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Josie HughesView profile

Assistant Professor

• Soft Robotics
• Robot Design
• Fabrication
• +4 more

Josie Hughes is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) within the School of Engineering's Institute of Mechanical Engineering. She leads the Computer Robot Design and Fabrication Laboratory (CREATE Lab) and serves on the doctoral program committee for Robotics, Control and Intelligent Systems. Her roles include teaching courses in product development, engineering design, and data-driven manufacturing methods while maintaining active research and student supervision. Her research centers on soft robotics with emphasis on adaptive design, fabrication techniques, and real-world applications. Key areas include agricultural robotics (exemplified by the GraspBerry raspberry harvester), biomimetic materials like self-healing e-skins, and developmental robotics (BabyBot project). She pioneers approaches integrating morphological computation, variable stiffness mechanisms, and machine learning for robotic control, while championing open-source principles and diversity in robotics through accessible educational initiatives like balloon robot kits. Analysis of her recent publications reveals dominant trends in soft robotics adaptability, particularly in variable-stiffness structures, sensor integration, and task-specific optimization. Her work bridges fundamental material science with practical applications in agriculture, food science, and human-robot interaction, frequently employing computational methods like Bayesian optimization and neural networks for design and control. Scientific awards: No specific awards were mentioned in the source materials. Dr. Hughes actively supervises 16 doctoral students across diverse projects including soft grippers for agriculture, biomimetic locomotion, and robotic manipulation systems. Her advising portfolio shows strong alignment with her research themes, with current students working on topics like modular soft arms, agricultural automation, and developmental robotics. While grant details weren't specified, her high publication volume indicates robust research funding. The CREATE Lab under her leadership focuses on holistic co-design of hardware and software for soft robotic systems, with notable projects including the GraspBerry agricultural harvester and BabyBot developmental platform. The lab emphasizes open-source development and educational outreach, maintaining strong industry and academic collaborations while pushing boundaries in reconfigurable robotics and human-centered applications.

• Electrical and Electronic Engineering
• Computer Hardware
• Computer Software
• +7 more

Professor Peter Y. K. Cheung is a Professor of Digital Systems at Imperial College London, holding dual affiliations within the Department of Electrical and Electronic Engineering and the Dyson School of Design Engineering. His work focuses on reconfigurable systems, FPGA architectures, and high-level synthesis tools. He co-founded one of the UK's leading FPGA research groups with Professor Wayne Luk, addressing challenges in variability mitigation, reliability, and application-specific FPGA deployments. His research spans Field-Programmable Gate Arrays (FPGAs) Reconfigurable computing Neural network acceleration Cryptographic protocols Embedded systems He has pioneered techniques such as logic shrinkage for FPGA-based neural networks and developed frameworks like LUTNet for efficient inference. His contributions also include fault-tolerant FPGA designs and methodologies for distributed computation protocols. Key collaborations include work with the Department of Computing on FPGA-based AI acceleration and cybersecurity applications. His recent work explores edge computing, secure decentralized systems, and pandemic modeling using adaptive control strategies. Notable projects include the DSCS protocol for secure distributed computation, acceleration of gravitational wave detection algorithms, and energy-efficient CNN implementations. His research bridges hardware-software co-design with real-world applications in healthcare, finance, and aerospace.

Benoit CombemaleView profile

Professor

• Software Engineering
• Model-Driven Engineering
• Software Language Engineering
• +7 more

