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Minxuan ZhouView profile

Assistant Professor

• Systems
• High Performance Computing
• Parallel Computing
• +5 more

Minxuan Zhou is an Assistant Professor of Computer Science at Illinois Institute of Technology. His research focuses on systems, high-performance computing, and parallel computing with an emphasis on processing-in-memory (PIM) acceleration, fully homomorphic encryption (FHE), and hyperdimensional computing. He holds a Ph.D. and M.S. in Computer Science from the University of California San Diego, and a B.S. in Computer Science and Technology from Beihang University. His work spans hardware-software co-design, cryptographic acceleration, and energy-efficient computing systems. Research Interests Processing-in-Memory (PIM) architectures for accelerating machine learning and graph processing Hardware acceleration for secure computation (FHE) Hyperdimensional computing frameworks for edge AI Thermal-aware design and memory optimization in 3D systems Recent Research Trends Recent publications emphasize co-optimization techniques for PIM architectures, FHE hardware acceleration, and privacy-preserving machine learning. His work often bridges theoretical algorithm design with practical hardware implementations using novel memory technologies like ReRAM and 3D-stacked memory. Grants & Labs Engaged in cutting-edge research through collaborations on projects involving PIM accelerators and encrypted computation systems. His lab focuses on developing full-stack solutions for next-generation computing architectures.

Christopher AyalaView profile

Professor

• Superconductor Electronics
• Adiabatic Computing
• Quantum Computing
• +2 more

Christopher Ayala is a Professor at Yokohama National University's Institute of Advanced Sciences, where he directs research in the Quantum Information Research Center. His work focuses on energy-efficient superconductor computing architectures, particularly adiabatic quantum-flux-parametrons (AQFPs) for next-generation computing systems. Dr. Ayala's group develops superconducting processors, memory systems, and control architectures for quantum computing applications. Research areas include: Adiabatic superconductor microprocessors Fully homomorphic encryption hardware Quantum computing control systems Superconductor logic families Cryogenic computing architectures His team pioneered the world's first adiabatic superconductor microprocessor, recognized by IEEE Spectrum and Superconductor Week. Dr. Ayala received the Best Young Scientist Paper Award at the European Solid-state Device Research Conference for his work on nanoelectromechanical switches.

• Cryptography
• Computer Security
• Privacy
• +3 more

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.

Srinivasan RaghuramanView profile

Lecturer

• Cryptography
• Blockchain
• Theory of Computation
• +3 more

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 SchützeView profile

Researcher

• Compiler Construction
• Homomorphic Encryption
• Quantum-Classical Computing
• +3 more

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.

• Parallel and Distributed Systems
• Cloud Computing
• Computer and Communication Networks
• +12 more

Deniz Turgay Altılar is a Professor in the Department of Computer Engineering at Istanbul Technical University (ITU), Faculty of Computer and Informatics. He has been an active academic since 1988, progressing from Research Assistant to Associate Professor in 2013 and later achieving the rank of Professor. His work is centered on distributed systems, cloud computing, and computer networks, with expanding interests in AI applications for agriculture and healthcare. Education Bachelor of Science, Control and Computer Engineering, Istanbul Technical University (1984–1988) Master of Science, Computer Engineering, Istanbul Technical University (1988–1992) Research experience at Queen Mary and Westfield College, University of London (1998–2002) His research interests include parallel and distributed systems, cloud computing, secure computation, deep learning, and cybersecurity. He applies these to interdisciplinary domains such as corn yield prediction, radar identification, and medical diagnostics using ECG signals. His recent work emphasizes efficient and secure AI systems. The publication trends show a strong focus on distributed deep learning, straggler mitigation, secure multiparty computation, and hardware-based attack detection. His work bridges theoretical computer science with real-world applications in agriculture, medicine, and national security. Recent articles highlight innovation in lightweight AI models, privacy-preserving computation, and cross-layer system design. Scientific Awards No specific awards are mentioned in the provided text. He actively supervises students and leads major research projects, including those on cache side-channel attacks in multi-tenant clouds, distributed OpenCL platforms, real-time scheduling, and molecular communication in nano-networks. These projects reflect sustained external funding and leadership in cutting-edge computing domains. His research group collaborates across disciplines and institutions, contributing to both national and international scientific communities. Labs and Teams : While not explicitly named, his role as Principal Investigator on multiple projects suggests leadership in a research lab focused on distributed systems, cybersecurity, and applied AI at ITU.

