جست‌وجوی استاد و پژوهشگر

• Control Theory
• Advanced Control Systems
• Model Predictive Control
• +۷ مورد دیگر

Liuping Wang is a Professor in the School of Electrical and Computer Engineering at RMIT University, Australia, since 2007. He serves as Head of Discipline for Electrical Energy and Control Systems since 2005 and teaches Advanced Control Systems (EEET 2100) and Real Time Estimation and Control (EEET 2221). Current academic rank: Professor Location: City Campus, Australia Industry collaborators: ANCA, Australian Power Academy, Advanced Manufacturing CRC His research interests span: Control Theory with applications to UAVs and industrial processes Development of Model Predictive Control systems System Identification using neural networks Robust Control for constrained systems Control of AC motors and power electronics Applications in biomedical research and food process monitoring The 15 most recent publications (2015-2025) demonstrate expertise in: UAV control systems with segmented surfaces Battery condition monitoring for electric vehicles Mult-agent robotics with coordination algorithms Smart grid security and electricity dispatch GPS-denied localization for mobile robots Disturbance observer control with input constraints As a supervisor, he oversees Masters Research and PhD projects but no specific student names are listed. His email is liuping.wang@rmit.edu.au for collaboration or supervision inquiries.

Tommy Löfstedtمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۴ مورد دیگر

Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.

Aravind Machiryمشاهده پروفایل

استادیار

• Software Security
• Embedded Systems
• Program Analysis
• +۹ مورد دیگر

Aravind Machiry is an Assistant Professor at Purdue University's Electrical and Computer Engineering Department and a founding member of the Purdue Systems and Software Security (PurS3) Lab . His research focuses on system security, particularly vulnerability detection, prevention, and secure system development using static/dynamic program analysis, fuzzing, type systems, and machine learning. Designing practical solutions for software and embedded system security Recipient of NSF CAREER and Amazon Research awards Active participant in SPLASH 2025 as OOPSLA Review Committee member His recent work includes automated vulnerability detection in embedded software, spatial memory safety enhancements, and security analysis of GitHub workflows. He has received recognition for his research through multiple distinguished paper awards and industry funding. Selected scientific awards include NSF CAREER Award (2024) Amazon Research Award (2022) Test of Time Award at FSE 2023 for DynoDroid Distinguished Paper Award at OOPSLA 2022 for 3c Qualcomm Innovation Fellowship (2025) His research team has developed frameworks like ARGUS for taint analysis of CI/CD workflows and FuzzUEr for UEFI interface fuzzing, discovering hundreds of critical vulnerabilities in open-source projects and thousands of command injection flaws in GitHub repositories.

João Magalhãesمشاهده پروفایل

استاد

• Vision and Language Understanding
• Multimodal Information Understanding
• Multimodal Conversational AI
• +۵ مورد دیگر

João Magalhães is a Full Professor in the Department of Computer Science at the Faculty of Science and Technology, Universidade NOVA de Lisboa, Portugal. He serves as Group Coordinator of the Multimodal Systems Group at the NOVA Laboratory for Informatics and Computer Science and leads the NOVASearch research group at FCT/UNL. His research focuses on vision and language information understanding, with particular emphasis on multimodal information understanding, multimodal conversational AI, multimedia search and summarization, temporal and memory models, and social media information quality. His work spans both theoretical foundations and practical applications across web, social media, and clinical domains. Analysis of his recent publications reveals a strong trajectory in multimodal conversational AI systems, with increasing sophistication in handling both voice and visual inputs. His research has evolved from foundational work in cross-modal embeddings to advanced large language models for dual-goal conversational settings, demonstrating consistent innovation in the field of multimodal understanding. 1st prize winner of the second Alexa TaskBot Challenge (2023) Award-winning solution in the Alexa TaskBot Challenge (2022) Best paper award at the Portuguese NLP conference (PROPOR) (2020) Best paper nominations at ACM conferences (2018) Professor Magalhães has advised numerous graduate students through the NOVASearch group and has secured substantial research funding through projects including Amazon Alexa TaskBot Challenge (2021-2023), iFetch (2020-2023), SmartyFlow (2017-2020), COGNITUS (2016-2019), GoLocal (2016-2020), QSearch (2012-2015), ImTV (2010-2013), and CS4SE (2010-2013). He actively serves the research community as ACM Multimedia 2026 Program Committee Chair and has held leadership roles in numerous conferences including ACM Multimedia 2022 General Chair and ECIR2020 General Chair. He leads the Multimodal Systems Group within the NOVA Laboratory for Informatics and Computer Science, where his team develops cutting-edge solutions for multimodal understanding with applications in conversational AI, multimedia search, and social media analysis.

