جست‌وجوی استاد و پژوهشگر

Sergey Gorbunovمشاهده پروفایل

دانشیار

• Cryptography
• Network Security
• Blockchain Technology
• +۴ مورد دیگر

Sergey Gorbunov is an Associate Professor in the Department of Computer Science at the University of Waterloo . He holds a Ph.D. from MIT (2015), an M.Sc. and H.B.Sc. from the University of Toronto (2012 and 2011, respectively). His research focuses on Cryptography, Network Security, Blockchain Technology, Secure Protocols, and Privacy-Preserving Systems . He explores advanced cryptographic techniques for decentralized systems, privacy-enhancing technologies, and secure communication protocols. His work includes pioneering contributions to blockchain security (e.g., mitigating front-running attacks, enhancing transaction privacy) and foundational cryptographic tools like homomorphic encryption and multi-signature schemes. Recent publications emphasize resilient consensus mechanisms, anonymous payment channels, and efficient cryptographic primitives for distributed systems. Notable projects include Astrape (anonymous payment channels), Algorand Agreement (fast Byzantine consensus), and StealthDB (encrypted SQL databases). His research bridges theoretical cryptography with practical applications in secure computing and decentralized technologies.

• Cryptography
• Information Security
• Authenticated Encryption
• +۳ مورد دیگر

Assoc Prof Wu Hongjun is an Associate Professor at the Division of Mathematical Sciences, School of Physical & Mathematical Sciences, Nanyang Technological University (NTU). His research focuses on cryptography and information security, with notable contributions to lightweight authenticated encryption algorithms like TinyJAMBU and ACORN, as well as cryptanalysis of stream ciphers (e.g., ZUC, HC-128) and hash functions (e.g., JH, SHA-3 candidates). His academic career includes over 15 years of contributions to cryptographic standards, IoT security frameworks, and secure cloud data management. Key areas of expertise encompass symmetric-key cryptography, algorithm design for resource-constrained devices, and vulnerability analysis of cryptographic primitives. Prof Wu has authored influential papers on authenticated encryption modes (AEGIS, MORUS), lightweight cipher optimizations (ACORN), and cryptanalysis techniques applied to Feistel networks and stream ciphers. His work bridges theoretical cryptography with practical implementations across telecommunications, IoT, and cloud computing domains.

• Cryptography
• Computer Security
• Coding Theory
• +۲ مورد دیگر

Elette Boyle is an Associate Professor at the Efi Arazi School of Computer Science, Reichman University (IDC Herzliya), Israel. She serves as the Director of the FACT Research Center and is a Senior Scientist at NTT Research. Additionally, she heads the RRIS International Program. Her academic career spans prestigious institutions including MIT, Technion, and Cornell. Dr. Boyle received her Ph.D. in Mathematics from MIT under the guidance of Shafi Goldwasser and Yael Tauman Kalai. Following her doctorate, she completed postdoctoral research at the Technion Israel Institute of Technology (2013-2015) hosted by Yuval Ishai, and a short-term postdoc at Cornell University (Summer 2013) hosted by Rafael Pass. She completed her undergraduate studies in mathematics at Caltech. Her research focuses on the theoretical foundations of computer security and cryptography, with particular expertise in secure multiparty computation, function secret sharing, distributed point functions, and memory checking protocols. Her work bridges theoretical computer science with practical cryptographic applications, developing protocols that balance security guarantees with computational efficiency. Dr. Boyle's research has significantly advanced the field of cryptography, particularly in understanding the fundamental limits and possibilities of secure computation protocols. Analysis of her recent publications reveals a strong focus on the theoretical foundations of secure computation, with particular emphasis on understanding computational and communication complexity limits. Her work spans multiple dimensions of cryptography including foundational protocols, complexity analysis, and practical implementations. A recurring theme in her research is developing efficient cryptographic primitives that minimize communication overhead while maintaining strong security guarantees. Her contributions to function secret sharing, distributed point functions, and pseudorandom correlation generators have been particularly influential in the field. Dr. Boyle has advised several graduate students including Pierre Meyer (Ph.D., co-advised with Geoffroy Couteau), Matan Hamilis (Ph.D.), and D'or Banon (MSc., co-advised with Ran Cohen). Her research has been supported by various grants enabling her to lead significant projects in cryptography and secure computation. As Director of the FACT Research Center, she leads a team focused on foundational aspects of computer science and cryptography. Her center collaborates with researchers worldwide and serves as a hub for advancing cryptographic research in Israel and internationally.

