جست‌وجوی استاد و پژوهشگر

Daniel Gardhamمشاهده پروفایل

مدرس

• Cryptography
• Post-Quantum Cryptography
• Information Security
• +۴ مورد دیگر

Dr. Daniel Gardham is a Lecturer at the University of Surrey, affiliated with the Surrey Centre for Cyber Security and the School of Computer Science and Electronic Engineering. He teaches modules such as Privacy Enhancing Technologies (COM3030) and Data Structures & Algorithms (COM1029). His research focuses on applied cryptography, including privacy-preserving techniques, authentication protocols, and post-quantum cryptography using lattice-based methods. Previously, he was a postdoctoral researcher at Royal Holloway, University of London, and earned his PhD from the University of Surrey in 2021 under Mark Manulis, exploring attribute-based signatures in classical and post-quantum contexts. Education: PhD in Computer Science (University of Surrey, 2021), MMath in Mathematics (University of Bath, 2017). Research interests include developing cryptographic protocols for secure communication, post-quantum security solutions, and enhancing user privacy through authentication mechanisms. His recent work addresses phishing awareness for older adults and oblivious PRFs leveraging homomorphic encryption. Publications span topics like asynchronous key generation, hierarchical attribute-based signatures, and biometric authentication systems. He contributes to cybersecurity through interdisciplinary research, bridging theoretical cryptography with practical applications. Labs/Teams: Active in the Surrey Centre for Cyber Security and the Computer Science Research Centre, advancing cryptographic innovation and cybersecurity education.

Mark Manulisمشاهده پروفایل

استاد

• Privacy-enhancing technologies
• Cryptography
• Cybersecurity
• +۵ مورد دیگر

Prof. Dr. Mark Manulis is a Full Professor at the Department of Computer Science, Universität der Bundeswehr München, Germany, and a Visiting Professor at the Surrey Centre for Cyber Security, University of Surrey, UK. He heads the Privacy and Applied Cryptography (PACY) Lab and specializes in cryptographic protocols, privacy-enhancing technologies, and cybersecurity. His research focuses on secure communication, blockchain, IoT security, and decentralized systems. Education: Earned his Dr.-Ing. (PhD) in 2007 from Ruhr-Universität Bochum under advisors Jörg Schwenk and David Pointcheval. Held positions at TU Darmstadt, University of Surrey, and UCL Crypto Group. Research interests include authentication protocols, cryptographic primitives, and privacy-preserving systems. Notable awards include the Best Paper Award at ISC 2016 and E-Teaching Award 2010. Supervises PhD students and mentors researchers in cryptographic protocol design and analysis. Publications span over 150 papers in top venues like ACM CCS, EUROCRYPT, and IEEE S&P. Active in academic service, including program chair roles at ACNS and SECITC. Leads research projects on post-quantum cryptography and decentralized key generation.

Yue Chengمشاهده پروفایل

استادیار

• Distributed Systems
• Cloud Computing
• High-performance Computing
• +۳ مورد دیگر

Yue Cheng is an Assistant Professor with dual appointments in Computer Science and Data Science at the University of Virginia's School of Engineering and Applied Science. His research explores the intersection of distributed systems and machine learning, focusing on cloud infrastructure optimization. His work spans serverless computing architectures , distributed machine learning systems , and efficient storage solutions for large models . Recent publications address challenges in GPU resource management, federated learning optimization, and decentralized web infrastructure. Key innovations include adaptive scheduling algorithms for serverless environments and compression techniques for large language models, demonstrating consistent focus on enhancing computational efficiency in cloud-native systems.

