جست‌وجوی استاد و پژوهشگر

Nils Fleischhackerمشاهده پروفایل

استادیار

• Cryptography
• Theoretical Computer Science
• Zero-Knowledge Proofs
• +۵ مورد دیگر

Nils Fleischhacker is an Assistant Professor for Cryptography at Ruhr University Bochum, where he leads the Theoretical Cryptography group within the Faculty of Computer Science. His research focuses on foundational aspects of cryptography with particular emphasis on zero-knowledge proofs, digital signatures, and secure computation. Prior to joining Ruhr University, he held postdoctoral positions at Johns Hopkins University and Carnegie Mellon University. Dr. Fleischhacker received his PhD in Computer Science from Saarland University in February 2017, advised by Dominique Schröder. During his doctoral studies, he was a research intern at Microsoft Research with Chris Brzuska and a research visitor at the University of Maryland, College Park with Jonathan Katz and Dana Dachman-Soled. His research spans theoretical cryptography with significant contributions to zero-knowledge proofs, multi-signature schemes, property-preserving hash functions, and security proofs for cryptographic primitives. Fleischhacker's work often addresses fundamental questions about the limits and possibilities of cryptographic constructions, with particular attention to black-box separations and lower bounds. His recent publications demonstrate a strong focus on lattice-based cryptography and post-quantum secure protocols. Fleischhacker's publications over the past five years reveal a consistent research trajectory focused on improving the efficiency and security of cryptographic primitives, with particular emphasis on signature schemes, zero-knowledge protocols, and secure computation frameworks. His work frequently appears in top-tier cryptography venues including CRYPTO, EUROCRYPT, and CCS. Dr. Fleischhacker actively mentors PhD students, with numerous successful graduations spanning from 2010 to 2024. His former PhD students include Önder Askin (2024), Floyd Zweydinger (2023), and Lars Schlieper (2022), among others. His research is supported through various academic channels and collaborations. He is affiliated with several prominent research organizations including the Horst Görtz Institute (HGI) for Information Security, the DFG Excellence Cluster CASA, the EU Marie Curie Network QSI, and the International Association for Cryptologic Research (IACR). These affiliations provide a strong institutional framework for his theoretical cryptography research.

• Secure Computing
• Hardware/Software Co-Design
• Machine Learning
• +۶ مورد دیگر

Farinaz Koushanfar is a Professor in the Department of Electrical and Computer Engineering at the Jacobs School of Engineering, University of California San Diego (UCSD) . She holds the Siavouche Nemat-Nasser Endowed Chair and serves as Founding Co-Director of the Center for Machine-Intelligence, Computing and Security . Her affiliations include NSF Trust-Hub (Co-PI) and NSF TILOS AI Institute . She also serves on the Editorial Board of The Proceedings of the IEEE . Research Focus: Prof. Koushanfar leads research in secure and efficient computing , including robust/safe AI , hardware/system security , AI-based optimization , and cryptographically secure privacy-preserving computing . Her work pioneered logic obfuscation/locking for chip security, automated co-design of AI systems , watermarking/tracing of deep learning models , and physical proofs of provenance . She explores co-design with cryptographic constructs for privacy preservation and manages nonlinearities in ciphertext domains. Article Trends: Recent publications show expertise in neural watermarking (deepfakes, media authentication), zero-knowledge proof frameworks , Trojan attack defenses in ML models, secure federated learning , and hardware acceleration of cryptographic protocols . Her work combines machine learning , cryptography , and physical design security across 2022-2025 publications. Scientific Awards: Fellow of ACM Fellow of IEEE Fellow of National Academy of Inventors (NAI) Fellow of Kavli Foundation of NAS Inducted to NAI 2024 Fellows Advising & Leadership: She has advised multiple PhD students who became faculty at top universities (e.g., Stanford, Purdue). She chairs conferences like ACM WiSec 2024 and co-led the NSF SaTC decadal review. Her lab ( ACES Lab ) produces award-winning graduates like Bita Rouhani (DAC Under-40 Innovators) and Shehzeen Hussain (UCSD Best Dissertation Award).

