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.
Corey Clark, Ph.D., serves as Deputy Director of Research and Assistant Professor in the Department of Computer Science and Engineering at Southern Methodist University's Lyle School of Engineering. He leads the Human and Machine Intelligence (HuMIn) Game Lab, pioneering research at the intersection of gaming, artificial intelligence, and human computation to solve large-scale problems in healthcare, education, and national security. Dr. Clark holds a Ph.D., M.S., and B.S. in Electrical Engineering from The University of Texas at Arlington, where he graduated Magna Cum Laude for his undergraduate degree. His doctoral research focused on nanoscale modeling and simulation techniques for Molecular Beam Epitaxy and Chemical Vapor Deposition of exotic materials. His research spans Artificial Intelligence, Game Development, Human Computation, Distributed Computing, Machine Learning, Computational Biology, and Educational Technology. Clark is renowned for transforming commercial video games into distributed computing platforms through human computation, enabling breakthroughs in medical diagnostics and educational technology. His technical innovations in HTML5/JavaScript multithreading have been featured at major international game conferences. Analysis of Clark's 15 most recent publications (2022-2024) reveals a dominant focus on generative AI applications integrated with gaming mechanics, particularly for knowledge graph enhancement, medical diagnostics, and computational thinking education. Key trends include explainable AI systems, human-AI collaboration through gameplay, blockchain applications, and privacy-preserving machine learning, demonstrating consistent innovation in applying game-based approaches to real-world problems. Dr. Clark has secured over $1.7 million in competitive research funding, including: Human Computation for Ocular Tomography Analysis ($62,924) with Retina Foundation Cryptocurrency Transactional Analysis via Gaming ($60,000) with Raytheon Game-Based Adult Literacy (XPrize) ($480,000) Flexible Electronics for Airborne Laser ($820,000) with Missile Defense Agency Networked C4ISR Chip ($850,000) with US Army As CTO for Dallas-based game technology companies, Clark has helped raise over $12 million in startup funding. His HuMIn Game Lab currently develops immersive gameplay systems for cancer treatment research using crowdsourced computation and machine learning, with recent projects including therapeutic discovery platforms and blockchain-based task completion systems.
Chaoyun Li is a Lecturer in Software Security at the Surrey Centre for Cyber Security, University of Surrey, since March 2023. Prior to this, he served as a postdoc researcher at the COSIC research group, KU Leuven (2020–2023) and completed his PhD in cryptography under Prof. Bart Preneel at the same institution in 2020. His academic journey includes a Marie Skłodowska-Curie ESR Fellowship (2015–2019) as part of the EU H2020 ECRYPT-NET project. Chaoyun holds a PhD in Electrical Engineering from KU Leuven (2020) and has conducted extensive research in applied cryptography, cryptanalysis, privacy-enhancing technologies, and mathematical foundations of cryptography. His work focuses on breaking cryptographic systems, designing secure primitives, and optimizing lightweight ciphers for constrained environments. Notable contributions include advancements in side-channel attacks, MDS matrix design, and de Bruijn sequence construction. His research interests span symmetric cryptography, fault analysis, and secure implementations. Recent projects emphasize practical intrusion detection over encrypted traffic and the integration of cryptographic ciphers with fully homomorphic encryption (FHE). Chaoyun has received several accolades, including the FWO Junior Postdoc Fellowship (2020–2023) and the CHES 2021 Challenge WhibOx Contest victory for best implementations (2021). He actively contributes to academic service, serving on program committees for conferences like Indocrypt and IMACC, and reviewing for journals such as IEEE Transactions on Information Theory and Designs, Codes and Cryptography. As a mentor, he co-supervises PhD student Indranil Thakur and has guided master’s students Ryan De Koninck, Edward Wiels, Michiel Verbauwhede, and Xueming Zhong. His collaborative efforts include affiliations with imec-COSIC (KU Leuven) and visits to institutions such as the Chinese Academy of Sciences and Orange Labs. Professional activities include invited talks at venues like the International Workshop on Coding Theory and Cryptography and the Chinese Academy of Sciences. Chaoyun’s work bridges theoretical cryptography and practical security applications, with a focus on real-world implementations and vulnerabilities in cryptographic systems.
Carmela Troncoso is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), heading the Security and Privacy Engineering Lab (SPRING Lab) in the School of Computer and Communication Sciences. She holds a PhD in Engineering from KU Leuven (2011) and has held academic roles at IMDEA Software Institute and Gradiant, along with postdoctoral research at COSIC Group. Research Focus: Security and privacy engineering, machine learning's societal impact, privacy-enhancing technologies, and decentralized privacy-preserving systems. Publications: Her work spans top venues like IEEE S&P, CCS, NeurIPS, and PETS, with recent emphasis on adversarial robustness, proximity tracing, and privacy metrics. Awards: IEEE S&P Distinguished Paper Award (2022) EPFL Latsis University Prize (2022) Caspar Bowden Award Runner-up (2021) CNIL-INRIA Privacy Protection Award (2017) ERCIM PhD Thesis Award (2011) She supervises active PhD students and collaborates with institutions like ICIJ. Her lab's SPRING Lab website details current projects.
