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Florian DosterView profile

Professor

• Subsurface Flow Phenomena
• Geological Carbon Storage
• Porous Media Modeling
• +3 more

Florian Doster is a Professor at Heriot-Watt University, affiliated with the School of Energy, Geoscience, Infrastructure and Society and the Institute for GeoEnergy Engineering. He leads the MuPhi Research Group, focusing on multi-scale modeling of subsurface flow phenomena, particularly in porous media and fractured reservoirs. His work addresses challenges in CO2 storage, hydrocarbon production, and groundwater management. He actively supervises PhD students, including Amanzhol Kubeyev, and collaborates with industry and research councils. Research interests include multi-scale simulation, reactive transport, and AI-driven uncertainty quantification, with applications to geological carbon sequestration and reservoir engineering. His group develops open-source tools, such as the Carbonate Reservoir Group HWU code. Notable projects include regional screening of saline aquifers in Malaysia and fracture mechanics studies. Publications highlight advancements in AI for subsurface modeling, CO2 leakage risk assessment, and pore-scale interactions. Funding sources include industry partnerships and research councils. The MuPhi Group’s activities span geomechanics, fracture network analysis, and environmental sustainability.

Jiangfeng ZhangView profile

Associate Professor

• Battery Management Systems
• Smart Grid Integration
• Renewable Energy
• +2 more

Jiangfeng Zhang is an Associate Professor in the Department of Automotive Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on electric vehicle technologies, battery management, renewable energy integration, and smart grid solutions. Research Areas: His work spans battery optimization, grid resilience, autonomous vehicle control, and sustainable energy planning. Recent projects include solid-state battery advancements, V2G integration, and AI-driven energy management. Publication Trends: His articles predominantly address optimization challenges in electric mobility and grid stability, utilizing machine learning, game theory, and control systems. Key themes include decarbonization, cyber-physical security, and renewable integration under uncertainty.

Allahyar AkhbariView profile

Lecturer

• Energy
• Power Electronics
• Controls
• +7 more

Allahyar Akhbari is a Lecturer and Research Assistant at Aarhus University's Faculty of Engineering and Science, specializing in Energy Section focusing on Offshore Renewable Energy Systems. He holds a PhD in Electrical Power Engineering from the University of Kashan (2017-2022) and conducted a Guest PhD at Aarhus University (2021-2022). His academic roles include multiple Lecturer positions in MATLAB, Electrical Basics, Power Electronics, and Electrical Machines Laboratories at the University of Kashan (2018-2021). Research Interests: His work centers on energy systems integration, particularly DC microgrids, wind turbine control, and stability analysis of renewable energy systems. He explores artificial intelligence applications in power systems and hybrid energy generation systems combining wind, microturbines, and constant power loads. His research emphasizes real-time control, fault ride-through mechanisms, and efficient power management strategies. Projects: He participates in major initiatives including the BlueBARGE project (EU-funded, 2024-2026) for renewable bunkering solutions and the Wave Energy Converter project (2023-2025) focusing on reliable energy generation. Additional projects include FOSS (floating offshore substations) and Damping System research for semi-submersible wind turbines. Advising & Grants: Though no student advisees are listed, he is actively involved in multiple EU-funded research projects as a project participant. His work aligns with renewable energy transition goals, emphasizing offshore systems and grid stability. Labs & Teams: Collaborates within Aarhus University's Energy Section teams, particularly in offshore renewable energy systems and wave energy converter analysis. Engages with cross-disciplinary groups in power electronics and control systems development.

• Computational Thinking
• Neural Networks
• Machine Learning
• +6 more

Anthony V. Robins is a Professor in the Department of Computer Science at the University of Otago, New Zealand. His work spans neural networks , computational cognition , computer science education , and machine learning . With over three decades of publication history, Robins has contributed to understanding catastrophic forgetting , prototype analysis , and reinforcement learning in artificial neural networks. His recent research (2024-2025) focuses on Hopfield networks and Boltzmann neural networks , particularly their applications in state classification , supra-classical logic , and sequential learning . Collaborations with researchers like Hayden McAlister and Lech Szymanski highlight his work in memory systems and Hebbian learning . Robins has also made significant contributions to high school computing education in New Zealand, analyzing curriculum implementation , student perspectives , and teacher challenges through publications in venues like ACM Trans. Comput. Educ. and ACE. His work on self-regulated learning and educational frameworks addresses systemic issues in computing education.

