جست‌وجوی استاد و پژوهشگر

• Mechanical Design
• Robotics
• Uncertainty Modeling
• +۶ مورد دیگر

Nicolas Binaud is a Lecturer at Paul Sabatier University (UPS), where he is a member of the Modeling of Mechanical Systems and Microsystems group (MS2M) at Espace Clément Ader in Toulouse, France. His research focuses on multidisciplinary design in uncertain environments with emphasis on robustness, reliability, and sensitivity analysis of mechanical systems. His research interests span across: Multidisciplinary design under uncertainty Modeling of uncertainties and variations of design variables Sensitivity analyses for mechanical systems Development of performance indices for preliminary design Mechanical and computer-aided design Digital tools for mechanics Probability and reliability engineering Dr. Binaud's scholarly work demonstrates expertise in mechanical systems design with particular strength in cable-driven parallel robots, structural reliability, and predictive maintenance. His research shows strong interdisciplinary connections between mechanical engineering, aerospace applications (particularly airframe maintenance), and materials science (especially electrostatic interactions in microsystems). Analysis of his publication record reveals consistent output with recent work focusing on sensitivity analysis of robotic systems and electrostatic characterization techniques. His notable contributions include: Development of comprehensive methods for sensitivity analysis of cable-driven parallel robots Research on model-based prognostics for fatigue crack growth in aircraft structures Studies on 3D charge localization in thin dielectric materials using atomic force microscopy Applications of Kalman filtering techniques for structural health monitoring Cost-driven optimization of predictive maintenance policies for aerospace structures

Andrew M. Rossمشاهده پروفایل

استاد

• Mathematical Modeling
• Operations Research
• Optimization
• +۴ مورد دیگر

Andrew M. Ross is a Professor in the Mathematics and Statistics Department at Eastern Michigan University, where he has served since Fall 2006. He holds dual roles as Data Science and Analytics Advisor and Mathematics Advisor. His expertise spans mathematical modeling, operations research, optimization, and queueing theory, with applications in telecommunications, power systems, and education. Dr. Ross earned his Ph.D. in Industrial Engineering and Operations Research from UC Berkeley in 2001, with minors in Statistics and Transportation. His research interests focus on mathematical modeling of real-world problems, particularly in optimization and stochastic systems. He has made significant contributions to queueing theory with daily cycles and uncertain parameters, as well as applications in electrical power grid systems and telecommunications. He actively trains the EMU Math Contest in Modeling (MCM) team and has developed educational materials for future math teachers through the NSF-sponsored MODULE(S2) project. His publication record demonstrates a strong trajectory from theoretical queueing research to practical applications across diverse fields including telecommunications, power systems, supply chain management, and game theory. His work shows consistent focus on modeling real-world stochastic systems with time-dependent parameters, bridging theoretical mathematics with practical engineering applications. Outstanding Teacher, ISE Dept. (2004-2006) INFORMS Telecom Dissertation Contest Honorable Mention (2002) IEOR Teacher of the Year, UC Berkeley (2000) Chavin Prize for Mathematical Writing, Harvey Mudd College (1996) Dr. Ross has supervised numerous graduate and undergraduate students, including three Ph.D. students. He has secured research funding as Co-PI on NSF grants including "Integrating the Engineering Curriculum" ($100,000) and "Ways and Means, 1935" ($5,000). His service includes coordinating the EMU Numb3rs Club, serving on multiple university committees, and judging regional science fairs. He is also active in professional organizations including the MAA and INFORMS.

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

Saber Mahboubi Heydarabad serves as a Lecturer in Robotics and Automation at the University of Salford within the School of Science, Engineering & Environment and is affiliated with the Centre for Future Engineering. His educational background includes a PhD in Robotics from the University of Salford funded by the Marie Curie program. Research interests span robotics, artificial intelligence, machine learning, control systems, autonomous vehicles, and industrial automation, with specialized focus on robotic manipulation, grasping technologies, and bioinspired mobility solutions. Analysis of his 2016-2018 publications reveals consistent innovation in adaptive control for robotic grasping under uncertainty and bioinspired locomotion mechanisms, addressing challenges in stable manipulation and environmental maneuverability through variable stiffness actuation and sliding mode control. Dr. Mahboubi has secured significant funding from the European Union, EPSRC, and Innovate UK for projects including autonomous self-driving systems and in-space manipulation technologies, with current involvement in the Advanced Manufacturing Institute (2023-2026) and electric vehicle drivetrain development. He actively contributes to the Centre for Future Engineering and collaborates on initiatives like the Advanced Manufacturing Institute, focusing on advanced robotics applications for industrial and nuclear decommissioning contexts.

