جست‌وجوی استاد و پژوهشگر

Tamara Gravaمشاهده پروفایل

دانشیار

• Integrable systems
• Nonlinear waves
• Random matrix theory
• +۵ مورد دیگر

Tamara Grava is an Associate Professor at the International School for Advanced Studies (SISSA) in Trieste, Italy, and a Professor at the University of Bristol, UK. Her research focuses on mathematical physics, particularly integrable systems, nonlinear waves, and random matrix theory. She maintains active collaborations between these two prestigious institutions, contributing significantly to both theoretical developments and interdisciplinary applications. Her educational background includes a PhD in Mathematical Physics from SISSA, completed on October 5, 1998, with a thesis titled "On the Cauchy problem for the Whitham equations" under the supervision of Boris Dubrovin. Professor Grava's research spans several interconnected areas in mathematical physics. She investigates integrable systems with random initial data, developing probabilistic Riemann-Hilbert methods to analyze soliton gases and their connections to statistical mechanics. Her work on dispersive nonlinear partial differential equations examines long-time and small-dispersion asymptotics, with particular attention to dispersive shock waves and rogue waves. She also makes significant contributions to random matrix theory, studying special functions, orthogonal polynomials, and their connections to Painlevé equations. Her research often bridges pure mathematics with applications in physics, revealing deep connections between seemingly disparate fields. Analysis of Professor Grava's recent publications (2021-2024) reveals a strong focus on the intersection of integrable systems, random matrix theory, and nonlinear wave phenomena. Her work increasingly explores soliton gases and their statistical properties, connecting microscopic soliton dynamics to macroscopic hydrodynamic descriptions. There's a clear progression toward more complex systems involving random initial conditions and the development of rigorous mathematical frameworks to handle these problems. Her research shows strong interdisciplinary connections between mathematical physics, statistical mechanics, and quantum field theory. Professor Grava actively supervises PhD students including Sacha Grover (Bristol), Dmitrii Rachenkov (SISSA), Xiao-Fan Zhang, Xiaodong Zhu, and Zechuan Zhang. Her editorial work includes service on the boards of Nonlinearity (since 2012), SIAM Journal of Mathematical Analysis (since 2022), Constructive Approximation (since 2023), and the Bulletin and Journal of the London Mathematical Society (starting 2025). She participates in numerous international conferences and workshops, with upcoming engagements in 2025 including events on enumerative combinatorics, integrable systems, and statistical physics at institutions including CIRM, Harvard University, and the MATRIX Institute.

Juseong Leeمشاهده پروفایل

استادیار

• Predictive Maintenance
• Industrial Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Juseong Lee is an Assistant Professor at the Eindhoven University of Technology (TU/e), affiliated with the Department of Industrial Engineering and Innovation Sciences. His research focuses on integrating data-driven models and artificial intelligence into industrial processes, with a specific emphasis on predictive maintenance approaches for engineering systems. PhD in Aerospace Engineering from Delft University of Technology (2022) MSc and BSc in Aerospace Engineering from Korea Advanced Institute of Science and Technology (2018, 2016) His work employs methodologies such as digital twins, multi-objective optimization, and deep learning to enable real-time decision-making in business contexts. He aims to improve the reliability, efficiency, and sustainability of Industry 4.0 systems through his research. Recent publications highlight his expertise in predictive maintenance, with applications to aircraft systems, landing gear brakes, and turbofan engines. Key techniques include Gaussian process learning, adaptive sampling, deep reinforcement learning, and stochastic Petri nets. These studies often involve collaborations with researchers like Mihaela Mitici. Lee's research is supported by the European Union's Horizon 2020 program (Grant No. 769288). He teaches courses on quality and reliability engineering, maintenance optimization, and service logistics.

