جست‌وجوی استاد و پژوهشگر

Milan Stojanovicمشاهده پروفایل

دانشیار

• Molecular Automata
• Biosensors
• Nanotechnology
• +۳ مورد دیگر

Milan Stojanovic serves as Associate Professor in Columbia University's Department of Biomedical Engineering within the Fu Foundation School of Engineering and Applied Science, with dual affiliation in the Department of Systems Biology. He concurrently holds the position of Associate Director in the Division of Clinical Pharmacology & Experimental Therapeutics. His pioneering research focuses on engineering self-operating molecular automata capable of information processing and therapeutic response, high-resolution sensor arrays for bodily fluid analysis, and molecular systems exhibiting programmed walking and self-organization through local interactions. This work bridges nanotechnology, synthetic biology, and clinical therapeutics. His publication record demonstrates consistent innovation in DNA-based molecular computation and biosensing, with key contributions spanning molecular robotics (2010), evolutionary sensor optimization (2012), and breakthrough Debye-length overcoming detection methods (2018). Recent work advances intradermal health monitoring through hydrogel microfilaments (2019). National Institute of Diabetes and Digestive and Kidney Diseases grant recipient His research program integrates biochemical engineering with computational principles to develop autonomous molecular systems for diagnostic and therapeutic applications, maintaining strong connections between fundamental nanotechnology and clinical translation through Columbia's medical and engineering infrastructure.

Wolfgang Schoppekمشاهده پروفایل

مدرس ارشد

• Complex Problem Solving
• Dynamic Decision Making
• Cognitive Science
• +۳ مورد دیگر

Dr. Wolfgang Schoppek is a Senior Lecturer at the University of Bayreuth's Department of Educational Sciences within the Faculty of Humanities. Since 2001, he has held the position of Academic Councillor at the Chair of Psychology. His research focuses on complex problem solving, dynamic decision making, cognitive processes, and quantitative methodologies. He serves as Chief Editor of the Journal of Dynamic Decision Making and is an active reviewer for journals such as Human Factors and Frontiers in Psychology . Education: He earned his doctorate in psychology from the University of Bayreuth in 1995 with a dissertation on competence and complex problem solving. Previously, he studied psychology at the Otto-Friedrich University of Bamberg (1983–1990) and held a Research Assistant Professor position at George Mason University (1999–2000). Research Interests: Dr. Schoppek’s work explores cognitive mechanisms underlying decision-making in dynamic systems, the role of structural knowledge, and the application of quantitative methods in educational and psychological research. He also investigates self-regulation strategies and the design of adaptive instructional systems. Publications: His recent work includes studies on cognitive models of music improvisation, dual processing theories, and open-access publishing advocacy. Key themes in his 20+ years of research include redefining complex problem-solving paradigms beyond traditional psychometric approaches and advancing interdisciplinary methodologies. Professional Roles: He chairs the academic staff at the University of Bayreuth and is a board member of the Bavarian State Association of Scientists. His contributions span editorial leadership, policy advocacy, and fostering collaborative research initiatives.

Hind CASTEL SAMOVARمشاهده پروفایل

استاد

• Computational Statistics
• Multimedia Data Engineering
• E-Health
• +۶ مورد دیگر

Prof. Hind CASTEL is a Professor at Telecom SudParis affiliated with the SAMOVAR Lab (formerly UMR 5157). Her research focuses on computational statistics, multimedia engineering, e-health, and distributed systems. She contributes to projects involving optical technologies, cybersecurity, and IoT networks. She participates in academic events such as the SOP Seminar on June 12, 2023, discussing advanced mathematical methods for optimization and boundary problems. Her work addresses challenges in memory management (disaggregated systems), high-speed optical communication (III-V-on-SOI lasers), and natural language interfaces for process data. Part of the NeSS group Active in doctoral student mentoring through Samovar's annual PhD Day events Recent publications explore garbage collection in distributed systems, laser-based optical networking, and AI-driven query interfaces for business processes.

