جست‌وجوی استاد و پژوهشگر

Adrian Perrigمشاهده پروفایل

استاد

• Network Security
• Cybersecurity
• Distributed Systems
• +۳ مورد دیگر

Adrian Perrig is a Full Professor at the Department of Computer Science at ETH Zürich. He leads research in network security, distributed systems, and internet architecture, focusing on projects like the SCION secure internet architecture and its commercialization through Anapaya Systems. His work emphasizes secure communication, denial-of-service defense, and public key infrastructure (PKI) innovations. Affiliations: ETH Zürich, Institute for Information Security Key Contributions: SCION, SAGE, RHINE, F-PKI Research interests include path-aware networks, cryptographic protocols, and resilient systems. His publications span over 295 results since 2005, with notable awards including the Best Paper Award (CoNEXT 2021) and ANRP 2023. He has contributed to foundational work in secure routing, DNS security, and GPU attestation. Scientific awards include Best Paper Awards at CoNEXT and ACM SIGCOMM, as well as recognition for applied networking research. His work bridges academia and industry, addressing challenges in global network security and scalability.

Tom Verhoeffمشاهده پروفایل

استادیار

• Software Engineering
• Model-Driven Engineering
• Domain-Specific Languages
• +۷ مورد دیگر

Tom Verhoeff is an Assistant Professor at the Faculty of Mathematics and Computing Science of Eindhoven University of Technology (TU/e) , working within the Software Engineering & Technology group. His research focuses on Model-Driven Engineering (MDE) , Domain-Specific Languages (DSLs) , and the intersection of mathematics, computing, and the arts . He teaches courses in data analytics, programming, algorithms, theoretical computer science , and logic . Verhoeff earned both his MSc and PhD in Technical Science (Mathematics and Computer Science) from TU/e. He is actively involved in promoting mathematics and informatics through initiatives like the annual Bridges conference , and serves as board member and treasurer of the Dutch Mathematics Olympiad , as well as chair of the Koos Verhoeff MathArt foundation . He has also held roles as guest lecturer in Lithuania and Finals Director for the ACM International Collegiate Programming Contest . Research Interests: Verhoeff’s work spans Model-Driven Engineering , domain-specific language development , and 3D geometric modeling . His scholarship often explores symmetry, recursion, and mathematical visualization , particularly through computational art and algorithmic puzzles . Recent publications highlight 3D rotation methods , knot theory , and mathematical art using lattice paths and geometric transformations . Scientific Awards: ACM ICPC European Founders Award (2004) IOI Distinguished Service Award (2007) Second Place in the 2022 Wolfram Computational Art Contest Notable Collaborations and Affiliations: He is affiliated with the Esprit Working Group on Asynchronous Circuit Design (ACiD-WG) , WIRE (TUE Mathematics Alumni) , ACM (Senior Member) , CSTA , IEEE Computer Society , and Royal Dutch Mathematical Society (KWG) .

Mohammad Dehghaniمشاهده پروفایل

استاد آموزشی

• Reinforcement Learning
• Simulation Optimization
• Healthcare Operations
• +۵ مورد دیگر

Mohammad Dehghani is an Associate Teaching Professor in the Department of Mechanical and Industrial Engineering at Northeastern University, where he also serves as Program Director of the Galante Engineering Business Program. He holds a Ph.D. in Engineering Management from Western New England University (2016), an M.S. in Industrial Engineering from Tarbiat Modares University (2011), and a B.S. in Industrial Engineering from Yazd University (2008). His research focuses on Reinforcement Learning (RL), Simulation Optimization, and Healthcare Operations, with applications in manufacturing, digital twin systems, and UAV routing. He has developed multiple courses in Industrial Engineering and Data Analytics, receiving the 2020 Fostering Engineering Innovation in Education Award and the 2025 DAIS Data Analytics Teaching Award. Education: Ph.D. in Engineering Management, Western New England University, 2016 M.S. in Industrial Engineering, Tarbiat Modares University, 2011 B.S. in Industrial Engineering, Yazd University, 2008 Dehghani’s research bridges AI and operations research, emphasizing practical applications. His work includes developing RL frameworks for manufacturing scheduling and UAV routing, as well as simulation-optimization models for healthcare and pandemic preparedness. He has collaborated on projects addressing supply chain resilience during the COVID-19 pandemic and multi-objective supplier selection processes. His publications span journals like Simulation and conferences such as Winter Simulation Conference (WSC). His honors include the 2015 Best Ph.D. Paper Award at WSC and recognition from the Institute of Industrial and Systems Engineers (IISE). He actively contributes to professional societies, including the American Society of Engineering Management and Institute of Industrial Engineers. His teaching focuses on integrating data analytics and simulation tools into engineering curricula, with courses emphasizing Python integration, simheuristics, and digital twin technology. Dehghani leads initiatives in the Galante Program to enhance engineering-business synergies, preparing students for industry roles through interdisciplinary training. His work emphasizes practical problem-solving, with grants supporting projects in healthcare logistics and sustainable construction in cold climates.

