جست‌وجوی استاد و پژوهشگر

Alexander Schiedمشاهده پروفایل

استاد

• Quantitative Finance
• Probability Theory
• Stochastic Analysis
• +۴ مورد دیگر

Alexander Schied is a Professor of Statistics and Actuarial Science at the University of Waterloo, holding the Munich Re Chair in Stochastic Finance and a University Research Chair. His research focuses on quantitative finance, probability theory, and stochastic analysis, with applications to risk measurement, financial modeling, and market microstructure. He co-authored the seminal textbook Stochastic Finance: An Introduction in Discrete Time (5th ed., 2025) and serves as Co-Editor of Finance and Stochastics . Before joining Waterloo, Schied held positions at the University of Mannheim, TU Munich, Cornell University, and TU Berlin. He earned his doctorate in mathematics from the University of Bonn. His work bridges theoretical advancements in stochastic processes with practical applications in finance, including robust optimization, model uncertainty, and high-frequency trading dynamics. His recent research explores rough volatility models , pathwise Itô calculus , and market impact games , with publications in top journals like Annals of Applied Probability and Mathematical Finance . His contributions to risk management and stochastic analysis have positioned him as a leading figure in mathematical finance. Awards & Roles: Munich Re Chair in Stochastic Finance (University of Waterloo) University Research Chair (University of Waterloo) Co-Editor, Finance and Stochastics Editorial Board Member: Applied Mathematics and Optimization , Mathematical Finance , and SIAM Financial Mathematics series Key Themes in Publications (2020–2025): Model-free portfolio theory and continuous-time optimization Rough stochastic volatility and Hurst parameter estimation Market impact dynamics and game-theoretic models Robust risk measures and optimization under uncertainty

Thomas P. Jensenمشاهده پروفایل

استاد

• Program Analysis
• Static Program Analysis
• Abstract Interpretation
• +۷ مورد دیگر

Thomas P. Jensen is a Directeur de recherche (Research Director) at INRIA, the French National Institute for Computer Science and Applied Mathematics. He has been affiliated with INRIA since 2010, previously serving as a researcher at CNRS (French National Center for Scientific Research) from 1993 to 2010. Currently, he leads the Epicure project-team on semantic analysis and compilation for secure execution platforms, and since 2022, he serves as director of the Laboratoire d'Excellence CominLabs. His educational background includes a Cand. scient. in Computing and Mathematics from the University of Copenhagen (1990), a PhD from Imperial College, University of London (1992), and a Habilitation à diriger des recherches from Université Rennes 1 (1999). Dr. Jensen's research focuses on program analysis and software security , with particular expertise in static program analysis based on abstract interpretation theory, language-based security, information flow control, and Software Fault Isolation. His work bridges theoretical foundations with practical applications in secure compilation and program verification. His recent publications reveal a strong trend toward formally verified security mechanisms, particularly in the integration of Software Fault Isolation with verified compilers like CompCert. His research spans both theoretical aspects of program semantics and practical security applications, with increasing emphasis on algebraic data types, relational analysis, and formal verification of program transformations. Best paper award at GPCE 2018 for Verification of High-Level Transformations with Inductive Refinement Types Editor of Strategic research and innovation roadmap for the SPARTA cybersecurity competence network (2022) Dr. Jensen has been actively involved in numerous research projects including JavaSec, Decert, Seccloud, AJACS, Anastasec, SPARTA, and SecurEval. His leadership roles in research teams and projects demonstrate his significant contributions to both academic research and practical cybersecurity applications. His work often involves collaboration with researchers across Europe on large-scale cybersecurity initiatives. He maintains strong connections with the international programming languages and security research communities, regularly publishing in top venues including PLDI, POPL, ICFP, SAS, ESOP, and CSF, and participating in program committees for major conferences in these fields.

Farshad Firouziمشاهده پروفایل

استادیار مدعو

• Edge Computing
• Internet of Things (IoT)
• Artificial Intelligence
• +۴ مورد دیگر

Farshad Firouzi serves as an Adjunct Assistant Professor in the Department of Electrical and Computer Engineering at Duke University, where he teaches EGR 393: Research Projects in Engineering. His academic work bridges hardware and software domains with a strong emphasis on practical applications in critical systems. His research spans Edge Computing, Internet of Things (IoT), Artificial Intelligence, Machine Learning, Healthcare Systems, Chip Design, and Reliability Engineering. This interdisciplinary focus manifests in projects ranging from Parkinson's disease monitoring using edge devices to LLM-enhanced chip design and security-hardened neural networks. His work consistently addresses real-world challenges in system dependability, particularly in healthcare and biomedical contexts where failure is not an option. Analysis of his 2024-2025 publications reveals three dominant research thrusts: (1) LLM applications in hardware design (ChipMnd, Spiced), (2) Edge-based healthcare monitoring systems (freezing of gait recognition, blood glucose prediction), and (3) Security and reliability in AI/ML systems (gradient inversion defense, silent data corruption mitigation). His work demonstrates a clear trajectory toward integrating cutting-edge AI techniques with hardware-aware solutions for mission-critical applications. No scientific awards were mentioned in the source materials. The absence of student listings or grant information suggests his current role may be primarily research-focused without formal advising responsibilities. Similarly, no dedicated labs or research teams were referenced in the available documentation.

