جست‌وجوی استاد و پژوهشگر

Jean-François Biasseمشاهده پروفایل

استاد

• Computational Number Theory
• Post-Quantum Cryptography
• Lattice-Based Cryptography
• +۲ مورد دیگر

Jean-François Biasse is Professor in the Department of Mathematics & Statistics at University of South Florida and Director of the Center for Cryptographic Research. His research specializes in computational number theory and post-quantum cryptography, particularly lattice-based and isogeny-based cryptographic schemes. He holds a PhD from École Polytechnique and has held positions at University of Waterloo and University of Calgary. His research develops: Algorithms for class group and unit group computation Cryptanalysis of post-quantum schemes Quantum-resistant cryptographic protocols Efficient implementations of number-theoretic algorithms He has contributed significantly to isogeny-based cryptanalysis. His publications address: Quantum attacks on cryptographic assumptions Subexponential algorithms in number theory Security of code-based signatures Ideal lattice problems in cyclotomic fields Recent work examines tradeoffs between classical and quantum attacks. Dr. Biasse leads research on privacy-preserving technologies through projects like Trace- and homomorphic encryption systems. He serves as associate editor for the International Journal of Computer Mathematics: Computer Systems Theory.

Kemal Akkayaمشاهده پروفایل

استاد

• Network Security
• IoT & Cyber-physical System Security
• Key Management
• +۵ مورد دیگر

Dr. Kemal Akkaya is a Professor at the Department of Electrical & Computer Engineering, Florida International University (FIU), where he leads the Advanced Wireless and Security Lab (ADWISE). He holds a Ph.D. in Computer Science from the University of Maryland Baltimore County and has expertise in Network Security, IoT/CPS Security, Blockchain Applications, and 5G Security. His professional roles include serving as Research Director for FIU’s Emerging Preeminent Program in Cybersecurity and as Program Director for the first BS degree in IoT in the U.S. Education: Ph.D. in Computer Science, University of Maryland Baltimore County M.S. in Computer Engineering, Middle-East Technical University, Turkey B.S. in Computer Science, Bilkent University, Turkey Research Interests: Dr. Akkaya focuses on securing IoT and cyber-physical systems, blockchain for micro-payments, and network defense mechanisms. His work emphasizes privacy-aware protocols, secure key management, and SDN/NFV-based solutions. Awards: FIU Faculty Senate Excellence in Research Award (2020) College of Engineering and Computing Faculty Research Award (2020) Top Cited Article Award from Elsevier (2010) Advisees and Grants: While no student names are listed, his lab (ADWISE) likely supports graduate research in IoT and cybersecurity. His grants include interdisciplinary initiatives at FIU, focusing on securing emerging technologies like 5G and blockchain. Labs/Teams: Leads the ADWISE Lab, collaborating on projects like secure IoT payment systems, drone communication security, and resilient smart grid networks.

• Cryptography
• Computer Security
• Privacy
• +۳ مورد دیگر

Ghada Almashaqbeh is an Assistant Professor of Computer Science at the University of Connecticut's School of Computing, affiliated with the Connecticut Advanced Computing Center (CACC) and the Engineering for Human Rights Initiative. Previously, she cofounded CacheCash (based on her PhD work) and worked as a Cryptographer at NuCypher. She is currently a scientific advisor at Sunscreen and The Melon, and holds 2023 fellowships from the Foresight Institute and Uniswap Foundation's TLDR program. She earned her PhD in Computer Science from Columbia University (2019), advised by Allison Bishop and Tal Malkin, and is supported by NSF, Protocol Labs, and UConn grants. Her research focuses on cryptography, systems security, and privacy, particularly in blockchain and distributed systems. Key areas include secure blockchain scalability (chainBoost, ammBoost), privacy-preserving smart contracts (smartFHE), unclonable cryptography using biochemical materials, and revocable proxy signatures. Her work bridges theory and practice, emphasizing real-world applications and rigorous validation. She has received awards including the 2023 Foresight Institute Fellowship and Uniswap Foundation's TLDR Fellowship. Her lab recruits postdocs and PhD students to tackle challenges in cryptography, privacy, and systems security. She emphasizes interdisciplinary approaches, combining cryptography with biochemistry and distributed systems for practical solutions.

