جست‌وجوی استاد و پژوهشگر

Kenneth P. Birmanمشاهده پروفایل

استاد

• Distributed Systems
• Cloud Computing
• Machine Learning Systems
• +۵ مورد دیگر

Kenneth P. Birman is the N. Rama Rao Professor of Computer Science at Cornell University, where he has had a long and impactful career in distributed systems, cloud computing, and AI/ML infrastructure. He is known for foundational contributions to reliable and scalable distributed systems, and for leading high-impact projects such as Cascade, Vortex, and Derecho. He is also the author of a widely used textbook on reliable distributed systems and has founded multiple companies based on his research. Education: Ph.D. in Computer Science, University of California, Berkeley M.S. in Computer Science, University of California, Berkeley B.A. in Computer Science, Columbia University Research Interests: Professor Birman's research focuses on building reliable, secure, and scalable distributed systems . His current emphasis is on AI and ML infrastructure , particularly in reducing latency and improving performance through hardware acceleration, RDMA-based communication, and edge computing. He explores how to eliminate data movement bottlenecks in AI pipelines and how to support real-time, mission-critical applications in domains like healthcare, smart grids, and industrial IoT. His work spans systems programming, cloud computing, fault tolerance, and formal verification . He has designed systems that have been deployed in high-stakes environments such as the New York Stock Exchange, the Swiss Exchange, and the French Air Traffic Control system. Scientific Awards: ACM Fellow (1999) IEEE Fellow (2014) IEEE Tsutomu Kanai Award for innovations in distributed computing Teaching and Mentorship: Professor Birman teaches two courses in the fall semester: CS4414: Systems Programming and CS5416: Cloud and ML Systems Programming . He has advised numerous Ph.D. and M.S. students, including Alicia Yang, Tiancheng Yuan, Yifan Wang, Weijia Song, Edward Tremel, Sagar Jha, Jonathan Behrens, and Mae Milano. He has announced that Fall 2025 will be his last semester teaching, and he is no longer recruiting new students, though he will continue supervising current ones. Labs and Projects: He leads the Derecho Project and the Cascade/Vortex Project , both focused on high-performance distributed systems. These projects are collaborative efforts with students and industry partners, and the software is released under open-source licenses. He also maintains strong ties with Cornell's systems group and collaborates with faculty across CS, ECE, IS, and the Cornell Tech NYC campus.

Achuta Kadambiمشاهده پروفایل

دانشیار

• AI
• Computational Imaging
• Computer Vision
• +۷ مورد دیگر

Achuta Kadambi, Ph.D., is an Associate Professor at UCLA in Electrical Engineering and Computer Science, leading an interdisciplinary research group focused on AI, computational imaging, and bias mitigation in medical technologies. He recruits PhD students from EE, CS, and Bioengineering departments and has commercialized research through two California-based companies. His research investigates the intersection of physics and artificial intelligence, with a focus on unbiased low-level vision systems. Current projects explore how light transport interacts with human skin variations to identify and correct imaging biases in facial recognition and medical devices. His work has produced over 70 patents, with 30+ issued, and a textbook Computational Imaging (MIT Press, 2022). NSF CAREER Award (2021) for light transport bias research DARPA Young Faculty Award (2021) for AI and medical imaging innovations ARO Young Investigator Program (2021) for computational sensing IEEE-HKN Under 35 Award (2022) for inclusive EECS inventions Forbes 30 Under 30 recognition His recent publications focus on polarization imaging, 3D Gaussian splatting, synthetic data generation for healthcare, and bias mitigation in machine learning. Collaborations with UCLA medical school faculty, including Dr. Laleh Jalilian, aim to deploy these innovations in clinical settings. Current teaching includes ECE 149: Foundations of Computer Vision (Fall 2024, Spring 2025) and ECE 102: Signals and Systems (Winter 2024).

