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Páraic CarrollView profile

Lecturer

• Transport Planning
• Travel Behavior
• Sustainable Mobility
• +3 more

Dr. Páraic Carroll is a Lecturer in Transport Planning at the University of Melbourne's Faculty of Architecture, Building and Planning, with a concurrent Visiting Professorship at University College Dublin. His expertise combines policy analysis, behavioral modeling, and infrastructure planning, with recent focus on sustainable mobility transitions. Research examines travel behavior and decarbonization pathways, including: Equitable EV charging infrastructure deployment Agent-based modeling of mode-shift interventions Airport access policy optimization Energy-transport nexus in rural contexts Publications (2022-2025) highlight three themes: 1) Micromobility integration (30% of recent works), analyzing e-scooter adoption and first/last-mile solutions; 2) Rural transitions (25%), evaluating public transport expansion and EV microgrids; and 3) Behavioral interventions (20%), applying stage-based models to mobility change. He advises Irish climate policy through the Climate Change Advisory Council and consults for local governments on micro-mobility regulation. No awards or funded studentships are documented in available sources.

• Materials Science
• Metallurgy
• Additive Manufacturing
• +3 more

Greta Lindwall is a Lecturer at the Royal Institute of Technology (KTH), specializing in materials science and additive manufacturing. She teaches courses such as Metallic Materials, Powder Metallurgy, and Materials in Design and Product Development. Her roles include examiner, course coordinator, and teacher for several advanced programs. Her research focuses on solidification processes in additive manufacturing, particularly using high-energy X-ray techniques and synchrotron imaging to study material behavior during electron beam and laser powder bed fusion. She has contributed to understanding microstructural evolution in steels, grain refinement mechanisms, and process optimization for materials like tool steels and stainless steel. Her work integrates experimental methods with computational modeling, including CALPHAD-based approaches for phase equilibria and microstructural prediction. Key research areas include real-time tracking of solidification, smoke mechanisms in electron beam processes, and the development of novel alloys for AM applications. Lindwall collaborates with initiatives like the LIGHTer Academy and has published extensively on topics such as phase transformations, material properties under AM conditions, and process monitoring technologies. Her contributions bridge fundamental materials science with advanced manufacturing techniques, aiming to improve material performance and process reliability.

• Control Systems
• Model Predictive Control
• Power Systems Engineering
• +3 more

Morten Hovd is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, model predictive control (MPC), power electronics, and optimization algorithms. He has contributed significantly to the development of robust control strategies for uncertain systems and has published extensively in leading journals and conferences in the field of control engineering. His research interests span several key areas in control systems engineering, including model predictive control, nonlinear control systems, optimization under uncertainty, and applications in power systems and energy efficiency. He is particularly known for his work on discrete-time bilinear systems, modular multilevel converters (MMCs), and the integration of machine learning techniques with control theory. His contributions address both theoretical advancements and practical implementations in industries such as energy and petroleum engineering. Hovd's recent publications highlight advancements in energy-efficient control systems, stochastic surrogate modeling for subsurface flows, and optimization algorithms tailored for complex engineering problems. His work often combines rigorous mathematical frameworks with real-world applications, such as improving the reliability of power systems and enhancing reservoir management through data-driven methods. He is actively involved in teaching courses such as TTK4210 (Advanced Control of Industrial Processes) and TK8118 (Mini-seminar in Cybernetics). His research has led to innovations in fault detection for power systems, energy-efficient building climate control, and robust MPC strategies for uncertain systems.

Emanuele CrisostomiView profile

Associate Professor

• Smart Grids
• Electric Vehicles
• Control Systems
• +2 more

Emanuele Crisostomi is Associate Professor of Electrical Systems at the University of Pisa's Department of Energy, Systems, Territory and Constructions Engineering. He holds PhD and graduate degrees in Automation Engineering from the University of Pisa, with research visits to University of Cambridge, University of Leicester, and Hamilton Institute. His research bridges control theory, energy systems, and smart mobility. His research integrates: Smart grid control and renewable energy integration Electric vehicle charging infrastructure and routing algorithms Urban traffic modeling and optimization Nonlinear system dynamics and Lyapunov stabilization Machine learning applications in energy systems Crisostomi's publications demonstrate consistent innovation in electric vehicle-grid integration, with recent focus on predictive charging control, highway EV circulation patterns, and hydropower monitoring. His work shows increasing application of deep reinforcement learning for multi-regional power systems and hybrid network models for infrastructure monitoring. He leads research on IoT-enabled energy efficiency in buildings and contributes to epidemic modeling through network science. Current projects examine battery sizing optimization, charging station scheduling under uncertainty, and synergetic building management systems integrating environmental monitoring with energy conservation.