Julien Romeroمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Knowledge Graphs
• Natural Language Processing
• +۳ مورد دیگر

Julien Romero serves as Maître de Conférences (Associate Professor) at Telecom SudParis, Institut Polytechnique de Paris, where he conducts research within the SAMOVAR laboratory. His work bridges artificial intelligence, database systems, and natural language processing with practical applications in knowledge representation and recommender systems. Romero earned his PhD in Artificial Intelligence from Institut Polytechnique de Paris in 2020 with a thesis on harvesting commonsense knowledge from web services. His research focuses on extracting and refining commonsense knowledge from unconventional sources including children's texts, query logs, and web content. He develops techniques for knowledge graph construction, query rewriting, and heterogeneous graph-based recommendation systems. Analysis of his 12 publications (2019-2024) reveals a strong trajectory from foundational database research toward applied AI systems. His work consistently addresses real-world challenges like job recommendation cold-start problems and commonsense knowledge base cleaning, demonstrating increasing technical sophistication through generative translation methods and heterogeneous graph approaches. Key publication venues include IEEE TKDE, CIKM, ISWC, and EMNLP. Romero actively mentors students through SAMOVAR laboratory projects, with research opportunities spanning knowledge engineering, natural language processing, and educational tool development. His Pyformlang library exemplifies his commitment to making theoretical computer science accessible. Current projects like TIMBRE (2024) indicate active industry-relevant research in professional recruitment systems. The SAMOVAR laboratory provides Romero's research environment, focusing on networks, computer science, and communication systems. His work within this framework emphasizes practical applications of theoretical concepts, particularly in knowledge representation and AI-driven recommendation systems. Ongoing projects suggest continued innovation in leveraging generative AI for knowledge base refinement and heterogeneous information network analysis.

• Modeling and verification of reactive systems
• Probabilistic model checking
• Stochastic systems analysis
• +۷ مورد دیگر

Christel Baier is a full professor and head of the Chair for Algebraic and Logical Foundations of Computer Science at TU Dresden since 2006. She holds a Diploma in Mathematics (1990), PhD (1994), and Habilitation (1999) from the University of Mannheim. Previously, she was an associate professor at the University of Bonn (1999–2006). She serves as Dean of the Faculty of Computer Science at TU Dresden (2025–2027) and has held leadership roles including Vice-Dean (2019–2024). Her research focuses on formal methods, probabilistic systems, and verification, with notable contributions in probabilistic model checking, temporal logics, and automata theory. She has received prestigious awards such as the Honorary Doctorate from RWTH Aachen (2022) and the Jean-Claude Laprie Award (2023). She leads major projects like SEMECO (Zukunftscluster) and contributes to initiatives like the Collaborative Research Center for Tactile Internet. Her editorial roles include editorship of Acta Informatica and ACM SIGLOG News . Baier's work bridges theoretical foundations with practical applications in system dependability and software engineering. She actively participates in international conferences and program committees, emphasizing the synthesis and analysis of component-based systems.

Andrew Melatosمشاهده پروفایل

استاد

• Gravitational Waves
• Neutron Stars
• Pulsar Timing
• +۷ مورد دیگر

Andrew Melatos is a Professor in the School of Physics at the University of Melbourne, Australia. His research focuses on gravitational waves, neutron star astrophysics, superfluid dynamics, and interdisciplinary topics like econophysics and art-science collaboration. He has held senior roles in major collaborations such as OzGrav, LIGO, and the Texas Symposium Series. Melatos leads a dynamic research group producing cutting-edge work on pulsar glitches, magnetic mountains, and gravitational wave detection algorithms. His group has pioneered simulations of superfluid vortex avalanches and developed novel statistical methods for LIGO data analysis. Melatos has advised over 40 PhD and Master's students, many of whom now hold academic or industry leadership roles globally. His work bridges fundamental physics with applications in climate science, finance, and public outreach. Education: BSc (University Medal), University of Sydney (1988-1991) PhD in Physics, University of Sydney (1992-1995) Miller Fellow, UC Berkeley (1997-2000) Research Fellow, Caltech (1995-1997) Key Appointments: Chair, Texas Symposium Steering Committee (2006-2008) LIGO Scientific Collaboration Council (2014-present) Australia Telescope Time Assignment Committee (2002-2004) Research Themes: Melatos' work spans gravitational wave astrophysics, pulsar timing anomalies, superfluid turbulence in neutron stars, and interdisciplinary projects like econophysics modeling and art-science collaborations with visual artist Briony Barr. His group's recent breakthroughs include explaining pulsar glitch recovery via superfluid vortex dynamics and developing hidden Markov models for glitch detection. Publications & Impact: Over 15 key articles in areas like neutron star magnetospheres, LIGO signal processing, and econophysics. Notable collaborations include simulations of magnetic mountains and contributions to the historic 2017 gravitational wave-neutrino electromagnetic counterpart discovery.

