جست‌وجوی استاد و پژوهشگر

Heinz Rienerمشاهده پروفایل

پژوهشگر

• Logic Synthesis
• Formal Methods
• Computer-Aided Verification
• +۴ مورد دیگر

Heinz Riener is a Researcher at the Integrated Systems Laboratory (LSI) within the School of Computer and Communication Sciences (IC) at EPFL, Lausanne, Switzerland. He holds a Ph.D. (Dr.-Ing.) in Computer Science from the University of Bremen, Germany. Previously, he worked at the German Aerospace Center (DLR) and the University of Bremen's Reliable Embedded Systems group. His research focuses on logic synthesis , formal methods , and computer-aided verification of hardware and software systems. Key areas include quantum computing (e.g., AQFP circuits), nanotechnology (e.g., RFET-based circuits), and emerging technologies like adiabatic quantum-flux parametron systems. Developed open-source tools like mockturtle and easy for logic synthesis and ESOP forms. Principal Investigator on projects like the Open Logic Synthesis Libraries initiative. Active in program committees for conferences like DAC, DATE, and FDL. Collaborates with institutions such as TU Graz, TU Hamburg, and UC Berkeley. His work emphasizes reproducibility and open-source collaboration in logic synthesis, contributing to benchmarks and libraries widely used in academia and industry.

Neil Robinsonمشاهده پروفایل

استاد مدعو

• Software Engineering
• Formal Methods
• Railway Systems Engineering
• +۳ مورد دیگر

Adjunct Professor Neil Robinson is affiliated with the School of Electrical Engineering & Computer Science at the University of Queensland. His research focuses on formal methods, railway systems engineering, and safety-critical systems. He has contributed to advancements in data refinement techniques, formal verification of railway control systems, and adaptation of CMMI for safety-critical applications. His work emphasizes practical implementations of formal methods in real-world systems. Education details are not explicitly stated in the provided text. His research interests include software engineering methodologies, formal specification tools, and railway signalling design automation. Notable contributions include the development of control table verification frameworks and incremental abstraction techniques for data refinements. Publications span formal verification applications in railway systems, process improvement methodologies for safety-critical software, and visualization tools for formal specifications. Collaborations include work with the Software Verification Research Center at the University of Queensland and industry partners in railway signaling design. No awards or grants are listed in the provided information. He has advised on multiple research projects related to railway systems and formal methods but no specific student names are recorded here.

• Programming Languages
• Type Theory
• Formal Semantics
• +۳ مورد دیگر

Max New is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan's College of Engineering. He specializes in programming languages, type theory, and formal semantics, with particular focus on gradual typing, functional programming, and category theory. His research emphasizes foundational aspects of programming languages and their semantic properties. He advises graduate students through weekly one-on-one meetings and hosts weekly group meetings at an on-campus café to foster collaboration. Students are expected to conduct research, publish papers, and maintain on-campus presence. Teaching responsibilities include serving as a Graduate Student Instructor (GSI) for 1-2 semesters. His recent work explores topics like stack-manipulating computation, denotational semantics of gradual typing, and formal category theory. Conference attendance is supported through research funds and college grants. Students typically take time off during major holidays and summer months.

Francesca Chiaromonteمشاهده پروفایل

استاد

• Statistics
• Bioinformatics
• Genomics
• +۷ مورد دیگر

Francesca Chiaromonte is a renowned statistician and professor holding dual faculty positions at the Sant'Anna School of Advanced Studies (Institute of Economics) and Penn State University (Department of Statistics). She is the scientific coordinator of the EMbeDS Department of Excellence and a key figure in interdisciplinary research, particularly at the interface of statistics, computation, and life sciences. Her work focuses on developing methods for analyzing high-dimensional, complex data, with applications in genomics, meteorology, and economics. Education: She earned a Laurea (cum laude) in Statistical and Economic Sciences from the University of Rome La Sapienza (1990s) and a Ph.D. in Statistics from the University of Minnesota (2000s). Her thesis work, under R. Dennis Cook, explored sufficient dimension reduction and envelope models. Research Interests: Methodological contributions include dimension reduction, functional data analysis, and computational techniques for significance assessment. Applied research spans Omics data integration, climate change socioeconomic impacts, and statistical validation of agent-based economic models. She has pioneered applications of statistical methods to meteorology, such as improving tropical storm forecasts and analyzing storm lifecycle dynamics. Awards: Elected Fellow of the American Statistical Association (2016) and Fellow of the Institute of Mathematical Statistics (2022), recognizing her contributions to statistical methodology and interdisciplinary training. Academic Leadership: Spearheads interdisciplinary training programs, including the PhD in Data Science (consortium with Scuola Normale Superiore and others) and the National Doctorate in Artificial Intelligence. Active in Penn State’s Huck Institutes of the Life Sciences and multiple research centers. Collaborations: Engages with institutions such as the Santa Fe Institute, UCLA’s Institute for Pure and Applied Mathematics, and the International Institute for Applied Systems Analysis.

