جست‌وجوی استاد و پژوهشگر

William Grovesمشاهده پروفایل

دانشیار

• Environmental Health Engineering
• Occupational Exposure Assessment
• Sensor Development
• +۷ مورد دیگر

William Groves is an Associate Professor in the John and Willie Leone Department of Energy & Mineral Engineering at the Pennsylvania State University, part of the College of Earth and Mineral Sciences. His work bridges environmental engineering, occupational health, and sensor technology, with a strong focus on exposure assessment and instrumentation. His research interests include development of sensors for organic vapors , breath analysis , physiologic monitoring , and mining safety . He has pioneered methods involving surface acoustic wave (SAW) sensor arrays, physiologic sampling pumps, and real-time exposure assessment tools. His work supports safer workplaces and better environmental monitoring. The recent publications reflect a consistent trajectory in environmental sensing , respiratory protection , and occupational exposure modeling . Key trends include the use of machine learning for ventilation prediction , low-cost particulate sensors , and advanced modeling of traffic and mining-related risks . William Groves has not been explicitly associated with any scientific awards in the provided text. He has collaborated extensively on research related to respirator performance , mining fatality analysis , and air quality modeling . While no specific grants are listed, his work appears to be supported by ongoing institutional and interdisciplinary research initiatives. He has advised or collaborated with multiple researchers across environmental and occupational health domains. There is no mention of specific labs or research teams led by William Groves in the provided content, though his work suggests strong ties to environmental health engineering and industrial hygiene research groups at Penn State.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Jiaqi Liu is an Associate Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology in Shenzhen, China. His research spans multiple domains within artificial intelligence, with a particular focus on autonomous systems, multimodal learning, and computer vision. He maintains active collaborations with researchers across China and internationally, particularly with Jian Sun and Peng Hang on autonomous driving projects. Dr. Liu's research interests encompass a broad spectrum of AI and computer science topics. His work in autonomous driving involves developing sophisticated decision-making frameworks for cooperative vehicle systems. In multimodal learning, he has pioneered approaches for handling missing modalities and creating adaptive fusion networks. His signal processing research includes innovative methods for sleep staging and EEG analysis using dynamic mode decomposition techniques. Additional research areas include photonic computing accelerators, graph neural networks, and quantum neural networks. The publication trend shows significant growth in output quality and quantity, with numerous papers in top-tier venues including IEEE Transactions, CVPR, AAAI, and IJCAI. His work demonstrates a strong interdisciplinary approach, bridging computer science with applications in healthcare, robotics, and telecommunications. Recent publications indicate increasing focus on the integration of large language models with reinforcement learning for autonomous systems. Dr. Liu has received recognition through publications in high-impact journals and conferences including: IEEE Transactions on Intelligent Transportation Systems IEEE Robotics and Automation Letters Expert Systems with Applications CVPR (Computer Vision and Pattern Recognition) AAAI Conference on Artificial Intelligence His research program involves collaborations across multiple domains, with current projects focusing on language-guided autonomous driving, multimodal sentiment analysis, and advanced signal processing techniques. He leads research efforts in developing novel deep learning architectures for complex real-world problems, particularly in the domains of autonomous systems and healthcare applications.

Lars Lundbergمشاهده پروفایل

استاد

• Computer Science
• Machine Learning
• Cloud Computing
• +۴ مورد دیگر

Lars Lundberg is a Professor and Dean at Blekinge Institute of Technology (BTH), affiliated with the Faculty of Computing and Department of Computer Science. He works in Karlskrona, Sweden, and can be contacted at lars.lundberg@bth.se . His research spans multiple domains in Computer Science, including: Cloud Computing Efficiency Machine Learning for Data Imbalance Scientometric Analysis of Research Directions Voice-Based Medical Diagnostics Information Retrieval Techniques Recent publications demonstrate his focus on computational pipelines for disease detection, adaptive data augmentation methods, and cloud optimization frameworks. His work intersects technical innovation with practical applications in healthcare and academic research. Current projects information is unavailable, but his research aligns with BTH's strategic emphasis on digital engineering and data-driven systems.