Vipul Goyalمشاهده پروفایل

دانشیار

• Cryptography
• Quantum Cryptography
• Security & Privacy
• +۱ مورد دیگر

Vipul Goyal is an Associate Professor at the Computer Science Department of Carnegie Mellon University and a Senior Scientist at NTT Research in California. He is on leave from CMU since joining NTT Research in June 2020 after a 7-year tenure at Microsoft Research. His research focuses on Cryptography , Quantum Cryptography , Security & Privacy , and Theoretical Computer Science , supported by grants from NSF, DARPA, Department of Energy NETL, JP Morgan, Cisco, PNC, Ripple, and CyLab initiatives.

Vincent John Mooney IIIمشاهده پروفایل

دانشیار

• Hardware-Software Co-Design
• Cyber Physical Systems Security
• System Level Design
• +۷ مورد دیگر

Vincent John Mooney III is an Associate Professor at the School of Electrical and Computer Engineering and an Adjunct Associate Professor at the School of Computer Science, Georgia Institute of Technology. His research focuses on Hardware-Software Co-Design , Cyber Physical Systems Security , and Low-Power Architectures . He has authored numerous publications on topics such as probabilistic computing, hardware security, and embedded systems design. Dr. Mooney has received prestigious awards including the NSF Career Award , National Semiconductor Fellowship , and ARCS Best Paper Award . Education: Ph.D. in Electrical Engineering (1998), Stanford University MA in Philosophy (1997), Stanford University MS in Electrical Engineering (1994), Stanford University Certificate of Graduate Study (1992), University of Navarra BS in Electrical Engineering and Computer Science (1991), Yale University Research interests span hardware/software codesign, cybersecurity in embedded systems, and synthesis of reconfigurable architectures. His recent work includes Gridtrust for decentralized supply chain cybersecurity and COPPER for computation obfuscation. Dr. Mooney has supervised numerous Ph.D. students and held leadership roles in conferences such as HOST and CASES . Scientific awards include NSF Career Award (2000) National Semiconductor Fellowship (1997-1998) AT&T Engineering Scholarship Program (1987-1991) NCAA Postgraduate Scholar (1991) Senior Member, IEEE (2003) ARCS 2012 Best Paper Award Advising and grants highlight his mentorship of students like Jun Cheol Park and Yudong Tan , along with grants such as the U.S. Air Force Summer Faculty Fellowship (2007). He leads the Hardware/Software Codesign for Security Group at Georgia Tech and has contributed to advancements in real-time operating systems and deadlock detection algorithms.

Anna Lysyanskayaمشاهده پروفایل

استاد

• Cryptography
• Privacy-Preserving Protocols
• Secure Communication
• +۳ مورد دیگر

Anna Lysyanskaya is the James A. and Julie N. Brown Professor of Computer Science at Brown University. She joined Brown in 2002 after earning her Ph.D. from MIT. Her research focuses on cryptography, particularly privacy-preserving protocols and anonymous credentials. She has received prestigious awards including the NSF Career Award, Sloan Foundation Fellowship, and Google/IBM Faculty Fellowships. Her work emphasizes secure communication systems and cryptographic foundations for privacy. Education: Ph.D. in Computer Science from MIT. Research interests include cryptographic protocols, anonymity, and secure authentication. She has authored over 140 publications and contributed to projects like PACIFIC for privacy-preserving contact tracing and Bruisable Onions for anonymous communication. Her grants support advancements in cryptographic security and privacy-enhancing technologies. Awards include the NSF Career Award, Sloan Fellowship, and multiple industry recognitions. She collaborates widely and advises on cryptographic standards for digital identity and secure computation.

Lisa Willsمشاهده پروفایل

استادیار

• Computer Architecture
• Hardware Acceleration
• Hardware-Software Co-Design
• +۵ مورد دیگر