• Artificial Intelligence
• Data Science
• Complex Systems
• +۵ مورد دیگر

Manuel Pita is an Assistant Professor at CICANT (University Lusófona de Humanidades e Tecnologias, Portugal) and a Research Associate in the complex systems group at ISTAR-Iscte (Instituto Universitário de Lisboa, Portugal) . PhD in Artificial Intelligence and Cognitive Science (2007, University of Edinburgh) Postdoctoral work: Prof. Luís Rocha (Indiana University, USA), Prof. Melanie Mitchell (Portland State University, USA) Principal Investigator: Project on collective states in online social conversations among Portuguese high school students His research focuses on the interaction between individual and collective behaviors in complex networks , with applications to social challenges like hate speech, polarization, and fake news. He has published articles in Q1 journals such as Sensors , PLoS Computational Biology , and BMC Medicine , with over 130 Web of Science citations. Manuel Pita's work spans computational modeling , artificial life , and data science , often applying cellular automata and NLP techniques to understand complex systems. His recent publications emphasize IoT applications and predictive epidemiology using machine learning approaches. He collaborates with institutions including Indiana University , Portland State University , and Lisbon institutions like ISTAR-Iscte and CICANT.

David Vinczeمشاهده پروفایل

پژوهشگر

• Fuzzy Systems
• Fuzzy Rule Interpolation
• Reinforcement Learning
• +۹ مورد دیگر

David Vincze is a researcher at the Department of Precision Mechanics, Chuo University, Japan. Previously affiliated with the University of Miskolc, Hungary, he has taught courses in Operating Systems, UNIX System Administration, and Modern Information Technologies. His research spans computational intelligence, fuzzy systems, reinforcement learning, and human-robot interaction. PhD in Computer Science Current Position: Researcher, Chuo University Previous Affiliation: University of Miskolc David Vincze's research focuses on Fuzzy Systems , particularly Fuzzy Rule Interpolation (FRI) , and its integration with Reinforcement Learning (e.g., FRIQ-learning). He applies these methods to Human-Robot Interaction (HRI) , drawing inspiration from ethology and animal behavior. His recent work explores applications in Parent-Child Interaction Therapy (PCIT) , using social robots to support child development and family communication. He also investigates Operating Systems Security , especially Linux kernel mechanisms. His publications show a consistent trend in developing and optimizing FRI-based learning methods for real-time, embedded, and robotic applications. He has worked on performance optimization, parallelization, and knowledge injection in FRIQ-learning, aiming to make fuzzy reinforcement learning more efficient and applicable in dynamic environments. Scientific Contributions: Developed FRIQ-learning: Fuzzy Rule Interpolation-based Q-learning Applied ethological models to robot behavior Designed fuzzy automata for HRI Optimized fuzzy inference for real-time systems Explored indoor localization for HRI experiments Vincze has advised students such as Alex Tóth and has been involved in student supercomputing teams (ASC 2014–2017). He has developed various Linux tools, including security extensions for Apache, video filters for MPlayer, and drivers for TV tuner cards. He maintains active research profiles on ResearchGate, Google Scholar, and Scopus. He leads projects in Mihoko Niitsuma's Lab at Chuo University, focusing on social robotics, behavior modeling, and real-time interaction systems.

Cosma Shaliziمشاهده پروفایل

دانشیار

• Nonparametric prediction of time series
• Learning theory
• Nonlinear dynamics
• +۱۵ مورد دیگر

Cosma Shalizi is an Associate Professor in the Statistics Department and Machine Learning Department at Carnegie Mellon University, and an External Professor at the Santa Fe Institute. His work bridges statistics, machine learning, and complex systems theory, with applications spanning neuroscience, statistical mechanics, and social networks. Shalizi's research focuses on nonparametric prediction of time series, learning theory, information theory, and causal inference. He has made significant contributions to computational mechanics, developing algorithms like CSSR (Causal State Splitting Reconstruction) for identifying optimal predictive states in complex systems. His work extends to heavy-tailed distributions, network analysis, and the statistical foundations of complex systems. He has pioneered methods for quantifying self-organization and developing nonparametric approaches to spatio-temporal prediction. His recent publications reveal a trend toward increasingly interdisciplinary work, connecting network science with causal inference, statistical learning theory with macroeconomic forecasting, and information theory with biomedical applications. His work consistently emphasizes rigorous statistical methodology applied to complex, dependent data structures across diverse scientific domains. Winner of the Best Student Paper and Best Poster awards Shalizi has advised students including Georg Goerg, who extended spatio-temporal prediction techniques to continuous-valued fields, and George Montañez, who developed fast approximate algorithms for prediction and explored information-theoretic explanations for machine learning. His collaborative network spans statistics, physics, neuroscience, and social sciences, reflecting his interdisciplinary approach to complex systems. His work with collaborators has led to significant contributions in network analysis, causal inference in social networks, and the development of nonparametric methods for complex data structures. He maintains active research programs in statistical network modeling, time series analysis, and the application of information-theoretic approaches to diverse scientific problems.

