جست‌وجوی استاد و پژوهشگر

Carl Ho (Ngai Man)مشاهده پروفایل

استاد

• Power Electronics
• Heavy-Duty Electric Vehicles
• Renewable Energy Technologies
• +۵ مورد دیگر

Dr. Carl Ho (Ngai Man) is a Full Professor and Canada Research Chair in Efficient Utilization of Electric Power at the University of Manitoba's Price Faculty of Engineering, Department of Electrical and Computer Engineering. Appointed Associate Head (Electrical Engineering) in July 2021, he leads the Renewable-energy Interface and Grid Automation (RIGA) Lab established with CFI funding in 2014. His educational background includes: PhD in Electronic Engineering (2007), City University of Hong Kong MEng in Electronic Engineering (2002), City University of Hong Kong BEng in Electronic Engineering (2002), City University of Hong Kong Dr. Ho's research focuses on power electronics applications for sustainable energy systems, with particular expertise in power conversion technologies for electric vehicles, renewable integration, and smart grid infrastructure. His work bridges industrial application and academic innovation, evidenced by over 40 IEEE journal publications, 80 conference papers, and 20+ patents. Current research emphasizes wide-bandgap semiconductor applications, power hardware-in-loop validation, and DC microgrid architectures for remote communities. Analysis of his recent publications reveals a strong trend toward practical implementation of power electronics solutions, with increasing focus on GaN/SiC devices, grid-forming converters, and modular architectures for microgrids. His work consistently addresses real-world challenges in efficiency, reliability, and cost-effectiveness across renewable integration, electric transportation, and power quality domains. Notable awards include: Second Place Winner for 2018 IEEE Transactions on Power Electronics Prize Paper Multiple IEEE JESTPE Star Associate Editor Awards (2022-2023) IEEE TPEL AE Excellence Award (2023) Best Student Team Regional Award in IEEE Empower a Billion Lives 2019 As an active mentor, Dr. Ho supervises numerous graduate students across multiple cohorts and leads significant research initiatives including NSERC Discovery Grants, MITACS collaborations with Power Integrations Inc., Research Manitoba Innovation Proof-of-Concept Grants, and Natural Resources Canada projects on zero-emission heavy vehicles. His RIGA Lab serves as a hub for industry-academic collaboration with Manitoba Hydro and transportation sector partners. The RIGA Lab, completed in 2016 and renovated in 2019, houses specialized equipment for power electronics prototyping, real-time simulation, and hardware-in-loop testing. Current projects include advanced wireless EV charging, GaN-based controller development, and DC microgrid solutions for remote communities, with recent recognition including a visit from Prime Minister Justin Trudeau in April 2023.

Poman Soمشاهده پروفایل

دانشیار

• Multiscale Modeling
• Biomedical Engineering
• Microwave CAD
• +۵ مورد دیگر

Poman So is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Victoria, Canada. He is a Professional Engineer (P.Eng) registered in British Columbia and holds memberships in IEEE (Senior Member), ACES, and CMBES. Dr. So co-founded Faustus Scientific Corporation, creator of MEFiSTo electromagnetic field modeling software. Prior to joining UVic in 1998, he worked as senior scientific staff at MDA in Quebec. Education: Ph.D. in Electrical Engineering (University of Victoria) M.A.Sc. and B.A.Sc. in Electrical Engineering (University of Ottawa) B.Sc. (University of Toronto) Research Interests: Focuses on multiscale electromagnetic field modeling, biomedical electromagnetics and instrumentation, time-domain microwave circuit analysis, and open-source computational electromagnetics (CEM) tools. His work integrates GPU-based parallel computing and meta-material applications in microwave CAD. Professional Activities: Administers the Talbot Memorial Fund Awards, established to honor engineering students based on academic excellence, community service, and leadership. Since 2007, the fund has awarded two $2500 scholarships annually. Teaching: Instructs courses including ECE 216 (Electricity and Magnetism), ECE 335 (Biosensors and Instrumentation), and ECE 521 (Computational Electromagnetics). Labs/Teams: Leads research in biomedical engineering instrumentation and microwave circuit design through Faustus Scientific’s MEFiSTo platform, emphasizing open-source software development.

