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Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, affiliated with multiple research centers including the Berkeley Artificial Intelligence Research Lab (BAIR) and the Berkeley Center for Responsible, Decentralized Intelligence (RDI). She holds leadership roles in AI safety, cybersecurity, and blockchain research. Her work bridges machine learning, security, and distributed systems, with notable contributions to formal verification, privacy-preserving technologies, and AI governance. Education: Ph.D. (2002) and M.S. (1999) in Computer Science from UC Berkeley and Carnegie Mellon University, respectively. She previously served as an Assistant Professor at Carnegie Mellon University before joining UC Berkeley in 2007. Research Interests: Dawn's work focuses on AI safety, cybersecurity, and the ethical implications of frontier AI. She explores topics such as large language model vulnerabilities, zero-knowledge proofs, blockchain security, and policy frameworks for AI governance. Her research combines theoretical rigor with practical applications, addressing challenges in secure systems, adversarial machine learning, and privacy-preserving computation. Publications: Her recent work includes groundbreaking studies on LLM memorization, smart contract decompilation, and AI agent cybersecurity evaluation. She also leads initiatives like the Singapore Consensus and California Report on AI safety priorities, emphasizing global collaboration in risk mitigation. Notable Awards: MacArthur Fellowship (2010), ACM Fellow (2019), IEEE Fellow (2019) Advising & Grants: She advises projects on AI policy and cybersecurity, securing grants from NSF, DARPA, and industry collaborations. Her lab develops tools like CyberGym for evaluating AI agents and zkPyTorch for secure machine learning. Labs & Teams: Her research groups at UC Berkeley focus on cutting-edge projects in AI safety, blockchain, and cybersecurity, collaborating with industry leaders and policymakers to advance both technical and ethical standards.

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +7 more

Abhishek Jain is an Associate Professor in the Department of Computer Science at Johns Hopkins University and a Senior Scientist at NTT Research. He is affiliated with the Data Science and AI Institute, the Information Security Institute, and the Algorithms and Complexity group. University: Johns Hopkins University Department: Computer Science Academic Rank: Associate Professor Institutional Roles: Co-director of the Advanced Research in Cryptography group, member of the Theory Group, and co-leader of the Cryptography Group Education: Ph.D. in Computer Science from the University of California Los Angeles (2012), advised by Amit Sahai and Rafail Ostrovsky. Recipient of the Symantec Outstanding Graduate Student Award during his Ph.D. Research Interests Cryptography Secure computation Proof systems Program obfuscation Privacy Blockchain Theoretical computer science Research Trends: His recent publications emphasize cryptographic protocols for homomorphic encryption, zero-knowledge proofs, and secure multi-party computation, alongside applications in blockchain and privacy-preserving systems. Key themes include scalability, verifiable evaluation, and adapting cryptographic techniques to dynamic and distributed environments. Scientific Awards 2020 NSF CAREER Award Best Paper Awards at Eurocrypt and the International Conference on the Theory and Applications of Cryptographic Techniques Advising & Grants: Mentors Ph.D. students and interns/postdocs at NTT Research. Research funded by NSF, DARPA, JP Morgan, Ethereum Foundation, Stellar, Cisco, Samsung, and JHU Catalyst awards. Labs & Teams: Co-leads the Cryptography Group at Johns Hopkins, participates in the DC Area Crypto Day, and organizes the weekly Theory Seminar. Collaborates with institutions including MIT CSAIL, BUSEC, and Microsoft Research New England.

