جست‌وجوی استاد و پژوهشگر

Dawn Songمشاهده پروفایل

استاد

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +۷ مورد دیگر

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

Adrian Wellerمشاهده پروفایل

استاد پژوهشی

• Artificial Intelligence
• Machine Learning
• Explainability
• +۹ مورد دیگر

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.

• Educational Technology
• Informatics Education
• Programming Pedagogy
• +۳ مورد دیگر

Snježana Babić serves as Associate Professor and Head of the Graduate Study Program Informatics - Teaching Major at the Faculty of Informatics in Pula , University of Pula. With a PhD obtained in 2016 and university affiliation since 2017, she teaches across undergraduate and graduate programs focusing on ICT fundamentals, web design, digital competencies, and informatics education methodology. Her educational background includes graduation in 1993 followed by doctoral studies culminating in 2016. Though specific institutions aren't detailed in the text, her career trajectory shows progression from foundational ICT teaching to specialized informatics education leadership. Babić's research centers on educational technology adoption with particular emphasis on programming pedagogy for young learners through the MEMA method. Her work investigates digital competencies across multiple educational contexts, from primary schools to higher education. Recent publications demonstrate rapid adaptation to emerging technologies, with 2025 studies examining ChatGPT integration in Croatian educational settings and cross-cultural applications of programming methods. Analysis of her 15 most recent publications reveals consistent focus on technology acceptance models applied to Croatian educators and students. The research trajectory shows evolution from foundational e-learning systems (2016-2020) to cutting-edge AI and robotics applications (2022-2025), maintaining strong methodological focus on teacher motivation , implementation barriers , and culturally-specific adoption factors . Her teaching portfolio demonstrates deep integration with research interests: Undergraduate: ICT Fundamentals, Web Design and Application Graduate: Digital Competencies in Education, Informatics Education Methodology, Practicum for IT Educators Cross-disciplinary: ICT in Nursing, Healthcare Informatics Babić actively contributes to institutional leadership as program head while maintaining robust research productivity. Her work bridges theoretical technology adoption frameworks with practical implementation challenges in Croatian educational contexts, particularly examining how emerging technologies like humanoid robots and AI assistants can be effectively integrated into teaching practices.

Eric Wongمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Explainable AI
• +۵ مورد دیگر

Eric Wong is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania, affiliated with the ASSET Center and leading the Brachio Lab. His research focuses on robust and reliable machine learning, including model debugging, adversarial robustness, and explainable AI. He holds a PhD from Carnegie Mellon University (CMU), advised by J. Zico Kolter, and completed a postdoc with Aleksander Madry. His work bridges theory and practice, addressing challenges in model interpretability, safety, and scalability. Teaching includes CIS 5200 (Machine Learning), CIS 3333 (Mathematics for Machine Learning), and a specialized course on debugging ML pipelines. Notable contributions include the FIX Benchmark for interpretable features and defenses against LLM jailbreaking attacks. He received an Amazon Research Award in 2024 and has published extensively in top conferences like ICML, NeurIPS, and ICLR. Affiliations: University of Pennsylvania (CIS), ASSET Center, Brachio Lab Education: PhD in Machine Learning (CMU), Postdoc at MIT Key Projects: FIX Benchmark, DOLPHIN framework, SmoothLLM defense Awards: Amazon Research Award (2024) Lab Focus: Safe AI, model debugging, neurosymbolic learning

• Learning Sciences
• Technology-Enhanced Learning
• Collaborative Learning
• +۴ مورد دیگر

