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جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۲۱-۷۳۵ از ۵٬۱۷۴ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Thorsten Vittمشاهده پروفایل

پژوهشگر

Julius-Maximilians-Universität WürzburgGermany

Thorsten Vitt is a Researcher at the Chair of Computational Philology and Modern German Literary History within the Faculty of Philosophy at the University of Würzburg . His role includes academic advising for Digital Humanities degree programs, IT support for the Chair, and technical leadership in digital edition projects such as the Faust Edition and TextGrid . He is affiliated with the ZPD (Zentrum für Philologie) and works on computational methods for literary analysis. Current Projects : Faust Edition, ESF-ZDEX, TextGrid, DARIAH-DE Technical Expertise : Markup Languages, Linguistic Query Languages, Database Systems Research Interests : Digital Humanities, Computational Philology, Authorship Attribution, Topic Modeling, Digital Editions, Data Visualization, Software Development for Humanities. His work bridges computer science with literary studies, focusing on algorithmic analysis of historical texts and digital scholarly editions. Publications highlight his contributions to computational analysis of Goethe's Faust, topic modeling tools, and distance measures like Burrows' Delta. He has developed software libraries for stylometric analysis and collaborates on infrastructures like TextGrid and DARIAH-DE. Education : Completed a diploma thesis on "Queries to Complex Corpora" (2005) and a student research paper on "Storing Linguistic Corpora in Databases" (2004) at Humboldt University Berlin. Technical Leadership : Deputy IT support for the Chair, contributor to GitHub repositories including pydelta and faust-web , and developer of tools for topic modeling and digital editions.

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Jun Wuمشاهده پروفایل

پژوهشگر

University of OxfordUnited Kingdom

Dr. Jun Wu is a Postdoctoral Research Assistant at the Department of Biology, University of Oxford, where he conducts research in the Animal Vibration Lab. Previously, he served as a research associate in the Department of Aerospace Engineering at the University of Bristol. His academic credentials include: PhD in Sound and Vibration Research from the University of Southampton Dr. Wu's research bridges vibration dynamics and biomedical engineering, with current work developing engineering technologies inspired by biological dynamic systems. His publication record reveals a significant pivot toward medical artificial intelligence since 2023, with dominant contributions in surgical vision-language models and medical image segmentation. This dual expertise enables unique cross-pollination between biological vibration principles and clinical AI applications. Analysis of his 2023-2025 publications shows concentrated innovation in adapting foundation models like Segment Anything Model (SAM) for medical contexts. Key advances address uncertainty quantification in segmentation, human-in-the-loop refinement systems, multi-modal diagnostic reasoning, and real-time surgical video analysis. These works consistently target clinical deployment challenges including safety verification, computational efficiency, and human-AI collaboration. Professional recognition includes: Associate Fellow of the Higher Education Academy (AFHEA) Member of the Royal Aeronautical Society (MRAeS) As a core member of Oxford's Animal Vibration Lab, Dr. Wu contributes to research investigating biological dynamic systems for engineering innovation while leading medical AI initiatives that translate these principles into surgical and diagnostic technologies.

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Venkat Venkatakrishnanمشاهده پروفایل

استاد

University of Illinois ChicagoUnited States

Venkat Venkatakrishnan is a Professor of Computer Science and Director of Research at the Discovery Partners Institute at the University of Illinois at Chicago. As Associate Dean for Research and Graduate Studies in the College of Engineering, he leads initiatives in cybersecurity research and education. His research focuses on Computer Systems Security , utilizing techniques from Compilers Operating Systems Formal Methods to defend against cyber attacks. He has developed innovative approaches to secure desktop systems, detect web vulnerabilities, and reconstruct attack scenarios. Recent publications highlight his work in areas including: Advanced Persistent Threat detection Web application security Automated exploit generation Security sanitization tools SQL injection prevention Content security policies Scientific achievements include Distinguished Paper Award at USENIX Security Symposium (2018) Best Paper Awards at IEEE QRS (2016), Oakland (2009), and ACSAC (2003) Finalist for AT&T Best Applied Security Research Paper (2010) He has mentored numerous students and postdoctoral researchers, many of whom have become faculty members at prestigious institutions or joined leading technology companies. His work bridges academic research with practical applications for businesses and websites seeking to patch security vulnerabilities.

