جست‌وجوی استاد و پژوهشگر

Damian Nale Dailisanمشاهده پروفایل

مدرس

• Computational Social Science
• Machine Learning
• Multi-Agent Systems
• +۳ مورد دیگر

Dr. Damian Nale Dailisan is a Lecturer in the Department of Humanities, Social and Political Sciences at ETH Zürich, affiliated with the Computational Social Science group. He holds a Ph.D. in Physics from the University of the Philippines, specializing in traffic modeling and machine learning applications. His research focuses on multi-agent systems, particularly in transportation and urban systems. He has held postdoctoral roles and contributed to projects like the ACCeSs@AIM lab. His work bridges computational methods with real-world challenges, including traffic control optimization, AI-driven decision-making, and smart city infrastructure. Notable projects include FAIRLANE for priority lane management and studies on democratizing traffic control systems. Dailisan’s publications span journals like Transportation Research Part C and IEEE Access, addressing topics such as reinforcement learning in traffic signals and ethical AI frameworks. He has presented at workshops like 'Back to the Future' at ETH Zurich and collaborates with interdisciplinary teams to enhance urban mobility solutions. His technical expertise includes Python, network analysis, and agent-based modeling, with contributions to open-source tools for earthquake networks and social systems analysis.

Olivier ALLIXمشاهده پروفایل

استاد

• Computational Mechanics
• Structural Mechanics
• Multiscale Modeling
• +۷ مورد دیگر

Olivier ALLIX is a Professor at the Laboratoire de Mécanique et Technologie (LMT) at École Normale Supérieure de Cachan (ENS-Cachan). His research focuses on computational mechanics, including multiscale modeling of composite materials, structural failure analysis, and non-intrusive coupling strategies. He has held leadership roles such as Head of LMT-Cachan and Vice-president of the International Association for Computational Mechanics (IACM). Expertise: Computational structural mechanics, material failure, inverse problems, and multiscale approaches. Editorial Roles: Associate editor of multiple journals including Computational Mechanics and Computer Methods in Applied Mechanics and Engineering . Awards: IACM Fellow, Euromech Fellow, and recipient of the Gay-Lussac Humboldt Prize (2019). His work integrates experimental mechanics with computational methods, emphasizing big data applications and model validation. He has organized major conferences like the World Congress on Computational Mechanics and co-led international research initiatives such as the IRTG ‘Virtual Material and Structures’ with Hannover University. Teaching includes advanced courses on structural dynamics, composite materials, and computational mechanics at the Master’s level. His research group collaborates with industries like Safran, IFPEN, and DGA on projects involving fatigue analysis, mooring systems, and composite testing.

• Occupational Health and Safety
• Ergonomics
• Risk Analysis and Management
• +۴ مورد دیگر

Piia Tint is a Professor at Tallinn University of Technology (TalTech), affiliated with the School of Business and Governance and the Department of Business Administration. She specializes in occupational health, ergonomics, and risk management. Her roles include leading the Ergonomics Laboratory and the Chair of Labour Environment and Safety, and she has organized significant events like the Baltic Sea Network in Occupational Health (BSN) Annual Meeting in 2015. Education: Doctorate in Chemical Processes (1977) from the Technical Institute of Lensoveta Postgraduate studies in Chemical Engineering (1972–1975) Advanced training in Occupational Health via Nordic Institute of Advanced Training (NIVA) Research Interests: Focuses on occupational safety, musculoskeletal disorder prevention, environmental risk assessment, and green shipping sustainability. Her work integrates ergonomic principles with digital tools and AI for workplace health improvements. Awards: 2018: Appreciation from Riga Technical University for cross-institutional collaboration 2012: Honors for scientific publications 2006: Best Researcher in Faculty of Economics Grants and Projects: Led over 16 projects, including EU-funded initiatives on telework for aging workers, green shipping impacts, and ergonomic interventions in healthcare. Key contributions include developing risk assessment methods and OHSAS 18001 compliance studies. Labs/Teams: Heads the Ergonomics Laboratory (accredited since 2003) and collaborates with international networks like the Finnish Ergonomics Society and Eurotox.

