جست‌وجوی استاد و پژوهشگر

Michalis Vazirgiannisمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Graph Analysis
• +۱۱ مورد دیگر

Michalis Vazirgiannis is a Professor at LIX, École Polytechnique in France, where he leads the Data Science and Mining group (DaSciM). He holds a degree in Physics and a PhD in Informatics from Athens University (Greece), and a Master's degree in AI from Heriot Watt University, Edinburgh (UK). His academic career spans multiple prestigious institutions including Fraunhofer and Max Planck MPI in Germany, INRIA/FUTURS in Paris, AUEB in Greece, Telecom-Paristech, ENS in France, Tsinghua and Jiaotong Shanghai in China, and Deusto University in Spain. Professor Vazirgiannis's research focuses on machine and deep learning methods for graph analysis, including community detection, graph clustering, node embeddings, and influence maximization. His work in text mining encompasses Graph of Words, word embeddings with applications to web advertising and marketing, event detection, and summarization. He has active collaborations with industrial partners in analytics and machine learning for large-scale data repositories across various application domains such as recommendations, meeting summarization, influence metrics for scientific and social networks, and predictive maintenance. His recent publications demonstrate a strong emphasis on Graph Neural Networks, multilingual NLP (particularly for French and Arabic), and applications of deep learning to diverse domains including social networks, legal text, and biomedical data. There's a clear trajectory toward developing more efficient, explainable, and specialized models that address real-world challenges in data analysis. ERCIM fellowship Marie Curie EU fellowship Tencent "Rhino-Bird International Academic Expert Award" (2017) Best Paper Award at IJCAI 2018 Best Paper Award at CIKM 2013 Professor Vazirgiannis has supervised 29 completed PhD theses and has attracted significant R&D funding from national and international sources, including research agencies and industrial partners such as Google, Airbus, Huawei, Deezer, BNP, and LVMH. He leads or has led several academic research chairs including DIGITEO (2013-15), ANR/HELAS (2020-25), and AXA (2015-2018). The DaSciM research group, which he leads at École Polytechnique, has extensive experience in real-world R&D projects involving large-scale data mining. The team maintains active collaborations with major industrial partners including AIRBUS, Google, BNP, Tencent, and Tradelab, working on cutting-edge machine learning projects. The group has co-organized major conferences such as ECML PKDD 2011 and ECML/PKDD 2017 and participates in the senior organization of AI and data mining events like AAAI and IJCAI.

Fedor Dokshinمشاهده پروفایل

استادیار

• Computational and Quantitative Methods
• Environmental Sociology
• Political Sociology
• +۴ مورد دیگر

Fedor Dokshin is an Assistant Professor in the Department of Sociology at the University of Toronto, Downtown Toronto (St. George) campus. His research bridges computational social science with environmental and political sociology, focusing on energy transitions, partisan dynamics, and social network structures. Key research areas include racial and income disparities in solar photovoltaic adoption, policy feedback mechanisms in renewable energy programs, and partisan influences on environmental decision-making. Fields of Study: Computational and Quantitative Methods, Environmental Sociology, Political Sociology, Social Networks Areas of Interest: Computational social science, Energy and the environment, Political polarization Research Trends: Dokshin's publications reveal a focus on energy justice, behavioral diffusion models, and political polarization. His work combines computational methods with environmental policy analysis, examining how socioeconomic factors and partisan identities shape renewable energy adoption. Articles demonstrate geographic heterogeneity in opposition to extraction projects, digital discourse analysis techniques, and institutional dynamics affecting scholarly knowledge production. Methodological Emphasis: Utilizes large-scale data analysis, spatial modeling, and automated textual analysis to explore energy-environment-society intersections. Research highlights the tension between technical solutions and social equity in energy transitions, with recurring themes of policy design, public engagement, and networked political behavior.

Tobias Neckelمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +۴ مورد دیگر

Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.

