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Li WanView profile

Lecturer

• Acoustics
• Speech Processing
• Machine Learning
• +4 more

Li Wan serves as a Lecturer at the Language Centre within Queen Mary University of London's School of Languages, Linguistics and Film. Despite her institutional affiliation with language education, her research output demonstrates deep expertise in acoustics, machine learning, and sensor networks, reflecting an unusual interdisciplinary profile spanning engineering and biomedical applications. Her research interests center on acoustics and speech processing, with significant contributions to machine learning applications in sensor networks, signal processing, and natural language processing. Recent work focuses on binaural audio rendering, emotion recognition systems, and biomedical diagnostics using electrocardiogram data, often addressing real-world challenges like missing sensor data and class imbalance in classification tasks. This research bridges engineering disciplines despite her Language Centre appointment. Analysis of her 15 most recent publications reveals a strong trend toward physics-informed neural networks for audio applications and sensor-based human activity recognition. Her work frequently appears in specialized challenges like the SHL and Odyssey competitions, emphasizing practical implementations for locomotion recognition, drone audition, and text segmentation. Key methodological themes include multimodal fusion, data augmentation, and cross-domain adaptation. Scientific Awards: No formal awards or fellowships documented in provided sources While her Language Centre role suggests teaching responsibilities in Chinese language, her publication record indicates active research mentorship in engineering domains. No grants or student advisement details are publicly available, though her challenge competition participation implies collaboration with technical research teams. The absence of lab affiliations in current documentation contrasts with her hardware-oriented work on drone audition and sensor systems.

• Sustainable Manufacturing
• Production Systems
• Virtual Reality
• +7 more

Björn Johansson is a Professor at Chalmers University of Technology, specializing in Production Systems. His research focuses on sustainability aspects of manufacturing through virtual tools, aiming to minimize environmental, social, and economic impacts. Key methodologies include flow simulation, dynamic environmental assessments, 3D visualization, and layout optimization. Primary research areas: Sustainable Manufacturing, Digital Twins, Environmental Impact Assessment Collaborations: Mélanie Despeisse, Henrik Söderlund, and others in automotive, battery production, and maritime industries His work emphasizes integrating digital technologies (e.g., VR, IoT) with sustainable practices, addressing challenges in supply chain resilience, human-robot collaboration, and circular economy models. Recent studies explore VR training environments, 5G-enabled manufacturing, and extended reality (XR) frameworks. Current projects involve 44 initiatives across battery systems, servitization, and digitalization for sustainability. Publications span 179 articles, including topics like digital twin implementation, ergonomic VR assessments, and hybrid simulation models for environmental analysis.

• Visual Analytics
• Human-Computer Interaction
• Trustable AI
• +5 more

David Ebert is a Gallogly Chair Professor of Electrical and Computer Engineering at the University of Oklahoma (OU), where he serves as Director of the Data Institute for Societal Challenges. He is also an Adjunct Professor at Purdue University and leads the Department of Homeland Security’s Visual Analytics Center of Excellence (VACCINE). Education: Ph.D., M.S., B.S. in Computer Science from The Ohio State University Research Interests : His work spans visual analytics , human-computer teaming , trustable AI , and data science with applications in energy/climate resiliency, defense/security, health, and digital humanities. Recent projects include predictive visual analytics for public safety and social media spambot detection. Scientific Awards : IEEE Fellow IEEE Computer Society vgTC Technical Achievement Award National Science Foundation's InTRO grant University Faculty Scholar DHS S&T Award of Excellence Leadership Roles : Associate Vice President of Research and Partnerships at OU, with directorships in multiple research centers focused on visual analytics and information assurance.

Eduard GröllerView profile

Professor

• Visual Computing
• Scientific Visualization
• Volume Visualization
• +5 more

Eduard Gröller is a Full Professor of Visualization at the Vienna University of Technology (TU Wien), leading the Research Unit of Computer Graphics and the Visualization Group within the Institute of Visual Computing & Human-Centered Technology (VC&HCT). He holds adjunct professorships at the University of Bergen, Norway, and chairs several academic committees. His academic journey began with a PhD in 1993 from TU Wien, followed by extensive contributions to visualization research. His research focuses on visual computing, scientific visualization, and medical imaging, with applications in energy modeling, climate analysis, and molecular biology. Gröller has pioneered methods like BEMTrace for BIM-based energy models and HORA 3D for flood risk visualization. He co-authored over 300 publications, including foundational work in IEEE Transactions on Visualization and Computer Graphics and Computer Graphics Forum . Gröller's awards include the IEEE VGTC Technical Achievement Award (2019), Eurographics 2015 Outstanding Technical Contributions Award, and Fellow of the Eurographics Association (2009). He actively contributes to conferences like EuroVis and IEEE Visualization as program chair and reviewer. His educational efforts span courses in visual computing, graphics, and data analysis, with a focus on immersive tools like ImNDT for material data exploration. Current projects include Climate-Sensitive Adaptive Planning for Resilient Cities, Visual Analytics in Radiation Therapy, and scalable web-based visualization techniques. He leads teams in VRVis, a research center for applied visualization, and collaborates internationally on medical imaging and environmental data challenges.