Sean Choiمشاهده پروفایل

استادیار

• Computer Networking
• Programmable Hardware
• Machine Learning Infrastructure
• +۳ مورد دیگر

Sean Choi is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University. He holds a Ph.D. in Electrical Engineering from Stanford University (2020), M.S. in Computer Science from Stanford (2013), and a dual B.S. in Computer Science and Business Economics & Management from Caltech (2011). His research focuses on programmable network hardware, including smartNICs, and their applications in machine learning, blockchain, and cloud computing. Education: Ph.D. in Electrical Engineering, Stanford University (2020) M.S. in Computer Science, Stanford University (2013) B.S. in Computer Science & Business Economics, California Institute of Technology (2011) Research Interests: Programmable Network Switches, SmartNIC Acceleration, Cloud Infrastructure, Serverless Computing, Blockchain Optimization, and Privacy-Preserving Machine Learning . Recent Work: Explores leveraging programmable hardware for low-latency machine learning inference, blockchain scalability via NIC-assisted consensus, and privacy-preserving federated learning through homomorphic encryption offload. His NSF-funded project develops ultra-low-latency ML systems using programmable networks. Awards: NSF CRII 2023 Award Hewlett Packard Enterprise Grant Labs/Teams: Leads research on network programmability and hardware acceleration within Santa Clara University's School of Engineering. Collaborates with industry on data center network optimization and cloud infrastructure projects.

Srinivasan Raghuramanمشاهده پروفایل

مدرس

• Cryptography
• Blockchain
• Theory of Computation
• +۳ مورد دیگر

Srinivasan Raghuraman is a Lecturer in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), and a Staff Research Scientist at Visa Research since June 2020. He holds a Ph.D. in Cryptography from MIT (2020), advised by Prof. Shafi Goldwasser, focusing on secure multiparty computation infrastructures in incomplete networks. Education: Ph.D. in Cryptography, MIT, 2020 Research Interests: His work bridges cryptography, blockchain, and theoretical computation. Key areas include secure multi-party computation, cryptographic protocols, network security, and privacy-preserving technologies. His Ph.D. thesis emphasized reliable communication and fault-tolerant systems in distributed networks. Research Contributions: He has published in top venues like CRYPTO, EUROCRYPT, and TCC, focusing on secure computation, homomorphic encryption, and cryptographic infrastructure. His work addresses challenges in network oblivious transfer, constrained pseudorandom functions, and spiking neural P systems. Labs/Teams: At Visa Research, he collaborates with the Advanced Cryptography and Blockchain teams to develop secure transaction protocols and cryptographic frameworks for modern financial systems.

Lars Schützeمشاهده پروفایل

پژوهشگر

• Compiler Construction
• Homomorphic Encryption
• Quantum-Classical Computing
• +۳ مورد دیگر

Dr. Lars Schütze is a researcher at the Chair for Compiler Construction within the Faculty of Computer Science at Dresden University of Technology (TU Dresden). Holding a PhD in Computer Science from TU Dresden, he currently serves as a PostDoc specializing in domain-specific compilers for verifiable Full Homomorphic Encryption (vFHE) and hybrid quantum-classical computing systems. His academic credentials from TU Dresden include: Bachelor's degree in Computer Science Master's degree in Computer Science PhD in Computer Science (awarded February 2025) Schütze's research centers on advanced compiler design for emerging computational paradigms. His foundational work explores context-oriented and role-based programming languages, focusing on runtime optimization and dispatch mechanisms. Recent efforts pivot toward post-quantum security through homomorphic encryption compilers and hybrid quantum-classical computing frameworks. His research bridges theoretical language design with practical compiler implementation, emphasizing verifiable security and performance efficiency in next-generation computing environments. Publication analysis reveals a clear evolution from context-oriented programming (2017-2020) toward cryptographic compiler development (2022-2025). Early work optimized role-based dispatch systems, while recent publications establish compiler frameworks for Fully Homomorphic Encryption using MLIR infrastructure. His research consistently addresses performance bottlenecks in dynamic language features while transitioning toward quantum-resistant cryptography solutions. Scientific Awards: No awards documented in source materials Dr. Schütze supervises student theses in Homomorphic Encryption and Quantum-Classical Computing frameworks, offering projects spanning Bachelor to Master levels. His research is funded through institutional projects including (verifiable) Full Homomorphic Encryption and Hybrid Quantum-Classical Computation, though specific grant details remain undisclosed. He actively develops compiler infrastructure for encrypted computation and quantum-classical orchestration. As core personnel in TU Dresden's Chair for Compiler Construction, Schütze contributes to the RoSI project (role-based software infrastructures) and leads current initiatives in vFHE. His team collaborates on building domain-specific compiler toolchains that address quantum computing threats through post-quantum cryptographic solutions while advancing hybrid execution models for emerging hardware architectures.