Tilman Wolfمشاهده پروفایل

استاد

• Computer Networks
• Cybersecurity
• Embedded Systems
• +۵ مورد دیگر

Tilman Wolf is a Professor of Electrical and Computer Engineering and holds senior administrative roles at the University of Massachusetts Amherst, including Deputy Chancellor for Operational and Organizational Strategies and Senior Vice Provost for Academic Affairs. He leads strategic initiatives such as carbon reduction, flexible learning programs, and campus space planning. His research focuses on computer networks, cybersecurity, embedded systems, and machine learning, with a notable contribution to the ChoiceNet project under NSF's Future Internet Architecture initiative. Education: D.Sc. in Computer Science, Washington University in St. Louis (2002) M.S. in Computer Engineering and Computer Science, Washington University in St. Louis (2000/1998) Diplom in Informatik, Universität Stuttgart, Germany (1998) Management Development Program, Harvard Graduate School of Education (2016/2018) Research Interests: Dr. Wolf's work spans network architecture innovation, router design, embedded system security, and IoT. He emphasizes practical implementations of prototypes for performance validation. His research has been supported by NSF, DARPA, and industry grants. Key Contributions: Co-authored Architecture of Network Systems . Established the University of Massachusetts Biomedical Engineering department. Developed the UMass Flex program for asynchronous remote education. Oversees teaching support units, including the Center for Teaching and Learning. Awards: IEEE Fellow (2023) Fulbright Specialist (2019–2024) Multiple Best Paper Awards and NSF CAREER Award (2005) Administration & Leadership: Managed interdisciplinary research grants (e.g., IRG program), coordinated international education programs (Shorelight), and led campus sustainability efforts. His tenure includes roles as Interim Department Head of Biomedical Engineering and Associate Dean for Engineering Research. Labs & Teams: Active in CIRTL (Center for the Integration of Research, Teaching and Learning), steering committees for IEEE/ACM Transactions on Networking, and global conferences like SIGCOMM and INFOCOM.

Joseph Cimpianمشاهده پروفایل

استاد

• Social Inequities
• Education Policy
• Gender Studies
• +۴ مورد دیگر

Joseph Cimpian is a Professor of Economics and Education Policy at New York University, affiliated with the Steinhardt School's Department of Applied Statistics, Social Science, and Humanities. His research focuses on social and educational inequities, gender gaps in STEM, LGBTQ+ youth well-being, and policy solutions. He holds a Ph.D. from Stanford University and has been recognized with the Spencer Foundation/National Academy of Education Dissertation Fellowship and Postdoctoral Fellowship. He advises doctoral students in Public Policy, Statistics and Computational Social Science, and collaborates across disciplines. His grants total over $10 million, including a $7.4M study on English learner classification policies. His work spans peer-reviewed journals like Science , Child Development , and Journal of Policy Analysis and Management , and has been featured in major media outlets. Key areas include analyzing institutional factors in STEM gender disparities, racial disproportionality in special education, and LGBTQ+ youth mental health. He co-directs the Education and Social Policy Master’s program and serves on multiple editorial boards.

Matthew Cookمشاهده پروفایل

استاد

• Computer Science
• Artificial Intelligence
• Neural Networks
• +۶ مورد دیگر

M.M. (Matthew) Cook, PhD is a Full Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Artificial Intelligence group at the Bernoulli Institute. His research focuses on theoretical computer science, neural networks, and biologically inspired computation. He holds a PhD and has extensive experience in neuromorphic engineering, cellular automata, and computational neuroscience. His work spans interdisciplinary areas including spiking neural networks for biosignal processing, self-assembly algorithms, and paradox resolution in causal inference. Key contributions include foundational studies on universality in cellular automata (e.g., Rule 110), neuromorphic hardware implementations, and neuroimaging analysis techniques for synaptic partner detection in Drosophila brain data. Recent publications highlight advancements in event-driven neural networks for epilepsy monitoring, low-power neuromorphic systems, and algorithmic exploration strategies. His research bridges theoretical computer science with practical applications in biomedical engineering and cognitive modeling.

• Distributed Optimization
• Machine Learning
• Data-Driven Decision-Making
• +۳ مورد دیگر