• Computational Mathematics
• Numerical Analysis
• Finite Element Methods
• +۳ مورد دیگر

Prof. Joachim Schöberl is a faculty member at TU Wien's Faculty of Mathematics and Geoinformation, leading the Scientific Computing and Modelling research group. His academic career includes roles as a university professor (Univ.Prof.) with engineering and technical doctorates (Dipl.-Ing., Dr.techn.). Research focuses on advanced numerical methods, including finite element methods, computational fluid dynamics, and partial differential equations. He has pioneered high-order schemes for fluid-structure interaction, shell mechanics, and electromagnetic simulations. Notable contributions include the NGSolve finite element library and innovative approaches to curvature approximation in discrete geometry. Recent work emphasizes nonlinear elasticity modeling, fractional diffusion problems, and shape optimization for biomembranes. His team collaborates on projects like metascreen upscaling, micromorphic continuum models, and eddy current simulations in laminated materials. Prof. Schöberl advises PhD students researching mixed finite element methods, fractional operators, and computational mechanics. His lab develops open-source tools for high-performance scientific computing.

Haochen Liمشاهده پروفایل

استادیار

• Environmental Fluid Dynamics
• Computational Fluid Dynamics
• Physics-informed Machine Learning
• +۷ مورد دیگر

Haochen Li is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Tennessee, Knoxville, within the College of Engineering. He leads the multidisciplinary Water Infrastructure Laboratory (Ψ Lab), which focuses on advancing urban water infrastructure through high-fidelity computational fluid dynamics (CFD), physical modeling, and physics-informed machine learning (ML). Education: PhD in Environmental Engineering, University of Florida, 2019 MS in Mechanical Engineering, University of Florida, 2019 MS in Civil Engineering, University of Florida, 2015 BS in Coastal Engineering, Hohai University, 2013 His research centers on environmental fluid dynamics , particularly multiphase and multiphysics flows in urban water systems. He investigates turbulence, particulate matter transport, pathogen fate, and chemical dynamics using advanced CFD simulations, volumetric particle image velocimetry (PIV), and AI-driven models. His lab develops open-source tools like InterAdsFoam for adsorption systems and integrates ML with CFD to optimize infrastructure design, retrofit, and regulatory frameworks. The recent publications reflect a strong trend toward hybrid CFD-ML frameworks for water infrastructure, with applications in clarifier design, stormwater basin optimization, and real-time sensing. His work emphasizes model validation, scalability, and practical deployment, including web-based tools for engineers. Scientific Awards: Rudolph Hering Medal, ASCE, 2023 Editor choice, Journal of Environmental Engineering ASCE, 2021 Editor choice, Journal of Environmental Engineering ASCE, 2020 Graduate School Fellowship, University of Florida, 2015 Academic Achievement Award, University of Florida, 2013 Haochen Li actively advises researchers and students in his lab, including Kai Liu, Mohamed Shatarah, and Ahmed Abdelmeguid. His team works on AI-empowered reactive flows, physics-informed ML, and CFD applications in energy and environmental systems. He has served as a reviewer for top journals and is a member of the ASCE/EWRI Computational Fluid Dynamics Committee. His lab is equipped with state-of-the-art HPC platforms and physical modeling facilities for experimental validation.

• Machine Learning
• Domain Adaptation
• Transfer Learning
• +۳ مورد دیگر

Dr. Sinno Jialin Pan is a leading researcher in machine learning and artificial intelligence at Nanyang Technological University, Singapore. His work focuses on domain adaptation, sentiment analysis, and efficient neural network optimization. Key research areas: Machine Learning, Domain Adaptation, Reinforcement Learning, Sentiment Analysis Recent publications demonstrate expertise in time-series classification (2025) using hierarchical domain adaptation, LLM efficiency (2025) through expert pruning, and graph generation (2024) via spectral diffusion. His work spans both theoretical advancements and practical applications in neural architecture optimization and adversarial learning. Scientific contributions include: 2025: Virtual-label hierarchical domain adaptation 2024: Spectral diffusion for graph generation 2024: Multilingual jailbreak analysis in LLMs Current trends show increasing focus on large language model optimization and robust neural architectures , with applications in fault diagnosis, recommender systems, and misinformation detection.

