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نمایش ۷۳۶-۷۵۰ از ۴٬۳۱۸ استاد راهنما
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استادیار
Asad Dossani is an Assistant Professor of Finance at Colorado State University's Department of Finance and Real Estate. His research focuses on financial econometrics, asset pricing, monetary policy impacts, and risk dynamics in currencies and commodities markets.
Prof. Dr.-Ing. Joachim Denzler is a Professor leading the Chair for Digital Image Processing (Lehrstuhl für Digitale Bildverarbeitung) at Friedrich-Schiller-Universität Jena. His research spans computer vision, machine learning, medical imaging, and remote sensing applications across diverse domains including biomedical analysis, environmental monitoring, and facial recognition systems. His research interests focus on explainable AI, causal discovery, physics-informed neural networks, and bias mitigation in deep learning models. He has made significant contributions to fine-grained classification, concept activation vectors, and privacy-preserving techniques in facial recognition. His work often bridges theoretical computer vision with practical applications in medical diagnostics, environmental science, and infrastructure monitoring. His recent publications demonstrate a strong trend toward causal discovery methods, physics-informed neural networks, and addressing bias in machine learning systems. His work spans both theoretical advancements in model interpretability and practical applications in medical imaging, environmental monitoring, and infrastructure safety. The interdisciplinary nature of his research is evident from collaborations with ecologists, neuroscientists, and civil engineers. Best Paper Award at International Conference on Pattern Recognition (ICPR) 2024 Prof. Denzler actively mentors numerous PhD students and postdoctoral researchers, with frequent co-authors including Maha Shadaydeh, Niklas Penzel, Gideon Stein, and Tim Büchner appearing as first authors on significant publications. His laboratory has secured research funding across multiple domains including medical imaging, environmental monitoring, and AI safety. His work on CausalRivers represents a major contribution to benchmarking causal discovery methods with real-world time series data. The Chair for Digital Image Processing maintains strong industry and clinical partnerships, particularly in medical imaging applications for facial palsy analysis and neonatal care. Prof. Denzler's team has developed novel techniques for dam deformation monitoring using remote sensing data and created advanced methods for analyzing plant diversity effects on ecosystem functioning.
دانشیار
Jia Hu is an Associate Professor in Computer Science at the University of Exeter. He holds a PhD in Computer Science from the University of Bradford (2010), and M.Eng/B.Eng degrees in Electronic Engineering from Huazhong University of Science and Technology. His research specializes in edge-cloud computing, federated learning, and AI-driven optimization for networks and IoT systems. Research Interests: Hu's work spans resource optimization, applied machine learning (particularly in distributed settings), network security, blockchain integration, and intelligent systems for electric vehicles and Industry 4.0. His recent projects focus on federated edge AI, 6G-enabled industrial IoT, and real-time federated learning via hardware-algorithm co-design. Publications: His 150+ papers emphasize federated learning, edge computing, and reinforcement learning applications. Recent works (2020–2025) show a strong trend toward optimizing AI at the network edge, with themes like digital twins, blockchain security, and EV-integrated systems dominating. Awards & Recognition: Best Paper Awards: IEEE SOSE'16, IUCC'14 Outstanding Service & Leadership Awards for IEEE conferences Top 4% contributor to EPSRC Peer Review Fellow of the Higher Education Academy (HEA) Grants & Projects: Secured €4.7M+ funding from EU Horizon, EPSRC, and Royal Society for projects including: SAILING (Secure AI for Smart Internet-of-Energy, €3.6M) REFINE (Real-time Air Quality Monitoring with UAVs, €897K) SustainAIRA6G (Energy-Efficient AI for 6G Networks, £118K) Advising: Supervised 12 PhD students to completion; currently mentoring 7 students in federated learning, edge computing, and AIoT.