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Mark Zylkaمشاهده پروفایل

استاد

University of North Carolina at Chapel HillUnited States

Mark Zylka serves as the W.R. Kenan, Jr. Distinguished Professor in the Department of Cell Biology and Physiology at the UNC School of Medicine, University of North Carolina at Chapel Hill. He directs a research laboratory focused on neurodevelopmental disorders and pain mechanisms, with affiliations at the UNC Neuroscience Center and support from multiple NIH institutes. His research spans autism etiology , Angelman syndrome , chronic pain pathways , and environmental neurotoxicology . The lab employs CRISPR/Cas gene editing, AAV vectors, single-cell RNA-seq, and behavioral analysis to investigate molecular mechanisms underlying neurodevelopmental disorders and pain sensation. Recent work emphasizes environmental chemical impacts on neurodevelopment and gene therapy approaches for autism-related conditions. Publications from 2023-2025 reveal strong trends in CRISPR-based therapeutics for Angelman syndrome, environmental chemical screening for neurodevelopmental risks, and computational pain assessment tools . Key interdisciplinary fields include neurogenetics, molecular toxicology, and translational neuroscience with emphasis on Wnt signaling pathways and epigenetic regulation. Scientific Awards: Rita Allen Foundation Award Alfred P. Sloan Foundation Fellowship Searles Scholars Program Whitehall Foundation Grant Ester A. & Joseph Klingenstein Fund Award Brain & Behavior Research Foundation Grant Laboratory funding demonstrates exceptional grant acquisition across federal and private sources. Major support comes from NIH/NIEHS, NIH/NIMH, NIH/NINDS, Rett Syndrome Research Trust, Angelman Syndrome Foundation, Autism Speaks, and Simons Foundation. The lab maintains active collaborations through the UNC Neuroscience Center and NC TraCS institute, with recent focus on developing PainFace software for standardized pain assessment and CRISPR-based therapies for neurogenetic disorders.

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Daniel Kneeshawمشاهده پروفایل

استاد

University of Quebec in Abitibi-TémiscamingueCanada

Daniel Kneeshaw is a Professor in the Department of Biological Sciences at the University of Quebec in Montreal (UQAM), where he holds the UQAM Strategic Research Chair on the resilience and vulnerabilities of temperate and boreal forests to climate change. He is a Regular Member of the Center for Forest Ecology (CEF) and has been actively contributing to forest ecology research since joining UQAM in 2001 after serving as a researcher at the Forest Research Branch of the Quebec Ministry of Natural Resources. His educational background includes a Post-doctorate in forestry sciences (1999, Laval University), Ph.D. in Environmental Sciences (1997, UQAM), M.Sc. in Forest Ecology (1992, University of British Columbia), and B.Sc. in Forestry (1989, University of Toronto). Before his academic career, he worked in softwood forests in Manitoba and British Columbia. Dr. Kneeshaw's research focuses on sustainable forest management tools, mortality and population dynamics in forest ecosystems, and the dynamics of forest gaps in maintaining old-growth forests. His work aims to develop and integrate indicators of sustainable forest management by better understanding forest dynamics, particularly in advanced development stages, tree mortality and regeneration, and competition in gaps. He employs modeling approaches to assess ecosystem responses to different management strategies across multiple spatial and temporal scales. His recent publications demonstrate a strong focus on climate change impacts on boreal forests, tree physiology in urban environments, forest insect outbreaks (particularly spruce budworm), and biogeochemical processes in forest ecosystems. His research spans from fundamental forest ecology to applied management solutions, with a growing emphasis on climate change adaptation and resilience. Prix de la Faculté des sciences de l'UQAM 2024 - Prix de la recherche - Professeur – volet carrière Prix de la ministre de l'éducation, du loisir et du sport for the book Aménagement écosystémique en forêt boréale (2009) Dr. Kneeshaw has supervised an extensive number of graduate students (over 50 Master's and PhD students listed) and has collaborated with numerous researchers across Canada and internationally. His work has been supported by various grants, including NSERC funding for his postdoctoral research and ongoing projects related to forest dynamics and climate change. He has contributed significantly to developing ecosystem-based management approaches that mimic natural disturbance regimes in boreal forests. His laboratory focuses on understanding forest dynamics at multiple scales, from tree-level physiological processes to landscape-level patterns. His team employs a range of methodologies including field measurements, dendrochronology, remote sensing (particularly LiDAR), and ecosystem modeling to address complex questions about forest resilience and sustainability in the face of climate change and other disturbances.