Lisa Wills serves as Assistant Professor of Computer Science at Duke University's Trinity College of Arts & Sciences and holds a joint appointment in Electrical and Computer Engineering at the Pratt School of Engineering since 2019. Her research bridges computer architecture and domain-specific applications, with a focus on hardware acceleration for computationally intensive fields. Dr. Wills earned her Ph.D. from Columbia University in 2014. Her academic journey reflects a deep commitment to advancing hardware-software co-design methodologies for real-world computational challenges. Her research centers on developing efficient hardware accelerators for big data analytics, particularly in genomics, graph processing, and database systems. She pioneers frameworks that simplify accelerator deployment while tackling critical bottlenecks in genomic data analysis, protein structure prediction, and privacy-preserving computing. Current work focuses on hardware-aware machine learning systems and energy-efficient architectures for emerging AI applications. Analysis of her publication record reveals a clear trajectory: from foundational work in database processing units (2014-2016) to specialized genomic accelerators (2019-2021), then evolving toward ML-enhanced design automation (2022-2023) and cutting-edge architectural abstractions (2024-2025). Her research consistently targets the intersection of hardware efficiency and domain-specific computational demands, with increasing emphasis on AI/ML workloads. Google ML and Systems Junior Faculty Award (2025) Dr. Wills actively mentors doctoral students including Chris Kjellqvist (lead architect of Beethoven accelerator framework), Mason Ma (PyTFHE FHE framework), and Mansi Choudhary (COCOSSim accelerator simulator). Her research is supported by significant grants including the NSF AI Institute: Athena ($20M, 2021-2027), Meta-funded ProSE accelerator project (2023-2026), and NSF CAREER award (2021-2026), totaling over $25M in active funding. She directs the APEX Lab (Application-driven Programmable Efficient Accelerated Systems), which develops open-source frameworks like Beethoven for FPGA/ASIC accelerator deployment and focuses on lowering barriers for non-hardware researchers to leverage custom acceleration in genomics, AI, and big data applications.

Lisa Wu Willsمشاهده پروفایل

استادیار

• Computer Architecture
• Hardware-Software Co-design
• Hardware Accelerators
• +۶ مورد دیگر

Lisa Wu Wills is an Assistant Professor in the Department of Computer Science and Electrical and Computer Engineering at Duke University, leading the APEX Lab (Application-driven Programmable Efficient Accelerated Systems Lab). Her research focuses on hardware acceleration for big data analytics in genomics, graphs, and databases to advance healthcare and natural sciences. Education: Ph.D. in Computer Science, Columbia University, 2014 Research Interests: Dr. Wills pioneers computer architecture and hardware-software co-design to create efficient accelerators for emerging applications. Her work targets genomics , graph analytics , and database systems , emphasizing simplified hardware deployment and energy efficiency for scientific breakthroughs in healthcare and AI. Publication Trends: Her 2022-2025 publications reveal a strong focus on open-source frameworks (Beethoven, PyTFHE) for accelerator development, hardware acceleration in privacy-preserving computing, and optimization for large language models. Key themes include transfer learning for EDA, domain-specific architectures for genomics, and energy-efficient image processing. Scientific Awards: Google ML and Systems Junior Faculty Award (2025) Advising and Grants: Dr. Wills mentors three PhD students: Chris Kjellqvist (Beethoven framework architect), Mason Ma (PyTFHE lead for FHE applications), and Mansi Choudhary (COCOSSim simulator creator). Her 2025 Google award funds research on accelerating vector databases and retrieval-augmented generation for LLMs. Labs and Teams: She directs the APEX Lab at Duke, developing tools like Beethoven (open-source accelerator composer) and PyTFHE for hardware-software integration, enabling domain scientists to leverage custom acceleration with minimal hardware expertise.

• Trustworthy Machine Learning
• Algorithmic Fairness
• Data Privacy
• +۷ مورد دیگر

Hui Wang is a Professor and Associate Chair for PhD Studies and Research in the Department of Computer Science at the Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology. She also serves as the Director of the Data Science PhD Program and holds leadership roles in multiple institutional committees, including the Doctoral Committee, Faculty Mentoring Program, and Strategic Planning initiatives at both departmental and university levels. Research Interests: Dr. Wang's research focuses on building trustworthy machine learning systems by integrating privacy, fairness, and accountability . Her work aims to fortify ML models against privacy attacks, eliminate algorithmic biases, and ensure auditable decision-making. She explores intersections between machine learning, data mining, and cybersecurity, with applications across domains requiring ethical and secure AI deployment. Recent Research Trends: Her recent publications and funded projects reflect a strong emphasis on privacy-preserving machine learning , fairness-aware systems , and verifiable computing . Themes include securing graph embeddings, federated learning with fairness guarantees, and audit mechanisms for black-box models. Supported by NSF, Cisco, and Google, her work bridges theoretical rigor with practical system design. Scientific Awards: NSF CAREER Award, 2014 Advising and Grants: Dr. Wang actively mentors PhD students and hosts visiting scholars. She leads multiple NSF-funded projects, including Securing Network Embedding against Privacy Attacks and Privacy for All: Ensuring Fair Privacy Protection in Machine Learning . Her research is supported by substantial grants from the National Science Foundation, Cisco, and Google, reflecting her leadership in trustworthy AI. Labs and Teams: While not explicitly named, Dr. Wang leads a research group focused on trustworthy machine learning, advising students and collaborating with industry partners. She is deeply integrated into the Data Science PhD program and CS faculty leadership, shaping research and academic strategy at Stevens.

