جستجوی استاد راهنمای دانشگاهی

Junhua Dingمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Software Engineering
• +۱ مورد دیگر

Junhua Ding is the Reinburg Endowed Professor Chair of Data Science at the University of North Texas, where he is based in Discovery Park, E292G. He holds a prominent academic position in the Department of Data Science, contributing to research and academic leadership in data-intensive fields. His educational background includes advanced degrees from institutions in both the United States and China: Ph.D. from Florida International University M.S. from Nanjing University, China B.S. from China University of Geosciences, China Dr. Ding's research spans several cutting-edge domains in computing and data science. His primary areas of interest include Data Science , Artificial Intelligence , Software Engineering , and Biomedical Computation , reflecting a strong interdisciplinary focus on applying computational methods to complex scientific and engineering problems. No recent publications are listed in the available information, so no trend analysis can be performed at this time. There are no scientific awards mentioned in the current profile. Dr. Ding advises students and may be involved in grant-funded research, though specific details about advisees or grants are not provided in the current text. He is likely affiliated with research initiatives at UNT's Discovery Park, a hub for technological innovation and interdisciplinary research, though specific lab or team affiliations are not detailed.

Xibin Zhangمشاهده پروفایل

استاد

• Bayesian inference
• Financial econometrics
• Nonparametric estimation and testing
• +۷ مورد دیگر

Xibin Zhang is a Professor in the Department of Econometrics and Business Statistics at Monash Business School, Monash University, Australia. He has been a faculty member since 2004, progressing from Lecturer to full Professor in 2021, and has established a strong research profile in econometrics and applied statistics. Educational Background: PhD in Econometrics, Monash University, Australia Doctoral Degree in Systems Engineering, Tianjin University, China MSc in Probability and Statistics, Nankai University, China BSc in Mathematical Statistics, Nankai University, China Research Interests: Professor Zhang’s research focuses on advanced econometric methodologies, including Bayesian inference, semiparametric and nonparametric estimation, panel data models, and hypothesis testing. His work extends into financial econometrics, actuarial studies, and energy economics, with a strong emphasis on empirical applications related to climate policy, energy transition, and economic sustainability. He applies sophisticated statistical techniques to real-world data, contributing to both methodological development and policy-relevant insights. Publication Trends: His recent publications (2019–2025) reveal a consistent focus on nonparametric and Bayesian methods in econometrics, with applications in energy economics, stochastic frontier analysis, and financial modeling. He frequently publishes in top-tier journals such as Journal of Econometrics , Energy Economics , and Economic Modelling , often in collaboration with leading researchers. A notable trend is the integration of kernel-based methods and Bayesian bandwidth selection across various modeling frameworks. Scientific Awards: Dean's Award for Excellence in Research (2009) Dean's Commendation for Excellence in Research Publication (2007) Advising and Grants: He has supervised over 15 PhD and Master’s students to completion and currently supervises multiple PhD candidates. His research is supported by competitive grants, including four Australian Research Council (ARC) Discovery Projects (2006–2016) and a DFAT-funded project (2017–2018) on Chinese tourism dispersal in regional Australia. Labs and Research Teams: While no formal lab is mentioned, Professor Zhang collaborates extensively within econometrics and business statistics research groups at Monash, particularly with colleagues like M.L. King, Jiti Gao, and Russell Smyth. He also engages in interdisciplinary work involving energy policy, finance, and actuarial science.

Aidan Burrellمشاهده پروفایل

دانشگاهی

• Critical Care
• Intensive Care Unit (ICU)
• Extracorporeal Membrane Oxygenation (ECMO)
• +۱۰ مورد دیگر

