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Benedikt BünzView profile

Assistant Professor

• Applied Cryptography
• Blockchain Technology
• Consensus Algorithms
• +4 more

Benedikt Bünz is an Assistant Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences. He is also a co-founder and chief scientist of Espresso Systems, where he applies his research expertise to real-world blockchain solutions. His academic work bridges theoretical cryptography with practical blockchain implementations, focusing on enhancing privacy, security, and usability of decentralized systems. Dr. Bünz's research centers around applied cryptography, consensus mechanisms, and game theory as they relate to cryptocurrencies. His work spans zero-knowledge proofs, verifiable delay functions, secure multi-party computation, and privacy-preserving protocols. He has made significant contributions to Bulletproofs, a zero-knowledge proof system deployed on blockchains like Monero, and pioneered research in verifiable delay functions which are now part of Ethereum 2.0's design. His recent work focuses on recursive proof systems, accumulation schemes, and efficient verification techniques for blockchain scalability. His publication record shows a consistent progression from foundational cryptographic primitives to practical blockchain implementations. Recent work demonstrates increasing sophistication in recursive proof systems (ProtoStar, HyperPlonk), novel accumulation techniques (ARC, DewTwo), and foundational work on randomness generation (VDFs). His research consistently bridges theoretical cryptography with real-world blockchain applications, resulting in protocols that are both theoretically sound and practically implementable across multiple blockchain platforms. Dr. Bünz actively contributes to the academic community through teaching and mentorship. He teaches courses on cryptography of blockchains and computer security at NYU, providing students with hands-on experience in blockchain security and cryptographic protocols. His industry engagement through Espresso Systems demonstrates his commitment to translating academic research into practical solutions for the blockchain ecosystem.

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).

Daniel GottesmanView profile

Professor

• Quantum Computing
• Algorithms and Theory
• Quantum Error Correction
• +3 more

Daniel Gottesman is the Brin Family Endowed Professor in Theoretical Computer Science at the University of Maryland, affiliated with the Department of Computer Science, Institute for Advanced Computer Studies (UMIACS), and the Joint Center for Quantum Information and Computer Science (QuICS). He holds a Ph.D. in Physics from Caltech (1997) and has held positions at institutions like the Perimeter Institute and Quantum Benchmark. His research focuses on quantum computing, quantum error correction, and fault-tolerant systems, with contributions to stabilizer codes and quantum teleportation-based gates. Education: Bachelor's in Physics, Harvard University (1992) Ph.D. in Physics, California Institute of Technology (1997) Research Interests: Quantum error correction and fault-tolerant architectures Quantum cryptography and secure communication protocols Quantum complexity theory and algorithm design Applications of stabilizer codes and topological quantum computing Scientific Awards: Fellow of the American Physical Society CIFAR Senior Fellow in Quantum Information Science Three U.S. Patents (e.g., quantum key distribution systems) Advising & Grants: Supervised over 30 students/postdocs and served on numerous thesis committees. Active in securing funding for quantum research through endowed professorships and industry partnerships (e.g., Quantum Benchmark). Labs/Teams: Member of QuICS and UMIACS, collaborating on quantum hardware-software integration and error correction challenges.

• Computer Architecture
• Machine Learning
• FPGA Acceleration
• +4 more

Hadi Esmaeilzadeh is an Associate Professor at the University of California, San Diego in the Department of Computer Science and Engineering . He previously served as an Assistant Professor at Georgia Tech (2013-2017) and holds the Halicioğlu Chair in Computer Architecture . As founder/director of the Alternative Computing Technologies (ACT) Laboratory and associate director of UCSD's Center for Machine Integrated Computing and Security (MICS) , his research drives cross-stack solutions for next-generation computer systems. Early tenure recipient at UCSD Coined the term "dark silicon" in computer architecture Developed Tabla/DnnWeaver open-source frameworks Research Interests span: Approximate Computing Neural Acceleration FPGA/ASIC Hardware Design Machine Learning Systems Dark Silicon Challenges Security/Privacy in Accelerated Systems Scientific Recognition : 4 CACM Research Highlights 4 IEEE Micro Top Picks Distinguished Paper Award (HPCA 2016) Inducted to ISCA Hall of Fame (2018) Teaching : Developed courses on accelerator design (CSE 240D) and alternative computing (CS 8803 ACT) Advocates for hands-on FPGA-based learning in Processor Design with FPGAs courses