Assoc. Prof. Dr. Nil Banu Tarım is an Associate Professor at Istanbul Technical University's Department of Electronics and Communication Engineering. She earned her PhD at the same institution and has held academic roles since 1989, including a Visiting Professor position at Sweden's Royal Institute of Technology (2002-2004). Education: PhD in Electronics and Communication Engineering (1993-1999) Current Role: Associate Professor (2010-present) Prior Role: Assistant Professor (2000-2010) Her research spans RF engineering, communication systems, and biomedical signal processing, with recent work focusing on fully homomorphic encryption for secure data handling in biomedical applications. She has contributed to LTE/WiMAX receiver design, X-band amplifiers, and CMOS-based RF components. Key research trends include: Privacy-preserving computation (2024) Adaptive RF hardware (2017-2018) CMOS process optimization (2007-2008) Wireless synchronization systems (2007) Contact: tarimn@itu.edu.tr
Ni Trieu is an Assistant Professor of Computer Science at Arizona State University, specializing in cryptography and security with a focus on secure computation and its applications. Her research has significant implications for privacy-preserving technologies in various domains including healthcare, data sharing, and machine learning. Dr. Trieu received her PhD and Master's degrees from Oregon State University under the supervision of Professor Mike Rosulek, followed by a postdoctoral position at UC Berkeley with Professor Dawn Song. During her graduate studies, she gained industry experience through research internships at Bell Labs, Visa Research, and Google. She completed her undergraduate education at St. Petersburg State Polytechnic University. Her research interests center around cryptography and security , with specific expertise in secure computation and its practical applications including private set intersection, private database queries, and privacy-preserving machine learning. Her work bridges theoretical cryptography with real-world security challenges, developing protocols that balance security guarantees with computational efficiency. Dr. Trieu has maintained an impressive publication record in top-tier security conferences including CCS, PETS, EuroS&P, and CRYPTO. Her recent work shows a clear trajectory toward more complex and practical secure computation scenarios, with increasing focus on multi-party settings, efficiency improvements, and applications to real-world problems like genomic privacy, contact tracing, and machine learning. The publications demonstrate consistent innovation in secure computation techniques while addressing practical constraints of real implementations. Dr. Trieu actively contributes to the academic community through conference service, having served on program committees for major security conferences and participated in NSF panel reviews. Her recent news indicates she's been invited to speak at prestigious venues including Simons Institute, VIASM, and NIST.
Nektarios Tsoutsos is an Assistant Professor at the University of Delaware, specializing in information security, applied cryptography, and hardware security. His research focuses on encrypted computation, homomorphic encryption, and secure hardware architectures. He holds a PhD from New York University, where he was previously a postdoc in the Modern Microprocessor Architectures laboratory. Education : PhD in Computer Science, New York University Research Interests : Homomorphic encryption frameworks and their acceleration Hardware-software co-design for secure computing Privacy-preserving technologies in additive manufacturing and distributed systems Cryptography for machine learning and IoT Awards : New York University Ph.D. Student Fellowship Pearl Brownstein Doctoral Research Award (NYU) Grants & Projects : CAREER Grant: Accelerating Encrypted Computation on Diverse Hardware (2023) CCRI Grant: Data-Driven Cybersecurity for Smart Manufacturing (2023) His work includes developing secure processors (e.g., Juliet), accelerating FHE algorithms (e.g., Ripple), and creating datasets for 3D printing security. He collaborates on frameworks like HELM and Mastic, addressing challenges in privacy, performance, and scalability.