Aidan Burrell is an Adjunct Clinical Associate Professor in Acute & Critical Care at Monash University, affiliated with the Australian and New Zealand Intensive Care Research Centre (ANZIC-RC). He is an Intensive Care Specialist at The Alfred Hospital, Melbourne, and an NHMRC Emerging Leader Fellow. He co-leads major national research initiatives including SPRINT-SARI Australia (the National ICU COVID-19 Registry) and the ECMO Research Program. As a Chief Investigator on grants exceeding $14 million (with over $4 million as CIA), he plays a pivotal role in advancing critical care research in Australia and internationally. His research interests center on critical care, extracorporeal membrane oxygenation (ECMO), resuscitation, and severe respiratory and cardiac failure . He employs clinical trials, registry data, simulation modeling, and device innovation to improve patient outcomes. His work contributes to UN Sustainable Development Goals related to health and well-being. The most recent articles highlight a strong focus on ECMO optimization—ranging from in-silico modeling of pharmacological interventions to novel cannula design and long-term functional outcomes post-ECMO . This reflects a multidisciplinary approach integrating clinical medicine, engineering, and epidemiology to tackle challenges in critical care. Scientific Awards: NHMRC Emerging Leader Fellow He is actively involved in research supervision, currently mentoring a Master’s student (Ms Natalie Robertson), and is accepting PhD students via Monash’s Supervisor Connect program. He has secured multiple active research projects, including platform trials for severe pneumonia and ECMO-related interventions, demonstrating sustained grant leadership and collaborative research activity. Dr Burrell is embedded in a robust research network through the ANZIC-RC and collaborates with national and international teams on large-scale critical care studies, contributing significantly to evidence generation in intensive care medicine.

Noah Rhodesمشاهده پروفایل

پژوهشگر ارشد

• Power Grid Optimization
• Mathematical Optimization
• Energy Security
• +۲ مورد دیگر

Noah Rhodes is a CNLS Postdoctoral Associate at Los Alamos National Laboratory's Center for Nonlinear Studies (CNLS), working within the Theoretical Division (T-Division). His research focuses on mathematical optimization applications for power grid challenges, particularly infrastructure flexibility and wildfire risk mitigation. Office contact: TA-3, Building 1690, Room 123; email: nrhodes@lanl.gov. Educational background includes: B.S. in Electrical Engineering, University of Wisconsin-Madison (2019) Ph.D. Minor in Energy Analysis and Policy, University of Wisconsin-Madison (2022) Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2024) Rhodes' research centers on enhancing power grid resilience through optimization techniques, with emphasis on reducing infrastructure needs and managing wildfire-related outages. His work bridges theoretical mathematics with practical energy security challenges, developing frameworks for public safety power shutoffs and grid restoration under extreme conditions. His 2021-2025 publications reveal a trajectory from foundational grid restoration algorithms toward integrated environmental considerations. Recent work combines carbon intensity analysis with grid operations while advancing open-source tools (PowerModelsRestoration.jl, ElectricityEmissions.jl) for academic and utility applications. Key trends include stochastic optimization for emergency response and topology-aware power flow formulations. No scientific awards are listed in the provided materials. No student advisement or grant information is documented in the source text. Rhodes operates within CNLS—a hub for nonlinear science research at LANL—which fosters interdisciplinary collaboration on complex systems. His work interfaces with T-Division's theoretical frameworks while addressing real-world energy infrastructure vulnerabilities through computational modeling and optimization.

Mohammad Ghassemiمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Natural Language Processing
• +۷ مورد دیگر

Mohammad Ghassemi is an Assistant Professor in the Department of Computer Science and Engineering at Michigan State University's College of Engineering. His work focuses on developing AI systems that integrate human and machine intelligence to address complex challenges in healthcare, decision-making, and social behavior. He has affiliations with MIT's Institute for Medical Engineering and Science, Standard & Poor's Financial Services, and Ghamut Corporation, and serves on multiple startup advisory boards. Education: PhD from MIT (Electrical Engineering and Computer Science), M.Phil. from University of Cambridge (Information Engineering), B.Sc. from New Mexico State University (Electrical Engineering & Applied Mathematics). Research: Combines machine learning, natural language processing, and crowdsourcing to predict health outcomes, enhance medical decision support, and design wearable AI for social interaction. Awards: Multiple US patents, media features in BBC, NPR, Wall Street Journal, and publications in Nature Scientific Data, Science Translational Medicine, and AAAI. Key projects include the Connect platform for health data sharing, large-scale medical databases, and an AI-driven wearable for social coaching. His lab emphasizes end-to-end systems for real-world impact in healthcare and human behavior analysis.