• Software Testing
• Model-Based Testing
• Search-Based Software Engineering
• +۶ مورد دیگر

Raluca Lefticaru is an Associate Professor in Computer Science at the University of Bradford's School of Computing, Software and Artificial Intelligence & Engineering (CSAI&E), within the Faculty of Engineering & Digital Technologies. She leads the BEng Software Engineering programme and holds a visiting researcher position at the University of Sheffield's Testing group. A Fellow of the Higher Education Academy (FHEA), she specializes in software testing methodologies, particularly model-based testing using evolutionary approaches, formal specification, and P systems verification. Her research interests span software testing, model-based testing, search-based software engineering, membrane computing, and formal verification. Recent professional activities include organizing conferences such as YISEC 2023, AIERC 2022, and A-MOST 2021, serving as a program committee member for SEFM 2025, ICTSS 2023, and ICSE 2022, and reviewing for WCCI 2022. She has held roles including Co-chair of YISEC 2023 and Communication Chair of CMC18 (2017). Raluca's work emphasizes interdisciplinary applications, from IoT security to medical imaging analysis. Her publications reflect contributions to testing frameworks for P systems, fault tree analysis, and AI-driven solutions for cyberbullying detection and industrial fire prevention. She actively contributes to academic journals in membrane computing, optimization, and software engineering. Awards: Fellow of the Higher Education Academy (FHEA) Key Projects: IoT safety-security integration, robotic system testing, and COPD self-management systems Conference Leadership: Organized over 10 international conferences since 2017

Bernd Finkbeinerمشاهده پروفایل

استاد

• Formal Verification
• Reactive Synthesis
• Hyperproperties
• +۴ مورد دیگر

Prof. Bernd Finkbeiner is a faculty member at CISPA Helmholtz Center for Information Security and holds a Professorship in Computer Science at Saarland University. He earned his Ph.D. in 2003 from Stanford University. Leading the Reactive Systems Group since 2003, now part of CISPA, his research focuses on ensuring safety and security in computer systems through formal methods like specification, program synthesis, and verification. Key projects include output-sensitive reactive synthesis (OSARES), hyperproperty logics (HYPER), and real-time monitoring (RTLOLA). Education : Ph.D. in Computer Science, Stanford University, 2003 His research interests span hyperproperties, formal verification, runtime monitoring of cyber-physical systems, and distributed synthesis. He has pioneered tools like StreamLAB and AutoHyper for hyperproperty analysis. His work on temporal causality and information-flow guided synthesis addresses challenges in distributed and secure systems. Key Achievements : Recipient of ERC Advanced Grant 2022–2027 for Project HYPER Best Paper Awards at ICALP 2009, FSEN 2007, and VMCAI 2012 Leader of the Reactive Systems Group at CISPA Grants & Funding : ERC Advanced Grant supporting research on hyperproperties His lab develops cutting-edge tools for formal methods, including BoSy for bounded synthesis and RTLola for runtime verification. Current research explores compositional synthesis, explainable reactive systems, and robust monitoring for medical and autonomous systems.

• Cybersecurity
• Data Leak Prevention
• Steganography
• +۵ مورد دیگر

Dr. Abdulaleem Al-Othmani is a Senior Lecturer in Computer Science at De Montfort University (DMU), serving as Programme Manager for APU/DMU dual award computing programmes. He holds a PhD in Computer Science from Universiti Teknologi Malaysia (UTM) and has held academic roles at University Kuala Lumpur (UniKL) and Asia Pacific University (APU) in Malaysia. His research focuses on cybersecurity, including steganography, digital watermarking, data leak prevention, and machine learning applications in security. Education: BSc in Computer Engineering, University of Baghdad (2002) Master of Computer Science (Information Security), UTM (2010) PhD in Computer Science, UTM (2016) Research Interests: Data Leak Prevention frameworks for educational institutions Cybersecurity in IoT and telemedicine systems Machine learning-driven threat detection E-learning security during pandemics Biometric authentication systems for healthcare Awards & Grants: Principal Investigator: RM99,380 FRGS grant for data leak prevention (2020–2022) UTM International Doctoral Fellowship (2011–2012) Certificate of Excellence for PhD thesis (2016) Advising & Professional Roles: Supervised over 20 PhD/Master students and 50+ bachelor dissertations Current PhD supervision: Safari Ismail Mussa (web app vulnerability scanners) External Examiner for MSc programmes at Birmingham City University (2021) Member of IEEE, BCS, ACM, and AIS professional bodies He is affiliated with the Cyber Technology Institute (CTI) at DMU and contributes to editorial boards of journals/conferences.