• Economic Growth
• Convergence Theory
• Energy Economics
• +۵ مورد دیگر

Professor İ Hakan Yetkiner is a faculty member in the Department of Economics at Izmir University of Economics, Turkey. He has been affiliated with the university since September 2004 and has played a leading role in organizing over 15 national and international conferences. Professor Yetkiner is recognized as the only economist in the Aegean region ranked in the top 10 among Turkish economists in REPEC statistics, highlighting his significant impact on economics research in Turkey. Professor Yetkiner's educational background includes: B.A. in Management from Middle East Technical University (METU) in 1989 M.S. in Economics from METU M.Sc. in Economics from London School of Economics Ph.D. in Economics from METU Ph.D. in Economics from the University of Groningen, Netherlands Two years of post-doctoral research at Hamburg University Professor Yetkiner's research primarily focuses on economic growth and development theories, with special emphasis on convergence phenomena across various economic indicators. His work extensively utilizes panel data methodologies to examine income inequality, financial development, and energy economics across different regions, particularly the EU-15 countries. He has made significant contributions to understanding the relationships between energy prices and economic growth, as well as the impact of fiscal policies on energy intensity in Turkish manufacturing. His recent publications reveal a methodological consistency in applying panel data analysis and Granger causality tests to explore economic relationships. The thematic evolution of his work shows expansion from traditional economic growth models into contemporary issues like CO2 emissions convergence, defense expenditure patterns across NATO countries, and the macroeconomic determinants of health outcomes, demonstrating an interdisciplinary approach that connects economic theory with practical policy applications. Professor Yetkiner has authored one textbook and three edited books in addition to numerous English and Turkish journal articles. His academic leadership extends beyond research through his active role in organizing scholarly events, having coordinated over 15 national and international conferences since 2004, fostering academic exchange and collaboration in the economics community.

• Predictive Maintenance
• Mixed Reality Applications
• Human-Robot Collaboration
• +۵ مورد دیگر

Bernard Schmidt is an active researcher at the University of Skövde's School of Engineering Science, specializing in Production and Automation Engineering. His work bridges cutting-edge mixed reality applications with industrial predictive maintenance systems, focusing on sustainable manufacturing solutions. Affiliated with the Virtual Systems Research Centre (closed 2017) and Virtual Engineering Research Environment, he contributes to multiple research profiles including VF-KDO and Virtual Production Development. His research interests center on predictive maintenance methodologies , human-robot collaboration interfaces , and cloud-enhanced manufacturing systems . Schmidt develops mixed reality training environments that reduce production line disruption while improving operator safety. His work integrates double ball-bar measurements with machine learning to create population-based maintenance models that achieve 40% cost reduction compared to traditional approaches. Current projects include ACCURATE (2021-2025) and VF-KDO (2019-2026), funded by Sweden's Knowledge Foundation. Analysis of Schmidt's 15 most recent publications reveals a strong trend toward real-time industrial decision support systems using mixed reality visualization. His work consistently addresses sustainable manufacturing through energy-efficient robotics and predictive maintenance frameworks that leverage cloud computing. Key subfields include virtual sensor integration, collision avoidance algorithms, and multi-objective optimization for robotic cells - demonstrating practical applications in automotive and elevator manufacturing. Research funding highlights include: Knowledge Foundation (KKS) projects: ACCURATE (2021-2025), VF-KDO (2019-2026), Virtual Factory with Knowledge-Driven Optimization (2018-2026) European Commission grant (637107) IPSI Industrial Research School collaboration with Volvo GTO and Volvo Cars Schmidt actively supervises bachelor's degree projects at ASSAR Industrial Innovation Arena in Skövde, with recent work involving Natalia Sempere Maciá and Celia Redondo Verdú. His research integrates with ABB Robotics systems through partnerships with experts like Tommy Y. Svensson. Current laboratory work focuses on HoloLens 2 implementations for robotic cell visualization and safety system development for human-robot collaborative environments.

• Simulation-Based Optimization
• Industry 4.0
• Production Engineering
• +۴ مورد دیگر