Sindri Magnússon serves as an Associate Professor in Machine Learning at Stockholm University's Department of Computer and Systems Science. His research focuses on distributed optimization, machine learning, and data-driven decision-making within complex network systems, with applications spanning power grids, IoT infrastructure, and satellite operations. His educational background includes a B.Sc. in Mathematics from the University of Iceland (2011), an M.Sc. in Applied Mathematics (Optimization and Systems Theory) from KTH Royal Institute of Technology (2013), and a Ph.D. in Electrical Engineering from KTH (2017). He completed postdoctoral research at Harvard University (2018-2019) following a 9-month visiting PhD stint there in 2015-2016. Magnússon's research integrates theoretical optimization frameworks with practical machine learning implementations, particularly emphasizing reinforcement learning, federated systems, and distributed algorithms for resource-constrained environments. His work bridges theoretical guarantees with real-world applications in critical infrastructure and industrial systems. Analysis of his recent publications reveals a strong focus on communication-efficient distributed learning, non-stationary reinforcement learning environments, and predictive maintenance systems. His team consistently develops novel algorithms addressing asynchronous updates, model mismatch in federated settings, and multi-objective optimization challenges across IoT and satellite networks. Major awards include the IEEE ICASSP best student paper award (as supervisor) and a prestigious Swedish Research Council (VR) Starting Grant. VR Starting Grant: Resource Constrained Machine Learning in Complex Networks (PI, 4 MSEK, 2021-2024) Digital Futures: DEMOCRITUS project on critical societal infrastructures (Co-PI, 4 MSEK, 2021-2024) Vinnova: Smart Converters for Climate-neutral Society (PI, 3 MSEK, 2022-2025) He actively supervises seven PhD candidates including main supervision for Ali Beikmohammadi, Shubham Vaishnav, and Mohsen Amiri, plus industrial PhD projects with Spotify. As Associate Editor for IEEE/ACM Transactions on Networking, he contributes significantly to the networking and machine learning research communities.

Richard Vuducمشاهده پروفایل

دانشیار

• High-Performance Computing
• Algorithms
• Machine Learning
• +۵ مورد دیگر

Richard Vuduc is an Associate Professor in the School of Computational Science and Engineering at the Georgia Institute of Technology (Georgia Tech), part of the College of Computing. His research focuses on high-performance computing (HPC), algorithms, performance engineering, and their applications in numerical simulation and data analysis. He leads the HPC Garage research lab and has held roles such as Associate Chair and Director of graduate programs in his department. He has received notable awards including the NSF CAREER Award, Gordon Bell Prize (2010), and teaching excellence recognition. Education and Background: PhD in Computer Science from UC Berkeley (2003) Postdoctoral scholar at Lawrence Livermore National Laboratory Research Interests: High-Performance Computing (HPC) systems and algorithms Parallel algorithms for graph processing and machine learning Performance analysis and optimization Tensor decomposition and multiway data analysis Key Contributions: Pioneering work in distributed-memory parallel algorithms Development of tools like Jack (autotuner) and SPARTan (PARAFAC2) Advances in HPC applications for bioinformatics and knowledge graphs Awards and Recognition: Gordon Bell Prize (2010) NSF CAREER Award Lockheed-Martin Aeronautics Teaching Excellence Award (2013) Best Paper Awards at SDM (2012) and IPDPS (2015) Professional Service: Chair of SIAM Activity Group on Supercomputing (2018-2020) Co-chair of SC16 Technical Papers Program Associate Editor for International Journal of High-Performance Computing Applications and IEEE TPDS Labs and Teams: The HPC Garage lab, focused on scalable HPC systems and applications, and contributions to interdisciplinary projects like the C21U Center.

Elad Michael Schillerمشاهده پروفایل

دانشیار

• Distributed Computing
• Fault-Tolerant Systems
• Self-Stabilizing Algorithms
• +۴ مورد دیگر

Elad Michael Schiller is an Associate Professor at the Department of Computer Science and Engineering, Chalmers University of Technology, within the Division of Networks and Distributed Systems. His affiliation includes membership in the Distributed Computing and Systems research group. He has held significant roles in organizing conferences, including co-chair positions at ApPLIED 2019/2018 and general chair at PODC 2017. His research focuses on fault-tolerant distributed systems, self-stabilizing algorithms, vehicular networks, and secure communication protocols. Dr. Schiller has led projects such as KARYON (predictable coordination in vehicular systems) and Chameleon-MAC (adaptive media access control for MANETs). He has contributed to testbeds like Gulliver for vehicular systems and Renaissance for SDN control planes. His work emphasizes practical applications of distributed systems in safety-critical domains like autonomous vehicles and intelligent transportation systems (ITS). He has served on technical program committees for events including SSS, ICDCS, and DISC. His research outputs span self-stabilizing consensus algorithms, Byzantine fault tolerance, and vehicular coordination mechanisms. Key themes in his publications include fault recovery, secure distributed messaging, and resilient network architectures.

