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Coleman JonathanView profile

Professor

• Nanomaterials for energy storage
• Graphene and 2D materials
• Nanomaterials for printed electronics
• +5 more

Professor Jonathan Coleman is an Erasmus Smith's Professor of Natural and Experimental Philosophy at the Department of Physics, Trinity College Dublin. He is affiliated with the CRANN Research Institute and holds simultaneous membership in the Chemical Sciences and Physics sections of the Royal Irish Academy. His research spans materials chemistry and nanotechnology, focusing on graphene, 2D materials, and printed electronics. With 378 peer-reviewed publications in top journals like Nature and Science, his work has accumulated over 107,000 citations (h-index 123) since 2001. Recent papers analyze charge transport limitations in nanosheet networks, defect engineering strategies for 2D materials, and battery electrode performance metrics. These publications reflect his expertise in solution processing techniques and low-dimensional nanostructures. Royal Irish Academy Gold Medal (2023) Tabor Medal, Institute of Physics (2022) Advanced Materials Hall of Fame (2022) Clarivate Highly Cited Researcher (2014-2021) Professor Coleman secures major EU funding through grants like Graphene Flagship 2D-PRINTABLE (€0.6M, 2023-2026) and HYPERSONICS (EIC Pathfinder, €0.9M, 2024-2028). He serves as Principal Investigator at CRANN while delivering invited plenary/keynote lectures at international conferences including Flatlands and Graphene23.

• Transportation Engineering
• Urban Planning
• Sustainable Mobility
• +5 more

Ravi Seshadri is an Associate Professor in the Transport Division at the Department of Technology, Management and Economics, Technical University of Denmark (DTU). His research focuses on designing equitable, efficient, and sustainable mobility solutions with a focus on fiscal instruments like congestion pricing and tradable permits, as well as emerging mobility modes such as shared and demand-responsive transit. He employs methods from transportation network equilibria, dynamic traffic assignment, and agent-based simulation. His research interests span transportation economics, urban planning, and intelligent transportation systems. Key areas include evaluating the impacts of automated mobility-on-demand systems, optimizing tolling strategies using predictive control and reinforcement learning, and integrating multi-modal transportation networks through game-theoretical frameworks. His work emphasizes real-world applications in urban freight systems, e-commerce logistics, and sustainable urban mobility policies. Recent projects include studying congestion pricing schemes via agent-based microsimulation, analyzing behavioral responses to decarbonization policies, and developing frameworks for tradable credit systems with peer-to-peer trading. He has contributed to both theoretical advancements (e.g., robust traffic assignment models) and applied tools like the SimMobility simulation platform. Ravi's research demonstrates a strong focus on bridging transportation engineering with policy analysis, using cutting-edge computational methods to address complex urban mobility challenges. His work spans academic publications, industry collaborations, and policy consultations to advance sustainable transportation systems.

• Vehicle Routing Problems
• Scheduling Algorithms
• Genetic Algorithms
• +3 more

Rémy DUPAS is an Associate Professor with HDR (Habilitation à Diriger des Recherches) specializing in operations research, scheduling algorithms, and vehicle routing optimization. His work focuses on dynamic systems, genetic algorithms, and real-time logistics solutions. He has contributed extensively to the field through publications in journals like European Journal of Operational Research and International Journal of Innovative Computing and Applications . His research interests include vehicle routing problems (VRP), cyclic scheduling in manufacturing systems, and metaheuristics for dynamic optimization. He has developed innovative approaches using genetic algorithms and neural networks to address real-world logistics challenges, such as time-dependent travel times and flexible time windows. He has advised four PhD students, including Xin ZHAO (2004–2008), Haiyan HOUSROUM (2002–2005), and Guillaume CAVORY (1997–2000), focusing on topics like dynamic vehicle routing and evolutionary algorithms. His HDR thesis, Amélioration de performance des systèmes de production : apport des algorithmes évolutionnistes aux problèmes d’ordonnancement cycliques et flexibles , underscores his expertise in optimizing industrial systems. Key contributions include book chapters on metaheuristics for vehicle routing in dynamic contexts and conferences on topics like the traveling repairman problem and simulation platforms for dynamic vehicle tours.