Benoit Combemale is a Full Professor of Software Engineering at the University of Rennes , currently on leave as Research Director at Inria . He is affiliated with the DiverSE research team (joint between IRISA and Inria) and the SM@RT team at IRIT. He serves as Editor-in-Chief of the Springer-Nature journal Software and Systems Modeling (SoSyM) and holds leadership roles in academic conferences like ACM SIGPLAN Intl. Conference on Software Language Engineering and MODELS . Education : Habilitation (2015) and PhD (2008) in Software Engineering from University of Rennes and University of Toulouse, respectively. Research : Focuses on Model-Driven Engineering , Digital Twins , and ICT for Sustainability , with applications in cyber-physical systems, scientific computing, and industrial systems. Recent Publications highlight trends in digital twin modeling, energy-aware software engineering, polyglot programming, and formal verification for heterogeneous systems. His scientific awards include: Editor-in-Chief of SoSyM Steering Committee member of ACM SIGPLAN Intl. Conference on Software Language Engineering General Chair for ICT4S 2023 and MODELS 2016 PC Chair for MODELS 2024, ECMFA 2019, and SLE 2014 Coordinator of Dagstuhl Seminars and Bellairs workshops Advising includes 30+ students in topics ranging from digital twin engineering to software testing and language design. He leads the GEMOC Initiative and contributes to projects like SciHook and GEMOC Studio .

Bernhard RumpeView profile

Professor

• Software Engineering
• Model-Based Software Engineering (MBSE)
• Domain-Specific Languages (DSLs)
• +16 more

Bernhard Rumpe is a Professor and Chair of Software Engineering at the Department of Computer Science 3, RWTH Aachen University, Germany. He leads a research group focused on model-based software engineering, domain-specific languages, and digital twins, with strong industrial collaborations and applications in embedded systems, AI, IoT, and autonomous vehicles. His research centers on improving software development through model-driven engineering, generative techniques, and formal modeling using UML, SysML, and the MontiCore language workbench. Key interests include digital twins, variability modeling, model composition, and the integration of cyber-physical systems with information systems. The recent publications highlight a consistent focus on model-driven digitalization, language workbenches, and system integration. Trends show increasing emphasis on digital twins in manufacturing and societal systems, formal verification of model transformations, and educational applications of model-driven low-code platforms. His work bridges theoretical foundations with industrial applicability. Keynote Speaker, OOPSLE 2025 General Chair, GPCE 2023 Session Chair, MODELS 2020 Program Committee Member, SLE, GPCE, ICSE, ECMFA He advises master’s and doctoral students and leads a vibrant research team that has successfully executed over 100 research projects. His group develops foundational tools like MontiCore and applies them in industrial contexts, contributing to software quality and developer efficiency. No formal grants are listed, but sustained project funding is evident. He is involved in several research labs and teams centered around the Software Engineering Chair at RWTH Aachen, focusing on language workbenches, model-driven development, and digital twin systems. The team actively contributes to open research through publications, tools, and industrial partnerships.

• Information Systems
• Operations Management
• Business Strategy
• +5 more

Henrique O'Neill is an Associate Professor (with Habilitation) in the Department of Marketing, Operations and General Management at ISCTE - University Institute of Lisbon, Portugal. He is an Integrated Researcher at ISTAR-Iscte - Research Center in Information Sciences, Technologies and Architecture. His research focuses on information systems adoption, organizational strategy, and process optimization in healthcare, finance, and public administration. Education: PhD in Business Organization and Management - University of Cranfield (1995) Master's in Electrical and Computer Engineering - Higher Technical Institute (1987) Bachelor's in Electrical Engineering - Higher Technical Institute (1983) Research Interests: His work spans business/IT strategy, systems modeling, process analysis, and technology implementation. Key domains include healthcare informatics, banking systems, and Industry 4.0 applications. He emphasizes practical solutions for organizational performance through technology integration. Publication Trends: Recent articles explore intelligent business systems, telemedicine, design science methodologies, and supply chain innovation. His work consistently bridges theoretical frameworks with sector-specific applications in healthcare, education, and logistics. Professional Engagement: Member: Portuguese Telemedicine Association, Order of Engineers Commissioner: INEM (National Institute of Medical Emergency) reform study Director: Center for IT Development (2010-2014) Advising & Projects: Supervises 7 graduate students (1 PhD, 6 Master's). Leads EU-funded projects including: Atlantic Crossing (2024-2025): US-Portugal academic collaboration in AI/cybersecurity AAL4ALL (2011-2015): Ambient Assisted Living ecosystem UNITE (2000-2002): Ubiquitous teamwork platforms