Henry Corrigan-Gibbsمشاهده پروفایل

استادیار

• Computer Security
• Cryptography
• Computer Systems
• +۵ مورد دیگر

Henry Corrigan-Gibbs is an Assistant Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science. He is affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and collaborates with the PDOS and CSS research groups. Henry co-hosts the MIT Security Seminar series. Education : PhD in Computer Science from Stanford University (advisor: Dan Boneh), Postdoc at École polytechnique fédérale de Lausanne (EPFL) (host: Bryan Ford), and B.S. in Computer Science from Yale University . Research Interests Henry focuses on computer security , cryptography , and systems research , building technologies that: Empower end users through privacy-preserving design Implement strong cryptographic security in real-world deployments Scale to millions of users while maintaining security Key projects include: Tiptoe for private web search Prio for privacy-preserving aggregate statistics used by Apple and Google Express for metadata-hiding communication SafetyPin and True2F for secure authentication Scientific Contributions Standards Influence : IETF and NIST standards recommend his private-aggregation systems Industry Impact : Deployed in Apple iOS , Google Android , and Mozilla Firefox Non-profit Deployment : Co-founder of Divvi Up for real-world Prio implementations Academic Recognition 2023 MIT EECS Jerome Saltzer Award for teaching excellence 2020 ACM Doctoral Dissertation Honorable Mention 2016 Caspar Bowden Award for PET research 2015 IEEE Security & Privacy Distinguished Paper Award Multiple IACR Best Young Researcher Paper Awards (2015–2018) Advising and Collaborations Current advisees include: PhD students: Alexandra Henzinger , Ryan Lehmkuhl Postdoc: Emma Dauterman Collaborates with industry (Apple, Google, Mozilla) and standards bodies (IETF, NIST) to translate research into practice.

• Theoretical Computer Science
• Machine Learning
• Algorithms
• +۴ مورد دیگر

Nicholas J. A. Harvey is a Professor of Computer Science at the University of British Columbia. His research spans multiple areas of theoretical computer science with significant contributions to algorithms, machine learning theory, and network coding. Harvey's research focuses on the theoretical foundations of computer science with particular emphasis on: Theory and analysis of randomized algorithms Machine learning theory, including online learning and neural networks Network coding and information theory Combinatorial optimization and graph algorithms High-dimensional probability and statistics His work combines deep mathematical insights with practical applications in computer systems and networking. Analysis of Harvey's recent publications (2022-2025) reveals a strong focus on theoretical aspects of machine learning and optimization. His work spans continuous prediction with experts' advice, tight analyses of optimization algorithms like subgradient descent, and fundamental questions in randomized algorithms. A significant portion of his recent research addresses challenges in high-dimensional statistics, privacy-preserving estimation, and graph neural networks. His publications demonstrate a consistent pattern of addressing foundational theoretical questions with implications for practical machine learning systems. Harvey has maintained a strong publication record with an h-index of 35 and over 6,000 citations according to Google Scholar metrics. His influential papers include 'SkipNet: A Scalable Overlay Network with Practical Locality Properties' (2003), 'On the capacity of information networks' (2006), and 'Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks' (2019). Harvey has supervised numerous graduate students and postdoctoral researchers, contributing to the development of the next generation of theoretical computer scientists. His research has been supported by various grants from funding agencies including the Natural Sciences and Engineering Research Council of Canada (NSERC) and collaborations with industry partners. While specific details about laboratory facilities are not available in the provided information, Professor Harvey appears to be part of the theoretical computer science research group at the University of British Columbia, collaborating with researchers across multiple institutions on fundamental problems in algorithms and machine learning theory.

Edoardo S. Biagioniمشاهده پروفایل

دانشیار

• Networking
• Wireless Networking
• Network Security
• +۹ مورد دیگر

Edoardo S. Biagioni is an Associate Professor at the University of Hawaii in the Department of Information and Computer Sciences , where he has been since 1997. His primary research focuses on decentralized networking , wireless security , and systems programming , with long-term projects including the Allnet project (since 2011) and earlier PODS micro-sensor networks (2000-2003). Current research since 2024 includes formal verification of file systems and correctness properties in systems programming Technical interests span protocol decomposition , language-based OS design , and congestion control Teaching emphasizes Java fluency , data structures , and algorithm complexity in courses like ICS 211 Prior roles included directing the Advanced Network Computing Laboratory (1997-2005) and contributions to the Fedora Core project. Students under his supervision have worked on projects ranging from network measurement to ATM transport protocols . His work bridges theoretical concerns like IP fragmentation theory with practical implementations in SML database systems and IPv6 transitions .

