جست‌وجوی استاد و پژوهشگر

Raviv Raichمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Optimization
• +۴ مورد دیگر

Raviv Raich is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University. He holds a B.Sc. and M.Sc. from Tel-Aviv University (1994, 1998) and a Ph.D. from Georgia Institute of Technology (2004). His research focuses on signal processing, machine learning, optimization, and probabilistic modeling with applications in adaptive sensing, manifold learning, and sparse signal reconstruction. He has advised numerous graduate students and contributed to impactful work in bioacoustics, hyperspectral imaging, and medical diagnostics. Notable awards include the NSF CAREER Award (2013) and multiple best paper awards. His academic leadership includes editorial roles at IEEE Transactions on Signal Processing and contributions to the IEEE Signal Processing Society. Education: B.Sc. Electrical Engineering, Tel-Aviv University (1994) M.Sc. Electrical Engineering, Tel-Aviv University (1998) Ph.D. Electrical Engineering, Georgia Tech (2004) Research Interests: Statistical signal processing and machine learning frameworks Adaptive sensing strategies for optimization in imaging and tracking Manifold learning for high-dimensional data analysis Sparse representations in signal reconstruction and inverse problems Publications: Over 100 peer-reviewed articles in top venues like IEEE Transactions on Signal Processing, IEEE Sensors Journal, and Pattern Recognition Letters, emphasizing theoretical guarantees and practical applications in bioacoustics, medical imaging, and environmental monitoring. Grants: Active funding from NSF, DARPA, and industry partnerships supporting research in optimization, multi-instance learning, and hyperspectral sensing.

Anton Dignösمشاهده پروفایل

دانشیار

• Temporal Databases
• Time Series Data
• Data Analytics
• +۳ مورد دیگر

Anton Dignös is a tenured Associate Professor at the Free University of Bozen-Bolzano, Faculty of Engineering, where he leads the Database Systems Research Group. He holds a PhD from the University of Zurich (2014) and has been a faculty member since 2014. His research focuses on temporal databases, time series data, and data analytics, with contributions to indexing, query optimization, and benchmarking tools like TSM-Bench and SEER. Education : BSc and MSc (Computer Science) - Free University of Bozen-Bolzano (2008, 2010) PhD (Dr. sc. in Computer Science) - University of Zurich (2014) Research Interests : Dignös specializes in temporal database systems, time series management, and efficient query processing. He develops novel indexing techniques (e.g., RD-INDEX) and tools for benchmarking time series systems (SEER, TSM-Bench). His work addresses challenges like anomaly detection, sparse data summarization, and parallel join algorithms. Awards : Best Paper Award at SSDBM 2023 Best Paper Award at ADBIS 2022 ACM Computing Reviews Best of Computing (2017) Teaching & Advising : Teaches Data Management and Applied Informatics courses at both BSc and MSc levels. Advises ~25 students (PhD/MSc/BSc) on topics like temporal databases and machine learning applications. Active in thesis supervision and course development. Labs/Teams : Leads the Database Systems Research Group, collaborating on projects like the Period Index for temporal data and VISOR for data visualization tools.

• Operations Research
• Finance
• Decision Analysis
• +۷ مورد دیگر

Doumpos Michalis is a Professor at the School of Production Engineering and Management (PEM) of the Technical University of Crete. His research focuses on decision analysis methodologies, financial risk assessment, sustainability criteria evaluation, and operations research applications in finance and energy sectors. He leads efforts in developing robust multicriteria decision support systems and optimization models for banking, corporate governance, and environmental policy analysis. Key research areas include portfolio optimization, credit risk modeling, ESG criteria analysis, and energy policy evaluation. His work integrates advanced analytical techniques like machine learning, robust optimization, and data envelopment analysis to address real-world challenges in finance, sustainability, and public policy. Recent studies explore the interplay between ESG metrics and corporate financial performance in energy sectors, bank failure prediction methodologies, and robust portfolio selection under uncertainty. He has contributed to policy frameworks for sustainable urban development and energy security assessments across EU countries. His publications span over 25 years, emphasizing methodological advancements in multiple criteria decision analysis (MCDA) and their applications in finance, energy, and environmental management. He collaborates with institutions globally to advance practical decision support solutions for complex socio-economic systems.

