جست‌وجوی استاد و پژوهشگر

Nathan Spragueمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Developmental Robotics
• +۳ مورد دیگر

Nathan Sprague is a Professor of Computer Science at James Madison University (JMU), affiliated with the College of Integrated Science & Engineering. He holds a Ph.D. in Computer Science from the University of Rochester (2004) and a Sc.B. from Brown University (1997). Before his academic roles, he worked as a Software Developer at Epic Systems Corporation (1997–1998) and was an Assistant Professor at Kalamazoo College (2004–2011) before joining JMU in 2011. His research focuses on Machine Learning, Computer Vision, and Developmental Robotics, with recent work extending to autonomous systems, human-robot interaction, and AI education. Key projects include trust assessment in autonomous golf carts, Bitcoin artifact analysis via deep learning, and developing educational tools for AI curricula. Notable contributions include prototyping autonomous vehicle test-beds, assistive technologies for mobility-impaired pedestrians, and teaching innovations in robotics and computer science education. His work bridges theory with practical applications, emphasizing interdisciplinary collaboration and real-world impact.

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +۳ مورد دیگر

Hong Qin is an Associate Professor at Old Dominion University (ODU), specializing in interdisciplinary research at the intersection of artificial intelligence, bioinformatics, and public health. His work focuses on developing machine learning models for healthcare diagnostics, pandemic forecasting, and computational biology. Qin’s research integrates deep learning, federated learning, and explainable AI to address challenges in disease detection, aging mechanisms, and urban environmental analysis. Key research interests include viral evolution modeling, medical image analysis, and algorithm optimization for genomics. He has contributed to projects like the REU Site: Interdisciplinary Computational Biology (iCompBio), fostering undergraduate research in computational biology and data science. His lab develops tools such as μPolar for image analysis and Fitmix for lifespan modeling in yeast. Recent studies explore the impact of social determinants on health outcomes, AI-driven solutions for cybersecurity threats, and urban growth modeling using remote sensing. Qin’s work bridges computational methods with real-world applications in healthcare, environmental science, and public policy.

Spyros Samothrakisمشاهده پروفایل

مدرس ارشد

• Reinforcement Learning
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Dr. Spyros Samothrakis is a Senior Lecturer and Chief Scientific Adviser at the School of Computer Science and Electronic Engineering, University of Essex. His primary roles include academic research and advisory work in artificial intelligence and machine learning. He holds a PhD in Computer Science from the University of Essex, an MSc in Intelligent Systems from the University of Sussex, and a BSc in Computer Science from the University of Sheffield. His research focuses on Reinforcement Learning (RL) , Machine Learning (ML) , Neural Networks , and Role-Playing Games (RPGs) . He explores applications in areas like causal inference, game theory, and supply chain management. Recent work includes developing frameworks for automated cost estimation in games and analyzing AI agents in complex socioeconomic systems. Notable contributions include studies on self-play algorithms, causal structure learning, and the societal implications of AI. His articles span topics from neurocontrol optimization to ethical considerations in AI deployment. Dr. Samothrakis advises PhD students in computer science and has supervised research on causal effect estimation, memory-driven exploration, and text generation. He has secured grants from organizations like the Alan Turing Institute and the ESRC for projects involving AI in micro-social change and data-driven decision-making. His collaborations extend to industry partners such as the National Theatre and NHS Trusts, applying AI to healthcare and cultural data analysis. He actively contributes to conferences and publishes in top-tier journals like Neural Networks and IEEE Transactions .

Hexu Liuمشاهده پروفایل

دانشیار

• Construction Engineering and Management
• Building Information Modeling (BIM)
• Discrete-Event Simulation
• +۷ مورد دیگر

Hexu Liu is an Associate Professor in the Department of Civil and Construction Engineering at Western Michigan University. His research focuses on integrating emerging technologies like BIM, discrete-event simulation, virtual reality, and AI into construction planning and management. He holds a Ph.D. from the University of Alberta, where he also served as a post-doctoral fellow. Dr. Liu received his bachelor's and master's degrees from Shaanxi, China, and has been recognized with awards such as the Joseph D. Thompson/Zurich Canada Graduate Award and the 2017 Outstanding Reviewer from the Journal of Automation in Construction. His work emphasizes automation in construction through BIM innovations, including semantic quantity take-off and BIM-simulation integration. Research Interests: Building Information Modeling (BIM) and its applications Discrete-event simulation for construction processes AI-driven predictive maintenance and safety monitoring Offsite construction technologies Occupant-centric facility management Recent work highlights include frameworks for highway cost indices during inflation, digital twin-based fire safety systems, and human-robot collaboration in construction. He actively reviews for journals like Automation in Construction and conferences like CRC 2018. Awards and Honors: Joseph D. Thompson/Zurich Canada Graduate Award 2017 Outstanding Reviewer Award (Journal of Automation in Construction) Ernie Tromposch Graduate Scholarship Labs/Teams: Leads the DSC (Data-Driven Sustainable Construction) Lab, focused on advancing smart technologies in construction through interdisciplinary research.

