جستجوی استاد راهنمای دانشگاهی

Mahdi Boloursaz Mashhadiمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Wireless Communications
• +۶ مورد دیگر

Mahdi Boloursaz Mashhadi is a Lecturer in Communications and AI at the Institute for Communication Systems (ICS), part of the School of Computer Science and Electronic Engineering at the University of Surrey, UK. He is a Surrey AI Fellow and an IEEE Senior Member, with a focus on advancing AI-driven wireless communication systems. He holds B.S., M.S., and Ph.D. degrees in mobile telecommunications from Sharif University of Technology, Tehran, Iran. Prior to joining Surrey, he served as a postdoctoral research associate at Imperial College London’s Intelligent Systems and Networks (ISN) Research Group (2019–2021). His research interests span AI/ML integration with wireless systems, including semantic communications, federated learning, generative AI for telecom, and beamforming optimization in massive MIMO architectures. He also explores edge computing, distributed deep learning frameworks, and energy-efficient communication designs using reconfigurable intelligent surfaces (RIS). Mahdi has been recognized with the IEEE EWDTS Best Paper Award and IEEE ComSoc Exemplary Reviewer Awards (2021–2022). He leads the UKTIN/DSIT 12M£ national project TUDOR and collaborates with industry on cutting-edge 5G/6G innovations. He has contributed to the ITU’s AI/ML in 5G challenge as a panel judge and serves as an editor for Springer’s Wireless Personal Communications Journal. His roles include advising on government/industry projects and advancing interdisciplinary research at the 5G/6G Innovation Centre. He emphasizes practical implementations of AI in telecommunications, aiming to bridge theoretical advancements with real-world applications.

Tony Smithمشاهده پروفایل

استاد

• Macroeconomics
• Environmental Economics
• Econometrics
• +۳ مورد دیگر

Tony Smith is the William K. Lanman, Jr. Professor of Economics at Yale University, where he previously served as Chair of the Department of Economics from 2019 to 2025. He holds a B.S. in Economics from MIT (1984) and a Ph.D. in Economics from Duke University (1990). He has also taught at Queen's University, Carnegie Mellon University, and the University of Rochester. As a Research Associate at the National Bureau of Economic Research, his research focuses on macroeconomics, particularly income and wealth heterogeneity, and econometrics, including simulation estimation of structural models. His recent work integrates macroeconomics with environmental economics, developing high-resolution global economy-climate models. He currently serves as Co-Editor of Macroeconomic Dynamics and has been an Associate Editor at Review of Economics Dynamics . His scholarly contributions emphasize computational methods and interdisciplinary approaches to economic challenges.

Anastasia Ivanovaمشاهده پروفایل

استاد

• Adaptive Designs
• Dose-finding Methods
• Enrichment Designs
• +۳ مورد دیگر

Anastasia Ivanova, PhD, is a Professor in the Department of Biostatistics at the University of North Carolina's Gillings School of Global Public Health. She specializes in clinical trial design, with over 20 years of experience in adaptive designs, dose-finding methods, and enrichment strategies. Her leadership roles include Principal Investigator of the NHLBI-funded PrecISE Network Data Center and co-PI of the BACPAC DAC. She holds a PhD in Statistics from the University of Maryland (1998) and a PhD in Mathematics from St. Petersburg State University (1998). Research focuses on innovative trial methodologies including adaptive trials for severe asthma, back pain consortia, and CAR-T cell therapies. Key publications span statistical methods in Biometrics , Statistics in Medicine , and Statistical Methods in Medical Research . Awards include the American Statistical Association Fellowship (2020) and the Stanley S. Schor Scholarship (2006-2007). Education: PhDs in Statistics (UNC, 1998) and Mathematics (St. Petersburg, 1992); MS in Mathematics (1988) Service: Associate Editor for Statistics in Medicine since 2006; North Carolina ASA leadership roles Key Projects: PrecISE Network (adaptive asthma trials), BACPAC (back pain research), CAR-T cell clinical trials Her work integrates statistical rigor with translational medicine, advancing precision interventions through multi-stage and biomarker-stratified designs.

