt, PRIDE, IntAct, and InterPro. These resources form the backbone of modern proteomics research and have been widely adopted across the life sciences. HUPO Distinguished Achievement Award in Proteomics (2004) EMBO Member (2011) ISCB Fellow (2015) President of Human Proteomics Organisation (2007-2008) As Director of EMBL-EBI, Apweiler oversees one of the world's leading bioinformatics institutions, managing extensive research programs, international collaborations, and major data infrastructure projects. His leadership in Open Targets demonstrates significant engagement with translational research connecting basic science to drug discovery. Apweiler's teams maintain several critical global resources including PRIDE (proteomics identifications), IntAct (molecular interactions), and UniProt (protein knowledgebase), forming an integrated ecosystem for biological data analysis.

• Genetic Epidemiology
• Metabolic Disorders
• Cardiometabolic Diseases
• +۳ مورد دیگر

Jun Liu is a Novo Nordisk Postdoctoral Research Fellow at the Nuffield Department of Population Health, University of Oxford. His research focuses on integrating multi-omics approaches to study cardiometabolic diseases, neurological disorders, and liver diseases. He holds a clinical medicine degree from Shanghai Jiao Tong University (2013), an MSc in genetic epidemiology from Erasmus Medical Center (Rotterdam), and a PhD in multi-omics integration for type 2 diabetes research. His work utilizes large-scale datasets such as the UK Biobank to explore genetic, metabolic, and proteomic biomarkers. Liu's research interests include metabolic dysfunction-associated liver diseases, genetic epidemiology, and the interplay of metabolome and microbiome in neurological conditions. His recent studies highlight novel genetic insights into amino acid roles in liver disease and the impact of ethnicity on dementia risk. Collaborations with Professor Cornelia van Duijn and Dr. Joanna Howson focus on cross-omics analyses of non-alcoholic fatty liver disease. His publications span journals like Nature , medRxiv , and J Hepatol , reflecting contributions to omics integration, Mendelian randomization, and multi-ancestry genetic studies. Key themes include biomarker discovery, therapeutic target identification, and population health disparities.

Eduardo Oriasمشاهده پروفایل

استاد پژوهشی

• Genetics
• Genomics
• Molecular Biology
• +۳ مورد دیگر

Eduardo Orias is a Research Professor and Professor Emeritus in the Department of Molecular, Cellular, and Developmental Biology at the University of California, Santa Barbara (UCSB). He earned his Ph.D. in Zoology from the University of Michigan in 1960, focusing on Tetrahymena genetics. His career spans over six decades, with visiting professorships at institutions such as the Carlsberg Foundation, Albert Einstein College of Medicine, and universities in Italy and Japan. He has held leadership roles in the Society of Protozoologists and editorial boards of journals like Journal of Protozoology . His research centers on Tetrahymena thermophila , a model organism for eukaryotic cell biology, genomics, and DNA rearrangement. Key contributions include mapping the Tetrahymena genome, coordinating the International Tetrahymena Genome Project, and investigating mating type switching and DNA rearrangement mechanisms. Collaborations, such as with Prof. Wei Miao’s group in China, have advanced understanding of genomic processes in ciliates. Dr. Orias has organized major international conferences on Tetrahymena genomics and pioneered genetic tools like gene knockout and RNAi in this organism. His work bridges fundamental biology with applications in comparative genomics and environmental science, as seen in studies on quantum dot toxicity in microbial systems.

Yuchao Jiangمشاهده پروفایل

دانشیار

• Statistical Genomics
• Bioinformatics
• Single-cell Omics
• +۳ مورد دیگر

Dr. Yuchao Jiang is an Associate Professor at Texas A&M University (TAMU), holding a joint appointment in the Department of Statistics (primary) and the Department of Biology within the College of Arts and Sciences. He joined TAMU in 2023 and previously served as an Associate Professor at the University of North Carolina at Chapel Hill. His academic background includes a B.Sc. in Biological Sciences from Cornell University (2012), an M.Sc. in Statistics from the Wharton School at the University of Pennsylvania (2014), and a Ph.D. in Genomics and Computational Biology from the University of Pennsylvania (2017). Dr. Jiang's research focuses on statistical and computational methods in genetics and genomics, particularly in analyzing large-scale cohort studies of human health and disease. His lab develops tools for detecting structural variants, assessing tumor heterogeneity, and studying genome-wide DNA damage and repair using single-cell omics approaches. Key projects include multi-omic analysis of circadian rhythms, HIV latency reversal, and tumor phylogeny inference. He currently co-directs the Center for Statistical Bioinformatics at TAMU and serves on the NIH's Cancer and Hematologic Disorders study section. His work is supported by grants like R35 GM138342 from the National Institute of General Medical Sciences. Notable contributions include the development of computational frameworks such as SCOPE, SCDC, and Destin for single-cell data analysis. His publications span journals like Cell Systems , PLOS Genetics , and Nature Communications .

