جست‌وجوی استاد و پژوهشگر

• Emergency Management
• Supply Chain Management
• Operations Research
• +۳ مورد دیگر

Professor Meng Qingchun is a second-level professor and doctoral supervisor at the School of Management, Shandong University. He leads the Digital Intelligence Management and Decision Simulation Laboratory (Class A) and focuses on emergency resource coordination, supply chain resilience, and disaster response strategies. His research has been supported by multiple National Natural Science Foundation projects and resulted in over 80 publications, including works in Transportation Research Part E , Production and Operations Management , and Risk Analysis . Key achievements include optimizing emergency material reserves, volunteer task allocation algorithms, and fairness-aware decision models. Education details are not explicitly mentioned in the provided texts, but his academic roles and publications indicate advanced training in operations research and management science. He serves as deputy secretary-general of the China Association for Optimization and chairs its Network Science Branch. His work bridges theoretical advancements with practical applications in disaster management and public policy. Research interests emphasize emergency governance, collaborative decision-making mechanisms, and government-enterprise partnerships. Recent projects address volunteer scheduling efficiency, risk integration under uncertainty, and loan subsidy policies post-disasters. His team’s work has been published in top-tier journals like Production and Operations Management (UTD 24) and Risk Analysis (ABS 4). Scientific awards include the National Teaching Achievement Award (2nd Prize) and Shandong Provincial Social Science Award (1st Prize). He has secured grants totaling four National Natural Science Foundation key/general projects and a National Social Science Foundation key project. His lab collaborates with industry leaders in manufacturing and public health, applying optimization theories to real-world challenges.

• Maritime Informatics
• Autonomous Systems
• Big Data Analytics
• +۴ مورد دیگر

Associate Professor Dimitris Zisis leads the Smart Move Laboratory at the University of the Aegean's Department of Product & Systems Design Engineering, focusing on autonomous vessels and marine robotics. His research spans maritime informatics, trajectory analytics, and Big Data applications. He has published over 80 works with 3,900+ citations, including a top-0.1% cited paper on cloud computing security. As a Horizon Europe National Representative for Cluster 4 (Digital, Industry, Space), he advises EU digital policy. He previously led BDVA's Data Science/AI Task Force and participated in EU High-Level Expert Groups on B2G data sharing. He chairs the IEEE's maritime systems committees and edits Elsevier's Future Generation Computer Systems journal. Projects: Horizon 2020 VesselAI, INFORE, BigDataOcean Awards: Top 2% scientist in Information Systems (2019-2021) Memberships: IEEE Senior Member, Hellenic Institute of Marine Technology Fellow His lab develops advanced maritime systems, including the Accu-Waves platform for port safety and the Ro-Boat Race for autonomous vessel innovation. Recent work emphasizes federated learning for vessel location forecasting, scalable maritime event detection, and digital twin applications.

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +۳ مورد دیگر

Timea Bezdan is a researcher at Singidunum University, specializing in artificial intelligence, machine learning, and optimization algorithms. She holds a doctoral degree in Advanced Protection Systems (2021-2024) from Singidunum University, following master's degrees in Contemporary Information Technologies (2020-2021) and bachelor's degrees in Software and Data Engineering (2017-2020) and Marketing and Management (2013-2017). Her research focuses on applying AI to environmental science, healthcare, and computational efficiency, with notable work on air pollution modeling, disease detection, and algorithm optimization. Key research interests include neural network architecture design, hybrid metaheuristic approaches, and bias mitigation in AI systems. Recent projects address demographic bias in age estimation models and energy-efficient cloud-edge computing. Her work spans interdisciplinary collaborations, integrating machine learning with environmental monitoring and medical diagnostics. Education: Doctoral studies: Advanced Protection Systems, Singidunum University (2021–2024) Master's: Contemporary Information Technologies, Singidunum University (2020–2021) Bachelor's: Software and Data Engineering, Singidunum University (2017–2020) Bachelor's: Marketing and Management, Singidunum University (2013–2017) Publications highlight contributions to AI-driven environmental analysis, healthcare applications (e.g., Parkinson’s disease detection), and optimization techniques for neural networks. Her work often combines metaheuristic algorithms like firefly, bat, and particle swarm optimization with machine learning frameworks. Research groups and collaborations include Singidunum University’s Institute for Applied AI, with active participation in international conferences such as IEEE Symposia and Sinteza. Ongoing projects focus on explainable AI for emotion recognition and AI-based crisis analysis of environmental pollutants.