Irene KoronakiView profile

Professor

• Thermodynamics
• Cooling Technology
• Heat Pumps
• +7 more

Irene Koronaki is a Professor at the National Technical University of Athens , affiliated with the School of Mechanical Engineering and the Thermal Engineering Section . She serves as Director of the Laboratory of Applied Thermodynamics, Cooling Technology & Refrigerated Vehicles since 2022 and has held academic roles at NTUA since 1999. Her research focuses include thermodynamics, heat pumps, energy efficiency, and renewable energy systems. Diploma in Mechanical Engineering, NTUA (1996) PhD in Thermal Engineering, NTUA (2000) Postdoctoral Researcher, NTUA (2002) Her research spans thermodynamics of cooling cycles, heat pumps, power cycles, energy saving in buildings, and thermal energy storage. She has pioneered work in nanofluids, solar cooling, and CO2 absorption systems. Her publications and projects reflect expertise in Stirling engines, hybrid solar collectors, and building energy optimization. Her recent articles highlight advancements in superfluid thermodynamics, solar PV/T systems, and medical robotics. Awards include the Edward F. Obert Award (2022) and leadership of the 2021 ASHRAE Student Design Competition winning team. She serves on ASME and ASHRAE committees and co-authored educational materials for refrigeration and energy inspection standards.

• Climate Change Economics
• Environmental Policy
• Sustainable Development
• +4 more

Mathias Kirchner is a Researcher at the University of Natural Resources and Life Sciences, Vienna (BOKU), affiliated with the wZGWN Centre for Global Change and Sustainability. Currently on parental part-time status (available Mondays and Wednesdays), he contributes to teaching and research in climate change economics, with a focus on policy-relevant modeling for Austrian sustainability transitions. His research spans : Climate Change Economics (CO2 pricing, decarbonization pathways) Environmental Policy (land use, stakeholder integration) Social Equity (distributional impacts, poverty assessments) Integrated Assessment Modeling (system dynamics, multi-model approaches) Work emphasizes Austrian contexts while addressing global challenges like SDG implementation and climate justice. Recent publications (2022-2025) reveal three dominant trends : (1) Economic modeling of carbon taxation with focus on revenue recycling and triple dividends, (2) Social equity analyses of decarbonization in housing/mobility sectors, and (3) Stakeholder-driven methodologies for SDG pathway validation. Key subfields include distributional impacts across income groups, land-use change under climate scenarios, and causal loop diagram applications. Dr. Kirchner operates within the wZGWN Centre for Global Change and Sustainability , an interdisciplinary hub at BOKU addressing climate adaptation, resource management, and policy coherence through integrated modeling approaches that bridge natural and social sciences.

Edward SUNView profile

Professor

• Data Science
• FinTech
• Mathematical Finance
• +7 more

Edward SUN is a Professor in Data Science and FinTech at Kedge Business School since 2018, with a PhD in Statistics and Mathematical Finance from the University of Karlsruhe (2007) and HDR in Management Sciences from the University of Bordeaux (2018). His research spans applied mathematics, operations research, artificial intelligence, and financial engineering, with notable collaborations with Nobel Laureate-affiliated supervisors. Key research areas: Data Science, FinTech, Mathematical Finance, Logistics 4.0, and Business Analytics Recent publications focus on spatiotemporal analysis, digital twin applications, and machine learning in financial services Teaching domains include data-driven decision-making and predictive analytics