• Cryptography
• Computer Security
• Privacy
• +5 more

Tal Malkin is a Professor of Computer Science at Columbia University, directing the Cryptography Lab and serving as inaugural chair of the Cybersecurity Center at Columbia's Data Science Institute. She holds a Ph.D. from MIT (2000), joined Columbia after AT&T Labs research experience, and focuses on cryptography, security, complexity theory with applications in secure computation, zero-knowledge proofs, and privacy-preserving systems . Education: B.S. in Math and Computer Science, Bar-Ilan University M.S. in Computer Science, Weizmann Institute of Science Ph.D. in Computer Science, MIT (2000) Research Interests: Malkin's work spans foundational and applied cryptography, including homomorphic encryption, lattice-based protocols, attribute-based encryption, and tamper-resilient systems . She explores intersections with machine learning and information theory , addressing challenges in multi-party computation , public-key encryption , and side-channel resistance . Scientific Contributions: Her publications include breakthroughs in non-malleable codes , secure computation , and privacy-preserving databases . Notable works involve continual leakage resilience , optimally-fair coin tossing , and garbled circuits for efficient cryptographic protocols. Awards & Grants: Recipient of the NSF CAREER award, IBM and Google faculty research awards, and the IACR Fellow designation. Her research is funded by NSF, NSA, DHS, NYSIA, IARPA, and industry partnerships with Google, IBM, Mitsubishi, and NEC. Professional Leadership: Former conference chairs at CRYPTO 2021 , CCS 2017 , and CT-RSA conferences. Active in program committees for over 20 leading cryptography and security events, including FOCS , Eurocrypt , and Real World Crypto . Advising: Supervised numerous Ph.D. students and postdoctoral researchers, including Marshall Ball , Chengyu Lin , and Negev Shekhel Nosatzki . Her lab has mentored graduates like Ghada Almashaqbeh (2019) and Fernando Krell (2016), focusing on decentralized networks , secure learning , and cryptographic primitives .

• Cryptography
• Computer Security
• Complexity Theory
• +8 more

Prof. Srdjan Capkun is a Full Professor in the Department of Computer Science at ETH Zurich and Director of the Zurich Information Security and Privacy Center (ZISC). He holds a Dipl.Ing. from the University of Split (1998) and a Ph.D. from EPFL (2004). His research focuses on system and network security, including wireless security (e.g., secure positioning), trusted computing, blockchain technologies, and privacy-preserving systems. He co-founded 3db Access (acquired by Infineon) and Sound-Proof, addressing secure distance measurement and authentication. Notable achievements include an ERC Consolidator Grant (2016), ACM Fellowship, and IEEE Fellowship. Education: Dipl.Ing., Electrical Engineering / Computer Science, University of Split, 1998 Ph.D., Communication Systems, EPFL, 2004 Research Interests: Wireless security (e.g., UWB, GNSS, cellular) Trusted execution environments (TEEs) Blockchain and cryptocurrency systems Privacy-preserving technologies Awards & Grants: ERC Consolidator Grant (2016) ACM Fellow and IEEE Fellow Leadership of ZISC Advising & Labs: Leads the System Security Group at ETH Zurich Guided postdoctoral researchers and graduate students in security and privacy Companies: 3db Access (secure distance measurement) Futurae (online authentication)

• Computer Security
• Network Security
• Wireless Security
• +4 more

Andrea Coladangelo is an Assistant Professor at the Allen School of Computer Science & Engineering , University of Washington , co-leading the Quantum group and contributing to the Theory and Crypto groups. He coordinates the NSF-funded Quantum@UW REU program , fostering undergraduate research in quantum information. Previously, he was a postdoctoral researcher at UC Berkeley and the Simons Institute , advised by Umesh Vazirani , following a PhD in Computer Science at Caltech under Thomas Vidick . His academic journey began with a B.A. in Mathematics from Oxford and a Master in Mathematics from Cambridge . He co-founded qBraid , a platform for quantum computing education. Research Interests : Andrea explores the intersection of quantum computation and cryptography , focusing on foundational questions in entanglement , quantum correlations , and quantum learning theory . His work investigates quantum pseudorandomness , device-independent security , quantum algorithms , and quantum copy-protection , often leveraging computational assumptions to bridge quantum information theory with cryptographic applications. Scientific Awards : 2025 Google Research Scholar Program Award in Quantum Computing 2023 CSE Undergraduate Teaching Award for his course on quantum computation 2019 Best Student Paper Award at QIP Teaching & Outreach : Andrea designed and taught CSE 434: Intro to Quantum Computation (Spring 2023, 2024, 2025), CSE 534: Quantum Information and Computation (Autumn 2023), and CSE 599C: Quantum Learning Theory (Winter 2025). He also delivered lectures at the 22nd Bellairs Crypto Workshop (2024) and led a quantum programming tutorial using qBraid . Labs & Teams : As co-leader of the Quantum group at the Allen School, he collaborates with researchers in Theory and Crypto , advancing quantum computing through interdisciplinary projects and educational initiatives.

