Complex system modeling and simulation Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۲۱-۷۳۵ از ۱۱٬۴۰۲ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Kurt Keutzerمشاهده پروفایل

استاد

University of California, BerkeleyUnited States

Kurt Keutzer is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, and a key member of the Berkeley AI Research Lab (BAIR). He holds a Ph.D. in Computer Science from Indiana University (1984) and was previously Chief Technical Officer at Synopsys, Inc. His research focuses on systems issues in deep learning, particularly for computer vision, speech recognition, NLP, and finance. He has published over 250 refereed articles and six books, and is a highly cited author in hardware and design automation. Keutzer has received multiple IEEE Fellowships, DAC awards, and best paper accolades at conferences like Embedded Vision Workshop and ICPP. Educations: 1984, PhD, Computer Science, Indiana University Kurt Keutzer's research interests span Artificial Intelligence , Computer Architecture , and Scientific Computing , with a focus on computational efficiency in AI systems. His work explores hardware-aware neural architecture search, domain adaptation, and quantization techniques to optimize models from edge to cloud. Recent publications highlight advancements in vision transformers , LLM inference efficiency , and autonomous driving . He also contributes to multimodal AI and self-supervised learning frameworks. Scientific Awards: Institute of Electrical & Electronics Engineers (IEEE) Fellow (1996) DAC's Most Influential Paper Award (2023) Top Ten Cited Author and Paper at DAC Best Paper Awards at Embedded Vision Workshop and ICPP Kurt Keutzer has advised numerous Ph.D. and Master’s students, including Forrest Iandola (co-founder of DeepScale), Sheng Shen, and Michael Murphy. His research teams have pioneered hardware-efficient deep learning solutions like SqueezeNet and FireCaffe. Current projects include optimizing large language models (LLMs) for edge deployment and advancing 3D reconstruction for autonomous vehicles. He is also involved in diffusion models , sparse attention mechanisms , and multi-agent coordination for complex tasks.

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Denis O'Carrollمشاهده پروفایل

استاد

University of New South WalesAustralia

Professor Denis O'Carroll is Deputy Head of the School of Civil and Environmental Engineering at the University of New South Wales (UNSW) and Managing Director of the Water Research Laboratory (WRL). His research focuses on environmental engineering challenges, particularly in water resource management and contaminant remediation. His primary research interests include: Development of nanoscale materials for environmental restoration PFAS contamination assessment and treatment technologies Groundwater remediation and contaminant transport modeling Green infrastructure performance evaluation Fate and transport of emerging contaminants in aquatic systems Electrochemical degradation of persistent pollutants Bioremediation and microbial transformation processes Professor O'Carroll's recent publications demonstrate a strong focus on PFAS research, with multiple studies examining global contamination patterns, degradation mechanisms, and innovative treatment technologies. His work also shows consistent attention to nanomaterial applications for environmental remediation, particularly sulfidated zerovalent iron systems for chlorinated solvent treatment. The research spans laboratory studies to field-scale validations. As Managing Director of the Water Research Laboratory, Professor O'Carroll leads significant research initiatives addressing water quality challenges. His laboratory conducts both fundamental research and applied studies with direct relevance to environmental policy and remediation practice.

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Xifan Wuمشاهده پروفایل

استاد

Temple UniversityUnited States

Xifan Wu is a Professor of Physics at Temple University, specializing in computational methods and materials science. His research focuses on first-principles computational approaches, particularly exploring the locality of Wannier orbitals to address physical problems in solids and liquids. Key interests include superlattice design and applications of order-N exact exchange functionals like PBE0 and GW quasi-particle approximations. He has authored numerous high-impact publications in journals such as Physical Review Letters and Physical Review B , covering topics like ferroelectric superlattices, X-ray absorption spectroscopy, and the dielectric properties of electrolyte solutions. His work bridges quantum mechanical models with machine learning potentials, advancing large-scale simulations of complex materials. Education/Background: Not explicitly detailed in the provided text. Grants/Awards: No specific awards listed, but his research is supported by Temple University’s Center for the Computational Design of Functional Layered Materials (CCDM). Labs/Teams: Collaborates with teams focused on computational design and materials modeling, possibly through Temple’s physics department and affiliated research centers. His recent work explores molecular-scale insights into electrical double layers at oxide-electrolyte interfaces and the impact of ions on X-ray spectra, demonstrating expertise in linking theoretical models with experimental phenomena.

