Ivan Dokmanic is an Assistant Professor at the Coordinated Science Laboratory (CSL) within the University of Illinois . His research bridges signal processing , machine learning , and applied inverse problems , with a focus on acoustics, biomedical imaging, and distance geometry. Current Role : Assistant Professor, CSL Email : dokmanic@illinois.edu Research Interests : Dokmanic explores machine learning applications in inverse problems , particularly distance geometry for molecular imaging and acoustics . His work includes unlabeled sensing , where distances between points are known but their arrangement is not. This has implications for powder diffraction , indoor localization , and echo modeling . Article Trends : His recent publications emphasize distance geometry in machine learning , acoustic signal processing , and inverse problem theory . Key areas include molecular imaging , audio encryption , and sensor positioning . Collaborative work spans medical imaging , cyberphysical systems , and geometric invariants . 2016 Google Faculty Award NSF Grant (1 year, $157,079) Students and Grants : Dokmanic mentors PhD students like Puoya, Shuai, and Anadi. His research is funded by the National Science Foundation , Google , VISA , and nVidia .
Peter Druschel is a Professor and founding Director of the Max Planck Institute for Software Systems (MPI-SWS) in Saarbrücken, Germany. He holds adjunct professorships at Saarland University and the University of Maryland. His research focuses on distributed systems, operating systems, and privacy-preserving technologies. He earned his Ph.D. from the University of Arizona in 1994 and has held roles at Rice University since 1994, including Professor of Computer Science (2002–2005). Education: Ph.D. in Computer Science, University of Arizona (1994) Research Interests: Distributed systems, operating systems, network security, accountable computing, and privacy technologies. Current projects include privacy compliance in data systems (Thoth), secure communication (EbN), and privacy-aware image capture (I-Pic). Awards: SIGOPS Mark Weiser Award (2008) NSF CAREER Award (1995) Member of Academia Europaea and German Academy of Sciences Leopoldina Grants & Leadership: Leads the ERC Synergy Project imPACT, chairs the Max Planck Society’s Chemistry, Physics, and Technology Section, and collaborates with institutions like Cornell and Google. Advises on policy issues related to technology and privacy. Labs/Teams: Distributed Systems Group at MPI-SWS, collaborations with Microsoft Research and MIT. Current team includes students and postdocs working on privacy, security, and distributed systems.
Sushmita Ruj is an Associate Professor in the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney. She serves as the Faculty of Engineering Lead for the UNSW Institute for Cybersecurity (IfCyber) and as the Taste of Research (ToR) Coordinator within the School of Computer Science and Engineering. Her academic journey includes previous positions as a Senior Research Scientist at CSIRO's Data61 (2019-2022), Associate Professor at the Indian Statistical Institute, Kolkata, and Assistant Professor at the Indian Institute of Technology (IIT), Indore. Dr. Ruj's primary research interests focus on applied cryptography, post-quantum cryptography, cybersecurity, blockchains, and data privacy. She designs practical, efficient, and provably secure protocols for real-life applications, with particular emphasis on critical infrastructure including smart grids, cloud computing, ad hoc networks, and data sharing frameworks. As quantum technology advances, her work increasingly focuses on developing quantum-safe algorithms to ensure a more secure Internet infrastructure. Her research spans multiple domains including cryptographic key management, proofs of storage, verifiable computation, vector commitments, and privacy-enhancing technologies for cloud and IoT environments. Her recent publications demonstrate a strong trend toward post-quantum cryptography solutions, with particular emphasis on blockchain applications, DNS security, and privacy-preserving protocols for industrial IoT. The research shows increasing focus on practical implementations of theoretical cryptographic concepts, with applications across multiple sectors including finance, healthcare, and critical infrastructure. Her work bridges the gap between theoretical cryptography and real-world security challenges, with growing emphasis on the transition from classical to quantum-resistant systems. Best Paper Award at ACISP 2024 JNCA Best