• Quantum Computation
• Quantum Cryptography
• Quantum Learning Theory
• +3 more

Kamalika Chaudhuri is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD), and also serves as a Director and Research Scientist with the FAIR team at Meta AI. Her research focuses on the foundations of trustworthy machine learning, including robust machine learning, learning with privacy, and out-of-distribution generalization. Dr. Chaudhuri has earned her PhD from UC Berkeley in 2007 with a dissertation on "Learning Mixtures of Distributions." Her academic journey has led her to become a leading researcher in machine learning theory with a particular emphasis on privacy and robustness. Her research interests span machine learning foundations with a strong focus on trustworthy AI . She investigates problems at the intersection of differential privacy , adversarial robustness , and out-of-distribution generalization . Her work addresses critical challenges in developing machine learning systems that maintain privacy while preserving utility, resist adversarial attacks, and generalize effectively beyond training data distributions. She has pioneered approaches in privacy-preserving machine learning, robust learning theory, and methods for detecting and mitigating data memorization in models. An analysis of her recent publications (2024-2025) reveals a strong focus on the intersection of privacy, security, and machine learning. Her work spans differential privacy mechanisms, membership inference attacks, memorization detection, and fairness certification. She has been particularly active in developing methods for privacy-preserving foundation models, with several papers on differentially private computer vision and language models. Her research demonstrates a consistent thread of addressing fundamental challenges in trustworthy AI while developing practical solutions that balance privacy, accuracy, and utility. Best Award at the ICLR 2024 Workshop on Privacy Regulation and Protection in Machine Learning Distinguished Paper Award at IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2024 Dr. Chaudhuri has advised numerous PhD students who have gone on to prominent positions at Google, DeepMind, Microsoft Research, and other leading AI institutions. Her group maintains an active research blog with guest posts from UCSD researchers. She has served in significant leadership roles including General Chair for ICML 2022 and Program Co-Chair for both ICML 2019 and AISTATS 2019, where she pioneered initiatives to improve reproducibility in machine learning research. Her research group at UCSD focuses on trustworthy machine learning, with current projects spanning privacy-preserving AI, robustness against adversarial attacks, and methods for ensuring reliable out-of-distribution generalization. The group collaborates closely with the FAIR team at Meta AI, where Dr. Chaudhuri serves as a Research Scientist.

• Machine Learning
• Trustworthy Machine Learning
• Differential Privacy
• +4 more

Ian Goldberg is a Professor at the Cheriton School of Computer Science , University of Waterloo, Canada. He holds the Canada Research Chair in Privacy Enhancing Technologies and leads the Cryptography, Security, and Privacy (CrySP) group . He is affiliated with the Cybersecurity and Privacy Institute and the Institute for Quantum Computing . Research Focus: Security, privacy, and privacy-enhancing technologies (PETs) for the Internet, including Off-the-Record (OTR) Messaging , censorship resistance , private information retrieval , and zero-knowledge proofs . Recent Publications: His work addresses metadata protection , deniable authentication , and secure multi-party computation , with applications to anonymous communication and censorship resistance. Scientific Awards: Canada Research Chair in Privacy Enhancing Technologies Students: He advises current and former PhD/Master’s students in projects like Lox , PRSONA , Walkie-Talkie , and SkypeMorph . Notable advisees include Sajin Sasy (Privacy-Preserving Communications), Justin Tracey (PETs Development), and Tao Wang (Website Fingerprinting Defenses). Labs & Teams: Goldberg is a core member of the CrySP group and contributes to the Tor Project and Off-the-Record Messaging open-source initiatives. His research bridges theoretical and applied cryptography, with deployments in real-world systems like Tor and PIR protocols.

• Security
• Privacy
• Privacy-Enhancing Technologies (PETs)
• +5 more

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.