Dr. Daniel Sanchez is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), where he leads the Computation Structures Group within the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on computer architecture, scalable systems, and memory hierarchies, with particular emphasis on multicore and many-core systems, quality-of-service guarantees, and runtime systems. Education: Ph.D. in Electrical Engineering, Stanford University (2012) M.S. in Electrical Engineering, Stanford University (2009) B.S. in Telecommunications Engineering, Technical University of Madrid (UPM) (2007) Research Interests: Dr. Sanchez’s work spans computer architecture innovations such as cache partitioning (KPart), software-defined caches (Jenga), and accelerators for sparse computing (Azul, Terminus). He designs systems that enhance performance, energy efficiency, and scalability in modern computing environments. His projects include the Swarm Architecture for ordered parallelism and architectures supporting fully homomorphic encryption. Articles Trends: Recent publications emphasize accelerators for sparse algorithms, cryptographic hardware (e.g., Fully Homomorphic Encryption), and high-performance computing. His work bridges hardware-software co-design to tackle challenges in irregular applications, memory hierarchies, and secure computing. Awards & Grants: No scientific awards explicitly listed, but his work has been recognized in top conferences (e.g., MICRO, ISCA). Advising & Teaching: He advises over 20 Ph.D. and master’s students, including current advisees Axel Feldmann and Hyun Ryong Lee. He teaches courses like 6.5900 Computer System Architecture and 6.191 Computation Structures at MIT. Labs & Teams: Leader of the Computation Structures Group at MIT CSAIL, collaborating with researchers like Joel Emer, Srini Devadas, and Armando Solar-Lezama. His team explores cutting-edge architectures and systems for next-generation computing.
Marco Calderini is a Researcher in the Department of Mathematics at the University of Trento. He holds an academic position focusing on theoretical and applied cryptography, with affiliations spanning multiple departments including the Department of Information Engineering and Computer Science. His teaching responsibilities include courses on Advanced Coding Theory, Applied Cryptography, and Programming Lab, where he emphasizes practical implementations alongside theoretical foundations. Research interests revolve around cryptographic functions (e.g., APN functions, S-box design), algebraic coding theory, and cryptanalysis techniques. He explores topics like parameter selection in homomorphic encryption (BFV scheme), geometric invariants in coding theory, and the security of block ciphers against key-recovery attacks. His work bridges algebraic structures with cryptographic applications, often addressing both theoretical properties and practical implementation challenges. Publications (2011–2025) highlight contributions to APN function analysis, CCZ equivalence, and cryptanalysis methodologies. No scientific awards are explicitly listed in the provided texts. Advising and grant details are not specified here, though his involvement in multiple academic programs suggests active mentorship roles. He collaborates across disciplines, reflecting the interdisciplinary nature of modern cryptography and information security research.
Dr. Juan Fumero Alfonso is a Researcher at the University of Manchester, specializing in high-performance computing and hardware acceleration. He holds a PhD from the University of Edinburgh where he researched GPU acceleration of interpreted programming languages through JIT and runtime optimizations (2013-2017). His research focuses on: Developing runtime systems for heterogeneous hardware (GPUs, accelerators) Optimizing virtual machines and managed languages (Java) Advancing hardware abstraction through frameworks like TornadoVM Exploring cryptographic acceleration and privacy-preserving computation His 23+ publications demonstrate consistent focus on GPU programming, runtime optimization, and hardware abstraction, with recent work exploring RISC-V vectorization and fully homomorphic encryption acceleration. Awards: Best Outstanding Output by Research Staff Award 2022 (University of Manchester) Dr. Fumero contributes to academic communities as an expert member of oneAPI Language/Hardware SIGs, co-chaired the ACM SIGPLAN Dynamic Languages Symposium (2019), and develops the RISC-V ecosystem. He maintains active collaborations across Europe in computer systems research.
Christian Schaffner is a Researcher at the University of Amsterdam and Centrum Wiskunde & Informatica (CWI) in the Algorithms and Complexity department, focusing on quantum cryptography and post-quantum secure protocols. He leads projects like 'Taming Quantum Adversaries' and holds a Veni Grant from NWO (2010). His work emphasizes cryptographic protocols resilient to quantum computing threats, including zero-knowledge proofs, secure multi-party computation, and verifiable delay functions. Key research areas include quantum-resistant cryptographic schemes, homomorphic encryption, and protocols in the quantum random oracle model. Notable contributions include advancements in NIZKs, oblivious transfer in noisy-storage models, and quantum secure computation frameworks. He has authored over 40 publications in top venues like CRYPTO, EUROCRYPT, and Nature Communications. His grants include funding for research on quantum cryptography and homomorphic encryption. Schaffner collaborates with institutions like CWI and presents regularly on quantum computing security at international conferences.
Eunsang Lee serves as an Assistant Professor in the Department of Contents Software at Sejong University since 2022. His academic journey began with a B.S. from Seoul National University in 2014, followed by a Ph.D. from the same institution in 2020. After completing post-doctoral research at Seoul National University (2020-2022), he joined Sejong University's faculty. Lee's research centers on the intersection of cryptography and artificial intelligence, with particular expertise in privacy-preserving machine learning through homomorphic encryption techniques. His work focuses on implementing deep learning models on encrypted data without decryption, addressing critical challenges in computational efficiency and accuracy preservation. The fingerprint analysis of his publications reveals strong concentration in Fully Homomorphic Encryption (100%), Approximation Algorithms (97%), and Privacy-Preserving Machine Learning (86%). His publication trend shows consistent output with 4 papers in 2022 and 2 papers in 2023, primarily appearing in prestigious venues like IEEE Access, IEEE Transactions on Dependable and Secure Computing, and ASIACRYPT. These works demonstrate progressive refinement of techniques for applying homomorphic encryption to neural networks, with particular emphasis on convolutional architectures and efficient polynomial approximations. Lee's research has garnered significant attention with multiple papers receiving substantial Scopus citations (89 for his PMLR 2022 paper, 47 for his IEEE TDSC 2022 paper), indicating strong impact in the field. His work has also been referenced in 1 patent, suggesting practical applications of his theoretical contributions.