Luis Ricardez-Sandovalمشاهده پروفایل

دانشیار

• Process Systems Engineering
• Multiscale Modelling
• Computer-Aided Catalyst and Materials Design
• +۷ مورد دیگر

Luis Ricardez-Sandoval is an Associate Professor in the Department of Chemical Engineering at the University of Waterloo, Canada. He holds the Tier II Canada Research Chair in Multiscale Modelling and Process Systems. His research focuses on developing theoretical and computational tools for optimizing chemical processes, with a strong emphasis on CO2 capture technologies, energy systems, and heterogeneous catalysis. His work integrates multiscale modeling, machine learning, and advanced control strategies to enhance process efficiency and sustainability. Education : Doctorate in Chemical Engineering, University of Waterloo (2008) Master of Science in Chemical Engineering, Instituto Tecnologico de Celaya (2000) Bachelor of Science in Chemical Engineering, Instituto Tecnologico de Orizaba (1997) Research Interests : Process Systems Engineering and Multiscale Modelling Computer-Aided Material Design for CO2 Utilization Advanced Process Control and Uncertainty Management Optimal Integration of Process Design, Control, and Scheduling Energy Systems and CO2 Capture Technologies Selected Awards : 2024 Canadian Society of Chemical Engineering D.G. Fisher Award 2017 Canada Research Chair (Tier II) 2015 Early Researchers Award (Ontario) Teaching : CHE 341: Introduction to Process Control CHE 521: Process Optimization CHE 522: Advanced Process Dynamics and Control Labs & Collaborations : Ricardez-Sandoval collaborates with industry and governments to advance carbon reduction technologies. His research group has partnerships with institutions like CONACyT (Mexico), China Scholarship Council, and Tampere University (Finland). Key projects include optimizing aquaculture systems, chemical looping combustion, and catalyst design.

Yuanyuan Yangمشاهده پروفایل

استاد

• Quantum Computing and Networking
• Edge Computing
• Machine-Learning Enabled Computing
• +۳ مورد دیگر

Yuanyuan Yang is a distinguished professor at Stony Brook University's Department of Electrical and Computer Engineering and Computer Science, part of the College of Engineering and Applied Sciences. She holds a BEng and MS from Tsinghua University and a PhD from Johns Hopkins University. Her research focuses on quantum computing, edge computing, machine-learning enabled systems, data center networks, wireless networks, and distributed systems. She is an IEEE Fellow and National Academy of Inventors Fellow, with significant contributions to interconnection networks and patents in networking technologies. Yang has led NSF programs, served as Associate Dean, and directed research labs. Her work bridges theoretical advancements and practical applications in high-performance computing and networking. Education: BEng, Tsinghua University (China) MSE, Johns Hopkins University PhD, Johns Hopkins University Research Highlights: Explores cutting-edge topics like quantum networking, edge computing architectures, and machine learning integration. Her work emphasizes scalable, efficient systems with applications in distributed computing and cloud infrastructure. Awards: Fellow, IEEE (2009) Fellow, National Academy of Inventors (2022) Best Paper Awards at IEEE GLOBECOM 2024, IEEE INFOCOM 2020 Outstanding Service and Leadership Awards in IEEE Grants & Sponsorship: Supported by NSF and Army Research Office grants. Her research addresses challenges in edge-cloud continuum, energy-efficient networks, and quantum computing infrastructure.