Andreu Sansó Rosselló is a Full Professor of Applied Economics at the University of the Balearic Islands (UIB), where he is affiliated with the Faculty of Economics and Business and the Department of Applied Economics. He previously served as Head of the Department of Applied Economics (2003-2007) and Dean of the Faculty of Economics and Business (2013-2020). He has also held significant positions outside academia, including General Director of Economy for the Government of the Balearic Islands (2007-2011) and Director of the Institute of Statistics of the Balearic Islands (2008-2011). His educational background includes a Degree in Economics and Business from the University of Barcelona (1992) and a PhD in Economics from the University of Barcelona (1996). He previously held academic positions as assistant professor and associate professor at the University of Barcelona, associate professor at UIB, and Professor-tutor at the Open University of Catalonia. Professor Sansó Rosselló's research focuses primarily on econometrics, with specializations in economic statistics and economic time-series analysis. His work spans multiple disciplines including tourism economics, health policy, energy economics, and public finance. He has published extensively in top-tier journals such as Journal of Econometrics, Journal of Business and Economic Statistics, and Tourism Management. His recent work demonstrates a strong interdisciplinary approach, connecting econometric methods with practical applications in tourism, healthcare, and environmental economics. His publications reveal a consistent focus on applying advanced statistical techniques to real-world economic problems, particularly in tourism (7 of his 15 most recent articles) and methodological econometrics (5 articles), with additional contributions to health economics, energy economics, and public finance. Professor Sansó Rosselló leads multiple research initiatives as Main researcher for the Econometrics and Data Science (ECD) Consolidated R+D+I Group and the Economics and Data Science (UECD) R+D+I Unit, while also serving as a Member of the Topological Models for Fuzzy Information Processing (MOTIBO) Consolidated R+D+I Group. He teaches Econometrics, Statistical Learning and Decision-Making, and supervises Final Degree Projects across multiple undergraduate and master's programs at UIB, including the Degree in Business Administration, Double Degree in Business Administration and Law, Double Degree in Business Administration and Tourism, and Master's programs in Big Data Analysis and Data Analytics.
Yanlei Diao is a Professor of Computer Science at École Polytechnique (France) with a joint appointment at the University of Massachusetts Amherst. She received her PhD from UC Berkeley in 2005. Her research focuses on scalable data systems, particularly in big data analytics, cloud computing optimization, and real-time stream processing. Research Interests: Her work spans cloud infrastructure optimization (UDAO project), explainable anomaly detection in data streams (EXAD), interactive data exploration (AIDEme), genomic data analysis (GESALL), and uncertain data management (CLARO). She leads the CEDAR team at Inria/LIX focusing on cloud-scale data exploration. Awards & Honors: ERC Consolidator Grant (2017-2023) CRA-W Borg Early Career Award (2013) NSF CAREER Award (2008) Keynote speaker at ACM DEBS 2021 and SWIFT 2023 AI Forum Best Paper Award at SIGMOD 2011 ACM SIGMOD Dissertation Honorable Mention (2005) Advising & Leadership: Mentored over 20 PhD students and postdocs, currently supervising 7 researchers. Served as PVLDB PC Co-Chair (2025-2026) and ACM SIGMOD Editor-in-Chief (2014-2019). Leads multiple projects with industry partners including Alibaba Cloud.