Guillaume ChiavassaView profile

Professor

• Wave Propagation
• Numerical Analysis
• Computational Fluid Dynamics
• +6 more

Guillaume Chiavassa is a Professor in Applied Mathematics at Ecole Centrale de Marseille, affiliated with the Laboratoire M2P2 (Mechanics, Modeling and Physical Processes Laboratory). He leads research in the Thermodynamics, Waves, Digital, Interfaces and Combustion team, focusing on advanced computational methods for complex physical phenomena. His research spans wave propagation in porous media, numerical modeling of plasma flows in Tokamak configurations, multilevel schemes for conservation laws, penalization methods for compressible flows, and wavelets in numerical analysis. Chiavassa's work demonstrates exceptional mathematical rigor applied to challenging physical systems, particularly in nonlinear wave dynamics and computational fluid mechanics. His methodologies bridge theoretical mathematics with practical engineering applications. Analysis of his recent publications reveals a strong focus on wave propagation phenomena across diverse media, with significant contributions to numerical methods for nonlinear systems. His work consistently addresses the mathematical challenges of modeling complex physical behaviors including material softening, fractional attenuation in porous media, and plasma dynamics in fusion devices. The interdisciplinary nature of his research connects applied mathematics with mechanical engineering, geophysics, and nuclear fusion technology. Chiavassa leads the PROSPERO Software project and participates in the ANR Espoir research initiative and the Consortium SEISCOPE. His teaching activities include courses on hyperbolic equations, finite elements, and heat transfer, with practical computational components developed for student instruction. He maintains an active research program through Laboratory M2P2, where his team develops advanced numerical methods for simulating complex physical phenomena with applications ranging from environmental engineering to nuclear fusion research.

• Convivial informatics
• Digital sustainability
• Participatory design
• +9 more

Aurélien Tabard is an Associate Professor at LIRIS (Laboratoire d'InfoRmatique en Image et Systèmes d'information), a joint research unit between Université Claude Bernard Lyon 1 and CNRS. His academic career spans over a decade with positions at prestigious institutions including the IT University of Copenhagen, University of Munich, and INRIA. He completed his PhD at Université Paris-Sud in 2009, focusing on supporting lightweight reflection on familiar information. Tabard's research centers on convivial informatics, exploring alternatives to computational platforms that consider digital limits and foster sufficiency, maintenance, autonomy, and durability. His work emphasizes participatory approaches to designing resilient infrastructures and tools for rapid prototyping by non-designers. He investigates software obsolescence, digital sufficiency, and the materiality of data physicalization through projects like Limites Numériques and PC4-Congrats within PEPR eNSEMBLE. His recent publications reveal strong trends toward sustainability in computing, examining how digital systems can be designed with longevity and environmental impact in mind. There's a clear progression from technical HCI research toward broader societal implications of digital technology, particularly focusing on how users experience aging devices and negotiate digital limits collectively. Honorable mention award for 'Obsolescence Paths: living with aging devices' at ICT4S 2023 Student Lea Mosesso received the prize for best MSc thesis from Conseil National du Numérique Tabard actively mentors PhD students including Maëva Calmettes (working on skill and expertise sharing), Edlira Nano (studying software obsolescence), and Lea Mosesso (researching digital sufficiency). He collaborates extensively with researchers across France and internationally, particularly with Inria Lille where he spent a sabbatical in 2023. His current projects involve understanding software obsolescence, digital sufficiency, and the interplay between micro design decisions and systemic forces shaping ICT. He leads the Limites Numériques team, which investigates how to design computational platforms that take digital limits into consideration. The team recently developed an exhibit on water in the digital sector that will travel to Rhône-Alpes in spring 2025. Tabard also contributes to participatory design tools and critical approaches to participatory design, working with colleagues like Nolwenn Maudet, Thomas Thibault, and Romain Rouvoy.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Prof. Dr. Mehmet Reşit Tolun is a full-time Professor in the Department of Software Engineering at Çankaya University (Turkey) since 2022. Previously held full-time professor positions at Konya Food and Agriculture University (2020-2022), Aksaray University (2013-2017), and TED University (2011-2013), along with a part-time professorship at Başkent University (2017-2020). Specializes in Artificial Intelligence , Machine Learning , and Data Mining , with a focus on deep learning applications in aerospace, biomedical data analysis, and software process improvement. PhD in Computer Science (University of Kent, 1985) MSc in Computer Science (University of Kent, 1982) BSc in Physics and Computer Science (University of Kent, 1981) Research Interests span deep learning frameworks, hybrid expert systems, software engineering methodologies, and biomedical signal processing. Publications emphasize practical implementations in medical diagnostics, robotics, and agricultural pest detection. Scientific Awards include the IEEE Third Millenium Medal (2000). Supervised over 55 graduate students, including Burak Çetin, Uğur Özotuk, and Mahinur Doğan. Collaborated with researchers from Orta Doğu Teknik Üniversitesi , Çankaya University , and Aksaray University .