Survey Award (2023) NSW Innovation Award (iAward) Merit Winner (2022) Women in Science Award from CSIRO (2020) ACM Senior Member (2016) IEEE Senior Member (2015) Samsung GRO award (2014) Dr. Ruj has successfully mentored numerous PhD and Master's students, with many of her former students now holding academic positions at institutions like IIT Indore, TU Wien, and CISPA Helmholtz Center. She has secured significant competitive funding including multiple Australian Research Council (ARC) grants, Samsung GRO Award, NetApp Faculty Fellowship, Cisco Academic Grant, and IBM Research grant. Her current research portfolio includes projects on blockchain-based quantum-safe digital medical passports, embedding trust in digital IDs, and resilience of supply chain unstructured data. As Faculty of Engineering Lead for IfCyber, Dr. Ruj plays a key role in UNSW's cybersecurity research initiatives. She has served on editorial boards for prestigious journals including IEEE Transactions on Information Forensics and Security and has held leadership positions in major conferences such as ACISP 2021 and Indocrypt 2020. She was also a member of the working group on "Blockchain For Cybersecurity" for the National Blockchain Roadmap of Australia and the first Blockchain Working group set up by the Reserve Bank of India.
Brent Waters is a Professor at the Department of Computer Science, University of Texas at Austin , where he has been since 2008. He received his Ph.D. in Computer Science from Princeton University (2004) and held a postdoctoral position at Stanford University (2004-2005). His research focuses on cryptography and computer security, with groundbreaking work in Identity-Based Encryption, Functional Encryption, Attribute-Based Encryption, and code obfuscation. He is a founder of Functional Encryption and Attribute-Based Encryption. Education Ph.D., Computer Science, Princeton University (2004) Research Interests Cryptography, Security Protocols Functional Encryption, Attribute-Based Encryption Indistinguishability Obfuscation, LWE-Based Systems Zero-Knowledge Proofs, Key-Dependent Message Security Selected Publications Trends Recent work (2025) addresses adaptive security in broadcast encryption, SNARGs, and multi-authority ABE systems using LWE and bilinear maps. Key themes include collusion resistance, witness encryption, and optimizing cryptographic assumptions like CRS size reduction. Scientific Awards IEEE Fellow (2025), IACR Fellow (2024), ACM Fellow (2021) Simons Investigator (2019), Grace Murray Hopper Award (2015) Presidential Early Career Award (2011), Packard Fellowship (2011) Advising Current Ph.D. students: Shafik Nassar, George Lu Past Ph.D. students: Rachit Garg (2024), Satya Vusirikala (2021), Rishab Goyal (2019), Venkata Koppula (2018), Yannis Rouselakis (2013), Allison Bishop (2012) Contact Email: bwaters@cs.utexas.edu Phone: (512) 232-7464 | Office: GDC 6.810
Michael J. Freedman is the Robert E. Kahn Professor of Computer Science at Princeton University and co-founder/CTO of Timescale. He received his Ph.D. from NYU’s Courant Institute and degrees from MIT. Current roles: Professor, Co-founder & CTO Affiliations: Princeton University, SNS Group, CITP Associate Education: Ph.D. (NYU), S.B./M.Eng. (MIT) His research spans distributed systems, networking, and security, with innovations like CoralCDN, DONAR, and Ethane. His work impacts decentralized content delivery, software-defined networking, and privacy-enhancing technologies. His recent publications address scalable fusion algorithms, GPU acceleration for data systems, and distributed GPU resource management. These works intersect with cloud infrastructure, network optimization, and security. Scientific honors include: Presidential Early Career Award for Scientists and Engineers (PECASE) Sloan Fellowship NSF CAREER Award Office of Naval Research Young Investigator Award Test of Time Award (Theory of Crypto Conference) ACM SIGOPS Mark Weiser Award He advises graduate students like Sam Ginzburg and Ashwini Raina, who joined Meta AI and Timescale post-PhD. His projects have secured substantial grants, including $110M Series C funding for Timescale. Key labs/teams: Princeton SNS Group Co-founder, Timescale (enterprise data platform) Co-founder, iobeam (IoT analytics, acquired by Timescale) Collaboration with FCC on Consumer Broadband Test Contributions to OpenFlow/SDN standardization