Giselher Pankratzمشاهده پروفایل

استاد

• Combinatorial auctions
• Heuristic approaches to solving decision-making problems in business
• Multi-objective optimisation
• +۴ مورد دیگر

Professor Giselher Pankratz serves as Head of University at the Deutsche Bundesbank University of Applied Sciences, where he teaches IT and IT management, process management, and payments systems. His courses include SAP exercises, IT project management, cashless payment instruments, bank management business games, and payment transaction case studies. He earned his economics degree specializing in business informatics from the University of Siegen and completed his PhD at FernUniversität in Hagen under Professor Hermann Gehring, receiving the Stinnes Logistics Award in 2003 for his dissertation. His research spans combinatorial auctions, heuristic decision-making approaches, multi-objective optimization, process management, requirements analysis patterns, cyber laundering technology, and Distributed Ledger Technologies. Professor Pankratz's publication history reveals an evolution from logistics optimization (2000-2010) toward blockchain applications (2019), with consistent focus on combinatorial auctions and transportation systems. His work bridges theoretical operations research with practical financial technology applications, particularly in payment systems and distributed ledgers. Stinnes Logistics Award 2003 (for doctoral dissertation) As a peer reviewer for international journals and coordinator of research projects sponsored by Germany's Federal Ministry of Economics and Technology, he has lectured in Bachelor's and Master's programs at Hagen Institute for Management Studies and Danube University Krems. His academic leadership extends to editing works like 'Intelligent Decision Support' (2008) and 'Intelligente Systeme zur Entscheidungsunterstützung' (2008). While specific lab affiliations aren't detailed, his research involved collaborations at FernUniversität in Hagen and practical industry projects, particularly in transportation logistics and payment systems development.

Jordan Gleesonمشاهده پروفایل

پژوهشگر

• Robotics
• Collision Avoidance Systems
• Maritime Robotics
• +۳ مورد دیگر

Jordan Gleeson is a PhD Researcher at the Centre for Robotics within Queensland University of Technology (QUT) . His research focuses on collision avoidance systems for robotic boats, particularly integrating the International Regulations for Preventing Collisions at Sea (COLREG) with advanced algorithms. He addresses challenges such as regulatory compliance, uncertainty mitigation via sensor fusion, and predictive planning for autonomous maritime navigation. His work bridges robotics engineering with maritime law, emphasizing real-world applicability of collision avoidance methodologies. Research interests include autonomous systems, sensor data integration, and regulatory frameworks for unmanned vehicles. Recent articles explore COLREG scenario classification, multi-vessel collision avoidance dynamics, and comparative analysis of reactive methods in complex environments. His contributions advance safe navigation strategies for uncrewed surface vehicles (USVs). Labs/Teams: Active member of QUT's Centre for Robotics, collaborating on maritime autonomy projects.

• Robotics
• Control Systems
• Multi-Agent Systems
• +۶ مورد دیگر

Dr. Hongjun Yu is a Postdoctoral Researcher at the Australian Centre for Field Robotics (ACFR), part of the Faculty of Engineering at The University of Sydney. His research focuses on advanced control systems for multi-agent and multi-robot systems, particularly in areas like formation control, collision avoidance, and autonomous vehicle coordination. He has contributed to methodologies addressing environmental adaptability, stochastic scheduling, and energy efficiency in robotics. His work integrates geometric path planning, event-triggered mechanisms, and distributed control strategies to enhance coordination and robustness in complex systems. Key applications include underwater vehicles, two-wheel vehicles, and multi-agent formations under dynamic environmental conditions. Dr. Yu's publications span journals like IEEE Transactions on Systems, Man, and Cybernetics, and conferences such as the IEEE International Conference on Systems, Man, and Cybernetics. His articles emphasize real-world challenges like hazard avoidance, topology manipulation, and stealth mode control. He is affiliated with the ACFR, a leading center in robotics innovation, and collaborates on projects involving environmental contours analysis and irregular robot systems. While no specific awards are listed, his research has consistently addressed cutting-edge problems in autonomous systems.