Fabien Duveauمشاهده پروفایل

پژوهشگر

• Evolutionary Biology
• Genetics
• Molecular Biology
• +۵ مورد دیگر

Fabien Duveau is a CNRS Researcher at the Laboratory of Biology and Modelling of the Cell (LBMC) at École Normale Supérieure de Lyon. He leads the eGRIDE project, funded by an ERC Consolidator Grant, investigating evolutionary mechanisms of gene regulation in dynamic environments. His work focuses on understanding how gene expression plasticity and mutational effects shape organismal adaptation. Duveau holds a PhD from Université Paris 7/CNRS (2011), followed by postdoctoral research at the University of Michigan and Université Paris 7. He joined LBMC in 2019 as part of Gaël Yvert’s team, supported by an ANR Young Researchers grant. His research integrates experimental and computational approaches, including high-throughput RNA sequencing and genetic mapping in yeast ( Saccharomyces cerevisiae ), to study how regulatory evolution balances mutational randomness and environmental selection pressures. Key areas include gene expression noise, cis/trans regulatory variation, and fitness outcomes under environmental stress. Recognitions include the prestigious ERC Consolidator Grant (2024), awarded for innovative projects advancing fundamental biological understanding. Duveau collaborates across disciplines, combining evolutionary genetics with systems biology to predict regulatory evolution in natural and pathological contexts.

Hoda Hassanمشاهده پروفایل

دانشیار

• Computer Networks
• Mobile Ad Hoc Networks (MANETs)
• Artificial Intelligence
• +۱ مورد دیگر

Hoda Hassan is an Associate Professor in the Department of Information Sciences and Technology at George Mason University. Her work bridges theoretical and applied domains in computer networks, Mobile Ad Hoc Networks (MANETs), and the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT). Education : PhD in Computer Engineering from Virginia Tech (2010), MSc and BSc in Computer Science from the American University in Cairo (2005, 1992). Research Interests focus on designing intelligent and secure network systems. She has developed innovative frameworks, including a BLE computer-aided design toolkit for MANETs, which was commercialized by Skymind Malaysia. Her work spans AI integration in IoT, anomaly detection, cloud computing models, and network architecture evolution. Academic Leadership includes pioneering roles in founding The Knowledge Hub (TKH) Universities in Egypt and leading the Computing and Computer Science Program at Coventry University's UK offshore branch (2019–2022). Her research has been supported by a significant 2 million Egyptian Pound grant from ITAC Egypt (2015–2017).

Ludo Ausielloمشاهده پروفایل

مدرس ارشد

• Audio Engineering
• Signal Processing
• Electro-Acoustics
• +۴ مورد دیگر

Dr. Ludo Ausiello is a Senior Lecturer in Electronics at the University of Portsmouth, affiliated with the School of Electrical and Mechanical Engineering and the Faculty of Technology. His roles include membership in the Portsmouth AI and Data Science Centre and the Portsmouth Centre for Advanced Materials and Manufacturing. He holds a Fellowship of the Higher Education Academy and is a member of the Institute of Acoustics. Education: Integrated Master’s in Electronic Engineering (University of Bologna), European PhD in Audio Engineering, and PGCert in Higher Education (2019). Professional training includes Fonoprint Studios (Bologna) and industry experience at Maserati, Magneti Marelli, and Harman. Research interests focus on audio engineering, signal processing, electro-acoustics, and innovative manufacturing. Recent work includes developing affordable measurement ecosystems for musical acoustics, optimizing soundboard designs via least-squares analysis, and applying AI to room acoustics prediction. His studies span topics like wood elasticity, loudspeaker design, and vocal fatigue in educational spaces. Awards: Fellowship of the Higher Education Academy (2019). Teaching and advising: Taught analog and digital oscillator design at the University of Bologna and contributed to curriculum development at Solent University. Active in mentoring students through collaborative industry projects. Labs/Teams: Engaged with interdisciplinary teams at Portsmouth, focusing on advanced materials and AI-driven acoustic solutions.