• Evolutionary ecology
• Quantitative ecology
• Meta-analysis
• +۵ مورد دیگر

Shinichi Nakagawa is a Professor of Evolutionary Ecology and Synthesis at the University of Alberta's Faculty of Science, Biological Sciences Department. He holds the Canada Excellence Research Chair in Open Science and Synthesis in Ecology and Evolution. His research focuses on quantifying biological variation through meta-analysis and synthesis, with a strong emphasis on open science practices. He leads a lab that explores topics in ecology, evolutionary biology, and environmental sciences, while also advancing meta-science to improve research transparency and reproducibility. Dr. Nakagawa earned his BSc(Hons) at the University of Waikato and PhD at the University of Sheffield. He previously held positions at the University of Otago (2008–2015) and the University of New South Wales (2015–2024). He is the founder of EcoEvoRxiv, a preprint server for ecology and evolution, and actively promotes open science through initiatives like the Society for Open, Reliable, Transparent Ecology and Evolutionary Biology (SORTEE). His research interests include quantitative ecology, statistical methodology for meta-analysis, and understanding biological variability. Key areas of focus include thermal physiology in ectotherms, sexual selection, and the impacts of climate change on biodiversity. He has authored influential papers on meta-analytic techniques, including location-scale models and variance-based analyses. Notable contributions include the development of the 'orchard plot' visualization tool for meta-analyses and advocacy for preregistration and open data practices. His work bridges ecological theory with data synthesis, emphasizing reproducibility and methodological rigor. Awards include the Canada Excellence Research Chair, recognizing his leadership in advancing open science and ecological synthesis.

Jia Wuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Mining
• Deep Learning
• +۷ مورد دیگر

Dr. Jia Wu is an Associate Professor and Research Director of the Centre for Applied Artificial Intelligence at Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (2009) and is an IEEE Senior Member. His research focuses on artificial intelligence, data mining, graph neural networks, and anomaly detection, with over 200 publications in top-tier journals/conferences like IEEE TPAMI, TKDE, and conferences like KDD, IJCAI, and NeurIPS. He has received awards including the Heidelberg Laureate Forum Fellowship (2019) and multiple best paper awards. Education: PhD in Computer Science (UTS, 2009). Current roles include Director of HDR (Higher Degree Research) and Associate Editor for IEEE TNNLS and ACM TKDD. He leads projects in AI-driven cybersecurity, personalized banking solutions, and disaster response systems. Research interests emphasize graph-based learning, fake news detection, and deep learning applications. His recent work explores hypergraph neural networks for fraud detection and brain graph analysis for neurological disorders. He has pioneered scalable semi-supervised clustering techniques and transformer-based hypergraph models for anomaly detection. Awards include CIKM'22 Best Paper Runner-Up, ICDM'21 Best Student Paper, and the 2023 Faculty of Science and Engineering Collaboration Award. His work spans 13 active research projects, including mitigating AI deepfakes in identity systems and enhancing disaster response networks through graph-based simulations. Labs/Teams: Leads teams in the Data Horizons Research Centre, Future Communications Research Centre, and Hearing Research Centre. Collaborates internationally in AI, data mining, and social network analysis.

Davide Donadioمشاهده پروفایل

استاد

• Molecular Modelling
• Materials Science
• Thermal Transport
• +۴ مورد دیگر

Davide Donadio is a Professor of Chemistry at the University of California, Davis. His research focuses on molecular modeling and simulations of materials, particularly in non-equilibrium processes, thermal transport, and nanostructure assembly. He leads the Naotheory Group, which develops predictive multiscale models for energy-related materials. Education : Habilitation in Materials Science, Italian Ministry for University and Research (2013) Ph.D. in Materials Science, University of Milano (2003) M.S. in Physics, University of Milano (1998) Research Interests : His work spans molecular-level understanding of energy conversion, thermal management, and nanostructure formation. Key areas include phononics, thermoelectrics, and interfacial phenomena in materials like ice surfaces, semiconductors, and clathrates. He employs machine learning and first-principles methods to bridge simulation and experiment. Awards : UC Davis Hellman Fellow (2017–2018) Young Scientist Award, Italian Institute for the Physics of Matter (1998) Grants & Labs : His funding and collaborations drive advancements in nanostructured materials and computational tools like PLUMED tutorials. The Naotheory Group actively publishes in high-impact journals and collaborates internationally on thermal transport and materials design.