Amos H. C. Ng is a Professor at the School of Engineering Science, University of Skövde, specializing in simulation-based optimization and Industry 4.0 technologies. His research bridges production engineering with human-robot collaboration, ergonomics evaluation, and cloud-based cyber-physical systems for manufacturing efficiency. Key Affiliations: University of Skövde (School of Engineering Science), Uppsala University (Industrial Engineering and Management) Research Themes: Multi-objective optimization, Digital Twin frameworks, Human-centric production systems, Reconfigurable manufacturing, Throughput bottleneck analysis Projects: ACCURATE 4.0 (Knowledge Foundation), VF-KDO (Virtual Factories with Knowledge-Driven Optimization), EWASS (Wire Harness Assembly Optimization) His recent publications demonstrate expertise in applying evolutionary algorithms, machine learning models, and digital human modeling tools to solve complex manufacturing problems ranging from crankshaft machining to wood supply chain robustness. Current work integrates motion capture technology with DHM tools for objective ergonomic assessments in assembly stations. Amos collaborates extensively with industrial partners like Volvo Penta and academic institutions, utilizing simulation-based approaches to enhance decision-making in production systems. His methodological focus includes non-dominated sorting genetic algorithms, surrogate modeling, and parallel computing architectures for optimization tasks.

John Daneshمشاهده پروفایل

استاد

• Epidemiology
• Cardiovascular Disease
• Genetic Epidemiology
• +۳ مورد دیگر

John Danesh is Professor of Epidemiology and Medicine and Head of the Department of Public Health and Primary Care at the University of Cambridge. He concurrently serves as a Faculty Member at the Wellcome Sanger Institute, Honorary Consultant at Cambridge University Hospital NHS Foundation Trust (Addenbrooke's Hospital), Professorial Fellow at Jesus College, Cambridge, Founder-Director of the Cardiovascular Epidemiology Unit, and Director of Health Data Research UK-Cambridge. His research spans cardiovascular epidemiology, genetic determinants of disease, and health data science. He pioneers the integration of genomics, metabolomics, and large-scale population data to unravel disease mechanisms, with emphasis on polygenic risk scores, metabolite-disease associations, and international collaborations like the BELIEVE cohort in Bangladesh. His work bridges molecular biology with public health applications for precision prevention. Analysis of his 2022-2025 publications reveals dominant themes in multi-omics cardiovascular risk prediction, including genetic architecture of coronary disease, iron homeostasis pathways, and regional risk variations in South Asian populations. Key methodologies involve individual-participant-data meta-analyses, Mendelian randomization, and computational modeling of biological networks. Scientific Awards: No specific awards listed in source materials Professor Danesh leads major grant-funded initiatives including the Cardiovascular Epidemiology Unit and Health Data Research UK-Cambridge. His advisory roles in national health data infrastructure and longitudinal research environments demonstrate significant influence in shaping large-scale epidemiological studies and translational data science applications. He directs the Cambridge Mathematics of Information in Healthcare (CMIH) hub, fostering cross-disciplinary teams that apply advanced computational methods to healthcare data. His laboratory focuses on genetic and metabolic drivers of cardiovascular disease through international cohort studies and innovative risk prediction modeling.

Emilia Fridmanمشاهده پروفایل

استاد

• Control Theory
• Time-Delay Systems
• Networked Control Systems
• +۴ مورد دیگر

Emilia Fridman is a Professor at the School of Electrical Engineering, Tel Aviv University, specializing in time-delay systems , networked control systems , and distributed parameter systems . Her work bridges robust control , singular perturbations , and nonlinear control , with over 200 journal papers and three monographs. PhD in Mathematics from Voronezh State University (1986) Highly Cited Researcher (2014) and IEEE/IFAC Fellow Recipient of the IFAC Harold Chestnut Prize (2023) and Kadar Family Award (2021) Her research focuses on stability analysis and control design for systems with delays, including sampled-data control , networked systems under communication constraints , and predictor-based methods for input delays . She has developed discretized Lyapunov functionals and descriptor system approaches to address robustness in time-delay systems. Recent publications emphasize finite-dimensional control of PDEs , event-triggered control , and extremum seeking with delays . Her group has secured grants from the Israel Science Foundation (ISF) and collaborates globally on distributed control and stochastic systems . Scientific Awards : IFAC Harold Chestnut Control Engineering Textbook Prize 2023 IEEE Fellow (2014) IFAC Delay Systems Life Time Achievement Award (2021) Stanford University Top 0.03% in Industrial Engineering & Automation (2022) Advising : Mentored over 15 PhD students including Kun Liu, Anton Selivanov, and Rami Katz. Her editorial roles include Automatica and SIAM Journal on Control and Optimization .