• Digital Circuits and Systems
• Energy Efficiency
• Smart Cities
• +۱۳ مورد دیگر

Enrico Macii is a Full Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Regional and Urban Studies and Planning (DIST) and the Department of Control and Computer Engineering (DAUIN). He leads the Electronic Design Automation (EDA) research group and holds key roles as Scientific Advisor for the Politecnico-STMicroelectronics partnership and Scientific Contact for the European Chips Joint Undertaking. Research Interests: His work spans digital circuits and systems, energy efficiency, smart cities, Industry 4.0, and smart manufacturing. He focuses on embedded and cyber-physical systems, low-power design, neuromorphic computing, AIoT, and sustainable urban development. Recent Publications: His recent research demonstrates strong trends in edge AI, neuromorphic computing, and smart energy systems. Articles highlight innovations in low-power hardware acceleration, federated learning, physics-informed AI, and digital twin applications for urban and industrial systems. There is a clear emphasis on deploying AI efficiently on constrained devices and integrating physical models with machine learning. J. William Fullbright Fellowship (1993) Best paper award IEEE European Design Automation Conference (1996) Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) DAC Service Award (2014) IEEE Fellow (2006) DATE Fellow (2014) Advising and Grants: He has supervised over 25 PhD students in computer engineering, AI, and urban systems. His research is funded by major EU programs (Horizon 2020, PNRR, KDT JU), national (PRIN, FAR), and regional grants, as well as industrial contracts with STMicroelectronics, Michelin, and Cefriel. He leads numerous high-impact projects in smart manufacturing, energy efficiency, and digital twins. Labs and Teams: He is a core member of the EDA Group, an interdepartmental research team at Politecnico di Torino focusing on VLSI-CAD, bioinformatics, smart cities, and Industry 4.0. He also contributes to IAM@PoliTo (Integrated Additive Manufacturing) and leads multiple EU and national research consortia.

Jason R. Greenمشاهده پروفایل

استاد

• Theoretical chemical physics
• Nonequilibrium statistical mechanics
• Data science
• +۴ مورد دیگر

Jason R. Green is a Professor in the Department of Chemistry at the University of Massachusetts Boston. With a PhD from Purdue University (2007) and postdoctoral experience at the Universities of Chicago, Cambridge, and Northwestern University, his research bridges theoretical chemistry, physics, and data science to explore nonequilibrium systems. His work focuses on transforming chemical energy into dynamically functional materials through interdisciplinary approaches. Education: B.S., Case Western Reserve University (cum laude, 2002) Ph.D., Purdue University (2007) with NASA Graduate Fellowship NSF Postdoctoral Fellow at University of Chicago and University of Cambridge Research Interests: Theoretical chemical physics Nonequilibrium statistical mechanics Data science applications in chemical systems His recent publications analyze electrochemical material dynamics (ACS Nano 2024), chemically driven self-assembly (Chemical Science 2024), and thermodynamic speed limits across disciplines (Nature Physics 2020, Physical Review X 2022). He has received prestigious fellowships including NASA's Graduate Student Researchers Program and NSF Postdoctoral Fellowship. The Green Research Group at UMB applies theory, computation, and data science to understand energy transformation in synthetic and biological materials.

Enrico Rukzioمشاهده پروفایل

استاد

• Human-Computer Interaction (HCI)
• Automotive User Interfaces
• Extended Reality (XR)
• +۵ مورد دیگر

Enrico Rukzio is a Full Professor of Human-Computer Interaction at the University of Ulm, leading the Human-Computer Interaction group and directing the Institute for Media Research and Media Development. His research spans interactive systems design, focusing on automotive UIs, extended reality, accessibility, and sustainable interaction. He holds a Ph.D. in Computer Science from the University of Munich and has held prior roles at Lancaster University and the Ruhr Institute for Software Technology. Roles: Faculty Dean (2017–2020), Admissions Committee Chairman, Doctoral Committee Chairman Educations: Ph.D., Munich; Lecturer qualifications from Lancaster and Duisburg-Essen Research emphasizes enabling efficient, inclusive, and sustainable interactions through novel interfaces. His work addresses automated vehicles, health-supporting tech, and accessibility for visually impaired users. Recent articles explore Bayesian optimization for UI design, automated vehicle communication, and urban air mobility visualization. Notable awards include best paper recognitions at CHI, EuroVR, and IEEE VR. His grants come from BMBF, DFG, and industry partners like Mercedes-Benz Group. Advises on over 20 funded projects and has mentored award-winning students.