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Zoran J. Stankovicمشاهده پروفایل

استاد

University of NišSerbia

Antenna Design and Optimization
Direction of Arrival (DoA) Estimation
Artificial Neural Networks in Electromagnetics
+۷ مورد دیگر

Zoran J. Stankovic is a Full Professor at the Faculty of Electronics, University of Niš, Serbia, in the Department of Telecommunications. He has been an integral part of the institution since earning his PhD in 2007, progressing from Assistant to Associate Professor (2020) and Full Professor (2024). He is actively involved in research, education, and academic leadership. Education: PhD in Telecommunications, Faculty of Electronics, University of Niš (2007) Master's in Telecommunications, Faculty of Electronics, University of Niš (2002) Bachelor's in Electronics and Telecommunications, Faculty of Electronics, University of Niš (1994) His research focuses on applying artificial neural networks to solve complex problems in electromagnetics and wireless systems. Key areas include antenna design optimization , direction-of-arrival (DoA) estimation , microwave cavity modeling , and smart textile antennas . His work bridges machine learning with electromagnetic theory, enabling efficient and adaptive RF system design. The analysis of his recent publications reveals a strong trend in using deep and hybrid neural networks for electromagnetic modeling and signal processing. His research emphasizes real-world applications such as wearable antennas, mobile source tracking, and intelligent antenna systems, often published in high-impact IEEE and Wiley journals. Scientific Awards: Commemorative Plaque from the Yugoslav Society for Microwave Techniques and Technologies (2005) for outstanding scientific results in microwave engineering Advising and Grants: While specific students are not listed, he has supervised numerous academic works. He has participated in 17 research projects (13 national, 4 international including DAAD, NATO, COST) and 8 educational development projects (including ERASMUS+, TEMPUS, WUS). These reflect sustained funding and academic collaboration. Labs and Teams: He is the founder and head of the Laboratory for Antennas and Propagation at the Faculty of Electronics, Niš. He is actively involved in organizing and leading the international conferences TELSIKS and ICEST , serving on their program and organizational committees, demonstrating strong leadership in the academic community.

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Moe Mojtahediمشاهده پروفایل

مدرس ارشد

University of New South WalesAustralia

Dr Moe Mojtahedi is a Senior Lecturer at the School of Built Environment, University of New South Wales (UNSW). He earned his PhD in 2014 from the School of Civil Engineering at the University of Sydney. As a certified Project Management Professional (PMP) and Professional Engineer (PEng) accredited by Engineers Australia, Dr Mojtahedi bridges academic research with practical application in construction management and disaster risk reduction. PhD (University of Sydney, 2014) MEngSc (University of New South Wales) B.E. (Industrial), Professional Engineer (Australia) His research focuses on the intersection of construction management , urban resilience , and disaster risk reduction , particularly examining: Climate change adaptation in infrastructure Post-disaster recovery frameworks Lean construction methodologies Decision support systems for risk management Evacuation planning optimization Resilient hospital infrastructure Recent publications analyze trends in disaster science using computational modeling, prefabricated construction for industrial buildings, and AI/ML applications in aged care facility evacuation. His 2025 ChemistryOpen article explores sustainable reaction media for chemoselective processes, demonstrating interdisciplinary reach. Scientific recognition includes: Research Excellence Awards (Engineers Australia, 2012 & 2013) Best Conference Paper (ICES, Salford, 2017) Elsevier Outstanding Contribution Award (International Journal of Project Management, 2017) Learning and Teaching Excellence (UNSW, 2017) As a supervisor, he guides 7 PhD candidates and has mentored 4 graduates, including Mahmoud Ershadi (project management office effectiveness) and Kamyar Kabirifar (construction waste management). He contributes to policy discussions on aligning National Construction Code with UN Sendai Framework and advocates for disaster science integration in built environment practices. His media contributions examine hospital flood risks and climate change adaptation in Australia.