دانشیار
Michela Mari is an Associate Professor of Economics and Business Management at the Department of Management and Law, Faculty of Economics, University of Rome Tor Vergata. She serves as Deputy Coordinator of the Master's Degree in Real Estate Economics and Management and the Departmental Research Center "Scientific Observatory on Female Entrepreneurship - OSIF," and is a Faculty Member of the PhD in Management. Since 2018, she has chaired the "Female Entrepreneurship" Track at the European Academy of Management Conference (EURAM) and has been an active member of the Global WEP Network since 2017. Her research centers on Female Entrepreneurship , Real Estate Management , and Service Management , with particular focus on work-family conflict dynamics, virtual retail environments, and public-private partnerships. Her interdisciplinary approach bridges business strategy with gender studies and urban development, resulting in publications in leading journals such as International Entrepreneurship and Management Journal , Cities , and The Service Industries Journal . She has pioneered research on Italian female entrepreneurs' experiences and global STEM entrepreneurship barriers. Analysis of her publication trends reveals strong thematic continuity in gender-focused business research since 2011, with increasing emphasis on sustainability and digital transformation in recent years. Her work consistently addresses practical implications for policy and management while maintaining rigorous academic standards. Mari's scientific recognition includes: borsa di studio per giovani ricercatori, XXXIII Convegno Nazionale AIDEA 2010 for corporate governance research As PhD faculty and OSIF Research Center Deputy Coordinator, she actively mentors emerging scholars in female entrepreneurship research while collaborating with international networks like Global WEP to shape policy frameworks. Her editorial leadership in special journal issues demonstrates scholarly influence beyond traditional publication metrics. She directs the Departmental Research Center "Scientific Observatory on Female Entrepreneurship - OSIF," which conducts empirical studies on gendered business practices and advises policymakers on women's economic empowerment initiatives through evidence-based research.
Yuanchang Xie serves as Professor in Civil and Environmental Engineering at UMass Lowell's Francis College of Engineering, where he leads research in the Center for Smart Cyber-Physical Systems. His work integrates computational methods with transportation infrastructure analysis, focusing on safety-critical applications through federal partnerships. Dr. Xie earned his Ph.D. in Civil Engineering from Texas A&M University (2007), preceded by M.S. and B.S. degrees in Transportation Engineering from Southeast University, China (2003, 2000). His academic foundation supports interdisciplinary research bridging civil engineering and cyber-physical systems. Research centers on traffic safety, intelligent transportation systems, and logistics optimization. He pioneers AI-driven approaches for crash prediction, connected vehicle operations, and infrastructure monitoring, emphasizing real-world implementation through partnerships with USDOT and state agencies. Current work explores multimodal data fusion for safety analytics in mixed-autonomy environments. Recent publications (2024-2025) reveal accelerating focus on deep learning applications: crosswalk detection via drone imagery, trajectory prediction in mixed traffic, and real-time work zone safety monitoring. This evolution demonstrates strategic alignment with emerging transportation technologies while maintaining core safety objectives. No scientific awards were explicitly documented in source materials. Dr. Xie has secured continuous funding as Principal Investigator through NSF, USDOT, DOE, and USDA programs. Key projects include Connected Vehicles: Toward the Understanding of "Firm Science" (NSF), Center of Multi-Scale Sensing Technologies (USDOT), and nuclear evacuation modeling for rural communities (USDA). His grants consistently address infrastructure resilience through cyber-physical integration. He directs research activities within UMass Lowell's Center for Smart Cyber-Physical Systems, which develops sensor networks and computational models for transportation infrastructure monitoring. The center's work on drone-based inspection systems and emergency response logistics demonstrates practical applications of his theoretical frameworks.