• Software Engineering
• Defect Prediction
• Automatic Software Repair
• +۳ مورد دیگر

Tracy Hall is Professor in Software Engineering at Lancaster University's School of Computing and Communications, where she holds a Chair in Software Engineering Research and serves as Director of Post Graduate Teaching. Previously, she was Professor and Head of Computer Science at Brunel University London, and has held visiting positions at University College London and adjunct roles at the University of Oslo. With over 20 years of empirical software engineering research experience, she maintains extensive industrial collaborations. Her research focuses on: Software defect prediction and automatic repair Code analysis methodologies Software testing frameworks Human factors in software development Empirical studies of developer behavior Tool development for software engineers She leads research in automated defect repair techniques and vulnerability prediction, with recent work exploring AI-driven approaches to software quality improvement. Her publication portfolio (100+ papers) shows consistent focus on software quality enhancement, with recent emphasis on explainable AI for vulnerability prediction (2025), developer-centric testing tools (2024), and human factors in bug resolution (2022). Research frequently involves large-scale empirical studies and industry partnerships. Awards include multiple best paper awards for her contributions to software engineering research. As Principal Investigator, she secured significant funding including: EPSRC Fixie project: £400,000 for defect prediction/repair (2018-2020) EPSRC Fault Analysis grant: £128,578 (2016-2019) Current PhD supervisees include Gaz Bennett, Jesse Phillips, and Miles Walker working on software engineering challenges. She contributes to the Cyber Security Research Centre , Security Lancaster , and DSI-Foundations research groups. Teaches courses on IT Architecture and Software Studio.

Yu Zhangمشاهده پروفایل

مدرس

• Text Mining
• Information Management
• Social Computing
• +۱۲ مورد دیگر

Dr. Yu Zhang is a Lecturer of Data Science at the School of Business, UNSW Canberra. His academic career focuses on text mining, knowledge and information management, social computing, and bibliometric analysis, with interdisciplinary applications in areas such as sustainable logistics, supply chain management, and net-zero energy solutions. Fields of Interest: Text Mining, Information Management, Social Computing, Bibliometric Analysis, Machine Learning for Information Systems, Heterogeneous Network Analysis, Data Mining for Asset Management, Sustainable Logistics, Supply Chain Management, Net-zero Energy in Green Buildings, and Transportation. Grants: Served as CI in projects like "Online health monitoring in Li-ion batteries via trustworthy AI" (ACT Government, $1.22M) and "Delivering net-zero energy buildings" (TRaCE Lab to Market, $1.05M). Awards: Best Paper Award (Runner-up) at ADMA 2024 and Excellent Paper Award at ICEBE 2024. Teaching: Coordinated courses in Data Analytics, Workforce Planning Research, Business Capstone, and Logistics Intelligence with Big Data Analysis. Supervision: Guided research on topics like federated learning for healthcare fraud detection, blockchain-based carbon offset management, and tier-based supply chain visibility. His publications span materials science and photovoltaic technologies, with a focus on thin-film solar cells and defect passivation methods. For collaboration or supervision inquiries, contact him at m.yuzhang@unsw.edu.au .

Jesper Linigerمشاهده پروفایل

دانشیار

• Marine Growth Engineering
• Power Fluid Engineering
• Wind Turbine Engineering
• +۶ مورد دیگر

Jesper Liniger is an Associate Professor at AAU Energy within the Faculty of Engineering and Science at Aalborg University. He works in the Esbjerg Energy Section focusing on Offshore Renewable Energy Systems and is affiliated with AAU BLUE – Marine & Maritime Research. His office is located at Niels Bohr Street 8, 6700 Esbjerg, Denmark. Research Interests Marine Growth Engineering and automated cleaning solutions for offshore structures Underwater robotics including Remotely Operated Vehicles (ROVs) and autonomous inspection systems Wind turbine engineering with emphasis on hydraulic pitch systems and fault detection Fluid power engineering applications in marine environments Development of robotic solutions for offshore renewable energy infrastructure Research Trends Dr. Liniger's recent publications demonstrate a strong focus on developing robotic solutions for offshore renewable energy infrastructure. His work bridges theoretical control systems with practical marine applications, particularly addressing marine growth (biofouling) challenges on offshore structures. The research shows increasing interdisciplinary collaboration, combining robotics, fluid mechanics, and wind energy systems to create integrated solutions that improve operational efficiency and reduce maintenance costs in offshore environments. Scientific Awards Innovation Project of the Year (2024) - For underwater robotics development Esbjerg Universitetspris (2018) - University award recognizing research excellence Advising and Research Leadership Dr. Liniger actively supervises PhD students and serves as principal investigator or supervisor on multiple major projects including "NextGen Robotics" for offshore wind farms and "Towards Enhancing Perception and Navigation for Autonomous Underwater Inspection Drone." His research portfolio includes collaborations with industry partners like Vattenfall and Business Center Funen, demonstrating strong industry-academia connections focused on practical applications with economic impact. Research Teams and Facilities Liniger is part of AAU BLUE – Marine & Maritime Research, which provides specialized facilities for marine robotics testing and development. His work involves close collaboration with researchers in control systems, fluid mechanics, and renewable energy. The research group has developed experimental frameworks for testing underwater and surface vehicle operations, with recent media coverage highlighting their innovative approaches to solving marine growth challenges on offshore structures.