Professor Nancy W. Y. Law is a leading scholar in Learning Sciences and Technology at the University of Hong Kong's Faculty of Education . She has served as Founding Director of the Centre for Information Technology in Education (CITE) (1998-2013) and currently holds the Chair of Learning Sciences and Technology . Her career spans over 25 years of international comparative studies, policy analysis, and large-scale educational innovation. Current roles: Associate Dean (Strategic Development), HKU Key affiliations: Sciences of Learning Strategic Research Theme (co-convener) Research Expertise Her work focuses on technology-enhanced learning , digital literacy , knowledge building communities , and multilevel educational change . She pioneered network models of teacher innovation and socio-technical frameworks for sustainable pedagogical transformation, particularly in STEM education and Cyberbullying prevention . Scientific Leadership With over 25 major research grants (2000-2023) exceeding HK$50 million, she leads projects like: Learning and Assessment for Digital Citizenship Jockey Club Self-directed Learning in STEM Design-Aware Learning Analytics A Fellow of the International Society of Learning Sciences and recipient of the HKSAR Humanities and Social Sciences Prestigious Fellowship , she has supervised numerous international comparative studies including SITES 2006, ICILS 2013, and longitudinal digital citizenship research (2019-2021).

• Financial Analysis
• Equity Valuation
• Life Insurance
• +۳ مورد دیگر

Demetris Christodoulou is an Associate Professor in Accounting, Governance and Regulation at the University of Sydney. He holds a BEcon from Piraeus University, an MSc(Fin) from the University of York (UK), and a PhD from Athens University of Economics and Business (AUEB). His research focuses on applying data analytics, econometrics, and visualization techniques to financial analysis, equity valuation, life insurance, and financial advice. He co-directs the PEMA research group, specializing in productivity and performance measurement analytics, and previously led the MEAFA research group (2007–2022). He has collaborated extensively with industry partners including Deloitte and Australian insurers, and developed training programs for over 1,000 executives. His work includes open-source contributions to Stata software and the Graph Workflow platform, alongside $662k in workshop-generated funds supporting academic programs. He has advised multiple PhD students and taught at leading universities globally. Education: BEcon in Economics (Econometrics), Piraeus University MSc in Finance, University of York (UK) PhD in Accounting and Financial Analysis, Athens University of Economics and Business His research interests span financial reporting models, life insurance underwriting strategies, and behavioral finance. Recent projects address dishonesty mitigation in insurance disclosures and the adviser effect on customer disclosures. He has published widely in top journals like the Review of Accounting Studies and Stata Journal , and his work was featured in The Australian for insights on insurance fraud reduction. He maintains international collaborations, including visiting roles at Columbia Business School and the University of Cyprus, and serves on advisory boards for organizations like Behaviour.ai. Publications highlight methodological innovations in econometrics and visualization, with 2025's upcoming Stata Journal paper advancing time-series analysis techniques. His grants include partnerships with industry on longitudinal studies of insured lives, aiming to improve risk modeling and public policy insights.

Takako Fujiokaمشاهده پروفایل

دانشیار

• Auditory Perception
• Neural Oscillations
• Auditory-Motor Coupling
• +۵ مورد دیگر

Takako Fujioka is an Associate Professor of Music at Stanford University, affiliated with the Center for Computer Research in Music and Acoustics (CCRMA). Her research focuses on the neural mechanisms underlying auditory perception, auditory-motor coupling, and music-supported therapy for neurorehabilitation. She holds a Ph.D. in Physiology from the Graduate University for Advanced Studies, Japan, and M.Sc./B.Eng. degrees in Electrical Engineering from Waseda University. Her work combines neurophysiological techniques such as MEG and EEG to study brain plasticity in development, aging, and stroke recovery. Notable contributions include investigating how music influences motor and cognitive recovery in stroke patients, as well as exploring the neural basis of musical perception through rhythmic synchronization and pitch discrimination studies. Supported by awards from the Canadian Institutes of Health Research during her postdoctoral work at the Rotman Research Institute, her research bridges clinical neuroscience and music cognition. Dr. Fujioka’s expertise spans auditory neuroscience, neurorehabilitation, and technology-assisted music therapy. She has pioneered studies on tactile mapping for cochlear implant users and networked music performance systems, emphasizing cross-modal perception and human-technology interaction. Her findings contribute to both theoretical understanding of auditory processing and practical applications in medical and educational settings. Awards: Canadian Institutes of Health Research Awards (postdoctoral phase) Labs/Teams: CCRMA, Stanford Music Perception Laboratory, Rotman Research Institute collaborations Key Themes: Neuroplasticity, Music-Mediated Rehabilitation, Auditory-Motor Integration, Multisensory Processing Her recent work examines aging-related changes in binaural hearing and the role of beta/gamma oscillations in rhythmic processing. She advocates for translational research that connects neural mechanisms with real-world therapeutic interventions.