• Water resources engineering
• Surface water hydrology
• Data science
• +7 more

Jeff Sadler is an Assistant Professor in the Department of Biosystems & Agricultural Engineering at Oklahoma State University, where he also serves as an Extension Specialist for Water Resources with OSU Extension. He leads the WaDE (Water Data and Education) Lab, focusing on data science and machine learning applications in water resources. Education: PhD in Civil and Environmental Engineering, University of Virginia (2019) MS in Civil Engineering, Brigham Young University (2015) BS in Civil Engineering, Brigham Young University (2013) Research Interests: Jeff’s research lies at the intersection of data science and water resources. He specializes in machine learning, particularly physics-guided and process-aware deep learning, for modeling stream temperature, water quality, flood dynamics, and hydrological forecasting. His work emphasizes real-time decision support, reproducible modeling, and integrating domain knowledge into data-driven systems. Recent Research Trends: His recent publications demonstrate a strong focus on advanced deep learning architectures (e.g., graph neural networks, recurrent models), data assimilation, multi-task learning, and surrogate modeling for environmental systems. Applications center on the Delaware River Basin and coastal Virginia, with implications for climate change adaptation and infrastructure resilience. Scientific Awards: No awards explicitly listed in the provided text. Advising and Grants: Jeff mentors graduate students and supervises master's and doctoral research. He is actively funded through multiple grants from the USDA, NOAA, and USGS, supporting projects in water quality monitoring, rural health, evapotranspiration forecasting, and integrated hydrological modeling. Labs and Teams: He leads the WaDE Lab, which develops data-driven tools for water resource education and management. He has collaborated extensively with researchers from the U.S. Geological Survey, University of Virginia, and other institutions on cyberinfrastructure, reproducible modeling, and environmental machine learning.

Gul CalikliView profile

Lecturer

• Empirical Software Engineering
• Human Aspects in Software Development
• Program Comprehension
• +2 more

Dr. Gül Calikli is a Lecturer in Software Engineering at the University of Glasgow specializing in empirical software engineering and human factors in software development. Her research investigates cognitive biases, code review practices, and human-centered aspects of software quality. Current work focuses on LLM-generated code evaluation, financial incentive design for software experiments, and variability management in software product lines. She develops methodologies to study developer behavior, team dynamics, and decision-making processes in software projects. Recent publications examine the impact of request formats on effort estimation, financial incentive guidelines for experiments, and vulnerability detection during code reviews. Her research bridges software engineering practice with cognitive psychology and organizational behavior. Dr. Calikli teaches software engineering courses and supervises research in human aspects of software development. She contributes to international collaborations on adaptive decision-support systems and privacy dynamics in social software.