Pratyush Mishra is an Assistant Professor at the University of Pennsylvania in the Department of Computer and Information Science, where he is affiliated with the Security and Privacy Laboratory. His research focuses on the intersection of cryptographic proof systems and computer security , particularly on efficient implementations of zero-knowledge proofs and secure computation protocols. Pratyush completed his PhD in Computer Science at UC Berkeley , advised by Alessandro Chiesa and Raluca Ada Popa , and holds a BSc in EECS from UC Berkeley, where he worked with David Wagner . His current research group includes PhD students Anubhav Baweja , Tushar Mopuri , Bharath Namboothiry , and Alireza Shirzad , along with Matan Shtepel , a former research assistant who moved to CMU for his PhD. His work spans advanced cryptographic techniques like zkSNARKs , accumulation schemes , and private delegation of provers , with applications in decentralized systems and secure inference. His recent publications focus on optimizing polynomial commitments , read-write streaming for SNARKs, and horizontally scalable proofs . Key awards include: ACM SIGSAC Doctoral Dissertation Award Runner-Up (2022) CSAW Applied Research Award (2016) for Hidden Voice Commands He teaches courses like CIS 5560: Cryptography and 7000-2: Theory and Practice of Succinct Zero Knowledge Proofs , covering topics such as symmetric cryptography, public-key encryption, digital signatures, zero-knowledge proofs, and secure multiparty computation. His lab contributes to the arkworks ecosystem for zero-knowledge proof libraries and co-founded the startup Aleo based on his research.
David Jao is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo. His research focuses on post-quantum cryptography, particularly leveraging isogenies of supersingular elliptic curves for secure cryptographic protocols. He is renowned for co-developing the Supersingular Isogeny Key Encapsulation (SIKE) protocol, a leading candidate for post-quantum cryptography standards. His work spans theoretical foundations and practical implementations, including optimizing isogeny-based systems for embedded devices and ARM processors. Research interests include isogeny-based cryptosystems, elliptic curve cryptography, zero-knowledge proofs, and cryptographic security against quantum attacks. He explores applications of expander graphs and Ramanujan graphs in cryptography, alongside algorithmic improvements for cryptographic protocols such as SIDH (Supersingular Isogeny Diffie-Hellman). Key contributions include advancements in key compression techniques for SIKE, side-channel attack mitigation, and formalizing security models for post-quantum key exchange. His publications analyze cryptographic hardness assumptions, such as the discrete logarithm problem in finite groups and the semidirect product structure in isogeny-based systems. Jao’s work bridges theoretical mathematics and applied cryptography, with a focus on ensuring practical security in next-generation cryptographic systems. His research addresses challenges in quantum-resistant authentication, key establishment, and digital signatures, often emphasizing efficiency and resistance to both classical and quantum attacks.
Alex Lombardi is an Assistant Professor of Computer Science at Princeton University, specializing in cryptography and theoretical computer science. His work explores cryptographic proof systems, post-quantum security, and quantum cryptography. Princeton University (Current) Simons-Berkeley Postdoctoral Fellow (Former) MIT (Graduate Training) Visiting Scientist, Cryptography 10 Years Later Program (2025) Education: PhD in Computer Science from MIT (advised by Vinod Vaikuntanathan) Master's Thesis on Provable Instantiations of Correlation Intractability and the Fiat-Shamir Heuristic Dr. Lombardi's research spans foundational cryptography, with a focus on indistinguishability obfuscation, worst-case assumptions, and quantum cryptographic protocols. His work on SNARGs and PPAD hardness has advanced cryptographic proof systems, while his recent projects address quantum verification and post-quantum security. He encourages prospective cryptography students to apply to Princeton's PhD program. His publications highlight advancements in LWE-based cryptography, quantum protocols, and complexity-theoretic foundations. Key themes include secure computation, hash function design, and cryptographic reductions under quantum assumptions. Scientific Awards: Simons-Berkeley Postdoctoral Fellowship Dr. Lombardi serves on program committees for STOC 2025, EUROCRYPT 2025, and other conferences. He has taught courses like COS 433/533 (Cryptography) and COS 533 (Advanced Cryptography) at Princeton.