Martin Fordeمشاهده پروفایل

مدرس

• Financial Mathematics
• Stochastic Volatility Models
• Lévy Processes
• +۵ مورد دیگر

Martin Forde is a Lecturer in Financial Mathematics at King's College London's Department of Mathematics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He joined King's in 2011 and previously held roles as a Research Fellow at Dublin City University and Visiting Assistant Professor at the University of California, Santa Barbara. His research focuses on asymptotics for stochastic volatility models, Lévy processes, and diffusion-type processes, with applications in financial mathematics and quantitative finance. His work often involves large deviations theory and explores topics like volatility smile dynamics, rough volatility models, and optimal trade execution strategies. Key research interests include rough volatility, price impact models, Gaussian fields, and robust hedging of exotic options. He contributes to events such as the London-Paris Bachelier Workshop in Financial Mathematics and maintains an active publication record in journals like Risk , Quantitative Finance , and Mathematical Finance . His recent work addresses small-time and large-time asymptotics in models like the Rough Heston and Stein-Stein frameworks, as well as the analysis of multiplicative chaos and log-correlated Gaussian fields. Publications highlight advancements in understanding the behavior of financial derivatives under stochastic volatility, including papers on the conditional law of Bacry-Muzy fields, rough Bergomi model skew flattening, and optimal execution strategies under drift uncertainty. His research bridges theoretical probability and applied finance, with a focus on rigorous mathematical analysis of market dynamics and derivative pricing.

• Network Routing
• Segment Routing
• Software Defined Networking
• +۶ مورد دیگر

Marco Polverini is an active computer networking researcher with a prolific publication record spanning over a decade, with 59 publications documented from 2012 to 2025. His work primarily focuses on advanced networking technologies including Segment Routing, Software Defined Networking, and Network Function Virtualization. His research interests center around network routing optimization, traffic engineering, and network monitoring. He has made significant contributions to Segment Routing technology, developing novel behaviors for low-latency communication, black hole detection mechanisms, and traffic matrix assessment techniques. His recent work integrates artificial intelligence approaches, particularly reinforcement learning, with traditional networking protocols to create more adaptive and efficient network systems. He has also been exploring the application of Digital Twin technology for network management and optimization. Analysis of his publication trends shows a clear evolution from foundational work on energy-efficient networking and traffic engineering to more recent innovations in Segment Routing, in-band network telemetry, and AI-driven network optimization. His publications consistently appear in top networking venues including IEEE JSAC, IEEE Transactions on Network and Service Management, INFOCOM, and NOMS, demonstrating his standing within the networking research community. Throughout his career, Polverini has maintained strong collaborative relationships, particularly with Antonio Cianfrani (54 joint publications), Marco Listanti (33 publications), and Francesco Giacinto Lavacca (16 publications), suggesting he works within a well-established research group focused on next-generation networking technologies.

• Flow in underground porous media
• Geoenergy
• Underground energy storage
• +۱۲ مورد دیگر

Dario Viberti is a Full Professor at the Polytechnic University of Turin, affiliated with the Department of Environmental, Land and Infrastructure Engineering (DIATI). He is a member of the Interdepartmental Center Ec-L - Energy Center Lab and actively contributes to the Master's and Continuing Education School. He serves as Coordinator for both Master’s levels I and II of the Natural Resources Development and Storage program and participates in doctoral colleges for Civil and Environmental Engineering and Materials, Sustainable Processes, and Systems for the Transition. His research focuses on flow in underground porous media , geoenergy , and underground energy storage , particularly hydrogen and CO₂ storage. His expertise spans numerical modeling, reservoir engineering, fluid mechanics, well testing, and energy transition systems. He is involved in commercial research projects with ENI Corporate University and leads initiatives related to sustainable energy applications. His recent publications reveal a strong trend in underground hydrogen storage , biogeochemical modeling , pore-scale simulation , and PVT analysis of gas mixtures . These works emphasize safety, efficiency, and long-term integrity of subsurface storage systems using advanced computational and experimental methods. Scientific Manager, Underground CO2 Storage Project (2022) Scientific Manager, Reservoir Modeling Project (2022) Coordinator, Master’s Program in Natural Resources Development and Storage Member, SEASTAR Competence Center Committee (2020–2025) He mentors several PhD students, including Michel Tawil, Marialuna Loffredo, Alice Raeli, and Alice Massimiani, supporting research in microfluidics, Lattice Boltzmann simulations, and thermodynamic characterization. He teaches courses such as Numerical Modeling for Multiphase Flow , Underground Energy Storage , and Well Logging and Testing across multiple degree programs. His work aligns with UN SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation, and Infrastructure), and 13 (Climate Action).

