جست‌وجوی استاد و پژوهشگر

• Matroid Theory
• Graph Theory
• Coding Theory
• +۵ مورد دیگر

Peter Nelson is an Associate Professor in the Department of Combinatorics and Optimization at the University of Waterloo, Canada. He currently serves as the Associate Chair for Undergraduate Studies, coordinating academic advising and managing departmental operations. His research focuses on structural and extremal matroid theory, graph theory, and their connections to coding theory, additive combinatorics, and finite geometry. He holds an NSERC Discovery Grant and has contributed to foundational work on matroid minors, binary matroid classification, and combinatorial enumeration. His recent interests include formalizing proofs in the LEAN theorem prover. Education: Ph.D. in Mathematics (University of Waterloo, 2008) with a thesis titled *Exponentially dense matroids*. His academic journey includes postdoctoral research and teaching roles prior to his current position. Research Interests: Structural matroid theory (e.g., minor-closed classes, forbidden configurations) Binary matroid extremal problems Applications to coding theory and additive combinatorics Formal proof systems like LEAN Advising & Grants: As Associate Chair, he oversees undergraduate academic advising via coundergrad.officer@uwaterloo.ca . His NSERC grant supports investigations into matroid density and extremal configurations. He has collaborated extensively with institutions globally, including co-authoring over 40 peer-reviewed publications. Labs/Teams: Active member of the Combinatorics and Optimization research group at Waterloo, contributing to collaborative projects on matroid theory and discrete mathematics.

• Lattice Algorithms and Geometry of Numbers
• Linear and Integer Programming
• Extended Formulations
• +۳ مورد دیگر

Daniel Dadush is a part-time Professor of Geometry of Optimization at Utrecht University and leads the Networks & Optimization group at Centrum Wiskunde & Informatica (CWI). He has held previous positions as a Simons Postdoctoral Fellow at the Courant Institute of Mathematical Sciences (New York University) and a PhD in the ACO program (Algorithms, Combinatorics, and Optimization) at Georgia Tech. Research Interests: Lattice Algorithms, Geometry of Numbers, Linear/Integer Programming, Extended Formulations, Discrepancy Theory, Convex Optimization, Asymptotic Convex Geometry. Awards: Van Dantzig Prize (2020), Best Paper Award at CCC'20 (2020), Tucker Prize (2015). Advising: Supervised PhD/MSc students including Ben Bals, Samarth Tiwari, Sander Borst, Sophie Huiberts, Huck Bennett, and Yilin Li. Recent Publications: His 15 most recent articles focus on strongly polynomial algorithms, exact integer programming, convex optimization in the oracle model, matrix discrepancy, circuit diameter bounds, and integrality gaps, spanning journals and conferences like STOC, SODA, FOCS, and Mathematical Programming. Professional Activities: Organizer of the Dutch Day on Optimization (2022), co-organizer of workshops on Discrepancy Theory, Lattices, and Discrete Optimization at institutions like HIM Bonn and the Simons Institute. Served on program committees for STOC 2025, SODA 2024, and other major conferences. Teaching: Lectured on Interior Point Methods, Straight-Line Complexity, and courses in Continuous Optimization at Utrecht University and Mastermath.

• Granular Mechanics
• Mechanical Engineering
• Neutron Diffraction
• +۵ مورد دیگر