Andrew Headمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Programming
• Reading
• +۱۳ مورد دیگر

Andrew Head is an Assistant Professor at the University of Pennsylvania in the Computer and Information Science department. His research focuses on human-computer interaction, programming, and reading, particularly in developing technology for interactive reading and reasoning. He advises PhD students Alyssa Hwang, Hita Kambhamettu, Litao Yan, Jeffrey Tao, and Jessica Shi, and co-leads the Penn Human-Computer Interaction (Penn HCI) group with Danaé Metaxa. His work is published in top venues like ACM CHI, UIST, and ICSE. Research Interests: Andrew's work bridges interactive systems with programming environments, aiming to enhance how scientists and programmers interact with their tools. Key areas include AI-assisted code understanding, math notation accessibility, and medical note interpretation through interactivity. He employs user studies to identify needs and builds interactive systems to address them. Recent Article Trends: His publications emphasize systems-centric HCI approaches, integrating AI into code and document interfaces. Topics span code explanation (e.g., Ivie), property-based testing (e.g., Tyche), math notation augmentation (e.g., FreeForm), and medical informatics (e.g., Explainable Notes). Recent work also explores notebook environments (e.g., Bolt-on, Tyche) and literate programming (e.g., Colaroid). Scientific Awards: Distinguished Paper Award, ICSE 2024 Best Paper Awards at CHI (2024, 2023, 2022, 2019), UIST (2023, 2018), and others. Nominated for Best Paper at CHI 2023 and VL/HCC 2015. Advising & Grants: Andrew advises multiple PhD students and has secured significant grants, including a $1M NSF award for 'Property-based Testing for the People' (2024). His group collaborates with Penn’s MindCORE center and teams like PLClub and PennNLP. Labs & Teams: He co-leads the Penn HCI group, which works closely with other Penn research teams and labs. The group focuses on creating interactive tools that enhance scientific and programming workflows, supported by grants and cross-institutional partnerships.

Andrew Childsمشاهده پروفایل

استاد

• Quantum Algorithms
• Quantum Information Processing
• Quantum Simulation
• +۲ مورد دیگر

Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.

Peter Selingerمشاهده پروفایل

استاد

• Quantum Computing
• Category Theory
• Mathematical Logic
• +۴ مورد دیگر

Peter Selinger is a Professor in the Department of Mathematics and Statistics at Dalhousie University , with a cross-appointment in Computer Science. He specializes in mathematical methods in computer science, particularly quantum computing and combinatorial game theory . His work on quantum programming languages like Quipper and foundational research in category theory has garnered international recognition. Education : Ph.D. in Mathematics (University of Pennsylvania, 1997), undergraduate studies in Mathematics (Technische Universität Darmstadt). Research Interests span quantum computing, category theory, and combinatorial game theory. He has pioneered formalisms for quantum programming languages, developed categorical models for quantum mechanics, and analyzed game-theoretic structures in games like Hex. His recent work includes linear dependent type theory , quantum circuit synthesis , and combinatorial game classification . Publications demonstrate expertise in quantum programming languages, categorical semantics, and game theory. Key trends include Hamiltonian simulation , Clifford+T circuits , and monotone game realization . Scientific Honors include the Killam Professorship (2017–2022), Faculty of Science Award for Excellence in Teaching (2023), and fellowships from the Alfred P. Sloan Foundation and German National Scholarship Foundation . Students he has supervised include PhD graduates Xiaoning Bian , Francisco Rios , and Neil J. Ross , along with MSc students like Fahimeh Bayeh and Seth Greylyn . He has advised 16 postdoctoral researchers.

David Bindelمشاهده پروفایل

دانشیار

• Applied Numerical Linear Algebra
• Eigenvalue Problems
• Network Analysis
• +۵ مورد دیگر

David Bindel is an Associate Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences, College of Engineering, and Cornell Ann S. Bowers College of Computing and Information Science. He earned his Ph.D. in Mathematics from the University of California, Berkeley in 2006. His research focuses on applied numerical linear algebra, eigenvalue problems, and their applications in plasma physics, network analysis, and nonlinear systems. He develops methods for analyzing complex systems, including magnetic confinement in stellarators, stability of MHD systems, and community detection in networks. His work bridges theoretical foundations with practical computational tools, such as formal verification of linear algebra algorithms and scalable Gaussian process models. Bindel’s research explores the interplay between structure and computation, leveraging eigenvalue analysis to address challenges in computer vision, opinion dynamics, and engineering design. He has contributed to advancements in numerical methods for large-scale systems, including iterative solvers, spectral approximation techniques, and stochastic optimization. His interdisciplinary approach spans applied mathematics, computer science, and physics, with applications in fusion energy, machine learning, and network science. Recent work highlights include high-order expansions for magnetic confinement, adaptive filtering for dynamical systems, and Bayesian optimization strategies. His publications emphasize rigorous analysis alongside computational scalability, addressing both theoretical and practical aspects of modern scientific computing. Despite no explicitly listed awards, his contributions reflect significant impact in his fields.