• Signal Processing
• Machine Learning
• Acoustics
• +7 more

Luis Antonio Azpicueta Ruiz is an Associate Professor in the Department of Signal Theory and Communications at Carlos III University of Madrid. He leads research in the Signal Processing and Learning Group (GTSA) and Machine Learning for Data Science (ML4DS) group, focusing on interdisciplinary applications spanning acoustics, telecommunications, and machine learning. Research Interests: His work bridges signal processing theory with practical applications in environmental acoustics, adaptive filtering systems, and machine learning. Key research themes include: Advanced adaptive filtering architectures for nonlinear systems Distributed estimation in sensor networks Acoustic echo cancellation and room equalization Psychoacoustic evaluation methods Machine learning applications in noise monitoring and sound analysis Research Projects: Principal investigator for multiple funded projects including: Diagnóstico del ruido de chorro en aeronaves (AEI, 2022-2025) LearnINg FLow and Noise Dynamics via AI (COMUNIDAD DE MADRID, 2024-2026) BODYinTRANSIT - Sensory-driven Body Transformation (EUROPEAN COMMISSION, 2022-2026) Aprendizaje Automático para análisis Big Data (MINISTERIO DE ECONOMÍA, 2018-2021)

• Industrial Ecology
• Life Cycle Assessment (LCA)
• Life Cycle Management (LCM)
• +4 more

Henrikke Baumann is a Professor at Chalmers University of Technology , affiliated with the Environmental Systems Analysis department within the Environmental Systems Analysis school. Her research focuses on interdisciplinary environmental sustainability, particularly in Life Cycle Assessment (LCA) , Life Cycle Management (LCM) , and sustainable business models . She pioneers work on societal material flows, plastic pollution, and organizational influences on environmental performance. Research Interests: Interdisciplinary research in industrial ecology Life Cycle Assessment (LCA) methodology and applications Business model innovation for sustainability Material flows and plastic pollution mitigation Organizational practices and environmental governance Recent Trends in Articles: Explores methodological rigor in LCA for carbon capture and utilization systems Analyses the environmental effectiveness of LCA in building product development Develops frameworks for assessing business models' environmental performance via BM-LCA Addresses social dimensions of sustainability in business models Grants and Projects: Principal investigator in projects like TwinWins: Measuring Business Model Environmental Performance Contributions to Mistra REES (Resource-effective and efficient solutions) Phase 2 Labs/Teams: Active in interdisciplinary teams at Chalmers focused on industrial ecology and sustainability science.

Mehdi MortazaviView profile

Academic

• Thermo-Fluid Sciences
• Multiphase Flow
• Hydrogen Energy Systems
• +5 more

Mehdi Mortazavi is an Associate Teaching Professor in the Department of Mechanical and Materials Engineering at Worcester Polytechnic Institute (WPI), where he has been since January 2023. He previously served as an Assistant and Associate Professor at Western New England University (WNE) from 2016 to 2022. His expertise lies in experimental thermo-fluid systems, multiphase flow, and clean energy technologies. Education: PhD in Mechanical Engineering (Michigan Tech University, 2014), MS in Mechanical Engineering (Michigan Tech University, 2011), BS in Mechanical Engineering (KN Toosi University, 2007). Research Interests : Multiphase Flow Dynamics Hydrogen Energy Systems Droplet Dynamics in Fuel Cells Decarbonization Strategies Additive Manufacturing in Heat Exchangers As director of the Multiscale Thermal Fluids Laboratory (MTFL) at WPI, Dr. Mortazavi leads research addressing thermo-fluid challenges in energy systems. His work bridges fundamental and applied research, with a focus on improving fuel cell efficiency, hydrogen production, and sustainable energy solutions. He also emphasizes pedagogical innovation, teaching courses like Fluid Dynamics, Thermodynamics, and Heat Transfer. His recent publications highlight advancements in PEM fuel cell optimization, hydrogen electrolyzers, and thermal modeling of renewable energy systems. While no specific awards are listed, his contributions to clean energy and experimental fluid mechanics are notable. Lab involvement includes directing the MTFL, which focuses on additive manufacturing, multiphase flow characterization, and energy storage. His teaching portfolio spans both undergraduate and graduate levels, reflecting his commitment to engineering education.