Albert TreytlView profile

Researcher

• Communication Technologies
• Security
• Distributed Systems
• +4 more

Albert Treytl is a Senior Researcher at the Danube University Krems , serving as Head of the Center for Distributed Systems and Sensor Networks and Deputy Head of the Department for Integrated Sensor Systems. He holds a Master’s degree in Electrical Engineering from the Vienna University of Technology (2001) and has over two decades of experience in communication technologies and security. Current roles: Head of Center, Deputy Head of Department Research focus: Security for embedded systems, IoT, digital twins, AI in energy optimization Involvement: IEEE, CEN TC 247 WG4, IEEE1588 standardization His work addresses distributed data management, smart grid security, and model predictive control strategies for energy efficiency in buildings and traffic systems. He leads multiple national and international projects funded by FFG and EU programs. Recent research projects include KI4HVACS (AI-driven HVAC optimization), Factories4Renewables (industrial renewable energy integration), and Dataskop (sensor-based data economy). He has authored over 100 peer-reviewed publications and serves as co-lecturer at the Vienna University of Technology.

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +7 more

Vahid Behzadan is an Assistant Professor in Data Science and Computer Science at the University of New Haven's Tagliatela College of Engineering. He leads the SAIL Lab, focusing on AI safety and security, particularly in autonomous systems like driverless cars and smart cities. His work addresses adversarial attacks on machine learning and reinforcement learning, with applications in cybersecurity and healthcare. Behzadan has held prior positions at Kansas State University, University of Nevada Reno, and University of Birmingham, UK. He holds a Ph.D. in Computer Science and an M.S. from the University of Nevada Reno, and a B.Eng. from the University of Birmingham. His research spans AI ethics, cybersecurity, and complex systems. Behzadan advises the UNH hacking team and actively participates in policy initiatives, including Connecticut's AI Working Group and the Connecticut AI Alliance. He has contributed to over 30 peer-reviewed articles and frequently engages in media discussions on topics like AI safety, facial recognition, and cybersecurity threats. Key research areas include adversarial machine learning, AI forensics, and ethical AI design. His work bridges theoretical advancements with real-world applications in transportation, healthcare, and national security. Behzadan collaborates with organizations such as the Transportation Research Laboratory (TRL) and Open Web Application Security Project (OWASP).

Juan Felipe Carrasquilla ÁlvarezView profile

Assistant Professor

• Condensed Matter Physics
• Quantum Computing
• Machine Learning
• +3 more

Juan Felipe Carrasquilla Álvarez is an Assistant Professor in the Department of Physics at the University of Toronto. His research focuses on the intersection of condensed matter physics, quantum computing, and machine learning, emphasizing quantum many-body systems, quantum device validation, and phase identification. He holds affiliations with the Acceleration Consortium and the Centre for Quantum Information and Quantum Control at the University of Toronto, and is a Perimeter Institute Visiting Fellow. Education: PhD in Physics from SISSA (Italy), followed by postdoctoral fellowships at Georgetown University (2011-2013), the Perimeter Institute (2013-2016), and a stint as a Research Scientist at D-Wave Systems Inc. Earlier, he completed the Abdus Salam ICTP Diploma Programme (2005-2006). Research interests span quantum Monte Carlo simulations, machine learning-driven analysis of quantum systems, and applications to quantum computing validation. His work bridges theoretical physics with computational methods, addressing challenges in both classical and quantum computing paradigms. Notable contributions include developing neural network architectures for quantum state reconstruction, error mitigation in quantum simulations, and optimal control strategies for quantum thermal machines. His publications explore topics like topological order detection, shadow tomography, and hybrid quantum-classical algorithms. Awards/Fellowships: Perimeter Institute Postdoctoral Fellowship (2013-2016), Georgetown University Postdoctoral Fellowship (2011-2013), SISSA PhD Fellowship (2006-2010), and Abdus Salam ICTP Diploma Programme Fellowship (2005-2006). Advising/Grants: No formal advisee list provided. Active in interdisciplinary collaborations through affiliations with major quantum research consortia and institutions. Lab/Teams: Part of the Acceleration Consortium and the Centre for Quantum Information and Quantum Control, contributing to cutting-edge quantum computing and machine learning research.