Shinichi Nakajimaمشاهده پروفایل

پژوهشگر

• Bayesian Inference
• Generative Modeling
• Explainable Artificial Intelligence
• +۴ مورد دیگر

Dr. Shinichi Nakajima is a Senior Research Lead at the Technical University of Berlin, affiliated with the BIFOLD (Berlin Institute for the Foundations of Learning and Data) and the AIP – RIKEN Center of Advanced Intelligence Project . He leads the research group “Probabilistic Modeling and Inference” at BIFOLD. His academic journey includes a Master’s in Physics from Kobe University (1995) and a PhD in Computer Science from Tokyo Institute of Technology (2006). Prior to academia, he worked at Nikon Corporation (1995–2014) on statistical analysis, image processing, and machine learning. His research focuses on Bayesian inference , generative modeling , explainable AI , and quantum computing , with applications in computer vision, natural language processing, and scientific computing. Notable projects include developing NeuLat (a neural sampling toolbox for lattice field theories) and advancing techniques for symbolic XAI to enhance AI transparency. Dr. Nakajima has published extensively on topics such as diffusion models, federated learning, and physics-informed neural networks. His work bridges theoretical foundations (e.g., Bayesian learning) with practical applications in quantum computing and biomedical imaging. He actively contributes to open-source tools and collaborates with industry and academic institutions globally. Key technical achievements include improving sampling efficiency in quantum eigensolvers, enhancing brain source reconstruction via 3D neural networks, and developing anomaly detection systems using self-supervised autoencoders. His research emphasizes computational efficiency and robustness against adversarial attacks, leveraging Langevin dynamics and gradient-based optimization methods.

• Computational Social Science
• Data Science
• Multivariate Social Networks
• +۱۰ مورد دیگر

Termeh Shafie is a full-time Professor in the Department of Politics and Public Administration at the University of Konstanz, specializing in Computational Social Science and Data Science. With a strong statistical foundation, she develops advanced methodologies for analyzing multivariate social networks while bridging archaeological network reconstruction with modern data science techniques through projects like NEXUS 1492. Key Research Areas: Multigraph modeling, network entropy analysis, isotope geoprovenance, and hypergraph representations Projects: NEXUS 1492 archaeological network reconstruction Her 15 most recent publications demonstrate significant contributions to network methodology (random multigraph models, centrality index analysis), archaeological applications (Caribbean attack networks, Iroquoian settlement patterns), and data privacy frameworks. Articles span 2012–2025 with interdisciplinary focus on statistical sociology, archaeological theory, and computational modeling. Current teaching includes courses on social network analysis, data science, and statistical learning. Office hours available via ILIAS booking system. Contact details provided for both academic and administrative correspondence.

• Machine Learning
• Cybersecurity
• Internet of Things (IoT)
• +۳ مورد دیگر

Firas Al-Doghman is a Lecturer at the School of Computer Science, University of Technology Sydney. With expertise in Machine Learning, Cybersecurity, and Internet of Things (IoT), he teaches subjects like iOS Applications, Networking, and Data Engineering while conducting research on smart algorithms and their cybersecurity applications. PhD in Computer Engineering and Data Management (UTS, 2020) Teaching since 2017 at UTS Former Associate Researcher/Lecturer at UNSW ADFA Research focuses on: Machine Learning integration with cybersecurity Blockchain applications in real estate Edge computing security frameworks Consensus-based data aggregation Publications highlight advancements in: Cybersecurity for supply chains Fractional NFTs in property ownership Carbon credit price prediction models Secure microservices orchestration Key collaborations include researchers from UNSW and industry partners.

Dana Drachsler Cohenمشاهده پروفایل

استادیار

• Formal Methods
• Deep Learning Security
• AI Safety Verification
• +۲ مورد دیگر

Dana Drachsler Cohen is an Assistant Professor at the Faculty of Electrical and Computer Engineering, Technion, leading the SAFE lab (Secure Artificial Intelligence via Formal Methods and Engineering). Her research bridges formal methods and deep learning to establish security guarantees for AI systems. Her research focuses on leveraging formal analysis and synthesis techniques to mathematically prove the safety of deep learning models against adversarial attacks. Key interests include neural network robustness verification, program synthesis for repair, and privacy-preserving AI. The SAFE lab emphasizes practical system development that scales beyond theoretical methods while enhancing model transparency through customized verification programs. Her publication trends show consistent advancement in robustness verification techniques, evolving from pixel-level attacks to global robustness guarantees and privacy certification. Recent work integrates program synthesis with formal verification to create repair mechanisms and quantification methods with mathematical safety guarantees. Dr. Drachsler Cohen actively recruits students (PhD/MSc/BSc) for research in AI security and formal methods. She maintains significant service contributions as committee member for major conferences including PLDI, SPLASH, and POPL.