Naresh R. Shanbhagمشاهده پروفایل

استاد

• Energy-efficient computing
• Machine learning hardware
• In-memory computing
• +۷ مورد دیگر

Naresh R. Shanbhag is the Jack Kilby Professor in the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. He serves as Director of the Systems on Nanoscale Information fabriCs (SONIC) Center and held the D.J. Gandhi Distinguished Visiting Professorship at IIT Mumbai from 2015-2020. Previously, he was a visiting faculty member at National Taiwan University (2007) and Stanford University (2014). Dr. Shanbhag received his doctorate from the University of Minnesota (1993) in Electrical Engineering. From 1993 to 1995, he worked at AT&T Bell Laboratories as the lead chip architect for AT&T's 51.84 Mb/s transceiver chips over twisted-pair wiring for Asynchronous Transfer Mode (ATM)-LAN and very high-speed digital subscriber line (VDSL) chip-sets. His research focuses on the design of energy-efficient machine learning, communications, and signal processing systems on resource-constrained embedded platforms. He explores fundamental trade-offs between energy efficiency, latency and accuracy of decision-making systems implemented in nanoscale technologies, with applications to computer vision, biomedicine, automatic target recognition, and imaging. His work spans four primary focus areas: Resource-efficient Machine Learning for the Edge, In-memory Computing (IMC), Energy-efficient High Data Rate Communications, and Shannon-inspired Statistical Error Compensation (SEC). Analysis of his recent publications reveals a strong emphasis on in-memory computing architectures (SRAM, MRAM, RRAM) for machine learning acceleration. His work consistently addresses energy-accuracy trade-offs, with increasing attention to security aspects of hardware implementations and applications to MIMO signal processing and edge AI systems. His research demonstrates a progression from theoretical foundations to practical silicon implementations. 2024 Semiconductor Research Corporation Innovation Award 2018 Semiconductor Industry Association/Semiconductor Research Corporation University Researcher Award 2018 IEEE International Symposium on Circuits and Systems Best Paper Award 2006 IEEE Fellow 1996 National Science Foundation CAREER Award Professor Shanbhag has mentored over 50 graduate students who now work at leading technology companies including Qualcomm, Amazon, Nvidia, Intel, and Apple. His research has been generously supported by the National Science Foundation, DARPA, AFRL, Semiconductor Research Corporation, Texas Instruments, Sandia National Laboratories, and industry partners including IBM, GlobalFoundries, and Intel Corporation. He led the Alternative Computational Models research theme (2006-2012) and was the founding Director of the SONIC Center (2013-2017), a 5-year multi-university center funded by DARPA and SRC. Currently, he leads research themes in the SRC and DARPA funded JUMP 2.0 Program's Center for Co-Design of Cognitive Systems and the Center for Ubiquitous Connectivity, and in the NSF IUCRC Center for Advanced Semiconductor Chips with Accelerated Performance (ASAP). As Director of the Systems on Nanoscale Information fabriCs (SONIC) Center, Professor Shanbhag leads a multidisciplinary team exploring novel computing paradigms for the nanoscale era. His group has benchmarked an extensive collection of in-memory computing and digital accelerator IC designs, maintaining a publicly available IMC benchmarking repository of metrics extracted from published IC prototypes. His research philosophy integrates concepts from information theory, statistical signal processing, detection and estimation, VLSI architectures, and digital and analog integrated circuits to develop energy-efficient systems from algorithms to silicon implementations.