• Environmental Sustainability
• Manufacturing Systems
• Circular Economy
• +۶ مورد دیگر

Mélanie Despeisse is an Associate Professor in Production Systems at Chalmers University of Technology. Her research focuses on the relationship between industry and environmental sustainability at varying levels, from operational resource efficiency in manufacturing processes to corporate social responsibility in strategic planning. Her primary research interests include sustainable manufacturing systems, circular economy implementation, and the integration of digital technologies with environmental sustainability goals. She develops practical tools and methodologies to help companies create sustainable social value through concepts like eco-efficiency and circularity. Her work spans multiple industries including automotive, aerospace, maritime shipping, and battery production systems, addressing challenges from resource efficiency to strategic sustainability planning. Analysis of her recent publications reveals a strong focus on the intersection of digital technologies (such as virtual reality, computer vision, and digital twins) with environmental sustainability in manufacturing. Her research increasingly addresses the twin transitions of digitalization and sustainability, exploring how Industry 4.0 technologies can support eco-efficient production systems while building organizational resilience. She has developed frameworks for eco-efficient and circular industrial systems, examining both technical and organizational aspects of sustainability transitions. Mélanie Despeisse is actively involved in numerous research projects across multiple domains, including XR for battery production, visibility of value networks for circularity, aircraft architecture integration, and sustainable manufacturing for maritime shipping. Her collaborative work demonstrates strong interdisciplinary partnerships across engineering, environmental science, and business management domains.

Aude Olivaمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Generative Models
• +۵ مورد دیگر

Aude Oliva serves as MIT director of the MIT-IBM Watson AI Lab and director of strategic industry engagement at the MIT Schwarzman College of Computing. As a Senior Research Scientist at MIT CSAIL, she leads the Computational Perception and Cognition group, driving interdisciplinary research at the intersection of human intelligence and artificial systems. Her roles position her at the forefront of translating academic AI research into real-world applications through major industry partnerships. Dr. Oliva earned her MS and PhD in cognitive science from Institut National Polytechnique de Grenoble, France, establishing her foundation in human perception and computational modeling. Her research integrates computer vision, deep learning, and cognitive neuroscience to understand visual information processing in both biological and artificial systems. She develops computational models that mimic human visual recognition while creating AI systems capable of compositional reasoning and efficient video understanding. Current work emphasizes neuroscience-inspired architectures, resource-efficient deep learning, and multimodal representation learning, with applications spanning healthcare, robotics, and human-computer interaction. Her cross-disciplinary approach uniquely bridges theoretical neuroscience with practical AI development. Analysis of recent publications reveals a clear trajectory toward tighter integration of neuroscience and AI, particularly through brain imaging datasets like BOLD Moments. Her group consistently advances efficient deep learning techniques (Trans-LoRA, VA-RED²) while exploring fundamental questions in visual cognition through projects like the Algonauts Challenge. The work demonstrates increasing industry relevance with strong representation in NeurIPS and Nature Communications. Her major recognitions include: NSF Career Award in computational neuroscience Guggenheim fellowship in computer science Vannevar Bush Faculty Fellowship in cognitive neuroscience As director of the $240M MIT-IBM Watson AI Lab, Dr. Oliva oversees substantial research funding while advising graduate students through MIT's EECS department. Her lab benefits from unique industry-academic synergy, with students gaining access to IBM resources and real-world deployment challenges. The collaborative environment fosters innovation in efficient AI systems with tangible societal impact. The Computational Perception and Cognition group operates as a dynamic hub where computer scientists, neuroscientists, and cognitive scientists collaborate on fundamental questions of intelligence. Current projects focus on making AI systems more human-like in visual reasoning while ensuring computational efficiency for real-world deployment, leveraging the unique resources of the MIT-IBM partnership.