• Circular Economy
• Material Recycling Technologies
• Sustainability in Production Systems
• +3 more

Prof. Kathrin Greiff is a University Professor at the Chair of Anthropogenic Material Cycles and Institute of Processing, Coking, and Briquetting at RWTH Aachen University's Faculty of Georesources and Materials Engineering. Her work focuses on advancing circular economy principles through innovative recycling technologies, sensor-based material flow analysis, and sustainability assessments. She leads research initiatives like the DACE Academy and the Material Assessment Lab, which explore sensor integration, waste valorization, and decarbonization strategies in industrial processes. Her research interests include optimizing mechanical recycling processes for plastics and construction materials, developing machine learning-driven sorting systems, and evaluating environmental impacts across material lifecycles. Recent projects highlight applications in lightweight packaging waste, steel forging decarbonization, and polystyrene insulation recycling. She collaborates closely with industry to translate research into scalable solutions for sustainable material management. Prof. Greiff’s publications emphasize technical feasibility studies, life cycle assessments, and cross-disciplinary approaches to circular economy challenges. Her work contributes to policy frameworks for sustainable resource use and eco-design principles in manufacturing.

Christian EgwimView profile

Researcher

• Artificial Intelligence
• Big Data Analytics
• Machine Learning
• +4 more

Christian Egwim is a Researcher in the Department of Computer Science at the University of Bath. With a PhD in Applied Artificial Intelligence from the University of Hertfordshire and a BSc in Computer Science from the University of Ibadan, he specializes in Artificial Intelligence, Big Data Analytics, Machine Learning, Software Engineering, Digital Technology Innovation, Environmental Monitoring, and Circular Economy. Education: PhD in Applied Artificial Intelligence, University of Hertfordshire BSc in Computer Science, University of Ibadan His research focuses on applying AI and IoT technologies to address environmental challenges, including air quality monitoring, pollution prediction, and sustainable building design. Current projects include the D-BuD initiative, which uses big data and machine learning to optimize pollution dispersion in architectural designs. His scholarly work spans topics like feature selection, ensemble modeling, and agentic AI. Collaborations include partnerships with institutions in the UK and Nigeria, contributing to advancements in environmental and construction-related AI applications.

Ashish VenkatView profile

Associate Professor

• Computer Architecture
• Compilers
• Computer Security
• +12 more

Ashish Venkat is an Associate Professor in the Department of Computer Science at the University of Virginia, part of the School of Engineering and Applied Science. He holds a Ph.D. from UC San Diego and has established himself as a leading researcher in computer architecture, compilers, and computer security. Research Interests: His research focuses on cross-disciplinary hardware and software techniques to build secure, high-performance computing systems. He investigates robust exploit mitigations that maintain energy efficiency and programmability, with a particular emphasis on speculative execution, memory safety, hardware security, and privacy-preserving computing. He also explores the application of machine learning to detect security threats and model execution behavior. Publication Trends: His recent publications (2020–2025) demonstrate a strong focus on hardware-based security, particularly microarchitectural vulnerabilities (e.g., micro-op cache attacks), memory safety via microcode capabilities, and secure accelerators for bioinformatics. There is a consistent trend of publishing in top-tier venues like ISCA, MICRO, IEEE S&P, and USENIX Security, often featuring novel hardware/software co-design solutions. Scientific Awards: NSF CAREER Award (2023) NSF CRII Award (2018) IEEE Micro Top Pick (2019) IEEE Design & Test Top Pick (2020, 2021) HPCA Best Paper Runner-Up (2019) DATE Best Paper Nominee (2023) UVA Research Achievement Award (2023) ISCA Prolific Author of the Decade (2013–2022) Advising and Grants: He actively mentors graduate and undergraduate students, many of whom have pursued advanced degrees or joined leading tech companies. He has secured significant funding as PI or co-PI from NSF, DARPA, SRC, and Intel, including a $4.9M DARPA HERCULES grant and an NSF CAREER award. His projects focus on holistic security solutions, speculative optimization, and privacy-preserving machine learning frameworks. Labs and Teams: He leads a research group focused on secure and efficient computing systems, collaborating with researchers at institutions like UC San Diego, UC Riverside, and UC Irvine, as well as industry partners including Intel and IBM.