Eric JessupView profile

Research Professor

• Transportation Modeling and Economics
• Freight Systems Modeling
• Applied Econometrics
• +3 more

Eric Jessup is a Research Professor and Director of the Freight Policy Transportation Institute at Washington State University's School of Economic Sciences. He holds a Ph.D. in Agricultural Economics from WSU (1998), an M.S. and B.S. in Agricultural Economics from the University of Kentucky (1992, 1988). His career includes roles at Informa Economics, Albert-Ludwig-University (Germany), and American Express. His research focuses on transportation modeling, freight systems, applied econometrics, and geo-spatial analysis. Notable awards include the 2008 Transportation Research Forum Best Paper Award and recognition as an Eno Fellow (1994). Research interests emphasize optimizing freight logistics, transportation policy, and infrastructure investment. Recent work addresses pandemic impacts on global trade, truck parking safety, and rail rate dynamics. He has directed major freight policy initiatives, including the Freight Policy Transportation Institute and the PacTrans Workforce Development Institute. Over 80 publications span journals like Transportation Research Forum and Journal of the Transportation Research Forum , with a focus on GIS applications, supply chain resilience, and safety critical events. Education: Ph.D. Agricultural Economics, WSU, 1998 M.S. Agricultural Economics, University of Kentucky, 1992 B.S. Agricultural Economics, University of Kentucky, 1988 Awards: 2008 TRF Best Paper Award 2006 Best Paper Award (Italy) 1996 Northwest UTC Student of the Year 1994 Eno Transportation Fellowship Labs/Teams: Leads the Freight Policy Transportation Institute and collaborates with the Transportation Research Group at WSU. Engaged with national initiatives like PacTrans and the Idaho Statewide Freight Data Project. Grants and advising are central to his work, though specific student advisees are not listed. His research bridges academia and industry, addressing real-world challenges in freight logistics, environmental policy, and transportation safety.

Yong-Bin KimView profile

Professor

• Integrated Circuit Design
• Nanoelectronics
• High-Speed System Integration
• +4 more

Yong-Bin Kim is a Professor in the Department of Electrical and Computer Engineering at Northeastern University, part of the College of Engineering. He has held prior positions at Intel Corp., Hewlett Packard Co., Sun Microsystems, and the University of Utah. His research focuses on integrated circuit design, nanoelectronics, bio-chip interfaces, and low-power VLSI systems. He has contributed to initiatives like the HPVLSI Lab and Microsystems and Electron Devices Lab. Education includes a B.S. in Electronic Engineering from Sogang University (Seoul, South Korea), an M.S. from the New Jersey Institute of Technology, and a Ph.D. in Computer Engineering from Colorado State University (1996). Research interests encompass high-speed low-power VLSI design, system-on-chip (SoC), physical VLSI CAD, and nanoelectronics. Specific areas include bio-sensor interface circuits, electronic neuron design, and adaptive robot controllers. Key projects involve compact power-efficient integrated circuits and high-speed transceiver design. Outstanding Paper Award, 2020 IEEE ISOCC South Korean Patent for Autonomous Impedance Calibration (2021) Best Paper Award, 2016 International SoC Design Conference Patent for Improved Receiver Circuit (2020) Patent for Method to Detect Trojan Circuits (2018) Advisees include graduate student Yixuan He. He has led research projects funded by Winchester Technology and Hynix Semiconductor, focusing on semi-self-calibration transceivers and tunable RF inductors. Labs include the HPVLSI Lab and Microsystems and Electron Devices Lab at Northeastern University, which focus on high-speed/low-power IC design and microfabrication technologies.

Hojjat AdeliView profile

Professor

• Structural engineering
• Computational neuroscience
• Civil engineering
• +7 more

Hojjat Adeli is an Academy Professor at The Ohio State University (OSU) with courtesy appointments in the Departments of Neurology, Neuroscience, and Biomedical Informatics. He has held the Abba G. Lichtenstein Professorship in Infrastructure Engineering (2003-2013) and served as Editor-in-Chief of Computer-Aided Civil and Infrastructure Engineering for 25 years. Academy Professor, OSU (2018–present) Professor of Neurology, Neuroscience, and Biomedical Informatics (by courtesy, since 2015-2002) His research interests span interdisciplinary domains at the intersection of engineering and neuroscience, focusing on: Computational neuroscience and neurocomputing Biomedical signal processing (particularly EEG-based diagnostics) Machine learning and computational intelligence applications Smart infrastructure systems and structural engineering Optimization algorithms in civil and biomedical contexts His research trends demonstrate a synergy between: Neural network development for medical diagnostics Wavelet and chaos theory in epilepsy detection Computational intelligence for structural engineering Hybrid models integrating fuzzy logic and genetic algorithms Scientific awards and honors include: Multiple IEEE Fellowships and AAAS Fellow Thomson Reuters Highly Cited Researcher in Engineering and Computer Science Hojjat Adeli Awards for Neural Systems and Innovation in Computing Elections to international academies in Poland, Lithuania, and Spain Scott Award for Engineering Education and Distinguished Member ASCE

Anja FeldmannView profile

Professor

• Internet Measurement
• Network Performance Debugging
• Software-Defined Networking
• +7 more