Meg Blume-Kohoutمشاهده پروفایل

استاد

• Applied Econometrics
• Public Policy Analysis
• Health Economics
• +۳ مورد دیگر

Meg Blume-Kohout is a Professor of Economics at Gettysburg College, holding a PhD in Policy Analysis from Pardee RAND Graduate School (2009), an MS in Environmental Health Sciences from UC Berkeley (2002), and a BA in Economics from Williams College (1996). Her interdisciplinary research examines how government interventions shape STEM workforce dynamics, higher education outcomes, scientific innovation, and entrepreneurial behavior through rigorous econometric analysis. Education: PhD in Policy Analysis, Pardee RAND Graduate School, 2009 MS in Environmental Health Sciences, University of California, Berkeley, 2002 BA in Economics, Williams College, 1996 Research Focus: Integrating methodologies from economics, psychology, sociology, and data science, her work investigates policy impacts on health insurance entrepreneurship (notably ACA effects), STEM education funding efficacy, and R&D funding dynamics in academic and biopharmaceutical contexts. She specializes in causal inference techniques to isolate policy effects from confounding variables. Publication Trends: Recent work (2022-2024) emphasizes health policy-entrepreneurship linkages and STEM workforce development, building on earlier research into innovation systems and biomedical labor markets. Her publications span economics, policy, and technology transfer journals, demonstrating consistent methodological rigor in analyzing natural experiments and longitudinal datasets. Teaching and Service: She teaches core economics courses including Principles of Microeconomics (ECON 103), Economics of Public Issues (ECON 105), Health Economics (ECON 270), and Advanced Econometrics (ECON 352). Her research has received support from the National Academies of Sciences, Engineering, and Medicine and the National Endowment for the Arts, though specific grant details aren't provided in the source text.

Manuel de Buenaga Rodríguezمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Text Categorization
• Biomedical Informatics
• +۱۲ مورد دیگر

Dr. Manuel de Buenaga Rodríguez is a Faculty Member in the School of Engineering at Universidad Europea de Madrid, where he leads research in the Department of Artificial Intelligence. His work bridges Natural Language Processing, Biomedical Informatics, and E-Health solutions. PhD in Computer Science from Universidad Complutense de Madrid (1996) Specializes in Text Categorization, Semantic Annotation, and Ontology-based Systems Research Highlights: Pioneered the POESIA open-source web filtering system and contributed to the NAVIga platform for elderly healthcare. His work integrates WordNet, UMLS, and Machine Learning in semantic disambiguation and user modeling. Key Publications Trends (2010-2013): Focus on Biomedical document annotation (BioAnnote) Virtual reality rehabilitation systems Lexical database integration for text classification Concept-based medical/tourism information access Spanish language processing challenges Collaborations: Works with José María Gómez Hidalgo, Simone Marchi, and European research networks like MAVIR. Projects span Madrid Region initiatives and translational medicine tools.

• Clinical Natural Language Processing
• Mental Health Informatics
• Temporal Information Extraction
• +۵ مورد دیگر

Dr. Sumithra Velupillai is a Research Fellow at King's College London's Institute of Psychiatry, Psychology & Neuroscience, Department of Psychological Medicine. With a PhD from Stockholm University completed in 2012, she has established herself as a leading researcher in clinical natural language processing with a specific focus on mental health applications. Her work bridges computer science, linguistics, and clinical medicine to extract meaningful insights from electronic health records. Dr. Velupillai's research interests center on clinical natural language processing, particularly for mental health applications. She specializes in temporal information extraction from clinical narratives, uncertainty detection in medical text, and developing NLP methods for analyzing mental health records. Her work has significant implications for improving clinical decision support systems, mental health diagnostics, and patient care through advanced text mining techniques. She has made substantial contributions to developing lexicons, annotation schemes, and computational models specifically tailored for healthcare contexts. Her recent publications demonstrate a strong trajectory in applying cutting-edge NLP techniques to mental health challenges, with particular emphasis on pain detection in mental health records, suicide risk assessment, and temporal reasoning in clinical narratives. Velupillai's work consistently addresses methodological challenges in healthcare NLP while maintaining clinical relevance. Dr. Velupillai maintains active collaborations with leading researchers in mental health informatics, including Robert Stewart, Angus Roberts, and Rina Dutta at King's College London. Her research is frequently published in top venues for biomedical informatics and computational linguistics, reflecting the interdisciplinary nature of her work.

