جست‌وجوی استاد و پژوهشگر

Thomas Zachariasمشاهده پروفایل

مدرس

• Cybersecurity
• Cryptography
• Privacy-Preserving Communication
• +۴ مورد دیگر

Dr. Thomas Zacharias is a Lecturer in Cybersecurity at the School of Computing Science, University of Glasgow. His research focuses on cryptographic approaches to Cybersecurity and Privacy, emphasizing formal modeling, design, and analysis of secure systems. He holds a PhD in Cryptography from the University of Athens, with prior work as a Senior Researcher at the University of Edinburgh. Education PhD in Cryptography, University of Athens MSc in Theoretical Computer Science BSc in Pure Mathematics BEng in Electrical and Computer Engineering Research Interests Dr. Zacharias specializes in electronic voting systems (e-voting), privacy-preserving communication, blockchain protocols, multi-party computation, and cryptographic foundations. His work bridges theoretical contributions with practical applications, such as the Pebble project for decentralized e-voting solutions. Recent research explores decentralization in distributed protocols like e-voting and blockchain. Publications His publications span advancements in cryptographic protocols, blockchain security, and e-voting systems. Key areas include decentralized voting, privacy-preserving mechanisms, and formal verification of security properties. Recent work addresses challenges in blockchain dynamics and contingent service payments. Awards & Grants Recipient of a $250,000 grant from the Tezos Foundation for the Pebble project , where he serves as Co-Investigator. This project aims to develop and commercialize decentralized e-voting systems. Teaching & Supervision Supervised PhD student Nikolaos Lamprou on e-voting systems in the quantum and classical settings. Teaches Cybersecurity Fundamentals (H) (COMPSCI4062) at the University of Glasgow. Labs & Professional Engagement A co-founder of the Pebble project and active member of the IET’s e-voting working group. Served on program committees for major conferences including CRYPTO, PKC, and PETS.

Shashi Raj Pandeyمشاهده پروفایل

استادیار

• Network Economics
• Game Theory
• Wireless Networks
• +۷ مورد دیگر

Shashi Raj Pandey serves as Assistant Professor in the Department of Electronic Systems at Aalborg University's Technical Faculty of IT and Design, Denmark. His research is anchored in the Connectivity section and Connectivity Classique-Center for Classical Communication in the Quantum Era, with office location at Fredrik Bajers Vej 7C, C1-111, 9220 Aalborg Øst. His core research spans Network Economics, Game Theory, and Wireless Networks, with specialization in Decentralized Machine Learning and Semantic/Goal-oriented Communications. Current work integrates Digital Twin technologies with 6G systems for industrial automation and earth observation, emphasizing resource-efficient protocols for Internet of Things and edge intelligence applications. Recent publications (2024-2025) reveal a clear trajectory toward AI-6G convergence, featuring semantic communications for satellite imaging, game-theoretic network resource allocation, and digital twin implementations for autonomous systems. Key themes include communication efficiency in distributed learning and physical-digital world integration. Notable recognitions include: Best PhD Thesis Nominee (2021) Excellent Paper at Korea Software Congress, KIISE 2021 Student Best Paper Award at APNOMS 2019 Best Paper at Korea Software Congress, KIISE, 2018 Brain Korea 21st Century Plus Fellowship Academic service includes external PhD examination for EU SNS projects and peer review for premier conferences (AAAI, ICLR, ICML). His lab work within the Connectivity Classique-Center explores classical communication frameworks applicable to quantum-era networks, with focus on semantic information theory and decentralized network architectures.

• Biophysics
• Cellular Biophysics
• Dynamic Self-Organization in Living Matter
• +۵ مورد دیگر

Matthias Weiss is a Professor of Experimental Physics at the University of Bayreuth since 2010. Previously, he held roles including BIOMS Junior Group Leader at the German Cancer Research Center (2004–2010) and postdoc positions at EMBL Heidelberg and the MEMPHYS-Center in Denmark. He studied physics at Frankfurt and Heidelberg, earning a Diploma (1997) and PhD in quantum chaos (2000). His research bridges physics and biology, focusing on dynamic self-organization in living systems. Key areas include transport processes, organelle formation, embryogenesis, and parasite-host interactions. Techniques include advanced light microscopy and computational modeling. His work spans projects like the physics of Giardia adhesion and Trypanosoma motility, funded under SPP 2332 PoP. Notable publications explore anomalous diffusion in nuclei, ER network dynamics, and microtubule roles in Trypanosoma. Collaborations involve institutions like DKFZ, EMBL, and the University of Würzburg. He leads the Experimental Physics I department at Bayreuth and advises on biophysical methodologies.