Prashant Nalini Vasudevanمشاهده پروفایل

استادیار

• Cryptography
• Complexity Theory
• Theoretical Computer Science
• +۲ مورد دیگر

Prashant Nalini Vasudevan serves as an Assistant Professor and NUS Presidential Young Professor in the Department of Computer Science at the National University of Singapore's Faculty of Computing. His research focuses on the theoretical foundations of cryptography and complexity theory, with particular emphasis on identifying computational hardness assumptions that can support cryptographic constructions. Dr. Vasudevan's research interests span Cryptography , Complexity Theory , and Theoretical Computer Science , with specific focus on statistical zero-knowledge proofs, fine-grained cryptographic primitives, and the intersection of cryptography with data privacy regulations. His work explores how computational hardness can be leveraged to build secure systems while addressing modern privacy concerns such as the right to be forgotten. Analysis of his recent publications reveals a strong trend toward statistical zero-knowledge systems, batch verification techniques, and formal approaches to data deletion. His research bridges theoretical computer science with practical privacy concerns, particularly in the context of machine learning systems and regulatory compliance. The work demonstrates increasing sophistication in cryptographic protocols while maintaining rigorous security proofs. NUS Presidential Young Professorship (2021-2026): Computational Hardness Assumptions and the Foundations of Cryptography NRF Fellowship (2022-2027): Fine-Grained Cryptography Dr. Vasudevan actively mentors multiple PhD students including Haoxing Lin, Yunqi Li, and Kel Zin Tan, along with postdoctoral researcher Rohit Chatterjee. His research group focuses on theoretical cryptography with applications to privacy-preserving systems. Current projects include developing new cryptographic primitives based on fine-grained hardness assumptions and exploring the theoretical foundations of data deletion in machine learning contexts.

• Cryptography
• Side-channel Analysis
• Deep Learning Security
• +۵ مورد دیگر

Stjepan Picek serves as an Associate Professor at Delft University of Technology in the Intelligent Systems school and maintains a secondary affiliation with Radboud University's Digital Security Group in Nijmegen, The Netherlands. His research bridges cryptographic theory with practical machine learning applications, particularly focusing on security vulnerabilities in modern computing systems. Dr. Picek's research spans multiple critical areas in cybersecurity, with particular emphasis on deep learning-based side-channel analysis where he has published extensively on improving attack methodologies and countermeasures. His work on cryptographic Boolean functions explores evolutionary computation approaches to design functions with optimal cryptographic properties. More recently, his research has expanded to AI security , investigating backdoor attacks, membership inference vulnerabilities, and covert channels in large language models. Analysis of his recent publications reveals a clear trajectory toward integrating traditional cryptographic techniques with modern machine learning paradigms. His work demonstrates consistent innovation across three main axes: (1) advancing side-channel analysis through deep learning innovations, (2) developing evolutionary methods for cryptographic primitive design, and (3) identifying and mitigating security vulnerabilities in emerging AI systems. This multidisciplinary approach positions his research at the critical intersection of theoretical cryptography and practical security implementation. Dr. Picek maintains extensive collaborations with researchers across Europe, particularly with Domagoj Jakobovic (104 co-publications), Luca Mariot (56), Lejla Batina (56), and Guilherme Perin (56), indicating a well-established research network and leadership in his field. His publication record shows remarkable productivity with over 380 indexed publications, including 63 in 2023, 53 in 2024, and 24 in 2025, demonstrating sustained research momentum.

George Barmpaliasمشاهده پروفایل

استاد

• Mathematical Logic
• Computability Theory
• Algorithmic Randomness
• +۳ مورد دیگر

George Barmpalias is a Professor at the Chinese Academy of Sciences (Institute of Software) , with a research focus on Mathematical Logic , Computability Theory , Algorithmic Randomness , and Kolmogorov Complexity . His work also extends to Network Science and Social Dynamics through Schelling segregation models. Ph.D. in Mathematics, University of Leeds (2004) Mathematics Degree, University of Thessaloniki (2000) Research Interests include: Algorithmic Randomness (Ω numbers, K-triviality, Martin-Löf randomness) Computability Theory (Turing degrees, reducibilities, effective closed sets) Network Dynamics (Schelling models, segregation tipping points, idemetric properties) Information Theory (Kolmogorov complexity, data compression, oracle use bounds) Scientific Awards : Distinguished Research Professor (CAS, 2022) National Natural Science grants (2015-2020) International Young Scientist Award (CAS, 2012) EPSRC Research Fellowship (2004) Teaching spans graduate courses in: Theory of Computation Network Science Mathematical Logic Computability and Randomness Supervision includes Ph.D./MSc students like Fang Nan, Cheng Shichao, and Charlotte Vlek. His software projects include high-performance C++ simulations for Schelling models and open-source tools for algorithmic experiments.