• Cryptography
• Theoretical Computer Science
• Zero-Knowledge Proofs
• +5 more

Charalampos Papamanthou is an Associate Professor of Computer Science at Yale University, where he also serves as Co-director of the Yale Applied Cryptography Laboratory and a member of the Yale Institute for Foundations of Data Science. He holds affiliations with the Yale Center for Algorithms, Data, and Market Design. Additionally, he is Chief Scientist at Lagrange Labs. His research focuses on computer security and applied cryptography, particularly verifiable and privacy-preserving computations, leakage-abuse attacks on searchable encryption, and scalable blockchains/cryptocurrencies. He has advised numerous students and postdocs, and his work is supported by NSF, Protocol Labs, and JP Morgan. Research Interests: His primary areas include cryptographic protocols, privacy-preserving systems, blockchain infrastructure, secure cloud computing, and distributed consensus mechanisms. He has pioneered advancements in zero-knowledge proofs, private information retrieval, and dynamic searchable encryption. Awards: He has received prestigious awards such as the CCS Test-of-Time Award (2022), JP Morgan Faculty Research Award (2022), and NSF CAREER Award (2017). His contributions span over 140 publications in top venues like CRYPTO, CCS, and SODA. Teaching: He has taught advanced courses in cryptography, algorithms, and computer systems security at Yale and previously at the University of Maryland and Brown University. Recently, he chairs Yale’s PhD admissions in Computer Science. Labs & Teams: Leads the Yale Applied Cryptography Lab, focusing on real-world applications of cryptographic research. Collaborates with industry partners like Lagrange Labs to develop privacy-preserving technologies.

• Computer Security
• Applied Cryptography
• Verifiable Computations
• +5 more

Ron Steinfeld is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University, Australia. He holds editorial roles in Designs Codes and Cryptography (since 2017) and has served on technical committees for top-tier conferences like ASIACRYPT, CRYPTO, and EUROCRYPT. His research focuses on quantum-safe cryptography, lattice-based cryptography, and blockchain security, with over 80 refereed publications and AUD$4M+ in research funding. Education: BSc Mathematics and Physics (Monash University, 1998) BE (Hons., First Class) Electrical and Computer Systems (Monash University, 2000) PhD Computer Science (Monash University, 2003) Research Interests: Design and analysis of cryptographic algorithms, quantum-safe protocols, lattice-based security foundations, and blockchain applications. His work underpins NIST standard algorithms like Kyber and Dilithium through structured lattice problem research. Recent Articles Trends: Focus on privacy-preserving blockchain protocols, post-quantum signature schemes, encrypted data search, and cryptographic applications in adversarial AI. Recent work includes fair Bitcoin watchtower schemes and genomic database privacy solutions. Awards: ASIACRYPT 2015 Best Paper Award Advising & Grants: Supervises PhD projects on quantum-safe cryptography and blockchain security. Leads initiatives like the AUD$4M ARC-funded Quantum Information Technology project. Collaborates with industry partners including CSIRO/Data61. Labs/Teams: Key contributor to Monash’s cybersecurity and cryptography research groups, involving interdisciplinary projects on materials discovery ( Æinstein initiative) and post-quantum blockchain security.

• Quantum-safe cryptography
• Lattice-Based Cryptography
• Post-Quantum Cryptography
• +4 more

Anwar Hithnawi is an Assistant Professor of Computer Science at the University of Toronto, where he leads the Privacy Preserving Systems Lab (PPS Lab). His research focuses on data privacy, applied cryptography, and secure systems, with emphasis on privacy-preserving machine learning, federated learning, and encrypted data processing. He holds a Ph.D. in Computer Science from ETH Zurich and was a postdoctoral researcher at UC Berkeley. Previously, he served as an Ambizione Fellow and research group leader at ETH Zurich. Research Interests: Data Privacy & Security Applied Cryptography (Homomorphic Encryption, Zero-Knowledge Proofs) Privacy-Preserving Systems (Federated Learning, Secure Analytics) IoT Security & Privacy Secure Collaborative Learning Awards: Google Research Award SNF Ambizione Grant ETH Medal for Outstanding Master Thesis (student Lukas Burkhalter) Microsoft Research Ph.D. Award (student Lukas Burkhalter) Lab Activities: The PPS Lab develops systems for privacy-preserving computation, secure collaborative learning, and encrypted data stream processing. Notable projects include Zeph, HECO, and Cohere. Recent achievements include acceptance of DPolicy at IEEE S&P 2025 and RoFL at Oakland 2023.