Melody Bucknerمشاهده پروفایل

استادیار

• educational technology
• digital literacies
• creative literacies
• +۳ مورد دیگر

Melody Buckner serves as Assistant Professor of Practice in the Department of Teaching, Learning and Sociocultural Studies within the University of Arizona's College of Education, while concurrently holding the administrative position of Associate Vice Provost of Digital Learning since 2019. In this dual capacity, she directs institutional strategy for online education implementation and alignment with university mission through collaboration with academic leadership. Her formal education includes: Bachelor of Science in Architecture from Arizona State University Master’s in Educational Technology from Northern Arizona University PhD in Teaching, Learning and Sociocultural Studies from the University of Arizona (2015) Dr. Buckner's research centers on educational technology , digital/creative literacies , and multicultural education , with emphasis on narrative-based pedagogical approaches. Her work investigates how digital storytelling enhances reflective assessment in global contexts, particularly within study abroad programs, while exploring technology's role in bridging cultural divides through innovative literacy practices. She examines gaming mechanics as motivational frameworks for learning and develops frameworks for 21st-century skill integration. Analysis of her publication trajectory reveals consistent focus on leveraging digital tools for experiential learning, with increasing emphasis on narrative pedagogy in instructional design since 2018. Her research demonstrates practical applications of sociocultural theory in online environments, particularly regarding non-traditional student engagement. She established Arizona Online's integrated office for instructional design, visual design, video production, and quality assurance, creating institutional infrastructure for digital learning. Her work with the Youth Literacy Network exemplifies community-based technology integration for mentoring programs, reflecting her commitment to practical applications of educational technology.

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۹ مورد دیگر

Ioannis Mitliagkas is an Associate Professor at the University of Montréal's Department of Computer Science and Operations Research, affiliated with the Faculty of Arts and Science. He is a core faculty member at Mila and a staff research scientist at Google DeepMind. His research focuses on machine learning, deep learning, optimization algorithms, and generative models. He holds the Canada CIFAR AI Chair and has led numerous projects on distributed training, causal discovery, and robust generalization. Education: While specific academic degrees are not explicitly listed in the text, his roles imply a PhD in Computer Science. His work bridges theoretical foundations and applied machine learning. Research Interests: Machine learning theory, optimization dynamics, generative models, out-of-distribution generalization, and causal inference. His work emphasizes scalable algorithms for deep learning and distributed systems. Awards: Canada CIFAR AI Chair (2018). Grants & Projects: Lead researcher on over 50 projects funded by CRSNG, MITACS, and others, including grants on optimization dynamics, causal discovery, and industrial applications (e.g., healthcare, energy, robotics). Students: Advised PhD and Master's students on topics like distributional shifts, generative models, and performative prediction.

Romaric Duvignauمشاهده پروفایل

دانشیار

• Distributed Systems
• Network Security
• IoT/CPS Applications
• +۷ مورد دیگر

Romaric Duvignau is an Associate Professor at Chalmers University of Technology since 2024, affiliated with the Department of Computer Science and Engineering within the IT Faculty. His primary research focuses on distributed systems, network security, and cyber-physical systems (CPS), with an emphasis on optimizing communication and computational efficiency in distributed applications. Key projects include the READY project for continuous monitoring systems and contributions to P2P energy sharing optimization via the DEEP and HyperP2P initiatives. He has been at Chalmers since 2017 and also holds teaching responsibilities in computer networks and data-driven systems. Research Interests: Distributed Algorithms & Optimization Cybersecurity & Privacy Peer-to-Peer Energy Sharing Vehicular Networks Edge Computing & Streaming Analytics Notable Articles: His recent works include advancements in P2P energy matching algorithms (2025), Byzantine fault-tolerant consensus (2025), and vehicular data localization (2021). Earlier contributions include impactful studies on vehicular networks (DRIVEN framework, 2020) and network traffic analysis (2019). Teaching & Grants: Responsible for courses like Computer Communication (EDA343) and Data-driven Support for CPS. Active in educational innovation, exploring AI impacts on teaching and active learning tools like Kahoot/Mentimeter. Labs/Teams: Member of the Distributed Computing and Systems group at Chalmers Networks and Systems division, collaborating with researchers like Marina Papatriantafilou and Vincenzo Gulisano on energy systems and distributed algorithms.