• Robotic Process Automation
• Process Mining
• Business Process Management
• +۳ مورد دیگر

Simone Agostinelli is a researcher and academic at Sapienza University of Rome specializing in business process management, robotic process automation (RPA), and process mining. He completed his PhD at Sapienza University of Rome in 2022 with a dissertation titled 'Generating executable robotic process automation scripts from unsegmented user interface logs.' His work demonstrates extensive collaboration with researchers including Andrea Marrella, Massimo Mecella, and several others across numerous publications. Dr. Agostinelli's research focuses on the intersection of business process management and automation technologies. His primary interests include developing methods for generating executable RPA scripts from user interface logs, applying process mining techniques to improve business processes, implementing AI-augmented business process management systems, and exploring blockchain applications for supply chain transparency and business confidentiality. His work bridges theoretical computer science with practical business applications, particularly in production planning, logistics, and supply chain management. Analysis of his publication trends reveals a consistent focus on automating business processes through innovative technical approaches. Starting with foundational work on RPA script generation, his research has expanded to include context-aware decision support systems, trust mechanisms in AI-augmented BPM, and blockchain applications for secure business collaboration. His recent work shows increasing sophistication in integrating multiple technologies (RPA, process mining, AI, and blockchain) to solve complex business process challenges. Dr. Agostinelli has been actively involved in major conferences including BPM, CAiSE, and ICPM, often contributing both research papers and demonstration tools like SmartRPA and DIS-PIPE. His work demonstrates strong industry relevance while maintaining academic rigor, with applications spanning healthcare, manufacturing, and supply chain domains.

Aalok Thakkarمشاهده پروفایل

استادیار

• Programming Languages
• Formal Logic
• Artificial Intelligence
• +۳ مورد دیگر

Aalok Thakkar is an Assistant Professor in the Department of Computer Science at Ashoka University, specializing in Programming Languages , Formal Logic , and Artificial Intelligence . He earned his Ph.D. in 2023 from the University of Pennsylvania , advised by Rajeev Alur and Mayur Naik. His research focuses on example-guided synthesis of relational queries , with applications in program verification, automated reasoning, and AI. Recent work includes the Mobius framework (OOPSLA 2023) and Relational Query Synthesis ⋈ Decision Tree Learning (VLDB 2023). Published in npj Digital Medicine (2021) on pandemic risk modeling Contributed to Natural Computing (2020) on Boolean network concurrency Workshop papers on synthesis algorithm complexity (SYNT 2022) and reactive systems (SYNT 2020) He was previously affiliated with Adobe Systems, Aptos Labs, Amazon AWS, and Bell Labs. Awards include the Alt Carbon Darjeeling Revival Fellowship . Teaching includes courses on Symbolic Logic , Rust programming , and Games on Graphs . He will host an IndiCS Seminar on Automated Synthesis in 2025.

Joost-Pieter Katoenمشاهده پروفایل

استاد

• Probabilistic Programming
• Model Checking
• Formal Verification
• +۴ مورد دیگر

Joost-Pieter Katoen is a full professor at RWTH Aachen University in Germany where he leads the Software Modeling and Verification (MOVES) group. Since 2013, he holds a distinguished professorship at RWTH Aachen University and is a member of the Academia Europaea. He also maintains a part-time association with the Formal Methods & Tools group at the University of Twente in the Netherlands. Professor Katoen's research primarily focuses on probabilistic programming, model checking, and formal verification. His work spans theoretical foundations of probabilistic systems, deductive verification techniques for probabilistic programs, and practical applications in software verification. He has made significant contributions to the development of algorithms for analyzing probabilistic models, including Markov chains and probabilistic automata. His research bridges theoretical computer science with practical verification challenges, particularly in the context of probabilistic systems. His recent publications reveal a strong focus on advancing probabilistic programming verification, with particular emphasis on generating functions, expected runtimes, and probabilistic bisimulation. The research trends show increasing sophistication in handling continuous distributions, parameter synthesis, and multi-objective verification problems. His work consistently appears in top-tier conferences including POPL, PLDI, SPLASH, and CAV. Scientific Awards and Recognition: Distinguished Professorship at RWTH Aachen University (since 2013) Member of Academia Europaea Professor Katoen has supervised numerous PhD students whose work spans various aspects of formal methods and probabilistic systems. His MOVES research group develops several verification tools including PROPHESY, PRINSYS, COMPASS, MRMC, and SMYLE. His editorial service includes membership on steering committees for major conferences such as CONCUR and ETAPS (where he serves as chair), and editorial boards for journals including STTT and PeerJ Computer Science.