Fiona LeeView profile

Professor

• Organizational Behavior
• Social Psychology
• Cultural Psychology
• +3 more

Fiona Lee is the Arthur F. Thurnau Professor of Psychology at the University of Michigan, with affiliations in Organizational Studies and the Advisory Committee. She holds a Ph.D. from Harvard University. Her research focuses on three core areas: Identity Integration (how individuals navigate multiple identities), Power Dynamics (effects of power on behavior), and Cultural Differences (impact of cultural contexts on organizations and individuals). She teaches courses on Organizational Psychology and Research Methods in Personality/Social Contexts. Lee’s work bridges social psychology and organizational behavior, emphasizing multicultural experiences and adaptation. Her recent studies explore refugee well-being, racial discrimination coping strategies, and the role of family in resettlement processes. Notably, she investigates how cultural identity integration influences creativity, decision-making, and social tolerance. Her publications span cross-cultural communication, biculturalism, and institutional barriers faced by minorities. Lee’s research often combines qualitative and quantitative methods to address real-world challenges in workplace dynamics and global mobility.

• 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.

Catherine O'SullivanView profile

Professor

• Particulate Soil Mechanics
• Discrete Element Modelling (DEM)
• Geotechnical Engineering
• +7 more

Prof. Catherine O'Sullivan is a Professor of Particulate Soil Mechanics at Imperial College London's Department of Civil and Environmental Engineering, part of the Faculty of Engineering. She leads the Geotechnics Section and serves as Editor-in-Chief of the ASCE Journal of Geotechnical and Geoenvironmental Engineering. Her research focuses on particulate soil mechanics, employing Discrete Element Modelling (DEM) and micro-CT imaging to study sand behavior, reservoir sandstones, and internal erosion. Notable recognitions include the 2016 Shamsher Prakash Research Award and the 2021 President’s Teaching Innovation Award. Education : PhD in Civil Engineering, University of California, Berkeley (2002) MEngSc in Civil Engineering, University College Cork (Ireland) BEng (Civil Engineering), University College Cork (Ireland) Research Interests : Prof. O'Sullivan's work integrates computational and experimental methods to explore granular material behavior. Key areas include DEM validation, μCT analysis, and pore network modeling. Her group collaborates across disciplines, involving physicists and mechanical engineers alongside civil engineers. Awards & Recognition : 2015 Geotechnique Lecture Student Choice Supervision Award (nominated twice) 2023 Alert Geomechanics Special Lecture Advising & Grants : She supports PhD and postdoctoral researchers through Imperial scholarships and fellowships. Her students often explore particulate soil behavior, with many securing prestigious awards. Labs & Teams : Leads the Geotechnics Section at Imperial, fostering interdisciplinary research in geomechanics and computational modeling.

• Computer Architecture
• Machine Learning
• Approximate Computing
• +3 more

Hadi Esmaeilzadeh is an Associate Professor at the University of California, San Diego in the Department of Computer Science and Engineering . He leads the Alternative Computing Technologies (ACT) Laboratory and serves as Associate Director of the Center for Machine Integrated Computing and Security (MICS) . Previously, he was an Assistant Professor at Georgia Institute of Technology. Ph.D., Computer Science and Engineering, University of Washington (2013) Research focuses on computer architecture , machine learning acceleration , and approximate computing His work has produced 15+ publications spanning IEEE Micro Top Picks , CACM Research Highlights , and ISCA . Key projects include: Tabla : Cross-stack ML acceleration framework DnnWeaver : Open-source DNN acceleration platform Major honors include: IEEE TCCA Young Computer Architect Award ISCA Hall of Fame Qualcomm Innovation Fellowship Georgia Tech PURA Award Teaching roles: CSE 141: Introduction to Computer Architecture CSE 240D: Accelerator Design for Deep Learning CSE 240A: Principles of Computer Architecture

