جست‌وجوی استاد و پژوهشگر

Chung-Hsing Yehمشاهده پروفایل

دانشیار

• Multicriteria Decision Analysis
• Applied Artificial Intelligence
• Intelligent Decision Support Systems
• +۶ مورد دیگر

Chung-Hsing Yeh is an Associate Professor at Monash University's Faculty of Information Technology, Department of Data Science & AI. He holds a visiting professorship at National Cheng Kung University, Taiwan, and has extensive experience in academic roles including Chief Examiner and Lecturer for numerous IT and business-related courses. His research focuses on multicriteria decision analysis, applied artificial intelligence, fuzzy logic, neural networks, and sustainable operations management. He has led collaborative projects on e-waste recycling, supply chain optimization, and public health policy, funded by organizations like the Ministry of Science and Technology (Taiwan) and the Australian Research Council. Education: PhD in Operations Research/Information Systems, Monash University (1988) MSc in Management Science, National Cheng Kung University (1982) BSc (Engineering) in Industrial Design, National Cheng Kung University (1977) Research Interests: His work spans decision support systems, optimization modeling, transport research, and recycling operations. Notable contributions include algorithms for production scheduling, AI-driven solutions for healthcare, and sustainable e-waste management strategies. Awards: Listed in Marquis Who's Who in the World Listed in Who's Who in Finance and Industry Listed in Who's Who in Science and Engineering Grants & Projects: Led 6 major projects, including 'Maximizing E-waste Recycling Profitability' (2019–2020) and 'Smoke-Free Policy Effectiveness' (2007–2010). Active in grant review roles for ARC and the Netherlands Organisation for Scientific Research. Teaching: Overseeing courses such as Fundamentals of Artificial Intelligence, Business Intelligence Modelling, and Management Information Systems.

Penina Axelradمشاهده پروفایل

استاد

• Astrodynamics
• Satellite Navigation
• Global Navigation Satellite Systems (GNSS)
• +۵ مورد دیگر

Dr. Penina Axelrad is a University of Colorado Distinguished Professor and Joseph T. Negler Professor of Aerospace Engineering Sciences at the University of Colorado Boulder. She has held academic roles since 1992, serving as Department Chair from 2012–2017. A member of the National Academy of Engineering since 2019, her research focuses on GNSS technology, satellite navigation, and remote sensing applications. She has authored over 223 publications and secured $17.5M in research grants. Education: Ph.D., Aeronautics and Astronautics, Stanford University, 1991 S.M., Aeronautical and Astronautical Engineering, MIT, 1986 S.B., Aeronautical Engineering (Avionics Option), MIT, 1985 Research Interests: Global Navigation Satellite Systems (GNSS), multipath mitigation, GNSS reflectometry, orbital dynamics, and quantum sensing for Earth science. Her work bridges astrodynamics, satellite navigation, and environmental monitoring. Awards: Member, National Academy of Engineering (2019) Women In Aerospace Educator Award (2016) Institute of Navigation Samuel Burka Award (2012) AIAA Summerfield Book Award (2011) Advising & Grants: Advised numerous students (no names listed) and led major grants including NASA Quantum Pathways Institute and Sentinel-6 orbit determination projects. Active in Institute of Navigation leadership roles. Labs/Teams: Colorado Center for Astrodynamics Research (CCAR), Quantum Pathways Institute, and collaborative efforts on CubeSat atomic clock experiments.

