جست‌وجوی استاد و پژوهشگر

María Encarnación Beato Gutiérrezمشاهده پروفایل

استاد

• Computer Science
• Artificial Intelligence
• Internet of Things (IoT)
• +۷ مورد دیگر

María Encarnación Beato Gutiérrez is a Professor of Computer Science and Artificial Intelligence at the School of Computer Science , Pontifical University of Salamanca . She leads the MARATON research group, focusing on disruptive technologies like the Internet of Things (IoT), semantic technologies, open data, and data processing. Her work addresses societal needs in the technological revolution through research, innovation, and development in these domains. Her research spans IoT frameworks, event-driven architectures, semantic data integration, and AI ethics. Notable projects include frameworks for smart tracking (SCIFI series), capacity control systems using BLE technology, and semantic tools for university transparency. Her publications highlight trends in IoT, AI ethics, and open data applications. The MARATON group collaborates on projects like UPSA-INNOVA , integrating mobile technologies and semantic solutions for e-learning and university innovation. Her work also explores human-computer interaction, software engineering, and the societal impact of AI. She earned her PhD from the University of Valladolid in 2004, with a thesis on Formal Verification of UML under the supervision of Dr. Manuel Barrio Solórzano and Dr. Carlos Enrique Cuesta Quintero.

Leonard Berradaمشاهده پروفایل

پژوهشگر

• Optimization
• Deep Learning
• Verification
• +۵ مورد دیگر

Leonard Berrada is a Research Scientist at DeepMind, focusing on robust and verified AI. He previously completed his DPhil/PhD at the University of Oxford under Andrew Zisserman and M. Pawan Kumar. Education: University of Oxford (DPhil/PhD, supervised by Andrew Zisserman and M. Pawan Kumar) Research Interests: His work spans optimization, deep learning, verification, and privacy-preserving machine learning. Recent publications highlight expertise in federated learning, biomedical image analysis, inverse problems, and tumor growth modeling. Publication Trends: His recent work (2022–2025) focuses on federated learning, biomedical image analysis, and inverse problems in personalized tumor modeling. Earlier contributions (2020–2018) address neural network training, verification, and loss function design. Keywords include Computer Science , Optimization , and Biomedical Engineering . Scientific Awards: Outstanding Reviewer Award at CVPR 2018 Top 10% Reviewer at NeurIPS 2020 Notable Contributions: Funded by Yougov and EPSRC during his PhD, he pioneered methods in neural network verification and federated learning. His thesis was accepted without correction by Andrea Vedaldi and Julien Mairal (2020). He also contributed to Gilbert Strang's book on linear algebra and learning from data.

Matthew D. Greenمشاهده پروفایل

دانشیار

• Cryptography
• Computer Security
• Applied Cryptography
• +۳ مورد دیگر

Matthew D. Green is an Associate Professor of Computer Science at Johns Hopkins University and a member of the Johns Hopkins University Information Security Institute. He is a nationally recognized expert in applied cryptography and cryptographic engineering with significant contributions to privacy-enhanced technologies and secure communication systems. Oberlin College: BMus in Technology of Music and Related Arts and BA in Computer Science (1998) Johns Hopkins University: MS in Computer Science (2005), PhD in Computer Science (2008) Green's research focuses on practical applications of cryptography, including privacy-enhanced information storage, anonymous payment systems, and bilinear map-based cryptography. His work bridges theoretical cryptography with real-world security challenges, particularly in blockchain technologies, end-to-end encrypted communications, and privacy-preserving systems. He has made significant contributions to understanding cryptographic vulnerabilities in widely deployed systems and developing more secure alternatives. His recent publications demonstrate a strong focus on emerging cryptographic challenges including machine unlearning, privacy in encrypted messaging, compliance-enabled digital assets, and detecting synthetic content from large language models. These works show his ability to address both theoretical foundations and practical implementations of security systems. NSF CAREER Award (2017) $100,000 Google Security and Privacy Research Award Green is actively engaged in both academic research and public discourse on security issues. He is the author of the popular blog 'A Few Thoughts on Cryptographic Engineering' and has over 140,000 followers on X (formerly Twitter). His work has been instrumental in analyzing security flaws in commercial products and contributing to the development of more secure cryptographic protocols. He was one of the creators of the Zerocash protocol used by Zcash cryptocurrency and founded the encryption startup Zeutro. His research group at Johns Hopkins focuses on practical cryptographic engineering challenges, with particular emphasis on how theoretical cryptographic constructions can be implemented securely in real-world systems. The group maintains strong connections with industry partners and regularly contributes to security standards discussions.