Syed Imtiazمشاهده پروفایل

استاد

• Process Control
• Process Monitoring
• Modelling and Simulation
• +۵ مورد دیگر

Syed Imtiaz is a Professor and Department Head of Process Engineering at the Faculty of Engineering, Memorial University of Newfoundland . Holding a PhD in Process Control from the University of Alberta and professional engineering credentials (P.Eng.), he specializes in Process System Safety, Risk Simulation , and Advanced Monitoring Tools for industrial applications. B.Sc., M.Sc. (BUET), M.Sc. (University of Calgary), PhD (University of Alberta) P.Eng. certification Joined Memorial University in March 2010 Research focuses on multivariate statistical tools and Bayesian belief networks for process fault diagnosis , particularly in offshore oil & gas and mineral processing industries. His work includes modeling Hydrogen Desulfurization processes and automating Managed Pressure Drilling (MPD) systems. Key article trends include: Advancing machine learning for fault detection in harsh industrial environments Developing Bayesian networks for risk assessment in offshore operations Optimizing reservoir production using entropy generation minimization Improving dynamic positioning for autonomous marine systems Scientific contributions are recognized through university awards and >30 peer-reviewed publications.

José Andrés Moreno Pérezمشاهده پروفایل

استاد

• Operations Research
• Optimization Algorithms
• Artificial Intelligence
• +۳ مورد دیگر

José Andrés Moreno Pérez is a Full Professor at the University of La Laguna, affiliated with the Department of Computer Science and Systems Engineering. His research focuses on optimization algorithms, heuristic methods, and operations research, particularly in facility location problems, variable neighborhood search, and combinatorial optimization. He holds leadership roles in the PhD programs in Mathematics and Statistics, and Industrial, Informatics, and Environmental Engineering. His work bridges theoretical advancements with practical applications in logistics, scheduling, and network design. Education: PhD in Mathematics from Universidad Complutense de Madrid (1986), thesis titled 'Localización minimax en grafos mixtos' under Dr. Francisco José Cano Sevilla. Research Interests: Development of metaheuristics (e.g., VNS, PSO), competitive location models, and algorithmic solutions for NP-hard problems. His contributions include seminal work on parallel variable neighborhood search and applications in telecommunications network optimization. Publications: Over 30 indexed publications since 1990, including influential papers on p-median problems, kernel search matheuristics, and leader-follower location models. His work has been cited over 400 times, reflecting significant impact in optimization communities. Labs/Teams: Lead researcher in the GCI (Computación Inteligente y Ciencia de Datos) group, fostering interdisciplinary projects in intelligent computing and data science.

Weijia Zhangمشاهده پروفایل

مدرس

• Causal Inference
• Machine Learning
• Weakly Supervised Learning
• +۳ مورد دیگر

Dr. Weijia Zhang is a Lecturer in Data Science and Applied Statistics at the University of Newcastle (Australia), focusing on causal inference and machine learning from ambiguous data. He holds a PhD from the University of South Australia (2018) and has held roles as Associate Professor at Southeast University (China) and Research Fellow at the University of South Australia. His research bridges causal inference and machine learning, addressing challenges in weakly supervised learning (e.g., multi-instance partial-label learning) and survival analysis under dependent censoring. Affiliations: University of Newcastle (current), Southeast University (2021–2023), University of South Australia (2018–2020) Teaching: Courses on predictive analytics, unsupervised methods, statistical decision-making, and time series forecasting Research interests include causal representation learning, weakly supervised learning from noisy/ambiguous labels, and applications in healthcare, telecommunications, and automotive industries. Key projects include developing causal methods for battery lifespan prediction with Chang'an Automobile and models for dependent censoring in survival analysis. Publications (over 30+): Focus on causal inference, weak supervision, and survival analysis. Notable contributions include CausalPLL+, MiplGp, and TEDVAE algorithms. Editor for Data Science and Engineering and reviewer for top conferences (NeurIPS, ICML, AAAI). Grants: $125k from Changan Automobile, $63.5k from National Natural Science Foundation of China, and institutional funding ($132.5k total) Awards: Top Reviewer Awards at AAAI (2021) and UAI (2022) Labs/Teams: Leads research on causal representation learning and survival analysis within the School of Information and Physical Sciences, collaborating internationally with institutions in China, Switzerland, and the US.