• Health Services Research
• Narrative Interventions
• Hypertension Control
• +۳ مورد دیگر

Dr. Jeroan Allison is Chair and Professor at UMass Chan Medical School's Department of Population and Quantitative Health Sciences. Trained as a primary care physician and epidemiologist (Harvard SPH), he leads research in narrative interventions for health behavior change, with continuous funding from NIH, AHRQ, and RWJF. His work spans hypertension control in Vietnam, AI-driven health communication, and implementation science for non-communicable diseases. Harvard School of Public Health: Master of Science in Epidemiology University of Alabama at Birmingham: MD Samford University: BS in Chemistry Research interests include narrative medicine , health disparities , mHealth , and health services research . He has published extensively on hypertension interventions in Vietnam, digital health tools, and tobacco cessation strategies using peer engagement. Recent publications focus on AI applications , global implementation science , and social determinants of health . His team has developed culturally adapted interventions for HPV vaccination and hypertension management using community storytelling methods. Scientific contributions include: Co-Editor-in-Chief of Medical Care Developing simulation-based training for research assistants Designing peer recruitment models for smoking cessation Dr. Allison holds multiple grants and leads the UMMS-Vietnam research collaboration , focusing on late-stage translation research in low-resource settings.

Saumen Mandalمشاهده پروفایل

استاد

• Optimal experimental design
• Biostatistics
• Data science
• +۷ مورد دیگر

Dr. Saumen Mandal is a Professor in the Department of Statistics at the University of Manitoba, Faculty of Science. He holds a PhD from the University of Glasgow, UK, and MSc/BSc (Gold Medal) from the University of Calcutta, India. His research focuses on optimal experimental design, biostatistics, data science, shrinkage estimation, and constrained optimization. He has received numerous teaching awards including the Dr. and Mrs. H.H. Saunderson Award for Excellence in Teaching, Students Choice Best Professor Award, and multiple Merit Awards. He is also a P.Stat. designee from the Statistical Society of Canada. Education: PhD (Statistics), University of Glasgow, UK MSc (Statistics), University of Calcutta, India (First Class First, Gold Medal) BSc Honours (Statistics), University of Calcutta, India Research Interests: Optimal design theory and applications Biostatistical methods for clinical trials and healthcare data Data science and machine learning techniques Shrinkage estimation and model selection Linear models and goodness-of-fit testing Publications span topics like optimal regression designs, response-adaptive clinical trial methods, and statistical models for healthcare data. His work emphasizes practical applications in medicine and data-driven decision making. Awards include: Teaching Excellence Awards (2005-2007) Merit Awards for Teaching and Research (2010-2019) Faculty of Science Innovation in Teaching Award (2020) He advises graduate students in statistics and contributes to research teams in biostatistics and data science. His office is temporarily located at 256 Parker Building during construction.

Guven Demirelمشاهده پروفایل

دانشیار

• Supply Chain Management
• Operations Research
• Network Science
• +۴ مورد دیگر

Dr. Guven Demirel is a Reader in Supply Chain Management at Queen Mary University of London's School of Business and Management, specializing in interdisciplinary research on complex systems. He holds a PhD in Physics from the Max Planck Institute and has prior roles as a Lecturer at the University of Essex and Research Fellow at the University of Nottingham. His research focuses on supply network dynamics, sustainability, innovation, and food waste reduction, employing methods from network science, operations research, and game theory. He is a Fellow of the Higher Education Academy and supervises doctoral research on topics such as vaccine supply chain coordination, digital innovation in logistics, and closed-loop battery supply chains. Current students include Ayomide Thompson-Ajayi, Mariam Saad Salib, Milad Asadpour, and Saeide Jamshidpour Poshtahani. Key research themes include supply chain resilience (e.g., food loss mitigation via 'ugly veg' supply chains), blockchain applications in finance, and bifurcation analysis for network instabilities. His work bridges operations research with natural science methodologies. Awards: Fellow of the Higher Education Academy Centers: Member of the Centre for Globalisation Research (CGR)