Brett Hemenway Falkمشاهده پروفایل

پژوهشگر

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

Brett Hemenway Falk is a Researcher in the Department of Computer and Information Science at the University of Pennsylvania. He serves as director of the Crypto and Society Lab, focusing on privacy and security in digital environments and facilitating transparency and trust. His work combines rigorous mathematical approaches with practical implementations in cryptography and blockchain technology. Education: Sc.B. in Mathematics from Brown University Ph.D. in Mathematics from UCLA Research Interests include: Cryptography and secure multi-party computation protocols Blockchain technology (cross-chain interoperability, financial network stability, on-chain governance) Privacy-preserving algorithms and data security Coding theory applications in secure systems Scientific Contributions include: Developing practical secure computation protocols Advancing ORAM (Oblivious RAM) architectures Analyzing decentralized governance mechanisms Exploring DeFi network stability and token economics His teaching activities feature the popular MCIT 582 Blockchain course at Penn. Research funding comes from NSF, DARPA, IARPA, ONR, ARL, NIH , and the Laura and John Arnold Foundation.

Aayush Jainمشاهده پروفایل

استادیار

• Cryptography
• Theoretical Computer Science
• Applied Cryptography
• +۳ مورد دیگر

Aayush Jain is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. Previously, he was a Postdoctoral Fellow at NTT Research and a PhD student at UCLA, advised by Professor Amit Sahai. His work bridges theoretical and applied cryptography with core computer science principles. Education PhD in Computer Science, University of California, Los Angeles (UCLA) Postdoctoral Fellowship, NTT Research Research Focus His research explores: foundational cryptography, indistinguishability obfuscation, functional encryption, lattice-based cryptography, secure multi-party computation, and post-quantum security. Work emphasizes rigorous theoretical frameworks with practical implications. Publication Trends Recent articles (2021-2024) demonstrate consistent focus on cryptographic primitives, obfuscation techniques, and security reductions. Dominant venues include CRYPTO, EUROCRYPT, FOCS, and STOC with emerging work in machine learning interfaces. Awards Best Paper Award at STOC 2021 for foundational contributions to indistinguishability obfuscation Advising and Collaboration Current PhD advisees: Alper Cakan, Quang Dao (co-advised), Sagnik Saha, Noah Singer (co-advised). Mentored postdocs: Mitali Bafna (2022-2023) and Rex Fernando (2022-2023). Teaches graduate courses in cryptography and theoretical tools. Leadership Leads the CMU Cryptography research group; organized the CMU Cryptography Workshop. Program committee member for FOCS, TCC, ITCS, and ICALP.

Rongxing Luمشاهده پروفایل

استاد مدعو

• Applied Cryptography
• Privacy Enhancing Technologies
• IoT Security
• +۴ مورد دیگر

Rongxing Lu is an Adjunct Professor at the Faculty of Computer Science, University of New Brunswick (UNB), Canada, since August 2016. Previously, he held positions at Nanyang Technological University (NTU), Singapore (2012–2016) and the University of Waterloo, Canada (PhD in 2012). His research focuses on applied cryptography, privacy enhancing technologies, and IoT-big data security. He has over 7,500 citations and received prestigious awards like the Governor General’s Gold Medal (2012) and the IEEE ComSoc Asia Pacific Outstanding Young Researcher Award (2013). He is an IEEE senior member and serves on editorial boards of journals like IEEE Network. **Education**: PhD in Electrical & Computer Engineering, University of Waterloo (2012), awarded Governor General’s Gold Medal Postdoctoral Fellow at University of Waterloo (2012–2013) **Research Interests**: Developing cryptographic protocols for IoT and big data systems Privacy-preserving techniques for distributed systems Secure communication in 5G/6G networks and vehicular systems **Awards and Recognition**: Recipient of multiple best paper awards in IEEE conferences 2016–2017 Excellence in Teaching Award at UNB **Editorial and Leadership Roles**: Symposium co-chair at IEEE Globecom’16 Secretary of IEEE ComSoc CIS-TC Organized special issues on fog computing security (Elsevier) and big data security (IEEE IoT Journal) **Key Contributions**: Pioneered privacy-aware data reporting schemes for vehicular networks Designed lightweight IoT authentication protocols Advanced secure machine learning frameworks with privacy guarantees