Dan Crisanمشاهده پروفایل

استاد

• Stochastic Analysis
• Fluid Dynamics
• Stochastic Partial Differential Equations (SPDEs)
• +۶ مورد دیگر

Professor Dan Crisan is a Professor of Mathematics at Imperial College London and Director of the EPSRC Centre for Doctoral Training in the Mathematics of Planet Earth. He holds a PhD in Mathematics from the University of Edinburgh and an MSci in Mathematics from the University of Bucharest. His research focuses on Stochastic Analysis, Stochastic PDEs, Fluid Dynamics, Nonlinear Filtering, and Data Assimilation. He leads the Stochastic Transport in Upper Ocean Dynamics (STUOD) project, supported by an ERC Synergy Grant, exploring stochastic models in geophysical fluid dynamics. His academic roles include directing the MPE CDT and teaching advanced courses like Stochastic Calculus with Applications to Non-Linear Filtering. He collaborates extensively with researchers globally on topics including stochastic fluid dynamics, particle methods, and Bayesian inference. His work bridges theoretical advancements with applications in climate modeling and environmental systems. Education: PhD (University of Edinburgh, 1996), MSci (University of Bucharest, 1992). Professional roles include Director of MPE CDT (2013–present), Professor at Imperial College (2011–present), and prior academic appointments at the University of Cambridge and Imperial College. His research emphasizes stochastic processes in fluid dynamics, with projects funded by EPSRC and EU grants. Current PhD supervision opportunities are available in areas like stochastic fluid dynamics and data assimilation. Key achievements include foundational contributions to particle filtering, numerical methods for stochastic PDEs, and stochastic transport models. His work on the Camassa-Holm equation and rotating shallow water models demonstrates expertise in nonlinear wave dynamics and stochastic parametrization. He actively engages in international collaborations and serves on editorial boards for leading journals in stochastic analysis.

Archontia C. Giannopoulouمشاهده پروفایل

دانشیار

• Graph Algorithms
• Structural Graph Theory
• Parameterized Algorithms and Complexity
• +۳ مورد دیگر

Archontia C. Giannopoulou is an Associate Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece. Her research focuses on Graph Algorithms, Structural Graph Theory, and Parameterized Algorithms and Complexity. She explores advanced topics such as matching minors, tree-cut width, immersion minors, and kernelization. Her work emphasizes algorithmic design and structural graph theory with applications in combinatorial optimization. Giannopoulou’s academic contributions include seminal papers on edge deletion problems, cutwidth obstructions, and polynomial kernelization. Notable publications include studies on bipartite graphs and their structural properties, as well as algorithmic advancements in parameterized complexity. Her research bridges theoretical foundations with practical algorithmic solutions. Her contact information includes an email at archontia.giannopoulou@gmail.com and a postal address at the University of Athens. Her work spans international collaborations, reflected in co-authored articles with leading researchers in the field.

John Leeمشاهده پروفایل

استاد

• Oil and Gas Reserves
• Resources Evaluation
• Production Forecasting
• +۱ مورد دیگر

John Lee is a Professor and Regents Professor in the Harold Vance Department of Petroleum Engineering at Texas A&M University, holding the Kelly L. '87 and William D. '87 Von Gonten, Jr. Chair. A member of the National Academy of Engineering, his research focuses on reservoir engineering and carbon storage technologies. Dr. Lee's educational background includes a Ph.D. in Chemical Engineering from Georgia Institute of Technology. His current research investigates enhanced oil recovery, carbon sequestration optimization, and geomechanical responses during CO2 injection. Recent publications focus on machine learning applications for reservoir management and experimental studies on caprock integrity. His articles consistently address challenges in carbon storage engineering, with recent work developing co-optimization frameworks and pressure management strategies for CCS projects. Publication trends show increasing focus on geochemical monitoring and machine learning applications in reservoir characterization. Lee has received numerous honors including the DeGolyer Distinguished Service Medal and Anthony F. Lucas Gold Medal. His research team develops novel experimental methods to evaluate reservoir rock behavior under injection conditions.