• Multi-Energy Systems
• Energy Communities
• Model Predictive Control
• +۶ مورد دیگر

Dr. Saeed Adelipour is a Research Fellow at Salford Business School, University of Salford, specializing in Multi-Energy Systems and Communities . His work focuses on resilient and sustainable energy communities, particularly vehicle electrification through bi-directional smart charging technologies within the DriVe2X project (funded by EU Horizon Europe and UKRI). He previously held a Postdoctoral Researcher role at Sharif University of Technology, exploring secure multi-party computation for cyber-physical systems with grants from the Iran National Science Foundation (INSF). Education: B.Sc. in Electrical Engineering (Control Systems), Iran University of Science and Technology (2011) M.Sc. in Electrical Engineering (Control Systems), Sharif University of Technology (2013) Ph.D. in Electrical Engineering (Control Systems), Sharif University of Technology (2020) Research interests include Model Predictive Control , Decentralized Control , Privacy-Preserving Computation , and Intelligent Transportation Systems . His publications bridge control systems and cybersecurity , with recent work on secure algorithms for smart grids and electric vehicle integration. He has participated in multiple international conferences including ICEE and UKACC.

Tianxiang Daiمشاهده پروفایل

استادیار

• Cyber Security
• Network Security
• Internet Infrastructure Security
• +۶ مورد دیگر

Dr. Tianxiang Dai serves as Assistant Professor in Cyber Security at Lancaster University's Data Science Institute since 2025, following postdoctoral research at Barkhausen Institut in Dresden. His academic credentials include: Dr.-Ing. (Ph.D.) summa cum laude in Computer Science from Fraunhofer SIT/TU Darmstadt, Germany Ingénieur (M.Eng.) in Computer Science from Télécom ParisTech/Eurecom, France B.Eng. in Computer Science from Tongji University, China His research specializes in security evaluations of networked systems including Internet infrastructures, IoT ecosystems, and cellular networks, with parallel expertise in Privacy Enhancing Technologies such as Confidential Computing and advanced cryptographic methods. This dual focus enables comprehensive security analysis across distributed architectures while developing privacy-preserving computational frameworks. His 2024 publications demonstrate significant contributions to secure machine learning, particularly through cryptographic frameworks for training Gradient Boosting Decision Trees. These works integrate Function Secret Sharing and Two-Party Computation to achieve privacy-preserving analytics at scale, reflecting a strategic research trajectory at the intersection of cryptography, network security, and practical machine learning deployment. Scientific Recognition: No specific awards or fellowships documented in source materials Academic mentoring activities and research funding details remain unspecified in available documentation, though his publication record indicates active collaboration with researchers in cryptographic protocol development. His prior affiliation with Barkhausen Institut suggests involvement in security research teams focused on network infrastructure, though current laboratory structures at Lancaster University are not detailed in the provided text.

Levent Ertaulمشاهده پروفایل

استاد

• Cyber Security & Privacy
• Cyber War
• Hacking Technologies
• +۹ مورد دیگر

Levent Ertaul is a Professor and Chair of the Department of Computer Science at California State University, East Bay (CSUEB). He holds a PhD in Computer Networks Security from the University of Sussex, UK, and advanced degrees in Electrical and Electronics Engineering from Hacettepe University, Turkey. His expertise spans Cyber Security & Privacy, Cyber War, Cryptology, and Wireless Security. He has led research in IoT Security, Post-Quantum Cryptography, and Enterprise Security Planning. Ertaul has held leadership roles including CSUEB Department Chair and served on committees such as the California Cybersecurity 4 Year Education Program Workgroup. His professional development includes summer faculty positions at Lawrence Livermore National Laboratory (LLNL) focusing on CyberDefenders and Global Security programs. He has authored over 50 publications and presented globally at venues like RSA Conference and IEEE events. Awards include the Cisco Instructor Excellence Award and Keynote Speaker recognitions. He teaches advanced courses in Cyber Security, Cryptology, and Network Security Management. Notable contributions include inventing the L4NE protocol for location-based services and the ERTAUL Crypto Algorithm. His grants exceed $15M, including work on National Military Messaging Systems and Firewall Development. He actively participates in cybersecurity competitions (HackCSUEB, CTFs) and advises on policy matters through roles like Bay Area Council Cyber Security Representative.