Daniel HellwigView profile

Professor

• Blockchain
• Supply Chain Management
• Production Management
• +2 more

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.

Anamaria CostacheView profile

Associate Professor

• Fully Homomorphic Encryption
• Privacy-Preserving Machine Learning
• Lattice-Based Cryptography
• +3 more

Anamaria Costache is an Associate Professor at the Norwegian University of Science and Technology (NTNU) in the Department of Information Security and Communication Technology. Previously, she held roles including a postdoc at Royal Holloway, University of London (RHUL), a Research Scientist at Intel AI Research, and a part-time position at Intel. She earned her PhD from the University of Bristol and undergraduate/master's degrees from the University of Warwick, focusing on number theory and algebraic structures like Brauer Groups. Her research interests center on fully homomorphic encryption (FHE), privacy-preserving machine learning, lattice-based cryptography, and post-quantum security protocols. Her work is supported by Intel, addressing challenges in secure computation on encrypted data. She has authored numerous publications in top venues like CRYPTO, PKC, and IEEE conferences, focusing on FHE optimizations, biometric systems, and cryptographic security analyses. Teaching includes courses on applied cryptography, network security, and quantum-safe encryption at NTNU. She co-chairs NIKT 2019 and the WAHC workshops (2022–2024), and serves on editorial boards for journals like IACR Communications in Cryptology. She actively participates in program committees for conferences such as MathCrypt and FHE.org. Professional service includes the Lattigo Advisory Committee and contributions to the Bristol Crypto Blog. Her current projects emphasize verifiable computation over encrypted data and resilient authentication mechanisms in biometric systems.

• GPU Architecture
• Hardware Accelerators
• Machine Learning
• +3 more

José L. Abellán is a Ramón y Cajal Fellow (Tenure-Track Associate Professor) and European R3 researcher at the University of Murcia's Department of Computer Engineering and Technology. He leads the EcoArTech research group and holds a Ph.D. in Computer Science from the University of Murcia (2012). His career includes postdoctoral positions at Boston University and previous faculty roles at Universidad Católica de Murcia. Dr. Abellán's research focuses on architectural enhancements for GPU systems and customized accelerators targeting machine learning and fully homomorphic encryption applications. His work spans hardware/software co-design, microarchitectural extensions for privacy-preserving computation, and efficient parallel processing. His extensive publication record demonstrates consistent contributions to GPU architecture, processing-in-memory, hardware acceleration for cryptography, and graph neural networks. Recent work has appeared in top computer architecture venues including MICRO, ASPLOS, and HPCA. Awards and Honors HiPEAC Paper Awards (2019, 2020, 2023, 2024) Best Paper Award at IPDPS 2011 Top Picks in Hardware and Embedded Security 2024 European R3 Certificate (2024) Dr. Abellán leads the EcoArTech research team and serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and Frontiers in Electronics. He is a Senior Member of IEEE and active in the HiPEAC European network.

