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نمایش ۶۹۱-۷۰۵ از ۲۰٬۹۱۱ استاد راهنما
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Professor Paolo Rapisarda is a faculty member at the University of Southampton , holding the position of Professor of Electronics and Computer Science. His academic journey began with a Ph.D. in Mathematical Systems and Control Theory at the University of Groningen, Netherlands, under the guidance of Prof. Jan C. Willems and Prof. H.L. Trentelman.
Dr. Johnson Xuesong Shen is an Associate Professor at the School of Civil and Environmental Engineering , University of New South Wales . His work integrates Digital Twins , Building Information Modeling (BIM) , and Construction Automation with a focus on robotics, AI, and LiDAR/UAS technologies. Research Interests: Digital Twins, BIM, Construction Robotics, Emissions Modeling, LiDAR/UAS, Structural Health Monitoring Education: Ph.D. in Construction Engineering and Management, The Hong Kong Polytechnic University His publications span 2025–2005, emphasizing construction automation , environmental impact reduction , and innovative tunneling solutions . Recent work includes IoT-Bayes fusion for real-time safety monitoring and life cycle analysis of construction waste. Scientific Awards: Vice Chancellor's Award for Teaching Excellence, UNSW, 2014 Best PhD Student Paper Award, CONVR, UK, 2013 Postdoctoral Fellowship, University of Alberta, 2011-2013 Best Paper Award, ASCE Construction Research Congress, 2010 Dr. Shen mentors 9 PhD candidates in areas like 3D object detection , fuel consumption modeling , and UAV-based LiDAR . His grants include $5.98M from the Australian Research Council (2022–2027) for resilient infrastructure systems and projects on modular construction and intelligent tunneling .
Professor Rosalyn Moran is a Professor of Computational Neuroscience and Deputy Director of King's Institute for Artificial Intelligence at King's College London. She holds roles in the Department of Neuroimaging and School of Neuroscience within the Institute of Psychiatry, Psychology & Neuroscience. Her research focuses on computational neuroscience, computational psychiatry, and neurology, particularly integrating brain connectivity with algorithmic principles like the free energy principle. She explores neurotransmitter roles in decision-making and disease modeling, with applications in artificial intelligence and neurodegenerative disorders. Moran serves as an editor for Neuroimage and collaborates with leading institutions. Key projects include global neuroimaging initiatives (UNITY) and low-field MRI advancements in low-resource settings. Her work bridges Bayesian inference, AI, and neurobiology, with recent emphasis on pediatric neuroimaging and treatment-resistant psychosis. Education & Research Interests Rosalyn Moran's research spans computational psychiatry, neuroimaging techniques, and AI applications in healthcare. Her lab investigates serotonin and dopamine signaling, brain connectivity patterns, and predictive coding frameworks. Notable contributions include modeling NMDA receptor dysfunction in encephalitis and developing super-resolution MRI methods for global health contexts. Grants & Collaborations Funded projects include MRC Human Functional Genomics (2024-2028), NIHR Maudsley BRC (2022-2027), and Gates Foundation initiatives for low-field MRI enhancement. Collaborators include Karl Friston (UCL), Read Montague (Virginia Tech), and Klaas Enno Stephan (University of Zurich). Recent events include presenting the Free Energy Principle's role in generative AI (May 2023). Labs & Teams Her lab focuses on computational psychiatry and AI-driven neuroimaging solutions, collaborating with the King’s Global Health Institute to advance medical imaging accessibility in low-income regions.
Michio Sugeno is a distinguished Professor at Tokyo Institute of Technology's Graduate School of Information Science and Engineering, Department of Computational Intelligence. With a career spanning over four decades, he has established himself as a leading figure in fuzzy systems and computational intelligence. His research interests encompass Fuzzy Systems, Computational Intelligence, Nonlinear Control, Choquet Integral theory, Brain-Computer Interfaces, and Linguistic Computing. Sugeno's work has fundamentally shaped modern fuzzy control theory, particularly through his development of the Takagi-Sugeno fuzzy model which has become a standard approach in industrial applications. Analysis of his recent publications reveals a continued focus on piecewise nonlinear modeling, stability analysis of fuzzy systems, and the application of Choquet calculus to various computational problems. His work demonstrates a consistent trajectory from theoretical foundations to practical implementations in control systems and intelligent computing. IEEE Pioneer Award in Fuzzy Systems IFSA Fellow Emanuel R. Piore Award Sugeno has mentored numerous researchers who have become prominent in their own right, including Tadanari Taniguchi, Luka Eciolaza, and Anh-Tu Nguyen. His laboratory has been instrumental in developing novel approaches to nonlinear control systems using piecewise bilinear models and fuzzy logic. Current research directions include brain-computer interfaces using EEG analysis and the development of everyday language computing systems that enable more natural human-computer interaction.