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Xiaoyuan Xieمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Xiaoyuan Xie is a Professor at the School of Computer Science, Wuhan University, where he leads a research group focused on software engineering, particularly in software testing, fault localization, and the intersection of AI and software engineering. His work bridges theoretical foundations with practical applications in software quality assurance and development processes. His research interests span software testing, fault localization, program slicing and analysis, debugging techniques, search-based software engineering, evolutionary computing, and machine learning applications in software engineering. He has pioneered work in metamorphic testing, particularly for AI systems, and developed innovative approaches for fault localization that consider exception trigger streams and parallel debugging contexts. Xie's publication trend shows a strong focus on applying AI techniques to software engineering problems, with recent work emphasizing metamorphic testing for AI systems, code search, debugging assistance, and fault localization enhanced by deep learning. His research bridges traditional software engineering with modern AI techniques, creating novel solutions for testing and debugging complex software systems. ICST 2025 Most Influential 10-years Journal First Paper Award Multiple ACM SIGSOFT Distinguished Paper Awards (2024, 2022, 2021) CCF Open Source Innovation Competition First Prize 2024 CCF NASAC Software Innovation Award for Young Researchers 2021 ACM SIGEVO HUMIES 2017 Silver Medal Luojia Young Scholar, Wuhan University 2016 Professor Xie has supervised numerous PhD and Master's students who have gone on to prestigious positions at companies like Alibaba, Tencent, and ByteDance, as well as academic institutions including Yale University and the Hong Kong University of Science and Technology. His research is supported by multiple national grants, including NSFC重点项目 (key projects), 国家重点研发计划 (key R&D programs), and the Research Fund for International Excellent Young Scientists from NSFC.

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Yi Liمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Yi Li is an Associate Professor at the College of Computing and Data Science, Nanyang Technological University (NTU), Singapore. He leads the Software Reliability and Security Lab (SRSLab@NTU) which focuses on program analysis and automated reasoning techniques for software engineering and security applications. Dr. Li received his BComp degree in Computer Science from the National University of Singapore in 2011, followed by MSc (2013) and PhD (2018) degrees in Computer Science from the University of Toronto. His educational background has positioned him at the forefront of software reliability research. His research interests span software engineering, program analysis, automated reasoning, and formal methods, with particular focus on software reliability, security, and smart contract analysis. Dr. Li's work develops practical solutions for constructing high-quality software systems that are both reliable and sustainable. His research has identified critical issues in API documentation, smart contract security, program termination, and library compatibility across multiple programming ecosystems. Dr. Li's publications demonstrate consistent contributions to software engineering research over the past five years, with recent work focusing on smart contract analysis, API documentation verification, and program termination detection. His research methodology often combines static and dynamic analysis techniques with machine learning approaches to address challenging problems in software reliability and security. Five ACM Distinguished Paper Awards at top conferences Two Best Artifact Awards recognizing research reproducibility Publications at premier venues including ASE, ISSTA, FSE, and ICSE Dr. Li actively contributes to the software engineering community through service on program committees of flagship conferences (ICSE, FSE, ASE, ISSTA) and co-chairing program committees for ICFEM'23, ICECCS'20, SEAIS'22, and ICFEM'19 Doctoral Symposium. His lab, SRSLab@NTU, has developed multiple influential tools including InvCon, DocCon, and EndWatch, while fostering research in software reliability and security for emerging technologies.