Azza AbouziedView profile

Associate Professor

• Human-Data Interaction
• Database Systems
• Human-Computer Interaction
• +7 more

Azza Abouzied is Associate Professor of Computer Science at New York University Abu Dhabi and Global Network Associate Professor at the Tandon School of Engineering. She serves as Vice Provost for Faculty Advancement and Engagement at NYUAD starting September 2024. Her research bridges database systems and human-computer interaction, focusing on intuitive tools for data querying and decision-making in uncertain, collaborative environments. PhD, Yale University (2013) MPhil, Yale University MSc, Dalhousie University BSc, Dalhousie University Her research centers on human-data interaction, designing systems that make data accessible to non-experts. She combines techniques from UI design, machine learning, and databases to build tools that simplify complex data tasks. Her earlier work focused on example-driven querying and synthetic data generation, while her recent work explores in-database prescriptive analytics and decision support in domains like disinformation mitigation and epidemic planning. Her publications span database and HCI venues, with a recurring theme of enhancing usability without sacrificing scalability. She co-founded Hadapt, a Big Data analytics platform, and has led interdisciplinary research through the Human-Data Interaction Lab and the Center for Interacting Urban Networks. Her teaching includes foundational courses such as Database Systems, Operating Systems, and Data, as well as the critical thinking course Techruption. VLDB Test of Time Award (2019) Best Paper Award in Database Systems Honorable Mention in HCI Publications Azza mentors undergraduate capstone students and advises prospective PhDs, research assistants, and postdocs. She is actively involved in academic leadership, having chaired NYUAD’s faculty council in 2024 and co-chaired the SIGMOD 2025 program. Her work emphasizes empowering users to critically engage with data and AI, both in research and education.

Jaakko Timo Henrik JärviView profile

Professor

• Generic Programming
• Programming Language Design
• Graphical User Interfaces (GUI)
• +7 more

Jaakko Timo Henrik Järvi is a Professor in the Department of Informatics at the University of Bergen, Norway, with additional affiliations at the University of Turku, Finland. His research focuses on programming language design, generic programming, and human-computer interaction, particularly in GUI frameworks and software reuse. His research interests include generic programming, programming language design (especially the Magnolia language), high-performance computing, array programming, and GUI engineering. He emphasizes formal methods and algebraic specifications to build reusable and efficient software systems. His work bridges theoretical foundations with practical applications in software development and education. The recent publications highlight a strong trend in declarative GUI frameworks, multi-selection models, and generic programming. His work explores domain-specific languages for GUI structure manipulation, reusable selection semantics across platforms, and optimizing array computations using the Mathematics of Arrays. These efforts reflect a consistent focus on software abstraction, correctness, and reusability. Jaakko Järvi has supervised doctoral students, including Tetiana Yarygina, whose dissertation explored microservice security. While no specific grants are detailed, his work on VisAST was supported by the Research Council of Norway (Project 250683), indicating active external funding. He frequently collaborates with researchers like Magne Haveraaen, Knut Anders Stokke, and Sean Parent. He contributes to tools and frameworks such as the MultiselectJS library and the VisAST educational tool. These are outcomes of collaborative research teams focused on improving software development practices and computer science education.

Anthony GachaganView profile

Professor

• Ultrasonics
• Non-Destructive Evaluation (NDE)
• Ultrasonic Transducer Design
• +13 more

Professor Anthony Gachagan is a leading academic at the University of Strathclyde, serving as Head of Department and Research Director in the Department of Electronic and Electrical Engineering (EEE) within the Faculty of Engineering. He has been Director of the Centre for Ultrasonic Engineering (CUE) since 2010, leading a multidisciplinary team of around 55 researchers with over £5M in active funding. He also holds leadership roles in RCNDE as Academic Chair and Management Board member, and participates in BINDT committees. His research spans ultrasonic transducer design, non-destructive evaluation (NDE), robotics, high-power ultrasound, and industrial process control. His work is highly collaborative, involving national and international partnerships with industry and academia, and contributes to sectors such as energy, aerospace, nuclear, and healthcare. He is actively involved in major research initiatives aimed at net zero, structural integrity, and advanced manufacturing. Recent publications highlight his focus on robotic ultrasonic inspection, adaptive signal processing, phased array techniques, and in-process monitoring of additive manufacturing. These works demonstrate a strong trend toward automation, real-time defect detection, and integration of ultrasonic systems in industrial and safety-critical applications. Scientific Awards: The BINDT Annual Conference Award (2019) Prof Gachagan has secured significant grants from EPSRC, Innovate UK, and industry partners, including projects on future ultrasonic engineering, lightning impulse testing, and robotic inspection for offshore wind. He supervises numerous research students and leads large collaborative teams. He also contributes to research commercialization through IAA projects. He leads the Centre for Ultrasonic Engineering (CUE), a vibrant research unit integrating electronic, mechanical, and biomedical engineers, physicists, and material scientists. The centre focuses on next-generation ultrasonic technologies, robotics integration, and industrial deployment.