Melanie Schienleمشاهده پروفایل

استاد

• Econometrics
• Statistical Methods
• Financial Risk Analysis
• +۴ مورد دیگر

Prof. Melanie Schienle is a Professor and Chair of Statistical Methods and Econometrics at the Department of Economics and Management, Karlsruhe Institute of Technology (KIT). She also holds a professorship in the Department of Mathematics at KIT since 2021. Her expertise spans statistical methods, econometrics, financial risk analysis, and forecasting. She leads the HKMetrics Network and the RespiNow Hub for respiratory disease forecasting. She serves as a Senior Fellow at the Rimini Center for Economic Analysis (RCEA), a steering committee member of the German Economic Association, and a member of the University Research Council at KIT. Education: Ph.D. (Dr. rer. pol.) in Economics from Mannheim University (2008), summa cum laude; Diploma in Mathematics (University of Karlsruhe, 2003) with a minor in theoretical physics. She has held academic positions at Leibniz University Hannover (2012–2015) and Humboldt University of Berlin (2008–2012). Research interests focus on financial networks, systemic risk, time series analysis, and machine learning applications in economics. She co-leads projects on nowcasting and forecasting, including collaborative efforts during the pandemic to predict hospitalizations. Her work integrates advanced statistical techniques with real-world policy implications. Prof. Schienle is an Associate Editor for the International Journal of Forecasting and Journal of Time Series Analysis . She has authored over 50 peer-reviewed publications and contributed to high-impact journals like Nature Communications and Journal of Business & Economic Statistics . She leads the Institute of Statistics at KIT and chairs the MathSEE initiative for interdisciplinary mathematical applications.

• Exercise Physiology
• Endurance Training
• Cardiovascular Health
• +۳ مورد دیگر

Jan Helgerud is a Professor in the Department of Circulation and Medical Imaging at the Norwegian University of Science and Technology (NTNU), Faculty of Medicine and Health Sciences. His research focuses on exercise physiology, endurance training, and cardiovascular health, with particular emphasis on aerobic capacity, muscle strength, and their clinical applications in chronic diseases and rehabilitation. Research Interests: Exercise physiology and its impact on health and disease High-intensity interval training (HIIT) adaptations in athletes and clinical populations Cardiovascular and metabolic responses to exercise Strength training in patients with rheumatic diseases, Parkinson's disease, and spinal cord injuries Physiological determinants of endurance performance in sports like cycling and cross-country skiing Recent Article Trends: Recent work emphasizes the efficacy of high-intensity training protocols for improving aerobic capacity (VO2max) and functional outcomes in diverse populations, including elite athletes and patients with long-term conditions like long-COVID or rheumatic diseases. Studies also explore mechanisms of muscle adaptation, heat tolerance in occupational settings, and digital health tools for exercise assessment. Advising & Grants: Collaborations with colleagues on randomized controlled trials for strength and endurance interventions Focus on translational research linking physiological insights to clinical outcomes Labs/Teams: Active in NTNU's Department of Circulation and Medical Imaging, contributing to interdisciplinary projects in sports science and rehabilitation medicine.

Emanuel Sallingerمشاهده پروفایل

استاد

• Information Systems Engineering
• Computational Logic
• Scalable Artificial Intelligence
• +۸ مورد دیگر

Emanuel Sallinger is a Full Professor at TU Wien's Databases and Artificial Intelligence Group and Vice Dean of Academic Affairs for Business Informatics and Data Science. He leads the Knowledge Graph Lab, focusing on scalable knowledge-based systems, reasoning in knowledge graphs, and AI integration. His research spans computational logic, database theory, and blockchain applications. Education: PhD in Computer Science (awarded 'sub auspiciis praesidentis rei publicae'), Master's degrees in Computational Intelligence and Informatics Management, and a Bachelor's in Software and Information Engineering. Research Interests: Knowledge graphs (construction, reasoning, scalability), logic-based systems, AI/ML integration with databases, enterprise architecture modeling, and financial knowledge systems. His work emphasizes practical applications like enterprise modeling, sustainable waste management, and regulatory compliance. Grants & Projects: Lead Vienna Science and Technology Fund (WWTF)-funded Knowledge Graph Lab. Involved in projects like 'Knowledge Graph-driven Tour Management' (sustainability), 'SustainGraph' (waste processing), and 'Enterprise Architecture Knowledge Graphs'. Teaching: Offers courses on Knowledge Graphs, Generative AI, Database Systems, and research methodology. Supervises doctoral and master's students in AI, databases, and knowledge representation. Labs/Teams: Knowledge Graph Lab at TU Wien, collaborating with industry on blockchain-based systems, financial AI, and enterprise architecture frameworks.