• Large-scale nonparametric and kernel methods
• Robust and trustworthy machine learning
• Multiresolution data and data fusion
• +۳ مورد دیگر

Professor Dino Sejdinovic is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. Previously, he held positions as Lecturer and Associate Professor at the University of Oxford's Department of Statistics (2014–2022). His academic qualifications include a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research focuses on the intersection of statistical methodology and machine learning, encompassing large-scale nonparametric methods, robust machine learning, multiresolution data fusion, and measures of dependence. He has contributed to kernel methods, Bayesian inference, causal discovery, and applications in climate science, quantum computing, and social science data analysis. Education: PhD in Electrical and Electronic Engineering, University of Bristol (2009) Diplom in Mathematics and Theoretical Computer Science, University of Sarajevo (2006) Sejdinovic's work emphasizes bridging theoretical foundations with practical applications, such as cloud type classification using vision transformers and machine learning-driven quantum device optimization. His recent publications explore topics like kernel-based causal inference, Bayesian neural networks, and uncertainty quantification in statistical models. Advising and grants: Eligible to supervise Masters and PhD students in machine learning and statistics, though specific grants or student advisees are not explicitly listed in the provided texts.

• Nonlinear Partial Differential Equations
• Mathematical Physics
• Semiconductor Theory
• +۷ مورد دیگر

Ansgar Jüngel is a Full Professor for Analysis of Nonlinear Partial Differential Equations (PDEs) at the Technische Universität Wien (TU Vienna), affiliated with the E101-Institute for Analysis and Scientific Computing. His academic journey includes roles at universities in Berlin, Konstanz, Mainz, and Vienna since 1991. He specializes in mathematical analysis of cross-diffusion systems, entropy methods, semiconductor models, and quantum fluid dynamics. Notable achievements include an ERC Advanced Grant (2021) and the Tsungming-Tu Award (2011). Research focuses on nonlinear PDEs with applications in physics, engineering, and biology, emphasizing rigorous existence theory, numerical methods, and entropy-based approaches. Recent projects include 'Emerging network structures and neuromorphic applications' and 'Taming complexity in partial differential systems.' His teaching includes courses on partial differential equations, calculus of variations, and computational finance. Publications span over 200 works, with key contributions on cross-diffusion models, quantum hydrodynamics, and energy-transport systems. He has supervised numerous PhD students and collaborates internationally on topics like semiconductor simulations and stochastic interacting particle systems. Grants include an FWF Special Research Programme and ERC funding.

Joanna C. S. Santosمشاهده پروفایل

استادیار

• Software Engineering
• Software Security
• Program Analysis
• +۴ مورد دیگر

Joanna C. S. Santos is an Assistant Professor at the University of Notre Dame's Department of Computer Science and Engineering. She leads the Security and Software Engineering research lab (S²E) and focuses on Software Engineering, Security, and Program Analysis. Her work bridges empirical studies with practical tool development. PhD in Computing and Information Sciences (Rochester Institute of Technology) M.Sc. in Software Engineering (Rochester Institute of Technology) B.Sc. in Computer Engineering (Federal University of Sergipe) Her research spans Software Security (vulnerability detection, ReDoS), Code Generation (LLM evaluation, benchmarking), and Program Analysis (taint tracking, call graphs). Recent articles show a strong focus on LLM-generated code quality and quantum computing applications. Scientific Awards : 2023 - Distinguished Reviewer (ESEC/FSE) 2020 - Research Pitch Winner (JOBS @MICRO) 2017 - Best Paper (ICSA) 2014 - CAPES Scholarship 2013 - ERBASE 3rd Place She actively contributes to conference committees (OOPSLA, ICSE, SCAM) and collaborates across institutions. Her lab S²E drives research in secure software development and empirical cybersecurity validation.