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Federica Sandroneمشاهده پروفایل

مدرس

Swiss Federal Institute of Technology in LausanneSwitzerland

Federica Sandrone is a Lecturer at the School of Architecture, Civil and Environmental Engineering (ENAC) at École Polytechnique Fédérale de Lausanne (EPFL), where she also serves as a Scientist at the Laboratory of Experimental Rock Mechanics (LEMR) within the Institute of Civil Engineering. Her academic career spans over 15 years with continuous contributions to tunnel engineering and rock mechanics research. Her research focuses on the intersection of rock mechanics and tunnel engineering, with particular expertise in tunnel pathology analysis, TBM performance in challenging geological conditions, and long-term tunnel behavior. Sandrone's work bridges theoretical analysis with practical engineering applications, addressing real-world problems in tunnel infrastructure management and maintenance. Her research methodology combines field investigations, laboratory testing, and numerical modeling to understand complex geomechanical behaviors. Analysis of her recent publications reveals a consistent focus on tunnel inspection methodologies, TBM performance prediction in difficult ground conditions, and the long-term behavior of tunnel structures. Her work has evolved from fundamental tunnel pathology studies to more advanced applications involving GIS integration, probabilistic modeling, and modern inspection techniques including laser scanning and image analysis. Engineer at SBB-Infrastructure (2008-present) responsible for Tunnels Management and Maintenance Assistant for Tunnel Engineering courses (2007-present) PhD supervision including Erika Paltrinieri's 2015 thesis on TBM performance Development of tunnel inspection methodologies and condition assessment procedures Her teaching activities include courses in Rock Mechanics and Underground Construction, where students learn about the mechanical behavior of rock materials, tunnel excavation and support design, planning and management of underground works, and risk assessment in tunnel construction.

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Attila Gursoyمشاهده پروفایل

استاد

Koç UniversityTurkey

Attila Gursoy is a Professor at the Department of Computer Engineering, College of Engineering, Koç University. He serves as the Dean of the College of Engineering and leads research in computational biology, bioinformatics, and high-performance computing. Education : PhD in Computer Science from University of Illinois (1994), MSc from Bilkent University (1988), BSc from Middle East Technical University (1986) His research focuses on protein-protein interactions , computational structural biology , and systems pharmacology , with applications in drug repurposing and inflammatory disease mechanisms . He has pioneered structural analysis of Ras signaling and developed tools like COSBI for computational systems biology. Recent publications highlight his work on viral protein mimicry , neurodegenerative pathways , and microbiome dynamics . His team maintains datasets like PPInterface and DiPPI for structural drug discovery. 2005 : Werner-von-Siemens Excellence Award

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Sebastian von Mammenمشاهده پروفایل

استاد

Julius-Maximilians-Universität WürzburgGermany

Prof. Dr. Sebastian von Mammen is a tenured professor at the University of Würzburg's Institute for Computer Science, where he heads the Games Engineering research group and contributes to the Chair for Human-Computer Interaction. His group leads the Games Engineering academic program. Previously, he completed his habilitation (2012-2016) at the University of Augsburg's Chair of Organic Computing and was a postdoctoral fellow at the University of Calgary. His research spans: Real-Time Interactive Systems : Visual programming, immersion techniques, software engineering Interactive Simulations : Serious games for healthcare/logistics/construction Artificial Life : Self-organisation, adaptive systems, evolutionary computation Artificial Intelligence : Agent-based modeling, procedural content generation Recent publications (2023-2025) demonstrate strong focus on: Virtual reality applications in education (femtoPro optics simulator, BrainBuilder neuroanatomy) Healthcare technology platforms (VIA-VR for medical serious games) Game mechanics analysis (Match-3, Jump'n'Run flow) AI-driven emotion recognition and interactive systems Computational modeling of biological systems He leads the Games Engineering research group and previously participated in the Evolutionary and Swarm Design group (Calgary) and LINDSAY project. His lab develops VR simulations for scientific training and serious games applications.