• Video Coding
• Machine Learning
• FPGA Acceleration
• +۳ مورد دیگر

Jarno Vanne is a Professor at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on video coding standards, real-time systems, and hardware acceleration, particularly in the context of FPGA implementations and open-source tools. He leads projects involving VVC (Versatile Video Coding), V-PCC (Volumetric Video Coding), and HEVC (High Efficiency Video Coding), with an emphasis on efficiency, low latency, and machine learning integration. Key research interests include point cloud compression, saliency-guided encoding, parallelization schemes, and real-time video communication protocols. His work often addresses challenges in multi-party video streaming, embedded systems, and encryption mechanisms for privacy protection. He has contributed to open-source projects like the UVG dataset, Kvazaar encoder, and CiThruS simulation frameworks. Recent publications highlight advancements in VVC intra encoding optimizations, machine learning-driven partitioning schemes, and FPGA-accelerated solutions for edge computing. His research bridges theoretical video coding algorithms with practical implementations, aiming to improve compression efficiency while maintaining real-time performance.

Babak Moaveniمشاهده پروفایل

استاد

• Structural Health Monitoring
• Bayesian Inference
• Earthquake Engineering
• +۷ مورد دیگر

Babak Moaveni is a Professor in the Department of Civil and Environmental Engineering at Tufts University, serving as the Associate Chair since September 2024. He also holds a joint appointment as a Professor in Electrical and Computer Engineering. His research focuses on structural health monitoring, Bayesian inference, earthquake engineering, and offshore wind energy systems. Moaveni earned his Ph.D. in Structural Engineering from the University of California San Diego (2007), following an M.S. (2001) and B.S. (1999) from Sharif University of Technology in Tehran, Iran. His research interests span probabilistic system identification, signal processing, uncertainty quantification, and verification/validation of computational models. Notable grants include leadership in the PIRE project on offshore wind energy digital twins and the Coastal Virginia Offshore Wind Pilot Project. He has supervised multiple Ph.D. and M.S. students, with current advisees including Mehdi Akhlaghi and Nasim Partovi-Mehr. Moaveni has received the Best Presentation Award at the 2022 EDGE Symposium and serves on editorial boards for journals like Structural Health Monitoring and Frontiers in Built Environment . His lab, the Structural Health Monitoring Lab, specializes in infrastructure management and offshore wind energy systems. Key professional activities include membership in the American Society of Civil Engineers (ASCE) and roles on Tufts' Tenure and Promotion Committee. His teaching includes courses on structural health monitoring, numerical methods, and structural reliability.

Jiang Hongboمشاهده پروفایل

استاد

• Distributed systems
• Mobile computing
• Smart sensing
• +۵ مورد دیگر

Hongbo Jiang is a Distinguished Professor and Vice Dean of the College of Computer Science and Electronic Engineering at Hunan University, China. He holds concurrent roles as Director of the Trusted Systems and Networking Key Laboratory of Hunan Province and Director of the Hunan International Technical Cooperation Base for High-Performance Computing and Distributed Systems. His academic journey includes tenures as a Professor at Huazhong University of Science and Technology and a Hong Kong Scholar Research Fellow at The Chinese University of Hong Kong. Education: PhD in Computer Science (Case Western Reserve University, 2008), B.S./M.S. in Mathematics (Huazhong University of Science and Technology, 2002). Research Interests: Distributed systems, mobile computing, smart sensing, wireless networks, IoT, and edge computing. Ongoing projects include mobile/wireless applications, data science in IoT, and edge computing platforms. His work emphasizes practical implementations such as DriverSonar for driving safety and SmileAuth for biometric authentication. Key Achievements: Elected Member of Academia Europaea (2022), Fellow of AAIA, IET, and BCS. Notable awards include the Wu Wenjun Science and Technology Award (2020) and multiple best paper recognitions. Over 100+ publications in top venues like ACM MobiCom, IEEE/ACM Transactions. Professional Contributions: Editorial roles across 8+ journals including IEEE Transactions on Mobile Computing and ACM Transactions on Sensor Networks. Conference leadership includes co-founding ACM TURC and EAI ICECI. Active in technical committees for INFOCOM, MOBIHOC, and ICDCS. Labs/Teams: Leads research groups focused on networking, IoT, and edge computing. Current openings for PhD/MSc students and PostDoc researchers with strong mathematical and systems backgrounds.

