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Adrian WellerView profile

Research Professor

• Artificial Intelligence
• Machine Learning
• Explainability
• +9 more

Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

Rachit AgarwalView profile

Associate Professor

• Systems
• Networking
• Theory of Computing
• +6 more

Rachit Agarwal is an Associate Professor in the Department of Computer Science at Cornell University, with research focusing on systems, networking, and theoretical problems arising in practical systems. He leads a research group working on resource disaggregation, host architecture, secure cloud storage, and datacenter design. PhD in Computer Science, Cornell University Undergraduate, IIT Kanpur His research spans three major directions: Resource Disaggregation (with $3M NSF and Google awards), Host Architecture (exploring terabit interconnects), and PANCAKE (secure oblivious cloud storage with $1M NSF award). He also contributed to foundational work in Near-optimal Datacenter Design and Data Plane Monitoring . Awards include the Sloan Fellowship, NSF CAREER, IRTF Applied Networking Prize, and multiple best paper awards. His recent publications focus on host network architecture, congestion control, oblivious data access mechanisms, and secure cloud storage systems. He has advised multiple Ph.D. and postdoctoral researchers who now hold faculty positions at leading institutions. Sloan Research Fellowship NSF CAREER award Kavli Fellowship IRTF Applied Networking Research Prize SIGCOMM Best Student Paper Award Usenix Security Distinguished Paper Award Tau Beta Pi Professor of the Year 2025 Rachit has advised numerous students including current Cornell advisees like Midhul Vuppalapati, Shreyas Kharbanda, and Omar Eqbal. Former advisees include Saksham Agarwal (UIUC), Qizhe Cai (UVA), and Mina Tahmasbi Arashloo (University of Waterloo). His research is supported by large NSF grants and industry awards, with deployments in real-world systems and open-source contributions.

Kamalika ChaudhuriView profile

Professor

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

Benjamin Carrison-SchaferView profile

Associate Professor

• Reconfigurable Computing
• FPGAs
• Electronic Design Automation
• +7 more

Benjamin Carrison-Schafer is an Associate Professor and Assistant Dean for Graduate Student Success at the Department of Electrical and Computer Engineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He leads the Design Automation and Reconfigurable Computing Laboratory (DARClab), focusing on systems, high-level design, and programming methodologies for VLSI computing systems and FPGAs. Education: MBA from McGill University, Canada (2012) PhD in Electrical and Electronic Engineering from University of Birmingham, UK (2003) Research Interests: His work spans modeling, analysis, synthesis, optimization, and implementation of VLSI systems. Current projects include: Approximate Computing, High-Level Synthesis Design Space Exploration, Hardware Security, Behavioral MPSoC Optimization, and Automatic Fault-Tolerant System Generation. His research blends theory and practice, using analytical and experimental techniques to solve real-world problems. Publication Trends: Recent publications demonstrate strong focus on hardware security, FPGA optimization, and automated design methodologies. Key themes include High-Level Synthesis innovations, hardware acceleration techniques, and cross-disciplinary applications of reconfigurable computing. His work frequently addresses challenges in hardware trustworthiness, energy efficiency, and design automation scalability. Professional Activities: Associate Editor: IEEE Transactions on Sustainable Computing (2022-present) Conference Chair: ICCD 2024, DCAS 2024 Program Committee: ASP-DAC, DATE, FCCM, GLSVLSI Advising & Labs: Supervises multiple PhD and Master's students at DARClab. Current research includes domain-specific architecture design, hardware security, sustainable computing, and ML for VLSI design. The lab collaborates with industry partners like Renesas Electronics and develops commercial tools through spin-off company highX Technologies.