Nelson Rodriguesمشاهده پروفایل

پژوهشگر

• Intelligent Manufacturing
• Reconfigurable Manufacturing Control Systems
• Artificial Intelligence
• +۵ مورد دیگر

Nelson Rodrigues is a Researcher at the Polytechnic Institute of Bragança (IPB) and a Ph.D. candidate at the University of Porto, conducting research since 2010 with involvement in H2020 PERFoRM and GO0D MAN projects, as well as FP7 ARUM and GRACE initiatives. His academic credentials include: Master of Science in Information Systems, Polytechnic Institute of Bragança His research centers on Intelligent and Reconfigurable Manufacturing Control Systems, investigating Artificial Intelligence applications for self-adaptive manufacturing evolution. Additional expertise spans Industry 4.0, Cyber-physical systems, Multiagent systems, Simulation, and Advanced Data Analytics, with emphasis on enhancing manufacturing flexibility through cutting-edge computational methods. Recent publication analysis reveals a strategic pivot toward energy systems optimization, particularly virtual power plants, utilizing high-performance computing, quantum annealing, and evolutionary algorithms. Concurrent research explores intelligent transportation frameworks, assistive robotics, and manufacturing business analytics, demonstrating interdisciplinary technical agility. He maintains active affiliations with LIACC - Artificial Intelligence and Computer Science Laboratory and the IEEE Technical Committee on Industrial Agents, contributing to over 29 publications with an h-index of 11. Research funding derives from European Union projects including H2020 PERFoRM and GO0D MAN, and FP7 ARUM and GRACE, where he developed intelligent manufacturing control architectures and service reconfiguration systems. Within LIACC and IEEE collaborations, he advances industrial agent technologies for real-time manufacturing adaptation, focusing on dynamic reconfiguration and human-in-the-loop cyber-physical production systems.

• Reinforcement Learning
• Internet of Things (IoT)
• Cognitive Robotics
• +۳ مورد دیگر

Iñaki Vazquez Gomez holds the position of Associate Professor at the University of Deusto, Spain, and is affiliated with the Deusto Institute of Technology (DeustoTech). He previously served as Director of DeustoTech from 2020 to 2023 and led the Smart Objects unit there. His research focuses on Reinforcement Learning, IoT, and Cognitive Robotics, with applications in human-robot interaction and smart environments. He earned a PhD in Computer Science and Artificial Intelligence from the University of Deusto and Lancaster University, UK. He is a member of the European IoT Council and the editorial board of the International Journal of Ambient Computing and Intelligence . He has organized and participated in conferences such as IoT, WoT, and IE, and has founded multiple technology startups. His work spans IoT protocols (e.g., WebTag, RFIDGlove), robotics software architectures, and adaptive systems. Recent research emphasizes federated learning, dynamic trajectory adjustment in robotics, and EEG-driven collaborative robotics. His publications reflect a long-term engagement with IoT challenges, context-aware systems, and embedded technologies. No scientific awards are explicitly listed. His advising record remains unspecified, though he has contributed to academic leadership roles. His office hours are 12:00–13:00 on Monday, Thursday, and Friday in Room 589-D (email required for scheduling).

Francisco Martínez Álvarezمشاهده پروفایل

استاد

• Time Series Analysis
• Big Data Analytics
• Deep Learning
• +۳ مورد دیگر

Francisco Martínez Álvarez is a Full Professor in the Computer Science Division at Universidad Pablo de Olavide's School of Engineering. He received his PhD in Computer Science in 2010, honored with the extraordinary doctoral prize. He has completed research stays at Université de Lyon, New York University, and Universidad de Chile. His research focuses on: Big data time series analysis Deep learning for forecasting Seismicity pattern recognition Explainable AI systems Recent publications cover applications in energy forecasting, environmental monitoring, and quantum machine learning. He has supervised 10 doctoral theses and co-founded the Data Science & Big Data Lab. Recognized among the world's top 2% scientists by Stanford University, he has led multiple national and European projects on big data streaming and sustainable AI.