Tsz-Yan Milly Loمشاهده پروفایل

دانشیار

• Pediatric Critical Care
• Data Informatics
• Neurocritical Care
• +۳ مورد دیگر

Dr. Tsz-Yan Milly Lo is an Honorary Reader at the University of Edinburgh’s Usher Institute and a Consultant Paediatric Intensivist at the Royal Hospital for Children and Young People in Edinburgh. She leads the Research Programme in Paediatric Critical Care Medicine and holds the NHS Research Scotland Career Research Clinician Award. Her work focuses on leveraging data informatics to improve outcomes for critically ill children, particularly in neurocritical care and brain trauma. She leads international initiatives like KidsBrainIT (a EU-funded pediatric brain trauma data initiative) and Window in the Brain (developing seizure detection tools). Dr. Lo’s education includes clinical training across Edinburgh, Birmingham, and Melbourne, complemented by a PhD and post-doctoral fellowships in Edinburgh and Toronto. Her research spans clinical informatics, multi-disciplinary data integration, and translational medicine. Key collaborations include academic, clinical, and industry partnerships (e.g., with BrainsView Ltd and engineers at the University of Edinburgh). Her scientific impact includes over 20 peer-reviewed publications, with recent work emphasizing intracranial pressure monitoring, EEG-based seizure detection, and pediatric critical care quality improvement. Her grants total over €1M, including EU NEURON funding and MRC support. She supervises a dynamic research team focusing on innovation in critical care informatics and clinical excellence. Grants: £90,953 (G-WiB), £298,740 (WiB-2), £103,164 (WiB-1), €621,834 (KidsBrainIT). Public Engagement: Launched Scotland’s first PPIE group for pediatric critical care (Intensive-Share). Her lab, IMPACT-ACE, emphasizes clinician-scientist collaboration to drive evidence-based improvements in pediatric critical care. Current projects include global seizure detection tools and pediatric brain trauma big-data analytics.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Li Wei is a distinguished academic affiliated with Tsinghua University, with a focus on interdisciplinary research spanning artificial intelligence, machine learning, and computer vision. His work often intersects with medical informatics, remote sensing, and signal processing, demonstrating a commitment to advancing technological solutions in healthcare, environmental monitoring, and engineering systems. Research interests include deep learning applications in clinical diagnostics, satellite data analysis for climate modeling, and optimization of energy storage systems. He has contributed to innovative solutions in areas such as UAV-enabled edge computing, privacy-preserving blockchain protocols, and thermal-based surveillance systems. His collaborative projects often involve multidisciplinary teams across institutions. Publications reflect a strong emphasis on practical applications, such as mobile health tools for tumor recognition, transformer-based super-resolution techniques for oceanography, and AI-driven risk classification models for respiratory diseases. While no specific awards or grants are listed, his prolific output across top-tier journals indicates sustained research impact. Professional activities include contributions to conferences like RecSys, MICCAI, and AAAI, and editorial roles are implied through his extensive publication record. Collaborations with industry partners (e.g., in energy systems and medical imaging) suggest engagement with real-world problem-solving.

• Control Systems
• Machine Learning
• Robotics
• +۶ مورد دیگر

Marko Tanasković is a researcher at Singidunum University's Faculty of Informatics and Computing, specializing in control systems, robotics, and electrical engineering. He holds a PhD from ETH Zurich (2015) in Information Technology and Electrical Engineering, following degrees from University of Belgrade (BEng, 2009) and ETH Zurich (MEng, 2011). His research focuses on adaptive control systems, machine learning applications in engineering, and sensorless motor control. Key contributions include: Development of predictive algorithms for traffic systems and industrial automation Innovations in rotor orientation determination for PMSM motors Integration of AI in fraud detection and building climate control Recent work includes a 2024 study on wearable health monitoring devices and a 2022 paper on drone forensics. He has authored/co-authored over 15 peer-reviewed articles and holds patents in motor control technologies. Current affiliations include Singidunum University's Department of Electrical Engineering and Collaboration with ETH Zurich alumni networks. Active in international conferences such as Sinteza and IEEE events.

Kursat Karaمشاهده پروفایل

استادیار

• Fluid Dynamics
• Aerodynamics
• Computational Fluid Dynamics (CFD)
• +۹ مورد دیگر