Yoram Alhassidمشاهده پروفایل

استاد

• Theoretical Nuclear Physics
• Many-Body Quantum Systems
• Nuclear Many-Body Problem
• +۱۵ مورد دیگر

Yoram Alhassid is the Frederick Phineas Rose Professor of Physics in the Department of Physics at Yale University, where he leads a research group focused on theoretical nuclear and many-body physics. His work bridges nuclear physics, mesoscopic systems, and ultracold atomic gases, using advanced computational methods such as quantum Monte Carlo and the configuration-interaction shell model. His research interests include the nuclear many-body problem, femtoscience and nanoscience (nuclei, quantum dots, nanoparticles), and cold atomic Fermi gases. He has developed and applied the shell model Monte Carlo (SMMC) method to study statistical and collective properties of nuclei, such as level densities, deformation, and pairing correlations. He has extended these methods to study cold Fermi gases, particularly in the unitary regime, where he has investigated pseudogap phenomena, heat capacity, and pairing gaps. The recent publications highlight a strong focus on nuclear level densities, γ-ray strength functions, deformation effects, and quantum Monte Carlo methodologies. There is a clear trend toward microscopic, ab initio calculations of nuclear and many-body properties, with increasing attention to odd-mass and deformed nuclei, as well as the interplay between pairing and collective phenomena. Scientific Awards: Frederick Phineas Rose Professor of Physics Alhassid advises students and postdoctoral researchers, as evidenced by numerous co-authored publications. His group has developed advanced computational tools and codes (e.g., HF-SHELL) for finite-temperature mean-field and shell model calculations. The research is supported by high-performance computing and has implications for nuclear astrophysics, radioactive beam facilities, and quantum simulation with cold atoms. His lab focuses on theoretical and computational modeling of finite-size quantum many-body systems, with close collaborations across nuclear theory, condensed matter, and atomic physics. The group emphasizes method development, benchmarking against mean-field theories, and extracting model-independent signatures of physical phenomena such as deformation and pairing.

• Human Computer Interaction
• User Experience
• Information systems
• +۸ مورد دیگر

Assoc Prof Geraldine Torrisi is the Deputy Head of School (Learning & Teaching) at the School of Information and Communication Technology at Griffith University. With a PhD in Educational Studies and degrees in Science (Physics/Mathematics) and Education, she focuses on Human-Computer Interaction (HCI) , User Experience , and Learning Theory . She is a Senior Fellow of the Higher Education Academy and Griffith Learning and Teaching Academy . Education: PhD in Educational Studies (Griffith University) Masters of Educational Studies (James Cook University) Bachelor of Education (Secondary Science and Maths) (James Cook University) Bachelor of Science (Physics/Mathematics) (James Cook University) Research Interests include Interactive Digital Media , Educational Technology , and Data Literacy . Her 2025 book chapters explore AI's role in education, while 2024-2023 articles address data literacy frameworks and hybrid teaching models. Scientific Awards include Griffith Award for Excellence in Teaching (2011) , Excellence in Teaching Award (2015) , and Senior Fellow of the Higher Education Academy (2018) . She also received multiple Group Learning and Teaching Commendations (2009, 2017, 2019). Grants include The Dignity Project (2024-2025), Promoting Renewable Energy Adoption (2022-2025), and Smart Environmental Sensing (2022-2023). She supervises PhD students on topics like Human-Machine Teaming and Instructional AR Design .

• Polyhedral combinatorics
• Extended formulations
• Combinatorial optimization
• +۲ مورد دیگر

Samuel Fiorini is Associate Professor in the Department of Mathematics at the Université libre de Bruxelles (ULB) , member of the Algebra and Combinatorics group (CP 216). His research centres on polyhedral combinatorics, extended formulations, combinatorial optimisation and approximation algorithms , with frequent overlap into structural graph theory. Research in depth: Fiorini’s work explores how high-dimensional polytopes can sometimes be expressed compactly through extended formulations, proving exponential lower bounds when they cannot. He has contributed new approximation algorithms for classical problems such as vertex cover, clique transversal and odd-cycle packing, and has advanced the understanding of sorting and entropy in partially ordered sets. His papers often combine tools from graph minors, communication complexity and polyhedral theory. Scientific recognition: Best Paper Award, 44th ACM Symposium on Theory of Computing (STOC 2012) Programme committees: FOCS, IPCO, APPROX, STACS, WAOA Organiser, Sixth Cargese Workshop on Combinatorial Optimization Advising & grants: He currently supervises PhD students Carole Muller and Matthew Drescher and has mentored six completed PhDs as well as more than a dozen post-doctoral researchers. His group has been supported by an ERC starting grant and other national and international projects focusing on polyhedral approaches to hard optimisation problems. Lab & team: Fiorini leads a vibrant team within the Algebra and Combinatorics cluster at ULB, maintaining active collaborations with researchers worldwide and hosting frequent visitors working on discrete optimisation and polyhedral combinatorics.