Hao Chenمشاهده پروفایل

دانشیار

• Anomaly/signal detection for streaming data
• Graph-based methods
• Two-sample test for high-dimensional and non-Euclidean data
• +۷ مورد دیگر

Hao Chen, Ph.D. is an Associate Professor in the Department of Statistics at the University of California, Davis. His research focuses on statistical methodology for high-dimensional and non-Euclidean data, including anomaly detection, graph-based methods, and change-point analysis. He also explores AI security, multimodal models, and geospatial applications. His work bridges statistical theory and practical machine learning challenges. Education: Ph.D., Graduate Group in Biostatistics, Stanford University Research Interests: Dr. Chen’s expertise spans statistical methods for streaming data, categorical data analysis, and allele-specific copy number variation. He has pioneered work in detecting signals in complex datasets and developing robust AI systems. His recent focus includes mitigating modality interference in LLMs, enhancing model safety via guardrails, and advancing geospatial AI through projects like GeoLM. Publications: His recent work addresses cutting-edge topics such as multimodal model vulnerabilities, unlearning algorithms, and clinical radiology applications. Key themes include improving model robustness, ethical AI design, and interdisciplinary data integration. Labs/Teams: Engages in collaborative projects at UC Davis, though specific lab names are not listed in the provided information.

Eric Rotenbergمشاهده پروفایل

استاد

• Computer Architecture
• High-Performance Computing
• Low-Power Design
• +۳ مورد دیگر

Dr. Eric Rotenberg is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. His research focuses on high-performance, low-power, and reliable processor architectures, with contributions to 3D stacked circuits, heterogeneous multi-core systems, and adaptive microarchitecture design. Education: Ph.D. in Computer Sciences, University of Wisconsin-Madison (1999) M.S. in Computer Sciences, University of Wisconsin-Madison (1996) B.S. in Electrical Engineering, University of Wisconsin-Madison (1991) Research Interests: Computer Architecture and Systems 3D-Stacked Integrated Circuits Heterogeneous Multi-Core Processors Microarchitecture Optimization Energy Efficiency in Processors Key Awards: IEEE Fellow (2015) NSF CAREER Award (2001) NC State Outstanding Teacher Award (2004) 2015 Micro Test-of-Time Award (co-authored) Professional Contributions: Developed the AnyCore adaptive processor framework and the H3 3D-stacked heterogeneous multi-core processor. Explored post-silicon microarchitecture adaptation and slipstream processors for improved performance and fault tolerance.

Debjani Sihiمشاهده پروفایل

استادیار

• Biogeochemistry
• Soil Carbon Cycle
• Greenhouse Gas Emissions
• +۵ مورد دیگر

Debjani Sihi is an Assistant Professor at North Carolina State University , with a joint appointment in the Departments of Plant and Microbial Biology and Crop and Soil Sciences . She joined the university in 2024 as part of the College of Agriculture and Life Sciences AI cluster. Prior to this, she was a faculty member at Emory University and held postdoctoral positions at the University of Maryland and Oak Ridge National Laboratory. Her doctoral research was completed at the University of Florida. Dr. Sihi's research focuses on the plant-soil-microbe-atmosphere continuum , integrating biogeochemistry, machine learning, and field/laboratory data to study soil carbon cycles, greenhouse gas emissions, and ecosystem responses to climate change. She leads the Sihi Biogeochemistry Lab , which collaborates with federal agencies (USDA, NSF, DOE) and industry partners like Valent Biosciences. Current projects include climate-smart agriculture, continental-scale soil carbon modeling, and microbial-driven carbon dynamics in diverse ecosystems. Her work emphasizes interdisciplinary approaches, leveraging sensors, imaging, and big data analytics to address challenges in food security and environmental sustainability. Notable projects include a $25M USDA-funded study on carbon sequestration in Southern Piedmont agriculture and a NSF-funded investigation into methane dynamics in northern forests. Dr. Sihi has mentored numerous graduate and undergraduate students, with recent achievements including student awards at the NC PSI BRIDGE Symposium. She actively engages in outreach, including workshops on regenerative agricultural practices for beginning farmers and veterans through USDA grants. Key funding sources include the National Science Foundation (NSF), Department of Energy (DOE), USDA, and Valent Biosciences. Her lab is currently recruiting postdocs, students, and researchers to expand its work in soil health, climate modeling, and ecosystem resilience.