Xavier Devroeyمشاهده پروفایل

استادیار

• Software Engineering
• Test Automation
• Search-Based Testing
• +۵ مورد دیگر

Xavier Devroey is an Assistant Professor of Software Engineering at the Faculty of Computer Science , University of Namur , where he co-leads the SNAIL Team . His research focuses on test automation for search-based and model-based software testing , test suite augmentation , DevOps , and variability-intensive systems . PhD in Computer Science (University of Namur, 2017) Master in Computer Science (University of Namur, 2010) Recent research highlights include: Automated Android safety/security audits (A3S3, 2025) REST API benchmarking infrastructure (2025) Energy consumption analysis through test execution (2025) Fuzzing approaches for Odoo integration testing (FuzzE, 2025) Scientific recognitions: 1st place Java Test Case Generation Tool Competition (2025) VAMOS 2024 Ten-Year Most Influential Paper SSBSE 2020 Best Paper Award ICST 2024 Distinguished Reviewer AST 2023 PC Reviewer Star He actively supervises PhD and master's students while organizing international conferences like ISSTA (2025) and Belgium-Netherlands Software Evolution Workshops (2024).

• Social Sciences
• Humanities
• Data Management
• +۳ مورد دیگر

Elina Late is a Senior Research Fellow at the Department of Communication Sciences, University of Tampere. Her work bridges social sciences, humanities, and computer science through research data management, image data interaction, and academic collaboration frameworks. Key Research Areas: Data interaction in humanities, open science, learned societies, image data modeling Recent Publications: Focus on collaborative workflows, FAIR data principles, and cross-border academic networks Activities: Conference presentations at CHIIR '25, STI2024, and invited lectures on digital image research Datasets: Principal contributor to national and European learned society datasets (2018-2023)

Luis A. Leivaمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Machine Learning
• Computational Interaction
• +۱۲ مورد دیگر

Luis A. Leiva is an Assistant Professor in the Department of Computer Science at the University of Luxembourg’s Faculty of Science, Technology and Medicine. He leads the Computational Interaction research group and serves as director of the Interdisciplinary Lab for Intelligent and Adaptive Systems (ILIAS). His academic background includes a PhD in Computer Science (with honors), an MAS in Pattern Recognition and Artificial Intelligence, an MSc in Electrical Engineering, and dual BSc degrees in Industrial Design and Industrial Engineering, all from the Universitat Politècnica de València (UPV). His research lies at the intersection of Human-Computer Interaction (HCI) and Machine Learning , with a focus on computational interaction . He investigates methods to enable, explain, and support user interaction through computational models and data-driven techniques. His key areas include implicit interaction —leveraging behavioral signals such as mouse movements and gaze—and synthetic data generation , particularly using kinematic models to simulate human-like gestures. His work has contributed to usability evaluation, user engagement prediction, adaptive interfaces, and stroke gesture modeling. His recent publications span top-tier journals and conferences such as ACM Transactions on Computer-Human Interaction (TOCHI) , IEEE Transactions , CHI , IUI , and SIGIR . The research trends reflect a strong emphasis on multimodal interaction, affective computing, brain-computer interfaces, and deep learning applications in user interface modeling and personalization. Best paper award at IUI'25 Marie Skłodowska-Curie Actions (MSCA) Seal of Excellence (2019) Extraordinary PhD thesis award (2014) Outstanding Reviewer Recognition at CHI, ICMI, MobileHCI, and UIST Quality Teaching Awards finalist at Aalto University (2020) Luis A. Leiva has advised numerous MSc and BSc students at the University of Luxembourg and Aalto University and has secured significant research funding as PI or co-PI for projects such as SYMBIOTIK (EIC), SCRIPTOR (FNR), BANANA (CHIST-ERA), and SAMUSE (Audacity). He is an Associate Editor for the International Journal of Human-Computer Studies and Machine Learning with Applications , and actively serves on the program committees of major HCI and AI conferences. He is also a co-founder and former CTO of Sciling, an SME specializing in machine learning solutions, and has held postdoctoral positions at Aalto University and the PRHLT Research Center (UPV). He leads the Computational Interaction research group and the ILIAS lab, both dedicated to advancing intelligent, adaptive, and user-centered computing systems through interdisciplinary research combining machine learning, cognitive science, and interactive technologies.