Associate Professor Christopher Wensrich is a faculty member in the School of Engineering at the University of Newcastle, Australia, specializing in Mechanical Engineering. He has a strong background in applied mechanics from both computational and experimental perspectives, with significant expertise in granular mechanics, neutron diffraction strain measurement, and Bragg-edge transmission strain tomography. Education: PhD, University of Newcastle Bachelor of Mathematics, University of Newcastle Bachelor of Engineering, University of Newcastle Professor Wensrich's research focuses on several interconnected areas within mechanical engineering and materials science. His primary expertise lies in granular mechanics, spanning from micromechanics and homogenization of granular systems to analytical modeling of granular dynamics (particularly the silo quaking problem) and computational modeling using the Discrete Element Method (DEM). He is also a pioneer in applying neutron diffraction strain scanning techniques to granular systems. In the broader field of applied mechanics, he has made significant contributions to neutron diffraction-based strain measurement, including breakthroughs in Bragg-edge Transmission Strain Tomography, where he demonstrated the world's first practical application outside of simple axisymmetric systems. His publication record demonstrates a consistent focus on developing and applying advanced techniques for strain measurement and reconstruction in granular and composite materials. His recent work has centered on tomographic reconstruction methods using neutron diffraction, with particular emphasis on Bragg-edge techniques for 2D and 3D strain field reconstruction. His research bridges theoretical mathematics, computational methods, and experimental validation, creating a robust framework for non-destructive stress measurement in complex materials. Professional Recognition: President of the Australian Neutron Beam User Group (ANBUG) since December 2022 Member of the ACNS Program Advisory Team at ANSTO (Australian Nuclear Science and Technology Organisation) since March 2019 Visiting Fellow at Clare Hall College, Cambridge University (January-June 2023) Visiting Researcher at Isaac Newton Institute for Mathematical Sciences (January-June 2023) Professor Wensrich has secured substantial research funding, with a total of $5,478,793 across 42 grants. His funding portfolio includes projects from the Australian Research Council (ARC), ANSTO, and international partners like Oakridge National Laboratory and Japan Proton Accelerator Research Complex. He has successfully supervised 11 PhD and Masters students to completion, with research topics spanning granular mechanics, conveyor systems, and neutron strain tomography. His current research involves collaborations with institutions worldwide, focusing on advanced strain measurement techniques and their application to complex material systems.

Pierre-Yves Lajoieمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• Artificial Intelligence
• +۵ مورد دیگر

Pierre-Yves Lajoie is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, a leading engineering school affiliated with Université de Montréal. His research focuses on robotics and artificial intelligence, with specialization in robotic perception for single and multi-agent systems. He has held research positions at prestigious institutions including the Massachusetts Institute of Technology (2019), Samsung AI Center (2023), and University of Oxford (2024). Education: Ph.D. in Computer Engineering, Polytechnique Montréal Dr. Lajoie's research interests span across robotics, computer vision, and distributed systems. He specializes in developing algorithms for robotic perception in challenging environments, with applications in aerial, underground, indoor, and space robotics. His work focuses on enabling robots to understand their surroundings through visual and sensor data, particularly in collaborative multi-robot scenarios where communication may be limited or unreliable. His primary research center of excellence is the Industry of the Future and Digital Society, with secondary centers in Modeling and Artificial Intelligence and New Frontiers in Information and Communication Technologies. His publication record demonstrates a strong focus on collaborative SLAM (Simultaneous Localization and Mapping) systems, with recent work addressing challenges in planetary exploration, swarm robotics, and pedestrian positioning. His research combines computer vision, machine learning, and distributed systems to create robust solutions for real-world robotic applications, particularly in environments with communication constraints. Scientific Awards: Vanier Canada Scholarship Best Paper Award at IEEE ICC 2024 Dr. Lajoie is actively recruiting graduate students for PhD and Master's programs, with openings for Fall 2025 and Spring 2026. He encourages students to apply for various scholarship opportunities including NSERC, FRQ, and IVADO scholarships at multiple academic levels. His research is supported by collaborations with academic and industrial partners, focusing on applications in space robotics, automated manufacturing, and service robotics. He has supervised research projects in areas such as search and rescue with sparsely connected swarms and distributed risk-aware exploration systems.

