جست‌وجوی استاد و پژوهشگر

• Surface Metrology
• Additive Manufacturing
• Precision Engineering
• +۴ مورد دیگر

Professor Jane Jiang is a distinguished academic at the University of Huddersfield , affiliated with the School of Computing and Engineering and the Department of Engineering . She specializes in surface metrology, precision engineering, and advanced manufacturing technologies. Her work focuses on optimizing measurement techniques for additive manufacturing, X-ray computed tomography, and optical systems. Research Interests Her research spans surface texture analysis , 3D vision systems , and metrology for smart manufacturing . She develops novel methods for characterizing complex surfaces using techniques like phase measuring deflectometry and chromatic confocal sensors. Her contributions bridge engineering and materials science, emphasizing practical applications in aerospace, biomedical, and industrial sectors. Recent Contributions Her 2025 work includes advancements in freeform optics design, vibration-resistant microscopy, and neural network-based surface characterization. Her studies on XCT measurement for additive manufacturing parts address critical challenges in precision and data analysis. Affiliations & Projects She leads the Centre for Precision Technologies and actively collaborates on EU-funded projects. She organized the 1st International Conference on Metrology and Standard (2024) and contributes to interdisciplinary initiatives in bio-engineering and advanced materials. Grants & Teams Her team includes researchers like Shan Lou and Paul Scott , focusing on metrology for medical implants and aerospace components. She supervises PhD students exploring AI-driven surface specification and manufacturing process optimization.

• Translational Genomics
• Statistical Genetics
• Computational Genomics
• +۴ مورد دیگر

Eleftheria Zeggini serves as Director of the Institute of Translational Genomics at Helmholtz Munich and holds the Liesel Beckmann Distinguished Professorship and Chair of Translational Genomics at the Technical University of Munich (TUM) School of Medicine. Her research leverages big data in genetics and genomics to translate genomic insights into disease mechanisms, advancing precision medicine through robust statistical and computational approaches integrating multi-omics data, electronic health records, and diverse population studies. Her educational background includes a PhD in Immunogenetics of Juvenile Arthritis from the University of Manchester School of Medicine (2003) and a Bachelor of Science (Hons) in Biochemistry from the University of Manchester Institute of Science and Technology (1999). Prior appointments include Group Leader at the Wellcome Sanger Institute (2008-2018) and Wellcome Research Career Development Fellow at the University of Oxford (2006-2008). Zeggini's research spans translational genomics, statistical genetics, and computational biology with emphasis on genetic architecture of complex diseases. Her team develops tools for multi-omics integration to dissect sequence variation in human health, focusing on osteoarthritis, type 2 diabetes, and cardiometabolic disorders. Recent work prioritizes health equity through tissue-specific multi-ancestry genomic studies and electronic health record integration, driving discovery of novel therapeutic targets and disease mechanisms. Analysis of her 15 most recent publications reveals dominant themes in osteoarthritis genomics (6 articles), type 2 diabetes heterogeneity (3 articles), and multi-ancestry genomic equity (3 articles). Her work consistently employs whole-genome sequencing, causal inference methods, and large-scale biobank analyses across diverse populations, with strong translational focus on precision medicine applications. Major scientific recognition includes: Elected Member of Academia Europaea (2024) Greek Excellence Award (2024) National Representative to the EMBL Council (2022) Elected Fellow of the Academy of Medical Sciences, UK (2020) Argo Award for Distinguished Greeks Abroad (2020) Honorary Senior Fellow, University of Cambridge (2018) Honorary Professor, University of Bristol (2018) Zeggini actively mentors early-career researchers while promoting diversity and inclusion in science. Her leadership in major consortia like UK Biobank analyses and ENABLE program demonstrates sustained grant acquisition capability. Current research direction focuses on developing computational frameworks for next-generation translational genomics, with particular emphasis on addressing health disparities through equitable genomic data representation. She directs Helmholtz Munich's Institute of Translational Genomics, which houses a multidisciplinary team of computational biologists, geneticists, and clinicians developing statistical tools for big data integration. The institute serves as a hub for precision medicine innovation, collaborating with TUM's medical faculty and international networks to accelerate genomic discoveries into clinical applications.