• Hybrid Control Systems
• Hierarchical Control Systems
• Distributed Consensus-Based Control
• +۳ مورد دیگر

Jörg Raisch is a Professor of Control Systems at the Technische Universität Berlin since March 2006. He previously held positions at Otto-von-Guericke University Magdeburg and the Max Planck Institute for Dynamics of Complex Technical Systems. His research focuses on hybrid and hierarchical control systems , distributed consensus-based control , and control of time-dependent event-discrete systems using tropical algebras. Alma Mater: University of Stuttgart (Technical Cybernetics), UMIST Manchester (Control Systems) Postdoctoral work: University of Toronto (Systems Control Group) His work spans applications in process technology , medical systems , and energy technology . Recent publications emphasize max-plus algebra , P-time event graphs , and over-the-air consensus in clustered networks. He has served on editorial boards of journals like Automatica and Discrete Event Dynamic Systems , and led the IFAC Technical Committee TC1.3 on Discrete Event and Hybrid Systems from 2017-2020. Awards: DFG fellowship for habilitation

• Software Testing and Verification
• Reinforcement Learning
• Program Repair
• +۵ مورد دیگر

Mahsa Varshosaz is an Associate Professor in Software Engineering at the IT University of Copenhagen, specializing in AI safety, formal verification, and program repair. Her work bridges theoretical research with practical applications in autonomous systems, particularly marine robotics. Current research focuses on model-based testing and formal specification for reinforcement learning systems Active in program repair techniques for concurrency bugs Collaborates with European Commission-funded initiatives in AI safety Recent publications highlight her expertise in autonomous underwater vehicle safety analysis and static program analysis for locking bugs. Her work intersects software engineering, artificial intelligence, and systems validation. Key projects include REMARO (Reliable AI for Marine Robotics) and DIREC (Verifiable and Safe AI), funded by major research foundations. She contributes to open-access datasets and peer-reviewed research through collaborations.

Liqian Chenمشاهده پروفایل

استاد

• Program Analysis
• Abstract Interpretation
• Automated Program Repair
• +۶ مورد دیگر

Liqian Chen is a Professor of Computer Science at the National University of Defense Technology (NUDT), Changsha, China. His research focuses on program analysis, abstract interpretation, and automated program repair, with a strong emphasis on numerical stability and software verification. Recent publications address advanced topics in floating-point error detection, vulnerability analysis, and invariant generation for neural networks. Key contributions include tools like F-IKOS (static analyzer for Fortran programs), AutoRNP (floating-point error repair tool), and Software Apron (numerical abstract domain library). Scientific Awards: ACM SIGSOFT Distinguished Paper Award (ISSTA 2024) He serves on program committees for major conferences such as ASE 2025, SAS 2025, and SETTA 2025, and acts as Guest Editor for journals like Automated Software Engineering Journal and Journal of Systems Architecture .

• Computer Networks
• Machine Learning
• Reinforcement Learning
• +۶ مورد دیگر

Alessio Sacco is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He's affiliated with the NETGROUP - Computer Networks Group and actively contributes to research in computer networks, machine learning, and software-defined networking. Current academic position: Assistant Professor Research group: NETGROUP Key collaborations: Guido Marchetto, Flavio Esposito Research Interests focus on: Computer networks and communications Dynamic programming and reinforcement learning Machine learning applications in network management Software-defined networks Cybersecurity and privacy analysis Intelligent Internet of Things systems Recent Publications demonstrate expertise across: Network security verification (PathSafe) AI-driven agricultural optimization (FertilizeSmart) Cross-layer congestion control (Flecto) Reinforcement learning for computing continuum scheduling Explainable AI for network transparency (ClearNET) Multi-agent exploration systems (MARS) Scientific Contributions include: Commercial research on network security and anomaly detection ERC sector expertise in distributed systems and machine learning Teaching in computer engineering programs since 2019 Advising involves: Supervising Federico Rinaudi (ongoing) Co-supervising Doriana Monaco (ongoing)