Zhengmao Liمشاهده پروفایل

استادیار

• Multi-energy Systems
• Electric Energy Storage
• Uncertainty Handling
• +۷ مورد دیگر

Zhengmao Li is an Assistant Professor at Aalto University's Department of Electrical Engineering and Automation. He holds a Ph.D. in Electrical Engineering from Nanyang Technological University (NTU), Singapore, and has served as a Research Fellow at Stevens Institute of Technology (2019-2021) and NTU/Singapore ETH Center (2021-2023). His research focuses on multi-energy system planning and operation, particularly in integrated power, thermal, and gas networks for microgrids, seaports, and smart buildings. Key areas include resilience enhancement via demand response, robust optimization under uncertainties, and advanced algorithms like deep reinforcement learning. Education: B.E. (Information Engineering), M.E. (Electrical Engineering) from Shandong University, China; Ph.D. (Electrical Engineering) from NTU, Singapore. Research emphasizes handling uncertainties in renewable energy, developing resilient energy systems, and applying optimization techniques such as distributionally robust stochastic methods. His work bridges theoretical advancements with practical applications in smart grids, maritime energy systems, and low-carbon industrial operations. Over 50+ peer-reviewed articles highlight his contributions to energy system resilience, multi-agent coordination, and sustainable energy technologies.

Maggie Chenمشاهده پروفایل

استاد

• Financial Mathematics
• Fintech
• Market Microstructure
• +۵ مورد دیگر

Professor Maggie Chen is a Personal Chair (Professor) in Financial Mathematics at Cardiff University, affiliated with the School of Mathematics and the Operational Research Group. She holds adjunct/visiting professorships at Columbia University and University College London. Her research focuses on interdisciplinary financial modeling using Hawkes processes and network approaches to address modern finance challenges, particularly in Fintech, market microstructure, and regulatory impacts. She co-leads the EPSRC UKFin+ Network for Financial Services and advises organizations such as the Royal Statistical Society and the Center for Research toward Advancing Financial Technologies (CRAFT). Education: BSc in Computer Science from Lanzhou Jiaotong University (China), MSc in Finance & Investment Management and PhD in Finance from the University of Aberdeen (UK). Prior to academia, she worked in software engineering and management at China Telecom and Dell (China). Research Interests: Liquidity provision, information dynamics in financial markets, Hawkes processes for modeling financial jumps, cryptocurrency analysis, ESG applications, and policy impacts. She collaborates with governments, companies, and start-ups to develop deployable solutions in Fintech. Grants & Projects: Principal Investigator/Co-Investigator on projects funded by Wales Data Nation Acceleration Institute, Turing Institute, and Data Transformation Innovation Institute. Her work on jump detection has been adopted by U.S. regulators including the Commodity Futures Trading Commission (CFTC) and IMF. Labs/Teams: Leads the Fintech theme at the Data Transformation Innovation Institute, Cardiff. Engages in collaborative networks like the UKFin+ Network and CRAFT.

Zoran Stajicمشاهده پروفایل

استاد

• Electrical Power Engineering
• Power Systems
• Synchronous Machines
• +۵ مورد دیگر

Zoran Stajic is a Full Professor at the Faculty of Electronics, University of Niš, Serbia, where he has been an academic since earning his PhD in 2001. He was promoted to Associate Professor in 2007 and Full Professor in 2012, both in the Department of Electrical Power Engineering. He holds a Master’s degree (1996) and a Bachelor’s degree (1993) from the same institution, all in Electrical Power Engineering. His research focuses on electrical power systems, with particular emphasis on synchronous and induction machines, mathematical modeling, and control systems. His work includes modeling of synchronous machine starting, rotor voltage effects in double-fed induction motors, and measurement systems for industrial applications such as pump station control. He also investigates power transformer protection and associated professional risks in engineering environments. The available publications span from 1997 to 2006 and reflect a strong focus on power engineering dynamics, modeling, and system protection. They appear in journals such as Facta Universitatis and Buletinul Stiintific al Universitatii "Politehnica" din Timisoara, covering topics in power systems, control, and safety. The research demonstrates consistent application of computational and mathematical tools to real-world electrical engineering problems. Currently, he is managing three research and development projects for the multinational company RADIUS Group, and is involved in two national research projects. His work bridges academic research and industrial application, particularly in power system design and engineering safety. He is affiliated with the Department of Electrical Power Engineering at the Faculty of Electronics, contributing to both education and applied research. While no formal advising of students is mentioned, his active project leadership suggests involvement in training researchers and engineers.