Asha Raoمشاهده پروفایل

استاد

• Cybersecurity
• Graph Theory
• Combinatorics
• +۷ مورد دیگر

Professor Asha Rao is a distinguished academic at RMIT University, holding the title of Professor in Mathematics and Cybersecurity. She has been instrumental in advancing cyber-mathematics to enhance cybersecurity. Her roles include Associate Dean of Mathematical Sciences (2017–2023) and Interim Director of the Australian Mathematical Sciences Institute (AMSI) (2020). She champions gender equity in STEM and frequently engages in public science communication. University: RMIT University School: School of Science Academic Rank: Professor Research Focus: Her work spans cybersecurity, coding theory, complex networks, and risk management, often integrating mathematical frameworks to address real-world problems like money laundering and small business digital security. She also explores fractal analysis and post-quantum cryptography. Recent Publications: Highlight recent advancements in post-quantum homomorphic encryption, small business cybersecurity frameworks (2021–2025), and data fusion techniques for internal network anomaly detection. Her research bridges technical innovation with policy implications. Scientific Recognition: 2019–2020 Science & Technology Australia Superstar of STEM 2021 Victorian Honour Roll of Women (Trail Blazer) 2021 India Australia Science and Technology Development Award 2016 RMIT Media Stars Award Teaching & Leadership: Actively supervises Masters and PhD students, emphasizing teamwork in cybersecurity education. She led curriculum design initiatives and addressed non-technical skills gaps in the industry (2020). Her leadership extends to national organizations like AMSI. Community Engagement: Promotes cybersecurity through media appearances (ABC News, Australian Financial Review) and events like Pint of Science. She co-founded Women in Mathematics and advocates for girls in STEM.

• Algebra
• Algebraic Coding Theory
• Boolean Algebras
• +۷ مورد دیگر

Massimiliano Sala is a Full Professor at the Department of Mathematics of the University of Trento. His expertise spans Algebra, Algebraic Coding Theory, Boolean Algebras, Cryptography, and Computational Algebra. He specializes in cryptographic protocols, post-quantum security, and elliptic curve cryptography. His recent work includes advancements in blockchain-agnostic multi-signature schemes, searchable encryption, and elliptic curve group structures. Research Focus: Dr. Sala's research integrates mathematical foundations with applied cryptography, emphasizing secure communication protocols, cryptographic algorithm design, and blockchain technologies. He has contributed to the analysis of Boolean functions, APN permutations, and secure multi-party computation. Recent Contributions: His 2024 publications address topics like cubic Boolean functions, blockchain-agnostic asset management, and randomized searchable encryption. He also explores post-quantum cryptographic frameworks and elliptic curve-based security mechanisms. Awards & Recognition: None explicitly listed in the provided materials. Teaching & Academic Leadership: Teaches Algebraic Cryptography at the Master's level, focusing on public-key systems and post-quantum cryptography. His academic contributions include editorial work for cryptographic journals and conference proceedings.

• Genetics
• Neuroscience
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Matthew Jensen is a Research Fellow at Yale School of Medicine, Yale University. His research focuses on genetics, neuroscience, and the application of artificial intelligence in healthcare. He explores topics such as psychiatric disorders, genetic variation, single-cell genomics, and privacy-preserving genomic data analysis. His work bridges computational biology with clinical insights, particularly in understanding neurodevelopmental disorders and leveraging AI for disease characterization. Key research interests include: Genetic modifiers and their impact on disease variability Single-cell analysis of brain and other tissues Development of AI tools for digital phenotyping and polygenic risk assessment Functional genomics and regulatory element dynamics in human health Recent publications highlight innovations in: Privacy-preserving genomic analysis using homomorphic encryption Multi-cohort studies of schizophrenia transcriptomes Comprehensive mapping of brain regulatory elements No scientific awards or grants are explicitly mentioned in the provided information.

Arif Harmanciمشاهده پروفایل

استادیار

• Next Generation Sequencing Data Analysis
• Bioinformatics
• Functional Genomics
• +۳ مورد دیگر