Anja Feldmann is Director at the Max Planck Institute for Informatics in Saarbrücken and Professor of Internet Network Architectures at Technische Universität Berlin (since 2006). Previously she held a full professorship at Technische Universität München (2002–2006) and conducted research at AT&T Labs Research , Saarland University , and Carnegie Mellon University , where she earned her Ph.D. in 1995. Education Ph.D. in Computer Science, Carnegie Mellon University, 1995 M.Sc. in Computer Science, Carnegie Mellon University, 1991 Diplom in Computer Science, Universität Paderborn, 1990 Research Interests Anja Feldmann’s research centers on measurement-driven understanding of the Internet. She tackles challenges such as software-defined networking , cloud-network interactions , performance debugging , and traffic characterization . A growing focus is the privacy and security of networked systems, evidenced by recent studies on online tracking, DNS security, and disinformation ecosystems. Her group designs scalable measurement platforms that combine passive and active monitoring , programmable data planes , and machine-learning analytics to dissect phenomena ranging from terabit-scale traffic to covert tracking on illegal streaming sites. Recent Publication Themes The 2021-2025 publications reveal a methodological evolution toward large-scale, longitudinal measurement . Topics include: Impact of global events (COVID-19, CrowdStrike outage) on Internet traffic Cross-country tracking ecosystems and privacy leaks DNS root and routing plane stability and security ML-driven real-time monitoring at terabit speeds Disinformation campaigns on encrypted messaging platforms Scientific Awards Gottfried Wilhelm Leibniz Prize (2011) – Germany’s highest research honor Berliner Wissenschaftspreis (2011) Elected Member of the German National Academy of Sciences Leopoldina (2009) Advising & Grants While individual student names are not listed, Prof. Feldmann leads a vibrant team at MPI-INF’s Internet Architecture department. She has supervised numerous doctoral candidates and post-doctoral researchers whose work is reflected in the co-authored papers. Funding sources include the German Research Foundation (DFG) via the Leibniz Prize and EU Horizon projects, although explicit grant numbers are not provided in the source material. Labs & Teams She heads the Internet Architecture department at MPI-INF, located at the Saarland Informatics Campus . The department operates state-of-the-art measurement infrastructure—including programmable switches, honeynets, and global vantage points—to support empirical network science.

Giuliano CasaleView profile

Professor

• Performance Engineering
• Cloud Computing
• Resource Management
• +6 more

Giuliano Casale is a Professor in the Department of Computing at Imperial College London, leading the Quality of Service Research Lab (QORE). His research focuses on performance assurance, resource management, and fault-tolerance in distributed systems. He teaches courses on Probability and Statistics and Scheduling and Resource Allocation at undergraduate and Master’s levels. Casale’s work spans cloud computing, edge AI, and machine learning applications in system modeling. Key contributions include methodologies for performance engineering, anomaly detection, and automated resource management in large-scale systems. He actively participates in international conferences, delivering keynote speeches on topics such as performance evaluation and AI-driven systems. His research integrates queueing theory, machine learning, and generative models to address challenges in distributed software systems. Casale also engages in service activities like PhD admissions tutoring and collaborates on projects involving resilience planning and cloud service optimization. His lab, QORE, emphasizes practical solutions for real-world distributed systems, including edge federations and serverless architectures. Casale’s work bridges theoretical performance analysis with industrial applications, contributing to advancements in both academia and industry.

Joe GeunesView profile

Professor

• Production Planning
• Supply Chain Management
• Logistics
• +3 more

Joe Geunes is a Professor and Associate Department Head for Graduate Affairs in the Department of Industrial & Systems Engineering at Texas A&M University, holding the Mike and Sugar Barnes Professorship. His research focuses on production planning, supply chain management, logistics, and operations optimization. He earned his Ph.D. in Business Administration (Management Science & Operations Research) and M.B.A. from The Pennsylvania State University in 1999 and 1993, respectively. Dr. Geunes has received notable accolades including Fellow of the Institute of Industrial Engineers (2015), Marilyn and L. David Black Faculty Fellow (2022), and Best Reviewer Award from Omega (2022). His work spans infrastructure network restoration, supply chain resilience, and optimization algorithms for logistics systems. Recent projects address railcar operations, distribution network fortification, and disaster response strategies. Education: Ph.D., Business Administration (Management Science & Operations Research), The Pennsylvania State University – 1999 M.B.A., The Pennsylvania State University – 1993 Awards: Fellow, Institute of Industrial Engineers – 2015 Marilyn and L. David Black Faculty Fellow – 2022 Best Reviewer Award, Omega – 2022 Best Application Paper, IISE – 2018 His research integrates mathematical modeling and computational methods to address real-world challenges in supply chain design, inventory management, and infrastructure resilience. Recent publications emphasize multi-modal logistics, robust optimization under uncertainty, and post-disaster network recovery strategies.