Wallapak Tavanapongمشاهده پروفایل

استاد

• Data Sciences
• Applied Machine Learning
• Multimedia Systems
• +۳ مورد دیگر

Wallapak Tavanapong is a Professor at Iowa State University, specializing in Data Sciences, Applied Machine Learning, and Multimedia Systems. His work integrates computational techniques with medical imaging and political science applications. He leads research in automated quality assessment for colonoscopy procedures and interpretable AI models for healthcare diagnostics. Research focuses on improving medical imaging analysis through machine learning, including real-time feedback systems during colonoscopies and developing datasets like IDCIA for cellular image analysis. He has pioneered methods for handling class imbalance in medical image classification and leveraging social media data for policy agenda analysis. His recent articles emphasize interpretable AI (e.g., CountXplain), confusion-based training strategies, and visual concept-based active learning. His work bridges technical innovation with clinical and policy applications, addressing challenges in healthcare quality and data-driven decision-making. No scientific awards are explicitly listed in the provided materials. His research has been applied in multi-center clinical trials for colonoscopy improvement and has contributed to advancements in endoscopic procedure monitoring systems.

• Quantum Computing
• Optimization Methods
• Smart Grid Technologies
• +۵ مورد دیگر

Junpeng Zhan is an Assistant Professor of Renewable Energy Engineering at the Inamori School of Engineering, Alfred University, NY, USA. He holds a Ph.D. and B.Sc. in Electrical Engineering from Zhejiang University (2014 and 2009). Before joining Alfred, he worked as a Research Associate at Brookhaven National Laboratory (2018–2020), a Postdoctoral Fellow at the University of Saskatchewan and Hong Kong Polytechnic University, and a Visiting Research Assistant at the University of Liverpool. His research focuses on quantum computing, optimization methods, smart grid technologies, machine learning, and renewable energy integration. Notable contributions include developing the Quantum Feasibility Labeling algorithm and the hybrid Variational Quantum Search, which address NP-complete problems like the Vertex Coloring problem. He currently leads NSF-funded projects on variational quantum algorithms for power system simulation and ISO-New England-funded work on quantum computing for unit commitment. Zhan is an Associate Editor of IET Generation, Transmission & Distribution and actively reviews for 20+ journals. His grants include $199,980 from the NSF (2022), $50,000 from ISO-New England (2024–2026), and a co-PI role in a $1.37M NYSERDA-funded workforce training initiative. His work bridges fundamental research and practical energy system challenges, emphasizing computational innovation and scalability. Labs and collaborations include the Engineering Laboratories at Alfred University, with partnerships extending to institutions like RIT and GE. His teaching spans courses in power systems, Python for research, and mechatronics.

Andrea M. Tonelloمشاهده پروفایل

استاد

• Power Line Communications
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Andrea M. Tonello is a distinguished academic researcher specializing in Power Line Communications (PLC), Machine Learning, and Signal Processing. His work bridges theoretical advancements with practical applications in communication systems, robotics, and smart grid technologies. He has authored over 200 publications across leading conferences and journals, with a focus on optimizing communication protocols, enhancing system robustness through noise mitigation, and applying AI techniques to solve challenges in wired/wireless networks. Research interests include: 1) Developing advanced PLC frameworks for smart grids and IoT deployments, 2) Designing machine learning algorithms for classification and signal processing under noisy conditions, 3) Investigating control systems for underactuated robotic mechanisms, and 4) Exploring full-duplex communication techniques for improved bandwidth utilization. Recent works emphasize energy-efficient sensor networks for agriculture and topology-aware learning models for PLC quality prediction. His articles consistently address cutting-edge topics like reinforcement learning for system stability, f-divergence based classification methods, and channel modeling innovations. Notable contributions include pioneering work on in-band full-duplex PLC systems and robust neural decoding techniques leveraging mutual information principles. Though no specific grants or advising information is listed here, his prolific collaboration network (evident from co-authorship patterns) indicates active participation in collaborative research initiatives. Laboratory activities focus on experimental validation of communication protocols and algorithmic performance evaluation in real-world scenarios.