Jim Steenburgh is a Professor of Atmospheric Sciences at the University of Utah specializing in mountain weather and climate, orographic and lake-effect precipitation, weather analysis and forecasting, and numerical weather prediction. He joined the University of Utah faculty in 1995 and served as Department Chair from 2005-2011. An avid skier, he shares his expertise through his popular blog Wasatch Weather Weenies and his book Secrets of the Greatest Snow on Earth . B.S. in Meteorology from The Pennsylvania State University (1989) Ph.D. in Atmospheric Sciences from the University of Washington (1995) Dr. Steenburgh's research focuses on winter storms in complex terrain, particularly in mountainous regions. His work spans mountain meteorology, lake-effect and sea-effect snow systems, and the interaction between weather systems and topography. He has conducted significant research on the Wasatch Mountains, Great Salt Lake region, Japan Sea, and other mountainous areas worldwide. His expertise in winter weather forecasting has practical applications for avalanche safety, ski industry forecasting, and understanding climate change impacts on mountain snowpack. Analysis of Steenburgh's recent publications reveals a strong emphasis on lake-effect and sea-effect precipitation systems, particularly their interaction with terrain. His research combines observational studies with numerical modeling approaches to understand mesoscale weather phenomena. A significant portion of his work focuses on the Wasatch Mountains and Great Salt Lake region, while also expanding to international locations including Japan and the European Alps. His publications demonstrate an evolving research trajectory incorporating climate change impacts on mountain snow systems. Fellow, American Meteorological Society (2021) Fulbright Scholar, University of Innsbruck (2019) Distinguished Teaching Award, University of Utah (2024) Russel L. DeSouza Award, NSF Unidata Program (2024) Named Session Award, AMS Mountain Meteorology Committee (2018) Hosler Alumni Scholar Medal, Penn State University (2017) Outstanding Service Award, National Weather Service Western Region (2002) Outstanding Teaching Award, University of Utah (2001) Steenburgh has secured substantial research funding from NSF, NASA, and other agencies, with current projects extending through 2025. His grants focus on mountain meteorology, lake-effect snow prediction, and improving winter weather forecasting in complex terrain. He has mentored numerous graduate students through projects like the Storm Peak Laboratory graduate education program and has been involved in several major field campaigns including the Ontario Winter Lake-effect Systems (OWLeS) and the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) program. Dr. Steenburgh leads the Wasatch Weather Weenies blog, a collaborative effort with other meteorologists that provides real-time weather analysis and commentary, particularly focused on Utah's mountain weather. He has been instrumental in connecting academic research with practical weather forecasting applications, working closely with the National Weather Service and avalanche centers. His research group frequently collaborates with international partners, particularly in Japan where sea-effect snow systems share similarities with Utah's lake-effect snow events.
Dr. Jose Paolo Talusan is a Research Scientist at the Department of Computer Science and Computer Engineering , Vanderbilt University, specializing in smart transportation systems , distributed computing , and cyber-physical systems . He is affiliated with ScopeLab , a research group focused on smart cyber-physical systems. Education: PhD from Nara Institute of Science and Technology, Japan (2020) Research Interests: His work addresses challenges in urban mobility through middleware architectures, optimization algorithms, and machine learning. Key areas include incident detection in transportation systems, privacy-preserving route planning, and vehicle-to-building charging optimization. Publication Trends: Recent publications focus on real-time transit optimization (2024-2025), leveraging reinforcement learning for heterogeneous agents in vehicle-to-building systems, and privacy-aware route planning in smart cities. His work integrates IoT , edge computing , and graph neural networks to tackle imbalanced data and sparsity issues in transit analytics. Labs & Teams: Actively contributes to ScopeLab at Vanderbilt University, collaborating on interdisciplinary projects with researchers in computer science, electrical engineering, and urban planning.
استادیار
Nikolaos Paterakis is an Assistant Professor of Power System Optimization and Electricity Markets with the Electrical Energy Systems research group at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). He is the founder and principal investigator of the Electricity Markets & Power System Optimization Laboratory (EMPSOLab) established in 2019, and a member of the Cyber-Physical Systems Center Eindhoven (CPSe). His research focuses on applying optimization and machine learning techniques to power system and electricity market problems, particularly regarding renewable energy integration and smart grid technologies. Dr. Paterakis received his Dipl.Eng. from Aristotle University of Thessaloniki in 2013, followed by a PhD in Industrial Engineering and Management (cum laude) from the University of Beira Interior in 2015. After serving as a post-doctoral fellow at TU/e from 2015-2017 and working as a consultant for the Energy Market Regulatory Authority of Turkey, he was appointed Assistant Professor at TU/e in April 2017. His research spans power system optimization, electricity market design, renewable energy integration, and the application of machine learning techniques to grid management problems. Recent work emphasizes distributed energy resource integration, local electricity markets, congestion management in low-voltage grids, and real-time grid control using advanced optimization techniques. His publications demonstrate a clear trajectory toward increasingly sophisticated methods for managing grid constraints while enabling market participation of distributed energy resources. Dr. Paterakis has received several prestigious awards including IEEE SEGE'15, SEST 2019, and SEST 2020 Best Paper Awards, and recognition as a Best Reviewer for IEEE Transactions on Smart Grid (2015, 2017) and IEEE Transactions on Sustainable Energy (2016). He serves as Associate Editor for multiple journals including IET Renewable Power Generation, IEEE Systems Journal, IEEE Transactions on Intelligent Transportation Systems, and Elsevier's e-Prime. He leads multiple research projects including MEGAMIND (NWO-funded), P2P-TALES (NWO-funded), and the Electricity Markets Game series (TU/e BOOST!-program). His educational contributions include teaching courses on power system analysis and optimization, electricity markets modeling, and developing innovative educational tools for power systems education. In 2021, he was elevated to Senior Member of the IEEE Power & Energy Society.