Paulo Bliksteinمشاهده پروفایل

دانشیار

• Technology in teaching and learning
• Learning design
• Computer modeling in Social and Behavioral Sciences
• +۶ مورد دیگر

Paulo Blikstein serves as Associate Professor of Communications, Media and Learning Technology Design at Columbia University. Previously, he was Assistant Professor of Education and (by courtesy) Computer Science at Stanford University and co-founded the Lemann Center for Brazilian Education (2008-2018). Education: Ph.D. in Learning Sciences, Northwestern University (2009) M.A. in Media Arts & Sciences, MIT Media Lab (2002) M.Eng. in Electronic Engineering, University of São Paulo (2000) B.S. in Metallurgical Engineering, University of São Paulo (1998) His research pioneers constructionist learning environments through digital fabrication, educational robotics, and tangible interfaces—focusing on equitable access for underserved communities. Inspired by Paulo Freire and Seymour Papert, he develops open-source tools like the GoGo Board robotics platform and leads the global FabLab@School initiative establishing fabrication labs in schools across four continents. Current work emphasizes multimodal learning analytics to study student interactions in maker-centered classrooms. Publications reveal strong focus on democratizing invention through maker education, with recurring themes in constructionist theory application, multimodal assessment, and context-specific technology adaptation. Brazilian education reform and low-cost computational solutions form significant threads, particularly in 2016-2017 publications. Scientific Awards: Two Google Faculty Awards National Science Foundation Early Career Award (highest U.S. government honor for early-career scientists) Blikstein directs the Transformative Learning Technologies Lab (TLTL) and co-founded Stanford's Center for Educational Entrepreneurship and Innovation in Brazil. His FabLearn conference established the first academic forum on Maker Movement applications in education. While specific grant details aren't listed, the NSF CAREER Award signifies major federal research funding. He spearheads the FabLab@School project deploying advanced fabrication labs in K-12 institutions worldwide and founded the FabLearn conference series. His work integrates teams of engineers, educators, and designers to create scalable solutions for resource-constrained learning environments.

Mark Aleksanyanمشاهده پروفایل

استاد

• Equity valuation
• Corporate reporting
• Financial market actors
• +۳ مورد دیگر

Mark Aleksanyan is a Professor of Accounting and Finance at the University of Glasgow's Adam Smith Business School, serving as Associate Head of the Accounting and Finance subject group since 2004. He holds a PhD in Accounting and Finance from the University of Glasgow (2004), an MPhil in International Finance (2000), and a bachelor’s degree in Mathematical Economics from Yerevan State University (1997). Prior to academia, he worked as a financial analyst, economist at the Armenian Ministry of Finance, and assistant to the Prime Minister of Armenia (1996–1999). Research Interests Focuses on relationships between market actors (sell-side/buy-side analysts, investor relations officers), equity valuation, corporate reporting, and empirical accounting. His work explores technology’s impact (e.g., generative AI) on financial information practices and regulatory frameworks like MiFID II. Current projects include analyzing the role of state visits in cross-border M&A and media effects on insider trading. Grants & Funding Received grants totaling over £100k from bodies like the Institute of Chartered Accountants of Scotland (2019), the Foreign and Commonwealth Office (2018), and the Adam Smith Research Foundation (2010). Teaching Teaches Financial Analysis & Equity Valuation (MSc), Financial Statement Analysis & Valuation (BSc), and PhD masterclasses. Previously instructed International Corporate Finance and MBA courses. Editorial Roles Serves as Associate Editor for Qualitative Research in Financial Markets and on editorial boards of Review of Business and Economics Studies and Behavioral Research .