Laurie Rickerمشاهده پروفایل

استاد

• Discrete-Event Systems
• Formal Methods
• Decentralized Control
• +۳ مورد دیگر

Dr. Laurie Ricker is Professor and Computer Science Program Advisor in the Department of Mathematics and Computer Science at Mount Allison University. Her research specializes in formal methods for discrete-event systems, focusing on decentralized control, communication protocols, and security properties like opacity enforcement. Her work develops theoretical frameworks for distributed supervisory control, addressing challenges in observability, communication delays, and coordination of decentralized agents. Recent publications advance verification algorithms for inferencing in supervisory control, mutual opacity in multi-adversary scenarios, and synthesis of communication protocols for pseudo-synchronous systems. Key contributions include development of parity-based architectures for decentralized control, methods for opacity enforcement under powerful attackers, and robustness analysis of communication protocols with bounded delays. Dr. Ricker's work bridges theoretical foundations with practical applications in distributed systems security and control.

Arunselvan Ramaswamyمشاهده پروفایل

مدرس ارشد

• Reinforcement Learning
• Multi-agent Learning
• Explainable Learning
• +۵ مورد دیگر

Arunselvan Ramaswamy is a Senior Lecturer in Data Science, AI, and Machine Learning Research at Karlstad University. His research focuses on the intersection of Computer Science and Mathematics, with primary interests in reinforcement learning, multi-agent systems, stochastic optimization, and control theory. He has held prior roles as Lecturer at Paderborn University and Junior Research Head at the Heinz Nixdorf Institute, Germany. His work spans theoretical foundations of learning algorithms and practical applications in cyber-physical systems, wireless sensor networks, and Industry 4.0. Key contributions include distributed optimization frameworks, reinforcement learning for autonomous systems, and stability analysis of stochastic approximation methods. He has published widely in top venues such as IEEE Transactions on Automatic Control, NeurIPS, and IFAC conferences. Ramaswamy has contributed to interdisciplinary projects including automated side-channel detection in cryptographic protocols and deep reinforcement learning for microgrid control. His teaching includes courses on machine learning fundamentals and project-based ML applications at Karlstad University. Research highlights include developing the DeepCAS algorithm for control-aware scheduling, analyzing convergence properties of distributed optimization under communication constraints, and exploring gradient-based methods with unbounded delays. Current work emphasizes scalable multi-agent learning and explainable AI systems.

Farinaz Koushanfarمشاهده پروفایل

استاد

• Hardware Security
• Integrated Circuit Security
• Physical Unclonable Functions
• +۵ مورد دیگر

Farinaz Koushanfar is a Professor in the Electrical Engineering and Computer Science department at the University of Michigan's College of Engineering. With an impressive h-index of 65 and over 27,000 citations, she has established herself as a leading researcher in hardware security and privacy-preserving computing. Her research interests span hardware security, integrated circuit protection, physical unclonable functions, hardware trojans, logic locking, and privacy-preserving machine learning. Koushanfar's work addresses critical security challenges throughout the semiconductor supply chain, developing innovative solutions for IP protection, anti-counterfeiting, and secure hardware design. Analysis of her recent publications reveals a strong focus on the intersection of hardware security and machine learning, particularly in federated learning security and privacy-preserving deep neural network inference. Her research shows a clear trajectory from foundational hardware security mechanisms toward more complex systems-level security challenges in emerging computing paradigms. Throughout her career, Koushanfar has made significant contributions to both theoretical frameworks and practical implementations in hardware security, with numerous highly influential publications that have shaped the field.