Konstantinos Mamourasمشاهده پروفایل

استادیار

• Programming Languages
• Formal Methods
• Runtime Verification
• +۶ مورد دیگر

Konstantinos Mamouras is an Assistant Professor at the Department of Computer Science, Rice University. His research focuses on programming abstractions for data streams, runtime verification for cyber-physical systems, program semantics, and Kleene Algebra with Tests. Rice University, Houston, TX Department of Computer Science Research Interests : Design of programming abstractions for streaming data Runtime verification of cyber-physical systems Formal methods in program verification Kleene Algebra with Tests (equational theories) Efficient automata-based processing Quantitative monitoring frameworks Recent Scientific Contributions : 2025: Membership Testing for Semantic Regular Expressions (PLDI 2025) 2024: HybridSA GPU regex acceleration (OOPSLA 2024) 2024: Efficient regex with lookaround (POPL 2024) 2023: Bit Vector Automata for regex (OOPSLA 2023) 2021: Quantitative monitoring framework (RV 2021) Scientific Awards : 🏆 Distinguished Paper Award, PLDI 2024 🏆 Best Paper Award, EuroSys 2021 🏆 EATCS Best Theory Paper Award, FoSSaCS 2015 Advising : Mentored PhD student Lingkun Kong (2023) Advised MSc student Zhifu Wang (2022) Current advisees: Alexis Le Glaunec and Agnishom Chattopadhyay Teaching : Spring 2023: IoT Programming and Data Analysis (COMP 418/518) Fall 2022: Reasoning about Algorithms (COMP 382) Spring 2021: Reasoning about Software (COMP 403/503)

Hossein Hojjatمشاهده پروفایل

استادیار

• Formal Methods
• Software Verification
• Hardware Verification
• +۱۱ مورد دیگر

Hossein Hojjat is an Assistant Professor in the Department of Computer Science at the Rochester Institute of Technology (RIT), within the Golisano College of Computing and Information Sciences. He received his PhD in computer science from EPFL, Switzerland in 2013 and was a postdoctoral researcher at Cornell University before joining RIT. He is currently on leave from his academic position since 2019. Research Interests: His research focuses on formal methods for the analysis and verification of software and hardware systems. He works extensively on program verification, network verification (especially in software-defined networking), automated program repair, Horn clauses, interpolation techniques, and model checking. His work bridges theoretical foundations with practical tools and applications. Publication Trends: His recent publications (2012–2017) show a strong trend in applying formal methods to networked and distributed systems, particularly using NetKAT and Horn clause solvers. He has made significant contributions to ensuring consistency in network updates, synthesizing repairs, and analyzing timed and concurrent systems. His work integrates logic, programming languages, and systems. Scientific Service: Program Committee: FSEN 2015, FSEN 2017, TTCS 2017, VSTTE 2017, SEFM 2017, SBMF 2018, FSEN 2019 (co-chair), PLDI 2019 (AEC) Editorial Board: Information Processing Letters Advising and Grants: He has advised PhD student Ali Shokri and has received research funding through the SaTC: CORE: Small grant on federated network security. His academic advising and grant activities reflect his leadership in advancing formal techniques for secure and reliable systems. Labs and Teams: While no specific lab is mentioned, his collaborations with researchers at EPFL, Cornell, and in tool development (e.g., FLATA, ELDARICA) indicate active participation in formal methods research teams focused on verification toolkits and network programming.

• Game Theory
• Formal Methods
• Controller Synthesis
• +۳ مورد دیگر

Thomas Brihaye is a Professor in the Mathematics Department at the University of Mons (UMONS) in Belgium and is affiliated with the Complexys Institute, a research center focused on complex systems. His academic position involves both teaching responsibilities and advanced research in theoretical computer science and applied mathematics. His primary research explores game theory and its applications across multiple domains including formal methods , controller synthesis , and urban planning . He investigates mathematical frameworks for decision-making in complex systems, with recent emphasis on renewable energy optimization, cybersecurity protocols, and stochastic process modeling. Key methodological approaches include Markov decision processes, timed automata, and equilibrium analysis in adversarial environments. Analysis of Brihaye's publications reveals consistent focus on: Theoretical foundations of game theory and computational complexity Applications in energy systems (renewable communities, grid management) Formal verification methods for security and system reliability Stochastic modeling and probabilistic systems His work bridges theoretical computer science with practical challenges in sustainability and infrastructure. Brihaye actively contributes to academic dissemination through: Regular presentations at international conferences (LATIN, RP, CONCUR, FORMATS) Educational outreach programs ('Mathipulations' workshops and public lectures) Interdisciplinary collaborations on urban systems and cognitive psychology projects