Yan Huang is an Associate Professor in the Department of Computer Science at Indiana University Bloomington, focusing on security and cryptography. His work bridges theoretical foundations with practical systems, emphasizing cryptographic protocols with strong security guarantees for generic computation. University: Indiana University Bloomington School: College of Arts and Sciences Department: Computer Science Academic Rank: Associate Professor His research combines theoretical computer science , program analysis , artificial intelligence , and software engineering to address real-world security problems. Key areas include secure computation , zero-knowledge proofs , and privacy-preserving technologies . Recent publications, such as Phecda (SP'25) and Dubhe (USENIX Security'23), highlight advancements in post-quantum cryptography , AES verification , and privacy-preserving deep packet inspection , reflecting his focus on scalable and practical cryptographic solutions. He has advised notable students including Changchang Ding (PhD, Computer Science) and Ruiyu Zhu (PhD, now at Facebook), and served on program committees for top conferences like ACM CCS and CRYPTO .
Dr. Xiaodong Lin is a Professor at the University of Guelph's School of Computer Science and an IEEE Fellow (2017) for contributions to secure vehicular communications. He leads the Blockchain Technology and Cybersecurity (BTC) lab, focusing on privacy-enhancing technologies, digital forensics, wireless network security, blockchain applications, and DeFi security. PhD, Beijing University of Posts and Telecommunications, China PhD, University of Waterloo, Canada His research bridges theoretical and applied domains in cybersecurity, particularly vehicular networks, smart grids, and decentralized systems. Recent work examines blockchain security, privacy-preserving protocols for IoT, and secure data aggregation in wireless networks. Key publication trends include secure fog computing for vehicular crowdsensing, privacy-preserving authentication in 5G, and cryptographic solutions for smart grids. Awards highlight multiple Best Paper recognitions at IEEE conferences. IEEE Fellow (2017) Best Paper Awards (IEEE INFOCOM 2018, GLOBECOM 2017, SECURECOMM 2016) Dr. Lin supervises graduate students in blockchain security, AI security, and digital forensics. His lab provides financial support to qualified students.
Qiang Tang is currently an Associate Professor (Level D) at the School of Computer Science of The University of Sydney. Previously, he was a Senior Lecturer (2021.1-2024.12) at USYD and an Assistant Professor at the Computer Science Department of New Jersey Institute of Technology (2016.8-2021.1), where he co-directed the JACOBI Blockchain Lab with Prof. Jian Pei and Prof. Zhenfeng Zhang. He completed his PhD at the University of Connecticut under Prof. Aggelos Kiayias and Prof. Alexander Russell, following postdoctoral research at Cornell University with Prof. Elaine Shi. His research spans applied and theoretical cryptography, blockchain technology, privacy, and computer security. His work is supported by ARC, Google, Ethereum Foundation, Stellar Foundation, Protocol Labs, Algorand Foundation, Oracle, and USYD. Previous funding includes NSF, JD.com, AFRL, DoE, and Particl Foundation. His research has led to significant contributions in consensus protocols, distributed randomness generation, secure multi-party computation, and privacy-preserving technologies. Tang's publications reveal a strong focus on practical cryptographic solutions for blockchain and distributed systems. His recent work demonstrates expertise in asynchronous consensus, optimal protocol design, and secure implementations for real-world applications. His research shows consistent innovation in improving efficiency, security, and scalability of distributed systems. Scientific Awards: 2025 DSN Best Paper Award 2024 ICDCS Distinguished Paper Award 2023 SOAR Prize, USYD 2023 Oracle for Research Award 2022 Stellar Foundation Research Awards 2022 Ethereum Academic Award 2019 MIT Technical Review, 35 Chinese Innovators Under 35 Tang actively mentors PhD and Master's students, with several alumni now holding faculty positions or research roles at institutions like City University Hong Kong, Chinese Academy of Sciences, and A*STAR Singapore. He has received significant research funding including a multi-year Google project on End-to-End Secure Cloud and an ARC DP grant on Order Fairness in Decentralized Systems. He leads the research in his lab focusing on blockchain protocols and cryptographic applications, with strong industry connections through collaborations with Google, Ethereum Foundation, Stellar Foundation, and Protocol Labs. His team regularly publishes in top security and cryptography venues including CRYPTO, CCS, USENIX Security, and S&P.