• Control Theory
• Autonomous Systems
• Reinforcement Learning
• +۳ مورد دیگر

Dr. Cheng-Chew Lim is a Professor in the School of Electrical and Mechanical Engineering at the University of Adelaide. He specializes in control theory, autonomous systems, and multi-agent reinforcement learning. His research focuses on trusted autonomous systems, secure cyber-physical networks, and decentralized decision-making models. He has published over 300 articles and supervised 50+ PhD and master’s students. Dr. Lim teaches courses in control systems, autonomous systems, and engineering project management. He has held editorial roles, including Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics, and is actively involved in professional associations like the IEEE Control and Aerospace Electronic Systems Joint Chapter. His current projects include physics-informed neural networks for medical imaging, secure distributed autonomous systems, and resilient formation control under cyberattacks. Dr. Lim has secured research grants from ARC and industry partnerships, emphasizing practical applications in robotics, cybersecurity, and smart systems.

Ghyslain Gagnonمشاهده پروفایل

استاد

• Radiofrequency circuits and antennas
• Microelectronics
• Wireless communications
• +۴ مورد دیگر

Ghyslain Gagnon is a Professor in the Department of Electrical Engineering at École de technologie supérieure (ÉTS) in Montreal, Canada. He leads research activities within the LACIME – Communications and Microelectronic Integration Laboratory, focusing on cutting-edge developments in microelectronics, sensors, and communication systems. His work bridges theoretical research and practical applications across multiple domains including health technologies, wireless communications, and quantum engineering. Education: B.Ing. from École de technologie supérieure M.Ing. from École de technologie supérieure Ph.D. from Université de Carleton Professor Gagnon's research spans several interconnected domains with emphasis on Radiofrequency circuits and antennas, Microelectronics, Wireless communications, Sensors and monitoring systems, Machine learning applications, Health technologies, and Quantum engineering. His work demonstrates a strong commitment to translating theoretical concepts into practical solutions with real-world impact, particularly in the areas of health monitoring systems and advanced communication technologies. His recent publications reveal a clear trajectory toward increasingly interdisciplinary research, combining traditional electrical engineering with machine learning, health monitoring, and quantum technologies. The trend shows growing emphasis on practical applications in automotive safety systems, wireless communications for next-generation networks, and health monitoring technologies that leverage flexible electronics and novel sensor designs. Professor Gagnon has successfully supervised numerous graduate students through their doctoral and master's research, with recent theses focusing on smart hearing protection devices, machine learning applications, energy monitoring systems, and flexible sensor technologies. His supervision record demonstrates consistent productivity and relevance to contemporary engineering challenges. He is an active member of the LACIME research laboratory, which focuses on six key areas: Functional materials, Micro- and nanofabrication processes, Conception and design of integrated circuits, Design and fabrication of hybrid components, Photonic and electronic microsystems, and Signal processing and communication. This environment provides students with access to cutting-edge tools and fosters innovation through interdisciplinary collaboration.

Alan Wassyngمشاهده پروفایل

استاد

• Software Engineering
• Formal Methods
• Safety-Critical Systems
• +۶ مورد دیگر

Alan Wassyng is a Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. With over four decades of academic contributions, he specializes in formal methods, safety-critical systems, and software assurance cases. Key research intersections: Automotive software safety Medical device software certification Cyber-physical systems engineering Model-driven development with domain experts His scholarly work emphasizes rigorous methodologies for software verification, particularly through tabular expressions and Workflow+ models. Recent projects include a $2M GM Canada partnership to advance automotive safety systems. Teaching leadership spans interdisciplinary capstone projects in biomedical engineering, covering software design, safety-critical development, and mechatronics applications.