• Computer Hardware
• FPGA Acceleration
• Artificial Intelligence
• +8 more

Professor Wayne Luk is a Professor of Computer Engineering at the Department of Computing, Faculty of Engineering, Imperial College London. He leads the Programming Languages and Systems Section and the Custom Computing Research Group, and directs the EPSRC Centre for Doctoral Training in High-performance Embedded and Distributed Systems and the Centre for Advanced Financial Engineering. He previously served as a Visiting Professor at Stanford University from 2006 to 2009. His research spans FPGA acceleration, quantum computing, deep learning optimization, and algorithm-hardware co-design, with affiliations to the CRUK Convergence Science Centre and the Engineering Secure Software Systems group. His research interests include computational modeling for particle physics, causal discovery in agent-based systems, and high-throughput digital electronics. Notable contributions include FPGA-accelerated algorithms for neural networks, quantum circuit simulation, and Bayesian optimization frameworks. His work emphasizes practical applications of reconfigurable hardware in fields like medical imaging, high-energy physics, and financial systems. Professor Luk is a Fellow of the Royal Academy of Engineering, IEEE, and BCS. His publications focus on advancing hardware-aware machine learning, FPGA-based acceleration techniques, and scalable design methodologies. His research bridges theoretical computer science with applied engineering, addressing challenges in real-time systems, embedded computing, and next-generation computing architectures. His academic leadership includes directing interdisciplinary centers and training programs, fostering collaboration across computing, engineering, and physics. Current projects explore quantum computing tools, causal inference systems, and high-performance graph neural networks for particle physics applications.

• Cybersecurity
• Software Security
• System Security
• +6 more

Ruoyu (Fish) Wang is an Associate Professor at the School of Computing and Augmented Intelligence, Arizona State University (Tempe campus). He also holds affiliations as Associate Director of Impact at the Global Security Initiative, Center for Cybersecurity & Trusted Foundations, and with the Biodesign Center for Biocomputing, Security and Society. His educational background includes: Ph.D. in Computer Science, University of California, Santa Barbara Professor Wang's research focuses on system security, with an emphasis on automated binary program analysis and reverse engineering of software. He is the co-founder and core developer of the angr binary analysis platform, which won third place in the DARPA Cyber Grand Challenge (2018). His work spans vulnerability discovery, fuzzing, and security tool development for binary program analysis. His current research interests include: Binary program analysis and reverse engineering Automated vulnerability discovery and mitigation Fuzzing techniques and robust testing Phishing and fraud detection in e-commerce Security of firmware and embedded systems Application of machine learning to security problems His recent publications (2024-2025) demonstrate cutting-edge research in fraud detection for e-commerce using LLMs, advanced fuzzing methodologies, and binary decompilation techniques. Key trends include bridging theoretical program analysis with practical security tools, as evidenced by extensions to the angr platform, and addressing emerging threats in financial ecosystems and client-side security. Dr. Wang has received notable recognition: Third place in DARPA Cyber Grand Challenge (2018) with team Shellphish As an active educator, he supervises graduate research (CSE 599/799) and teaches core cybersecurity courses including Software Security (CSE 545) and Information Assurance (CSE 365). His teaching spans multiple semesters through 2025, covering practicums, internships, and special topics in computing security. Dr. Wang co-founded the angr binary analysis platform and contributes to Arizona State University's security research ecosystem through leadership roles in the Center for Cybersecurity & Trusted Foundations and Biodesign Center for Biocomputing, Security and Society.