Sanjay Krishnanمشاهده پروفایل

استادیار

• Data Science
• Databases
• Machine Learning
• +۵ مورد دیگر

Sanjay Krishnan is an Assistant Professor of Computer Science at the University of Chicago. His research focuses on managing uncertain data in databases and information systems, including accuracy guarantees in incomplete databases, query evaluation under corruption, and data leakage detection. He leads the ChiDATA research group within the University of Chicago's Department of Computer Science, collaborating with researchers like Michael Franklin and Raul Castro Fernandez on large-scale data analysis and systems. His academic work spans foundational areas such as AI/ML integration with data systems, scalable data processing, and resource-efficient database design. He is affiliated with the CERES Center for Unstoppable Computing and the Systems Group, exploring resilient computing systems and software engineering challenges. His lab's research has been featured in UChicago CS news for innovations like geometric database query processing and interdisciplinary collaborations with the Museum of Science & Industry. Key contributions include frameworks like EdgeServe for decentralized model serving, CrocodileDB for resource-efficient query execution, and ActiveClean for interactive data cleaning in ML pipelines. His work bridges theoretical computer science with practical system implementations, emphasizing real-world data challenges in IoT, healthcare, and environmental monitoring.

Kevin Verbeekمشاهده پروفایل

استادیار

• Computational Geometry
• Graph Drawing
• Information Visualization
• +۳ مورد دیگر

Kevin Verbeek is an Assistant Professor in the Applied Geometric Algorithms group at Eindhoven University of Technology (TU/e), affiliated with the EAISI Foundational research institute. His research focuses on computational geometry, information visualization, and algorithms for cartographic challenges. He holds a PhD from TU/e (2012) and conducted postdoctoral research at UC Santa Barbara (2012–2014). He coordinates the TU/e Honors Academy's 'Competitive Programming and Problem Solving' track and is a member of the Eindhoven Young Academy of Engineering. Education: PhD in Applied Geometric Algorithms, TU Eindhoven (2012) Postdoctoral Researcher, UC Santa Barbara (2012–2014) Research Interests: Algorithmic stability for dynamic data visualization Geometric algorithms for social networks and cartography Topological data analysis and computational topology Braided river network modeling Key Projects: NWO Veni Award-funded project on stable geometric algorithms (2015–present) GLAMMap: Interactive geo-visualization for cultural heritage metadata Teaching & Outreach: Coordinator of Honors tracks in Competitive Programming Instructor for courses like 'Heuristic Algorithms' and 'Computer Science Research Project' Awards: NWO Veni Award for 'Stable Geometric Algorithms' (2015) Labs/Teams: Active in EAISI Foundational, focusing on algorithmic spatial data analysis and foundational AI research.

Prashant Doshiمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Multiagent Systems
• +۶ مورد دیگر

Prashant Doshi is a Professor of Computer Science at the University of Georgia's School of Computing. He leads the THINC Lab , focusing on AI and Robotics, particularly multiagent decision-making frameworks like the Interactive POMDP (I-POMDP) . His work bridges game theory and decision theory, addressing challenges in uncertain, multiagent environments. Notable contributions include applications of I-POMDPs in counter-terrorism, defense simulations, and human behavior modeling. Doshi has published over 150 papers and holds editorial roles at Springer's Journal of AAMAS . Research interests span inverse reinforcement learning (IRL) , robotic learning from observation, SLAM in occluded settings, and semantic Web technologies. Awards include UGA’s Creative Research Medal (2011) and NSF CAREER Award (2009). He has been recognized three times with the CS Department’s Outstanding Faculty Research Award (2009, 2012, 2018). Key Projects: Open Human-Robot Collaboration Systems, I-POMDP framework development, cybersecurity intent recognition. Grants: NSF CAREER Award, Collaborative Research grants on multiagent systems. Labs: THINC Lab (focusing on human-robot interaction and decision-making). Recent work explores open multiagent systems , where agents dynamically join/leave, and occluded IRL for robots learning from partial observations. His research has practical applications in disaster response robots, healthcare resource allocation, and autonomous vehicle decision-making.

Jens Albertsمشاهده پروفایل

پژوهشگر

• Enterprise Information Management
• Business Intelligence
• Collaboration Systems
• +۳ مورد دیگر

Jens Alberts is a Researcher in the Enterprise Information Management group at the University of Koblenz, under the supervision of Prof. Williams. He holds a Bachelor of Science in Business Administration from Aachen University of Applied Sciences and a Master's in Business Information Systems from the University of Münster. His work bridges academic research with industry practice, focusing on collaboration technologies, digital transformation, and enterprise systems. Education: Bachelor of Science in Business Administration, Aachen University of Applied Sciences Master's in Business Information Systems, University of Münster Research interests include collaboration software, work practices in distributed teams, narrative networks, enterprise architecture, and business intelligence. His recent work emphasizes analyzing digital traces in collaboration systems and developing frameworks for technology adoption. Publications span topics like enterprise systems, sales planning under uncertainty, and business analytics, reflecting a multidisciplinary approach to solving modern organizational challenges. He has practical experience as an intern, working student, and business analyst in the pharmaceutical and life sciences industries. He also served as a student assistant under Prof. Dr. Eggert (Aachen) and Jun.-Prof. Dr. Dennis Riehle (Münster).