Keenan CraneView profile

Associate Professor

• Computer Science
• Mathematics
• Scientific Computing
• +7 more

Keenan Crane is the Michael B. Donohue Associate Professor of Computer Science and Robotics at Carnegie Mellon University , with membership in the Center for Nonlinear Analysis and mentorship in the Geometry Collective . His research bridges differential geometry and computer science to develop fundamental algorithms for geometric data processing. Education : BS from University of Illinois at Urbana-Champaign, PhD from Caltech Fellowships : Google PhD Fellow, NSF Mathematical Sciences Postdoctoral Fellow Research focuses on Discrete Differential Geometry , addressing PDE solutions, mesh processing, and geometric modeling through methods like: Walk on Spheres for PDEs Intrinsic Triangulations for robust geometry Repulsive Energy formulations for collision avoidance Recent publications span 2025–2021 , emphasizing grid-free algorithms , anisotropic mesh generation , and differentiable systems . Scientific accolades include Packard Fellowship and NSF CAREER Award . Students include Nicole Feng , Olga Gutan , and Zoë Marschner . During his 2024 sabbatical at Roblox , he does not accept new researchers. Key software contributions include Penrose (math diagram generation) and I♥Mesh (domain-specific language for mesh algorithms).

Timothy MenziesView profile

Professor

• Artificial Intelligence
• Software Engineering
• Data Science
• +5 more

Timothy Menzies is a full Professor in the Department of Computer Science at North Carolina State University's College of Engineering. He serves as the director of the Irrational Research lab (mad scientists r'us) and holds editorial positions as editor-in-chief of the Automated Software Engineering journal and associate editor for IEEE Transactions on Software Engineering. With over 300 publications and more than 24,000 citations, Menzies is a globally recognized leader in software engineering research. Menzies' research focuses on developing computer systems that make optimal decisions with minimal data, specializing in artificial intelligence, intelligent agents, data sciences, analytics, and software engineering. His pioneering work in data-driven, explainable, and minimal AI for software systems has redefined defect prediction, effort estimation, and multi-objective optimization. He is particularly known for his contributions to empirical software engineering, emphasizing transparency and reproducibility. As the co-creator of the PROMISE repository, he helped establish modern empirical software engineering by demonstrating that small, interpretable AI models can outperform larger, more complex ones. Menzies' recent publications reveal several key trends in his research: a growing emphasis on ethical considerations in AI deployment, particularly in sensitive domains like legal systems; continued innovation in software analytics with a focus on hyperparameter optimization tailored specifically for software engineering tasks; exploration of causal relationships in software analytics; and development of techniques that work effectively with limited data, including landscape analysis, surrogate learning, and active learning approaches. Mining Software Repositories Foundational Contribution Award (2017) Carol Miller Graduate Lecturer Award (2016) IBM Faculty Award (2016, 2017) ACM Fellow (2025) ASE Fellow (2024) IEEE Fellow Professor Menzies has advised 24 Ph.D. students throughout his career, with recent completions including Andre Motta (April 2025) and Xueqi Yang (October 2024). His research has secured over $19 million in funding from prestigious agencies including NSF, DARPA, and NASA, as well as industry partners like Meta, Microsoft, and IBM. Current grants focus on improving machine learning model efficiency, adapting empirical software engineering methods to computational science, vulnerability detection, and software analytics at scale using transfer learning across 10,000+ GitHub projects. Menzies has developed innovative approaches to help developers navigate the challenges of AI implementation while maintaining ethical standards and practical effectiveness. As director of the Irrational Research lab, Menzies leads a team focused on creating AI tools that are not only intelligent but also fair, transparent, and trustworthy. The lab's work emphasizes practical applications of AI in software engineering while addressing the human factors involved in developer-AI collaboration. Current projects include developing methods for better fuzzing with L3harris, improving vulnerability detection through smart pruning techniques, and creating AI platforms for workforce empowerment through credential gap diagnostics.