• Formal Verification
• Model Checking
• Concurrent Systems
• +۶ مورد دیگر

Parosh Abdulla is a Professor in the Division of Computer Systems within the Department of Information Technology at Uppsala University, Sweden. His research focuses on formal methods and program verification with emphasis on concurrent systems, memory models, and verification techniques. With an extensive publication record spanning over three decades, he has established himself as a leading researcher in his field. His primary research interests include formal verification, model checking, concurrent systems analysis, memory models (particularly TSO and PSO), string constraints, timed automata, parameterized verification, and program analysis. His work addresses fundamental challenges in verifying complex software systems, especially those involving concurrency and weak memory models. In recent years, he has expanded his research to include quantum systems verification and the application of machine learning techniques to formal methods. Analysis of his recent publications (2023-2025) reveals a continued focus on advancing verification techniques for concurrent systems, with growing interest in quantum computing verification and the integration of graph neural networks with traditional formal methods. His work demonstrates consistent innovation in reducing the state space explosion problem while maintaining verification completeness. Professor Abdulla has established significant collaborations with researchers including Mohamed Faouzi Atig, Bengt Jonsson, and others, resulting in numerous high-impact publications in top-tier venues. His research has practical implications for developing reliable concurrent software systems and advancing formal verification methodologies. While specific details about his advising activities and grants aren't explicitly mentioned in the available information, his extensive publication record suggests active supervision of graduate students and involvement in multiple research projects. His work likely contributes to formal methods research groups at Uppsala University, focusing on verification tools and theoretical foundations of program analysis.

• Theorem proving
• Decision procedures
• SAT/SMT solving
• +۸ مورد دیگر

Philipp Rümmer is a Professor of Theoretical Computer Science at the University of Regensburg and a Senior Lecturer/Associate Professor at the Department of Information Technology , Uppsala University. His career spans over two decades of academic and industrial collaboration in formal verification, SMT solvers, and program analysis. Professor (2022–present), Faculty of Informatics and Data Science, University of Regensburg Senior Lecturer/Associate Professor (2018–present), Department of Information Technology, Uppsala University His research focuses on formal verification of software and embedded systems, SMT solvers (e.g., Princess, Norn), model checking (Eldarica), and integrating machine learning with verification techniques. He has pioneered tools like JayHorn for Java verification and Sloth for string constraint solving. Recent publications emphasize probabilistic verification of parameterized systems, timing analysis in embedded updates, heap invariants via Horn clauses, and graph neural networks in solving word equations. These works reflect his commitment to advancing formal methods through interdisciplinary approaches. Awards include: Oscarspris (2013), Uppsala University SAP Award for top Computer Science graduate (2005) Bosch Telecom Award for natural science excellence (1998) He has secured significant grants from the Swedish Research Council , Knut and Alice Wallenberg Foundation , and Microsoft Research , supporting projects like UPDATE (2020–2024) and WebSec (2018–2023). His leadership in tools such as Princess and Eldarica underscores his influence in automated reasoning and verification.

• Hardware Trojan Design
• Netlist Reverse Engineering
• Neuro Engineering
• +۲ مورد دیگر