Jonny Holmströmمشاهده پروفایل

استاد

• digital innovation
• digital transformation
• digital entrepreneurship
• +۳ مورد دیگر

Jonny Holmström serves as Professor at Umeå University's Department of Informatics and directs the Swedish Center for Digital Innovation (SCDI), which he co-founded. He holds an additional affiliation as Professor at the Centre for Transdisciplinary AI, focusing on bridging theoretical research with practical AI applications across sectors including forestry, banking, and public services. His work appears in premier journals such as MIS Quarterly, Information Systems Journal, and Journal of Information Technology. His research centers on digital innovation, transformation, and entrepreneurship, examining how organizations navigate digital change through platform governance, AI integration, and entrepreneurial storytelling. Recent work investigates generative AI's impact on business model design, data work practices, and organizational transformation, emphasizing practical frameworks for managing digital transitions while addressing resistance and ethical considerations. Analysis of his 15 most recent publications (2024-2026) reveals a dominant focus on generative AI's organizational implications, particularly its role in reshaping platform governance, facilitating innovation through prompting, and transforming business models. Concurrent themes include digital platform evolution, data flow management in innovation networks, and citizen-centric digital government design, reflecting a consistent emphasis on practical implementation challenges in real-world contexts. Holmström leads significant research initiatives including a 28 MSEK program at Umeå University and the Kempe Foundation-funded SCDI AI Business Lab. His current project 'Using No-Code AI to Teach Machine Learning in Higher Education' (2024) aims to democratize AI education. He serves on editorial boards for CAIS, EJIS, Information and Organization, and JAIS, and heads the Swedish Center for Digital Innovation research group while participating in 'AI and society' collaborations. He founded and directs the Swedish Center for Digital Innovation (SCDI), which operates the SCDI AI Business Lab exploring practical AI applications for businesses. His work integrates with the Centre for Transdisciplinary AI to advance cross-sector AI implementation, particularly in public services and sustainable business models within the circular economy framework.

Liu Hongyanمشاهده پروفایل

استاد

• Big data management and analysis
• machine learning
• data mining
• +۷ مورد دیگر

Professor Liu Hongyan is a full-time Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management, where he has served since 1994, achieving the rank of Professor in 2011 after previously holding positions as Associate Professor (2003-2011) and Teacher. His research bridges theoretical data science with practical applications across e-commerce, healthcare, and social media platforms. Education: PhD in Management, School of Economics and Management, Tsinghua University (2001) His research focuses on big data management , machine learning , and business intelligence with specialized expertise in personalized recommendation systems , medical/financial data analysis , and computer vision applications . Recent work integrates large language models and causal inference to solve complex problems in short video platforms, live streaming, and healthcare analytics, emphasizing real-world impact through industry collaborations. Analysis of his 15 most recent publications (2023-2025) reveals a strong trajectory toward multimodal AI systems combining recommendation engines with computer vision, particularly in 3D animation for advertising and healthcare. Key trends include LLM-enhanced display advertising, emotion-aware facial animation, and medical image annotation using adversarial learning, while maintaining core contributions to behavioral data mining in social networks. Scientific recognition includes: National Archives Administration's Outstanding Scientific and Technological Achievement Award Multiple Best Paper Awards at international conferences Outstanding Doctoral Dissertation Supervisor designation from the Society for Management Science and Engineering Special Award for National Natural Science Foundation project on user behavior pattern discovery Professor Liu has secured leadership roles in major National Natural Science Foundation projects including Innovation Research Groups and international cooperation initiatives. His industry impact is demonstrated through patented recommendation systems adopted by multiple companies, particularly in personalized content delivery for live streaming and short video platforms. As an Outstanding Doctoral Dissertation Supervisor, he mentors the next generation of data science researchers. He serves as Deputy Director of Tsinghua University's Center for Artificial Intelligence and Management Research and holds key positions in national academic societies including the E-Commerce and Cyberspace Management Committee (China Management Modernization Research Association) and the Information Systems Engineering Committee (Chinese Society for Systems Engineering).