Matteo Maffei is a Full Professor at TU Wien, leading the Security and Privacy group. He joined in 2017 after 11 years at Saarland University's CISPA. He holds a Ph.D. in Computer Science from Ca’ Foscari University of Venice (2006). He coordinates the TU Wien Cybersecurity Center, the SecInt Doctoral School, and the FWF Special Research Program SPyCoDe. His research focuses on formal methods for security and privacy, blockchain technologies, and web security. Roles: Full Professor, Coordinator of TU Wien Cybersecurity Center, Module Head of Christian Doppler Lab for Blockchain Technologies (CDL-BOT), Board Member of Vienna Cybersecurity and Privacy Research Cluster (ViSP). His work emphasizes formal verification of cryptographic protocols, smart contracts, and decentralized systems. Key achievements include ERC Advanced (2024) and Consolidator (2018) Grants, and leadership roles in conferences like IEEE Computer Security Foundations Symposium (CSF). Research Interests: Formal methods, smart contracts, blockchain scalability, web security, privacy-preserving protocols, and decentralized systems. His recent projects include optimizing Lightning Network channels, secure multi-hop payments, and AI-driven robustness verification. Publications: Over 200 publications in top venues like CCS, IEEE S&P, and CRYPTO. Recent work includes advancements in blockchain interoperability (e.g., Alba bridges), light client protocols (Blink), and neural network verification techniques. Grants & Awards: ERC Advanced Grant (2024), ERC Consolidator Grant (2018), DFG Emmy Noether Fellowship (2009). Led projects funded by EU Horizon, FWF, and industry partners like ABC Research GmbH. Advising & Labs: Supervised over 20 PhD/Master theses. Active in the Christian Doppler Lab for Blockchain Technologies and the SPyCoDe SFB. Collaborates with institutions like SBA Research and Stanford University.
Chee-Wooi Ten is a tenured Professor in the Department of Electrical and Computer Engineering at Michigan Technological University, where he has served since 2010 and achieved tenure in 2016. He concurrently holds an Affiliated Professor appointment in Applied Computing and directs both the PSERC Site and ICC CPS Center. His institutional roles emphasize cyber-physical security integration within power infrastructure. His educational background includes: PhD in Electrical Engineering from University College Dublin (2009) MSc in Electrical Engineering from Iowa State University (2001) BSc in Electrical Engineering from Iowa State University (1999) Ten's research pioneers cyber-informed security engineering strategies for bulk power systems, focusing on quantifying rare events through system risk models and data science. His work bridges power grid interactions with robotics and transportation systems to advance decarbonization and electrification. Key methodologies include validating cyber-physical security frameworks against steady-state and dynamic grid approaches, with emphasis on attack/defense combinatorics and smart home technologies. This transdisciplinary approach supports the fourth industrial revolution's resilience requirements. His publication trends reveal strong focus on risk-aggregated substation testbeds using generative adversarial networks, cyber insurance models for power systems, and cascading failure analysis from switching attacks. Recent works increasingly integrate machine learning with physics-based modeling to address cybersecurity threats in inverter-based resource integration and distribution emergency operations. Ten has secured over $6.5M in active funding including: $2M DOE grant (MTU portion $105,000) for CyDERMS Center on DERs/Microgrids cybersecurity $704,409 CyManII award for secure digitalization in smart manufacturing $1.05M DOE ARPA-E grant for decarbonized freight transportation modeling NSF CyberCorps Scholarship for Service program ($3.38M) His grants consistently address risk management through data-driven and physics-based modeling, with industry partnerships through PSERC and utility collaborations. As ICC CPS Center Director, he leads research on cyber-physical security testbeds and coordinates the PSERC Summer Transformation School. His team develops validation frameworks for NERC CIP compliance while addressing practical pain points in OT cybersecurity for grid operators.