Leonard J. Schulmanمشاهده پروفایل

استاد

• Algorithms
• Communication Protocols
• Combinatorics
• +۶ مورد دیگر

Leonard J. Schulman is a Professor of Computer Science at the California Institute of Technology (Caltech), where he has been on the faculty since 2000. He is affiliated with the Caltech Center for the Mathematics of Information (which he directed from 2003 to 2017) and the Institute for Quantum Information and Matter. His academic appointments have included positions at UC Berkeley, the Weizmann Institute of Science, the Georgia Institute of Technology, and the Mathematical Sciences Research Institute. Schulman received his BSc in Mathematics in 1988 and his PhD in Applied Mathematics in 1992, both from the Massachusetts Institute of Technology (MIT). Schulman's research spans several overlapping areas in theoretical computer science and applied mathematics. His work focuses on algorithms and communication protocols , combinatorics and probability , coding and information theory , and quantum computation . More recently, his research has expanded into causal inference and machine learning , particularly in the areas of mixture models, causal discovery, and structure learning. His approach combines deep theoretical insights with practical applications across multiple domains. An analysis of Schulman's recent publications (2019-2025) reveals a strong focus on causal inference and machine learning, particularly in the areas of mixture models, causal discovery, and structure learning. His work bridges theoretical computer science with statistical learning, often developing novel algorithms with provable guarantees. He has also maintained his foundational work in coding theory, algorithms, and quantum computation, demonstrating remarkable breadth across theoretical computer science. IEEE Schelkunoff Prize (2004) ACM Notable Paper (2012) UAI Best Paper Award (2016) FOCS Test of Time Award (2022) S. A. Schelkunoff Transactions Prize Paper Award (2004) SIAM Fellow NSF CAREER award NSF mathematical sciences postdoctoral fellowship MIT Bucsela prize in mathematics Schulman has advised numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. His former students and postdocs include notable researchers such as Ashwin Nayak, Yaoyun Shi, Sean Hallgren, Jie Gao, and Michael Langberg. He served as Editor-in-Chief of the SIAM Journal on Computing from 2013 through 2018 and has been on the editorial boards of several prestigious journals including the Journal of the ACM, ACM Transactions on Algorithms, and SIAM Journal on Discrete Mathematics. Schulman directs the Caltech Center for the Mathematics of Information, a research center focused on the mathematical foundations of information processing, communication, and computation. His work often involves interdisciplinary collaborations across computer science, mathematics, physics, and economics.

Vanessa Magnanimoمشاهده پروفایل

استاد

• Discrete Element Method (DEM)
• Soil Micromechanics
• Granular Materials
• +۴ مورد دیگر

Vanessa Magnanimo is a Full Professor at the MESA+ Institute, University of Twente, specializing in the micromechanics of granular materials and clays using advanced Discrete Element Method (DEM) simulations . Her research bridges micro- and macro-scale behavior of soils, focusing on computational modeling , small-strain stiffness , and material stabilization . She has led 107 research outputs and 2 datasets , including studies on wet granular systems , bio-cemented soils , and vegetated soil mechanics . Recent work includes 2025 publications on coarse-grained DEM and mixing indices in industrial processes. Notable activities include keynote talks at international events (2018–2020), visiting researcher appointments at institutions like 3SR Laboratory (2017) and Politecnico di Bari (2020), and editorial contributions. Her research spans geotechnical engineering , powder technology , and multi-scale modeling , with applications in soil stabilization , industrial powder handling , and eco-mechanical systems .

• Information Systems
• E-business
• E-government
• +۷ مورد دیگر

Professor Angelika Kokkinaki is Dean of the School of Business and MBA Director at the University of Nicosia. With a PhD in Computer Science from the University of Louisiana at Lafayette (1995), a M.Sc. from Northeastern University (1991), and a Diploma from Patras University (1987), her academic credentials are rooted in Information Systems and Computer Science. PhD in Computer Science (1995), University of Louisiana at Lafayette M.Sc. in Computer Science (1991), Northeastern University Diploma in Computer Engineering and Informatics (1987), University of Patras Her research spans e-business, e-government, e-learning, and smart city technologies, focusing on semantic search engines, distributed systems, and circular economy applications. Recent work emphasizes Smart Cities through projects like OpenDCO, CRISIS, and DEVOPS. Key article trends include smart city resilience, AI applications in education, e-learning adoption, marine sensor networks, and open data workforce development. Her scholarly output aligns with 6 UN Sustainable Development Goals , particularly in education, sustainability, and urban innovation. Scientific Awards : Innovation award by the Republic of Cyprus (for quality management system) She leads over 30 EU/national projects and contributes to the Center of Equality, Diversity, and Inclusion (CEDI) and OECD policy monitoring. Her work integrates Petri Net modeling , software agent systems, and 100+ publications in information systems.

• Artificial Intelligence in Manufacturing
• Production Engineering
• Circular Economy in Manufacturing
• +۴ مورد دیگر

Dr. Amon Göppert serves as Professor and Chair of the Intelligence in Quality Sensing group at RWTH Aachen University's Laboratory for Machine Tools and Production Engineering (WZL). His work integrates artificial intelligence into manufacturing processes to enhance quality control, production efficiency, and sustainable practices. Göppert's research spans intelligent manufacturing systems with core expertise in AI-driven production engineering, circular economy applications, and advanced assembly systems. He investigates how machine learning optimizes production ramp-up, disassembly processes, and flexible manufacturing while developing sensor-based quality control solutions for industrial applications. His recent publications reveal strong trends toward AI implementation in production planning, with emphasis on worker assistance systems for disassembly, digital twin applications for real-time control, and mobile robotics in line-less assembly environments. Key focus areas include sustainable manufacturing, metrology innovation, and adaptive scheduling systems. Göppert leads multiple high-impact research initiatives: AI-driven Product Development: Machine learning for smart measurement strategies in metrology MetaVision Consortium: Industrial metaverse applications for AI-supported vision systems Generative AI for Non-Destructive Testing optimization Cluster of Excellence Internet of Production participation As Chief Engineer at WZL, he oversees technical implementation of research projects and collaborates with industry partners to translate innovations into practical manufacturing solutions, particularly in adaptive assembly systems and quality sensing technologies.