• Mathematics Education
• Interactive Multimedia in Teaching
• Educational Technology
• +۵ مورد دیگر

Marina Milovanović is a Professor at the University of Singidunum, Faculty of Informatics and Computing, Department of Mathematics. She holds dual doctoral degrees from the Faculty of Science, University of Kragujevac (Department of Mathematics, 2014) and Faculty of Entrepreneurial Business, Union University (2008), along with Master's and Bachelor's degrees from the Faculty of Mathematics, University of Belgrade (2000-2005 and 1995-2000 respectively). Faculty of Science, University of Kragujevac, Department of Mathematics (PhD, 2014) Faculty of Entrepreneurial Business, Union University (PhD, 2008) Faculty of Mathematics, University of Belgrade (Master's, 2000-2005) Faculty of Mathematics, University of Belgrade (Bachelor's, 1995-2000) Svetozar Marković High School, science and mathematics major (1991-1995) Professor Milovanović specializes in Mathematics Education and Educational Technology, with particular expertise in interactive multimedia applications for teaching mathematics. Her research consistently bridges theoretical mathematics with practical educational technology solutions, evolving from traditional multimedia approaches to incorporating cutting-edge AI and machine learning techniques. She has authored multiple books including 'Interactive multimedia in mathematics teaching' (2015) and collections of solved mathematics problems for entrance exams. Her recent publication record through 2025 demonstrates active engagement in interdisciplinary research, particularly at the intersection of educational technology, artificial intelligence, and practical applications in fields ranging from software engineering to medical diagnostics. Her work shows a clear trajectory from foundational educational technology research toward more sophisticated AI-enhanced learning systems. Professor Milovanović has made significant contributions to semantic web applications in education, particularly through Moodle LMS enhancements, and has explored SCADA applications in industrial contexts. Her collaborative research spans multiple countries and institutions, reflecting an international scholarly network. She has extensive experience developing computer tools for engineering education and has published on diverse topics including petroleum industry processes, environmental management, and financial mathematics. Her work demonstrates consistent application of computational approaches to solve domain-specific problems across multiple disciplines.

• Probabilistic Machine Learning
• Physics-Informed Machine Learning
• Industrial Applications of Data Science
• +۴ مورد دیگر

Markus Lange-Hegermann serves as Professor of Mathematics and Data Science at Ostwestfalen-Lippe University of Applied Sciences (TH OWL) since 2018 and holds a board position at the Institute for Industrial Information Technology (inIT). His career bridges academic research and industrial applications, with expertise in translating machine learning theory into practical engineering solutions for automation and manufacturing sectors. His educational foundation includes a Diplom (Master equivalent) in Computer Mathematics from RWTH Aachen University (2004-2008) followed by a Dr. rer. nat. (PhD equivalent) in algorithmic differential algebra (2008-2014). Prior to academia, he gained industry experience at FEV GmbH as an R&D engineer (2014-2017) and P3 automotive GmbH as a Data Science Consultant (2017-2018). Lange-Hegermann's research centers on probabilistic machine learning with distinctive emphasis on physics-informed approaches. He develops Gaussian process methodologies that incorporate differential equations to model time dependencies, uncertainties, and physical constraints in industrial systems. His work enables robust data-based modeling and optimization for cyber-physical systems, with applications spanning predictive maintenance, process control, and quality assurance in manufacturing. Analysis of his 15 most recent publications (2024-2025) reveals consistent innovation in physics-integrated machine learning, particularly using Gaussian processes to solve partial differential equations and optimal control problems. The research demonstrates strong industrial applicability across domains including medical imaging, material science, automotive engineering, and brewing processes, with recurring themes of anomaly detection in time-series data and uncertainty-aware decision making. His scientific contributions have earned significant recognition: Forschungspreis TH OWL (2024) Top reviewer award at NeurIPS (2023) Outstanding reviewer award at NeurIPS (2021) Best poster award at Bosch AI CON (2019) Borchers Plakette for outstanding dissertation (2014) Springorum Denkmünze for outstanding diploma (2009) As chairman of the Data Science study program and vice chairman of undergraduate examination boards, Lange-Hegermann actively shapes academic curricula while supervising graduate theses. His governance roles include serving on professorship search committees at multiple institutions and contributing to examination regulations. He maintains active research funding through collaborations with industrial partners and reviews proposals for initiatives like It's OWL and 3IA Côte d’Azur. Lange-Hegermann leads the Mathematics and Data Sciences research group within inIT, fostering collaboration between theoretical machine learning and industrial automation. He co-founded AICOmmunityOWL and the Informatics Europe working group on Data Analysis and Reporting, while organizing machine learning reading groups and data science hackathons to bridge academic research with industrial problem-solving.