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Maria Ribera Sanchoمشاهده پروفایل

استاد

Polytechnic University of CataloniaSpain

Dr. Maria Ribera Sancho is a Professor at the Polytechnic University of Catalonia (BarcelonaTech), holding roles as Dean of the Faculty of Informatics of Barcelona (2004–2010), Vice-Dean (1998–2004), and currently Manager of the Education and Training Department at the Barcelona Supercomputing Center (BSC-CNS). She chairs the EQANIE Accreditation Committee and serves on the ACM-W Europe Executive Committee. Her research focuses on Software Engineering, Conceptual Modeling, Ontologies, Learning Analytics, and IoT applications. Notable contributions include work on automated design using conceptual models, model-driven software development, and semantic-based IoT infrastructure monitoring. She leads projects like LinDaFIX (social welfare data tools) and REMEDiAL (ontology-driven software automation). Key awards include the UPC Quality in Teaching Prize (2005, 2021), Jaume Vicens Vives Distinction (2005), Sapiens Award (2011), and Festibity Award (2011). She advises doctoral and master’s students on topics like IoT semantic monitoring and educational ontology development. Academic director of the inLab FIB Talent Program, she also directs the PRACE Advanced Training Center at BSC. Main projects include TINTIN (SQL integrity tool), e-Catalunya (collaborative government platform), and PILARES (learning analytics for secondary education). Her work bridges academia and industry through competitive projects with companies and institutions.

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Ignacio Arganda Carrerasمشاهده پروفایل

دانشیار

University of the Basque CountrySpain

Ignacio Arganda Carreras is an Associate Professor at the Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU) and an Ikerbasque Research Associate, affiliated with the Donostia International Physics Center (DIPC). His research focuses on biomedical computer vision, with a strong emphasis on deep learning applications in microscopy and medical imaging. Key areas include bioimage analysis pipelines, domain adaptation for cross-modal image segmentation, and AI-driven solutions for healthcare diagnostics. He has contributed extensively to open-source tools like BiaPy, CartoCell, and DL4MicEverywhere, which advance accessibility to deep learning in bioimaging. His work bridges computational methods with biological and medical challenges, addressing issues like 3D object detection, super-resolution imaging, and automated classification in microscopy and clinical settings. Research highlights include developing the MitoEM and Nucmm datasets for mitochondria and neuronal nuclei segmentation, as well as innovative applications in wound healing modeling and aquaculture monitoring. His methodologies emphasize reproducibility, generalization, and mitigation of overfitting in deep learning models.

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Thomas Banitzمشاهده پروفایل

پژوهشگر

Helmholtz Centre for Environmental ResearchGermany

Thomas Banitz is a Researcher in the Department of Ecological Modelling at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. His work focuses on developing computational models to study ecological systems, population dynamics, and ecosystem functions. Key research areas include grassland biodiversity responses to environmental changes, microbial ecology, and causal reasoning in social-ecological systems. He leads projects like BioDT (biodiversity digital twin) and contributed to CauSES (causation in social-ecological systems). Education: PhD in Environmental Sciences from Universität Osnabrück (2011), Dissertation: 'Modelling bacterial growth, dispersal and biodegradation'. Research Highlights: Developed individual-based models for microbial systems (e.g., McComedy tool) Investigated microbial interactions and ecosystem processes in disturbed environments Advanced causal explanation frameworks for complex systems Explored spatiotemporal dynamics of biodiversity under environmental stressors Labs/Teams: Member of FORMIND vegetation model team and EcoEpi ecological epidemiology initiative. Collaborates with interdisciplinary groups on digital twins and sustainability science.