Michael Carbinمشاهده پروفایل

دانشیار

• Programming Languages
• Software Engineering
• Probabilistic Programming
• +۵ مورد دیگر

Michael Carbin is an Associate Professor at the Massachusetts Institute of Technology in the Department of Electrical Engineering and Computer Science, where he leads the Programming Systems Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research spans programming languages, software engineering, systems, and security with a focus on language-driven systems that operate in uncertain environments including perception, neural networks, and unreliable hardware. Carbin's research interests center on programming languages and systems that operate effectively in uncertain environments. His work spans probabilistic programming, quantum computing, neural network optimization, and approximate computing. He investigates how to design language-driven systems that can handle uncertainty in perception, implementation (neural networks or approximate transformations), and execution (unreliable hardware). His research bridges theoretical foundations with practical systems implementation, often developing novel programming models, compilers, and runtime systems. His publication record shows consistent contributions across multiple domains including programming languages (PLDI, POPL), machine learning (ICLR, NeurIPS), and systems (ASPLOS, OSDI). Recent work focuses on neural network pruning, quantum programming, probabilistic inference, and compiling programs to neural networks. His research demonstrates strong interdisciplinary connections between programming languages, machine learning, and systems. Carbin has received numerous prestigious awards including the Sloan Research Fellowship (2020), NSF CAREER Award (2018), and multiple best paper awards at top conferences including ICLR (2019), OOPSLA (2014, 2013), and ICFP (2019). He has also been recognized with the MIT Frank E. Perkins Award for Excellence in Graduate Advising (2020). Carbin actively serves the academic community as a program committee member for major conferences including MLSys (as Program Co-Chair in 2023), POPL, PLDI, and NeurIPS. He has advised numerous graduate students who have gone on to positions at leading technology companies and research institutions. His Programming Systems Group at CSAIL focuses on creating robust, efficient systems that can handle uncertainty across multiple dimensions of computing.

• Robotics
• Motion Planning
• Multi-robot Systems
• +۳ مورد دیگر

Liding Zhang is a Ph.D. candidate and researcher at the Chair of Robotics, Artificial Intelligence and Real-Time Systems at the Technical University of Munich (TUM), supervised by Prof. Alois Knoll. His work focuses on advanced robotics with emphasis on motion planning and multi-robot systems. His educational background includes: Master’s degree in Mechanical Engineering and Automation Technology from Technical University of Clausthal (2022) Bachelor’s degree in Mechanical Engineering from Rhine-Waal University of Applied Sciences (2020) Zhang's research centers on sampling-based asymptotically optimal motion planning, high-DOF multi-robot manipulation, coordinated control of mobile robot fleets, and real-time performance optimization. His work explores geometric reasoning in complex robotic systems, addressing challenges in manipulation, navigation, and control through innovative algorithmic approaches that balance computational efficiency with solution quality. Key contributions include adaptive sampling techniques and optimization frameworks for dynamic environments. His publication record (2023-2025) demonstrates consistent contributions to top robotics venues including ICRA, IROS, and IEEE Transactions. The body of work reveals a cohesive research trajectory advancing sampling-based planning through innovations in informed search strategies, adaptive batch processing, and physics-inspired optimization. Recurring themes include handling high-dimensional configuration spaces, ensuring real-time performance, and developing robust solutions for multi-robot coordination and deformable object manipulation. Zhang actively contributes to the robotics community as a reviewer for major conferences (ICRA, IROS, Humanoids) and journals (RA-L, T-ASE, T-Mech), and chaired the Robot Motion Planning IV session at IROS 2024. As a researcher, he participates in nationally and EU-funded projects including Bavarian State Project KI.FABRIK. He mentors bachelor's and master's students on topics spanning LLM-based risk mapping, task and motion planning, and multi-robot coordination, with several theses resulting in peer-reviewed publications. He operates within TUM's Robotics, AI and Real-Time Systems group, which pioneers real-time control systems and advanced planning algorithms for industrial and service robotics applications, maintaining strong industry collaborations and state-of-the-art experimental facilities.