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Emilie Carretierمشاهده پروفایل

استاد

Aix-Marseille UniversityFrance

Emilie Carretier is a Professor at Aix-Marseille University (AMU) , affiliated with the Procédés Membranaires research team. Her work focuses on membrane separation technologies, particularly for industrial applications in pharmaceuticals, water treatment, and nuclear waste management. Research Interests : Membrane processes (pervaporation, reverse osmosis), solvent regeneration, radioactive effluent treatment, catalyst recovery, and industrial sustainability. Publications highlight advancements in ceramic membranes, VOC removal, and membrane aging studies, with applications in pharmaceuticals, microelectronics, and nuclear industries. Laboratory : Active within the M2P2 research center, specializing in membrane process innovation for complex industrial matrices.

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Hüsnü Dalمشاهده پروفایل

استاد

Middle East Technical UniversityTurkey

Hüsnü Dal is a Professor at Middle East Technical University (METU) in Ankara, Turkey, specializing in computational mechanics of materials. His research bridges engineering and biomedical applications through advanced computational modeling techniques. Education: Bachelor's Degree, Middle East Technical University, 2001 Master's Degree, University of Stuttgart, 2005 PhD, Dresden University of Technology, 2011 Research Focus: Prof. Dal's work centers on computational micromechanics, multiscale and multifield problems, and materials theory. He investigates fracture in multiphysics media with applications in lithium-ion batteries and tissue mechanics, developing novel constitutive models for complex material behaviors under extreme conditions. His research integrates thermomechanical coupling, viscoplasticity, and data-driven approaches to solve engineering challenges in both synthetic polymers and biological systems. Publication Trends: Recent publications (2023-2025) reveal a dominant focus on data-driven constitutive modeling and phase-field fracture methods. His work spans rubber mechanics, polymeric foams, biological tissues, and battery materials, characterized by strong interdisciplinary connections between materials science, biomechanics, and computational engineering. Key themes include anisotropic hyperelasticity, thermo-viscoplastic fracture, and spatial property variations in additively manufactured materials.

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Kirsty Kittoمشاهده پروفایل

دانشیار

University of Technology SydneyAustralia

Kirsty Kitto is an Associate Professor at the University of Technology Sydney in the Learning Design and Technology Unit. With a background in theoretical physics and computer science, her research focuses on developing quantum-inspired models of contextuality to understand complex human behavior in educational systems. She explores the intersection of Artificial Intelligence (AI) , Learning Analytics , and Educational Theory , seeking to bridge the divide between big data and pedagogical frameworks. Current Appointments : Associate Professor (2020-present), Senior Lecturer (2017-2020) Past Academic Roles : Senior Research Fellow (QUT), Senior Lecturer (QUT), Associate Lecturer (Flinders University) Her funded research includes projects on: Quantum models in cognitive systems (ARC Fellowship DP1094974) AI literacy in digital workplaces (IEEE Transactions study) Contextual learning analytics (DVC Education and Students Division) Skills passports for lifelong learning (UMAP 2024 paper) Kitto's recent work examines human-AI interaction in writing assessment, data storytelling for teacher-centered analytics, and causal modeling to strengthen educational theory-data connections. She actively supervises Masters and PhD students and contributes to policy debates through government submissions on generative AI in education. Her methodology combines mathematical formalism with sociotechnical analysis to address educational complexity.