Simon Knightمشاهده پروفایل

مدرس

• Epistemic Cognition
• Learning Analytics
• Educational Technology
• +۳ مورد دیگر

Dr. Simon Knight is a Lecturer at the University of Technology Sydney , specializing in epistemic cognition , learning analytics , and educational technology . His work bridges philosophy, psychology, and digital education to examine how individuals conceptualize knowledge and navigate information in the age of search. Key Research Areas : Epistemic literacy, source evaluation, collaborative learning tools, and analytics-driven pedagogy. Recent Projects : Development of performance assessments for multiple-document processing, analysis of epistemic commitments in online tasks, and integration of writing analytics with epistemic features. Methodologies : Mixed-method studies combining chat logs, pageview tracking, and discourse analysis to map learner cognition. Selected Publications explore epistemic uncertainty, search engine tasks, and science communication challenges. Knight emphasizes meta-discourse (e.g., source critiques, date considerations) and exploratory talk as critical for modern learners.

• Network Science
• Robustness and Resilience of Complex Networks
• Dynamics on Networks
• +۳ مورد دیگر

Shreyas Sundaram is the Marie Gordon Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His primary affiliations include the Department of Electrical and Computer Engineering at Purdue University's West Lafayette campus. He holds a PhD from the University of Illinois at Urbana-Champaign (2009), an MS from the same institution (2005), and a BASc in Computer Engineering from the University of Waterloo (2003). His research focuses on network science, robustness of complex systems, dynamics on networks, fault-tolerant control, and graph theory. Key interests include secure control systems, distributed algorithms, and resilience in cyber-physical systems. His work spans applications in epidemic modeling, sensor networks, and multi-agent systems. Recent contributions include frameworks for epidemic control, resilient distributed optimization, and reinforcement learning for energy systems. He has developed methods for distributed state estimation, adversarial attack mitigation, and resource allocation in interdependent infrastructures. His research has been applied to real-world challenges such as autonomous vehicle charging, disaster recovery planning, and cybersecurity in multi-robot systems. Notable publications address topics like SIR epidemic mitigation, Byzantine-resilient algorithms, and sensor scheduling in adversarial environments. Sundaram's work integrates theoretical rigor with practical applications, emphasizing cross-disciplinary approaches to complex systems. His ongoing efforts aim to enhance the reliability and security of networked systems through mathematical modeling and control strategies.

Noshir Contractorمشاهده پروفایل

استاد

• Network Science
• Computational Social Science
• Web Science
• +۳ مورد دیگر

Noshir Contractor is a distinguished Professor of Behavioral Sciences in the McCormick School of Engineering, School of Communication, and Kellogg School of Management at Northwestern University. He holds the Jane S. and William J. White endowed chair and is affiliated with the Science of Networks in Communities (SONIC) research group. His work bridges network science, computational social science, and web science, with applications in business, healthcare, and space exploration. Educated at the University of Southern California (PhD, MA in Communication) and the Indian Institute of Technology Madras (BTech in Electrical Engineering), he has over 30 years of NSF-funded research. Research focuses on how social and knowledge networks form and perform in complex systems. Notable contributions include the book Theories of Communication Networks (co-authored with Peter Monge), recognized with the 2003 Book of the Year Award and 2021 Fellows Book Award. He hosts the podcast Untangling the Web , exploring societal-technology interactions. Awards include Fellowships from ACM, AAAS, and ICA, alongside distinguished scholar and service awards. Professional service includes editorial roles across journals like Network Science and Journal of Communication , and leadership as ICA Past President. His recent work addresses team dynamics in space missions, AI integration in teams, and computational models for innovation and decision-making.