Satish NagarajaiahView profile

Professor

• Structural Dynamics
• Earthquake Engineering
• Seismic Isolation
• +12 more

Satish Nagarajaiah is a tenured Professor at Rice University with joint appointments in the Civil & Environmental Engineering Department , Mechanical Engineering Department , and affiliation with Materials Science and Nano-Engineering . His research spans structural dynamics , earthquake engineering , seismic isolation , and adaptive stiffness systems , with significant contributions to negative stiffness , smart tuned mass dampers , and strain sensing using nanomaterials . Joint appointments across Civil, Mechanical, and Materials Science departments Developed 3D-BASIS software for nonlinear dynamic analysis of base-isolated structures Recipient of NSF CAREER Award , ASCE Moissieff Award , and multiple ASCE medals Research Interests include: Nonlinear dynamic analysis of seismic systems Adaptive passive stiffness and control mechanisms Machine learning and computer vision for sparse structural identification Nanocomposite-based strain sensing (contact/noncontact) Development of smart skins and optical sensors for structural health monitoring Scientific Awards include: NSF CAREER Award (1998-1999) ASCE Moissieff Award (2015) Raymond C. Reese Research Prize (2017) Nathan M. Newmark Medal (2020) George W. Housner Medal (2025) Fellow, U.S. National Academy of Inventors (2019) Distinguished Member, ASCE (2021) Editorial Leadership includes Senior Editor of Mechanical Systems and Signal Processing (Elsevier, 2017-2019, 2024-present), Editor of Structural Control and Health Monitoring (Wiley, 2008-2022, 2024-present), and Editor-in-Chief of Structural Monitoring and Maintenance (Techno-Press, 2014-2024).

Igor JankovicView profile

Associate Professor

• Groundwater Hydrology
• Environmental Engineering
• Contaminant Transport
• +2 more

Igor Jankovic is an Associate Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research focuses on groundwater flow and contaminant transport in heterogeneous aquifers, with particular emphasis on the impact of aquifer heterogeneity on solute movement and transport modeling. Education: PhD in Civil Engineering, University of Minnesota (1997) MS in Civil Engineering, University of Minnesota (1993) BS in Civil Engineering, University of Split, Croatia (1990) His work addresses critical issues in groundwater hydrology including: Advective transport mechanisms in heterogeneous media Breakthrough curve prediction and analysis Effective hydraulic conductivity modeling Upscaling of flow and transport parameters Application of the Analytic Element Method (AEM) for complex aquifer simulations Comparison of transport models (CTRW, MRMT) in heterogeneous environments Research trends in his publications reveal a focus on: Three-dimensional heterogeneous aquifer modeling Non-Fickian and anomalous transport behavior Impact of spatial variability on contaminant migration Development of numerical algorithms for large-scale groundwater simulations Validation of stochastic transport theories against field experiments (e.g., MADE and Borden aquifers) Interaction between physical and chemical heterogeneity in reactive transport

• Software Engineering
• Model Driven Engineering
• Software Product Lines
• +7 more

Jean-Marc Jezequel is a Professor of Software Engineering at University of Rennes , affiliated with CNRS , Inria , IRISA , and Institut Universitaire de France (IUF) . His research focuses on Model-Driven Engineering , Software Product Lines , Dynamic Adaptation , and Executable Meta-languages . Key Contributions : Pioneering work in aspect-oriented and model-driven approaches for software evolution Foundational research on model transformations (e.g., UMLAUT framework) Advances in testing and validation of distributed systems Research Trends from his recent publications include: Intelligent modeling assistance integrating machine learning Contextual variability modeling for complex systems Runtime model execution for self-adaptive systems Formal methods and constraint resolution for UML validation Collaborations include researchers from Luxembourg, Montreal, Colorado State University, and INRIA.