Brandon Reagen is an Assistant Professor in the Department of Electrical and Computer Engineering at New York University's Tandon School of Engineering, with affiliations in Computer Science, the Center for Advanced Technology in Telecommunications (CATT), and the NYU Center for Cybersecurity (CCS). He holds a PhD in Computer Science from Harvard (2018) and undergraduate degrees in Computer Systems Engineering and Applied Mathematics from the University of Massachusetts, Amherst (2012). His research focuses on computer architecture, hardware acceleration for deep learning and privacy-preserving computation, and VLSI design. He pioneered efficient deep learning accelerator designs through unsafe optimizations and contributed to benchmarking frameworks like Aladdin and MachSuite. His work spans privacy-preserving machine learning, secure computing systems, and hardware-software co-design for cryptographic protocols. Key achievements include the NSF CAREER Award (2024) and Siebel Scholar recognition (2018). His research centers on advancing secure computing through innovations like zero-knowledge proof accelerators (e.g., zkSpeed), fully homomorphic encryption frameworks (Orion), and entropy-guided privacy techniques for large language models. He leads interdisciplinary efforts at CATT and CCS to bridge hardware design and cybersecurity challenges. Reagen's contributions include over 50 publications in top-tier conferences (e.g., ISCA, ASPLOS, MLSys) and industry collaborations at Facebook AI. His work emphasizes practical solutions for encrypted computation efficiency, privacy-preserving inference, and scalable secure systems.
Dr. Kanchana Thilakarathna is a Senior Lecturer in Distributed Computing at the University of Sydney's School of Computer Science, and a member of the Centre for Distributed and High Performance Computing. They hold a PhD from the University of New South Wales (UNSW) and a B.Sc. Eng (Hons) from the University of Moratuwa, Sri Lanka. Prior to academia, they worked as a Research Scientist at CSIRO/Data61 and had industry experience as a Mobile Radio Network Engineer. Research Interests : Dr. Thilakarathna focuses on cybersecurity, privacy in mobile and IoT systems, mixed reality privacy, and distributed computing platforms. Their work emphasizes user-centric solutions like the Yalut social media app, which enables decentralized data sharing. Key themes include privacy-preserving techniques, edge computing, and secure federated learning frameworks. Recent Work : Recent articles (2023–2025) explore machine unlearning for large language models, federated learning security, and IoT network slicing using P4 programmability. Their work on synthetic video traffic generation (VideoTrain++) and drone detection (DronePrint) demonstrates cross-disciplinary innovation. Awards : Malcolm Chaikin Prize (2015), Meta Research Awards (2020/2022), and Heidelberg Laureate Fellowship (2019). Grants : ARC Research Hub for Future Digital Manufacturing (2024), NSW Defence Innovation Network Projects (2024/2021), and Facebook Research Awards (2022/2020). Students : Advising 4 current PhD students on topics like wireless trust establishment and machine unlearning. Labs/Teams : Part of the Centre for Distributed and High Performance Computing and Sydney Nano Institute.