مدرس ارشد
Dr James Shucksmith is a Senior Lecturer in Water Engineering at the School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. After completing his undergraduate degree and PhD at the same department, he joined the academic staff in 2010 following a KTP associate role with Yorkshire Water. His research focuses on urban flooding hydrodynamics, water quality modeling, and sustainable drainage systems. Co-director of EPSRC Centre for Doctoral Training in Water Infrastructure and Resilience Current projects: Real Time Abstraction Management (with Severn Trent Water), Centaur FloodInteract Research interests include: Urban flood hydrodynamics and drainage-surface flow interactions Water quality forecasting tools for surface water abstraction Development of local real-time control systems for urban drainage Experimental validation of flood models using PIV measurements His publications (2010-2025) cover topics like contaminant transport in flooded sewer systems, longitudinal dispersion modeling, and real-time control optimization. Recent work focuses on data-driven approaches for Cryptosporidium prediction and E. coli forecasting.
استاد پژوهشی
Laura M. Wallace serves as a Research Professor at the University of Texas Institute for Geophysics (UTIG) with a joint appointment at GNS Science in New Zealand. Her pioneering work focuses on geodetic analysis of crustal deformation at plate boundaries, particularly slow slip events in subduction zones like New Zealand's Hikurangi Margin. In 2018, she co-led IODP Expedition 375 aboard the JOIDES Resolution, drilling into active slow slip zones to install long-term monitoring observatories. Education: Ph.D. in Earth Sciences, University of California, Santa Cruz B.S. in Geology, University of North Carolina at Chapel Hill Research Focus: Wallace's work centers on tectonics and crustal deformation , utilizing land-based GPS and seafloor geodetic instruments to study subduction zone dynamics . Her 2002 discovery of slow slip events at Hikurangi revolutionized understanding of fault behavior, revealing how fluid pressure and lithological heterogeneity control slip patterns. She integrates geodetic data with seismic and drilling results to model earthquake cycles. Publication Trends: Recent work (2023-2025) analyzes spatiotemporal evolution of slow slip using multi-instrument approaches (GNSS, InSAR, seafloor sensors), with emphasis on New Zealand's seismic hazard models. Key themes include fluid-mediated fault weakening, seamount subduction effects, and probabilistic forecasting of megathrust events. Leadership & Grants: Wallace secured major funding through IODP for Expedition 375, enabling core sampling and observatory deployment at Hikurangi. She contributes to national hazard assessments for New Zealand, developing geodetic strain rate models and deformation frameworks for seismic hazard maps. Collaborative Infrastructure: She leverages UTIG's geodetic networks and GNS Science partnerships, utilizing JOIDES Resolution drilling data and SMART subsea cable initiatives for real-time offshore monitoring. Her lab integrates field observations with numerical modeling to predict subduction zone behavior.