Dr. Arif Harmanci is an Assistant Professor in the Department of Bioinformatics and Systems Medicine at UTHealth Houston. He holds a PhD and MS in Electrical Engineering from the University of Rochester, and a BS from Bilkent University. His research focuses on computational biology, including functional genomics, non-coding RNA modeling, cancer genomics, and genomic privacy preservation. Dr. Harmanci has contributed to major consortia such as ENCODE and modENCODE, and pioneered methods for secure genomic data analysis. His work addresses ethical challenges in sharing genomic data while advancing precision medicine applications. Education: PhD, Electrical and Computer Engineering, University of Rochester (2010) MS, Electrical and Computer Engineering, University of Rochester (2005) BS, Electrical and Computer Engineering, Bilkent University (2004) Research Interests: Dr. Harmanci’s work spans computational methods for next-generation sequencing analysis, functional genomics, and privacy-preserving bioinformatics tools. He explores how genomic data privacy can be maintained during clinical analysis and sharing, developing frameworks like FSLearning and COLLAGENE. His cancer genomics research includes meningioma classification, glioma immunology, and the molecular mechanisms driving tumor progression. Publications: Over 100 peer-reviewed articles, including high-impact journals like Nature and Science. Recent work emphasizes federated learning for medical data, secure genomic imputation, and single-cell tumor profiling. Grants & Collaborations: Key member of ENCODE and 1000 Genomes Project. Involved in interdisciplinary projects at Yale University and UTHealth Houston, focusing on translational bioinformatics and computational oncology.

Daniel Hellwigمشاهده پروفایل

استاد

• Blockchain
• Supply Chain Management
• Production Management
• +۲ مورد دیگر

Dr. Daniel Hellwig serves as Professor and Chair of Production Management within the Supply Chain Management Group at WHU - Otto Beisheim School of Management in Vallendar, Germany. His academic expertise is complemented by his professional role as Principal at Kepler Cannon in New York since 2014, demonstrating a strong integration of theoretical research and practical business applications in the field of supply chain innovation. His educational background includes: M.Eng. from University of California at Berkeley (2013) B.S.E. from The Wharton School, University of Pennsylvania (2011) Dr. Hellwig's research focuses on blockchain technology applications in supply chain management, with particular emphasis on distributed ledger systems for trade finance, resource allocation, and supply chain resilience. His work bridges technical blockchain implementation with business process optimization, exploring how cryptographic techniques like fully homomorphic encryption can enable secure information sharing among competing retailers. He has developed innovative simulation frameworks such as the DISASTER token trading game to study supply chain behavior during crises, demonstrating practical applications of theoretical concepts. His publication trajectory reveals an evolution from foundational blockchain concepts to increasingly sophisticated supply chain applications. Early works established fundamental blockchain knowledge through comprehensive guides, while recent research focuses on behavioral aspects of blockchain-enabled markets and privacy-preserving technologies for supply chain collaboration. This progression demonstrates both technical depth and growing business relevance in his scholarly contributions. As an educator, Dr. Hellwig has been a consistent guest lecturer at WHU since 2018, teaching blockchain programming and introduction courses at both bachelor's and master's levels. His teaching approach emphasizes practical implementation alongside theoretical understanding, reflecting his dual expertise in academic research and professional consulting. He frequently collaborates with Goran Karlic and Professor Arnd Huchzermeier, suggesting strong research partnerships within WHU's Supply Chain Management Group.

• Secure Computation
• Privacy-Preserving Machine Learning
• Homomorphic Encryption
• +۴ مورد دیگر

M. Sadegh Riazi is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego's Jacobs School of Engineering. He completed his PhD at UCSD in 2020 with a dissertation titled 'Towards A Private New World: Algorithm, Protocol, and Hardware Co-Design for Large-Scale Secure Computation.' Dr. Riazi's research focuses on the critical intersection of cryptography, machine learning, and hardware security, with particular emphasis on making secure computation practical for real-world applications. His work spans secure multi-party computation, homomorphic encryption, privacy-preserving machine learning, and hardware acceleration for cryptographic protocols. He has made significant contributions to optimizing secure computation frameworks for deep learning applications, developing techniques that balance security guarantees with computational efficiency. His publication record shows a clear progression from foundational secure computation techniques to increasingly sophisticated applications in privacy-preserving AI. Notably, his work on HEAX (Homomorphic Encryption Acceleration) and XONN (XNOR-based Oblivious Neural Network) demonstrates practical approaches to making encrypted deep learning feasible. The research trends in his publications indicate a consistent focus on bridging theoretical cryptographic security with practical system implementations. Dr. Riazi has established a strong collaborative network, most prominently with Professor Farinaz Koushanfar's research group at UCSD, with whom he has co-authored numerous papers across multiple domains including biometric security, secure hardware design, and privacy-preserving machine learning. His research has been published in top-tier venues including IEEE Security & Privacy, USENIX Security, ASPLOS, and CCS, reflecting the high impact and quality of his work in both the security and systems communities.