Ilias Georgakopoulos-Soaresمشاهده پروفایل

استادیار

• Cancer genomics and transcriptomics
• Gene regulation
• DNA and RNA secondary structures
• +۵ مورد دیگر

Ilias Georgakopoulos-Soares, PhD, is an Assistant Professor at Pennsylvania State University with joint appointments in the Department of Molecular and Precision Medicine and the Department of Public Health Sciences, and is also affiliated with the One Health Microbiome Center and the Penn State Cancer Institute. Research Interests: His laboratory integrates computational biology and genomics to address fundamental and translational questions in cancer biology, gene regulation, and human disease diagnostics. Central themes include: Cancer genomics and transcriptomics, especially determinants of mutagenesis and immune microenvironment interactions. Gene regulatory mechanisms, focusing on cis-regulatory elements and the impact of DNA/RNA secondary structures such as G-quadruplexes and R-loops. Development of novel computational methods and software tools for high-throughput genomic data analysis, including nullomer-based diagnostics and massively parallel reporter assays. Scientific Output: Since 2021, Dr. Georgakopoulos-Soares has published more than 40 peer-reviewed articles spanning Nature , Nature Communications , Nature Genetics , Genome Biology , Nucleic Acids Research , and other leading journals. His work highlights converging interests in multi-omics cancer analyses, immune cell profiling, sequence-structure-function relationships, and method development for genomic sequence mining. Laboratory & Collaborations: The Georgakopoulos-Soares lab employs an interdisciplinary approach, collaborating widely across Penn State’s Cancer Institute, microbiome initiatives, and external consortia to translate genomic insights into diagnostic and therapeutic advances.

Valeriia Haberlandمشاهده پروفایل

مدرس

• Cancer Genetics
• AI in Healthcare
• Epidemiology
• +۳ مورد دیگر

Dr Valeriia Haberland is a Lecturer in Computer Science at Brunel University London within the College of Engineering, Design and Physical Sciences, appointed in 2023. Her expertise bridges computing and biological sciences with a focus on translational health applications. She holds a PhD in Computer Science from King's College London (2015) and was awarded Fellow of the Higher Education Academy (FHEA) in October 2023. Educational background includes advanced training in computational methods and data science. Her research centers on cancer genetics and AI-driven healthcare solutions , specializing in modeling cancer aggressiveness through machine learning analysis of molecular profiles. She pioneers epidemiological causal inference techniques to identify genetically translatable disease factors and develops specialized web infrastructure for biomedical research. Current projects target breast cancer wound monitoring and laminopathy mechanisms. Publication analysis reveals a distinct evolution from early grid computing research (2012-2017) to contemporary health data science leadership. Recent work dominates cancer genomics, proteomics, and osteoarthritis therapeutics using UK Biobank data, with flagship contributions to EpiGraphDB and MR-Base platforms. Her methodology integrates Mendelian randomization, graph databases, and high-throughput sequencing analytics. Dr Haberland secures active research funding including a Brunel Partners-funded project on remote breast cancer wound monitoring (May-July 2024). She supervises MSc dissertations while teaching Modern Data, Logic and Computation, and final-year projects. Her academic service includes placement tutoring and curriculum development in data-intensive healthcare applications.

• Artificial Intelligence
• Machine Learning
• Healthcare Informatics
• +۷ مورد دیگر

Mahmoud ElBATTAH is a Research Fellow at the University of Picardie Jules Verne (UPJV) specializing in Optimization and Cryptography with a focus on AI (OCIA). His work bridges artificial intelligence with healthcare applications, particularly in emergency medicine and autism spectrum disorder research. Through cross-disciplinary methodologies, he integrates simulation modeling, machine learning, and natural language processing to address complex clinical challenges.

Adi Tarcaمشاهده پروفایل

استاد

• Reproductive Medicine
• Obstetrics
• Gynecology
• +۵ مورد دیگر

Adi Tarca is a tenured Professor in the Department of Obstetrics and Gynecology at Wayne State School of Medicine, and concurrently holds an Adjunct Professor appointment in the Department of Computer Science, College of Engineering at Wayne State University. He is actively engaged in teaching, having offered the graduate course MGG7050 – Bioinformatics: theory and practice every Winter Term since 2023. Education details are not explicitly provided in the supplied text. Research Interests High-dimensional biology and multi-omics integration in pregnancy Machine-learning approaches to predictive modeling in reproductive health Epigenetic and proteomic biomarkers of obstetrical syndromes such as preeclampsia and preterm birth Evolutionary genomics of placental adaptation Vaginal and amniotic microbiome dynamics across gestation Computational method development for pathway and network analysis (e.g., PADOG, SPIA) Dr. Tarca’s recent publications reveal a sustained trajectory toward precision obstetrics: leveraging crowdsourced data, single-cell RNA-seq, proteomics of extracellular vesicles, and large language models to dissect parturition biology and translate findings into point-of-care diagnostics. Scientific Awards No specific awards are enumerated in the provided text. Advising & Grants While the text lists no individual students, Dr. Tarca’s extensive multi-cohort longitudinal studies and leadership in consortia such as the Microbiome Preterm Birth DREAM Challenge imply active mentorship of graduate students and post-doctoral fellows in both the medical and computational sciences. Laboratories & Teams Dr. Tarca is expected to lead a trans-disciplinary laboratory embedded within the Perinatology Research Branch and affiliated with the Center for Molecular Medicine and Genetics at Wayne State University, integrating wet-lab pregnancy biology with advanced computational pipelines.