Nicole Megowمشاهده پروفایل

استاد

• Combinatorial optimization
• Discrete optimization
• Mathematical optimization
• +۱۱ مورد دیگر

Nicole Megow is a Professor holding the chair for Combinatorial Optimization in the Faculty of Mathematics and Computer Science at the University of Bremen since 2016. She is affiliated with several research clusters including Humans on Mars Initiative, Minds, Media, Machines, and Dynamics in Logistics. Her academic journey includes positions at TU Berlin, Max Planck Institute for Informatics, TU Darmstadt, and TU Munich. Professor Megow's research focuses on mathematical optimization, algorithm design and analysis, and operations research. Her specific interests span combinatorial and discrete optimization, efficient algorithms, scheduling theory, resource allocation, packing problems, network design, routing, and uncertainty models including online, stochastic, robust, and explorable approaches. Her work bridges theoretical foundations with practical applications in logistics and decision-making systems. Her recent publications demonstrate a strong trend toward integrating prediction models with traditional optimization frameworks, particularly in scheduling and matching problems. She has made significant contributions to understanding the role of uncertainty in optimization problems, developing algorithms that work effectively with incomplete or uncertain information. Her work spans multiple prestigious venues including Mathematical Programming, Algorithmica, SODA, STACS, and NeurIPS. Dissertation Award by the German Operations Research Society (2007) Berlin Science Award for Young Researchers (2013) Heinz Maier-Leibnitz Prize (2013) Listed among Germany's top 40 researchers below 40 (Capital, 2014, 2015) Professor Megow actively supervises PhD students and postdocs, including Max Stahlberg, Joes Biburger, Sarah Morell, Bart Zondervan, Zhenwei Liu, and Alexander Lindermayr. She serves on numerous program committees for major conferences including SODA, IPCO, and STOC, and holds editorial positions for several prestigious journals. Her current research projects include Optimization under Explorable Uncertainty (DFG funded), How robots learn how to use structure (seed grant from MMM research cluster), and Scheduling Invasive Multicore Programs Under Uncertainty (within TCRC 89).

• Tribology
• Materials Science
• Surface Engineering
• +۵ مورد دیگر

Auezhan Amanov is an Associate Professor at the Faculty of Engineering and Natural Sciences, Tampere University, specializing in the Engineering Materials Science (EMS) department. His research focuses on tribology, surface engineering, and advanced materials processing. He leads the 'Tribology and Surface Modification' research group, aiming to enhance machine element performance through surface treatments and manufacturing innovations. Dr. Amanov is an active member of international tribology societies (STLE, JAST, KTS), chairing the 'Surface Engineering' committee at STLE. His work emphasizes improving wear resistance, fatigue life, and tribological performance of materials like titanium alloys, high-entropy alloys, and thermal spray coatings. His research integrates additive manufacturing, laser-based processes, and severe plastic deformation techniques to optimize material properties. Key contributions include studies on ultrasonic nanocrystal surface modification (UNSM) for enhancing mechanical and tribological characteristics. Collaborations with industries and academic institutions globally drive his mission to translate research into practical solutions for manufacturing efficiency and sustainable development. Dr. Amanov holds an h-index of 34 (Google Scholar) and has authored numerous peer-reviewed articles on materials science and tribology advancements. Teaching responsibilities include tribology and fatigue-related courses, reflecting his expertise in both academic and applied engineering domains. His vision includes advancing circular economy practices through bearing restoration technologies and improving 'Made in Finland' manufacturing competitiveness through material science innovations.