Kursat Kara is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Oklahoma State University (OSU), leading the Kara Aerodynamics Research Laboratory. He holds a Ph.D. in Aerospace Engineering from Old Dominion University (2008), and has held academic positions including Assistant Professor at Khalifa University (2010–2018), where he received the President’s Faculty Excellence Award for Teaching (2015). His research focuses on fluid dynamics, computational aerodynamics, hypersonic flows, quantum computing, and flow separation control using techniques like CFD and miniPIV. He has advised numerous graduate and undergraduate students, and collaborates on projects such as hypersonic boundary-layer stability, quantum computing for fluid dynamics, and urban wind field modeling for UAS navigation. Dr. Kara’s expertise spans experimental and numerical fluid dynamics, including work on sweeping jet actuators, boundary-layer transition, and aerodynamic design optimization. He is a member of AIAA (Senior), APS, and ASME, and has contributed to facilities like the $3.5M Khalifa University Low-Speed Wind Tunnel. His teaching includes courses on computational fluid dynamics, quantum computing, and unsteady aerodynamics. Recent research highlights include applications of machine learning in wind field prediction and interdisciplinary projects like interface learning for multiphysics systems. Scientific achievements include publications on hypersonic flow stabilization, quantum solvers for Burgers’ equation, and reduced-order models for urban wind simulation. His lab engages students from high school to PhD levels, emphasizing project-based learning and computational tools. Key collaborations involve NASA, the DOD, and industry partners like Sikorsky Aircraft Corp.

Efthymios N. Karatzasمشاهده پروفایل

استادیار

• Numerical Analysis
• Computational Mathematics
• Optimal Control
• +۵ مورد دیگر

Efthymios N. Karatzas serves as an Assistant Professor in the Department of Mathematics at Aristotle University of Thessaloniki, Faculty of Sciences, within the Computer Science and Numerical Analysis Section. He maintains an active research profile in computational mathematics with strong institutional affiliations including collaborations with SISSA mathLab and FORTH Institute of Applied and Computational Mathematics. His academic credentials include: PhD in Mathematics, National Technical University of Athens (2015) Master's in Applied Mathematical Sciences – Computational Mathematics, NTUA (2009) Master's in Applied Mathematics, University of Patras (2001) Bachelor's in Mathematics (Computational Mathematics), University of Patras (1999) Dr. Karatzas' research program centers on advanced numerical techniques for partial differential equations , with pioneering work in reduced order modeling , embedded boundary methods , and optimal control systems . His expertise spans computational fluid dynamics, uncertainty quantification, and biomechanical applications, characterized by methodological innovation in handling geometrically complex domains through cut finite element approaches and shifted boundary formulations. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on developing efficient numerical frameworks for parametrized PDE systems. His work consistently bridges theoretical rigor with practical implementation, particularly in advancing reduced basis methods for fluid-structure interaction and biological modeling, demonstrating significant contributions to computational mathematics through high-impact journal publications. No major scientific awards are documented in the available sources. Dr. Karatzas demonstrates research leadership through project management roles including Scientific Manager for the ELIDEK project at NTUA (2019-2021) and Project Manager for the European Social Fund HEaD initiative at SISSA (2017-2019). His grant administration experience encompasses coordinating interdisciplinary teams and securing external funding for computational mathematics research. He maintains active collaborations with the SISSA mathLab in Trieste (particularly with Prof. Gianluigi Rozza's group) and the FORTH Institute in Crete, participating in international workshops including the Reduced Order Methods in CFD Summer School (2019) and SIAM UQ conferences. His research network spans computational mathematics groups across Europe with emphasis on advancing numerical methodologies for real-world engineering and biological applications.

Kangkang Yinمشاهده پروفایل

دانشیار

• Computer Animation
• Computer Graphics
• Humanoid Robotics
• +۳ مورد دیگر

Kangkang Yin is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on computer animation, computer graphics, humanoid robotics, machine learning, and multimedia analysis. He teaches courses such as Computer Animation and Scientific Computing, and holds a PhD from the University of British Columbia (2007), MSc from Zhejiang University (2000), and BSc from Zhejiang University (1997). His work bridges robotics and animation through projects like physics-based character controllers, motion diffusion models, and robotic manipulation. Key contributions include the SIMBICON biped locomotion framework and research into emotion-driven dance animation. Recent efforts emphasize reinforcement learning applications in motion synthesis and robust visual navigation for unmanned ground vehicles. Yin's publications span over two decades, addressing challenges in motion control, physics-based simulation, and machine learning applications. His lab contributes to both academic advancements and practical robotics solutions. Current research trends show strong emphasis on combining generative AI with traditional animation techniques, as seen in recent work on auto-regressive motion models (AAMDM) and physics-augmented reinforcement learning (PARC).