Debjit PalView profile

Research Fellow

• Machine Learning for Electronic Design Automation (EDA)
• System-on-Chip (SoC) Verification
• Hardware Verification
• +6 more

Debjit Pal is a Post-Doctoral Associate at the School of Electrical and Computer Engineering, Cornell University, and a member of the Computer Systems Laboratory. His research focuses on machine learning techniques for hardware verification, SoC validation, and FPGA optimization. Education: Ph.D. in Computer Engineering (University of Illinois at Urbana-Champaign, 2019) M.S. in Computer Science (IIT Kharagpur, 2012) B.E. in Electronics Engineering (Jadavpur University, 2008) Research Interests: Machine Learning for Electronic Design Automation (EDA) System-on-Chip (SoC) Verification Edge Intelligence as a Service Compiler Optimizations for Reconfigurable and High-Performance Computing Scientific Awards: IEEE CEDA Student Research Award (2016) Best Paper Nomination (ICCAD 2015, DAC 2018, ASP-DAC 2019) E. J. McCluskey Best Doctoral Thesis Competition Semi-Finalist (2020) Travel Grants for ICCAD/DAC/ASPDAC (2018-2019) Professional Roles: Technical Program Committee Member (DAC, VLSID), Reviewer (IEEE TVLSI, DATE, ICCAD). Collaborates with researchers like Zhiru Zhang and Shobha Vasudevan.

Wolfgang KunzView profile

Professor

• Hardware Safety and Security
• Formal Hardware Verification
• Embedded Computing Systems
• +5 more

Wolfgang Kunz is a Full Professor (C4, W3) and Chair of Electronic Design Automation at the Technische Universität Kaiserslautern since 2001. His academic career spans multiple prestigious institutions, including Goethe-University Frankfurt/Main and the University of Massachusetts, Amherst. He has held leadership roles such as Dean (2005-2007) and Vice-Dean (2007-2009) at TU Kaiserslautern. Habilitation (Dr. rer. nat. habil.), Computer Science, University of Potsdam (1996) Doctoral degree (Dr.-Ing.), Electrical Engineering, University of Hannover (1992) Dipl.-Ing. degree, Karlsruhe Institute of Technology (1989) His research focuses on hardware verification, security, and optimization, particularly in embedded systems and processors. His work on formal verification methods has been commercialized by companies like Synopsys, Mentor Graphics, and Siemens EDA. His 2016-2021 publications address critical security issues such as Spectre/Meltdown and introduce innovative verification frameworks adopted by industry leaders like Infineon and OneSpin Solutions. Scientific awards include the IEEE Fellow (2006), German IT Society Award (2005), and TU Kaiserslautern Distinguished Teaching Award (2016). He has served on editorial boards of major journals and coordinated the Erasmus Mundus European Master Program in Embedded Computing Systems since 2010. Key students: Jörg Bormann, Raik Brinkmann, Tobias Ludwig Collaborations: Siemens EDA, Infineon, AbsInt, Intel SCAP Spin-offs: LUBIS EDA, OneSpin Solutions