Volkan Atalayمشاهده پروفایل

استاد

• Machine Learning
• Bioinformatics
• Data Stream Analysis
• +۳ مورد دیگر

Volkan Atalay is a Professor in the Department of Computer Engineering at Middle East Technical University (METU), Ankara, Turkey. His research focuses on machine learning applications in bioinformatics, data stream analysis, and drug discovery. He has held leadership roles including Vice President for Research at METU (2010–2016) and chaired the Department of Computer Engineering (2007–2008). Education: Ph.D. in Computer Science from Université Paris Descartes (1993), followed by postdoctoral work at Virginia Tech (2004–2005). Research Interests: Machine learning for bioinformatics (drug-target interaction prediction, protein function annotation), data stream clustering algorithms, and deep learning applications in drug discovery. Key projects include CROssBAR (biomedical knowledge graphs) and DEEPScreen (drug-target interaction prediction tools). Recent publications (2020–2023) emphasize hybrid deep learning models, data fusion for energy forecasting, and open-source protein annotation benchmarks. Awards include the Best Poster Award at GLBIO 2017 for iBioProVis visualization work. Current grants: SmartHome Consortium (data stream analytics) and projects on biomedical relation curation. Labs/Teams: Leads the Cancer Systems Biology Laboratory and collaborates with international consortia like the CAFA challenge.

Ed Gehringerمشاهده پروفایل

استاد

• Computer Science Education
• Peer Assessment Systems
• Educational Technology
• +۱ مورد دیگر

Ed Gehringer is Professor in Electrical and Computer Engineering at North Carolina State University. Research specializes in educational technologies, particularly automated peer assessment systems and computer science pedagogy. Authored books on parallel processing and capability architectures. Recent publications focus on AI-enhanced assessment tools and software engineering education. Research trajectory shows progression toward intelligent tutoring systems and automated feedback mechanisms for programming education. Current projects develop natural language processing techniques to evaluate student submissions and peer reviews.

Daifeng Wangمشاهده پروفایل

استادیار

• Machine Learning
• Bioinformatics
• Functional Genomics
• +۴ مورد دیگر

Daifeng Wang is an Assistant Professor in the Department of Biomedical Informatics at Stony Brook University, with affiliations in Electrical and Computer Engineering, Computer Science, and Applied Mathematics and Statistics. He holds a Ph.D. in Electrical and Computer Engineering from the University of Texas at Austin and completed postdoctoral training at Yale University (2012–2016). His research focuses on developing interpretable machine learning methods and bioinformatics tools to analyze multi-omics data for understanding functional genomics in brain disorders and cancers. Key contributions include predicting regulatory networks in the brain, linking genetic variants to psychiatric disorders like schizophrenia, and advancing deep-learning models for genomic data integration. Recent work, published in Science (2018), demonstrates a 6-fold improvement in disease prediction using a conditional Deep Boltzmann Machine architecture. This model integrates regulatory networks and QTL linkages to prioritize genes and annotate hidden nodes for precision medicine applications. No scientific awards were explicitly mentioned in the provided text. His research spans interdisciplinary collaborations, leveraging elastic-net modeling, single-cell data deconvolution, and Hi-C datasets to address molecular mechanisms in diseases.

• Experimental Psychology
• Cognitive Neuroscience
• Neuroimaging
• +۴ مورد دیگر

Maarika Traat is a Research Fellow in Experimental Psychology at the University of Tartu's Institute of Psychology, Faculty of Social Sciences. Her research examines cognitive processes, attention modulation, and neuroimaging methodologies, with recent focus on fatigue effects on mismatch negativity and cerebral venous flow dynamics. She holds a PhD in Informatics from University of Edinburgh (2006), Master's in Psychology from University of Tartu (2023), and Master's in Neuroimaging from Cardiff University (2018). Her interdisciplinary background spans computational linguistics, neuroscience, and experimental psychology. Dr. Traat has received multiple awards including the Estonian-Latvian Language Award (2020) and Medal of the Baltic Assembly (2015). She coordinates translation projects and Baltic academic networks, maintaining research collaborations with Ghent University Hospital and San Camillo Hospital.