• Machine Learning
• Artificial Intelligence
• Fraud Detection
• +۷ مورد دیگر

Bart Baesens is a full professor at the Faculty of Economics and Business (FEB) at KU Leuven, affiliated with the Information Systems Engineering Research Group (LIRIS). He is a member of Leuven.AI, the university's artificial intelligence institute, and leads the education commission for the Master in Information Management program at FEB. His research focuses on machine learning, fraud detection, credit risk, network analytics, and explainable AI, with applications in finance, insurance, agriculture, and smart systems. Baesens is known for developing frameworks like IML4DQ and GARG-AML, and has contributed to foundational work in sensor data analysis and graph-based learning for AML systems. His recent research emphasizes practical applications of AI, such as improving museum archive accessibility via large language models and enhancing agricultural sustainability through precision irrigation systems. He collaborates with industry partners on projects like predictive maintenance optimization and customer lifetime value modeling, leveraging techniques like temporal fusion transformers and self-supervised anomaly detection. Baesens' work bridges academia and industry, with publications addressing ethical AI, model interpretability, and real-world fraud detection challenges. His contributions span over 150 peer-reviewed articles, including influential studies on credit scoring, social network analytics for insurance fraud, and multi-task learning for environmental monitoring.

Dong Wangمشاهده پروفایل

دانشیار

• Social Sensing
• Human-Centered AI
• AI for Social Good
• +۹ مورد دیگر

Roles and Affiliations: Associate Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, affiliated with the Siebel School of Computing and Data Science, Illinois Informatics, and multiple research centers including NCSA and CSBS. Director of the Social Sensing & Intelligence Lab. Education: PhD in Computer Science from University of Illinois Urbana-Champaign. Research Focus: Social sensing, human-centered AI, responsible AI, edge computing, and smart cities. Develops systems that integrate human and machine intelligence for societal challenges like disaster response, healthcare, and environmental sustainability. Authored two books on social sensing and edge computing. Recent Trends in Publications: Focuses on AI fairness, crowd-AI collaboration, misinformation detection, and federated learning applications. Recent work addresses demographic bias mitigation in AI models and symbiotic learning frameworks for healthcare policy adherence. Awards and Honors: NSF CAREER Award, ARO YIP Award, Google Faculty Award, and multiple teaching excellence recognitions. Senior Member of IEEE. Grants and Projects: Active projects include metacognitive calibration interventions, disaster scene assessment systems, and environmental contaminant modeling. Completed work includes smart water crowdsensing and health policy projects. Labs and Teams: Leads the Social Sensing & Intelligence Lab, collaborating on interdisciplinary initiatives across computer science, informatics, and environmental science.

Yuxin Chenمشاهده پروفایل

استادیار

• Interactive Learning Systems
• Machine Learning
• Machine Teaching
• +۷ مورد دیگر

Yuxin Chen is an Assistant Professor in the Department of Computer Science and a faculty member of the Committee on Computational and Applied Mathematics (CCAM) at the University of Chicago. His research focuses on interactive learning systems, machine learning, and machine teaching with applications in optimization, reinforcement learning, and multi-fidelity modeling. PhD in Computer Science from ETH Zurich (2017) MS in Computer Science from the University of Kansas (2011) His work spans Bayesian optimization, active learning, and policy improvement algorithms, addressing challenges in data-efficient decision-making and robustness. Recent publications highlight advancements in constrained optimization, neuromorphic computing, and sustainability applications. Scientific awards include the Best Paper Award at ICML Workshop on Constructive Machine Learning (CML), 2015 and the MSDE Recent HOT Article, 2025 . He actively seeks PhD students for his research group.

William Regliمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Computational Modeling
• +۹ مورد دیگر

William Regli is a Professor at the University of Maryland's Clark School of Engineering, holding appointments in Computer Science, Electrical and Computer Engineering, and the Institute for Systems Research. He also directs the Applied Research Laboratory for Intelligence and Security (ARLIS), overseeing over 75 researchers focused on defense and intelligence challenges. Regli's career spans academia, government leadership (including DARPA's Defense Sciences Office), and industry, with over 250 publications and five foundational U.S. patents in 3D CAD search. His research integrates AI, robotics, and computational modeling to address interdisciplinary problems in engineering, materials science, and national security. Education: Ph.D. and B.S. in Mathematics from the University of Maryland and Saint Joseph's University, respectively. He is a Fellow of AAAS and IEEE, recognized for contributions to 3D search and intelligent manufacturing. Research interests include AI agents, cyber-infrastructure for engineering data sustainability, advanced materials design, and robotic interoperability using category theory. His recent work emphasizes applying AI to national security, including trustworthy AI evaluation and social science integration. Awards include the DARPA Meritorious Public Service Medal (2018), AAAS Fellowship (2020), and IEEE Fellowship (2017). His articles reflect a focus on multi-agent systems, robotic task planning, and formal methods in autonomous systems. He advises multiple PhD students and has spun off two tech startups. Regli's laboratories include ARLIS, where he develops solutions for defense missions, and collaborates with the Maryland Robotics Center. His work bridges academia and government, emphasizing real-world impact through innovative computing research.