Tianyi ZhangView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +4 more

Tianyi Zhang is a Tenure-Track Assistant Professor in the Department of Computer Science at Purdue University, part of the College of Science. He leads the Human-Centered Software Systems Lab, focusing on AI-driven systems that synergize human expertise with machine intelligence to enhance programming productivity and software reliability. Prior to Purdue, he was a Postdoctoral Fellow at Harvard University under Dr. Elena Glassman and earned his Ph.D. from UCLA (2019) and B.Sc. from Huazhong University of Science and Technology (2013). Education: Ph.D. in Computer Science, University of California, Los Angeles (2019) Bachelor's in Computer Science, Huazhong University of Science and Technology (2013) Research Interests: His work spans Software Engineering, Human-Computer Interaction, and AI. Key areas include program synthesis, interactive debugging tools, autonomous driving system testing, and mitigating biases in AI models. He develops systems like Interpretable Program Synthesis and SQLucid to bridge human and machine intelligence. Recent Trends in Publications: Recent work emphasizes human-in-the-loop AI, including mixed-initiative systems for data wrangling (Dango), interactive program repair, and bias analysis in text representations (STILE). He also explores challenges in autonomous driving testing and LLM-based code generation errors. Awards & Grants: NSF Career Award (2024) Amazon Research Award Showalter Trust Research Award for pre-diabetes research $1.5M NSF grant for software supply chain security Best Paper Honorable Mentions at CHI and VAHC Advising & Teams: Supervises 12+ PhD/Master's students and 30+ research interns. Notable advisees include Bonan Kou (API misuse studies) and Yuan Tian (text-to-SQL systems). Collaborates with Harvard Medical School on healthcare data analysis. Labs & Initiatives: Directs Purdue's Human-Centered Software Systems Lab. Co-founded the Societal Impact Fellows program. Active in open-source projects like Examplore for API usage visualization and JShrink for Java debloating.

Adam FinkelsteinView profile

Professor

• Computer Science
• Audio Processing
• Image Processing
• +3 more

Adam Finkelstein is a Professor in the Department of Computer Science at Princeton University, where he has been a faculty member since 1997. He holds a PhD and Master's in Computer Science from the University of Washington and a dual degree in Physics and Computer Science from Swarthmore College. Finkelstein is renowned for his interdisciplinary work at the intersection of computer graphics, audio processing, and machine learning, and he co-organized the Art of Science exhibition at Princeton. Education: PhD, Computer Science, University of Washington MS, Computer Science, University of Washington BA, Physics and Computer Science, Swarthmore College His research spans audio processing (e.g., speech enhancement, voice conversion, audio metrics), computer graphics (e.g., line drawing algorithms, stylized rendering, image manipulation), and machine learning (e.g., self-supervised learning, differentiable programming). His work often bridges technical and creative domains, exemplified by collaborations at Pixar and Adobe Creative Technologies Lab. Recent publications highlight advancements in audio super-resolution and voice conversion using deep learning frameworks, as well as stylized line rendering for animated 3D models. His contributions to perceptual audio metrics and shader optimization further underscore his impact on human-centric computational systems. Scientific Awards: NSF CAREER Award Alfred P. Sloan Fellowship Fellow of the Association for Computing Machinery (ACM) Finkelstein has secured foundational grants for his research and actively mentors students, though no specific advisees are listed. He also explores collaborative tools for internet music performance, reflecting his broader interest in distributed systems and user interfaces.

Amelia SimpsonView profile

Associate Professor

• Constitutional Law
• Discrimination Law
• Federalism
• +3 more

Dr. Amelia Simpson holds the position of Honorary Associate Professor at the Australian National University (ANU), specializing in constitutional law with a focus on discrimination principles and federalism. She earned her BA Hons and LLB Hons from ANU, followed by an LLM and JSD from Columbia University. She is a practicing Barrister and Solicitor in the High Court of Australia. Her research critically examines constitutional frameworks, particularly regarding state residence discrimination under Section 117 and interstate free trade jurisprudence. Her work has been cited in landmark High Court and Federal Court rulings, solidifying her reputation as a leading scholar in public law. Amelia’s contributions include co-authoring Hanks Australian Constitutional Law: Materials and Commentary (2016) and contributing to the Oxford Handbook of Australian Constitutional Law (2018). Her research on social equality and functionalist approaches to non-discrimination has advanced scholarly discourse on constitutional values. Notably, her 2017 paper Social Equality in Australia critiques the historical neglect of equality principles in constitutional interpretation. Her publications span constitutional doctrine, class actions reform, and environmental policy, reflecting her interdisciplinary approach. She ranks among Australia’s top 20 most prolific legal scholars in high-impact journals (2000–2010). Her work bridges theoretical analysis with practical legal challenges, influencing both academia and judicial practice.