Deian StefanView profile

Associate Professor

• Security
• Programming Languages
• Systems
• +6 more

Deian Stefan is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD). His research focuses on building secure systems through interdisciplinary approaches spanning security , programming languages , and systems . Key areas include WebAssembly security , JavaScript JITs , constant-time programming , memory safety , information flow control , and program analysis tools. He co-founded Intrinsic , a web-security startup later acquired by VMWare, and contributed to standards like W3C WebAppSec and Node.js Security Working Groups. Education: PhD in Computer Science from Stanford University (2015), advised by David Mazières, John C. Mitchell, and Alejandro Russo BE and ME in Electrical Engineering from Cooper Union (2011) Research Trends: His work targets secure systems (Web frameworks, sandboxing, runtime systems), language-based security (constant-time programming, memory safety), and verification for security . Recent publications focus on mitigating speculative execution attacks, hardware-assisted sandboxing, and WebAssembly security. Awards & Recognition: Distinguished Paper Award at POPL 2021 and IEEE MICRO Top Picks 2024 Best Paper Award at CollaborateCom 2010 2010.01 Prize by Daniel J. Bernstein (DJB) Most Influential Paper Award at ICFP 2022 Teaching: He has taught graduate and undergraduate courses including Graduate Computer Security (CSE 227) , Building Secure Systems with Rust , and Programming Languages: Principles and Paradigms at UCSD since 2018. Prior teaching experience includes Stanford and Cooper Union.

Giovanni De MicheliView profile

Professor

• Integrated Circuit Design
• Hardware Synthesis
• Logic Optimization
• +5 more

Giovanni De Micheli is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), holding appointments in both the School of Computer and Communication Sciences (IC) and the School of Engineering (STI). He is affiliated with the Integrated Systems Laboratory (LSI) and serves as Scientific Director of EcoCloud. His primary office is located in INF 341 building at EPFL's Lausanne campus where he conducts research and teaching activities. Professor De Micheli's research spans multiple domains in electronic design automation and integrated circuit design. His primary interests include: Integrated Circuit Design and Hardware Synthesis Logic Optimization and Boolean Methods Nanoelectronics and 2D Electronics Quantum Computing and Emerging Technologies Low-Power Electronics and Energy-Efficient Systems Electronic Design Automation for Novel Computing Paradigms His recent publications demonstrate a strong focus on advancing logic synthesis techniques for both conventional and emerging technologies. Professor De Micheli's work bridges theoretical foundations with practical applications, particularly in adapting traditional EDA methods to novel computing paradigms like quantum computing and superconducting electronics. His research group has made significant contributions to the development of efficient algorithms for circuit optimization across multiple technology domains, with particular emphasis on area, power, and delay optimization while maintaining functional correctness. Professor De Micheli has supervised numerous doctoral students throughout his career at EPFL, mentoring over 40 PhD candidates whose research spans various aspects of electronic design automation, circuit design, and emerging technologies. His students have gone on to make significant contributions in both academia and industry. As Scientific Director of EcoCloud, Professor De Micheli leads research initiatives focused on energy-efficient computing systems and sustainable cloud infrastructure. His laboratory, the Integrated Systems Laboratory, serves as a hub for interdisciplinary research connecting computer science, electrical engineering, and materials science, with particular focus on next-generation computing technologies.

Charalampos PapamanthouView profile

Associate Professor

• Computer Security
• Applied Cryptography
• Verifiable Computations
• +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.

Alexandra BoldyrevaView profile

Professor

• Cryptography
• Information Security
• Searchable Encryption
• +3 more

Alexandra Boldyreva is a Professor at the Georgia Institute of Technology, holding joint appointments in the School of Cybersecurity and Privacy and the School of Computer Science. She serves as Associate Chair for Graduate Studies in the School of Cybersecurity and Privacy and coordinates the Information Security Master’s program in the College of Computing. Her affiliations include the Institute for Information Security & Privacy (IISP), the Algorithms, Combinatorics and Optimization (ACO) program, and the Algorithms and Randomness Center (ARC). She earned her Ph.D. in Computer Science from the University of California, San Diego, and holds bachelor’s and master’s degrees in applied mathematics from St. Petersburg State Technical University, Russia. Her research focuses on cryptography and information security, with notable contributions to encryption methods, authentication protocols, and privacy-preserving systems. Boldyreva’s work emphasizes provable security analysis of protocols like FIDO2 and TLS 1.3, as well as searchable encryption and data privacy techniques. Her recent studies include secure communication channel establishment, leakage quantification in encryption systems, and applications of fuzzy search in encrypted databases. She has received Test of Time Awards for foundational contributions to cryptography. Boldyreva leads initiatives in cybersecurity education and has secured grants for research in secure communication protocols and human-computing approaches to key exchange. Her interdisciplinary collaborations span computer science, mathematics, and privacy engineering.