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Tomomi Kitoمشاهده پروفایل

استاد

Waseda UniversityJapan

Tomomi Kito is a Professor (non-tenure-track) at the Faculty of Science and Engineering, Graduate School of Creative Science and Engineering, Waseda University. She previously held positions as Associate Professor at Waseda University (2018-2023), Assistant Professor at University of Tsukuba (2015-2018), Senior Research Fellow at University of Oxford's Cabdyn Complexity Center (2013-2015), and Assistant Professor at The University of Tokyo (2012-2015). Dr. Kito earned her Ph.D. in Engineering from The University of Tokyo. Her academic journey includes research positions at prestigious institutions including University of Oxford's Saïd Business School (2008-2011) and The University of Tokyo's Research into Artifacts, Center for Engineering (2008). Dr. Kito's research focuses on the intersection of complex systems, supply chain management, and business strategy. Her work employs network science and agent-based modeling to understand the emergent properties of industrial ecosystems, particularly in the automotive sector. She investigates how firm strategies, product characteristics, and geographic factors shape supply network structures and their resilience. Her current research explores the heterogeneity of supply networks, corporate portfolio strategies, and urban transportation networks using big data analytics and network science approaches. Her extensive publication record demonstrates a consistent focus on understanding supply network complexity through empirical analysis of real-world data. Dr. Kito's work bridges theoretical network science with practical business applications, particularly in manufacturing and automotive industries. She has developed methodologies for analyzing industrial clusters, supply network resilience, and firm interdependencies using complex network theory. Dr. Kito has secured multiple research grants from Japanese funding agencies including the Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology. Her current projects include 'Modeling and empirical analysis of emergent principles of diversity and heterogeneity in social networks' and 'Development of a general-purpose dynamic model of supply networks and its verification by simulation.' She actively supervises graduate students through various seminars and thesis guidance in Strategic Management for Value Creation and Management Design at Waseda University's Graduate School of Creative Science and Engineering. Her teaching portfolio includes courses on Production Management, Global Production Systems, and Business Design.

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Janarthanan Rajendranمشاهده پروفایل

استادیار

Dalhousie UniversityCanada

Janarthanan Rajendran is an Assistant Professor and the Sexton Chair in Reinforcement Learning at the Faculty of Computer Science, Dalhousie University, in Halifax, Nova Scotia, Canada. He is actively involved in research, teaching, and mentoring, with a focus on deep reinforcement learning and its applications in complex, dynamic environments. Education: Postdoctoral Fellow, Mila Quebec AI Institute and University of Montreal, Canada (2023) PhD in Computer Science and Engineering (AI stream), University of Michigan, Ann Arbor, USA (2021) MTech and BTech in Electrical Engineering, Indian Institute of Technology Madras, India (2016) His research focuses on enabling machines to learn through interaction, with core interests in deep reinforcement learning, model-based RL, multi-agent systems, transfer learning, and applications in materials science and economics. He also explores the integration of large language models and foundation models into reinforcement learning frameworks. His work emphasizes adaptivity, lifelong learning, and societal implications of AI. The most recent publications show a strong trend in advancing cooperative multi-agent systems, developing adaptive and memory-efficient RL methods, and applying RL to real-world challenges such as crystal design and dynamic pricing. His research bridges theoretical innovation with practical application, often in interdisciplinary contexts. Scientific Awards: Sexton Chair in Reinforcement Learning Dr. Rajendran is actively involved in mentoring graduate students and fostering an inclusive research environment. He is currently recruiting PhD and MCS students at Dalhousie University. He has no formal grants listed in the text, but his research chair and active publication record suggest strong funding support. He is also engaged in the broader AI community, having organized and participated in major conferences such as the Atlantic Canada AI Summit and NeurIPS. Labs and Research Groups: He leads a research group focused on deep reinforcement learning at Dalhousie University, working on topics including model-based RL, off-policy learning, and leveraging external knowledge sources. The group emphasizes inclusivity and supports underrepresented groups in computer science research.