David Carreraمشاهده پروفایل

دانشیار

• Computer Engineering
• Supercomputing
• Data-Intensive Computing
• +۵ مورد دیگر

David Carrera is an Associate Professor in the Department of Computer Architecture at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), and serves as the head of the Data-Centric Computing group at the Barcelona Supercomputing Center (BSC-CNS). His research focuses on holistic integration of emerging supercomputing technologies, infrastructure management and optimization for data-intensive workloads, and collaboration with global industry leaders in Computer Science. Areas of expertise include task placement strategies, performance modeling for heterogeneous workloads, and data location strategies for data analytics. He founded the start-up NearbyComputing SL (2018), specializing in orchestration of 5G and IoT services. His work emphasizes technology transfer and industrial collaboration. Scientific Awards & Grants: Agustín de Betancourt y Molina Medal (2018) ERC Starting Grant (HiEST project, 2014) ICREA Acadèmia Award (2015) ERC Proof of Concept (Hi-OMICS, 2017) MIT-Spain "la Caixa" Seed Fund (2018)

Madeline Cruiceمشاهده پروفایل

استاد

• Aphasia Rehabilitation
• Quality of Life in Communication Disorders
• Stroke Recovery
• +۳ مورد دیگر

Professor Madeline Cruice, based at City, University of London since 2002, is a leading expert in aphasia rehabilitation research. She holds a PhD in Speech Pathology from the University of Queensland (2001) and has pioneered innovations in clinical education, including the Classroom-to-Clinic program and Communication Skills Intensive in the NHS. Research Focus: Quality of life in aphasia, WHO ICF frameworks, communication disability assessment, and technology-enhanced interventions Grants: £609,000 (EPSRC), £410,000 (NIHR), £1,096,000 (NIHR HSDR), and £477,809 (Barts Charity) for projects like DARA, TANGO, COM-STAR, and MARS Her recent publications span virtual reality therapy (EVA Park), personalized language treatments , and communication partner training . She serves as Co-Chair of the international Collaboration of Aphasia Trialists (CATs-4) and has supervised 11 PhD/MRes students. Awards: Vice Chancellor’s Award for Local Community Contribution (2016), Research Supervision Award (2016), Student Voice Award (2011), and Walter and Eliza Hall Travelling Scholarship

• Modeling and Simulation
• Energy System Analysis
• Vehicle Grid Integration
• +۳ مورد دیگر

Prof. Dr.-Ing. Marco Pruckner leads the Chair of Communication Networks at the University of Würzburg since 2022, following his assistant professorship in Energy Informatics at Friedrich-Alexander-University Erlangen-Nürnberg (2016–2022). His research focuses on energy system analysis, vehicle grid integration, and future mobility systems using modeling and simulation methodologies. University of Würzburg (2022–present) Friedrich-Alexander-University Erlangen-Nürnberg (2016–2022) UC Berkeley (Visiting Scholar, 2020) Key research areas: Modeling and simulation of integrated energy systems Digital twin development for local energy systems Optimization of electric vehicle charging strategies Water-energy nexus analysis and sustainability metrics Recent publications (2022–2025) demonstrate expertise in: Smart charging algorithms for vehicle grid integration Heat pump electrification and seasonal performance modeling Battery state-of-health estimation techniques Multi-objective optimization for data centers and energy systems His team at University of Würzburg includes Daniel Bayer, Paul Benz, Jonas Schiller, and Leo Strobel. Methodologies combine system dynamics, discrete-event simulation, and machine learning for energy and mobility system analysis.

Ethan Goldمشاهده پروفایل

مدرس

• Cyber Systems
• Computer Science
• Ocean Data Management
• +۳ مورد دیگر

Ethan Gold serves as a Lecturer in the Department of Electrical Engineering and Computing at the United States Coast Guard Academy, joining the Cyber Systems program in 2019. His professional background integrates 9 years of oceanographic fieldwork (including 50+ research cruises aboard USCGC Healy) with 8 years of corporate IT security experience. His educational qualifications include: M.S. in Computer Science, Columbia University (2001) B.A. in Computer Science and Math Correlate, Vassar College (1997) Mr. Gold's research spans Cyber Systems , Ocean Data Management , and Robotics , with emphasis on data visualization for marine science. His interdisciplinary work bridges field oceanography and cybersecurity, leveraging extensive hands-on experience from research vessels to corporate environments. Early robotics publications evolved toward specialized ocean data systems, reflecting practical problem-solving in extreme operational settings. His publication history shows a clear trajectory from robotics research (2000-2001) to marine data management innovations (2014-2019), demonstrating consistent focus on data-intensive systems for challenging environments. Key themes include real-time data processing, visualization tools, and infrastructure design for scientific fieldwork. Mr. Gold has not listed any scientific awards or fellowships. He serves as Capstone Advisor for senior projects while teaching core courses including Introduction to Computing, Operating Systems, and Software Engineering. His industry and field experience provides practical context for student projects in cybersecurity and data systems. As part of the Cyber Systems faculty, Mr. Gold contributes oceanographic data management expertise to the academic program, connecting classroom instruction with real-world maritime operational challenges through his unique blend of seagoing and IT security experience.