Jim HarkinView profile

Professor

• Embedded Systems
• Neural Networks
• Hardware Systems
• +5 more

Professor Jim Harkin serves as Head of the School of Computing, Engineering and Intelligent Systems at Ulster University's Magee Campus in Derry~Londonderry. He is a prominent member of the Computational Neuroscience and Neuromorphic Engineering team within the Intelligent Systems Research Centre (ISRC), where he leads cutting-edge research bridging biological neural processes with hardware implementations. Harkin's research focuses on developing intelligent embedded systems capable of self-repair under error conditions, drawing inspiration from neural network models. His work explores how computer models of neural networks can be mapped to hardware to build highly efficient and reliable embedded computers. Key innovations include Networks-on-Chip strategies and hardware implementation of self-repairing Spiking Neural Networks. His research spans multiple domains including fault tolerance, neuromorphic computing, and AI hardware acceleration, with applications in healthcare, structural monitoring, and energy systems. Analysis of his recent publications reveals a strong trend toward practical applications of neuromorphic computing, particularly in healthcare monitoring systems, structural health assessment, and energy-efficient computing. His work shows increasing integration of spiking neural networks with real-world hardware implementations, demonstrating a clear trajectory from theoretical models to deployable systems with commercial applications. Harkin has received numerous scientific accolades including the Life and Health Startup Company of the Year 2019 from InventNI Ulster Distinguished Learning Support Fellowship Multiple awards for innovative routing strategies in neural network hardware implementations Professor Harkin has secured significant research funding exceeding £3.5 million from diverse sources including EPSRC, MRC, Innovate UK, HSC R&D, InvestNI, and DEL. His grant portfolio demonstrates strong industry and healthcare sector engagement, particularly through his co-founded startup Respiratory Analytics which focuses on medical analytics. He has supervised numerous research students and has been instrumental in Ulster University's Computer Science submissions to major research assessment exercises including RAE 2008, REF2014, and REF2021. As Head of School and leader within the Intelligent Systems Research Centre, Harkin oversees multiple research teams focusing on computational neuroscience, neuromorphic engineering, and intelligent embedded systems. His lab has developed specialized FPGA-based platforms for simulating and implementing self-repairing neural networks, including the AstroByte multi-FPGA architecture for accelerated simulations of fault-tolerant spiking astrocyte-neuron networks.

• Hydraulic Fluid Power
• Fluid Mechanics
• Positive Displacement Pumps
• +5 more

Massimo Rundo is an Associate Professor at the Polytechnic of Turin 's Department of Energy (DENERG) and coordinator of the Fluid Power Research Laboratory (FPRL) . With over 70 Scopus-indexed publications, his work focuses on modeling, simulation, and experimental validation of fluid power systems. Research Interests: Positive displacement pumps (gerotor, gear, vane, piston) Hydraulic valves (flow forces, pressure control) Mobile hydraulics (excavators, telehandlers) CFD and lumped parameter modeling Incomplete filling phenomena at high speeds Recent Publications analyze flow dynamics in pumps, vibration-based fault detection, and energy-efficient hydraulic architectures through 1D/3D simulations. He has coordinated industry-funded projects with companies like Casappa and Pierburg Pump Technology. Honors: MDPI Energies Editor's Choice (2018) SAE Excellence in Oral Presentation (2004) SAE Certificate of Appreciation (2000) Rundo teaches graduate courses in Mobile and Industrial Fluid Power and advises master's theses, while serving as Associate Editor for the International Journal of Fluid Power and committee member for major conferences including ASME/BATH Symposium and International Maha Fluid Power Conference.

• Coastal Adaptation
• Climate Resilient Design
• Urban Ecology
• +3 more

Kees Lokman is an Associate Professor and Chair of Landscape Architecture at the University of British Columbia (UBC) School of Architecture and Landscape Architecture. His research addresses design challenges related to sea level rise adaptation, water/food shortages, and energy transitions, with a focus on community values. He directs the UBC Coastal Adaptation Lab and leads the transdisciplinary project Living with Water , funded by the Pacific Institute for Climate Solutions. Education: MDesS, Harvard Graduate School of Design MSc, Wageningen University BSc, Wageningen University His work intersects coastal adaptation, urban ecology, and climate-resilient design, emphasizing collaboration with Indigenous communities and governmental stakeholders. Research outcomes have been published in journals like Journal of Landscape Architecture and Landscape Research . Scientific Trends: Recent articles explore fluid geographies, flood-resilient landscapes, and integrating Indigenous knowledge into coastal adaptation. Key subfields include design frameworks for uncertainty, GIS-based risk visualization, and urban metabolism applications. Awards & Funding: CELA 2019 Excellence in Research and Creative Work Award (Junior Level) Natural Resources Canada Social Sciences and Humanities Research Council Creative Industries Fund NL Hampton New Faculty Grant Professional Practice: He has practiced at Piet Oudolf’s office in the Netherlands and Terry Guen Design Associates in Chicago, and founded Parallax Landscape . His lab collaborates with Squamish and Tsleil-Waututh First Nations on coastal flooding solutions.