• Cognitive Psychology
• Social Psychology
• Communication Studies
• +۳ مورد دیگر

Nicolas Fay is an Associate Professor at the University of Western Australia (UWA), affiliated with the School of Psychological Science. He holds academic qualifications including a BSc from the University of Stirling and a PhD from the University of Glasgow. His research focuses on cognitive psychology, social psychology, and communication studies, particularly in areas like misinformation mitigation, AI ethics, and symbolic communication evolution. Education: BSc (Stirling University), PhD (Glasgow University). Research Interests: Fay's work explores the intersection of technology and human behavior, including the impact of artificial intelligence on social perception, the evolution of symbolic communication systems, and strategies to combat misinformation. His studies often involve experimental simulations, cultural transmission models, and behavioral interventions in social media contexts. Grant Projects: He leads major research initiatives such as 'Developing Efficient Interventions to Combat Information Warfare' (2024–2026), investigating scalable solutions for disinformation. His project 'The Role of Large Language Models in Mitigating Disinformation' (2024–2025) examines AI’s dual potential as both a tool and a threat in information ecosystems. Advising & Grants: Fay has supervised 10 students and secured significant funding from institutions like the ARC Australian Research Council and UWA. His work emphasizes practical applications of psychological principles to real-world challenges like social media governance and historical myth analysis.

Emelie Fröbergمشاهده پروفایل

پژوهشگر ارشد

• Investor Behavior
• Salary Behavior
• Consumer Behavior
• +۳ مورد دیگر

Emelie Fröberg is a Research Fellow at the Stockholm School of Economics (SSE) Institute for Research (SIR), where she conducts interdisciplinary research across financial markets, consumer behavior, and academic quality assessment. Her work bridges theoretical frameworks with practical applications in investment management, gender equality studies, and sustainable consumption patterns. Dr. Fröberg's research spans four interconnected domains: Investor Behavior examines fund manager information-seeking practices, particularly regarding MiFID II unbundling effects on investment performance; Salary Behavior investigates belief updating mechanisms in labor markets and gender-based pay disparities through experimental methodologies; Consumer Behavior analyzes how online versus in-store shopping channels influence sustainable consumption patterns, especially regarding environmental and social labels; and Academic Management focuses on developing quality assessment models for research and higher education institutions. Her publication portfolio reveals a distinct trajectory from foundational work in financial accounting (2016) toward increasingly policy-relevant research on sustainable finance (2024), with methodological evolution from experimental finance to longitudinal consumer studies. Notably, her recent work demonstrates growing interdisciplinary integration, combining behavioral economics with sustainability science to address contemporary market challenges. Dr. Fröberg's research receives competitive funding from prominent Swedish foundations including Jan Wallanders och Tom Hedelius stiftelse, Tore Browaldhs stiftelse, and Hakon Swenson stiftelsen. She collaborates closely with researchers such as Jenny Säve-Söderbergh (salary behavior), Sara Rosengren (consumer behavior), and Pär Mårtensson (academic management), indicating strong institutional connections within SSE's research ecosystem. Her methodological approach combines rigorous experimental design with real-world data analysis, particularly evident in her longitudinal studies of online grocery channel adoption and sustainable consumption patterns.

• Pattern Recognition
• Applied Machine Learning
• Document Analysis
• +۷ مورد دیگر

Fischer Andreas is a Professor at HES-SO (University of Applied Sciences and Arts Western Switzerland) , specifically affiliated with the School of Engineering and Architecture of Fribourg (HEIA-FR) . His research spans Pattern Recognition , Applied Machine Learning , Handwriting Recognition , and Graph-based Methods . He has secured significant grants from TAINA Technology , Swisscom , and the Hasler Foundation for projects including automatic handwriting recognition for tax forms , Swiss German translation , and graph-based keyword spotting in Vietnamese steles . Education : BSc in Computer Science from HEIA-FR. Research Highlights : Graph Neural Networks for Büchi automata classification Annotation-free alignment in historical documents Graph edit distance optimization for keyword spotting Hybrid deep learning for Vietnamese stele analysis Graph-based tumor budding analysis in digital pathology Collaborative Impact : Fischer's work bridges historical document analysis and medical imaging , demonstrated through frameworks like DIVA-DAF and tools like GammaFocus for histopathology. His 2024 conferences suggest ongoing exploration of LLM integration in document processing and OCR-free models for information extraction.