Paul Cariniمشاهده پروفایل

دانشیار

• Environmental Microbiology
• Microbial Genomics
• Soil Science
• +۳ مورد دیگر

Paul Carini is an Associate Professor in the Department of Environmental Science at the University of Arizona. His research focuses on microbial genomics, environmental microbiology, and microbial diversity in extreme environments. He is affiliated with the School of Animal and Comparative Biomedical Sciences through a joint Micro Graduate Program. His work emphasizes genomic sequencing of understudied microbes, particularly those from arid soils and subseafloor sediments. He explores microbial adaptation strategies to nutrient-poor and extreme conditions, including anaerobic respiration and toxic gas utilization. Recent studies highlight culturomics advancements, such as high-throughput cultivation and predictive modeling of microbial growth. Key contributions include genome-based taxonomic frameworks for uncultivated archaea and bacteria, and insights into microbial roles in climate change mitigation. His research bridges traditional cultivation methods with modern genomic tools, addressing challenges in capturing Earth's microbial biodiversity.

Jiaxiang Zhangمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Cognitive Neuroscience
• +۲ مورد دیگر

Jiaxiang Zhang is Professor of Artificial Intelligence in the Department of Computer Science at Swansea University's Faculty of Science and Engineering. He holds a PhD in Computational Neuroscience from the University of Bristol and previously held positions at the University of Birmingham, MRC Cognition and Brain Sciences Unit (Cambridge), and Cardiff University where he founded the Cognition and Computational Brain Lab. Zhang's research integrates computational modeling, machine learning, brain imaging (MEG/EEG/fMRI), and experimental approaches to study human cognition, aging, and neurological disorders. Key focus areas include: Neural mechanisms of decision-making and problem-solving Computational models of cognitive processes AI applications in healthcare diagnostics and neuroimaging Brain network dynamics in neurological conditions Recent publications emphasize deep learning models for neural data, multimodal brain connectivity, decision-making impairments in Parkinson's disease, and neuroinformatics tools. His work shows strong clinical translation through epilepsy biomarker development and emergency department outcome prediction. Zhang has led research grants from ERC, MRC, BBSRC, and Wellcome Trust. As primary investigator for multiple projects, he oversees significant computational neuroscience initiatives. He is available for postgraduate supervision.

Wenwen Liمشاهده پروفایل

استاد

• Geographic Information Science
• Cyberinfrastructure
• Big Data
• +۸ مورد دیگر

Wenwen Li is a Professor at Arizona State University (ASU), holding roles as Director of the CyberInfrastructure and Computation Intelligence (CICI) Lab and Research Director at the Spatial Analysis Research Center (SPARC). She specializes in geographic information science, cyberinfrastructure, and geospatial big data. Her work focuses on developing intelligent cyberinfrastructure for environmental and urban studies, leveraging AI and semantic technologies. Li's research has been supported by NSF, USGS, and Google.org, among others. Education: Ph.D. in Earth System and Geoinformation Science (George Mason University, 2010), M.S. in Signal and Information Processing (Chinese Academy of Sciences, 2007), and B.S. in Computer Science (Beijing Normal University, 2004). Research Interests : Cyberinfrastructure, spatial-temporal data mining, semantic interoperability, GeoAI applications in climate science, and urban studies. Her lab, CICI, pioneers projects like the Arctic Permafrost Thaw analysis and disaster response systems. Awards : 2023 AAG and UCGIS Fellowships, 2021 NSF Mid-Career Award, 2015 NSF CAREER Award, and the 2024 Greg Leptoukh Lecture Award (AGU). She chairs AAG's Cyberinfrastructure Specialty Group and serves on editorial boards of key journals. Grants & Service : Leads NSF-funded projects on GeoAI, climate modeling, and Arctic science. Active in AAG leadership roles and global initiatives like the Polar Cyberinfrastructure Portal. Recruits Ph.D. students in Geography and Computer Science. Labs & Teams : Directs the CICI Lab, advancing interdisciplinary GeoAI research and training. Collaborates with global partners on environmental and computational geography projects.