• Robotics
• Control Systems
• Autonomous Systems
• +6 more

Zehui Lu is a Visiting Assistant Professor at the Department of Aeronautics and Astronautics, Purdue University. Their research focuses on robotics, control systems, autonomous systems, and optimization, with applications in multi-agent systems, aerospace engineering, and battery management. Dr. Lu’s work integrates machine learning techniques with advanced control methodologies to address challenges in trajectory planning, distributed algorithms, and real-time mission execution in complex environments. Key research trends in their publications emphasize distributed optimization for multi-robot systems, adaptive control strategies for autonomous navigation, and deep learning-based approaches for dynamic system modeling. Notable contributions include innovations in spacecraft swarm coordination, battery network management under extreme conditions, and human-in-the-loop robotic formation control. Dr. Lu’s interdisciplinary work bridges theoretical control frameworks with practical engineering implementations, particularly in aerospace and mechatronics domains. No scientific awards or grants are explicitly listed in the provided information. Advising details are not available, but their research group actively explores topics such as adaptive policy learning, distributed motor-motion co-design, and real-time cluttered environment navigation. The full scope of their lab activities and collaborations remains unspecified in the current data.

• Multi-scale Modeling
• Powder Forging
• Mechanical Engineering
• +6 more

Mr. Yi Wang is a researcher at the Department of Mechanical Engineering, Imperial College London, specializing in Multi-scale Modeling of Powder Forging and related fields. His work bridges mechanical engineering, energy systems, and artificial intelligence. University: Imperial College London Department: Department of Mechanical Engineering Research Interests include electric vehicle-grid integration, microgrid resilience, reinforcement learning applications in energy systems, and multi-energy network optimization. His publications emphasize AI-driven strategies for low-carbon transitions and grid stability. Email: yi.wang12@imperial.ac.uk

Miloš MravikView profile

Researcher

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +3 more

Miloš Mravik is an academic affiliated with Singidunum University's Faculty of Informatics and Computing, specializing in research involving artificial intelligence, cybersecurity, and data science. He holds a doctoral degree in 'Advanced Protection Systems' from Singidunum University (2020–2023), preceded by a Master’s in 'Contemporary Information Technologies' (2018–2019) and a Bachelor’s in 'Informatics and Computing' (2014–2018). His work emphasizes applying machine learning techniques to real-world challenges like healthcare diagnostics, cybersecurity frameworks, and pandemic-era education systems. Research Interests: AI-driven cybersecurity solutions for IoT and blockchain Machine learning applications in health informatics Optimization of predictive algorithms using metaheuristics E-learning strategies during crises Recent articles focus on explainable AI for metaverse security, sentiment analysis using BERT models, and blockchain node detection via XGBoost. His work bridges theoretical computer science with practical applications in emergency education and pandemic response. No scientific awards listed. Advised no formally registered students. Active in conference organizing and software prototyping, as seen in projects like a scheduling web application and network management systems.

Paulette ClancyView profile

Professor

• Computational Materials Science
• Machine Learning for Materials Discovery
• Bayesian Optimization
• +12 more

Paulette Clancy is the Edward J. Schaefer Professor in Engineering at the Whiting School of Engineering, Johns Hopkins University, where she leads a prominent research group in computational materials science. She is the director of research for the JHU Data Science and AI Initiative and associate director of the Johns Hopkins Center for Integrated Structure-Mechanical Modeling and Simulation (CISMMS), and a fellow of the Hopkins Extreme Materials Institute (HEMI). Education: Bachelor’s in Chemistry, Queen Elizabeth College, University of London (1974) DPhil in Physical Chemistry, Oxford University (1977) Postdoctoral research at Cornell University and London University Her research focuses on the atomic- and molecular-scale modeling of semiconductor materials, particularly organic and hybrid systems. Key areas include advanced organic materials (covalent organic frameworks, organic electronics), algorithm development (machine learning, Bayesian optimization, force fields), electronic materials (III-V semiconductors), and nucleation and crystal growth (perovskites, quantum dots). She develops physics-informed machine learning frameworks like PAL 2.0 to accelerate materials discovery. The recent publications highlight a strong trend in integrating machine learning (especially Bayesian optimization) with computational chemistry to model and predict material behavior during processing. Topics span perovskite crystallization, gallium nitride growth, polymer thermoelectrics, and quantum dot self-assembly, reflecting a broad impact in semiconductors , renewable energy materials , and nanomaterials . Scientific Awards and Honors: Fellow, Royal Society of Chemistry Fellow, American Institute of Chemical Engineers (AIChE) AIChE National Women’s Initiatives Mentoring Award Alice Cook Award (Cornell) Zellman Warhaft Award (Cornell) Clancy is a dedicated mentor, having advised numerous PhD and master’s students who have gone on to careers in academia and industry. Her group has received funding for AI-driven microelectronics training through a $2.7M NSF award. She is also a founding chair of Women in Science and Engineering at Cornell and a strong advocate for diversity in STEM. Labs and Teams: Clancy Research Group, JHU – focused on computational materials discovery Collaborator with Mitra Taheri’s group on real-time materials monitoring Director of Research, JHU Data Science and AI Initiative Associate Director, CISMMS Fellow, HEMI