Rodney Howellمشاهده پروفایل

دانشیار

• Real-Time Systems
• Self-Stabilization
• Petri Nets
• +۵ مورد دیگر

Rodney Howell is an Associate Professor and Undergraduate Programs Director in the Department of Computer Science at Kansas State University (KSU). He holds a Ph.D. (1988) and B.S. (1984) in Computer Science from the University of Texas at Austin and Wichita State University, respectively. His research expertise includes Real-Time Systems, Self-Stabilization, Petri Nets, and Computational Complexity. He authored the textbook Algorithms: A Top-Down Approach (2023) and has directed seven Master’s theses. Key awards include the Kansas State Scholarship (1980–1984), McGregor Scholarship (1980–1984), and Microelectronics and Computer Development Fellowship (1984–1986). He has organized the KSU High School Programming Contest since 1992 and contributed to grants such as the NSF-funded Software Control Laboratory (1993–1995). His advising includes students like Muralidhar Venkatrao, Thiagarajan Rajagopalan, and Vineet Tadakamalla. His work spans over 30 years at KSU, including roles like Special Assistant to the Dean (2019–2020) and involvement in academic outreach.

• Computer Science
• Data Analytics
• Software Engineering
• +۱ مورد دیگر

Dave Voorhis is a Lecturer in Computing at the University of Derby's Department of Electronics, Computing and Mathematics. His research bridges computer science and environmental applications, with expertise in data processing architectures, game design evolution, and cloud-based computational frameworks. Research interests focus on relational database systems , big data analytics in educational contexts , and computational methods for environmental monitoring . His work demonstrates consistent innovation in applying computing principles to solve practical problems across diverse domains. Publication analysis reveals sustained contributions to: Satellite data processing techniques for climate science Foundational database architecture research Pedagogical approaches for data science education This interdisciplinary output establishes significant connections between computational theory and real-world implementations.

Bijan Jabbariمشاهده پروفایل

استاد

• Wireless Networks
• 5G and LTE Systems
• IoT
• +۵ مورد دیگر

Bijan Jabbari is a Professor in the Department of Electrical and Computer Engineering at George Mason University, affiliated with the Volgenau School of Engineering. He holds a PhD from Stanford University and has dual MS degrees in Electrical Engineering and Engineering Economics from Stanford, along with a BS from Arya-Mehr University. His research focuses on wireless networks, IoT, machine learning applications in networking, and cognitive networks. Notable contributions include work on LTE/5G systems, edge computing, and spectrum management. He has led projects funded by the National Science Foundation, Naval Research Laboratory, and KDDI Corporation. Jabbari is a Fellow of IEEE and IET, recipient of the IEEE Third Millennium Medal, and has pioneered initiatives like the Heart’s Delight charity event. Jabbari teaches graduate courses including ECE 528 (Random Processes), ECE 642 (Computer Network Design), and ECE 629 (Wireless Networks). He directs the Communications and Networks Laboratory and collaborates with Telecom ParisTech. His work emphasizes resilient network design, cross-layer protocols, and machine learning-driven optimization in telecommunications. Research Grants: Secure MAC Layer Protocols (NRL, 2016–2018) Cross-Layer Resilient Networking (NRL, 2012–2015) Network Virtualization (KDDI, 2010) Awards: Washington DC Engineer of the Year Award GMU Outstanding Faculty Research Award Labs/Teams: Communications and Networks Lab (CNL) at GMU Collaborations: Telecom ParisTech (France)