Alper Uzunمشاهده پروفایل

دانشیار

• Big Data
• Computational Biology
• Data Mining
• +۹ مورد دیگر

Alper Uzun is an Associate Professor of Pathology and Laboratory Medicine with a secondary appointment in Pediatrics at Brown University , where he serves as Director of Cancer Bioinformatics at the Legorreta Cancer Center . His research integrates genomics , bioinformatics , and network biology to investigate the genetic architecture of preterm birth , preeclampsia , and cancer . His work spans next-generation sequencing pipeline development, comparative network analysis , and machine learning applications in translational research. He has created key bioinformatics tools including VIVA (Variant Visualization) and Proteinarium (Protein-Protein Interaction Analysis). Research highlights include: Genomic studies of pregnancy complications Systems biology approaches to cancer research Software development for biomedical data visualization Machine learning algorithms for disease pathway analysis Scientific awards include: 2011 PLoS Computational Biology LabLIFE comic design winner His work has been supported by grants from the Burroughs Wellcome Fund , Legorreta Cancer Center , and NIH .

• Artificial Intelligence
• Machine Learning
• Computational Creativity
• +۶ مورد دیگر

Dr. Haixia Liu serves as a Senior Lecturer in Computer Science at the University of the West of England, Faculty of Engineering and Technology, Department of Computer Science and Creative Technology. She is also the Deputy Programme Leader for Undergraduate Computer Science (2022/2023) and holds a Vice-Chancellor's Early Career Researcher Development Award (2023-2025). Prior to her academic career, she worked in industry as a Machine Learning Engineer and Software Engineer. Dr. Liu holds a BSc, MSc, and PhD in Computer Science, with additional qualifications including FHEA (Fellow of the Higher Education Academy) and PGCert. Her educational background focused on automatic idea generation and analysis using NLP and Machine Learning techniques. Her research spans multiple domains within Artificial Intelligence, with particular expertise in Machine Learning, Computational Creativity, Natural Language Processing, and Computer Vision. Dr. Liu's work combines theoretical exploration with practical applications across diverse fields including medical imaging, idea mining, and novel idea computation. Her research demonstrates strong interdisciplinary connections between computer science and real-world problem solving. Analysis of Dr. Liu's publication record reveals consistent contributions to neural network architecture, image classification, and computational creativity. Her recent work shows increasing focus on medical applications of AI and systematic approaches to understanding machine learning in complex systems like anaerobic digestion. The trajectory of her publications demonstrates both depth in core AI techniques and breadth across application domains. Fellow of Higher Education Academy (FHEA) Vice-Chancellor's Early Career Researcher Development Award 2023-2025 Dr. Liu leads the Machine Learning Algorithms module in the MSc AI Programme and supervises a diverse range of student projects at undergraduate, master's, and PhD levels across Computer Science, Business Computing & Software Engineering, and Data Science. She has taught numerous modules including Business Intelligence and Data Visualization, Biocomputation, Intelligent Systems, and Computer Systems Architecture, demonstrating both depth and breadth in her teaching expertise. Her work connects with multiple research centers including the Big Data Enterprise and Artificial Intelligence Laboratory and the Centre for Machine Vision at UWE Bristol, contributing to the university's strategic focus on AI and data science applications.