Masao Yanagisawa is a Professor at Waseda University's School of Fundamental Science and Engineering, with over 25 years of academic experience since 1998. An IEEE and ACM member, he holds a Doctor of Engineering degree from Waseda University.
Stacey Jeffery serves as Professor of Quantum Information at the University of Amsterdam's Korteweg-de Vries Institute for Mathematics since 2023 and Senior Researcher at Centrum Wiskunde & Informatica (CWI) and QuSoft since 2017. Her foundational work bridges theoretical computer science and quantum information processing, with emphasis on algorithmic frameworks and cryptographic security in quantum systems. Her educational trajectory includes a PhD in Computer Science from the University of Waterloo (2014), where she was affiliated with the Institute for Quantum Computing, following prior research at Caltech as an IQIM Postdoctoral Fellow. Jeffery's research centers on quantum algorithms and quantum cryptography, with pioneering contributions to quantum walk frameworks that enable quadratic speedups for graph problems. She has developed critical frameworks for secure delegation of quantum computation and advanced models for quantum error correction. Her work on multidimensional quantum walks and subroutine composition establishes new paradigms for quantum algorithm design, while investigations into space-time tradeoffs challenge fundamental limits of quantum computation. These theoretical advances directly enable practical quantum cryptographic protocols and error mitigation strategies. Analysis of her 15 most recent publications reveals a cohesive evolution toward unified frameworks for quantum algorithm composition, particularly through quantum walk methodologies. Her 2023-2025 works demonstrate increasing sophistication in error reduction techniques and space-efficient quantum computation, while maintaining strong connections to cryptographic applications like software leasing and multi-party computation. The consistent focus on foundational complexity questions—span programs, query complexity, and graph connectivity—underscores her commitment to advancing theoretical underpinnings of quantum computing. Her scientific recognition includes: NWO WISE Fellowship NWO Veni Grant ERC Starting Grant QDNL Award (for co-founding WIQD) CIFAR Fellow in Quantum Information Science Jeffery leads significant research initiatives including an ERC Starting Grant and NWO-funded projects. Her academic mentorship extends through QuSoft's collaborative environment, though specific advisee names aren't publicly cataloged. Professional service includes co-chairing QIP 2022, chairing TQC's steering committee (2021), and directing the Lorentz Center Informatics Advisory board. She actively shapes community standards through roles in QCrypt and ACM SIGACT. As a core QuSoft researcher and co-founder of WIQD (Women in Quantum Development), Jeffery drives both technical innovation and diversity initiatives. Her laboratory focuses on quantum algorithm frameworks with real-world cryptographic applications, while WIQD—awarded the inaugural QDNL Award—creates vital networking and development opportunities for women across quantum technology sectors. Current efforts target quantum subroutine composition and space-efficient error correction for near-term quantum devices.
Michel Kinsy is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence and Director of the Secure, Trusted, and Assured Microelectronics (STAM) Center. His work bridges hardware security, cryptographic systems, and efficient computing architectures. Education: PhD in Computer Science from Massachusetts Institute of Technology His research focuses on hardware security , including secure architectures, trusted execution environments, quantum-proof cryptography, polymorphous architectures, and zero-trust computing systems. Recent projects explore privacy-preserving machine learning, zero-knowledge proofs, and homomorphic encryption acceleration. Key publication trends include: Hardware security for post-quantum cryptography (2020-2025) Secure distributed systems (2021-2025) Privacy-preserving machine learning implementations (2018-2025) Root-of-trust mechanisms in edge devices (2019-2023) Cryptographic protocol acceleration (2020-2024) Scientific Recognition: MIT Presidential Fellow CRA-WP Inaugural Skip Ellis Career Award He teaches graduate-level courses in computer architecture, research methodology, and thesis/dissertation advising, with recent offerings including CSE 792 Research , EEE 599 Thesis , and CEN 799 Dissertation . As STAM Center director, Kinsy leads initiatives in secure microelectronics and collaborates with hardware/software co-design teams. His research website provides detailed project information: https://stamcenter.asu.edu