Dong Gongمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Dong Gong is a Senior Lecturer and ARC DECRA Fellow (2023-2026) at the School of Computer Science and Engineering (CSE), UNSW. He holds an adjunct position at the Australian Institute for Machine Learning (AIML), University of Adelaide. His research focuses on machine learning challenges in dynamic environments, including continual learning, foundation models, generative models, and applications in interdisciplinary areas like mining and agriculture. Research interests include learning with non-ideal supervision, foundation model adaptation, generative models, and interdisciplinary problems combining CV/ML with domain-specific applications. His work often addresses real-world scenarios such as mineral exploration and soil trait analysis using CV/ML technologies. Outstanding Reviewer: NeurIPS 2018 Outstanding Area Chair: ACM MM 2024 ARC DECRA Fellowship (2023-2026) Advising and grants: Actively supervises PhD/MPhil students in computer vision and ML. Collaborates with industry and government on research projects. Utilizes advanced infrastructure like UNSW's Katana supercomputing cluster and Gadi (NCI). Labs/Teams: Involved in interdisciplinary research groups at UNSW CSE and AIML, focusing on dynamic learning paradigms and real-world applications of AI.

Hsiao-Dong Chiangمشاهده پروفایل

استاد

• Nonlinear System Theory
• Power System Stability and Control
• Optimal Power Flow
• +۱۲ مورد دیگر

Hsiao-Dong Chiang is a Professor in the School of Electrical and Computer Engineering at Cornell University. He holds a Ph.D. in Electrical Engineering from the University of California, Berkeley, and has made significant contributions to nonlinear system theory and power system stability. His research spans theoretical development and practical applications in electric power systems, nonlinear optimization, and machine learning. B.S., Electrical Engineering, National Taiwan University, 1979 M.S., Electrical Engineering, National Taiwan University, 1981 Ph.D., Electrical Engineering, University of California, Berkeley, 1986 Chiang's research interests focus on nonlinear system theory , power system stability and control , nonlinear optimization , and their applications to modern power grids with high penetration of inverter-based resources. He is renowned for developing the BCU method and TRUST-TECH methodology , which have enabled fast direct stability assessment and global optimization in complex systems. His work bridges fundamental theory with industrial deployment through his companies, Bigwood Systems, Inc. and Global Optimal Technology, Inc. His recent publications (2024–2025) reflect a strong trend toward integrating machine learning and deep neural networks with power system analysis , particularly in state estimation, optimal power flow, and voltage control. There is a clear emphasis on handling uncertainty, non-convexity, and multi-scale dynamics in active distribution networks and integrated energy systems . His work increasingly focuses on resilience , real-time control , and user-centered methodologies for modern grid operations. Chiang has received numerous scientific honors, including: IEEE Fellow (1997) United States Presidential Young Investigator Award (1989) Multiple DOE Grid Optimization Challenge Awards (2020–2023) Best Paper Awards from IEEE Transactions and Conferences Outstanding Education Award, Cornell University (1990) He has successfully managed over 100 research projects and holds 28 U.S. and international patents. As the founder of Bigwood Systems, Inc., he has commercialized advanced software for utility companies across the U.S. and Japan. His team has published over 480 refereed papers and received more than 17,500 citations. He advises a large research group and leads innovations in computational methods for energy systems. His lab is actively involved in developing next-generation tools for grid security, optimization, and machine learning integration.