Joseph Eremondiمشاهده پروفایل

استادیار

• Programming Languages
• Type Theory
• Dependent Types
• +۱۲ مورد دیگر

Joseph Eremondi is an Assistant Professor in the Department of Computer Science at the University of Regina, Faculty of Science, Canada. He began his tenure in 2024 after serving as a Royal Society Newton International Fellow at the University of Edinburgh, where he conducted postdoctoral research with Ohad Kammar in the Laboratory for Foundations of Computer Science. He earned his PhD from the University of British Columbia (UBC) under the supervision of Ron Garcia at the UBC Software Practices Laboratory. His research is centered on programming languages theory, with a strong focus on type systems that enhance software reliability and usability. He is particularly known for his work in dependent types, gradual typing, and the integration of both paradigms. His research interests include: Dependent pattern matching and its semantic foundations Gradual dependent types and approximate normalization Error message generation and usability in dependently typed languages Static analysis using set constraints and SMT solvers Theoretical properties of reversal-bounded counter automata and shuffle operations His recent publications, appearing in premier venues like POPL, ICFP, and CPP, reflect a consistent trajectory toward making advanced type systems more accessible and practical. Key themes include coverage semantics for dependent pattern matching, formal models of gradual dependent typing, and improving the developer experience through better tooling and error diagnostics. Notable scientific recognitions include the NSERC Discovery Grant (awarded in 2025) and the prestigious Royal Society Newton International Fellowship. These awards underscore the impact and promise of his research program on the usability of dependently typed programming languages. Joseph is actively mentoring and recruiting graduate students, particularly in areas such as dependently typed programming (Lean, Agda, Idris, Coq), gradual typing, live programming environments, and static analysis. He emphasizes close collaboration within a small, focused research group. He has also served on program committees, including for TyDe and POPL Artifact Evaluation, demonstrating active engagement in the programming languages community. His work bridges theoretical rigor with practical implementation, evident in his artifact releases on GitHub and integration with tools like Ott and DrRacket. He maintains a personal website and open-source repositories that support reproducibility and community involvement.

• Reconfigurable Computing
• Field-Programmable Gate Arrays (FPGA)
• High-Level Synthesis
• +۳ مورد دیگر

Paolo Ienne is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), where he leads the Processor Architecture Laboratory (LAP) within the School of Computer and Communication Sciences. His research focuses on advancing reconfigurable computing systems through innovative FPGA architectures and high-level synthesis methodologies. His primary research domains include reconfigurable computing, FPGA architecture design, dynamically scheduled dataflow circuits, and hardware acceleration techniques. Recent work emphasizes memory system optimization for FPGAs, formal verification of circuit transformations, and rapid C-to-hardware compilation flows. He has pioneered approaches for handling thousands of outstanding memory misses in FPGA accelerators and developed novel techniques for switch-block exploration without explicit pattern enumeration. Analysis of his 2023-2025 publications reveals a strong trend toward practical FPGA deployment challenges, with increasing focus on HBM integration, virtual memory systems for PCIe-attached devices, and formally verified circuit transformations. His work consistently targets real-world bottlenecks in high-level synthesis toolchains while maintaining theoretical rigor in dataflow architecture design. Professor Ienne's laboratory receives support from the Swiss National Science Foundation and industry partners including Huawei, enabling cutting-edge research in FPGA-based acceleration. His collaborative network spans major semiconductor companies and academic institutions worldwide, with frequent co-authorship on conference proceedings and journal publications in IEEE and ACM venues.

Jundong Liمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Graph Machine Learning
• +۱۲ مورد دیگر

Jundong Li is an Assistant Professor at the University of Virginia with primary appointment in the Department of Electrical and Computer Engineering and secondary appointments in Computer Science and the School of Data Science. He is affiliated with the School of Engineering and Applied Science and conducts research at the intersection of machine learning, data mining, and artificial intelligence. Education: Ph.D. in Computer Science, Arizona State University, 2019 M.Sc. in Computer Science, University of Alberta, 2014 B.Eng. in Software Engineering, Zhejiang University, 2012 His research focuses on graph machine learning , trustworthy and fair AI , and large language models . He investigates how to make deep learning models more interpretable, robust, and equitable, especially in graph-structured data and NLP applications. His work combines causal inference, feature selection, and model explanation techniques to build reliable AI systems. His recent publications (2024–2022) reveal a strong trend toward large language models , with topics including in-context learning, knowledge editing, and collaborative reasoning. Simultaneously, he continues pioneering research on fairness and interpretability in graph neural networks , addressing structural bias, adversarial attacks, and node attribution. His work is highly interdisciplinary, spanning computer science, data science, and social impact. Scientific Awards: SIGKDD Rising Star Award (2024) PAKDD Best Paper Award (2024) NSF CAREER Award (2022) SIGKDD Best Research Paper Award (2022) JP Morgan Faculty Research Award (2021, 2022) Cisco Faculty Research Award (2021) Stanford/Elsevier Top 2% Scientist (2024) Jundong Li actively advises graduate students, as seen in his co-authored papers with researchers like Song Wang, Yushun Dong, and Binchi Zhang. His research is generously funded by the National Science Foundation (NSF) through multiple programs including CAREER, III, SaTC, SAI, and S&CC, as well as by the Department of Energy (DOE) , Office of Naval Research (ONR) , Jefferson Lab , and industry partners including JP Morgan, Cisco, Netflix, and Snap . He leads a dynamic research group focused on advancing the frontiers of graph learning and trustworthy AI, with projects on causal inference, model unlearning, and explainable systems. His lab contributes to both theoretical foundations and real-world applications in public health, transportation, and network security.