Abolfazl Simorghمشاهده پروفایل

پژوهشگر

• Aircraft Trajectory Optimization
• Climate Change Mitigation in Aviation
• Robust Control
• +۳ مورد دیگر

Abolfazl Simorgh is a researcher at Charles III University of Madrid's Department of Aerospace Engineering, specializing in climate-optimized aviation systems. His work bridges mathematical control theory with practical climate impact mitigation, focusing on robust trajectory optimization under environmental and operational uncertainties. He leads development of open-source tools for sustainable flight planning while contributing to major European aviation initiatives. Education: B.Sc. in Control Engineering (2017) M.Sc. in Control Engineering (2020) Ph.D. in Aerospace Engineering from Charles III University of Madrid Dr. Simorgh's research centers on developing mathematical frameworks that reconcile aircraft trajectory optimization with climate impact reduction. His expertise spans robust control systems, optimization under uncertainty, and climate modeling integration, with particular emphasis on non-CO₂ emissions. His methodology addresses both CO₂ and non-CO₂ climate forcing mechanisms through computationally efficient algorithms that account for weather variability and climate metric uncertainties. This work directly supports aviation's decarbonization by providing operational strategies that reduce environmental footprint without prohibitive cost increases. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on operationalizing climate-optimal flight planning. His work consistently integrates climate science with aerospace engineering through robust optimization frameworks, demonstrating particular innovation in handling multiple uncertainty sources (weather, climate models, emissions). The publications cluster around three interconnected themes: 1) Development of open-source computational tools (ROOST, CLIMaCCF), 2) Network-scale implementation of climate-aware air traffic management, and 3) Risk analysis of climate mitigation strategies. This body of work establishes new methodological standards for quantifying and minimizing aviation's total climate impact. Scientific Awards: Luis Azcárraga Aeronautical Innovation Award for collaborative research impact Best Paper Award (2022) from a high-impact aerospace journal Dr. Simorgh secures significant research funding through European Commission projects including FlyATM4E (climate-optimized flight planning), ALARM (aviation emissions reduction), and RefMAP (sustainable aviation pathways). His grant portfolio emphasizes practical implementation of climate mitigation strategies, with strong industry-academia collaboration. He mentors junior researchers through project teams and has developed three major open-source Python libraries (CLIMaCCF, ROOST, ROC) that have become community standards for climate impact assessment in aviation research. His current work focuses on scaling climate-optimized trajectories to continental airspace while addressing operational constraints and economic viability. He leads a research group focused on climate-aware air traffic management, developing the ROOST simulation framework for GPU-accelerated trajectory optimization and the CLIMaCCF library for standardized climate metric calculations. His team collaborates with European air navigation service providers and aircraft manufacturers to transition research into operational practice, with current projects emphasizing real-time implementation and regulatory compliance frameworks.

Haibo Heمشاهده پروفایل

استاد

• Computational Intelligence
• Neural Networks
• Reinforcement Learning
• +۵ مورد دیگر

Dr. Haibo He is the Robert Haas Endowed Professor in the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island (URI). As an IEEE Fellow and NSF CAREER awardee, his research focuses on computational intelligence, neural networks, and reinforcement learning with applications to smart grids and microgrid systems. Ph.D. in Electrical Engineering, Ohio University, 2006 M.S. in Electrical Engineering, Huazhong University of Science and Technology, 2002 B.S. in Electrical Engineering, Huazhong University of Science and Technology, 1999 His research interests include: Computational Intelligence Adaptive Dynamic Programming Reinforcement Learning Deep Learning for Power Systems Distributed Control in Microgrids Imbalanced Data Learning Recent research trends from publications (2018-2025) show a focus on: Multi-agent reinforcement learning for energy systems Digital twin frameworks for grid security Event-triggered control mechanisms Finite-time convergence algorithms Cyber-attack resilient control systems Evolutionary computation in power networks Awards: IEEE Fellow (2018) NSF CAREER Award (2017) Dr. He leads the Computational Intelligence and Self-Adaptive Systems (CISA) Laboratory at URI, which conducts fundamental research on computational intelligence methods with applications to power systems, data mining, and neural networks.