• Psycholinguistics
• Language Use in Conversation
• Cognitive Psychology
• +۳ مورد دیگر

Herbert H. Clark is the Albert Ray Lang Professor of Psychology at Stanford University. His research focuses on communication processes in everyday language use, particularly the mechanisms of conversational grounding, common ground establishment, and the interplay between speech and non-verbal cues. He has authored seminal works like Psychology and Language and Using Language , critically analyzing statistical methodologies in psycholinguistics and exploring discourse dynamics. Affiliations: Stanford University (Psychology Department), Former FRIAS Fellow (Oct-Dec 2012). Clark's research interests span psycholinguistics, pragmatics, and social cognition. He pioneered studies on 'grounding theory'—how conversational participants establish shared understanding—and investigated phenomena like indirect speech acts, quotation, and speech disfluencies. His FRIAS project explores performative depictions in discourse, analyzing how gestures, demonstrations, and other embodied actions contribute to communication beyond verbal speech. Honors include Guggenheim Fellowship, membership in the American and Royal Dutch Academies of Arts and Sciences, and a lifetime achievement award from the Society for Text and Discourse. His work bridges psychology and linguistics, emphasizing collaborative aspects of communication.

• Data Mining
• Social Network Analysis
• Machine Learning
• +۳ مورد دیگر

Jaideep Srivastava is a Professor affiliated with Qatar Foundation (Doha, Qatar), University of Minnesota (Minneapolis, USA), and holds a PhD from University of California Berkeley. His research spans data mining, social network analysis, time series modeling, and health informatics. Recent work focuses on clinical deterioration prediction, sleep research, and computational analysis of pandemic behaviors. Key contributions in clustering algorithms and graph neural networks Active in multimodal learning and misinformation detection His publications from 2024-2022 demonstrate expertise in hierarchical clustering , large language model applications , and health data analytics . Articles often integrate machine learning , social network dynamics , and clinical monitoring systems . Current projects involve Covid-19 in-hospital mortality prediction , virtual influencer analysis , and low-light imaging techniques . Collaborations span institutions in Qatar, USA, and India with applications in urban mobility and precision medicine.

Uli Sauerlandمشاهده پروفایل

استاد

• Linguistics
• Semantics
• Pragmatics
• +۵ مورد دیگر

Prof. Dr. Uli Sauerland is Deputy Director at the Leibniz Centre for General Linguistics (ZAS) in Berlin and Adjunct Professor at the University of Potsdam since 2018. He leads Research Area 4 'Semantics & Pragmatics' and oversees key projects including the ERC-funded Realizing Leibniz’s Dream , DFG-ANR Boolean Connectors , and CRC 1412 projects A05 and B06 . PhD in Linguistics (MIT, 1998) Habilitation (Tübingen, 2003) Rehabilitation (Potsdam, 2007) His research focuses on semantic-pragmatic interfaces , register variation , language acquisition , and cognitive modeling of linguistic phenomena . Recent work includes LMBayes (Bayesian language modeling), DUAL (semantic-pragmatic interactions), and studies on quantifier scope in children. Publications span formal grammar , cognitive development , and language processing . Notable scientific awards : CNRS Fellowship (2024) Academia Europea Membership (2019) ESF Community of Experts (2018) He contributes to editorial boards of First Language , Journal of Semantics , and Language Acquisition , and co-founded the CRC 1412 Register research program.

Christine Paulمشاهده پروفایل

پژوهشگر

• Multilingualism in School
• German as Second and Foreign Language
• Varieties: Spoken Language, Educational Language
• +۳ مورد دیگر