Nicolas Halbwachsمشاهده پروفایل

پژوهشگر

• Embedded Systems Verification
• Synchronous Programming
• Formal Methods
• +۳ مورد دیگر

Nicolas Halbwachs is a Research Director at CNRS and Director of the Verimag Laboratory, affiliated with the University of Grenoble. His work focuses on formal methods for embedded systems, synchronous programming, and verification techniques. He co-developed the Lustre language, which forms the basis of the industrial tool Scade, and contributed to Linear Relation Analysis for program verification. Halbwachs has held positions including Invited Professor at Stanford University (1992-93) and has been recognized with the 2004 Michel Monpetit Award. He has supervised 10 PhD theses and contributed to major research projects like the SYRF Esprit project and the ASSERT initiative. His research spans verification of real-time systems, abstract interpretation, and automated invariant discovery in array-manipulating programs. Education: State Thesis in Mathematics (Grenoble, 1984), Third Cycle Thesis in Computer Science (Grenoble, 1979) Research Highlights: Synchronous Programming Foundations, Linear Relation Analysis, Lustre Language Development Leadership: Editor of Formal Methods in System Design, Program Committee Chair for CAV'99, TACAS'05, and EMSOFT'09 His recent work addresses invariant discovery in array-based programs, extending abstract interpretation techniques to handle complex data structures.

Jessica Chenمشاهده پروفایل

دانشیار

• Multimedia (online video streaming)
• Data analysis and machine learning
• Formal specification and verification
• +۲ مورد دیگر

Dr. Jessica Chen is an Associate Professor at the School of Computer Science, University of Windsor. She holds a BSc from Beijing University and a PhD from Scuola Normale Superiore di Pisa. Her research focuses on multimedia systems, data analysis, formal verification, communication protocols, and software design. Her work includes optimizing adaptive video streaming algorithms, studying network conditions, and formal methods for concurrency. Publications span ACM journals and SIGCOMM conferences. She advises students in co-op programs and contributes to experiential education initiatives. Dr. Chen’s office is located in LT 8100, and she can be reached at xjchen@uwindsor.ca. Her research bridges theoretical computer science with practical applications in streaming and network optimization.

• Web Services
• Formal Verification
• Software Security
• +۳ مورد دیگر

Xiang Fu is a researcher in the Department of Computer Science at Hofstra University, with affiliations to institutions including Georgia Southwestern State University, University of California, Santa Barbara, and Massachusetts Institute of Technology. His work spans formal verification of web services, software security, zero-knowledge proofs, and automated testing frameworks. His research interests include: Web Services Formal Verification Software Security Zero-Knowledge Proofs Automated Testing Software Engineering Recent publications focus on zero-knowledge auditing for financial systems, formal verification of web services, and secure software analysis. Notable tools developed include APOGEE for automated grading and WISEngineering for scalable online learning. No explicit scientific awards are documented in the provided data.

Anthony Widjaja Linمشاهده پروفایل

استاد

• Automated Reasoning
• String Constraint Solving
• Formal Language Theory
• +۵ مورد دیگر

Anthony Widjaja Lin is a Full Professor (W3) in Theoretical Computer Science (Automated Reasoning) and Max-Planck Fellow at University of Kaiserslautern-Landau, Germany. Previously, he was an Associate Professor in Programming Languages at Oxford University Department of Computer Science and Governing Body Fellow at Kellogg College (2016-2019), and an Assistant Professor at Yale-NUS, Singapore (2014-2016). He completed his PhD in Informatics at University of Edinburgh in 2010 under Leonid Libkin (supervisor) and Richard Mayr (co-advisor). Dr. Lin's educational background includes: PhD in Informatics, University of Edinburgh (2010) MSc, University of Toronto BSc (Honours), Melbourne University Dr. Lin's research focuses on automated reasoning, particularly over strings, formal language theory, learning/synthesis, and foundations of machine learning. His work has significant applications in software verification, program synthesis, querying graph databases, and computer security. He leads the development of the OSTRICH string solver, which won the QF_S (Single Query Track) in SMT-COMP 2023. His research has evolved from foundational work on string constraint solving to applications in verification of string-manipulating programs and more recently to connections with machine learning models like transformers. Dr. Lin has received numerous prestigious awards including an ERC Consolidator Grant (2023), Amazon Research Award (2021), ERC Starting Grant (2017), Google Faculty Award (2017), and the LICS Kleene Award (2010). Dr. Lin has advised several PhD students to completion, including Pascal Bergsträßer, Chih-Duo Hong, and Xuan-Bach Le, who have gone on to become Assistant Professors at institutions like National Chingchi University and Nanyang Technical University. He currently supervises multiple PhD students and postdocs working on string solving, automated reasoning, and verification. Dr. Lin leads the AV-SMP project (Algorithmic Verification of String-Manipulating Programs), which was supported by an ERC Starting Grant (2017-2022) and an Amazon Research Award (2021). His research group develops tools like OSTRICH, SLOTH, and CertiStr for string constraint solving and verification.