Bassam BamiehView profile

Professor

• Control Systems
• Dynamical Systems
• Quantum Control
• +7 more

Bassam Bamieh is a Professor of Mechanical Engineering at the University of California, Santa Barbara (UCSB), with affiliate roles in Electrical and Computer Engineering and the Center for Control, Dynamical Systems and Computation (CCDC). His research focuses on control systems, dynamical systems, and their applications in fluid mechanics, quantum control, and network science. He holds fellowships from IEEE and IFAC and has received accolades such as the NSF Early Career Award and IEEE Distinguished Lecturer designation. Education: B.Sc. in Electrical Engineering and Physics from Valparaiso University (1983), M.Sc. and Ph.D. in Electrical and Computer Engineering from Rice University (1986, 1992). Formerly an Assistant Professor at the University of Illinois at Urbana-Champaign (1991–98). Research interests span robust and optimal control, distributed systems, shear flow turbulence, and thermoacoustic energy conversion. He has authored over 200 publications and pioneered work in spatially invariant systems and network controllability. His teaching includes courses on linear systems, vibrations, and control systems design. Awards include the IEEE Axelby Award (twice), Hugo Schuck Best Paper Award, and recognition for student research mentorship (e.g., Outstanding Student Paper Awards at CDC and IFAC NecSys22). His group collaborates across disciplines, integrating mathematical analysis with engineering applications.

Minglun GongView profile

Professor

• Visual Computing
• Computer Graphics
• Computer Vision
• +6 more

Dr. Minglun Gong is a Professor and Director of the School of Computer Science at the University of Guelph (since 2019). Previously, he served as Professor and Head of the Department of Computer Science at Memorial University of Newfoundland. He holds a Ph.D. from the University of Alberta (2003), M.Sc. from Tsinghua University (1997), and B.Engr. from Harbin Engineering University (1994). His research focuses on visual computing, including computer graphics, computer vision, visualization, image processing, and pattern recognition. He has authored over 150 referred papers and holds patents in the field. He is an Associate Editor for Pattern Recognition and IEEE Signal Processing Letters , and has received awards such as the Izaak Walton Killam Memorial Award and multiple best paper awards. Dr. Gong has advised numerous students, including Ph.D./M.Sc. candidates and visiting scholars. His lab's recent work includes UAV path planning for urban reconstruction, image stylization techniques, and 3D human pose estimation. He actively participates in academic service, including editorial roles, conference program committees, and administrative roles at multiple institutions. His teaching spans courses in image processing, computational photography, and technical communication. He is also involved in administrative committees, such as Graduate Studies and Promotion at Memorial University. Key research contributions include advancements in transparent object modeling, underwater 3D reconstruction, and image-to-image translation. His work emphasizes practical applications in fields like medical imaging, autonomous systems, and environmental modeling.

Yongwan ChunView profile

Professor

• Geographic Information Science (GIS)
• Spatial Statistics
• Urban Geography
• +5 more

Professor Yongwan Chun is a faculty member in the Geospatial Information Sciences program at the University of Texas at Dallas , affiliated with the School of Economic, Political and Policy Sciences. His academic journey includes a PhD in Geography (2007) and MS in Applied Statistics (2006) from the Ohio State University. Research Interests: Methodological advancements in GIScience, spatial statistics, environmental justice, urban crime analysis, population migration, and commodity flow modeling. Awards: Emerging Scholar Award (2017) from the Spatial Analysis and Modeling Specialty Group of the Association of American Geographers. Grants: Multiple awards from National Science Foundation and National Institute of Health totaling $1.4 million. Article Trends reflect expertise in spatial autocorrelation (Moran's I, Geary's C), location-allocation modeling (p-median, p-dispersion), and applications to healthcare accessibility, urban crime patterns, housing market segmentation, and climate impacts. His work integrates GIScience with Bayesian methods, eigenvector filtering, and big data uncertainty. Collaborators: Daniel A. Griffith, Hyun Kim, and students like Dongeun Kim and Changho Lee. Recent Topics: Trauma center accessibility, snowpack variation modeling, and spatiotemporal crime analysis. Contact: ywchun@utdallas.edu | Office: GR 3.208, UT Dallas