Alexander Aueمشاهده پروفایل

استاد

• Time Series Analysis
• Change-Point Problems
• Functional Data Analysis
• +۳ مورد دیگر

Alexander Aue is a Professor in the Department of Statistics at the University of California, Davis. His research focuses on time series analysis, change-point problems, functional data analysis, and high-dimensional statistics. He holds a Ph.D. and has contributed to foundational methodologies in these areas. His work emphasizes developing robust statistical techniques for analyzing complex data structures, including spectral analysis, bootstrap methods, and functional time series. Notably, he has advanced change-point detection methodologies without relying on dimension reduction. Awarded the prestigious AAAS Fellowship for his contributions, his recent publications explore topics like high-dimensional hypothesis testing, stationarity testing for functional time series, and error estimation in time series predictions.

Hoseung Songمشاهده پروفایل

استادیار

• Statistical Data Science
• Decision Making
• Change-Point Analysis
• +۴ مورد دیگر

Hoseung Song is an Assistant Professor at KAIST (Korea Advanced Institute of Science & Technology), affiliated with the Department of Industrial and Systems Engineering and the Graduate School of Data Science. His research focuses on statistical data science, decision making, and biomedical applications, particularly in areas like change-point analysis, two-sample tests, and spatial clustering. His work bridges theoretical statistics with practical biomedical and healthcare challenges. Research interests include advanced statistical methodologies for analyzing complex biological and healthcare data, such as viral genomics, microbiota associations, and immune cell clustering. He develops scalable algorithms and kernel-based methods to address high-dimensional and non-Euclidean data challenges. Recent work highlights applications in infectious diseases (e.g., HSV-2) and postmenopausal health through association studies and differential analysis. His publications emphasize robust statistical testing frameworks, including permutation-based limitations, batch effect corrections, and graph-based methodologies. These contributions enhance reliability in biomedical research and safety-critical data applications. His lab likely integrates computational statistics with real-world healthcare datasets to drive translational insights.

Karim Oweissمشاهده پروفایل

استاد

• Neuroscience
• Biomedical Engineering
• Electrical Engineering
• +۵ مورد دیگر

Karim Oweiss is a Pre-eminent Professor at the University of Florida, with joint appointments in the Department of Biomedical Engineering (Herbert Wertheim College of Engineering), Electrical and Computer Engineering, and Neuroscience (McKnight Brain Institute). He holds a Ph.D. in Electrical Engineering and Computer Science from the University of Michigan (2002). His research focuses on neural mechanisms of sensorimotor integration and the development of clinically viable brain-machine interfaces (BMIs) to restore damaged neurological function. His work spans computational neuroscience, neural decoding, optogenetics, and advanced neurotechnology, with a strong emphasis on closed-loop systems and neural plasticity. 2025 : Chemogenetic stimulation of phrenic motor output and diaphragm activity 2024 : Chemogenetic phrenic motoneuron activation enables increased tidal volume 2023 : Compressive sensing of functional connectivity maps from patterned optogenetic stimulation Oweiss has received the NSF Excellence in Neural Engineering Award (2001) and is a Senior Member of the IEEE. He has published extensively on topics including neural decoding, compressive sensing, and multiscale neural interfacing. As editor of Statistical Signal Processing for Neuroscience and Neurotechnology (2010), he has contributed significantly to the field's methodological foundations. His lab develops tools like NeuroQuest for large-scale neural data analysis and implantable neuroprocessors for wireless BMI applications.