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Darrell S Croweمشاهده پروفایل

استادیار

Air Force Institute of TechnologyUnited States

Lt Col Darrell S. Crowe, PhD, is an Assistant Professor of Aerospace Engineering in the Department of Aeronautics and Astronautics at the Air Force Institute of Technology (AFIT), part of the Graduate School of Engineering and Management at Air University. He is an active military officer and educator contributing to advanced aerospace research and graduate education within the U.S. Air Force. Education: PhD in Aeronautical Engineering, Air Force Institute of Technology, 2014 MS in Aeronautical Engineering, Air Force Institute of Technology, 2008 BS in Aerospace Engineering, Texas A&M University, 2003 Dr. Crowe's research focuses on propulsion aerodynamics, computational fluid dynamics (CFD), supersonic and hypersonic flows, jet interaction effects, and store separation dynamics. His work involves high-fidelity simulations of exhaust nozzles, thermal distortion modeling, and active flow control, often in collaboration with military and aerospace applications. He investigates complex phenomena such as hot streaks in serpentine nozzles, film cooling, and cavity acoustics, contributing to improved aircraft and propulsion system design. His recent publications demonstrate a strong trend in advancing CFD methodologies for defense-related aerospace problems, particularly in propulsion-airframe integration, weapon bay aerodynamics, and supersonic/hypersonic flow control. The articles span both experimental validation and numerical modeling, emphasizing accuracy, turbulence modeling, and multi-physics coupling in extreme environments. Scientific Awards and Honors: AFIT Dean's Distinguished Teaching Professor, 2023 AIAA Associate Fellow, 2020 Air Force Meritorious Service Medal (2018, 2021) Joint Service Commendation Medal, 2017 Southwestern Ohio Council for Higher Education Faculty Excellence Award, 2015 Field Grade Officer of the Quarter, Air University, 2015 Air Force Commendation Medal, 2011 Company Grade Officer of the Quarter (2005, 2009) Air Force Achievement Medal, 2006 Dr. Crowe advises MS thesis students in aerospace engineering and teaches graduate-level courses in his domain. He has been involved in flight testing and simulation projects, often funded through U.S. Air Force research programs. His work supports critical defense capabilities in aircraft performance, propulsion efficiency, and weapon system integration. He is actively involved in professional organizations such as the American Institute of Aeronautics and Astronautics (AIAA) and contributes to major conferences and workshops, including the Propulsion Aerodynamics Workshops. His research is conducted within AFIT’s advanced simulation and modeling environment, leveraging tools like Kestrel and BCFD for high-fidelity analysis.

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Sheng Sangمشاهده پروفایل

استادیار

Bethany Lutheran CollegeUnited States

Sheng Sang is an Assistant Professor in the Department of Engineering Sciences at Bethany Lutheran College. His research lies at the intersection of Mechanical Engineering and Biomedical Engineering, with a strong emphasis on machine learning applications in composite materials and elastic metamaterials. His research interests include: Mechanical & Biomedical Engineering Machine Learning on Composites Elastic Metamaterials and Composites Optimization of Medical Devices Finite Element Modeling and Simulation Dr. Sang's recent publications demonstrate a consistent focus on integrating deep learning techniques with mechanical systems, particularly in predicting composite microstructures, tracking particles in complex systems, and optimizing wave propagation in metamaterials. His work frequently employs 3D CNNs and other neural architectures to solve inverse problems in material science. Scientific awards and recognition include: Dr. Lehtola Fellowship Research Grant ($9,000, PI), 2021–2023 Graco Engineering Lab Development Grant ($60,000), 2020–2022 He has been actively involved in teaching a wide range of engineering courses such as Fluid Mechanics, Solid Mechanics, Thermodynamics, and Computer-Aided Design. His research is supported by external grants, indicating active supervision and project leadership. Dr. Sang has collaborated with researchers across disciplines, including neuroscience and medical imaging, particularly in studies involving deep brain stimulation and fMRI. He is affiliated with research teams working on: Active elastic metamaterials design Machine learning for material characterization Optimization of biomedical devices using swarm intelligence Development of advanced simulation tools for composite systems

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