Panos Constantinidesمشاهده پروفایل

استاد

• Digital Transformation
• Digital Health
• Digital Platforms
• +۳ مورد دیگر

Panos Constantinides is a Professor of Digital Innovation at Alliance Manchester Business School, University of Manchester. He holds a PhD from the Judge Business School, University of Cambridge, and is a Fellow of the Cambridge Digital Innovation Centre. A co-founder of the European Digital Platforms Research Network (EU-DPRN), his research focuses on human-AI collaboration and digital platform governance. He has secured grants from the British Academy, Innovate UK, and UKRI, with publications in top-tier journals like Information Systems Research and MIS Quarterly . Education: PhD in Business (2005), Judge Business School, Cambridge MPhil in Business (2002), Judge Business School, Cambridge BSc in Computer Science (2000), New York University Research Interests: Exploring ethical and governance frameworks for AI integration in healthcare and autonomous systems Designing platform ecosystems resilient to crises like pandemics Advancing digital health through ecosystem approaches Studying innovation dynamics in decentralized systems Key Contributions: Authored Digital Transformation in Healthcare: An Ecosystem Approach (2023) Host of the Digital Podcast on YouTube/Spotify Advisor to corporations and policymakers on digital strategy Grants & Projects: Leading the £1.2M Applied AI and Quantum Technology in the Port Environment project (2024-2026) Recipient of British Academy grants (2015-2016) Co-PI on digital resilience research (2023) Labs/Initiatives: Principal Investigator at the Digital Futures Beacon Member of Christabel Pankhurst Institute Editorial roles at MIS Quarterly (2016-2025)

• Supply Chain Risk Management
• Blockchain Technology
• Sustainability & Environmental Initiatives
• +۶ مورد دیگر

Srinivas (Sri) Talluri is the John Hoagland - Richard Metzler Professor in Purchasing and Supply Management at the Department of Supply Chain Management, Michigan State University (Broad College of Business). He holds the title of DSI Fellow and served as the faculty director of the Axia Institute, focusing on advancing supply chain research and education. His work emphasizes supply chain resilience, risk management, and sustainability through innovative applications of blockchain, generative AI, and quantum computing. Dr. Talluri's research spans demand-supply uncertainty modeling, sustainable supply chain design, and strategic sourcing. Notable contributions include frameworks for blockchain integration in logistics, eco-efficient vaccine distribution during pandemics, and data-driven optimization for remanufacturing systems. His work frequently addresses macroeconomic disruptions, geopolitical risks, and environmental compliance under carbon tax schemes. His scholarly recognition includes the Fulbright-Hanken Distinguished Chair (2016-2017) and top 100 Operations Management scholar rankings. His research has been published in leading journals such as International Journal of Production Research and IEEE Transactions on Engineering Management . Key Professional Contributions: Over 140 peer-reviewed articles, leadership in supply chain education, and industry collaborations addressing global supply chain challenges. Labs/Initiatives: Active in the Axia Institute, focusing on supply chain innovation and policy.

• Soft Materials
• Quantum Mechanics
• Machine Learning
• +۳ مورد دیگر

Professor Nicholas E. Jackson is an Assistant Professor of Chemistry at the University of Illinois, affiliated with the College of Liberal Arts & Sciences and the Beckman Institute for Advanced Science and Technology. He holds the Lincoln Excellence for Assistant Professors Scholar distinction. His research focuses on soft materials, quantum mechanics, and machine learning, particularly in developing coarse-grained electronic structure methods and sustainable polymer design. Education: B.A. in Physics from Wesleyan University (2011), Ph.D. in Chemistry from Northwestern University (2016). Postdoctoral work as a Named Fellow and Assistant Scientist at Argonne National Laboratory (2016–2021). Joined the University of Illinois faculty in 2021. Research interests include conjugated materials theory, electronic structure predictions, and interdisciplinary applications of machine learning in materials science. His work bridges computational chemistry, materials engineering, and AI, addressing challenges in renewable energy and sustainable materials. Recent recognition: 2025 Kavli Foundation Emerging Leader in Chemistry Award for innovative research in young scientists under 40. His contributions to AI-driven materials design and thermoset fracture modeling have been highlighted in peer-reviewed journals. Advances include novel approaches to polymer reactivity prediction, molecular-level understanding of conjugated polymers, and machine learning workflows for molten salt mixtures. His lab collaborates across disciplines to advance soft materials design and computational tools for chemistry.