• Semantic Web
• Internet of Things
• Stream Reasoning
• +5 more

Pieter Bonte is a FWO Senior postdoctoral fellow and IMEC Postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology. His research focuses on Semantic Web technologies, stream reasoning, and Internet of Things applications. His research interests span Semantic Web, Internet of Things, Stream Reasoning, Knowledge Graphs, Context-aware Systems, RDF Processing, Linked Data, and Healthcare Informatics. His work bridges theoretical semantic technologies with practical applications, particularly in healthcare and IoT domains. Bonte's publication record shows a strong focus on streaming data processing, with numerous papers on Streaming Linked Data, context-aware query derivation, and semantic reasoning frameworks. His research demonstrates a progression from foundational semantic web technologies toward practical implementations in healthcare and IoT applications, with an increasing emphasis on privacy considerations and efficient processing techniques. His work frequently involves collaborations with Femke Ongenae, Filip De Turck, and other researchers at Ghent University and IMEC, indicating strong institutional research networks. His publications appear in respected venues including the Journal of Web Semantics, Semantic Web Journal, and various conference proceedings in the semantic technologies field.

Imran QureshiView profile

Associate Professor

• Gas Turbine Engines
• Experimental Fluid Dynamics
• Computational Fluid Dynamics
• +10 more

Dr. Imran Qureshi is an Associate Professor in the Department of Mechanical Engineering at the University of Birmingham Dubai Campus, part of the School of Engineering. He previously served as an Assistant Professor at the American University of Sharjah, UAE, before joining the University of Birmingham. His academic and industrial expertise lies in mechanical and aerospace engineering, particularly in gas turbine systems and sustainable energy technologies. BEng (Hons) in Aerospace Engineering, NUST, Pakistan MSc in Mechanical Engineering, University of Leeds, UK DPhil in Engineering Science, University of Oxford, UK Chartered Engineer (CEng), IMechE UK Dr. Qureshi's research focuses on experimental and computational fluid dynamics , gas turbine engines , heat transfer , and sustainable energy systems . His work bridges industrial applications and academic inquiry, particularly in improving turbine performance and advancing renewable energy technologies. He has investigated aggressive inlet swirl effects, combustor temperature nonuniformity, and dynamic engine modeling using artificial intelligence. His recent publications reflect a growing emphasis on sustainability, with studies on net-zero energy buildings , photovoltaic and concentrated solar technologies , and hydrogen propulsion . The integration of AI in turbine diagnostics and performance monitoring marks a significant trend in his scholarly output. His work spans both fundamental aerothermal investigations and applied sustainability solutions. Dr. Qureshi has no listed scientific awards in the provided text. He teaches undergraduate modules including Thermodynamics and Fluid Mechanics , Sustainable Energy and Environment , Engineering Mathematics , Powertrain and Vehicle Engineering , and supervises final-year individual engineering projects. There is no mention of students he has advised or research grants he has led. No specific lab or research team is referenced in the text.

• Edge Computing
• Next-Generation Networks
• Internet-wide Measurements
• +5 more

Nitinder Mohan is an Assistant Professor in the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS) at Delft University of Technology (TU Delft), where he leads the Systems and Protocols for Edge-Enabled Internet (SPEAR) Lab. His research focuses on edge computing, next-generation network protocols, and Internet-wide measurements. Previously, he was a senior researcher at Technical University of Munich and holds a PhD from University of Helsinki (awarded IEEE TCSC Outstanding Dissertation Award). Research interests span: Edge computing infrastructure and orchestration Next-generation network protocols (MPTCP, QUIC) Internet-wide measurements and performance analysis Satellite networking (Starlink/LEO networks) Distributed machine learning at edge His publications demonstrate strong focus on practical systems research in edge computing (container orchestration, Oakestra framework), network protocol innovation (segment routing, MPTCP), and empirical Internet measurements (Starlink/CDN analysis). Recent work shows growing emphasis on satellite networking and edge AI. Awards & Honors: IETF/IRTF Applied Networking Research Prize (2025) ACM EdgeSys Best Paper Award (2025) RIPE Academic Cooperation Fellowship (2025) ACM EuroSys Best Poster Award (2025) IEEE Future Networks Best Paper (2023) IEEE TCSC Outstanding PhD Dissertation (2020) Advises multiple PhD and master's students on edge computing, networking, and distributed systems. Secured significant funding including EU Horizon 2020 grant (€5.4M for EDGELESS project). Founded Oakestra - an open-source edge orchestration framework. Leads the SPEAR Lab at TU Delft focusing on edge-enabled Internet systems. Organizes conferences (TMA 2026) and workshops (LEO-NET). Active in IETF/IRTF standards community and industry collaborations with Microsoft Research, Airbus, and Siemens.