Andrew Pavlo is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on database management systems, particularly in the areas of transaction processing, in-memory databases, and self-driving database systems. He leads a productive research group that has published extensively in top database venues including VLDB, SIGMOD, and CIDR. Pavlo's research interests span database management systems, transaction processing, in-memory databases, non-volatile memory databases, and self-driving database systems. His work often bridges theoretical database concepts with practical system implementation, focusing on performance optimization, query processing, and system architecture. Recent work has explored machine learning applications for database tuning, novel storage techniques, and innovative approaches to transaction processing. An analysis of his recent publications reveals a strong focus on self-driving database systems, with significant work on the Database Gym framework for training machine learning models to optimize database performance. His research also examines columnar storage formats, transaction scheduling, and novel approaches to user-defined function optimization. The work demonstrates a consistent trajectory toward making database systems more autonomous and efficient through a combination of systems techniques and machine learning. Pavlo has been instrumental in mentoring numerous PhD students who have become active contributors to the database research community. His research has been supported by significant grants that have enabled the development of innovative database technologies and frameworks. His research group operates within CMU's vibrant database ecosystem, collaborating with other researchers on projects related to database systems, storage engines, and query processing frameworks. The group maintains close connections with industry partners to ensure practical relevance of their research contributions.
پژوهشگر
Bo Xiong is a researcher at the University of Stuttgart in the Analytic Computing group. His research focuses on machine learning and knowledge graphs , with a particular emphasis on geometric embeddings and hyperbolic neural networks. His research interests include: Knowledge graph embeddings Hyperbolic and pseudo-Riemannian geometry in AI Temporal knowledge graph reasoning Structured multi-label prediction Recent publications highlight his work on geometric relational embeddings, complex query answering, and temporal fact reasoning using advanced manifold-based techniques.
استادیار
Ashley J Thomas is an Assistant Professor at Harvard University, specializing in infant and child social cognition. Her research explores how young humans develop naive sociology, focusing on social hierarchy, intimacy perception, and moral judgments of parenting decisions. Education: PhD in Psychology (2018), MA in Psychology (2015) from UC Irvine, BA in Architecture (2008) from UC Berkeley. Her work reveals infants use caregiver interactions to evaluate social partners and demonstrates that saliva sharing serves as a key cue for relationship recognition. She examines how implicit theories of intelligence correlate with brain plasticity beliefs, showing that malleable intelligence theories align with environmental influence acceptance. Scientific awards include the NIH National Research Service Award and multiple fellowships. She has organized symposia on social cognition at the Cognitive Development Society and Society for Research on Child Development conferences.
Dimitris N Kiosses serves as Professor of Psychology in Psychiatry at Weill Cornell Medical College's School of Medicine since 2021. His academic affiliations center within the Department of Psychiatry with a focus on geriatric mental health services and research. His educational background includes a B.S. from Tufts University (1992), M.A. (1994), and Ph.D. (1999) from Adelphi University, establishing his foundation in clinical psychology with specialization in geriatric populations. Dr. Kiosses' research primarily investigates late-life depression, suicide prevention in older adults, and cognitive-behavioral interventions for comorbid conditions. His work emphasizes Problem Adaptation Therapy (PATH) development and implementation, particularly for depressed elders with cognitive impairment or chronic pain. Recent publications demonstrate growing focus on digital mental health tools, ecological momentary assessment, and international applications of geriatric interventions. His studies frequently examine emotion regulation mechanisms, neurocognitive correlates of depression, and implementation science approaches for real-world settings. Analysis of his 15 most recent publications reveals consistent thematic progression toward transdiagnostic approaches for late-life mental health, with increasing emphasis on technology-enabled interventions, cross-cultural adaptation, and precision medicine applications. The work spans clinical trials, implementation studies, and neurobiological investigations, predominantly targeting depression-suicide pathways in aging populations. Dr. Kiosses maintains active research mentorship through his leadership of multiple NIH-funded projects. His grant portfolio demonstrates particular strength in securing R01-level funding from NIMH and NIA for innovative geriatric mental health interventions. As Principal Investigator, he directs the Problem Adaptation Therapy research program which has evolved from home-based depression treatment (PATH) to specialized adaptations including PATH-Pain and suicide prevention applications. His work increasingly incorporates digital health components and focuses on implementation within primary care and community settings.