• Industrial Engineering
• Applied Optimization
• Simulation Models
• +۹ مورد دیگر

Miguel Vieira is an Assistant Professor at the Faculty of Engineering , Lusófona University, Lisbon, and a Researcher at RCM2+. He also holds Visiting Researcher positions at Instituto Superior Técnico and Universidade de Coimbra. PhD in Leaders for Technical Industries (2017) from Instituto Superior Técnico (part of MIT Portugal Program) Focus on industrial engineering challenges, including optimization methods, simulation models, and machine learning for supply chain and production systems Developed mathematical programming models (LP, IP, MILP, MINLP) and decomposition methods for complex manufacturing problems His research spans deterministic and stochastic scenarios, with a focus on Industry 4.0, human-robot interaction, and decision-support systems. He has published 8 journal articles, 11 book chapters, and contributed to 7 events, including organizing 7 conferences. Recent publications emphasize data-driven optimization , risk assessment , and sustainable strategies in biomass and fuel distribution supply chains, alongside machine learning integration in manufacturing. His work includes a Q-Learning algorithm for assembly lines and stochastic programming applications. Awards & Honors : 6 scientific awards (specifics not listed) Miguel has supervised 3 MSc dissertations and collaborated with 53 researchers. His projects include risk assessment in process design, dynamic scheduling for Industry 4.0, and enhancing industrial optimization via deep learning.

Bruno Gaujalمشاهده پروفایل

استاد پژوهشی

• Performance Evaluation
• Optimization
• Discrete Event Dynamic Systems
• +۵ مورد دیگر

Bruno Gaujal is a Research Professor at Inria Grenoble-Rhône-Alpes, affiliated with Université Grenoble Alpes. He obtained his PhD from the University of Nice in 1994 under François Baccelli's supervision and has held positions at AT&T Bell Labs, INRIA, and École Normale Supérieure de Lyon. He previously led the MESCAL (now POLARIS) research group focused on large-scale computing until 2015. His research interests center on performance evaluation, optimization, and control of discrete event dynamic systems with stochastic inputs. Specific areas include: Markov Chains and Markov Decision Processes Reinforcement Learning and stochastic optimization Queueing theory and scheduling algorithms Energy-efficient computing in distributed systems Game-theoretic approaches in network optimization Gaujal's recent publications show strong emphasis on reinforcement learning applications in queueing networks, energy optimization for real-time systems, and scalable algorithms for Markov Decision Processes. His work bridges theoretical frameworks like Whittle indices with practical implementations in cloud computing and distributed systems. He has supervised numerous PhD students including Nicolas Gast (now Inria researcher), Anne Bouillard (Huawei researcher), and Emmanuel Hyon (Paris Nanterre professor). Current students include Hélène Arvis and Romain Cravic. Gaujal co-founded RTaW, a startup specializing in real-time network design tools. At Inria, he leads research in the POLARIS group, focusing on optimization methods for large-scale distributed computing infrastructures. His work involves collaborations with 85+ co-authors across institutions globally.

• Computational Methods
• Numerical Analysis
• Control Theory
• +۹ مورد دیگر

Boris Kramer is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California San Diego , affiliated with the Jacobs School of Engineering . He leads research in computational methods for control , optimization , and uncertainty quantification of complex systems, with applications in space weather modeling , systems biology , and soft robotics . Center for Extreme Events Research (CEER) Center for Computational Mathematics (CCoM) Air Force Center of Excellence Multi-Fidelity Modeling His work focuses on reduced-order modeling (ROM) and data-driven methods that preserve physical structures (e.g., energy conservation in Hamiltonian systems). Recent projects include collaborations with Samsung Electronics for semiconductor manufacturing optimization and leadership in DOE's PSAAP IV program for radiation resilience modeling. Kramer's research has been featured in Nature Computational Science and Science News , with grants from NSF , DoD , and AFOSR . His group has advised students like Opal Issan (published in Journal of Computational Physics) and Nate Linden (Nature Communications). Scientific awards include: NSF CAREER Award (2022) DoD Newton Award for Transformative Ideas (2020) Outstanding PhD Student Award (2024-2025, UCSD MAE) MURI funding for Digital Twins (2023) Outreach efforts include participation in the Barrio Logan Science & Art Expo and the Southeast San Diego STEM Ecosystem , emphasizing science communication for K-12 audiences.