José F. Torresمشاهده پروفایل

استادیار

• Big Data Analytics
• Time Series Forecasting
• Artificial Intelligence
• +۱ مورد دیگر

José F. Torres is an Assistant Professor at Universidad Pablo de Olavide and visiting lecturer at UNED and Instituto Politécnico de Beja. His research specializes in Big Data analytics, temporal forecasting, and deep learning applications. He has authored numerous publications in high-impact journals and participates in international research projects on earthquake resilience and environmental modeling. Research areas: Development of predictive models for energy markets Hybrid quantum-classical machine learning Urban resource consumption forecasting His recent work demonstrates strong emphasis on practical applications of AI to environmental and urban challenges, with projects including earthquake-resilient infrastructure development and water resource management. Research collaborations span multiple European countries and industries.

Lixin Shenمشاهده پروفایل

استاد

• Applied and Computational Harmonic Analysis
• Optimization
• Imaging Science
• +۴ مورد دیگر

Lixin Shen is a Professor in the Department of Mathematics at Syracuse University, part of the College of Arts and Sciences. His research focuses on applied and computational harmonic analysis, optimization, imaging science, and information processing. Shen holds a Ph.D. in Mathematics from Sun Yat-Sen University (1996), an M.Sc. from Peking University (1990), and a B.Sc. from Peking University (1987). Education: Ph.D., Mathematics, Sun Yat-Sen University, 1996 M.Sc., Mathematics, Peking University, 1990 B.Sc., Mathematics, Peking University, 1987 Research Interests: Shen’s work emphasizes sparse optimization, image and signal processing, computational mathematics, and their applications. Notable areas include wavelet analysis, tensor decomposition, and robust algorithms for noise removal and high-resolution imaging. Grants & Awards: NSF Grant: Collaborative Research: Sparse Machine Learning and Sparse Optimization (2022–2025) Excellence in Graduate Education Faculty Recognition Award, Syracuse University (2024) Air Force Summer Faculty Fellowship (2024, 2023, 2021, 2020) Service & Leadership: Chair of Graduate Committee, Mathematics Department (2024–2025) Editor, Frontiers in Applied Mathematics and Statistics Organizer, SIAM Conferences on Optimization and Imaging (2023–2020) Teaching: Recent courses include Numerical Linear Algebra, Numerical Methods with Programming, Sparse Optimization, and Partial Differential Equations.

• Control Theory
• Learning Theory
• Optimization
• +۳ مورد دیگر

Dr. Yingying Li is an Assistant Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois Urbana-Champaign, with affiliations in the Coordinated Science Laboratory, Electrical and Computer Engineering, and Mechanical Science and Engineering departments. Her research integrates control theory, learning theory, and optimization with applications in smart grids and multi-agent systems. Recent publications focus on online learning for control systems, robust system identification, adaptive control, and optimization under uncertainty. Dr. Li leads research in safe learning-based control, distributed reinforcement learning, and non-asymptotic system analysis.

• Optimization
• Deep Learning
• Photonics
• +۵ مورد دیگر

Nicolas Roy is a Researcher affiliated with the University of Namur's Faculty of Sciences, Department of Physics. He is part of the Namur Institute of Structured Matter (NISMa) and the Namur Institute for Complex Systems (NICS). His work focuses on interdisciplinary research at the intersection of computational intelligence, photonics, microscopy, and historical materials analysis. He holds a Doctorate in Sciences and has a h-index of 6 based on Scopus citations. Education: Doctorate in Sciences, University of Namur Research Interests: Nicolas Roy's research emphasizes optimization algorithms integrating fuzzy logic and particle swarm techniques, applied to photonic device engineering and microscopy. He develops data-efficient deep learning approaches for photonics applications and applies unsupervised topological methods to analyze birefringence in materials. Additionally, he pioneers spectral and imaging techniques for studying historical parchments and manuscripts, combining computational tools with archaeological and chemical analysis. His work bridges computational methodologies with practical applications in physics, optics, and cultural heritage preservation. Articles Trends: Recent publications (2021–2025) highlight advancements in adaptive optimization algorithms, microscopy imaging techniques, and interdisciplinary methods for historical document analysis. A notable trend involves leveraging topological data analysis and deep learning to solve complex problems in photonics and materials science, while his work in cultural heritage integrates cutting-edge spectroscopic tools with machine learning. Scientific Awards: Prix Victor Mersch (2019) Advising & Grants: While no formal advisees are listed, his research collaborations include visiting Stanford University (Aug–Sept 2023) and participation in the 2022 IEEE Congress on Evolutionary Computation. His work is supported by grants focused on computational intelligence and photonics engineering. Labs & Teams: He contributes to the Namur Institute of Structured Matter (NISMa) and the Namur Institute for Complex Systems (NICS), where he collaborates on projects requiring interdisciplinary expertise in materials science and complex system analysis.