Dr Stathis Tingas is a Lecturer at Edinburgh Napier University's School of Computing Engineering and the Built Environment. His research focuses on hydrogen fuel systems, combustion engineering, and sustainable transportation technologies. With numerous publications in high-impact journals and conference proceedings, Dr Tingas has established himself as a significant contributor to the field of alternative energy systems. Dr Tingas' research interests center on hydrogen and ammonia as alternative fuels for transportation, with particular emphasis on combustion characteristics, engine performance, and emissions control. His work spans theoretical modeling, computational analysis, and practical applications for decarbonizing various transportation sectors including aviation, heavy-duty vehicles, and maritime transport. Recent publications demonstrate his focus on hybrid propulsion systems combining fuel cells with traditional engine technologies. Dr Tingas' publication record shows consistent productivity with research outputs spanning from fundamental combustion science to applied engineering solutions. His work often employs computational singular perturbation techniques for analyzing complex combustion phenomena, with recent focus shifting toward practical applications of hydrogen and ammonia fuels in real-world engine systems. The trend in his publications indicates growing emphasis on zero-emission transportation solutions aligned with net-zero targets. Dr Tingas serves as a second supervisor for PhD students, including Richard Wallace who is working on subsurface hydrogen storage simulation. He has successfully secured multiple research grants from UK government bodies including the Department for Science, Innovation & Technology, Scottish Government, and the Royal Society of Edinburgh, with projects totaling over £500,000 in funding. His current research portfolio includes projects focused on accelerating clean energy technology development, creating sustainable cities, advancing electromobility, and developing zero-carbon hydrogen engines for heavy transport applications. These projects demonstrate his commitment to addressing practical challenges in the transition to sustainable energy systems.
استادیار
Marco Letta is a Tenure-Track Assistant Professor at the Department of Social and Economic Sciences , Sapienza University of Rome. His research focuses on economic development , regional economics , policy evaluation , and applied econometrics , with a strong emphasis on climate change impacts, food security, and machine learning applications in economic policy. University: Sapienza University of Rome Department: Department of Social and Economic Sciences Email: marco.letta@uniroma1.it His recent work explores the climate migration nexus , household resilience , and policy targeting , often leveraging machine learning and empirical econometric methods . Publications span topics such as local inequalities during the COVID-19 crisis , temperature shocks in rural Tanzania , and machine learning applications in state aid regulation . Notable trends in his research include: Integration of machine learning with traditional econometric techniques Focus on climate resilience and migration patterns Analysis of policy impacts in developing economies Investigation of local mortality estimates during global crises Development of cross-country empirical frameworks Current projects include assessing agrifood system vulnerabilities and refining counterfactual policy evaluation methodologies.
Professor Valentyn Panchenko is a leading academic in Economics at the UNSW Business School, specializing in advanced econometric methodologies and financial modeling. Holding a PhD from the University of Amsterdam and an MPhil from the Tinbergen Institute, his research bridges theoretical econometrics with real-world financial applications, emphasizing big data analysis, network structures, and dependence modeling in economic systems. His expertise spans financial econometrics, time series analysis, non-parametric statistics, and agent-based economic simulations. He focuses on Granger causality, model evaluation, structural economic modeling, and bounded rationality with heterogeneous agents. His work has secured significant grants including ARC Discovery Projects and DECRA fellowships, enabling cutting-edge research on market dynamics and economic interactions. Professor Panchenko's publications appear in top-tier journals like the Journal of Econometric Theory, AEJ: Micro, Journal of Economic Dynamics & Control, and Journal of Banking & Finance. His methodological contributions include novel approaches to copula-based forecasting, nonlinear causality testing, and evolutionary learning models in strategic economic environments. While specific student advising details aren't provided, his research leadership demonstrates sustained impact across econometric theory, financial markets, and experimental economics.
Dr. Prabodh Bajpai is a Professor in the Department of Sustainable Energy Engineering at the Indian Institute of Technology Kanpur. Previously, he served as an Associate Professor at the same department from July 2022 to December 2022, and before that at the Electrical Engineering Department of IIT Kharagpur from July 2014 to June 2022. He began his academic career as an Assistant Professor at IIT Kharagpur from June 2008 to July 2014. His educational background includes a Ph.D. in Electrical Engineering (Power Systems) from IIT Kanpur (2008), M.Tech in Energy Studies from IIT Delhi (2001), and B.E. in Electrical Engineering from IIT Roorkee (1997). Dr. Bajpai's research spans renewable energy integration, power system operation and control, microgrid technologies, and smart grid applications. His work focuses on practical implementation of sustainable energy solutions with emphasis on power electronics, energy storage, and grid stability. He has made significant contributions to the fields of distributed generation integration, protection schemes for renewable-rich systems, and energy management in microgrids. His recent publications show a strong focus on DC microgrids, multi-port power converters, and advanced control strategies for renewable integration. The research demonstrates increasing sophistication in power electronics applications for sustainable energy systems, with particular emphasis on practical implementations for commercial and agricultural applications. Dr. Bajpai actively mentors graduate students, currently supervising seven students across PhD and M.Tech programs. His teaching portfolio includes core courses in electrical power engineering, renewables-integrated smart power systems, and energy systems modeling and analysis at IIT Kanpur, building on his extensive teaching experience at IIT Kharagpur where he developed several new courses in renewable energy systems. His research group maintains a Hybrid AC/DC Microgrid test facility and has developed a Renewable Hybrid Energy Power Plant for stand-alone applications, demonstrating his commitment to translating theoretical research into practical implementations.