Achim Edelmannمشاهده پروفایل

دانشیار

• Sociology of Culture
• Social Networks
• Computational Methods
• +۳ مورد دیگر

Achim Edelmann is an Associate Professor of Sociology at Sciences Po's médialab, specializing in the sociology of culture, social networks, and computational methods. He habilitated at the University of Bern, was a visiting scholar at UC Berkeley, and completed postdoctoral training at Duke University's Network Analysis Center. His research examines the interplay between cultural meanings and social networks, the co-evolution of scientific ideas and public debates, and the application of big data to sociological questions. Education: Habilitation in Sociology (University of Bern), Postdoctoral Trainee (Duke Network Analysis Center) Affiliations: Sciences Po (médialab), University of Bern, UC Berkeley, Duke University Key research projects include analyzing the interaction between cultural meanings and social networks, studying scientists' public support of risky science, semantic analysis in interviews, and misinformation spread through private messaging. He increasingly employs relational databases and cloud computing for large-scale data analysis. His Google Scholar publications (2014–2023) demonstrate expertise in computational social science, symbolic boundary theory, military-to-civilian transitions, and climate science networks. Notable works include analyzing gender differences in political education outcomes (2023), uncertainty in research methodology (2022), and formalizing cultural boundaries (2018). Methodologically, Edelmann combines traditional sociological theory with computational approaches, focusing on structural analysis of cultural systems, network ecology, and big data applications in social research. He supervises PhD theses and has contributed to MOOC-Ed network datasets.

János Tóthمشاهده پروفایل

استادیار مدعو

• Ensemble Systems Optimization
• Big Data Analytics
• Machine Learning
• +۳ مورد دیگر

Dr. János Tóth is an Adjunct Assistant Professor at the Department of Data Science and Visualization within the Faculty of Informatics at the University of Debrecen. His research focuses on optimization of ensemble systems and big data analytics. Institution: University of Debrecen School: Faculty of Informatics Department: Data Science and Visualization Rank: Adjunct Assistant Professor Email: toth.janos@inf.unideb.hu Research Interests: Dr. Tóth specializes in optimizing ensemble systems for medical image analysis and big data processing. His work spans: Parameter optimization techniques for ensemble models Integration of classical image processing with deep learning Medical data visualization solutions Applications in diabetic retinopathy detection Handling unstructured Hungarian medical reports Cloud-based machine learning pipelines Publication Trends: Over the past decade, Dr. Tóth's research has shown consistent focus on medical image analysis (particularly fundus and Pap smear imaging) combined with ensemble learning approaches. His work includes algorithm optimization (2014-2022), data visualization techniques (2012-2024), and natural language processing for Hungarian medical reports (2024). Recent publications (2024) demonstrate continued innovation in machine learning applications for both healthcare and business domains.

Adelaide M. Arruda-Olsonمشاهده پروفایل

استاد

• Clinical research informatics
• Risk stratification
• Cardiovascular epidemiology
• +۷ مورد دیگر

Adelaide M. Arruda-Olson, M.D., Ph.D. serves as Professor of Medicine at Mayo Clinic College of Medicine with primary appointments as Consultant in the Division of Preventive Cardiology and Joint Appointment as Consultant in the Division of Echocardiography and Cardiovascular Imaging, Department of Cardiovascular Medicine. She currently holds the administrative position of Chair for Research in the Division of Preventive Cardiology. Dr. Arruda-Olson's educational journey spans both Brazil and the United States, including her MD from Federal University of Goiás, Ph.D. from Paulista Medical School, Federal University of São Paulo, and extensive postgraduate training at Mayo Clinic including Internal Medicine residency, Cardiovascular Disease fellowship, and a Masters in Biomedical Sciences Clinical and Translational Science. Her research focuses on development and application of novel electronic tools (e-tools) using natural language processing and structured data algorithms to mine electronic health records, addressing knowledge gaps regarding predictors of cardiovascular events. Key focus areas include clinical research informatics, risk stratification, cardiovascular epidemiology, and echocardiography. She aims to develop new e-tools enabling risk stratification linked to clinical decision support at the point of care to facilitate shared decision-making and improve patient outcomes. Analysis of her recent publications reveals a strong emphasis on artificial intelligence applications in cardiology, particularly in echocardiography interpretation, cardiovascular risk prediction, and clinical decision support systems. Her work bridges the gap between traditional cardiology practice and cutting-edge informatics approaches, with increasing focus on ethical implementation of AI in clinical care. 2024 Key-Note Speaker at Opening Ceremony, 13th Congress of the Brazilian Department of Cardiovascular Imaging 2024 Reviewer Award for AMIA Informatics Summit 2012 Top Educator for Internal Medicine Residents 2011 Cardiovascular Diseases Research Achievement Award Multiple awards from Brazilian Society of Cardiology including Best Abstract Awards Dr. Arruda-Olson maintains active roles in numerous professional societies including as Fellow of the American Heart Association, American Society of Echocardiography, and American Medical Informatics Association. She serves on multiple committees including the American College of Cardiology Cardiovascular Disease Standard Setting Panelist and the Council of Medical Specialty Societies AI/Technology Task Force. Her laboratory work focuses on developing and validating clinical decision support algorithms with particular attention to debiasing clinical care algorithms.