Ambrogio Volontéمشاهده پروفایل

پژوهشگر ارشد

• Arctic Cyclones
• Extratropical Cyclones
• Sting-Jet Dynamics
• +۴ مورد دیگر

Dr. Ambrogio Volonté is a Senior Research Fellow at the Department of Meteorology, University of Reading, and a member of the National Centre for Atmospheric Science (NCAS). His research focuses on cyclone dynamics, particularly Arctic, extratropical, and Mediterranean cyclones, sting jets, and monsoon systems. He leads and contributes to projects funded by NERC and international collaborations, such as the THINICE field campaign investigating Arctic cyclones and the MiLCMOP project studying monsoon progression. He holds a PhD from the University of Reading (2018) and has extensive experience in numerical weather prediction, Lagrangian analysis of air masses, and process-based weather phenomena. His work includes studying the impact of sea ice on Arctic cyclones, the role of midlatitude dry air in monsoon withdrawal, and evaluating AI models against traditional forecasting methods. Key projects include the global climatology of sting-jet cyclones and the dynamics of extreme windstorms like Storm Ciarán and Eunice. Volonté collaborates on interdisciplinary projects such as COSMIC (convection-scale modeling in China) and INCOMPASS (monsoon dynamics in India). His research has been published in journals like Quarterly Journal of the Royal Meteorological Society and Weather and Climate Dynamics . He currently serves as a researcher in several grants, focusing on Arctic cyclones, sting jets, and monsoon processes.

Brenda Rubensteinمشاهده پروفایل

دانشیار

• Electronic Structure Methods
• Quantum Chemistry
• Molecular Computing
• +۲ مورد دیگر

Brenda Rubenstein is an Associate Professor of Chemistry and Physics at Brown University and Director of the Data Science Institute. Her research bridges quantum chemistry and materials science, developing stochastic electronic structure methods for accurate large-scale simulations. She also explores molecular computing and computational biophysics. Her group emphasizes diversity, collaboration, and work-life balance. Recent work includes quantum Monte Carlo techniques for materials discovery, machine learning approaches for protein dynamics, and quantum computing applications. She received a named professorship in 2025 and leads the Levermore Global Scholars program. Her publications focus on computational methods across chemistry, physics, and materials science.

Cristobal Pagan Canovasمشاهده پروفایل

استاد

• Cognitive Linguistics
• Conceptual Integration
• Multimodal Communication
• +۴ مورد دیگر

Cristobal Pagan Canovas is a Permanent Professor (tenure-track) at the Department of English Philology, University of Murcia, where he co-directs the Daedalus Lab and the Murcia Center for Cognition, Communication, and Creativity. He is also a member of the international consortium Red Hen Lab, focusing on multimodal communication research. Education includes: PhD in Ancient and Modern Greek Literature from University of Murcia BA+MA in Classics and BA+MA in English from University of Murcia MA in Classics from University College London His research explores human cognition and communication through interdisciplinary approaches combining humanities and sciences. Primary interests include: Conceptual integration networks in emotional expression Multimodal communication patterns across language, gesture, and prosody Temporal representation in creative artifacts Cognitive foundations of poetic metaphor and verbal art Cultural evolution of integrative patterns in social interactions Recent publications demonstrate consistent focus on temporal cognition, multimodal communication, and creativity across domains including poetry, music, and gesture. Research employs corpus analysis, big data approaches, and cognitive modeling to examine how humans integrate perceptions into meaningful wholes. Scientific awards and fellowships: Ramón y Cajal Grant (elite national scheme) Alexander von Humboldt Fellowship in Quantitative Linguistics EURIAS Fellowship at Netherlands Institute for Advanced Studies FBBVA Leonardo Fellowship Marie Curie Fellowship ENSAYA'10 Award for scientific essay He leads multiple research grants including ERASMUS PLUS KA220-HED (MULTIDATA) and national grants MULTIFLOW and CREATIME. Supervised trainees include postdoctoral researchers (Marie Curie, Juan de la Cierva), MA students, undergraduates, and data scientists. The Daedalus Lab develops interdisciplinary methods to study cognition and communication, while Red Hen Lab enables large-scale multimodal dataset analysis through international collaboration.