• Embedded Systems
• Software Verification and Validation
• Model Checking
• +۵ مورد دیگر

Stefan Kowalewski serves as Professor of Embedded Software at RWTH Aachen University, leading the Chair of Embedded Software (Informatik 11) within the Department of Computer Science. His research spans critical domains including medical cyber-physical systems, automotive software, and industrial automation, with over 150 publications demonstrating sustained scholarly impact. Professor Kowalewski's work focuses on three interconnected research pillars: Embedded Systems Verification: Pioneering model checking techniques for PLC code, particularly addressing state space challenges in GRAFCET-based specifications Medical Cyber-Physical Systems: Developing safety-critical software for mechanical ventilation, extracorporeal membrane oxygenation, and ARDS diagnosis systems with strong clinical collaborations Automotive Software: Creating verification frameworks and safety architectures for automated vehicles through projects like UNICARagil Recent publications reveal an increasing integration of AI techniques with traditional verification methods, particularly for medical applications involving neonatal care and critical respiratory support. His 2024-2025 work shows particular emphasis on timing isolation in vehicle communication systems, middleware performance evaluation, and robust AI models for medical diagnosis. Professor Kowalewski maintains active collaborations with RWTH Aachen University Hospital's medical departments and automotive industry partners. His laboratory operates specialized facilities including the Cyber-Physical Mobility Lab for vehicle research and in-vivo testing setups for medical device validation. He has supervised numerous doctoral candidates, with recent students focusing on topics like ARDS classification algorithms, GRAFCET verification techniques, and safety architectures for software-defined vehicles. His educational contributions include developing remote teaching platforms for cyber-physical systems education.

• Networked Epidemic Modeling
• Graph Signal Processing
• Control Systems
• +۲ مورد دیگر

Victor M. Preciado is a Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania. His research focuses on network science , control theory , and graph signal processing . Research Interests: Modeling and controlling spreading processes on complex networks Optimization algorithms for time-varying systems Applications in public health and cyber-physical security Selected Publications: Recent work includes machine learning for operator inference (2022), hybrid systems stability analysis (2021), and pandemic modeling frameworks (2021). Earlier contributions focus on spectral analysis of epidemics (2009-2016) and geometric optimization (2014).

Jesús del Alamoمشاهده پروفایل

استاد

• Semiconductor Devices
• Transistor Physics
• III-V Compound Semiconductors
• +۷ مورد دیگر

Jesús del Alamo serves as the Donner Professor of Science within MIT’s Department of Electrical Engineering and Computer Science, leading cutting-edge research in semiconductor device physics with applications spanning logic, high-frequency, and power electronics. His work bridges fundamental materials science with practical device engineering to address next-generation computing challenges. Academic Credentials: PhD, Stanford University MS, Stanford University Research Focus: Professor del Alamo’s expertise centers on transistor physics and semiconductor device innovation, particularly III-V compound semiconductors (InGaAs, GaN) and diamond MOSFETs. Current investigations target reliability mechanisms in GaN transistors for RF/power applications, novel analog computing architectures, and electrochemical ionic synapses for neuromorphic hardware. His group pioneers atomic-scale fabrication techniques like thermal atomic layer etching for sub-5nm devices while exploring quantum confinement effects in vertical nanowires. Publication Evolution: Recent work (2023-2025) demonstrates a strategic shift toward neuromorphic computing, with 60% of publications focusing on electrochemical synapses and ferroelectric memories for AI acceleration. This builds upon decades of transistor scaling research, now converging with materials innovations in HfZrO 2 ferroelectrics and protonic conductors to enable energy-efficient analog deep learning hardware. Award Recognition: Louis D. Smullin Award for Excellence in Teaching Amar Bose Award for Excellence in Teaching Intel Outstanding Researcher Award Semiconductor Research Corporation Technical Excellence Award Semiconductor Industry Association-Semiconductor Research Corporation University Researcher Award Collaborative Leadership: He directs research within MIT’s Microsystems Technology Laboratories (MTL), collaborating with faculty including Bilge Yildiz (electrochemical systems) and Ju Li (computational materials). Current projects integrate device physics with neuromorphic algorithms, supported by semiconductor industry partnerships focused on translating fundamental discoveries into practical AI hardware solutions. Research Infrastructure: His group operates within MIT’s MTL cleanroom facilities, utilizing advanced characterization tools for in-situ device analysis and leveraging partnerships with industry leaders in semiconductor manufacturing to prototype novel transistor architectures.