Rachel Cummingsمشاهده پروفایل

دانشیار

• Differential Privacy
• Machine Learning
• Algorithm Design
• +۶ مورد دیگر

Rachel Cummings is an Associate Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia University, with a courtesy appointment in the Department of Computer Science. She serves as Co-chair of the Cybersecurity Research Center at Columbia’s Data Science Institute. Previously, she was faculty at Georgia Tech’s School of Industrial and Systems Engineering (ISyE), holding a courtesy appointment in Computer Science. She holds a Ph.D. in Computing and Mathematical Sciences from Caltech, with research visits at UPenn, Hebrew University, Microsoft Research, and the Simons Institute. Her research focuses on differential privacy, integrating tools from machine learning, algorithm design, economics, optimization, statistics, HCI, usable security, and public policy. She emphasizes practical applications of theoretical privacy-preserving methods. Key roles include Managing Editor for the Journal of Privacy and Confidentiality , service on the ACM U.S. Technology Policy Council, IEEE Standards Association, and Future of Privacy Forum’s Advisory Board. She has advised on the U.S. Census Bureau’s Scientific Advisory Council and served as a Fellow at the Center for Democracy & Technology. Recent work includes papers on privacy elasticity, synthetic control methods, and differential privacy under class imbalance. Her awards include NSF CAREER, DARPA Young Faculty Award, and Best Paper recognitions at DISC, CCS, and SaTML. She actively chairs conferences (e.g., DEF CON Crypto) and mentors students like Tingting Ou (PhD 2025) and Peihan Liu (PhD 2024–present). Her lab explores privacy-preserving technologies, policy implications, and interdisciplinary collaborations.

Andrew D. Whiteمشاهده پروفایل

دانشیار

• Large language models
• Deep learning in chemistry
• Molecular dynamics
• +۵ مورد دیگر

Andrew D. White is an Associate Professor of Chemical Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the University of Washington (2013). His research focuses on automating scientific discovery through AI, particularly leveraging large language models (LLMs) and deep learning techniques in chemistry. His lab develops agents that integrate literature analysis, hypothesis generation, and experimental design to advance fields like molecular dynamics and drug discovery. Education: PhD in Chemical Engineering, University of Washington, 2013 BS/MS (not explicitly stated in text, inferred from career timeline) Research Interests: Large language models for scientific automation Deep learning applications in chemistry and materials science Molecular dynamics simulations Scientific agents and autonomous systems Publications: His work includes groundbreaking studies on closed-loop AI systems for chemistry, federated learning in molecular property prediction, and multi-agent systems for drug discovery. Recent highlights include the Robin system and ChemCrow tools. Awards: Recipient of the NSF Career Award (2018), NIH Outstanding Investigator Award (2020), and the Curtis Teaching Award (2019). He also advises biotech companies and serves on the National Academy of Sciences' Chemical Sciences Roundtable. Grants & Funding: Supported by DOE, NSF (multiple grants including CBET-1751471), NIH (R35GM137966), and LLNL projects. Collaborates with institutions like Argonne National Lab and Qubit Pharmaceuticals. Labs & Teams: Leads the White Lab at Rochester and co-founded FutureHouse, a nonprofit advancing AI-driven scientific discovery. Supervises a multidisciplinary team of PhD students and postdocs in computational chemistry, AI, and biophysics.

Norbert Lutkenhausمشاهده پروفایل

استاد

• Quantum Communication
• Quantum Key Distribution (QKD)
• Quantum Information Processing
• +۴ مورد دیگر

Prof. Norbert Lütkenhaus is a Professor and Executive Director of the Institute for Quantum Computing (IQC) at the University of Waterloo, cross-appointed to the Department of Applied Mathematics. He holds affiliations with Perimeter Institute and the Centre for Applied Cryptographic Research. His research focuses on quantum communication theory, particularly quantum key distribution (QKD) and quantum repeaters. He has pioneered methods to bridge abstract quantum protocols with practical optical implementations, emphasizing secure key rate calculations and overcoming quantum channel limitations. Education: PhD (2003) in Physics from University Erlangen-Nürnberg, MSc (1993) and BSc (1990) from Ludwig-Maximilians-Universität München and RWTH Aachen, respectively. Awards include the 2015 American Physical Society Outstanding Referee Award and a 2009 University of Waterloo Excellence Award. Research interests span QKD protocols (e.g., decoy-state BB84, phase-error mitigation), quantum repeater architectures, and entanglement verification. He develops numerical tools for key rate analysis and addresses implementation security loopholes. His work includes theoretical frameworks for long-distance quantum communication and practical QKD system optimizations. Teaching includes courses on quantum information processing (PHYS 768/QIC 890) and mechanics (PHYS 115). He contributes to international standards via ETSI’s QKD-ISG and the QCrypt steering committee. His patents cover QKD system designs and phase-randomization techniques.