Chengnian SunView profile

Associate Professor

• Software Engineering
• Programming Languages
• Compiler Testing
• +4 more

Chengnian Sun is an Associate Professor at the Cheriton School of Computer Science , University of Waterloo, Canada. His research focuses on software engineering and programming languages with an emphasis on software reliability and programming productivity. Education : Ph.D. in Computer Science from National University of Singapore (2013) His work spans compiler testing (EMI, Dfusor, Kitten), program reduction (Perses, Vulcan, PPR), Android testing, and DNN testing. He has received multiple grants including Google Research Scholar Program (2025) and NSERC Discovery Grants (2024-2029). His recent publications focus on LLM-based compiler testing, weighted delta debugging, and ransomware resilience. Scientific Awards : Most Influential Paper Award at SANER (2022) NUS Research Scholarship (2008-2012) ACM SIGSOFT Distinguished Paper Award at ASE (2012) IBM Cup Campus Innovation Contest First Prize (2005) He advises Ph.D. and MMath students in software engineering, compiler testing, and program analysis, including several who have contributed to top-tier conferences like ICSE, ISSTA, and ASPLOS. His service includes program committee roles in ICSE, OOPSLA, and ISSTA.

• Electrochemical Energy Storage
• System Design Methodologies
• Energy Systems Optimization
• +5 more

Prof. Dr. Walter Commerell serves as Professor at Technische Hochschule Ulm (THU) , holding the role of International Representative for the Faculty of Production Engineering and Production Management. He is the Spokesperson for ASIM (Simulation of Technical Systems Division) and Head of Steinbeis Transfer Center System Design , while also being a member of the International Energy Competence Center (IECN) and Institutes for Energy and Drive Technology/Vehicle Systems Technology.

• Bioinformatics
• Genomics
• Machine Learning
• +2 more

Professor Jinyan Li is an Adjunct Professor at the University of Technology Sydney's Data Science Institute, where he leads the Bioinformatics Program. His research develops computational methods for genomic analysis, protein interaction prediction, and biomedical data mining. Educational background: PhD in Computer Science (University of Melbourne) M.Eng in Computer Engineering (Hebei University of Technology) B.Sc in Applied Mathematics (National University of Defense Technology) Research spans: Genomic error correction algorithms Protein binding prediction Single-cell multi-omics analysis CRISPR design optimization Machine learning in bioinformatics With 140+ journal publications and 100+ conference papers, his work appears in leading venues including Bioinformatics, Nucleic Acids Research, and IEEE TKDE.

Joshua I GlaserView profile

Assistant Professor

• Computational Neuroscience
• Neural Control of Movement
• Interpretable Machine Learning
• +7 more

Joshua I Glaser is an Assistant Professor at Northwestern University, holding dual appointments in the Department of Neurology (Feinberg School of Medicine) and the McCormick School of Engineering. His research focuses on computational neuroscience, particularly the neural control of movement and interpretable machine learning. Glaser earned a BS in Physics and Math from the University of Illinois Urbana-Champaign (2011), a PhD in Neuroscience from Northwestern University (2018), and completed postdoctoral training at the University of Pennsylvania and Columbia University. Research Interests: Glaser’s lab develops interpretable machine learning tools to understand neural computations underlying movement and behavior. Key areas include neural population interactions, flexible behavior control, and disease-related neural dynamics. Projects include interpretable dimensionality reduction, decoding models linking neural activity to behavior, and multi-population neural interaction analysis. Lab & Collaborations: The Glaser Lab collaborates with experimental neuroscientists and statisticians. Current projects involve developing novel methods for analyzing neural data while maintaining interpretability. They host a team of PhD, master’s, and undergraduate students. Grants & Awards: While no specific awards are listed, his work reflects sustained funding in computational neuroscience and engineering. Code & Resources: Lab-developed tools include Neural_Decoding (machine learning for neural decoding), SSM (state-space models), and Sparse Component Analysis (interpretable latent factor analysis). These are publicly available on GitHub.