Sazadur RahmanView profile

Assistant Professor

• Hardware Security
• Supply Chain Security
• Electronic Design Automation
• +7 more

Sazadur Rahman is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Central Florida (UCF), affiliated with both ECE and Computer Science departments under the Cyber Security and Privacy Cluster. He holds a Ph.D. and M.Sc. from the University of Florida and a B.Sc. from Bangladesh University of Engineering and Technology. Previously, he worked as a Security Architecture Engineer at Intel Corporation, focusing on processor security hardening and threat modeling. His research interests include hardware security, semiconductor supply chain security, and AI-assisted chip design. He has published over 20 peer-reviewed papers and contributed to patents and textbooks. Education Ph.D. Electrical Engineering, University of Florida, 2022 M.Sc. Electrical and Computer Engineering, University of Florida B.Sc. Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology Research Interests His work centers on securing electronic design automation (EDA) tools, semiconductor supply chains, and hardware systems against reverse engineering, piracy, and attacks. Key areas include: Hardware obfuscation techniques (e.g., logic locking, watermarking) CAD tools for security assessment and mitigation Machine learning applications in secure chip design Secure heterogeneous integration and FHE acceleration Professional Activities Technical Program Committee Member: IEEE HOST Conference (2024) Reviewer: Top journals/conferences like IEEE Transactions on CAD, ACM TODAES, and DAC Awards 2022 IEEE/ACM DAC PhD Forum Finalist 2023 IEEE/ACM DATE Best Paper Nomination 2022 IEEE VTS TTTC Best Thesis Runner-Up Research Contributions His publications focus on innovative solutions like LLE for IC piracy mitigation, iPROBE for probing attack protection, and ReTrustFSM for RTL obfuscation. He has developed tools for threat modeling using NVD databases and evaluated cloud-based EDA platforms' security.

• Lattice-based Cryptography
• Homomorphic Encryption
• Privacy-Preserving Protocols
• +3 more

Jeongeun Park is an Associate Professor at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU). Her research focuses on lattice-based cryptography, homomorphic encryption, and privacy-preserving protocols, with a particular emphasis on fully homomorphic encryption (FHE) and its applications. She holds a PhD in Mathematics from Ewha Womans University (2021) and was a postdoctoral researcher at KU Leuven (2021–2024). Her educational background includes a BSc in Mathematics (2015) and a combined master-doctoral degree in Cryptology (2021), both from Ewha Womans University. Research interests span theoretical and practical advancements in cryptography, including FHE algorithms, privacy-preserving machine learning, and secure multiparty computation. She has contributed to foundational work on NTRU-based FHE, oblivious RAM, and privacy-preserving protocols like Pancea and SortingHat. Key awards include the 2017 Crypto Contest Special Award and NRF-funded research grants. Her work has been published in top venues such as Asiacrypt, SCN, and IEEE Transactions. She actively participates in academic governance, serving on program committees for PETS 2026, ISC 2025, and others, and reviews for major conferences and journals.

Tomohiro IView profile

Associate Professor

• Algorithms on Strings
• String Pattern Matching
• Indexing
• +9 more

Tomohiro I is an Associate Professor in the Department of Artificial Intelligence at Kyushu Institute of Technology, Japan. He has been in this position since January 2019, following a research associate role at the same institution from 2015 to 2018. Prior to that, he held postdoctoral positions at Kyushu University and TU Dortmund, Germany. His academic foundation includes a Ph.D. in Science from Kyushu University, awarded in 2012. His research primarily centers on string algorithms , with a strong emphasis on compressed data structures , pattern matching , indexing , and algorithmic efficiency . Key interests include Lyndon factorization, Lempel-Ziv compression, palindrome matching, and reverse engineering of string data structures. He frequently collaborates with prominent researchers like Hideo Bannai and Shunsuke Inenaga, producing high-impact work in theoretical computer science. His recent publications demonstrate a consistent focus on improving algorithms for string processing in compressed formats. Work on Re-Pair , RLBWT , and SLP encoding highlights his expertise in space-efficient computation. The 2022 Best Paper Award at IWOCA for work on Lyndon subsequences underscores the quality and recognition of his contributions. His research bridges theoretical analysis with practical algorithm design. Best Paper Award, International Workshop on Combinatorial Algorithms (IWOCA) 2022 Tomohiro I advises graduate students in his laboratory, although he currently notes that the lab is not accepting new research students. His work involves significant algorithmic research, often supported by theoretical grants or institutional funding, leading to numerous publications in peer-reviewed journals and conferences. He has also presented his work in invited talks, such as at CompressedAI2025 and WCTA 2024, indicating active engagement with the research community. He leads a research laboratory at Kyushu Institute of Technology, focused on advanced string processing and compressed data structures. His team collaborates extensively on algorithm design and analysis, contributing to the broader field of combinatorial pattern matching.