Murali Maniمشاهده پروفایل

استاد

• Database Systems
• Data Provenance
• Generative AI
• +۱۲ مورد دیگر

Murali Mani is a Professor in the Department of Computer Science, Engineering, and Physics at the College of Innovation and Technology, University of Michigan-Flint. He is actively involved in teaching courses such as Database Design (CSC 384, CSC 584) and Independent Graduate Study in Computer Science (CSC 591), and serves as Principal Investigator on multiple research grants focused on computing education and data science. His research interests span database systems, data provenance, generative AI for data augmentation, computing education, and the societal impact of technology . He has developed educational tools including epidemiology calculators and market basket analysis modules to support interdisciplinary learning. His work emphasizes integrating computing skills across disciplines such as health sciences and management. The 15 most recent scholarly contributions reflect a strong focus on data management, AI-augmented data curation, educational technology, and the cognitive aspects of learning programming. These publications appear in venues such as VLDB, IEEE FIE, and ACM conferences, with several under review or in preparation for top-tier journals like Communications of the ACM and the VLDB Journal. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: Murali Mani actively mentors students through independent graduate studies and collaborative research projects. He has secured funding from the National Science Foundation (SGER grant on provenance metadata) and internal university sources, including the CIT/CHS Joint Grant and the Office of Research at UM-Flint, supporting projects on civic literacy, computational skills integration, and AI for social science data archiving. Labs and Teams: While no formal lab name is mentioned, Murali Mani leads a research group focused on data systems and computing education, collaborating with colleagues across departments and institutions. He contributes to initiatives such as the Michigan Institute for Data & AI in Society (MIDAS) and the Academic Data Science Alliance (ADSA), and has presented at conferences including IASSIST, FIE, and ICCTAC.

Vinod Vaikuntanathanمشاهده پروفایل

استاد

• Cryptography
• Fully Homomorphic Encryption
• Lattice-Based Cryptography
• +۴ مورد دیگر

Vinod Vaikuntanathan is the Ford Foundation Professor of Engineering in the MIT EECS department and a principal investigator at MIT CSAIL. He holds a BTech from IIT Madras (2003), and SM/PhD degrees from MIT (2005/2009). His research focuses on cryptography, particularly fully homomorphic encryption (FHE), lattice-based cryptography, and quantum-resistant systems. He co-founded Duality Technologies as Chief Cryptographer. **Education:** BTech in Computer Science (2003), Indian Institute of Technology Madras SM in Electrical Engineering & Computer Science (2005), MIT PhD in Computer Science (2009), MIT **Research Interests:** His work spans FHE (enabling computations on encrypted data), lattice-based cryptography (post-quantum security), and intersections with quantum computing, machine learning, and privacy. He explores applications in secure computation, algorithm design, and cryptographic protocols. **Awards:** Recipient of the Gödel Prize (2022), Simons Investigator (2023), and MacVicar Faculty Fellow (2024). His work on FHE and lattice algorithms has earned widespread acclaim in cryptography and theoretical computer science. **Teaching & Mentorship:** Advanced cryptography courses at MIT (e.g., 6.5630, 6.876J) Advised PhD students (e.g., Sergey Gorbunov, Tianren Liu) and postdocs (e.g., Nir Bitansky, Mark Zhandry) now leading positions in academia and industry **Collaborations:** Organizer of the Charles River Crypto Day and MIT Cryptography Seminar Principal investigator on grants from NSF, DARPA, and Microsoft

Nathan Fiskمشاهده پروفایل

استادیار

• Cybersecurity Education
• Online Safety
• Behavioral Cybersecurity
• +۷ مورد دیگر

Nathan Fisk is an Assistant Professor at the University of South Florida College of Education , specializing in cybersecurity education . His research bridges educational theory with cybersecurity practice, focusing on behavioral aspects, policy analysis, and socio-technical systems. Key research themes include: Understanding user motivations for adopting cybersecurity practices Designing privacy-preserving data collection methods Examining economic stressors' impact on online scam susceptibility Critiquing digital inequality in medical fundraising platforms His recent work explores AI-driven cybersecurity awareness campaigns and anti-oppressive pedagogies in online learning environments. Scientific Awards : EAGER grant (2021) for SaTC AI-Cybersecurity research Contact: fisk@usf.edu