• Internet of Things
• Edge Computing
• Digital Twin Networks
• +۷ مورد دیگر

Yan Zhang is a Professor in the Department of Informatics at the University of Oslo's Faculty of Mathematics and Natural Sciences, with a dual affiliation at Simula Research Laboratory in Oslo, Norway. Zhang leads cutting-edge research at the intersection of networking, artificial intelligence, and next-generation communication systems, with particular focus on digital twin networks, 6G technologies, and intelligent edge computing. Zhang's research interests span Internet of Things , Edge Computing , Digital Twin Networks , Wireless Communications , Vehicular Networks , and Federated Learning . Their work bridges theoretical foundations with practical implementations, addressing critical challenges in network architecture, resource optimization, and AI integration for future communication systems. Recent projects have focused on applying diffusion models to network optimization, developing secure federated learning mechanisms resistant to data poisoning, and creating energy-efficient solutions for maritime IoT networks. Analysis of Zhang's recent publication portfolio reveals a strong trend toward integrating artificial intelligence with next-generation networking infrastructure. The research demonstrates significant contributions to digital twin technology for 6G networks, with increasing focus on practical implementation challenges including location uncertainties, imperfect prediction conditions, and energy efficiency constraints. Zhang's work consistently addresses real-world deployment scenarios across multiple domains including intelligent transportation, maritime networks, and UAV swarms. Zhang has served as guest editor for special issues on Digital Twin for 6G Internet of Everything and Empowering Future Mobile Networks With Large Models, reflecting leadership in these emerging research areas. Current research directions include applying generative AI techniques like diffusion models to network optimization problems, developing robust security mechanisms for federated learning in IoT environments, and creating seamless service migration frameworks for mobile edge computing systems. Zhang's work frequently addresses the practical challenges of implementing theoretical concepts in real-world networking scenarios, with growing emphasis on energy efficiency and reliability under uncertain conditions.

• Information Systems
• Data Analytics
• Operations Management
• +۷ مورد دیگر

Yan LI is a Professor at ESSEC Business School Singapore, specializing in Information Systems, Data Analytics, and Operations. She holds a Ph.D. from the National University of Singapore (2007) and has held academic roles including Visiting Professorships at NUS and Stanford University. Her research focuses on open source software adoption, mobile commerce, green IT, and strategic decision-making in uncertain environments. Education : Ph.D. in Information Systems (National University of Singapore, 2007) Master of Science in Information Systems (National University of Singapore, 2003) Bachelor of Arts in Computer Science (Nanjing University, China, 1999) Research Interests : Her work bridges technology adoption (e.g., open source, mobile platforms), organizational strategies, and sustainable IT practices. She explores how human factors and leadership influence technology implementation, with applications in healthcare, supply chain management, and e-government. Article Trends : Recent studies emphasize Big Data’s impact on decision-making, green IT’s dual goals of sustainability and profitability, and consumer behavior in mobile commerce. Earlier work addressed OSS adoption challenges and online learning participation dynamics. Awards : Best Paper Award (WCIC 2006) NUS Dean’s Graduate Award for Research Excellence (2006) Advising & Grants : Supervised projects include strategic initiatives for Renault, Michelin, and tech startups like Yek Mobile. She co-organizes conferences on HCI in Business and chairs editorial boards for journals like Information & Management . Labs/Teams : Active in ESSEC’s Singapore campus research groups, focusing on digital transformation and sustainable innovation.

Rochell McWhorterمشاهده پروفایل

دانشیار

• Virtual Human Resource Development (VHRD)
• Leadership Development in Virtual Environments
• Emerging Technologies in Education
• +۳ مورد دیگر

Rochell McWhorter is an Associate Professor in the Department of Human Resource Development at the University of Texas at Tyler’s Soules College of Business. She holds a Ph.D. from Texas A&M University, an M.Ed., and a B.S. from UT Tyler. Her research focuses on virtual scenario planning (VSP) for leadership development in uncertain environments and virtual human resource development (VHRD), with a particular interest in emerging technologies like AI, augmented reality, and eSports. She champions service-learning initiatives and integrates innovative pedagogical tools such as digital badges and social media in teaching. Her work explores the intersection of technology and education, including studies on the impacts of collegiate esports, workplace spirituality, and ethical AI applications in HRD. McWhorter has published extensively on topics ranging from digital divide reduction through iPad training for older adults to qualitative research methods in organizational learning. Key contributions include conceptualizing VHRD frameworks, analyzing teacher readiness for AI-driven medical education, and investigating systemic barriers to hybrid work participation for women of color. She actively participates in faculty development through service-learning communities and advocates for green computing and sustainable virtual learning environments.