Caroline Lemieuxمشاهده پروفایل

استادیار

• Programming Languages
• Software Engineering
• Fuzz Testing
• +۶ مورد دیگر

Caroline Lemieux is an Assistant Professor at the Department of Computer Science, University of British Columbia (UBC), with research focused on advancing software correctness, security, and performance through innovative testing and synthesis techniques. Her work bridges Programming Languages and Software Engineering , particularly in fuzz testing, specification mining, and program synthesis. PhD from University of California, Berkeley (2021), advised by Koushik Sen Postdoctoral researcher at Microsoft Research, NYC (2021-2022) Key contributions: FuzzFactory , CodaMOSA , Arvada , and Gauss Her research integrates machine learning with traditional testing methods, exemplified by projects like RLCheck (reinforcement learning for test generation) and AutoPandas (neural synthesis for dataframes). Recent publications analyze generator-based fuzzing challenges and propose hybrid strategies combining coverage feedback with AI-driven insights. Scientific Awards : ACM/SIGSOFT Best Paper Award (ESEC/FSE 2019) ACM/SIGSOFT Tool Demonstration Award (ISSTA 2019) ACM/SIGSOFT Distinguished Artifact Award (ISSTA 2019) NSERC Postgraduate Scholarship-Doctoral (PGS D) UBC Governor General's Silver Medal (2016) Teaching roles include: 2025W2: CPSC 539L - Topics in Programming Languages 2024W2: CPSC 410 - Advanced Software Engineering 2023W2: CPSC 410 (with Alex Summers) She supervises graduate and undergraduate researchers working on projects like ExploTest (automated unit test generation) and GRIMOIRE (grammar extraction from pseudo-rules). Her research team collaborates with institutions including Microsoft Research, Google, and academic partners in systems security and AI-driven testing.

Juan Zhaiمشاهده پروفایل

استادیار

• Software Engineering
• Program Languages
• Natural Language Processing
• +۶ مورد دیگر

Juan Zhai is an Assistant Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. She co-directs the Laboratory for Advanced Software Engineering Research (LASER) and is a member of the UMass NLP group. Her research advances software engineering through automated techniques for building high-quality systems with emphasis on behavioral specifications, AI safety, and trustworthy AI. Her work addresses the fundamental challenge of aligning software behavior with intended specifications through two main directions: automated specification synthesis (translating natural language comments to formal specifications via tools like C2S and LLMCup) and defect detection/repair (developing frameworks for AI system testing, bias mitigation, and training diagnostics). Her vision integrates these into end-to-end assurance systems that continuously validate, repair, and audit evolving software in dynamic environments. Recent publications (2024-2025) reveal dominant trends at the software engineering/AI intersection: formal specification synthesis for IoT and code generation, comment maintenance using LLMs, deep learning framework testing (DevMuT, Citadel), bias detection in LLMs, and automated training repair (AutoTrainer, DREAM). These contributions appear in top venues including ICSE, FSE, ASE, ISSTA, and ACL. Professor Zhai currently advises PhD student Gehao Zhang (focusing on Software Engineering and AI Safety) and actively recruits new PhD/Master's students. Her LASER lab develops practical tools for specification inference, LLM-driven synthesis, and trustworthy AI, while collaborating with the UMass NLP group on language-centric software analysis. The LASER lab, co-directed by Zhai, pioneers techniques for behavioral specification enforcement across traditional and AI-powered systems. Key projects include CPC for bidirectional code-comment analysis, ModelMeta for deep learning framework testing, and frameworks for bias mitigation across the ML lifecycle. The lab emphasizes practical, scalable tools that enhance correctness, robustness, and fairness in critical AI applications.