James Clauseمشاهده پروفایل

دانشیار

• Software Engineering
• Testing
• Program Analysis
• +۵ مورد دیگر

James Clause is an Associate Professor and Graduate Program Chair in the Department of Computer and Information Sciences at the University of Delaware. His research focuses on advancing software engineering practices through innovative work in testing, program analysis, and energy-efficient software development. His educational background includes: PhD in Computer Science from Georgia Institute of Technology (2011) MS in Computer Science from University of Pittsburgh (2005) BS in Computer Science from Allegheny College (2003) Clause specializes in improving software quality and developer productivity. His research investigates dynamic analysis techniques for bug detection, memory leak identification, and security vulnerability analysis. He pioneers energy-aware software engineering, developing methods to optimize application energy consumption and test suite efficiency. His work on privacy-preserving field data usage enables safer handling of sensitive user information while maintaining software quality. Current projects focus on energy-efficient application development, durable test creation, and privacy-conscious software improvement. His publication trajectory reveals a clear evolution from foundational security and memory analysis toward energy-conscious software engineering. Early work established critical dynamic taint analysis frameworks (e.g., Dytan), while recent research addresses energy consumption in web applications and test optimization. This shift demonstrates growing industry relevance of sustainable computing practices within software engineering. His scientific contributions are recognized through prestigious awards: ISSTA 2017 Impact Paper Award for seminal work on dynamic taint analysis ICSME 2016 Distinguished Reviewer Award for exceptional peer review contributions Clause actively mentors graduate students including Tedis Agoli, Emily Evans, Daniel Gaston, and Benwen Zhang. His research is supported by grants focused on energy-efficient development, high-quality testing methodologies, and privacy-preserving data usage. He serves the academic community through program committee roles for ICST 2018 and ICSE 2018, demonstrating leadership in software testing and engineering conferences. His research group operates within the University of Delaware's Department of Computer and Information Sciences, contributing to the institution's reputation in software engineering innovation through continuous exploration of energy-aware development and advanced debugging techniques.

Marco Aldinucciمشاهده پروفایل

استاد

• Parallel Computing
• High-Performance Computing (HPC)
• Cloud Engineering
• +۷ مورد دیگر

Marco Aldinucci is a Full Professor and Head of the Parallel Computing group at the University of Torino's Computer Science Department. He leads the HPC Key Technologies and Tools (HPC-KTT) national lab under CINI, involving 38 Italian universities. His expertise spans parallel programming models, HPC systems, federated learning, and energy-efficient computing. Aldinucci has secured over €10M in EU research funding, contributed to frameworks like Fastflow and Streamflow, and pioneered initiatives like the HPC4AI lab and the CINI HPC-KTT lab. His research focuses on advancing exascale computing, cloud-HPC integration, and AI-driven medical solutions. Notable projects include the Gaia AVU-GSR solver for exascale systems and the DeepHealth Toolkit for medical AI. He has held governance roles in EuroHPC and chairs the Observatory on Trends and Applications of Supercomputing in Italy. Aldinucci’s publications (150+) address parallel algorithms, distributed learning, and sustainable HPC infrastructure. His work has been recognized with awards from HPC Advisory Council, NVIDIA, IBM, and Autodesk. Current initiatives include the Software & Integration lab at the Italian National HPC Centre (ICSC) and leadership in the OpenScience working group at Torino. His advising includes Iacopo Colonelli, whose thesis won CINI’s 2023 best award. He actively engages in EU projects, workflow systems, and standards for hybrid computing environments. Aldinucci’s labs and collaborations drive innovations in HPC portability, energy efficiency, and AI scalability.

Bowen Xuمشاهده پروفایل

استادیار

• Software Engineering
• Machine Learning
• Program Analysis
• +۱۲ مورد دیگر

Bowen Xu is an Assistant Professor in the Department of Computer Science at North Carolina State University (NC State), College of Engineering. His research focuses on software engineering, machine learning, and program analysis, particularly in securing AI models and improving code quality. He holds a PhD from Singapore Management University (SMU), where he also conducted postdoctoral research. Education: PhD in Computer Science, Singapore Management University (SMU) Postdoctoral Researcher, SMU School of Computing and Information Systems Research Interests: AI for Code, Backdoor Attacks on Code Models, Vulnerability Detection Code Representation Learning, Model Compression, Safety of AI Systems Chatbot Development for Developers, Automatic Code Review Key Contributions: Developed PTM4Tag+, a Stack Overflow tag recommendation system using pre-trained models Explored stealthy backdoor attacks in code and reinforcement learning systems Pioneered work on automatic vulnerability repair using LLMs and broader input analysis Awards: 2022: Honorable Mention Award (ACSAC) 2018: Highly Commended Full Paper Award (ESEM) Service Roles: Editorial Board Member, Empirical Software Engineering Journal Program Committee Co-chair for ICSE/FSE Research Tracks Organized workshops like FORGE, MaLTeSQuE, and SEA4DQ Labs & Teams: Leads the Softmax Lab at NC State, advising 12+ students across PhD, Master's, and undergraduate levels. Alumni include industry professionals at Microsoft, Barclays, and Marvell Semiconductor.