Pascal Felberمشاهده پروفایل

استاد

• Distributed Systems
• Concurrent Systems
• Trusted Execution Environments
• +۷ مورد دیگر

Pascal Felber is a Full Professor at the Institute of Computer Science within the Faculty of Science at the University of Neuchâtel. His career spans significant industry experience at Oracle Corporation and Bell Labs before transitioning to academia, where he has established himself as a leading researcher in complex computing systems. His research interests focus on the theoretical foundations and practical applications of complex computing systems, particularly reliable, distributed, concurrent, and secure systems. Felber's work addresses critical challenges in large-scale systems including cloud computing, Internet of Things, and big data technologies. His research bridges theoretical computer science with practical system implementations. Analysis of his recent publications reveals a strong emphasis on Trusted Execution Environments (TEEs), secure computing, blockchain security, and privacy-preserving technologies. His work spans multiple domains from secure DNA alignment to phishing detection in smart contracts, demonstrating both theoretical depth and practical impact in addressing security challenges in modern distributed systems. Professor Felber has participated as a (co-)applicant in approximately twenty research projects funded by the EU (including VELOX, SRT-15, LEADS, ParaDIME, SafeCloud, SecureCloud, EBSIS, LEGaTO, VEDLIoT) and the Swiss National Science Foundation (SNSF). His teaching portfolio includes Bachelor's degree courses in French (Programming I & II, Languages and compilation, Web and network technologies, Concurrent and distributed systems) and Master's courses in English (Concurrency: Multi-core programming and data processing, Hot topics in operating systems seminar).

Jim Alves-Fossمشاهده پروفایل

استاد

• Cybersecurity
• Formal Verification
• Operating Systems
• +۲ مورد دیگر

Jim Alves-Foss is a University Distinguished Professor of Computer Science at University of Idaho's College of Engineering, specializing in high-assurance computing systems and cybersecurity. Core research domains: Formal methods for security verification Operating system security architectures Critical infrastructure protection Automated security policy enforcement Publications demonstrate consistent focus on: Rigorous verification of security properties (e.g., seL4 kernel) Development of automated analysis tools (fuzzing, SDN policy) Industrial control system protection frameworks Adversary-aware risk assessment methodologies

• Highways
• Road Materials
• Materials in Civil Engineering
• +۱۰ مورد دیگر

Kürşat Yıldız is an Associate Professor in the Department of Civil Engineering at the Faculty of Technology, Gazi University , where he has been serving since 2011. He also holds an Assistant Professor position at Gazi University's Institute of Science. His academic career is deeply rooted at Gazi University, where he completed all his degrees. His educational background includes: Doctorate (Dr.) in Construction Education, Gazi University, Institute of Science (2004–2009) Master's (MSc) in Construction Education, Gazi University, Institute of Science (2000–2003) Bachelor's in Construction Education, Faculty of Technical Education, Gazi University (1996–2000) Dr. Yıldız's research focuses on highway materials, asphalt and concrete technology, self-healing pavements, railway logistics, supply chain management, and sustainable construction . He integrates advanced methods such as machine learning, image processing, and multi-criteria decision-making (MCDM) into civil engineering applications. His recent publications highlight innovations in recycled asphalt prediction, microwave healing with fly ash, and logistics modeling . His scholarly contributions include over 80 publications, with a significant number in SCI-indexed journals. The trends in his recent articles show a strong emphasis on sustainable materials, smart infrastructure, and data-driven decision models in transportation and construction. His scientific recognition includes: REACHING AND REACHING Türkiye 2053 INTER-UNIVERSITY R&D IDEA COMPETITION (Ministry of Transport and Infrastructure, 2024) Publication Incentive (UBYT, 2022) International Scientific Publication Incentive Program (TUBITAK, 2021) He has supervised 11 theses and served as a consultant on various doctoral and master’s studies in areas such as railway logistics, asphalt performance, and traffic safety. He has led funded research projects, including those on self-healing asphalt and urban traffic impact assessments. Additionally, he has contributed to academic service through peer reviewing for journals such as Sustainability , Gazi University Journal of Science , and TÜBAV Science Journal , and has participated in scientific project evaluations for institutions like Bursa Technical University and Gazi University Technopark. Dr. Yıldız is actively involved in research groups and collaborations focusing on transportation systems, sustainable materials, and smart logistics , contributing to both national and international academic and industrial initiatives.

Qiang Liuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Probabilistic Graphical Models
• +۸ مورد دیگر

Qiang Liu is an Associate Professor in the Department of Computer Science at the University of Texas at Austin. He leads the Statistical Learning & AI Group, focusing on probabilistic graphical models, variational and Monte Carlo inference, deep reinforcement learning, and kernel methods with applications in crowdsourcing, computer vision, and bioinformatics. NSF CAREER Award recipient Co-organized workshops at ICML 2019 and NIPS 2013 Specializes in rectified flow for generative modeling and optimal transport His recent work includes fast inference via straight trajectories in ODE models and certifiable robustness in neural architectures. Publications span conferences like NeurIPS, ICML, ICLR, and JMLR.

• Computational Social Science
• Data Science
• Social Network Analysis
• +۴ مورد دیگر

Termeh Shafie is a Professor of Computational Social Science and Data Science at the University of Konstanz, Department of Politics and Public Administration. She specializes in statistical network analysis, multigraph modeling, and interdisciplinary applications across social sciences and digital humanities. Her research includes network entropy, data privacy, and archaeological network reconstruction.