Susanna M. Thonمشاهده پروفایل

دانشیار

• Renewable energy conversion and storage
• Photovoltaics
• Optoelectronics
• +۴ مورد دیگر

Susanna M. Thon is an Associate Professor of Electrical and Computer Engineering at Johns Hopkins University and a Marshal Salant Faculty Scholar. She serves as Associate Director of the Ralph O’Connor Sustainable Energy Institute (ROSEI) and is affiliated with the Data Science and AI Institute. Education: B.Sc. in Physics (MIT, 2005), M.Sc. and Ph.D. in Physics (UC Santa Barbara, 2008, 2010) Postdoctoral Fellow (University of Toronto) Her research focuses on nanostructured materials and devices for renewable energy and optoelectronics, particularly solar energy conversion and sensing. Key projects include plasmonic-photocatalytic systems using aluminum nanoparticles, colloidal quantum dots for multicolored/transparent solar cells, and machine learning-driven solar cell optimization. Funding: National Science Foundation, American Chemical Society, Maryland Energy Innovation Institute, U.S. Army, Cohen Translational Engineering Fund Professional Memberships: American Chemical Society, American Physical Society, IEEE, Optica, Materials Research Society, Association for Women in Science Her team has published over 50 peer-reviewed papers and actively mentors students like Kyle, Jill, Sreyas, Ekin, Ariel, Delancy, Joseph, Sasha, and Kevin, who have presented at Design Day and published in journals like Coordination Chemistry Reviews and Optics Express. Scientific awards include the Johns Hopkins Catalyst Award, Discovery Award, and WSE Excellence in Mentoring Award.

Jayanta Sahuمشاهده پروفایل

استاد

• Optical Fiber Technology
• Photonics
• High Power Lasers
• +۳ مورد دیگر

Professor Jayanta Sahu is a Professorial Fellow-Research at the University of Southampton , actively accepting PhD students. His work focuses on advanced optical fiber technology, particularly in the development of high-power fiber lasers and amplifiers. Key affiliations: Optoelectronics Research Centre (ORC) Research areas: Silica fiber fabrication, nonlinear multimode optics, and ultra-wideband amplification His projects include collaborations with institutions like EPSRC, European Union, US Air Force, and industry partners such as LiComm Co Ltd and QPeak Inc. Recent publications highlight innovations in bismuth-doped fiber amplifiers, erbium-doped lasers, and radiation-resistant fiber systems. Students: Robin Elliott, Rosemary Catriona Clark Grants: EPSRC, European Union, US Air Force Office of Scientific Research Professor Sahu leads research groups like Smart Lasers and Special Fibres , with expertise in cladding-pumped lasers, distributed feedback systems, and photonic manufacturing. His recent work explores mode control in multimode fibers and their applications in coherent transmission systems.

• 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.

• Game Theory
• Epistemic Game Theory
• Quantum Game Theory
• +۷ مورد دیگر

Adam M. Brandenburger is the J.P. Valles Professor at the Leonard N. Stern School of Business, New York University, with additional appointments as Distinguished Professor at the Tandon School of Engineering, Faculty Director of the NYU Shanghai Program on Creativity + Innovation, and Global Network Professor. He previously served as a professor at Harvard Business School from 1987 to 2002. His academic foundation includes a B.A., M.Phil., and Ph.D. from the University of Cambridge. His research focuses on game theory , epistemic game theory , quantum game theory , and business strategy . He has made foundational contributions to the understanding of rationality, belief hierarchies, and the intersection of quantum mechanics with decision theory. His work spans economics, philosophy, computer science, and cognitive neuroscience, reflecting a deeply interdisciplinary approach. The recent publications show a strong trend toward integrating quantum foundations , information theory , and behavioral economics . Themes include epistemic reasoning in non-classical systems, the role of symmetry in strategic interactions, and the neural basis of decision-making. His work increasingly explores the limits of classical probability and cognition, using tools from quantum information and sheaf theory. While no formal scientific awards are listed, his publications in Nature Communications , Econometrica , and Philosophical Transactions of the Royal Society indicate high scholarly impact. His collaborations with leading researchers across disciplines suggest a central role in advancing interdisciplinary science. He advises and collaborates extensively, though specific students are not listed. His leadership in the NYU Shanghai Program on Creativity + Innovation highlights his commitment to global education and innovation. He has not received any grants explicitly mentioned, but his sustained publication output suggests active funding support. He is associated with research initiatives bridging game theory, quantum information, and cognitive science. His recent unpublished work on quantum-assisted observatories and large language models points to forward-looking research at the intersection of AI, quantum technology, and strategic reasoning.