Ketan P. DetrojaView profile

Associate Professor

• Control Theory
• State Estimation
• Fault Diagnosis
• +9 more

Ketan P. Detroja is an Associate Professor in the Department of Electrical Engineering at Indian Institute of Technology Hyderabad . With a Ph.D. from IIT Bombay (2006) and a specialization in Multivariable Control, Distributed State Estimation, and AI/ML for Control Applications , his research focuses on ensuring fault-tolerant and economical operation of microgrids , developing distributed Kalman filtering techniques , and applying machine learning to process monitoring and control . Education: B.E., D. D. Institute of Technology, Gujarat, India Ph.D., IIT Bombay, 2006 Research Interests span Advanced Process Control, Fault Detection and Diagnosis, Cooperative Control, Microgrid Optimization and Control , and Hardware-Software Design for Control Systems . His work addresses challenges in data-driven process monitoring , distributed state estimation , and machine learning-based controller design , particularly for large-scale systems. Scientific Contributions include innovative approaches to inverse-free Kalman filters , reinforcement learning-based PI controllers , and data reduction algorithms for fault diagnosis. His publications reflect a focus on control theory, optimization, and machine learning integration , with applications in power systems, wind energy, and fault diagnostics . Scientific Awards: Best Paper Award at ADCONIP - 2011 Best Paper Award at DYCOPS-2013 Advising has been a significant part of his career, with supervision of Ph.D. students working on topics like fault detection in induction motors , microgrid scheduling , and adaptive control using fuzzy logic . His M.Tech students have explored areas such as visual object tracking, battery SOC estimation, and wind farm control , many of whom are now in prominent roles across academia and industry.

• Robotics
• Simultaneous Localization and Mapping (SLAM)
• Sensor Fusion
• +5 more

Maurice Fallon is a Professor of Engineering Science at the University of Oxford and a Royal Society University Research Fellow, leading the Dynamic Robot Systems Group (Perception) at the Oxford Robotics Institute. His research focuses on robust probabilistic methods for localization and mapping in challenging environments through advanced sensor fusion. Education: Electronic Engineering, University College Dublin PhD in Acoustic Source Tracking, University of Cambridge Research Interests: Dr. Fallon specializes in probabilistic state estimation , legged robot navigation , and dynamic motion planning for autonomous systems operating in vision-denied or complex natural environments. His work emphasizes robustness through multi-sensor integration , with applications spanning disaster response, forestry, and industrial inspection. Key innovations include terrain-aware locomotion and long-term autonomy frameworks. Publication Trends: Recent work (2024-2025) demonstrates a strategic shift toward forest robotics and long-term industrial inspection , leveraging legged and aerial platforms. There is strong emphasis on vision foundation models for place recognition, scalable 3D reconstruction using neural radiance fields, and open-vocabulary scene understanding . The research consistently addresses real-world challenges like lighting variations, sensor dropout, and environmental dynamics. Scientific Awards: Royal Society University Research Fellowship 4x Best Paper Awards at ICRA Nominations at Intelligent Vehicles, AAAI, and Humanoids conferences Advising and Grants: Dr. Fallon has secured major funding as PI/Co-I for EU/UK projects including ORCA, RAIN, THING, MEMMO, and the DARPA SubT-winning CERBERUS team. Current initiatives include the Horizon Europe DigiForest project and UKAEA collaborations. He mentors PhD students and postdocs in robotics systems development, though specific advisees aren't listed in source materials. Labs and Teams: He directs the Dynamic Robot Systems Group, which achieved global recognition through DARPA Robotics Challenge participation and SubT Challenge victory. The team operates specialized facilities for legged robot testing and maintains partnerships with nuclear energy and forestry sectors for field deployment.

• Design automation for intelligent cyber-physical systems
• Internet-of-Things applications
• Safe and robust machine learning for embodied AI
• +7 more