Lucie Luمشاهده پروفایل

مدرس ارشد

• Asset Pricing
• Credit Risk
• International Finance
• +۱ مورد دیگر

Dr. Lucie Lu is a Senior Lecturer in the Department of Finance at the University of Melbourne, affiliated with the Faculty of Business and Economics. She joined the institution in 2023 and specializes in asset pricing, credit risk, international finance, and sustainable finance. Her research examines investor behavior in global markets, risk transmission mechanisms, and sustainable investment strategies. Dr. Lu holds a Ph.D. in Finance from McGill University, an MSc in Finance and Economics from the London School of Economics and Political Science, and a BA in Economics from Fudan University. Her work investigates topics such as heterogeneous investors' roles in risk-sharing, default risk propagation in credit markets, and institutional investors' preferences for sustainable investments. Her recent publications explore cross-market risk transmission, institutional investment dynamics, and structural credit modeling. While no specific awards are listed, her contributions to finance theory and empirical analysis are evident in her scholarly output. Dr. Lu does not currently list advisees or grants in the provided information. Her research extends to interdisciplinary areas, combining quantitative finance with global economic trends, reflecting her expertise in both theoretical and applied financial frameworks.

Ricardo Alonsoمشاهده پروفایل

استاد

• Organizational Economics
• Contract Theory
• Information Economics
• +۳ مورد دیگر

Professor Ricardo Alonso is a Professor of Management at the London School of Economics (LSE), Department of Management since 2014. Previously, he held academic positions at the University of Southern California and Kellogg School of Management. He holds a PhD in Managerial Economics & Strategy from Northwestern University and an MS in Electrical Engineering from the Universidad Politécnica Madrid. His research focuses on organizational economics, contract theory, and information economics, with notable contributions to Bayesian persuasion, delegation mechanisms, and political economy. Key publications include work on voter persuasion, optimal delegation, and organizational adaptation. His academic leadership roles include directing the LSE Master’s in Management Programme and the PhD Programme in Business Economics. Alonso has been recognized with the Excellence in Refereeing Award from the American Economic Review (2009) and has served as a referee for top journals like Econometrica and the Review of Economic Studies. His work bridges theoretical economics with practical applications in management strategy and policy design. Education: PhD, Managerial Economics & Strategy, Kellogg School of Management, 2007 MSc, Management Economics, University of Essex, 2001 MS, Electrical Engineering, Universidad Politécnica Madrid, 1996 Research Interests: Organizational design, strategic communication, political persuasion, and incentive structures in multi-agent systems. Professional Service: Research Fellow at CEPR (2015–present), grant reviewer for NSF and ERC, and discussant at major academic conferences. Industry Experience: Over a decade in telecommunications leadership roles at Telefónica across Europe and the Americas, including launching greenfield operators and e-commerce platforms. His research explores how organizations optimize decision-making through delegation and information design, with applications in corporate strategy and public policy. Recent work analyzes persuasion strategies in elections and investor contexts, leveraging tools from game theory and behavioral economics.

L.J. Sluysمشاهده پروفایل

استاد

• Computational Mechanics
• Computational Mechanics of Materials
• Modelling of Failure Processes
• +۱۲ مورد دیگر

Professor L.J. Sluys is a Full Professor and Chair of Computational Mechanics at the Faculty of Civil Engineering and Geosciences, Delft University of Technology (TU Delft). He has been a leading figure in computational mechanics since 1999, heading the Computational Mechanics group and serving as head of the Department of Materials, Mechanics, Management and Design (3MD) from 2018 to 2024. His research is centered on the computational modeling of material behavior, particularly focusing on failure processes and high-performance materials. His research interests include computational mechanics of materials, modeling of failure and fracture processes, multi-scale methods, and the computational modeling of high-performance materials such as composites and concrete. He employs advanced numerical techniques including the finite element method, extended finite element method (XFEM), level-set methods, and cohesive zone modeling to simulate complex mechanical behaviors under static and dynamic loading conditions. His work spans civil, mechanical, and materials engineering domains, with applications in infrastructure, energy, and sustainable materials. The recent publications highlight a strong trend in modeling fracture, fatigue, and degradation in heterogeneous materials such as composites, concrete, and geological formations. His work integrates multi-physics and multi-scale approaches, often coupling mechanical, thermal, and chemical effects. There is a consistent focus on numerical robustness, model validation, and the development of adaptive computational frameworks for simulating progressive damage and failure. Research Fellow of the Netherlands Academy of Arts and Sciences (KNAW) Professor Sluys has taught core courses such as Introduction to the Finite Element Method and Computational Methods in Non-linear Solid Mechanics for over a decade, indicating a strong commitment to academic education. He has supervised numerous students, though specific names are not listed in the provided texts. He leads an active research group in computational mechanics, contributing to both fundamental and applied research in solid mechanics. His work involves collaboration with international institutions and industry partners, particularly in the areas of infrastructure durability and advanced materials.