• Data Privacy
• Applied Cryptography
• Privacy-Preserving Systems
• +5 more

Stefano Tessaro is a Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. He holds the Paul G. Allen Career Development Professorship. His research focuses on cryptography, theoretical computer science, and computer security, emphasizing practical applications of cryptographic techniques. He co-leads the cryptography group at UW with Andrea Coladangelo and Huijia Lin, and is part of the theory group. Education: PhD in Computer Science from ETH Zurich (2010) Postdoctoral researcher at MIT CSAIL (2010-2014) and UC San Diego (2014-2019) Joined UW in 2019 as faculty Research Interests: His work spans theoretical cryptography, privacy-preserving systems, cryptographic protocols, and security foundations. He explores practical applications of cryptography, including secure protocols, post-quantum cryptography, and efficient cryptographic constructions. Articles Trends: Recent publications emphasize threshold signatures, memory-tight security proofs, lattice-based cryptography, and privacy-preserving systems. Key themes include adaptive security, formal verification, and efficiency improvements in cryptographic protocols. Awards: NSF CAREER Award Sloan Research Fellowship Hellman Fellowship Research awards from Cisco, JP Morgan, and Microsoft Grants & Advising: His research has been supported by NSF grants and industry partnerships. He advises students in cryptographic theory and applications, though no specific student names are listed in the provided text. Labs & Teams: Co-leads the UW cryptography group, collaborating on projects like LERNA (secure aggregation) and Twinkle (threshold signatures). Engages with interdisciplinary teams in theoretical computer science and security.

• Cryptography
• Computer Security
• Theoretical Computer Science
• +3 more

Florian Kerschbaum is a Professor and NSERC/RBC Industrial Research Chair in Data Security at the Cheriton School of Computer Science, University of Waterloo. His research focuses on data security and privacy, applied cryptography, and confidentiality in data science. Research interests span data collection/preparation management, secure multi-party computation, homomorphic encryption, differential privacy, and machine learning robustness/privacy. His work develops cryptographic solutions for practical data management challenges in distributed systems.

• Data security and privacy
• Confidentiality and integrity in data science
• Applied cryptography
• +4 more

Ari Juels is the Weill Family Foundation and Joan and Sanford I. Weill Professor at Cornell Tech (Cornell University) and a faculty member in the Department of Computer Science. He co-founded and co-directs the Initiative for CryptoCurrencies and Contracts (IC³) and serves as Chief Scientist at Chainlink Labs. His research focuses on blockchain technology, cryptocurrency, smart contracts, applied cryptography, authentication, and privacy. Juels holds a Ph.D. in Computer Science from UC Berkeley and previously held roles as Chief Scientist and Director of the RSA Laboratories at EMC/Dell EMC until 2013. His work spans foundational contributions to decentralized systems, including Chainlink’s oracle networks and Ekiden’s privacy-preserving smart contracts. Juels has authored influential books like *The Oracle* (2024) and *Tetraktys* (2009), blending cryptography with thriller narratives. He advises numerous students and alumni, many of whom have joined prominent blockchain firms like Chainlink Labs, Mysten Labs, and Espresso Systems. Recent publications explore topics such as liquidity in blockchain assets, oracle network security, and economic analysis of smart contracts. His research emphasizes practical applications of cryptographic principles to real-world challenges in digital systems and decentralized finance. Juels’ lab collaborates on projects like HyDRA (automated bug bounty systems) and Lanturn (economic security metrics). He actively participates in academic and industry partnerships, bridging theoretical computer science with applied blockchain innovation.

• Blockchain Technology
• Cryptocurrency
• Smart Contracts
• +3 more

Prof. Sanjam Garg is an Associate Professor at the University of California, Berkeley in the Computer Science Division under the College of Engineering. His work focuses on cryptography and its applications to security and privacy , with affiliations to the Berkeley Center for Responsible, Decentralized Intelligence (RDI) and the Simons Institute for the Theory of Computing (SITC). Education: Ph.D., Computer Science, University of California, Los Angeles (2013) B.Tech, Computer Science and Engineering, Indian Institute of Technology, Delhi (2008) His research spans cryptography , secure computation , and zero-knowledge proofs , with recent publications on threshold encryption , zkSNARKs , and private set intersection . Trends in his work emphasize blockchain security , obfuscation , and secure multiparty computation . Scientific Honors: ACM Doctoral Dissertation Award (2013) Sloan Research Fellowship (2020) Okawa Research Grant (2016) IACR EUROCRYPT Best Paper Award (2013) FOCS Test of Time Award (2023) Bakar Fellows Spark Award (2023) Prof. Garg has advised researchers like James Bartusek (2024), Akshayaram Srinivasan (2020), and Peihan Miao (2019), who are now at institutions such as Microsoft Research and University of Toronto. He teaches CS 170 on algorithms at Berkeley.

• Cryptography
• Security
• Privacy
• +3 more