Christine Paul serves as a Scientific Associate in the Department of German Didactics within the Department of Education and Psychology at Freie Universität Berlin, specializing in linguistic and cultural education. Currently on leave while fulfilling duties at Technische Universität Dresden, her expertise spans multilingual pedagogy, German as a second language, and conversation analysis, with significant contributions to corpus linguistics through the Wendekorpus project documenting German reunification narratives. Education: PhD in Linguistics (2016), Freie Universität Berlin First State Examination in German and Spanish (2006), Freie Universität Berlin Graduate Fellowship, Evangelisches Studienwerk Villigst (2009-2013) Study Abroad, Pontificia Universidad Católica del Peru, Lima (2001-2002) Research Focus: Dr. Paul investigates multilingual identity construction in educational settings, analyzing how students navigate spoken vs. academic language varieties through conversation analysis and cognitive linguistics frameworks. Her work bridges theoretical linguistics with classroom practice, particularly examining meaning negotiation in multilingual dialogues and historical language shifts in post-reunification Germany, often utilizing corpus-based methodologies to reveal subtle pragmatic patterns. Publication Evolution: Early career works (2010-2015) established her expertise in temporal inference and retrospective utterances within conversation analysis. Recent publications (2019-2023) demonstrate strategic expansion into digital education spaces and multilingual identity, culminating in 2023's examination of linguistic diversity on school websites. Her research consistently centers on language as a tool for identity construction within institutional contexts, with growing emphasis on intersectional factors like gender and migration background. Academic Engagement: As co-developer of the Wendekorpus audio archive and contributor to the Learning and Research Workshop for Literary Education, she actively shapes pedagogical resources. Her 2024 conference presentation on multilingual female students' self-identification continues her trajectory of connecting linguistic theory to real-world educational challenges through empirical discourse analysis.

Liran Einavمشاهده پروفایل

استاد

• Industrial Organization
• Health Economics
• Insurance Markets
• +۴ مورد دیگر

Liran Einav is a Professor in the Department of Economics at Stanford University, where he serves as Research Associate Director of the Industrial Organization Program. His work spans multiple NBER programs including Industrial Organization, Economics of Aging, and Economics of Health. Einav's research focuses on industrial organization, health economics, and insurance markets. His work examines healthcare spending patterns, market design in insurance markets, nursing home quality, racial disparities in healthcare, and the economic implications of medical technologies. He frequently collaborates with Amy Finkelstein and other leading economists in the field. His recent publications analyze racial differences in nursing home quality, clinician behavior in response to skin-tone affecting medical tests, the economic impact of having a child with Down syndrome, and market design considerations in regulated health insurance markets. His work often leverages large administrative datasets and natural experiments to draw causal inferences about economic behavior in healthcare markets. Einav has made significant contributions to understanding selection and moral hazard in insurance markets, building on the empirical framework he developed with Finkelstein and Cullen (2010). His work has implications for health insurance design, Medicare policy, and understanding healthcare spending patterns in the United States. He maintains active research collaborations across economics and health policy, with recent work examining telemedicine adoption, emergency department utilization, and end-of-life healthcare spending. His research combines theoretical insights with rigorous empirical analysis to address pressing questions in health economics and industrial organization.

• Optimal Control Theory
• Numerical Methods
• Kinetic Models
• +۵ مورد دیگر

Dr. Jan Bartsch is a Lecturer at the Institute of Mathematics, University of Würzburg, Germany. He has been working as a Scientific Employee at the Institute of Mathematics since 2024. Prior to this, he was a Postdoc at the University of Konstanz from 2021 to 2024 in the Collaborative Research Center 1432 "Fluctuations and Nonlinearities in Classical and Quantum Matter Beyond Equilibrium." Dr. Bartsch's educational background includes: PhD in Scientific Computing from the University of Würzburg (2018-2021), dissertation title: "Theoretical and numerical investigation of optimal control problems governed by kinetic models" Master's degree in Mathematics from the University of Würzburg (2016-2018), thesis title: "Optimal control problems governed by Liouville models - Mathematical analysis and implementation" Bachelor's degree in Computational Mathematics from the University of Würzburg (2013-2016), thesis title: "Optimal Control of Androgen Suppression of Prostate Cancer" His research focuses on optimal control theory, numerical methods for partial differential equations, and kinetic models. Dr. Bartsch specializes in Monte Carlo methods for solving kinetic models, optimal control of stochastic differential equations, and numerical methods for hyperbolic differential equations. His work bridges theoretical mathematics with practical computational approaches, particularly in the context of control problems governed by complex physical models. Dr. Bartsch's recent publications show a strong focus on applying adjoint-based methods to optimal control problems, particularly involving stochastic processes and kinetic models. His research demonstrates expertise in developing numerical frameworks that combine Monte Carlo approaches with control theory to solve complex mathematical problems arising in physics and engineering applications. Contact Information: Email: jan.bartsch@uni-wuerzburg.de Phone: +49 931 31-80733 Office: Building 40 (Mathematics East), Room 00.012, Emil-Fischer-Straße 40, 97074 Würzburg