• Thermoelectric Energy Harvesting
• Waste Heat Recovery
• Sustainable Energy Systems
• +7 more

Eduard Massaguer Colomer is an Assistant Professor in the Department of Mechanical and Industrial Construction Engineering at the University of Girona, where he is part of the School of Engineering. He is a Serra Húnter Fellow, reflecting his strong research and academic trajectory. He is affiliated with the Research Group in Fluids, Energy and Environmental Engineering (GREFEMA) and leads research in thermoelectric energy harvesting and waste heat recovery. His research focuses on improving the efficiency of thermoelectric generators for heat recovery in automotive, industrial, and renewable energy systems. Key areas include: Thermoelectric generator design and optimization Waste heat recovery from exhaust gases and industrial processes Integration with solar, biomass, and marine systems Dynamic modeling and experimental validation Technology transfer and spin-off development His recent publications reveal a consistent focus on energy conversion efficiency, transient thermal behavior, and practical applications of thermoelectric systems across transportation, industrial, and domestic sectors. He has contributed significantly to hybrid energy systems and low-cost solutions for rural electrification. His scientific awards include: Extraordinary Doctoral Award 2016 (UdG) Volkswagen Think Blue Award 2015 Seal of Excellence 2019 (European Commission) Multiple awards from Repsol Foundation, Iberdrola Foundation, and La Caixa He has supervised two doctoral theses and led multiple competitive research projects, securing over €320k as Principal Investigator and €500k in total funding. His work has led to four patents and five technology transfer contracts. He co-founded the spin-off Nabla Thermoelectrics SL (2016–2021), demonstrating strong industry collaboration and innovation impact. He is actively involved in research leadership, having served as guest editor for a special issue on Solar Thermoelectric Generators in MDPI Energies. His lab work centers on the Heat and Cold Laboratory at the University of Girona, where experimental validation and prototype development are conducted.

Ciriaco D'AmbrosioView profile

Researcher

• Combinatorial Optimization
• Heuristics
• Metaheuristics
• +4 more

Ciriaco D'Ambrosio is a Research Fellow at the Department of Mathematics, University of Salerno, specializing in combinatorial optimization and its applications to wireless sensor networks. He teaches courses in operations research and maintains regular reception hours for students on Tuesdays (3:00-5:00 PM) and Wednesdays (4:00-5:00 PM), conducted both in-person and via Microsoft Teams. Education: PhD in Computer Science, University of Salerno (2015) - Thesis: models and algorithms for coverage in Wireless Sensor Network Laurea cum laude in Computer Science, University of Salerno (2011) Research Focus: D'Ambrosio specializes in combinatorial optimization, developing heuristics, metaheuristics, and math-heuristics for mixed integer linear programming problems. His work addresses challenging optimization problems in wireless sensor networks, particularly network lifetime maximization under coverage, connectivity, and interference constraints. His research bridges theoretical computer science with practical applications in sensor network design, seismic monitoring systems, and resource allocation problems. His methodology often combines exact approaches with sophisticated heuristic techniques to solve computationally difficult problems. Publication Trends: Analysis of D'Ambrosio's publications (2017-2025) reveals a progression from foundational work on sensor network lifetime problems toward increasingly sophisticated algorithmic approaches for combinatorial optimization. His recent work shows expansion into seismic monitoring applications while maintaining strong focus on knapsack problem variants and network optimization. His publications appear in high-quality journals including Soft Computing, Computers & Operations Research, and Networks, demonstrating both theoretical rigor and practical relevance of his research. Professional Activities: Member of the Italian Operations Research Society (AIRO) Associate Editor for Soft Computing, A Fusion of Foundations, Methodologies and Applications Active collaborator with researchers including Andrea Raiconi, Raffaele Cerulli, and Francesco Carrabs Research Infrastructure: D'Ambrosio works within the Department of Mathematics at University of Salerno's Fisciano Campus (Building F2, Room 040). His research contributes to the university's growing expertise in computational optimization and has practical applications in environmental monitoring systems like SEISMONOISY.