• Optimization Algorithms
• Machine Learning
• Multi-objective Optimization
• +۳ مورد دیگر

Dr Angus Kenny is a Casual Academic at UNSW Canberra within the Engineering and Technology Teaching school. His research focuses on optimization algorithms, machine learning, and their applications in engineering and mining. He has contributed to advancements in multi-objective optimization, automated machine learning pipelines, and structural design problems. Key research interests include developing hybrid algorithms for optimization tasks, improving hyperparameter search in machine learning frameworks (e.g., TPOT), and addressing complex problems in mining engineering such as constrained pit optimization and production scheduling. His work bridges theoretical algorithm development with practical engineering challenges. Kenny has published extensively in conferences like GECCO and journals like Scientific Reports, focusing on evolutionary computation and optimization techniques. He has no listed scientific awards but maintains an active publication record in computational engineering and machine learning domains. No advising or grant details are provided in the available text. His research activities are centered on algorithmic innovation and their real-world applications in engineering systems.

• Machine Learning
• Signal Processing
• Quantum Computing
• +۴ مورد دیگر

Andreas Spanias is a Professor at Arizona State University specializing in Machine Learning , Signal Processing , and Quantum Computing with applications in renewable energy, healthcare, and wireless networks. His research includes Photovoltaic Fault Detection , Quantum Machine Learning , and Real-Time Energy Monitoring . Key research areas: Machine Learning, Quantum Computing, Renewable Energy Systems, Medical Imaging, Wireless Sensor Networks. Notable collaborations: Glen S. Uehara, Cihan Tepedelenlioglu, Sunil Rao, Jayaraman J. Thiagarajan. Recent Publications (2025–2024) focus on quantum machine learning for photovoltaic fault classification, Bayesian optimization in circuit design, and real-time solar array monitoring. Trends include quantum algorithms for signal processing, energy-efficient ML, and educational innovations in quantum computing. Educational Initiatives include REU programs in Quantum Machine Learning and integrating ML into signals and systems courses. He leads international collaborations like the ASU-DCU Sensors and ML Workforce Development Program.

Emrah Dönmezمشاهده پروفایل

دانشیار

• Robotics
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Emrah Dönmez is an Associate Professor at the Software Engineering Department of Bandırma Onyedi Eylül University's Faculty of Engineering and Natural Sciences. He previously held academic roles at institutions like Malatya Turgut Özal University and Inönü University. His research focuses on robotics, AI applications in logistics, cybersecurity, and medical imaging. He has led projects on autonomous robots, smart logistics, and cybersecurity systems. Education: PhD and research assistantships at Istanbul Technical University and Bingöl University. His academic contributions include co-authoring a book on accounting standards and publishing extensively in IEEE journals and conferences. He teaches courses on artificial intelligence, robotics, and logistics systems. Research highlights include developing vision-based mobile robot control systems, deep learning models for medical diagnostics, and cybersecurity frameworks. He has led over 10 projects, including smart pathology lab systems and autonomous robot navigation. Grants: TÜBİTAK-funded projects on multi-robot control and smart logistics infrastructure. Labs: Robotics and Computer Engineering labs at Bandırma University. Advising: Supervised master's theses on plant disease classification and robotics.

Srbinovska Mareمشاهده پروفایل

دانشیار

• Environmental Science
• Air Quality Monitoring
• Wireless Sensor Networks
• +۴ مورد دیگر

Srbinovska Mare is an Associate Professor at the Faculty of Electrical Engineering and Information Technologies (FEIT), Ss. Cyril and Methodius University of Skopje. She leads research in the Institute of Electrical Measurements and Electrotechnical Materials, focusing on environmental monitoring, energy efficiency, and smart sensor systems. Her work integrates meteorological data, machine learning, and green infrastructure to address air quality challenges in urban environments. Research Interests: Her primary areas of expertise include air pollution prediction using statistical models like LASSO regression, optimization of wireless sensor networks for energy efficiency, and the application of green infrastructure (e.g., green walls) to mitigate particulate matter (PM). She also explores the impact of meteorological factors on pollution dispersion and the effects of pandemic-related restrictions on urban air quality. Articles Trends: Recent publications (2020–2025) highlight advancements in low-cost air quality monitoring systems, virtual power plant analytics, and interdisciplinary approaches combining environmental science with electrical engineering. A key focus is translating sensor data into actionable strategies for sustainable urban development. Collaborations: Her team collaborates with FEIT laboratories on inter-laboratory comparisons for measurement accuracy and participates in international calibration standards. Research outputs emphasize practical applications, such as improving energy consumption in sensor networks and optimizing green infrastructure placement.