Omid Elmiمشاهده پروفایل

پژوهشگر

• Remote Sensing
• Hydrology
• Geodesy
• +۳ مورد دیگر

Omid Elmi is a Researcher at the Department of Geodesy within the Institute of Geodesy at the University of Stuttgart. His work focuses on advancing satellite-based remote sensing techniques for hydrological applications, particularly in monitoring global water resources and dynamics. Key areas of research include satellite altimetry, river discharge estimation, and the integration of geodetic data with hydrological models. Elmi’s research emphasizes innovative methods such as bin-space-time (BiST) retracking, probabilistic drought characterization, and automated tools for analyzing satellite imagery (e.g., Google Earth Engine). His contributions address critical challenges in water resource management, environmental sustainability, and climate change adaptation through cutting-edge geodetic and remote sensing technologies. His publications highlight advancements in satellite mission design for river monitoring, spatio-temporal data analysis, and the application of Bayesian methods to improve inland water altimetry. Elmi’s work bridges geodesy, hydrology, and environmental science, offering practical solutions for global change monitoring.

Martin Eigelمشاهده پروفایل

پژوهشگر

• Numerical Mathematics
• Uncertainty Quantification
• Stochastic Partial Differential Equations
• +۵ مورد دیگر

Martin Eigel is a Researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) , specializing in numerical methods for stochastic partial differential equations, uncertainty quantification, and machine learning applications in computational mathematics. His work bridges tensor networks, Bayesian inversion, and quantum simulations. Research Interests : Adaptive stochastic Galerkin finite element methods Low-rank tensor approximations for high-dimensional problems Machine learning integration with PDE solvers Quantum circuit simulation techniques Bayesian inverse problems and error control Key Article Trends : His recent publications focus on merging deep learning architectures (e.g., ResNet, CNNs) with stochastic and tensor-based numerical methods for solving parametric PDEs, Bayesian inversion, and quantum systems. Topics include Hamilton-Jacobi-Bellman equations, Langevin dynamics, and risk-averse optimization under uncertainty.

• Register variation in linguistics
• Head-Driven Phrase Structure Grammar (HPSG)
• Corpus linguistics
• +۳ مورد دیگر

Dr. Felix Bildhauer is a researcher affiliated with the Institute for the German Language (IDS Mannheim) and Freie Universität Berlin , actively contributing to the Collaborative Research Center 1412 'Register: Language Users’ Knowledge of Situational-Functional Variation' . He serves as a key member of project A04 'Building register into the architecture of language – an HPSG account' and maintains an ORCID profile . His research focuses on register variation and Head-Driven Phrase Structure Grammar (HPSG) , with methodological expertise in Bayesian inference , corpus annotation , and computational modeling . He has developed the COReX feature extraction framework and contributes to the PreCOX20 corpus for register analysis. Recent publications include: 2023: Situating register phenomena across languages and cultures in Frontiers in Psychology 2023: Register-specific attributive clause variation in German 2022: Formalizing register in HPSG at Nagoya University Contact: bildhauer@ids-mannheim.de | felix.bildhauer@fu-berlin.de

• Linguistics
• Language Evolution
• Phylogenetic Methods
• +۴ مورد دیگر

Olena Shcherbakova is a doctoral researcher at the Department of Linguistic and Cultural Evolution of the Max Planck Institute for Evolutionary Anthropology. Her work focuses on phylogenetic methods, spatiophylogenetic modeling, and causal inference to study large-scale patterns in language evolution and typological variation. She holds a BA and MA in Philology from the National Technical University of Ukraine (Kyiv Polytechnic Institute) and completed an exchange program at Friedrich Schiller University Jena. Her research explores grammatical complexity dynamics, sociolinguistic influences on language structure, and the evolutionary pathways of gender systems. Key findings include evidence that grammatical complexity (fusion and informativity) is primarily inherited rather than adapting to sociolinguistic changes, and that societies of strangers do not correlate with simpler languages. She has contributed to cross-linguistic studies on argument structure signaling and collaborated on global analyses comparing music and speech acoustics. Shcherbakova's methods include computational phylogenetics, spatiotemporal modeling, and comparative approaches across language families like Austronesian, Bantu, and Indo-European. She co-developed the CHIELD database for tracking causal hypotheses in evolutionary linguistics and has published in journals such as Science Advances , Journal of Language Evolution , and Scientific Reports .