Hüseyin DEMİRCİمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Metaheuristic Algorithms
• +۹ مورد دیگر

Hüseyin DEMİRCİ is an Assistant Professor at Sakarya University's Faculty of Computer and Information Sciences, Department of Information Systems Engineering. He holds a doctorate in Computer and Information Engineering from Sakarya University, where his thesis focused on designing a novel metaheuristic algorithm inspired by electricity movement in resistive media. Education: PhD (2015), MSc (2014), and BSc (2012) in Computer-related fields His research spans artificial intelligence, optimization algorithms, and decision-making systems, with a particular emphasis on metaheuristic methods like particle swarm optimization and genetic algorithms. He has applied these techniques to problems in surface reconstruction and real-time systems. Hüseyin also contributes to education through his role as a Research Assistant and has explored interdisciplinary domains such as feature selection, data clustering, and sustainable development-aligned computational approaches.

• Control Theory
• Fractional Calculus
• System Identification
• +۳ مورد دیگر

Rachid Malti is a Professor in the Automatic Control research group at the University of Bordeaux, France, specifically working within the CRONE team at IMS (Laboratoire de l'intégration, du matériau au système). His academic career spans over two decades with significant contributions to fractional calculus applications in control engineering and system identification. Professor Malti's research focuses on: Development of fractional order system identification methods Stability analysis of fractional and time-delay systems Experimental design for fractional model validation Applications in energy systems, thermal networks, and battery modeling CRONE Toolbox development for fractional differential signal processing His publication record demonstrates consistent theoretical and applied contributions, with recent work expanding into district heating networks, lithium-ion batteries, and climate modeling while maintaining strong theoretical foundations in fractional calculus. Professor Malti frequently collaborates with researchers including Stéphane Victor, Abir Mayoufi, and Patrick Lanusse across international boundaries. Professor Malti has published extensively in top-tier journals including Automatica, Communications in Nonlinear Science and Numerical Simulation, and Fractional Calculus and Applied Analysis, establishing himself as a leading researcher in fractional order control systems.

• Maritime Engineering
• Digital Twin Technology
• Marine Robotics
• +۳ مورد دیگر

Hans Petter Hildre is Head of Department at the Department of Ocean Operations and Civil Engineering , part of the Faculty of Engineering at the Norwegian University of Science and Technology (NTNU). His work focuses on maritime engineering, digital twin technology, and marine operations. Research interests include: Digital Twin Applications in Maritime Industry Offshore Operations and Wind Turbine Installation Marine Robotics and Autonomous Systems Wave Field Estimation and Environmental Load Analysis Human-Machine Interaction in Maritime Contexts Co-simulation and Real-time Monitoring Recent publications highlight trends in: Wave shielding effects for offshore vessels Knowledge transfer from automotive/aviation to maritime Crane path planning using digital twins Visual attention zone recognition systems Hydrodynamic modeling and sensitivity analysis Smart city-maritime integration Scientific collaborations span institutions including: European Commission (Future Skills Reports) Royal Institution of Naval Architects The American Society of Mechanical Engineers (ASME) IEEE Transactions on multiple domains Springer Publishing