• Network Security
• Cryptographic Protocols
• Industrial Control System Security
• +7 more

Professor Ernest Foo is a distinguished academic at Griffith University's School of Information and Communication Technology, specializing in cybersecurity with a focus on industrial control systems and cryptographic protocols. With over 15 years of experience in computer networking, he has established himself as a leading expert in SCADA security and smart grid cybersecurity. His research has significant practical applications in critical infrastructure protection, and he has developed hands-on security training programs that have trained professionals from major Australian utilities and government agencies. Professor Foo's educational background includes: Bachelor of Engineering with Honours in Electronic and Computer Engineering from University of Queensland Doctor of Philosophy from Queensland University of Technology Professor Foo's research interests center around secure cryptographic protocols with specific applications in industrial control system security and cyber physical systems. His work spans SCADA security, smart grid protection, wireless sensor network security, and post-quantum cryptography. He has made significant contributions to understanding vulnerabilities in industrial protocols like Modbus and DNP3, and has pioneered the application of process mining and data mining techniques for attack detection in critical infrastructure systems. His research bridges theoretical security concepts with practical implementations in real-world industrial environments. Professor Foo's recent publications demonstrate a clear trajectory toward increasingly sophisticated security frameworks for critical infrastructure. His work shows a progression from foundational SCADA security research to advanced applications of artificial intelligence, machine learning, and formal methods in cybersecurity. A notable trend is the integration of zero trust principles with industrial control systems, alongside growing emphasis on post-quantum cryptographic solutions. His publications span high-impact journals and conferences in cybersecurity, with increasing focus on anomaly detection in cyber-physical systems and the application of graph-based machine learning techniques to network security challenges. Professor Foo's notable scientific achievements include: Best paper award at the 2nd International Cyber Resilience Conference for "Gap analysis of Intrusion Detection in Smart Grids" Professor Foo has secured significant research funding including an ARC Linkage grant with Powerlink Queensland focused on cyber security for electricity sub-stations. He currently leads multiple research projects including Westpac Micro-Credentials in Financial Crime Investigation, Digital Banking Micro-credentials with ANZ, and research on quantum-safe cryptography. As a dedicated educator, he serves as Program Director for multiple cybersecurity programs including the Master of Cyber Security, and has supervised numerous doctoral and masters students. His Cyber Security: Industrial Control System course, conducted annually from 2013-2018, featured innovative hands-on training with real-world participants from major Australian utilities. Professor Foo has been instrumental in establishing the SCADA security research laboratory with multiple vendor system miniatures running industrial PLCs. His work bridges theoretical security concepts with practical implementations, making significant contributions to the security of industrial control systems and critical infrastructure worldwide.

Deian StefanView profile

Associate Professor

• security
• programming languages
• systems
• +7 more

Deian Stefan is an Associate Professor at the University of California San Diego (UCSD) in the Department of Computer Science and Engineering . His research spans security , programming languages , and systems , with a focus on building principled and practical secure systems. He has served as a co-founder and Chief Scientist at Intrinsic (acquired by VMWare) and contributed to standards bodies like the W3C WebAppSec and Node.js Security Working Groups . His research interests include: Secure Systems : Web frameworks, browser designs, sandboxing, runtime systems Language-Based Security : Constant-time programming, memory safety, information flow control Verification : Security verification, static/symbolic analysis tools WebAssembly and JavaScript JITs security Deian Stefan has received multiple scientific awards , including several Distinguished Paper Awards at venues like POPL, ICFP, and USENIX Security, as well as the IEEE Cybersecurity Award for Practice (2022) and CSAW 2020 First Place for Applied Research. He has taught courses on Computer Security (CSE 127, CSE 227) and advanced topics in Building Secure Systems (CSE 291) using Rust, WebAssembly, and blockchain security. His work has been supported by collaborations with industry and academia, including projects like RLBox and COWL .