Wei David Daiمشاهده پروفایل

استادیار

• Robust Deep Learning
• Data Quality
• Distributed Computing Systems
• +۴ مورد دیگر

Dr. Wei David Dai is an Assistant Professor of Computer Science at Purdue University Northwest and Director of the Advanced Intelligence Software (AIS) Lab. His research focuses on robust deep learning, data quality, and public safety technologies like gunshot detection systems. He previously worked at IBM China as a senior engineer and served in Arkansas state government as a data scientist. Education: Ph.D. in Computer and Information Sciences (University of Arkansas at Little Rock, USA, 2020) M.S. in Information Science (University of Arkansas at Little Rock, USA, 2016) M.S. in Software Engineering (South China University of Technology, China, 2013) B.S. in Computer Science (Central South University, China, 2007) Research Interests: His work spans robust deep learning models, distributed computing systems, and privacy-preserving technologies. Notable projects include public safety innovations such as acoustic gunshot detection and AI-driven campus security systems. Articles Trends: Recent publications emphasize public safety applications (e.g., mass school shooting simulations) and deep learning robustness evaluation (e.g., the Accuracy-Stability Index metric). Earlier works address cloud computing optimization and data quality frameworks. Awards: Recipient of the 2024 Excellence in Research Award and multiple IBM honors for technical excellence and instruction. Grants & Advising: Leads the Indiana Space Grant Consortium-funded satellite imaging project and Purdue Provost Grant for gunshot detection. Advises doctoral and master’s students on AI ethics, distributed systems, and public safety. Labs: The AIS Lab develops AI tools for public safety, equipped with GPU resources for audio and image analysis.

Hing-Ho Tsangمشاهده پروفایل

استاد

• Civil Engineering
• Structural Engineering
• Seismic Design
• +۷ مورد دیگر

Professor Hing-Ho Tsang is the Chair in Civil and Structural Engineering at the University of Dundee, with over a decade of academic experience in Australia and Hong Kong. His research focuses on advancing sustainable infrastructure solutions, earthquake resilience, and green technologies to enhance building and infrastructure safety against natural disasters. He holds a Chartered Professional Engineer (CPEng) certification and advises governments and industries on building codes and seismic design guidelines. Tsang chairs the Global Network for Geotechnical Seismic Isolation (GSI) and serves as the Australian National Delegate to the International Association for Earthquake Engineering (IAEE). His expertise spans geotechnical seismic isolation, recycled materials in construction, and structural dynamics, contributing to UN Sustainable Development Goals (SDGs) like resilient infrastructure, circular economy, and climate action. With over 200 publications and a career-long impact ranking in Civil Engineering, Tsang has received prestigious awards including the R W Chapman Medal and Research Impact Award. Research Focus: Seismic design, sustainable construction, bio-inspired materials, and geotechnical isolation systems. Awards: Top Cited Article (2021–2022), Teaching Excellence Award (2022), and multiple international recognitions. Leadership Roles: Editorial board member for Geosynthetics International , organizer of the 18th World Conference on Earthquake Engineering technical session. His work emphasizes equity-driven engineering solutions, fostering inclusive and disaster-resilient communities. Current projects include innovative modular building systems and AI-driven seismic response models.