• Empirical Software Engineering
• Software Process Improvement
• Requirements Engineering
• +۷ مورد دیگر

Andreas Jedlitschka is a researcher at Fraunhofer IESE in Kaiserslautern, Germany, affiliated with the University of Kaiserslautern. He has made significant contributions to empirical software engineering, software process improvement, and experience management. His research focuses on evidence-based decision support, technology transfer, and quality in agile and AI-driven software development. PhD, University of Kaiserslautern (2009) His research interests include empirical software engineering, requirements engineering, software quality, experience management, and AI in software development. He investigates how to improve software processes through data-driven methods, empirical validation, and knowledge reuse. His work often bridges industry and academia, emphasizing practical applicability. The recent articles highlight a growing focus on AI-enabled systems, sustainability in software engineering, and data-driven technical debt management. His work spans both theoretical frameworks and industrial case studies, particularly in agile environments and safety-critical systems. He has co-organized workshops and edited conference proceedings, indicating active participation in the research community. He mentors researchers through collaborative projects and has contributed to doctoral research frameworks. His work is supported by collaborations across Europe, particularly in EU-funded projects related to software quality and AI. He is involved in initiatives like the Q-Rapids project, which supports decision-makers in managing quality in rapid software development, and contributes to building AI innovation labs with industry partners.

Veronika Cheplyginaمشاهده پروفایل

استاد

• Machine Learning
• Medical Image Analysis
• Pattern Recognition
• +۴ مورد دیگر

Veronika Cheplygina is a Professor at the IT University of Copenhagen and leads the Data-intensive Systems and Applications research group within the Pattern Recognition Revisited lab. Her work focuses on Machine Learning , Medical Image Analysis , and Pattern Recognition , with particular emphasis on Open Science practices and Bias in Medical AI . Primary Affiliation: Department of Data Science and Artificial Intelligence Research Metrics: 20 publications, 2 projects (2021-2025), 2 datasets Her research addresses critical challenges in medical imaging datasets, including annotation bias , data reuse , and fairness in AI through active collaboration with institutions across 5 countries. She has contributed to major conferences like NeurIPS and MICCAI. Scientific Funding & Recognition: Principal Investigator (PI) for Making Metadata Count (MMC) (Danmarks Frie Forskningsfond, 2022-2025) PI for Choosing a Transfer Source for Medical Image Classification (CATS) (Novo Nordisk Foundation, 2021-2025) She maintains an active role in academic governance as head of an assessment committee for faculty recruitment (2023) and has been featured in media discussions about medical AI practices.

Mojtaba Nayyeriمشاهده پروفایل

پژوهشگر

• Knowledge Graph Embedding
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Mojtaba Nayyeri is a Researcher at the University of Stuttgart, working within the Analytic Computing group under the KI institute. His office is located at Universitätsstraße 32, Room 2.302, 70569 Stuttgart, Germany, with contactable phone number +49 711 685 88105. His research centers on Knowledge Graph Embeddings, where he pioneers mathematical frameworks including hypercomplex spaces, stochastic processes, and differential equations to model structured knowledge. This work bridges Artificial Intelligence, Machine Learning, and Natural Language Processing, with significant contributions to semantic web technologies and knowledge representation systems. Nayyeri's publication record (2020-2023) reveals a consistent trajectory toward geometric and topological innovations in knowledge graph representation. He has developed ultrahyperbolic embeddings for heterogeneous structures, neural Itô processes for stochastic trajectory modeling, and hypercomplex space integrations for temporal knowledge graphs, demonstrating how non-Euclidean geometries outperform traditional methods in capturing hierarchical and relational complexities. He actively contributes to the Analytic Computing research group at the University of Stuttgart, which specializes in advanced computational methodologies for knowledge-intensive systems and large-scale data analysis.