• Manufacturing Engineering
• Systems Engineering
• Simulation Modeling
• +۴ مورد دیگر

Bruno Vallespir is a Professor at Universite de Bordeaux, affiliated with the Production Engineering research group and MEI team. His work focuses on lean manufacturing, industry 4.0 integration, and enterprise interoperability using simulation frameworks. Key research themes: Lean techniques evaluation Co-simulation for manufacturing systems Organizational interoperability Safe work activity design His recent publications address: Combining lean methods with Industry 4.0 technologies Human-machine interaction in production environments Verification of collaborative processes Performance metrics for dynamic industrial contexts Collaborations include institutions like IMS Bordeaux , INCOSE , and industrial partners such as STMicroelectronics , Thales , and Stellantis .

• Computational physiology
• Software testing, verification and validation
• Computational logic and formal languages
• +۲ مورد دیگر

Dr. Weiwei Ai is a Research Fellow at the Auckland Bioengineering Institute , University of Auckland, New Zealand. With a multidisciplinary background in biomedical engineering and computational modeling, he focuses on developing energy-consistent physiological models and closed-loop validation frameworks for implantable medical devices. Education PhD in Bioengineering, University of Auckland (2019) Master of Engineering (ME) in Electrical Engineering, Beijing University of Technology (2005) BSc in Electronic Engineering, Qingdao University (2002) Dr. Ai's research centers on computational physiology and medical device validation , utilizing bond graph formalisms and hybrid automata to create thermodynamically consistent models for glucose transport, cardiac pacemakers, and gastrointestinal systems. His work bridges mathematical modeling with clinical applications through formal verification techniques. His recent publications highlight trends in closed-loop biomedical device design and energy-based physiological modeling , including: (1) bond graph models for SLC transporter dynamics, (2) adaptive respiratory pacemaker frameworks with biofeedback, (3) formal verification of cardiac devices using timed automata, and (4) compositional cyber-physical epidemiology models. He also explores AI-driven integration of digital twins in healthcare through FAIR data principles. Supervision Opportunities : Dr. Ai is an accredited PhD supervisor at the University of Auckland, offering projects on AI-driven energy-based platforms for credible digital twins in healthcare. Labs : Affiliated with the Auckland Bioengineering Institute, focusing on computational models and in-silico validation systems.

• Computational Mathematics
• Applied Mathematics
• Partial Differential Equations
• +۷ مورد دیگر

Frédéric Valentin is a Senior Researcher at the National Laboratory for Scientific Computing (LNCC) in Brazil, where he led the Department of Computational and Mathematical Methods from 2015 to 2021. He holds an INRIA International Chair (2018–2023) and served on the applied mathematics board of Brazil's National Science Foundation (CNPq) from 2017 to 2020. He currently leads the IPES Research Group and acts as the Brazilian Scientific Director for the Inria-Brasil partnership. His work focuses on computational and applied mathematics, particularly in developing innovative numerical methods for multiscale phenomena in engineering and life sciences. National Laboratory for Scientific Computing - LNCC (2015–2021) INRIA International Chair (2018–2023) Inria-Brasil Partnership (Scientific Director) Valentin's research involves partial differential equations, finite element methods, domain decomposition, and numerical analysis. He explores the integration of numerical algorithms with machine learning and high-performance computing, including applications to supercomputing projects like the Brazil-European Community's HPC4E initiative. His publications span over 60 high-impact journal articles and book chapters, primarily in computational mathematics and numerical analysis. He has co-advised over a dozen PhD students and postdoctoral researchers, contributing to international collaborations and advancing multiscale modeling techniques. Notably, he played a pivotal role in developing the Santos Dumont petaflop supercomputer at LNCC, the most advanced in Latin America.