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Mitchell Pryorمشاهده پروفایل

استاد پژوهشی

University of Texas at AustinUnited States

Mitchell Pryor is a Research Professor in the Department of Nuclear and Radiation Engineering at the University of Texas at Austin. He specializes in applied robotics and automation, particularly in hazardous environments such as nuclear facilities, energy sectors, and defense applications. Pryor earned his BSME from Southern Methodist University (1993), followed by an MS (1999) and PhD (2002) in Mechanical Engineering from UT Austin. His research focuses on robotics for hazardous environments, including nuclear waste management, autonomous systems, and human-robot collaboration. He leads the Nuclear & Applied Robotics Group (NRG) and co-founded RAPID, an industry affiliate program supporting automation in energy sectors. His work spans robotics in extreme environments, augmented reality interfaces, and task planning frameworks. Key collaborations include Army Futures Command (AFC), NASA, DARPA, and DOE national labs such as INL, LANL, and ORNL. Pryor’s contributions include developing the TeMoto software framework for robotic autonomy, virtual fixture systems, and radiation surveying methodologies. He chairs the ANS Robotics & Remote Systems Division and actively participates in IEEE, ASME, and other professional societies. His research emphasizes interdisciplinary approaches to deploy robotics in complex, uncertain environments for tasks like material handling, mobile manipulation, and environmental monitoring. Notable projects include AR-STAR for real-time robot data modification, MaRMOT for object tracking, and TeMoto 2.0 for task execution formalisms. His work bridges theoretical robotics with practical applications in nuclear safety, energy infrastructure, and defense systems.

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Diomidis Spinellisمشاهده پروفایل

استاد

Athens University of Economics and BusinessGreece

Professor Diomidis Spinellis is a renowned academic in Software Technology at Athens University of Economics and Business (AUEB). He specializes in software engineering practices, code quality, AI ethics, and system architecture. His work bridges theoretical advancements with practical applications in industry, emphasizing reproducibility and empirical methods. Recipient of the IEEE Computer Society's prestigious 'Distinguished Contributor Recognition,' Spinellis is the sole Greek scientist to achieve this honor. His research spans software evolution, security, and open-source ecosystems, with a focus on methodologies like refactoring, static analysis, and debugging strategies. Key research interests include AI-generated content detection, modular data analytics, and incident management systems. His studies often leverage large-scale datasets (e.g., Unix evolution, Linux supercomputing analysis) to uncover patterns in software behavior and development practices. Publications frequently address emerging technologies' societal impacts, such as energy-efficient computing and ethical AI deployment. He advocates for reproducible research through tools like the Alexandria3k framework and contributes to open-source initiatives.

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Andy Hopperمشاهده پروفایل

استاد

University of CambridgeUnited Kingdom

Sir Andy Hopper is a Professor and Head of the Digital Technology Group within the Computer Laboratory at the University of Cambridge. He is a Fellow of Corpus Christi College, Cambridge. His research focuses on creating a world where everything is always connected in a personalized way and developing sentient computing systems that observe the real world to ensure planetary sustainability. His research interests span ubiquitous computing, sentient computing, distributed systems, computer networking, green computing, and provenance systems. He has pioneered work in location-aware systems, energy-efficient computing, and provenance in distributed environments. His vision extends computing to automatically adapt to real-world conditions for sustainable solutions. Recent publications indicate a strong focus on provenance systems, data reliability in cloud environments, and the integration of renewable energy in datacentre computing. His work often involves collaboration with researchers at the University of Cambridge and beyond, addressing challenges in virtualization, MapReduce, and resource accounting. Professor Hopper leads the Digital Technology Group, which is part of the Computer Laboratory. The group conducts cutting-edge research in networking, systems, and sustainable computing, contributing to both theoretical advances and practical applications in the field.