Alexander Hepp serves as a Lecturer at the Chair of Security in Information Technology, Technical University of Munich (TUM), where he contributes to both teaching and cutting-edge hardware security research. His current teaching responsibilities include C++ Lab, Embedded Systems and Security, and Strategic Management for Engineers, where he acts as primary lecturer for the latter course. His research centers on hardware trojan design/identification and netlist reverse engineering within the chair's broader security initiatives. His research portfolio demonstrates deep specialization in hardware security vulnerabilities, with emphasis on hardware trojan insertion/detection methodologies and reverse engineering countermeasures. Recent work explores neuro engineering applications and RISC-V-based security implementations, often leveraging open-source hardware frameworks. His publications reveal consistent innovation in hardware obfuscation techniques and netlist analysis tools, addressing critical gaps in pre-silicon security validation. His 12 publications from 2021-2024 show strong focus on hardware security conferences like DATE and DDECS, with recurring themes in trojan detection, reverse engineering challenges, and cryptographic hardware validation. The research trajectory indicates growing emphasis on practical countermeasures against physical attacks and standardized vulnerability assessment frameworks. Scientific Awards: Best Paper Award at DDECS 2022 for 'Hardware Obfuscation of Digital FIR Filters' No verifiable information exists regarding student advising or grant funding in available sources. He operates within Prof. Sigl's research ecosystem which actively pursues projects in fault attacks, IoT security, physical unclonable functions, and post-quantum cryptography implementations, with recent work emphasizing hardware reverse engineering challenges in advanced semiconductor designs.

Justin Pearsonمشاهده پروفایل

دانشیار

• Constraint Programming
• Combinatorial Optimisation
• Artificial Intelligence
• +۷ مورد دیگر

Justin Pearson is an Associate Professor at the Department of Information Technology; Division of Computing Science at Uppsala University . He is a member of the university's optimisation group and coordinates the IT department's mentor programme for new employees . Research Interests include Constraint Programming , Combinatorial Optimisation , Artificial Intelligence , Software Testing , and Complexity Theory . His work focuses on theoretical and practical aspects of constraint satisfaction, local search algorithms, and symmetry breaking in constraint programming. Teaching involves courses such as Algorithms and Data Structures II (1DL231) and Introduction to Machine Learning for Bachelor Students (1DL034), with a PhD-level course on Category Theory offered periodically based on demand. Scientific Contributions span publications in constraint programming for air traffic management, sensor networks, and industrial applications. Recent work includes parameterised treewidth in constraint models, time-series constraints, and symmetry breaking techniques. Students supervised include PhD candidates Frej Knutar Lewander (co-supervised with Pierre Flener), Yi Zhao (co-supervised with Di Yuan), and previously graduated students Gustav Björdal , María Andreína Francisco Rodríguez , and Joseph Scott .

Robert L. Givanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Reinforcement Learning
• Knowledge Representation
• +۴ مورد دیگر

Robert L. Givan is an Associate Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. His academic affiliations include Purdue’s Department of Electrical and Computer Engineering and the Max W and Maileen Brown Family Hall. He holds a B.S. in Mathematics and Biology from Stanford University (1987), an M.S. in Computer Science from Stanford (1987), and a Ph.D. in Computer Science from MIT (1996). His research focuses on artificial intelligence, reinforcement learning, automated reasoning, knowledge representation, and stochastic modeling. Notable projects include applying automated reasoning to AI planning domains and exploring reinforcement learning techniques for stochastic control problems. He has contributed to areas such as probabilistic planning, heuristic search, and formal verification. His recent publications span topics like stochastic enforced hill-climbing, automated feature induction, and formal verification methods. His work often bridges theoretical foundations with applied challenges in AI and robotics. Givan’s research has been supported by NSF grants in AI and computer engineering. Labs and collaborations include involvement with Purdue’s AI and robotics initiatives, though no specific lab names are mentioned. He emphasizes student collaboration through directed research and thesis opportunities, particularly in AI and related fields.

• Dependent Type Theory
• Verification
• Interactive Theorem Proving
• +۵ مورد دیگر