Miklos Korenمشاهده پروفایل

استاد

• International Trade
• Economic Development
• Labor Economics
• +۴ مورد دیگر

Miklós Koren is a Professor of Economics at the Central European University (CEU), Vienna. He serves as Head of the MS in Business Analytics Program and a Core member of the Economics Doctoral School. His research focuses on international trade, economic development, and the role of management in economic growth. He holds a Ph.D. from Harvard University (2005), an M.A. from CEU (2000), and a B.A. from Corvinus University Budapest (1999). Education: Ph.D. in Economics, Harvard University, 2005 M.A. in Economics, Central European University, 2000 B.A. in Economics, Corvinus University Budapest, 1999 Research Interests: Dr. Koren’s work examines the impact of managerial practices on economic development, trade policies, and firm performance. He has explored topics such as the effects of international experience on managerial talent, the role of expatriate CEOs in enhancing productivity, and the long-term implications of social distancing on industries reliant on face-to-face interactions. His research integrates empirical analysis with theoretical frameworks, often using large datasets to address questions of economic volatility and diversification. Key Contributions: He leads the CEU MicroData research group and has received an ERC Starting Grant for his work on trade and development. His publications span journals like the Quarterly Journal of Economics and PLoS ONE, addressing issues ranging from managerial skills in post-communist economies to the predictive power of machine learning in geospatial flows. Awards: Recipient of the Peter Kenen Fellowship and the Nicholas Káldor Prize, recognizing his contributions to economic theory and applied research. Grants & Labs: His research has been supported by grants from the European Research Council. He contributes to the development of research tools like the Stata package 'eventbaseline' for event study analysis.

Natalia Villanueva-Rosalesمشاهده پروفایل

دانشیار

• Semantic Web Technologies
• Smart Cities
• Data Integration
• +۵ مورد دیگر

Natalia Villanueva-Rosales is an Associate Professor in the Department of Computer Science at The University of Texas at El Paso (UTEP). As Co-Principal Investigator at the NSF-funded Cyber-ShARE Center of Excellence, she leads the iLink Research Group focusing on semantic technologies and smart city initiatives. Ph.D. in Computer Science, Carleton University (2011) M.Sc. in Artificial Intelligence, University of Edinburgh (2005) B.Sc. in Computer Science & Statistics, Universidad Panamericana & CINVESTAV-IPN (2002) Her research bridges Semantic Web technologies with Smart Cities applications, particularly in Water Sustainability and Senior Mobility . Key projects include ontology-based frameworks for freight performance data integration and community-driven smart mobility solutions. Recent publications demonstrate her interdisciplinary approach across Environmental Informatics (2022-2025) and Urban Mobility (2019-2022). She holds editorial and leadership roles in semantic science initiatives while actively mentoring through the ACM-W WICS student group. 2019 HEENAC Education Award 2019 NCWIT Undergraduate Research Mentoring Award Her NSF grants include IRES-1658733 for US-Mexico Smart Cities collaboration and OAC-1835897 for the SWIM water sustainability project. The iLink Research Group under her leadership develops ontological frameworks for cross-domain data integration and trust establishment in collaborative environments.

Andreas Grotheyمشاهده پروفایل

مدرس ارشد

• Stochastic Programming
• Interior Point Methods
• Decomposition Methods
• +۵ مورد دیگر

Andreas Grothey is a Senior Lecturer in the School of Mathematics at The University of Edinburgh, a position he has held since 2011. He completed his MSc in Numerical Algebra and Mathematical Computing at the University of Dundee (1995) and his PhD in Optimization at the University of Edinburgh (2001), supervised by Ken McKinnon. His research focuses on stochastic programming, interior point methods, decomposition approaches, high-performance computing, and energy systems optimization. He has contributed to energy planning, power grid reliability, and emergency response strategies for power networks. Grothey has advised seven PhD students, including work on unit commitment, top-percentile traffic routing, and power flow optimization. His projects include the OOPS solver, CESI energy integration center, and the Structured Modelling Language (SML). Recent work addresses pandemic policy optimization and exascale computational challenges. Education: MSc in Numerical Algebra and Mathematical Computing (University of Dundee, 1995) PhD in Optimization (University of Edinburgh, 2001) Research Interests: Stochastic Programming Interior Point Methods Decomposition Methods High-Performance Computing Energy Systems Optimization Advising & Projects: PhD Supervision (7 students, 2007–2022) OOPS Parallel Solver Development CESI Energy Systems Integration SML Structured Modelling Language Labs/Teams: Member of the Edinburgh Research Group on Optimization, leading projects in power grid stability and energy planning.