• Computer Vision
• 3D Reconstruction
• Image Processing
• +۳ مورد دیگر

Margrit Gelautz is an Associate Professor at TU Wien, leading the research group on Image and Video Analysis & Synthesis. She holds a PhD from Graz University of Technology (1997) and conducted postdoctoral research at Stanford University's Radar Interferometry Group. Her work focuses on 3D reconstruction, stereo analysis, and applications in autonomous driving and robotics. She directs the Doctorate College 'TrustRobots' and has pioneered projects like the AlphaMatting.com evaluation system. Key roles include heading the 'Precision Livestock Farming' unit and serving as a university lecturer in Computer Vision. Education: PhD in Computer Science, Graz University of Technology (1997) Postdoctoral Research, Stanford University (1999–2001) Research Interests: 3D scene reconstruction, stereo analysis, image matting, autonomous driving systems, and robotics. Her group develops tools for 3D film production, novel view synthesis, and real-time depth estimation using structured light. Current projects include CarVisionLight (autonomous driving vision systems) and Precise3D (cost-efficient 3D scanning workflows). Grants & Awards: Interactive TV Award 2009 Microsoft Research Stipend 2007 Funding for 10 PhD positions in the 'TrustRobots' Doctorate College Labs & Teams: Her research group collaborates on EU initiatives like 3D-ConTourNet and maintains the AlphaMatting.com platform. Projects span dynamic vision sensors, event-based stereo systems, and semantic SLAM for robotics.

• Combustion Diagnostics
• Laser-induced Incandescence
• Thermometry
• +۳ مورد دیگر

Qian Wang is a Researcher at the Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU). Their research focuses on combustion diagnostics, laser-based measurement techniques, and advanced imaging systems for fluid dynamics analysis. Key areas include soot characterization, flame tomography, and the development of cost-effective diagnostic tools. Research interests encompass combustion kinetics, thermometry, and particle dynamics, with applications in propulsion systems and material science. Recent work emphasizes the use of machine learning and deep learning for data-driven diagnostics, particularly in turbulent combustion environments. Publications highlight innovations in volumetric reconstruction, time-resolved imaging, and optimization of optical systems under constrained conditions. No scientific awards are mentioned in the provided text. Qian’s work involves collaboration with engineering teams to advance combustion diagnostics through interdisciplinary approaches, though specific grants or advising roles are not detailed here.

• Algorithms
• Data Structures
• String Algorithms
• +۳ مورد دیگر

Panagiotis Charalampopoulos is a Lecturer (Assistant Professor) in the Department of Informatics at King's College London, part of the School of Computing and Mathematical Sciences. He holds a PhD in Computer Science from King's College London, an MSc in Advanced Computing from the same institution, and a BA in Mathematics from the University of Cambridge. Previously, he was a Lecturer at Birkbeck, University of London and a postdoctoral fellow at Reichman University and the University of Haifa. His research focuses on algorithms and data structures, particularly string algorithms (e.g., pattern matching, indexing) and planar graph algorithms (e.g., shortest path computations). Applications of his work include bioinformatics, text mining, and medical imagery analysis. He teaches modules such as 'Software and Programming I' and 'Problems in Mathematics.' His research has produced impactful contributions in both theoretical and applied domains, addressing challenges in efficient algorithm design and practical implementations. His work often bridges foundational computer science principles with real-world applications. Dr. Charalampopoulos' publications span topics like resilient pattern mining, efficient string processing, and planar graph optimization, reflecting his dual focus on algorithmic efficiency and practical utility. His academic journey includes research internships at the University of Warsaw and IDC Herzliya, highlighting his international collaboration and expertise in computational theory.