استادیار
Yunji Zhang is an Assistant Professor at the Department of Meteorology and Atmospheric Science within The Pennsylvania State University . He also serves as the Assistant Director of the Penn State Center for Advanced Data Assimilation and Predictability Techniques (ADAPT) and is affiliated with the Alliance for Education, Science, Engineering and Design with Africa (AESEDA) . Research Focus: Dr. Zhang investigates the dynamics and predictability of convectively driven severe weather , including tropical cyclones, mesoscale convective systems, and severe thunderstorms. His work emphasizes ensemble-based data assimilation techniques using satellite and radar observations to enhance numerical weather prediction models. Current projects involve improving forecasts of hurricanes like Harvey and Midwest derecho events through advanced assimilation of all-sky microwave and infrared radiances. Publications Trends: His recent studies (2021-2025) highlight advancements in Ensemble data assimilation of dual-polarization radar and satellite data Predictability of extreme rainfall events in Zhengzhou and China Multi-sensor approaches for convection initiation forecasting Microphysical parameterization impacts on hurricane prediction Automated boundary layer depth estimation techniques Global-to-regional nested modeling for tropical cyclones Advising: Dr. Zhang mentors current graduate students Zhu Yao and Abhisek Das, while former advisees include Ph.D. recipient Keenan Eure and M.S. graduate Paul Mykolajtchuk . He actively invites students interested in M.S. or Ph.D. research to contact him via email. Labs & Centers: His work is supported by the Penn State Center for Advanced Data Assimilation and Predictability Techniques (ADAPT) and collaborations with the Alliance for Education, Science, Engineering and Design with Africa (AESEDA) .
دانشیار
Hakan Ergun serves as an Associate Professor at KU Leuven's Faculty of Engineering Science within the Department of Electrical Engineering (ESAT). He leads the Subdivisie EnergyVille Electa - Ergun and holds key roles in EnergyVille initiatives, including membership in the Division EnergyVille and the Council of the Faculty of Engineering Science. His research focuses on power systems engineering with specialization in HVDC grid technology, renewable energy integration, and stochastic optimization. Current projects include predictive maintenance for wind farms, congestion management for offshore HVDC grids, and development of hybrid AC/DC grid software tools. His work addresses critical challenges in grid resilience, uncertainty modeling, and multi-national offshore grid coordination. Recent publications demonstrate strong trends in hybrid AC/DC grid optimization under uncertainty, with emphasis on stochastic programming, polynomial chaos expansion, and risk-based operational models. Key themes include offshore grid protection, frequency stability with energy storage, and spatio-temporal variability in power system planning. Ergun actively supervises doctoral candidates including K. Phillips and C.K. Jat. His research portfolio includes significant EU-funded projects such as CROCODILE (Cross-border Coordination of Offshore Grids) and advanced HVDC grid development initiatives with multi-year funding through 2028-2029. He directs the EnergyVille Electa subdivision focused on electrical energy systems applications, collaborating with industry partners on real-world grid implementation challenges. Current work emphasizes practical solutions for multi-GW offshore energy hubs and resilient power systems leveraging HVDC transmission flexibility.
استادیار
John Paparrizos is an Assistant Professor of Computer Science and Engineering at The Ohio State University's College of Engineering, where he directs The DATUM Lab (Data Analytics, Understanding, Mining, and Management Lab). He maintains an adjunct affiliation with the School of Informatics at Aristotle University of Thessaloniki. His research spans databases, data science, machine learning, and artificial intelligence , with focus areas including: Time-series analysis (clustering, anomaly detection) Scalable data mining for structured/unstructured data Adaptive algorithms for resource-constrained environments Foundational technologies for data-intensive applications His work addresses real-world challenges across relational, time-series, multimedia, text, graph, web, and IoT data domains. Notable recognition includes: 2025 ACM SIGMOD Test-of-Time Award for k-Shape time-series clustering 2023 IEEE TCDE Rising Star Award ACM SIGMOD Research Highlight Award NetApp Faculty Award His research has been featured in New York Times (front page), Washington Post , Forbes , and adopted by Fortune 500 companies (Exelon, Nokia) and the European Space Agency. He actively serves on program committees for premier conferences including ACM SIGMOD, VLDB, IEEE ICDE, ACM SIGKDD, and NeurIPS. His open-source tools have exceeded 100,000 downloads and are integrated into academic curricula at Brown, Columbia, Purdue, and University of Chicago.
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