Giorgio Scorzelliمشاهده پروفایل

پژوهشگر

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +۶ مورد دیگر

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

Andrea Cristofaroمشاهده پروفایل

دانشیار

• Robotic Systems
• Multi-Agent Systems
• Hybrid Systems
• +۴ مورد دیگر

Andrea Cristofaro is an Associate Professor at the Department of Computer, Control and Management Engineering, Sapienza University of Rome. He holds an M.Sc. in Mathematics (2005) from Sapienza University of Rome and a Ph.D. in Information Science and Complex Systems (2010) from the University of Camerino. His academic trajectory includes postdoctoral positions at INRIA Rhône-Alpes (France) and the Norwegian University of Science and Technology, followed by faculty appointments at the University of Camerino and University of Oslo before joining Sapienza in 2019. His research spans: Robotic systems : Fault-tolerant control, multi-agent coordination, and hybrid dynamics Distributed parameter systems : Stability analysis, observer design, and optimal control of PDEs Hybrid systems : Control synthesis for systems with state-driven jumps and switching logic Recent publications (2024-2025) predominantly explore fault-tolerant control architectures for multi-robot systems, decentralized task allocation, adaptive observer designs for biological and thermal systems, and optimal control of reaction-diffusion equations. This reflects a consistent focus on robustness in complex dynamical systems with applications ranging from industrial robotics to biomedical engineering. He serves as Associate Editor for Automatica and IEEE Transactions on Control Systems Technology , and is a member of the IEEE CSS Conference Editorial Board.

• Electronic Design Automation
• VLSI Design
• Computer Systems
• +۷ مورد دیگر

Shinji Kimura is a Professor at Waseda University's Faculty of Science and Engineering, specializing in VLSI design and electronic systems. He holds a Doctor of Engineering from Kyoto University and has been with Waseda since 2002, previously serving as Associate Professor at Nara Institute of Science and Technology (1993–2002) and Assistant Professor at Kobe University (1985–1993). Kimura's research spans low-power circuit design , approximate computing , FPGA optimization , video coding (HEVC) , and hardware acceleration for AI . His work focuses on energy-efficient architectures for applications like neural networks, computer vision, and ultra-high-definition video processing. Recent publications emphasize hardware-efficient multipliers, neural network compression, and 3D-stacked memory systems. Awards include the LSI IP Design Award (2000, 1999) and the Information Processing Society of Japan Encouragement Award (1993). He leads projects on HEVC encoding/decoding, non-volatile memory optimization, and 3D integrated circuits, with VLSI implementations achieving real-time 8K video processing.

Filipe Rodriguesمشاهده پروفایل

دانشیار

• Machine Learning
• Intelligent Transportation Systems
• Transportation Science
• +۷ مورد دیگر

Filipe Rodrigues is an Associate Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where he conducts research in intelligent transportation systems and transportation science. His work integrates machine learning, artificial intelligence, and behavioral modeling to improve urban mobility and public transport systems. His research interests lie at the intersection of machine learning , transportation science , and behavioral modeling . He specializes in discrete choice modeling , reinforcement learning , graph neural networks , and smart card data analytics . His work contributes to sustainable urban mobility, leveraging big data and AI for proactive traffic control and public transport optimization. The recent publications highlight a strong trend toward integrating AI and behavioral science in transportation. Key themes include ride-sourcing driver behavior , public transport trip validation , autonomous fleet control , and causal machine learning . These works predominantly employ deep learning , Bayesian modeling , and offline reinforcement learning techniques, often applied to real-world datasets from Denmark and beyond. Scientific Contributions: Active contributor to journals like Transportation Research Part C and Journal of Choice Modelling . Supervises multiple PhD projects on AI in transportation and causal modeling. Regular presenter at major transportation and AI conferences. Advising and Grants: Filipe Rodrigues is the main or co-supervisor of several PhD students including O. B. Lassen, F. M. F. Santos, A. Nguyen, and X. Wu. He leads and participates in funded research projects such as 'Proactive traffic control through AI and Big Data' and 'Causal Graph Neural Networks for machine learning meta-modelling', indicating sustained grant support. His collaborative network spans institutions in Europe and beyond. Labs and Teams: He is part of the Intelligent Transportation Systems research group at DTU, collaborating closely with researchers like F. C. Pereira and C. M. L. Azevedo. The team focuses on data-driven mobility solutions, combining simulation, machine learning, and behavioral insights.