• Security and Privacy
• Cryptography
• Blockchain
• +4 more

Andrew Miller is an Associate Professor in the Electrical and Computer Engineering department at the University of Illinois, specializing in Programming Languages, Formal Methods, Software Engineering, Security and Privacy, and Systems and Networking. His research focuses on blockchain technologies, cryptography, and secure systems. Professor Miller's research spans multiple critical areas in modern computer security. His work primarily focuses on blockchain technologies , where he has made significant contributions to understanding and improving the security, privacy, and performance of systems like Bitcoin and Ethereum. He has conducted empirical analyses of privacy in the Lightning Network and developed protocols for confidential smart contracts. His work in cryptography includes research on multiparty computation, zero-knowledge proofs, and formal methods for cryptographic protocol design. Miller also investigates security vulnerabilities in proof-of-stake systems and resource exhaustion attacks, contributing to the robustness of decentralized systems. His applied security research extends to privacy-preserving health applications, as evidenced by his work on the Safer Illinois platform for COVID-19 contact tracing. Miller's publication record demonstrates consistent contributions to top security and systems venues including IEEE Security & Privacy, ACM CCS, Financial Cryptography, and USENIX Security. His research shows a clear trajectory from foundational work in blockchain security to more applied systems addressing real-world privacy and security challenges. Recent work focuses on making multiparty computation services publicly auditable and developing decentralized identity solutions that maintain compatibility with existing systems. Distinguished Reviewer Award, IEEE Security & Privacy 2018 Professor Miller has advised students including Vivek Nair, who joined the prestigious Hertz Fellows program in 2022. He has taught various courses including Introduction to Algorithms & Models of Computation, Advanced Computer Security, Cryptography, Applied Cryptography, and Ideal Functionality in Cryptography. His research has received support through grants including the SaTC: CORE: Medium project on "Automated Support for Writing High-Assurance Smart Contracts" in 2018. Miller is actively involved in research groups focusing on blockchain security, cryptographic protocols, and privacy-preserving systems. His lab appears to collaborate extensively with researchers across multiple institutions, as evidenced by the diverse author lists on his publications. Current work seems to be focused on making decentralized systems more secure, privacy-preserving, and accessible for real-world applications.

Fred B. SchneiderView profile

Professor

• Trustworthy Systems
• Fault-Tolerant Distributed Systems
• System Security
• +6 more

Fred B. Schneider is the Samuel B. Eckert Professor of Computer Science at Cornell University , where he has been a faculty member since 1978. His career spans foundational work in trustworthy systems , fault-tolerant distributed systems , and system security . He served as department chair from 2014–2018 and previously earned a B.S. in Engineering from Cornell (1975) and a Ph.D. in Computer Science from Stony Brook University (1978).

• Secure Decentralized Systems
• Blockchain Technology
• Systems Security
• +3 more

Prof. Bryan Ford leads the Decentralized/Distributed Systems (DEDIS) lab at EPFL. He focuses on secure decentralized systems, including blockchain technology, privacy, and systems security. He earned his Ph.D. from MIT and held faculty positions at Yale University and EPFL. His work spans distributed consensus protocols, peer-to-peer networking, and privacy-preserving systems. Key projects include QuePaxa (timeout-free consensus), UIA (global connectivity for mobile devices), and MedCo (secure healthcare data sharing). He advises numerous PhD students and contributes to open-source projects like Bitcoin collective signing and privacy networks like Riffle. Education: Ph.D., MIT; Postdoctoral work at Yale Research interests include blockchain scalability, consensus algorithms, and cryptographic privacy. His lab develops systems like TRIP for coercion-resistant voting and F3B to mitigate blockchain front-running. His work on NAT traversal and peer-to-peer protocols (e.g., STUN/ICE) remains foundational in network architecture. He emphasizes practical, auditable security solutions such as CertiKOS and atomic cross-chain transactions (Atom). Notable contributions: CoSi (collective signing), OmniLedger (sharded blockchain), and privacy-preserving protocols like PURBs (Protected Unsealable Recursive Boxes). His lab collaborates with Swiss Post to audit e-voting systems and designs democratic cryptocurrencies like PoPCoin.