Uduak Inyang-Udohمشاهده پروفایل

استادیار

• Control Theory
• Graph Theory
• Physics-Guided Machine Learning
• +۵ مورد دیگر

Uduak Inyang-Udoh is an Assistant Professor in the Department of Mechanical Engineering at the University of Michigan, affiliated with the Autonomous & Intelligent Systems (AI-Sys) Lab. Her research focuses on control theory, graph theory, and physics-guided machine learning applied to data-rich advanced manufacturing, thermal systems, and energy storage. Education: PhD (Rensselaer Polytechnic Institute, 2021), BSc (University of Lagos, 2016) Research Areas: Controls, Energy, Manufacturing, Mechatronics & Robotics Email: udinyang@umich.edu Research interests integrate theoretical frameworks with practical applications in real-time optimal control, nonlinear system analysis, and additive manufacturing optimization. Her group has published extensively in control algorithms, thermal management systems, and data-driven industrial processes. Recent publications highlight advancements in neural co-state regulators, hybrid thermal control systems, and machine learning integration in droplet-based manufacturing. She emphasizes control theory applications to complex systems with input constraints and transient dynamics. Scientific Awards ASME Dynamic Systems and Control Division Rising Star Award (2022) ASME Rudolf Kalman Best Paper Award (2024) Mentoring style prioritizes student ownership of projects, collaborative peer mentoring, and structured progress reporting. Funding supports conference attendance (ACC, MECC) through travel grants and lab resources. Lab policies balance research productivity with vacation periods during academic breaks.

Can Liuمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Artificial Intelligence
• Spatial Computing
• +۵ مورد دیگر

Dr. Can Liu is an Assistant Professor at the School of Creative Media, City University of Hong Kong, where she leads the ERFI Lab (Laboratory of Empirical Research for Future Interfaces). Her research focuses on designing future interfaces for ubiquitous technologies through empirical understanding of human cognition and behavior, with emphasis on multimodal interaction combining physical and digital elements. Education: PhD in Human-Computer Interaction, Université Paris-Sud (France), INRIA labs ex)situ and ILDA MSc in Media Informatics, RWTH Aachen University (Germany) Dr. Liu's research spans three primary domains: AI-assisted Input (using LLMs/NLP to enhance text manipulation and speech interfaces), Spatial Computing (multimodal interfaces for AR/VR and large displays), and Hybrid/Remote Collaboration (supporting intuitive remote interaction through understanding collocated collaboration). Her work integrates empirical user studies with real-world system deployments in public spaces. Recent publications demonstrate strong trends toward LLM-integrated interfaces, wearable computing applications, and novel interaction techniques for foldable devices. Her team consistently publishes at top venues including CHI, UIST, and CSCW, with increasing focus on practical AI integration for everyday tasks. Awards and Recognition: Best Paper Award at ACM CHI 2014 (top 1%) Honourable Mention at ACM CHI 2012 (top 5%) Dr. Liu actively mentors PhD students and researchers while securing substantial research funding including Google Faculty Research Awards, National Natural Science Foundation grants, and RGC Early Career Schemes. She serves on numerous program committees including ACM CHI (Associate Chair 2024, 2022, 2021, 2020, 2019, 2017) and co-organizes research initiatives like the HCIX Summer Research Program. Her laboratory ecosystem includes the ERFI Lab, affiliation with the Augmented Materiality Lab and Kowloon Interaction Center, and active participation in the Greater Bay HCI community, supporting both fundamental research and industry collaboration with partners including Google, Huawei, and Lenovo.

Werner Dietlمشاهده پروفایل

دانشیار

• Programming Languages
• Static Analysis
• Software Security
• +۴ مورد دیگر

Werner Dietl is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His work focuses on programming languages, static analysis, software security, and formal verification techniques. He has contributed to type system design, low-power computing, and approximate data types through projects like EnerJ. His research also addresses challenges in compiler design, cryptographic protocol validation, and runtime enforcement mechanisms. Key research areas include: Type systems for imperative and domain-specific languages Static analysis of implicit control flow (e.g., Java reflection, Android intents) Approximate computing and energy-efficient computation Formal verification of security properties Publications span topics from unit measurement type inference to ownership-based security models, reflecting a focus on practical formal methods. His work emphasizes scalability and precision in type systems while addressing real-world software engineering challenges.