• Computational Social Science
• Natural Language Processing
• Human-Computer Interaction
• +۳ مورد دیگر

Cristian Danescu-Niculescu-Mizil is an Associate Professor in the Department of Computer Science at Cornell University's College of Engineering, where he leads research at the intersection of computational social science and human-computer interaction. His work examines how language shapes and reflects social dynamics in digital environments, with applications for platform design and community health. His research focuses on computational social science , natural language processing , and human-computer interaction , particularly analyzing online discourse evolution, social influence mechanisms, and community dynamics. Key methodologies include large-scale data analysis, machine learning, and longitudinal modeling of language behavior across platforms like Reddit, Twitter, and Wikipedia. Analysis of his recent publications reveals strong trends in cross-platform misinformation tracking , AI-driven content moderation , and linguistic adaptation in support communities . His work consistently bridges technical NLP advances with sociological insights, emphasizing ethical implications and real-world interventions for healthier online ecosystems. Scientific recognition includes: NSF CAREER Award for foundational work in computational social science Multiple industry research awards from Google and Facebook ACM SIGCHI Best Paper Award for community health research He actively mentors doctoral students in computational social science, with several advisees now faculty at top institutions. His research is supported by NSF grants focused on social dynamics modeling and NLP for community health, often collaborating with social scientists and industry partners. Current projects investigate AI moderation systems and longitudinal language evolution in online communities through the Digital Discourse Lab .

G. Scott Watsonمشاهده پروفایل

استاد

• Early Universe Cosmology
• Inflation
• Dark Matter
• +۳ مورد دیگر

Prof. G. Scott Watson is a Professor in the Department of Physics at Syracuse University, affiliated with the College of Arts & Sciences. His research focuses on the interplay between fundamental particle physics and cosmology, particularly early universe cosmology, inflationary models, dark matter/energy, and string theory applications. He holds a Ph.D. in Physics from Brown University (2005) and B.S. degrees in Mathematics and Physics from the University of North Carolina at Wilmington (2000). Key research interests include string phenomenology as a quantum gravity framework, probing inflationary scenarios through cosmic microwave background (CMB) studies, and exploring dark matter origins. He leads major projects like CMB-S4 and contributes to the CMBPol mission concept. Watson has received the American Physical Society Outstanding Referee Award (2021) and serves on high-profile collaborations such as the Inflation Probe Study Analysis Group (IPSAG). Teaching responsibilities include advanced courses like Quantum Field Theory, Relativity and Cosmology, and Quantum Mechanics II. He actively mentors students through independent studies and advises on graduate admissions. Watson has secured significant grants, including a Department of Energy-funded project on theoretical particle physics and cosmology (2013–2025) and NSF support for cosmic acceleration research (2018–2023).

• Neuroscience
• Artificial Intelligence
• Neuronal Modeling
• +۵ مورد دیگر

Eilif B. MULLER is a Professor in the Department of Neurosciences at Université de Montréal, Principal Investigator of the Architectures of Biological Learning Lab (ABL-Lab) at CHU Sainte-Justine Research Center, and Associate Faculty at Mila (Quebec AI Institute). His work bridges neuroscience and artificial intelligence, focusing on understanding how sensory perception is learned in the neocortex through biophysical simulations and deep learning models. He holds affiliations with IVADO (Institute for Data Valorization) and contributes to strategic initiatives like the UNIQUE Québec Center. His research integrates empirical neurophysiology with computational models, exploring dendritic processing and synaptic plasticity to inform both biological understanding and AI advancements. Teaches NSC-6044 and NSC-6045 (Neuroscience Colloquia) at Université de Montréal. Leads projects on neocortical learning mechanisms and their implications for neurodevelopmental disorders. Recipient of grants from CRSNG (Natural Sciences and Engineering Research Council), FRSQ (Health Research Fund), and institutional funding. Publications span topics in computational neuroscience, neural network modeling, and interdisciplinary AI-neuroscience research. Collaborates extensively across institutions to advance large-scale brain simulations and data-driven models.