Mahmoudreza Babaeiمشاهده پروفایل

پژوهشگر

• Social Networks
• Data Mining
• Information Consumption
• +۳ مورد دیگر

Mahmoudreza Babaei is a researcher at the Max Planck Institute for Software Systems, focusing on computational social science, algorithmic fairness, and social network analysis. His research examines information propagation efficiency, trust dynamics, and divisiveness in social media ecosystems. Research interests span social computing, data mining, and NLP, with emphasis on fairness in influence maximization, truth perception in news, and adversarial learning techniques for equitable AI systems. Honors include the WSDM Student Travel Scholarship and visiting fellowships at KAIST. His recent publications focus on graph embeddings for fairness, time-critical influence models, and cognitive biases in information consumption.

Ekkart Kindlerمشاهده پروفایل

دانشیار

• Model-Based Software Engineering
• Process Mining
• Petri Nets
• +۴ مورد دیگر

Ekkart Kindler is an Associate Professor in the Department of Applied Mathematics and Computer Science at DTU Compute, Technical University of Denmark. His research focuses on model-based software engineering, process mining, and road condition assessment using vehicle sensor data. He leads the Competence Centre for Model-Based Software Engineering, supporting industry adoption of advanced software development methodologies. Education: M.Sc. (1990), Ph.D. (1995) from Technische Universität München; Habilitation in Computer Science (2001) from Humboldt-Universität zu Berlin. He held visiting professorships at German universities (2000–2007) and has extensive experience in formal methods, business process modeling, and Petri nets. Research interests include declarative process modeling, complexity metrics for cognitive load assessment, and the integration of formal methods into industrial software development. His work contributes to UN Sustainable Development Goals related to sustainable infrastructure (SDG 9) and innovation (SDG 9). Notable collaborations include road condition assessment projects using vehicle sensor data (LiRA-CD dataset) and contributions to Petri net standards (PNML). He has supervised PhD students such as K. I. Simonsen (protocol software) and A. Skar (road assessment). Grants and projects: Live Road Assessment with Vehicle Sensors (2019–2022), Model Transformation Tools (2013–2016), and waste management modeling (2012–2016). Active in open-source datasets and tool development for indoor climate control (climify.org).

• Performance Engineering
• Cloud Computing
• Machine Learning
• +۳ مورد دیگر

Emilio Incerto is an Assistant Professor at the IMT School for Advanced Studies Lucca, Italy. His research focuses on performance engineering, cloud computing, and formal methods applied to systems optimization. He specializes in developing frameworks for performance prediction, autoscaling solutions for microservices, and integrating machine learning techniques into performance modeling. Key research areas include: Performance modeling and optimization of distributed systems Autoscaling algorithms for dynamic cloud workloads Formal verification of real-time software systems AI-driven performance control mechanisms He has organized international workshops such as AIPerf (Artificial Intelligence for Performance Modeling), showcasing leadership in bridging AI and performance engineering. His work frequently explores reproducibility in performance studies and quantitative evaluation of complex systems like flocking behaviors in biological simulations. Notable contributions include μOpt (an efficient autoscaler for microservices) and μP (a performance prediction framework). His research emphasizes practical implementation of theoretical models through frameworks like SystemC-based environments for HW/SW co-design.

Jip Dekkerمشاهده پروفایل

پژوهشگر ارشد

• Constraint Programming
• Optimization
• Programming Languages
• +۵ مورد دیگر

Jip Dekker is a Research Fellow at the Department of Data Science & AI, Monash University. His expertise lies in constraint programming, optimization, and neuro-symbolic AI. He holds a PhD from Monash University (2021), an MSc from Uppsala University (2017), and a BSc from Radboud University (2014). Research interests include constraint-based modeling languages, SAT solving techniques, hybrid optimization solvers, and applications in transportation scheduling. He has contributed to projects such as the Next-Gen Lazy Clause Generation (NLCG) and sports scheduling optimization frameworks. Recent work spans neuro-symbolic AI explainability, circadian rhythm protocols, and rail dispatch algorithms. His articles demonstrate cross-disciplinary impact across computer science, operations research, and biomedical research. Received the OPTIMA Education Award (2022) and recognition for SETU results (2020) Contributed to 6 peer-reviewed publications since 2017, with 15+ citations Advising roles include collaborative projects on sports fixtures and constraint solver development. Active in organizing international conferences like CPAIOR and AAAI, and maintains visiting researcher ties with Uppsala University.