Ardalan Amiri SaniView profile

Associate Professor

• Mobile Systems
• Operating Systems
• Security
• +5 more

Ardalan Amiri Sani is an Associate Professor in the Computer Science Department at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information & Computer Sciences. He leads the Trustworthy Systems Lab (TrussLab), focusing on secure and reliable mobile systems, operating systems, and virtualization. His research bridges mobile computing, security, and OS design, addressing challenges in I/O devices, kernel hardening, and verifiable provenance. Education: Ph.D. and M.Sc. from Rice University (ECE), B.Sc. from Sharif University of Technology. Awards include the NSF CAREER Award, Google ASPIRE Award, and UCI Dean's Mid-Career Research Award. He has advised numerous graduate and undergraduate students, many of whom now work in academia and industry. Research highlights include Tabellion (secure legal contracts on mobile devices), ProvCam (verifiable video provenance), and work on minimizing smartphone TCBs via hardware isolation. His grants include NSF funding for OS kernel security and NSA support for provenance systems. Education: Ph.D., Electrical and Computer Engineering, Rice University, 2014 B.Sc., Electrical Engineering, Sharif University of Technology Grants: $500K NSF Award (with UCR) for OS kernel security (2020) NSF SaTC Award for GPU security in browsers Google ASPIRE Award for Android system call filtering Labs/Teams: Trustworthy Systems Lab (TrussLab), collaborating with industry partners like Intel and Broadcom.

• Cryptography
• Information Security
• Cryptographic Automation
• +3 more

Christina L. Garman is an Assistant Professor in the Department of Computer Science at Purdue University, where she joined in Spring 2018. Her research focuses on practical cryptography and cryptographic automation to make secure system development accessible to non-experts through error-resistant design methodologies. Her educational background includes: Bachelor of Science in Computer Science and Engineering from Bucknell University (2011) Bachelor of Arts in Mathematics from Bucknell University (2011) Master of Science in Engineering in Computer Science from Johns Hopkins University (2013) Doctor of Philosophy in Computer Science from Johns Hopkins University (2017) Professor Garman's work centers on real-world cryptographic system security, spanning protocol analysis (e.g., RC4 in TLS, Apple iMessage flaws), decentralized anonymous systems (Zerocash/ZCash), and cryptographic automation. She pioneered techniques for removing human error in cryptographic deployments through automated tools and frameworks. Her research bridges theoretical cryptography with practical implementation challenges in privacy-preserving technologies and secure infrastructure. Analysis of her 2021-2025 publications reveals expanding research horizons: hardware security vulnerabilities (Rowhammer, SGX), privacy network enhancements (Tor onion services), software supply chain security (SBOM tools), and advanced cryptographic protocols (zkSNARKs, MPC). This evolution demonstrates consistent focus on real-world security impact while diversifying into hardware-software cross-layer threats and formal verification methods for cryptographic implementations. Her major scientific recognitions include: NSF CAREER Award (2021) for cryptographic automation research ACM CCS Best Paper Award (2016) for iMessage security analysis IEEE Test of Time Award (2024) for foundational Zerocash work Professor Garman co-founded ZCash, a privacy-focused cryptocurrency based on her Zerocash protocol, and her NSF CAREER grant supports cryptographic automation development. Her research has received significant media coverage in The Washington Post, Wired, and The New York Times, highlighting real-world relevance. While specific student advising details aren't public, her active publication record indicates ongoing mentorship of graduate researchers in security and cryptography. Her work maintains strong industry connections through ZCash development and Tor network contributions, with recent projects like keyless CDNs demonstrating practical applications of cryptographic automation. She remains a leading voice in cryptographic research communities through conference participation and collaborative projects addressing evolving security challenges.