Amin Sakzadمشاهده پروفایل

دانشیار

• Lattice-Based Cryptography
• Wireless Communications
• Post-Quantum Cryptography
• +۶ مورد دیگر

Dr. Amin Sakzad is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University's Faculty of Information Technology. His research focuses on lattice-based cryptography, wireless communications, and post-quantum security protocols. He holds a PhD in Applied Mathematics from Amirkabir University of Technology (2011) and has held academic roles at Carleton University and Monash since 2012. Dr. Sakzad’s expertise spans lattice coding theory, MIMO systems, and privacy-preserving technologies for genomic databases and blockchain applications. He leads multiple ARC-funded projects, including work on secure databases (SRDBMS) and post-quantum cryptographic primitives for FinTech and energy sectors. His research has been recognized through awards such as the FIT Dean’s Award for Teaching Excellence (2021). Key collaborations include projects on blockchain security (CollinStar Lab), genomic data privacy, and energy market cybersecurity. His work addresses UN SDGs through contributions to quality education (SDG 4) and industry innovation (SDG 9). Recent publications highlight advancements in lattice-based cryptography (e.g., CRYSTALS-Kyber variants), privacy-preserving energy trading, and secure blockchain protocols like FPPW watchtower systems. His research bridges theoretical cryptography with practical implementations in embedded systems and 5G telecommunications. Grants: 16 active/completed projects including $1.2M in ARC funding Advising: Supervising PhD projects on lattice applications in post-quantum crypto and blockchain Labs: Core member of Monash’s Software Defined Telecommunications (SDT) Lab and CollinStar Lab

Cosette Crisanمشاهده پروفایل

دانشیار

• Mathematics Education
• Curriculum Development
• Teacher Education
• +۳ مورد دیگر

Dr Cosette Crisan is an Associate Professor (Teaching) in Mathematics Education at University College London’s IOE, ranked World Number 1 in Education. She specializes in curriculum design, subject-specific mentoring, and integrating digital technologies into mathematics education. Prior to joining UCL IOE in 2010, she taught mathematics at secondary and university levels for 16 years. Her research focuses on enhancing mathematics teaching practices through collaborative mentorship frameworks and technology integration. She co-leads the Curriculum and Subject Specialism Research Group and the ROPE group, driving pedagogical innovation. Notable achievements include the 2021 UCL Faculty Education Team Award for pandemic-era teaching support and leadership roles such as Academic Head of Learning and Teaching (2020–2023). She holds a PhD in Mathematics Education from London South Bank University and is a Principal Fellow of the Higher Education Academy. Current initiatives include developing a Mathematics and Secondary Mathematics Education BSc (QTS) Teacher Degree Apprenticeship program. Her research emphasizes revitalizing geometry education and preparing mentors to effectively guide novice teachers. Publications span topics like computer-aided assessment (STACK), pandemic-era teaching adaptations, and cross-cultural mentorship studies. Dr Crisan’s work bridges theory and practice, advocating for mathematics education as a design science. She actively contributes to the London Mathematical Society’s Education Committee, promoting public engagement with mathematics. Awards include the UCL Faculty Education Team Award (2021) and recognition for her role in transitioning teaching to online platforms during the pandemic. Her teaching portfolio includes leadership of the MA Mathematics Education program and supervision of doctoral candidates. Recent interests include exploring intersections between mathematics and cognitive neuroscience, inspired by her daughter’s career path.

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.