Galadrielle Humblot-Renauxمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Galadrielle Humblot-Renaux is a Research Fellow at Aalborg University's Technical Faculty of IT and Design, affiliated with the Department of Architecture, Design and Media Technology and the Section for Media Technology in Aalborg, Denmark. Her work focuses on AI-driven solutions for computer vision, robotics, and uncertainty quantification in machine learning systems. Key Research Areas Out-of-Distribution Detection and Robustness Testing 3D Semantic Segmentation and Point Cloud Processing Uncertainty Quantification in Renewable Energy Systems Human-Robot Interaction and Speaker Identification Marine Ecology Image Analysis via Multi-Annotator Datasets Scientific Contributions She has created two influential datasets: JAMBO (2024) for underwater benthic habitat classification and Why Talk to People When You Can Talk to Robots? (2021) for far-field speaker identification challenges. Her publications across 2018-2025 demonstrate interdisciplinary expertise bridging AI theory with practical applications in robotics, automotive systems, and ecological monitoring.

• Medical Imaging
• PET Reconstruction
• Deep Learning
• +۳ مورد دیگر

Xue Gu is a Research Fellow in Molecular Biophysics and Biochemistry at Yale University, specializing in advanced medical imaging technologies. Her research develops deep learning frameworks for PET and SPECT image reconstruction, denoising, and analysis, with particular focus on cardiac and neurological applications. Recent work includes innovative approaches for low-count PET reconstruction, attenuation correction without CT, and dynamic motion correction. Gu's publications demonstrate consistent focus on improving quantitative accuracy in nuclear medicine through generative adversarial networks (GANs), diffusion models, and self-supervised learning techniques. Her methods address critical challenges in cardiac SPECT denoising, Rb-82 PET imaging, and synaptic density estimation from MRI. The BodyGPS project represents a significant contribution to anatomical positioning systems for medical imaging analysis.

Hermann Maurerمشاهده پروفایل

استاد

• Networked Multimedia Systems
• Societal Implications of Informatics
• e-Learning
• +۷ مورد دیگر

Hermann Maurer is Full Professor of Computer Science at Graz University of Technology (since 1978), with additional roles as Honorary Visiting Professor at Danube University and Honorary Research Fellow at University of Auckland (till 2012). He has held leadership positions including Dean of Computer Science (2004-2007) and Chair of Informatics Section in Academia Europaea (2007-2012). Key Institutions: Graz University of Technology, Austria-Forum, JOANNEUM RESEARCH, Academia Europaea Academic Honors: Member of Finnish Academy of Sciences, Cross of Honour for Arts and Science (Austria), multiple honorary doctorates Research Focus: Networked Interactive Digital (NID) Books, Austria-Forum (1.1M entries), hypermedia systems, anti-monopoly digital frameworks, and societal impacts of technology. He pioneered second-generation web systems and co-founded conferences like ED-MEDIA and I-KNOW. Key Trends in Publications: Recent works emphasize NID systems, interactive encyclopedias, web reliability, and the future of digital libraries. His research spans e-learning, information integration, and combating web-based plagiarism. Scientific Awards: Honorary Doctorates (St. Petersburg, Karlsruhe, Calgary) AACE Fellowship (2003) Integrata Prize (2000) Enter Prize (1999) Advising & Grants: Supervised over 400 M.Sc. and 50 Ph.D. theses. Led multimillion-dollar projects including Hyperwave AG, COSTOC, and Austria-Forum. His work on optical storage and color-graphic microcomputers (MUPID) exemplifies technological innovation.

Charles Hauserمشاهده پروفایل

دانشیار

• Bioinformatics
• Plant Genetics
• Environmental Genomics
• +۳ مورد دیگر

Charles Hauser is an Associate Professor of Biological Sciences at St. Edward's University, where he established the Bioinformatics Program in 2004. His research focuses on soil metagenomics and nanoparticle toxicity, with expertise in plant genetics and chloroplast biology. Dr. Hauser earned his Ph.D. in Biochemistry and Biophysics from the University of Houston, followed by postdoctoral work at Duke University on chloroplast gene regulation in Chlamydomonas reinhardtii . Education highlights include a B.S. in Botany/Chemistry from the University of Texas, Austin, and postdoctoral training at Duke University. His work on the Chlamydomonas genome project contributed foundational gene models. Research interests include bioinformatics education, genomics CUREs (Course-Based Undergraduate Research Experiences), and environmental impacts of nanoparticles. His awards include the 2016 School of Natural Sciences Distinguished Research Award and NIH Postdoctoral Fellowship (1990). He leads grants such as the NSF-funded 'Integrating Bioinformatics into the Life Sciences' and USDA's Ag-STEM Pipeline initiative. Dr. Hauser is involved in national education initiatives like the Genomics Education Partnership (GEP) and Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE). Professional involvement includes advisory roles for GEP and NIBLSE, and membership in the Genetics Society of America and Texas Academy of Science. His lab focuses on undergraduate research training, leveraging genomic data analysis and bioinformatics tools to engage students in cutting-edge biological questions.