Talia Ringer is an Assistant Professor at the University of Illinois Urbana-Champaign, affiliated with the Grainger College of Engineering and the Siebel School of Computing and Data Science. Her research focuses on proof engineering, formal verification, and bridging neural and symbolic proof automation. She holds a PhD in Computer Science from the University of Washington and a BS in Mathematics and Computer Science from the University of Maryland. Prior to academia, she worked at Amazon as a software engineer. Ringer is known for founding initiatives like SIGPLAN-M and the Computing Connections Fellowship, fostering inclusivity in computer science research. She has received prestigious awards including the 2023 ACM SIGPLAN Distinguished Service Award and the DARPA Young Faculty Award. Education: PhD, University of Washington (2021); BS, University of Maryland (2012) Research Areas: Dependent Type Theory, Verification, Interactive Theorem Proving, Proof Automation, Formal Methods Awards: ACM SIGPLAN Distinguished Service Award (2023), ESEC/FSE Distinguished Paper Award (2023), DARPA Young Faculty Award (2023) Her work emphasizes making formal verification accessible to programmers through tools like Proof Repair and Baldur , while advocating for ethical AI research and LGBTQ+ inclusivity. She advises a diverse team of graduate and undergraduate students in the Illinois Theorem Provers (ITP) lab, exploring topics including proof repair, reinforcement learning for proofs, and quotient type equivalences.

Andrew Clarkمشاهده پروفایل

دانشیار

• Cyber-Physical Systems (CPS)
• Control Theory
• Network Security
• +۶ مورد دیگر

Andrew Clark is an Associate Professor in the Department of Electrical & Systems Engineering at Washington University in St. Louis (WashU), part of the McKelvey School of Engineering. Previously, he served as an Associate Professor at Worcester Polytechnic Institute (WPI). He holds a PhD from the University of Washington (2014) and degrees from the University of Michigan (BSE 2007, MS 2008). His research focuses on control, security, and resilience of cyber-physical systems (CPS) and complex networks, leveraging game theory, control theory, and optimization to ensure safety in adversarial environments. Key areas include secure control frameworks for autonomous systems, power grids, and submodular optimization for scalable algorithms. Education: PhD, Electrical Engineering, University of Washington, 2014 MS, Mathematics, University of Michigan, 2008 BSE, Electrical Engineering, University of Michigan, 2007 Research Interests: Clark’s work emphasizes resilience engineering, game-theoretic security modeling, and cyber-physical system security. He develops methods for robust control under attacks, including controlled islanding for power systems, stochastic control barrier functions for safety, and submodular optimization for resource allocation. His research also addresses challenges in autonomous systems, smart grids, and adversarial machine learning. Articles Trends: Recent publications focus on safety-critical control under adversarial conditions, secure CPS design, and submodular optimization for network resilience. His work bridges theoretical advancements with practical applications in energy systems and autonomous vehicles. Scientific Awards: NSF CAREER Award (2020) AFOSR Young Investigator Program (YIP) Award (2022) Multiple best paper awards at venues like WiOpt, GameSec, and ICCPS Grants & Advising: Clark has secured grants from NSF, AFOSR, and ONR totaling over $1.6M. He advises PhD students (e.g., Luyao Niu, Zhouchi Li) and MS students, focusing on CPS security and resilience. His lab has won awards at VehicleSec and other conferences. Labs & Teams: His research group at WashU collaborates on projects like resilient CPS design, with a focus on real-world applications such as autonomous systems and smart grids.

• Hardware Security
• Cryptographic Circuits
• Formal Methods
• +۳ مورد دیگر

Devanshi Upadhyaya is a Researcher at the Institute of Computer Architecture and Computer Engineering under the University of Stuttgart. Her work focuses on hardware security, cryptographic circuit protection, and formal methods for analyzing vulnerabilities in embedded systems. She is actively engaged in advancing secure hardware design principles through research in side-channel attacks, fault injection, and logic locking mechanisms. Her research interests include cryptographic circuit security, formal verification of hardware systems, and securing neural network accelerators. She has contributed to developing frameworks that assess vulnerabilities in mixed-signal and approximate computing architectures, emphasizing practical defenses against both software and hardware-based attacks. Notable projects include optimizing waveform accurate fault attacks and enabling power side-channel simulations for AI hardware accelerators. Her work bridges theoretical security models with real-world hardware implementation challenges. Devanshi holds a M.Sc. degree and is affiliated with the Hardware Oriented Computer Science department at the University of Stuttgart, where she collaborates on interdisciplinary projects involving cryptography, embedded systems, and machine learning hardware.