Eduardo Mercado III is a Professor in the Department of Psychology at the University at Buffalo, College of Arts and Sciences. His research focuses on bioacoustics, cognitive psychology, and marine ecology, particularly the vocal behavior of humpback whales and its implications for understanding human impact on marine ecosystems. He is also known for his work in perceptual learning, autism spectrum disorder, and comparative cognition. Scientific Awards Guggenheim Fellowship Harvard Radcliffe Institute Fellowship Research Trends His recent publications emphasize bioacoustic analysis of humpback whale songs, including their spectral entropy, cyclical variations, and adaptive adjustments to anthropogenic noise. Additional work explores perceptual learning mechanisms in autism, neural network modeling for acoustic classification, and cognitive processes in canines and rodents. Projects Mercado’s “Singers as Sentinels” project combines acoustic analysis of humpback whale songs with public awareness initiatives about ocean noise pollution. The project will produce a book, Why Whales Sing and Dolphins Don’t , and a web-based interface for public engagement.
Facundo M. Fernandez is a Regents' Professor and Vasser-Woolley Chair in Bioanalytical Chemistry at the Georgia Institute of Technology, where he leads the Fernandez Research Group within the School of Chemistry and Biochemistry in the College of Sciences. His research spans multiple cutting-edge areas of analytical chemistry with significant applications in medicine, forensics, and basic science. Dr. Fernandez earned his M.Sc. in Chemistry (1996) and Ph.D. in Analytical Spectrometry/Metallomics (1999) from the Facultad de Ciencias Exactas y Naturales at Buenos Aires University, Argentina. His research program focuses on Bioanalytical Mass Spectrometry with particular emphasis on Ambient Sampling/Ionization & Molecular Imaging, Ion Mobility Spectrometry, Metabolomics, and Pharmaceutical Forensics. His work has pioneered new approaches in ambient ionization techniques that enable direct analysis of complex samples without extensive preparation. His recent publications reveal a strong trend toward spatial metabolomics, particularly in traumatic brain injury and ovarian cancer research, with increasing integration of machine learning approaches for data analysis. His work also extends to pharmaceutical quality control, exercise physiology through the MoTrPAC consortium, and prebiotic chemistry investigations. The interdisciplinary nature of his research is evident in collaborations across Georgia Tech's campus and with external institutions. NSF CAREER Award (2007) 3M Non-tenured Faculty Award (2008) CETL/BP Junior Faculty Teaching Excellence Award (2009) Ron A. Hites Award for Outstanding Research Publication (2010) Sigma Xi (GT Chapter) Best Faculty Paper Award (2010) Vasser-Wooley Faculty Fellow (2012) Dr. Fernandez has secured significant funding for his research, including NSF CAREER support, and leads projects related to metabolomics for ovarian cancer detection, pharmaceutical forensics through the CODFIN network, and participation in the large-scale Molecular Transducers of Physical Activity Consortium (MoTrPAC). His laboratory develops advanced instrumentation for mass spectrometry applications and maintains strong collaborations with the Integrated Cancer Research Center, the College of Computing, and the Center for Chemical Evolution at Georgia Tech.
پژوهشگر
Yu Xia is a Post Doc at the Department of Chemistry, Stockholm University, Sweden. He is affiliated with the Tom Willhammar Research Group, focusing on advanced electron microscopy and diffraction techniques for structural characterization of materials. PhD (2019–2023) from a joint program between the University of Birmingham (UK) and the Southern University of Science and Technology (China). Research emphasizes fabrication of metallic nanoparticles with non-equilibrium structures and shapes using gas-phase condensation and thermal shock methods. Specializes in scanning transmission electron microscopy (STEM), in-situ heating experiments, and electron energy loss spectroscopy (EELS) for nanoparticle analysis. Current work prioritizes 4DSTEM imaging for electron beam-sensitive materials and Python-based post-processing of electron microscopy datasets. Yu Xia's research spans Materials Science , Nanotechnology , and Electrocatalysis , with applications in photocatalytic hydrogen evolution , graphene composites , and advanced electron microscopy techniques . His work often integrates computational image processing with structural characterization to optimize material properties. Publications highlight innovations in heterostructure engineering , metallic alloy catalysts , and electron beam-sensitive material imaging . No scientific awards are explicitly mentioned in the provided text. Yu Xia's technical expertise includes Python scripting for image analysis, in-situ electron microscopy , and multifunctional graphene-based materials .
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