• Cryptography
• Computer Systems Security
• Privacy
• +4 more

Ghada Almashaqbeh is an Assistant Professor in the School of Computing at the University of Connecticut. She is affiliated with the Connecticut Advanced Computing Center (CACC) and the Engineering for Human Rights Initiative. Her academic journey includes a PhD in Computer Science from Columbia University (2019), advised by Allison Bishop and Tal Malkin. PhD in Computer Science, Columbia University, 2019 Member of Columbia's Cryptography Lab and Data Science Institute Former Cryptographer at NuCypher Co-founder of CacheCash, a startup based on her PhD research Scientific Advisor at Sunscreen and Melon Her research focuses on cryptography, computer security, and privacy, particularly in blockchain-based systems and distributed protocols. Projects include secure sidechain frameworks for Web 3.0 scalability ( chainBoost , EuroS&P 2024), privacy-preserving smart contracts via homomorphic encryption (EuroS&P 2023), timed cryptographic delegation (ISC 2024), and biochemical token-based cryptography (Eurocrypt 2022). Her work bridges theoretical rigor with practical implementations. Key research areas include: Blockchain scalability and provable security Zero-knowledge proofs for privacy Cryptographic delegation mechanisms Biochemical security primitives Web 3.0 economic protocols Collateral management algorithms Scientific honors include 2023 Foresight Institute Fellow 2023 TLDR Fellow at Uniswap Foundation Her research is funded by the National Science Foundation (NSF), Protocol Labs, Uniswap Foundation, and the UConn Research Excellence Award. She actively recruits PhD students and postdocs for interdisciplinary projects combining cryptography, systems security, and blockchain innovation.

• Cryptography
• Computer Science
• Theoretical Computer Science
• +4 more

Vinod Vaikuntanathan is an Associate Professor of Computer Science at the Massachusetts Institute of Technology (MIT) and serves as Chief Cryptographer at Duality Technologies. He is renowned for co-inventing modern fully homomorphic encryption systems and lattice-based cryptographic primitives that ensure post-quantum security. His research focuses on theoretical and applied cryptography, with implications for secure computation and data privacy. Education : PhD and SM in Computer Science from MIT; BTech in Computer Science from IIT Madras Research Areas : Cryptography, Theoretical Computer Science, Lattice-Based Cryptography Scientific Awards : George M. Sprowls PhD thesis award (2009) IBM Josef Raviv Fellowship (2008) Sloan Faculty Fellowship (2013) Microsoft Faculty Fellowship (2014) NSF CAREER Award (2014) DARPA Young Faculty Award (2018) Harold E. Edgerton Faculty Award (2018)

• Big Data Processing
• Heterogeneous Computing
• GPU Acceleration
• +4 more

Maria Xekalaki is a Research Associate and PhD student in the School of Computer Science at the University of Manchester, affiliated with the Advanced Processor Technologies research group. Her work focuses on accelerating Big Data stacks using heterogeneous hardware resources while optimizing cost and energy efficiency. SDG Contributions: Sustainable Development Goals (SDGs) related to energy efficiency and poverty eradication Research Interests: Her research spans heterogeneous computing, GPU acceleration, Java application optimization, and energy-efficient Big Data processing. She explores techniques for leveraging RISC-V vectorization, improving cross-language interoperability, and advancing GPU-accelerated Fully Homomorphic Encryption (FHE) systems. Article Trends: Her publications emphasize transparent acceleration of Java programs, FHE optimization for privacy-preserving machine learning, and heterogeneous resource management. Key technologies include TornadoVM, OpenCL, and FPGA integration with Big Data frameworks.