Professor Zhu Qi is a faculty member in the Department of Electrical and Computer Engineering at Northwestern University's McCormick School of Engineering, with courtesy appointment in Computer Science. He leads the IDEAS Lab (Design Automation of Intelligent Systems Lab) where his research focuses on design automation for intelligent cyber-physical systems and Internet-of-Things applications. His research interests include safe and robust machine learning for embodied AI systems, cyber-physical security, energy-efficient CPS, and system-on-chip design. Professor Zhu's work particularly addresses safety, robustness, security, adaptability, resiliency, and energy challenges in the design and operation of embodied AI systems. His applications span connected and autonomous vehicles, robotics, advanced manufacturing, wearable computing, smart buildings and infrastructures, and IoT. Professor Zhu's recent publications reveal a strong focus on safety verification of neural network controlled systems, robust reinforcement learning methods, and applications of large language models in autonomous systems. His work often combines formal verification techniques with machine learning approaches to provide safety guarantees for AI-enabled cyber-physical systems. DATE 2022 Best Paper Award AutoSec 2021 Best Short Paper Award ACM TODAES 2016 Best Paper Award IEEE TCCPS Early-Career Award (2017) Humboldt Research Fellowship for Experienced Researchers (2017) NAE US Frontiers of Engineering participant (2020) Professor Zhu has secured multiple research grants from NSF (including FM, DESC, and Fuse grants), DOE, ONR, and industry partners including GM and Toyota. His advising includes PhD students Shuyue Lan and Hengyi Liang, and postdoc Chao Huang who became a Lecturer at University of Liverpool. He leads the IDEAS Lab which focuses on cross-layer design, verification, and adaptation of learning-enabled cyber-physical systems.

Nikolaos SamarasView profile

Professor

• Algorithm Analysis
• Computational Complexity
• Algorithmic Operations Research
• +9 more

Nikolaos Samaras is a Full Professor at the Department of Applied Informatics, School of Information Sciences, University of Macedonia in Thessaloniki, Greece. He has been serving as director of the Computational Methodologies & Operations Research (CMOR) Laboratory since April 2016. His academic career includes positions as Assistant Professor (2007-2012) and Lecturer (2003-2007) at the same institution, and earlier as an adjacent Lecturer at the Technological Institute of Western Macedonia (1998-2000). Dr. Samaras earned his Diploma in Applied Informatics from the University of Macedonia in 1996 and his Ph.D. in Applied Informatics from the same university in 2001. His educational background forms the foundation for his extensive research in computational optimization and operations research. Professor Samaras's research focuses on the interface between computer science and operations research, with particular expertise in linear and nonlinear optimization, network optimization, integer optimization, and scientific computing including HPC and GPU programming. His work has resulted in the development of new algorithmic families for optimization problems, efficient GPU implementations of the revised simplex algorithm, and novel algorithms and software for operations research. His research spans theoretical algorithm development, practical implementation, and real-world applications across various engineering and scientific domains. His extensive publication record includes over 35 journal papers in prestigious venues such as Computers and Operations Research, European Journal of Operational Research, and Journal of Artificial Intelligence Research, more than 85 conference papers, and four textbooks (two in English and two in Greek). His work has been recognized through citations and the Thomson ISI/ASIS&T Citation Analysis Research Grant in 2005. ACM Senior Member (2016) Thomson ISI/ASIS&T Citation Analysis Research Grant (2005) Editorial board member of Operations Research: An International Journal Reviewer for numerous top journals including Mathematical Programming Computation and European Journal of Operational Research Professor Samaras has supervised four current Ph.D. students working on hybrid simplex algorithms, large-scale optimization using Apache Hadoop, algorithmic procedures in matrix theory, and smoothed complexity analysis. He has successfully guided five Ph.D. students to completion, including Nikolaos Ploskas who won the 2014 HELORS Doctoral Dissertation Award. Additionally, he has supervised 45 master's theses and 84 bachelor's theses. His research group has secured funding from diverse sources including the European Union, Greek Secretariat of Research and Technology, and industry partners like Veltio Greece LTD. The Computational Methodologies & Operations Research (CMOR) Laboratory, which he directs, focuses on developing and implementing optimization algorithms with applications in transportation, energy systems, and business process design. The lab has produced notable software tools including Euclides and Visual LinProg, which have educational applications in linear programming.

• Mechanical Engineering
• Artificial Intelligence
• Tribology
• +3 more

Hassan Shirvani is a Professor of Engineering Design and Simulation at the School of Engineering and the Built Environment, Anglia Ruskin University. He serves as Director of the Engineering Analysis Simulation and Tribology (EAST) Research Group, focusing on industry collaborations to solve engineering challenges. PhD in Mechanical Engineering, University of Bath MSc in Mechanical Engineering, University of Birmingham Member, Institute of Mechanical Engineers (IMechE) His research spans mechanical engineering, artificial intelligence, and biomedical applications, including: Thermal system optimization Machine learning in clinical decision-making Composite metal foil manufacturing Virtual reality medical training systems Flow dynamics in heat exchangers and nozzles AI-assisted diagnostics Hybrid manufacturing processes Hassan's publications reflect expertise in computational modeling, multi-physics simulations, and industrial applications. Notable areas include deep learning for suicide prediction, thermodynamic analysis of sustainable energy systems, and tribology in mechanical components.