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Nicholas Pearceمشاهده پروفایل

استادیار

Linköping UniversitySweden

Nicholas Pearce is an Assistant Professor at Linköping University, affiliated with the Department of Physics, Chemistry and Biology (IFM) and the Bioinformatics (BIOIN) division. He leads the Data-Driven Determination of Macromolecular Structures (D3MS) group, also known as PearceLab @ LiU, and is affiliated with SciLifeLab and the Wallenberg Centre for Molecular Medicine (WCMM) through the Data-Driven Life Sciences (DDLS) program. His research focuses on advancing structural biology by developing computational and experimental methods that capture the dynamic nature of proteins. Key interests include protein flexibility, disorder, and functional dynamics, with applications in drug discovery and macromolecular modeling. The group employs techniques such as X-ray crystallography, cryo-EM, machine learning, and statistical modeling to improve the resolution and interpretability of macromolecular structures. The recent publications highlight a strong focus on software and method development for structural biology, particularly in multi-dataset analysis and crystallographic data processing. The work contributes to open-source tools like the CCP4 suite and introduces novel approaches such as PanDDA and PanDEMIC.adp for detecting ligand binding and modeling disorder. While no formal scientific awards are listed in the provided text, his involvement in major collaborative projects and national research initiatives underscores his growing impact in the field. He actively mentors students and welcomes Master’s candidates to join his research group. Nicholas began his independent research career at Linköping University on October 1, 2022, with funding supporting the launch of his lab. His group emphasizes data-driven decision-making in structural analysis and collaborates extensively within Sweden and internationally. Current projects include multi-dataset analysis for fragment screening and decomposition of protein disorder using elastic net models. The D3MS group is active in outreach and scientific communication, maintaining a blog and public presence, including a Twitter feed. They have participated in international research visits, such as a recent trip to the University of Hamburg, and national conferences like Sweprot in Tällberg.

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Jan Kierfeldمشاهده پروفایل

استاد

TU Dortmund UniversityGermany

Prof. Dr. Jan Kierfeld is a faculty member in the Department of Physics at Technical University of Dortmund, where he leads a research group focused on soft matter theory and biological physics. His work bridges statistical physics, mechanics, and hydrodynamics of soft and biological systems, with strong interdisciplinary connections to materials science and biophysics. His research interests include polymer physics , cytoskeletal filaments (actin and microtubules), elastic capsules and shells , active matter , and the development of novel simulation techniques such as event-chain Monte Carlo. He is particularly interested in how chemical energy (e.g., ATP/GTP hydrolysis) drives mechanical forces in biological systems, and in the mechanics of semiflexible polymer networks and microswimmers. His group also pioneers the application of machine learning to problems in soft matter, such as pendant drop tensiometry and traction force microscopy. The recent publications of Prof. Kierfeld span topics in biophysics, soft matter, and computational physics, showing a strong trend toward integrating theoretical modeling with experimental collaboration, especially in microswimmer dynamics, microtubule mechanics, and interfacial phenomena. His work frequently appears in journals such as Soft Matter , Physical Review , Biophysical Journal , and Nature Communications . He has no listed scientific awards in the provided text, but his active publication record and leadership in DFG programs (e.g., SPP1726 Microswimmers) indicate significant recognition in the field. He advises students and postdoctoral researchers in theoretical and computational soft matter physics, though specific names are not listed. His research is supported by grants from German funding agencies such as the DFG. Prof. Kierfeld’s group develops and applies advanced simulation methods and collaborates with experimentalists on problems involving elastic instabilities (buckling, wrinkling), microswimmers , and chemomechanical models of cellular structures. The group maintains strong technical development in numerical algorithms and data analysis tools, including open-source software like MLFTM for traction force microscopy.

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