• Computer Security
• Machine Learning
• Hardware Design
• +۶ مورد دیگر

Farinaz Koushanfar is a Professor at the University of California, San Diego (UCSD), with a former affiliation at the University of California, Berkeley. Her research focuses on advancing security, machine learning, and hardware design through interdisciplinary approaches. Key areas include adversarial defense mechanisms, cryptographic systems, federated learning, and zero-knowledge proofs. She has collaborated extensively with institutions and researchers globally, contributing to over 360 publications. Her work emphasizes practical security solutions, such as watermarking for intellectual property protection and methods to counteract adversarial attacks in neural networks. Recent trends in her publications highlight innovations in cache compression, robust watermarking for large language models, and securing wireless communication systems against modality-agnostic attacks. Collaborations with industry and academia underscore her commitment to real-world applications of theoretical advancements. Awards and grants are not explicitly listed here, but her prolific publication record and leadership in high-impact projects indicate significant recognition in her field. Advising and mentoring students and junior researchers are central to her academic contributions, though specific student names are not detailed in the provided text. Her lab’s work often intersects with emerging technologies like blockchain, edge computing, and privacy-preserving machine learning.

Filip Turobośمشاهده پروفایل

استادیار

• Mathematical Analysis
• Automotive Safety Engineering
• Transportation Systems
• +۳ مورد دیگر

Filip Turoboś is an Assistant Professor at the Division of Insurance and Capital Markets , Lodz University of Technology . His academic profile indicates active research engagement without mention of part-time status, retirement, or former institutional affiliations. Research Focus: Dr. Turoboś demonstrates interdisciplinary expertise spanning: Mathematical Analysis : Including semimetric spaces, fixed-point theorems, and topological extensions Automotive Safety Engineering : Specializing in precrash velocity modeling using machine learning and optimization techniques Urban Mobility & Transport : Analyzing behavioral patterns during crises (e.g., pandemics) and regulatory impacts Emerging Technologies : Blockchain applications in security and ontological ecosystems Publication Trends (2020-2024): Recent works cluster into three dominant themes: Mathematical foundations of generalized metric spaces and functional analysis (30% of publications) Data-driven transportation studies focusing on pandemic-related behavioral shifts and urban policy effects (40%) Automotive crash reconstruction using AI/statistical methods and blockchain cryptography (30%)

• Artificial Intelligence
• Machine Learning
• Probabilistic Inferencing
• +۵ مورد دیگر

Barry Drake serves as an Adjunct Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he is also affiliated with the Faculty of Engineering and Information Technology and the Data Science Institute. Additionally, he holds the position of Co-Chief Scientist at the Digital Health Cooperative Research Centre and participates in government advisory committees. His career bridges academic research and industry application, with significant experience in translating research into practical technology solutions for business and government sectors. Drake earned his PhD in Computer Science from UNSW Australia (1998-2003) and a BSc with First Class Honors in Computer Science from UTS (1991-1996). His educational background is complemented by a Certificate of Proficiency for Radio Fitter/Mechanic Electronic Systems from the Royal Australian Navy. His research focuses on algorithms and software for practical smart systems, with particular expertise in applications of AI and machine learning, probabilistic inferencing and knowledge compilation, learning probabilistic models, and fast-efficient near-neighbor searching in ultra-high dimensional spaces. His work demonstrates a clear trajectory toward health informatics applications , especially in health systems and delivery of health services, where he has made significant contributions through projects like the Lumos statewide linkage programme. The analysis of his publication record reveals a consistent pattern of research bridging theoretical computer science with practical healthcare applications. His recent work shows increasing focus on health data integration, patient journey modeling, and privacy-preserving synthetic data generation for healthcare applications, while maintaining his foundational expertise in probabilistic models and efficient search algorithms. Inventor on 20 filed patents ORCID identifier: 0000-0003-0572-9936 Drake actively supervises Masters and PhD students according to his profile, and his funded research portfolio includes multiple grants from the Digital Health CRC and NSW Health. His industry experience, particularly his 13 years at Canon Information Systems Research Australia where he served as Senior Principal Engineer and lead researcher for machine learning, informs his approach to technology research methods from a commercial perspective. His current projects focus on the impact of integrated care in New South Wales, synthetic data generation, and patient journey modeling. As Co-Chief Scientist at the Digital Health CRC, Drake contributes to a major national initiative focused on digital health innovation. His work with the Lumos programme has created Australia's first statewide linked data asset across primary care and other health settings, providing unique insights about cross-setting healthcare utilization. This initiative represents a significant contribution to health data infrastructure in Australia.