• Security
• Theoretical Computer Science
• Formal Methods
• +۷ مورد دیگر

Christian Johansen is a Professor in the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU), Faculty of Information Technology and Electrical Engineering. He leads the Systems Security group (S2G) and is affiliated with the Center for Cyber and Information Security (CCIS), the Norwegian Cyber Range, and the S2G Playground. Professor Johansen's research focuses on Security and Theoretical Computer Science, with emphasis on developing formal methods and tools for ensuring reliability of complex systems. His work spans security, safety, and concurrency properties in software systems, cyber-physical systems like Smart Grids, Internet of Things security, and modeling concurrency in multi-core and high-performance computing. Among his notable contributions are the Timed Distributed pi-calculus, ST-structures, Dynamic Structural Operational Semantics, Synchronous Kleene Algebra, and Higher Dimensional Modal Logic. His research interests include modeling of security protocols, programming language semantics, verification of distributed systems, concurrent systems modeling, and legal electronic contracts. His recent publications show a strong trend toward concurrency theory, security, and privacy, with significant contributions to pi-calculus variants, higher-dimensional automata, attribute-based encryption, and semantic access control frameworks. Many of his papers appear in top venues such as CONCUR, ATVA, FM, POST, CCS, JLAMP, IJCIP, FMSD, and LMCS. Professor Johansen actively mentors students and collaborators, having worked with Manish Shrestha on the LightSC Security Classification Method for Smart Grids and IoT, and with Bjørnar Luteberget on the SAT modulo Discrete Event Simulation method for railway capacity verification. He has secured funding from competitive sources including EU-FP7-FET-Young-Explorers, Horizon-2020, NFR-FRINATEK, UK's EPSRC, and ECSEL-JU. His work often bridges theoretical computer science with practical security applications in critical infrastructure domains.

Milad Haghaniمشاهده پروفایل

مدرس ارشد

• Transport Safety
• Human Factors in Transport
• Crowd Dynamics
• +۴ مورد دیگر

Dr. Milad Haghani is a Senior Lecturer at the School of Civil and Environmental Engineering , UNSW Sydney , and holds an Australian Research Council (ARC) Discovery Early Career Researcher Award (DE210100440) for his work in crowd evacuation planning. He earned his PhD in Transport Engineering from the University of Melbourne and has held fellowships at the University of Sydney and University of Melbourne. His research spans transport safety, human factors, and meta-science, with particular emphasis on pedestrian dynamics and disaster preparedness. Education: PhD in Transport Engineering, University of Melbourne MSc in Transport Engineering, Sharif University of Technology BSc in Civil Engineering, Iran University of Science and Technology Research Themes: Crowd safety and evacuation modeling Transport psychology and road safety Econometric and choice modeling Meta-research and science of science Disaster mitigation and urban planning Behavioral interventions in safety Scientific Recognition: ARC DECRA Fellow (DE210100440, 2021-2024) His publications (15 most recent) cover topics including pedestrian behavior , crowd accident analysis , emergency evacuation optimization , and transport safety policy . As a DECRA Fellow, he leads projects on behavioral interventions in crowd management. His work intersects transport engineering , human psychology , and disaster response systems , with applications in urban design, public security perception, and emergency logistics. Dr. Haghani contributes to science of science through bibliometric analyses, examining research trends in fields like coronavirus studies , wildfire science , and digital health technologies . His methodological expertise spans discrete choice modeling and simulation-based research , including virtual reality applications for safety training.

Palma Pacioccoمشاهده پروفایل

دانشیار

• Criminal Law
• Criminal Procedure
• Evidence Law
• +۳ مورد دیگر

Palma Paciocco is an Associate Professor at Osgoode Hall Law School within York University . Her work focuses on criminal law, criminal procedure, evidence law, sentencing, professional ethics, and interdisciplinary approaches via law and the humanities. Education: BA (McGill), BCL/LLB (McGill), SJD (Harvard) Professional Affiliations: Co-director of the Juris Doctor/Master of Arts in Philosophy Program, Board of Directors, Canadian Law and Society Association Her research addresses systemic challenges in criminal justice, including trial delays, prosecutorial ethics in plea bargaining, and judicial discretion. She has contributed to leading journals and co-authored Canada’s premier evidence law textbook. Scientific Awards: Osgoode Hall Law School Teaching Award (2018) SSHRC Doctoral Fellow Thomas Shearer Stewart Travelling Fellow Landon H. Gammon Fellow Professor Paciocco supervises graduate students in socio-legal studies and law-and-humanities frameworks, serving on examination committees for Katherine Poehlmann (Master's) and Danardo Jones (PhD).