Qiang Wangمشاهده پروفایل

پژوهشگر

• Taxonomy
• Evolution
• Phylogenetics
• +۷ مورد دیگر

Qiang Wang is a Researcher at the Institute of Botany, Chinese Academy of Sciences, with expertise in plant taxonomy, systematics, and phylogenetics. His work focuses on the classification and evolutionary relationships of Lamiaceae and Campanulaceae families, employing molecular, morphological, and cytological approaches. He holds a PhD in Science and has held visiting scholar positions, including at the Royal Botanic Gardens, Kew (2016–2017). Research interests include: Phylogenomics of plant families using plastome and chloroplast data Taxonomic revisions of genera like Pseudocodon and Codonopsis Species delimitation in Lamiaceae and Campanulaceae Application of DNA barcoding and cytotaxonomy His publications emphasize integrative approaches combining genomic, morphological, and ecological data to resolve taxonomic uncertainties and uncover evolutionary patterns. Notable contributions include descriptions of new species (e.g., Codonopsis atriplicifolia) and reclassification of plant groups based on molecular evidence.

• Reinforcement Learning
• Robotics
• Artificial Intelligence
• +۳ مورد دیگر

George Dimitri Konidaris is a Professor at Brown University in Providence, RI, USA, specializing in artificial intelligence and robotics. His research focuses on developing advanced algorithms for reinforcement learning, robotic planning, and hierarchical skill acquisition. He leads investigations into model-based reinforcement learning, temporal abstraction, and human-robot interaction frameworks. Konidaris's research bridges fundamental machine learning concepts with applied robotics. Key interests include: Hierarchical reinforcement learning for complex decision-making Robotic skill transfer and generalization Language-guided agent learning Continuous control in high-dimensional spaces Uncertainty-aware planning systems His publications demonstrate consistent focus on reinforcement learning theory applied to robotic systems, with recent emphasis on language integration and hierarchical abstraction. Work frequently appears in premier venues including ICML, NeurIPS, ICRA, and IROS.

Dimitrije Markovićمشاهده پروفایل

پژوهشگر

• Cognitive Neuroscience
• Theoretical Neuroscience
• Machine Learning
• +۴ مورد دیگر

Dimitrije Marković is a Researcher in the Department of Psychology at Technische Universität Dresden, affiliated with the Faculty of Science. His work bridges cognitive neuroscience, theoretical neuroscience, and machine learning, focusing on computational models of human decision-making and adaptive behavior. He employs concepts from information theory and probability to develop and test experimental predictions from these models. Education: PhD in Physics (2013), Goethe University Frankfurt, advised by Prof. Claudius Gros Diploma in Theoretical and Experimental Physics (2007), Belgrade University Undergraduate studies in Physics (2002–2007), Belgrade University Research Focus: His research emphasizes understanding neurophysiological and computational mechanisms underlying human decision-making, particularly in dynamic environments. He explores topics like adaptive learning, Bayesian inference, and neural modeling of uncertainty and agency perception. Publications Trends: Recent work includes advancements in active inference frameworks, machine learning models for decision-making, and applications to mental health (e.g., depression). His 2020–2025 publications highlight innovations in scalable models (e.g., AXIOM), Bayesian methods, and interdisciplinary approaches linking neuroscience with AI. Professional Experience: Postdoc at TU Dresden (2014–present) Guest Researcher, Max-Planck Institute for Human Cognitive and Brain Sciences (2013–2015) Postdoc, University Clinic Jena (2013–2014) Labs/Teams: Affiliated with TU Dresden’s Chair for Neuroimaging, focusing on neuroimaging techniques and collaborative projects in computational psychiatry and AI-driven neuroscience.