Jens Grothمشاهده پروفایل

عضو هیئت علمی

• Cryptography
• Cryptographic Protocols
• Zero-Knowledge Proofs
• +۵ مورد دیگر

Jens Groth is an Honorary Professor at the Department of Computer Science, University College London (UCL), and serves as Chief Scientist at Nexus. His primary research focuses on cryptography, with an emphasis on cryptographic protocols, zero-knowledge proofs, and privacy-preserving technologies. Groth has contributed significantly to advancements in digital signatures, homomorphic encryption, and secure multi-party computation. He holds a leadership role as Program Chair for the 15th IMA International Conference on Cryptography and Coding (2015) and has been a key figure in shaping modern cryptographic standards. His work often bridges theoretical foundations with practical implementations, emphasizing efficiency and security. Groth's research interests include but are not limited to: cryptographic protocol design, lattice-based cryptography, and the application of zero-knowledge proofs in real-world systems such as blockchain and voting systems. His publications span venues like CRYPTO, EUROCRYPT, and ASIACRYPT, reflecting his impact on the field. Notably, he advocates for open access to research and has contributed to strategies ensuring conferences adopt de facto open-access policies. His current focus at Nexus centers on verifiable computation and distributed cryptographic systems.

Anand V Natarajanمشاهده پروفایل

استادیار

• Quantum Information Theory
• Quantum Complexity Theory
• Nonlocality and Bell Inequalities
• +۳ مورد دیگر

Anand V Natarajan is the ITT Career Development Professor in Computer Technology and Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, with affiliation to the Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds a PhD in Physics from MIT (2018), advised by Aram Harrow, and previously served as a postdoc at Caltech's Institute for Quantum Information and Matter. His research focuses on theoretical quantum information, emphasizing quantum complexity theory (e.g., MIP* and QMA(2) classes), nonlocality (Bell inequalities, nonlocal games), and semidefinite programming hierarchies. He teaches courses such as Quantum Cryptography, Quantum Systems Engineering, and Introduction to Algorithms at MIT. Notable contributions include resolving the MIP* = RE problem and the FOCS 2019 Best Paper Award for NEEXP ⊆ MIP*. He advises PhD students in quantum computing topics and collaborates extensively on quantum verification protocols and entanglement testing.

Stefania Dumbravaمشاهده پروفایل

دانشیار

• Formal methods for databases
• Graph databases
• Property graphs
• +۷ مورد دیگر

Stefania Dumbrava is an Associate Professor of Computer Science at ENSIIE (École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise) and a permanent member of the ACMES team in the SAMOVAR laboratory at Télécom SudParis, Institut Polytechnique de Paris. She is also actively involved in the Property Graph Schema Working Group and the European Research Network on Formal Proofs (EuroProofNet). Education PhD in Computer Science, Université Paris-Sud (2016) MSc in Computer Science, Jacobs University Bremen (2012) BSc in Mathematics, Jacobs University Bremen (2010) Research Interests Dumbrava's research lies at the intersection of formal methods and data management . She designs and verifies algorithms and systems for graph databases , with emphasis on property graphs , schema discovery , query optimization , and distributed graph processing . Recently, her work focuses on certifying large-scale distributed graph systems under the ANR JCJC VERDI project (2025–2029). Awards & Honors SIGMOD Best Paper Award 2023 – “PG-Schema: Schemas for Property Graphs” SIGMOD Research Highlight Award 2023 – “Threshold Queries” VLDB 2022 Best Regular Research Paper Runner-Up – “Threshold Queries in Theory and in the Wild” SIGMOD 2025 Distinguished Reviewer Award ICDE 2025 Best Program Committee Member Award EASST Best Software Science Paper Award, ICGT 2025 Students & Grants Dumbrava has supervised numerous research interns and is actively recruiting PhD students for her ANR VERDI project on verified foundations of large-scale distributed graph systems. She has also served on six PhD thesis committees as examiner since 2021. Labs & Teams She leads the ACMES research group within the SAMOVAR laboratory (Télécom SudParis, Institut Polytechnique de Paris), where her team develops formally verified graph-database engines and tools such as GRASP, VerDILog, and DatalogCert.