جست‌وجوی استاد و پژوهشگر

• Computational Logic
• Answer Set Programming
• Bioinformatics
• +۶ مورد دیگر

Alessandro Dal Palu' is an Associate Professor at the Department of Mathematical, Physical, and Computer Sciences at University of Parma. He holds a PhD in Computer Science from University of Udine and has been with University of Parma since 2005, transitioning from Researcher to Associate Professor in 2014. His teaching portfolio includes courses on Computer Architecture, Constraint Programming, and Algorithms & Data Structures. His research spans computational logic, bioinformatics, and GPU computing. Notable achievements include the 2007 GULP award for his Ph.D. thesis and the ICLP 2010 best paper award. He has led Italian INdAM-GNCS research projects on GPU applications (2011) and Logic Programming in cancer genomics (2016). Recent publications focus on explainable AI frameworks, bioinformatics applications, and sustainable logistics solutions. His work integrates Answer Set Programming with biomedical challenges like protein structure analysis and cancer genome evolution. He chairs the International Conference on Logic Programming (ICLP 2018) and has organized multiple international workshops on constraint programming. 2007 GULP Award ICLP 2010 Best Paper PI for INdAM-GNCS projects (2011, 2016) Program Committee member for international conferences

• Artificial Intelligence
• Optimization
• Classification
• +۳ مورد دیگر

Lucas Martínez Bernabéu is an Associate Professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante , where he also serves as a senior technician at the University Institute of International Economics . His work bridges artificial intelligence with geographic analysis. Academic Training Doctorate in Information Society Technologies (2012) Computer Engineering (2003) His research focuses on AI applications for solving optimization and classification problems in functional regionalisation , particularly local labor markets and spatial interaction patterns. Recent publications demonstrate methodological advancements and critical evaluations of data sources like mobile phone records for geographic analysis. Teaching includes courses on Intelligent Systems and Programming 1 , delivered across undergraduate programs in Robotics Engineering and Computer Engineering with Business Administration. Over the last 10 years, he has taught these subjects multiple times at the University of Alicante. Research projects involve collaborations with international institutions and organizations like the Office for National Statistics and UTE Cercle Territorio Paisaje y Arquitectura , funded by Spanish ministries and local entities. Notably, he contributed to studies on mobile phone data validation for commuting analysis and adaptive evolutionary algorithms for labor market delineation. His publications and conference participation (e.g., NECTAR Conference 2024, SCORUS Conference 2024) highlight cross-disciplinary work at the intersection of AI, geography, and policy. No formal doctoral students have been supervised in the past five years.

Tolga Ovatmanمشاهده پروفایل

استاد

• Algorithms and Computing Theory
• Computer Software and Software Engineering
• Cloud Computing
• +۶ مورد دیگر

Prof. Dr. Tolga Ovatman is a faculty member at the Department of Computer Engineering, Istanbul Technical University , where he has been serving as Head of Department since 2024. His academic career spans roles from Research Assistant (2004-2012) to Associate Professor (2019-2023) and full Professor (2023-present). He previously held administrative roles such as Vice Dean (2018-2022) and Deputy Head of Department (2016-2018). PhD in Computer Engineering (2005-2011) MS in Computer Engineering (2003-2005) BSc from Hacettepe University (1999-2003) His research focuses on model checking , replicated state machines , cloud computing , and object-oriented software . Recent work addresses computation offloading in 6G networks , collaborative text editing data structures , and energy-efficient environmental monitoring systems . Key projects led include Design of a Multiplexed State Machine Storage System for Edge Computing (2022-2024) and Microservice Compatible Symphony Infrastructure Research (2020). He has supervised numerous theses on topics ranging from collaborative text editing to AI applications in watershed management . Publications span IEEE Transactions , Springer , and conferences like CSCE and CLOSER .

• Artificial Intelligence
• Strategic Reasoning in Multi-Agent Systems
• Formal Verification
• +۱۰ مورد دیگر

Aniello Murano is a Professor in Computer Science at the Department of Electrical Engineering and Information Technology of the University of Naples Federico II. He serves as Scientific Director of the ASTREA (Automated Strategic REAsoning) Laboratory and has been actively involved in numerous research projects including H2020-MSCA project SEAL as Principal Coordinator and PNRR Research Unit Coordinator on Resilient Strategic Reasoning in AI (2023-2025). His primary research interests focus on Artificial Intelligence, Formal Aspects for Strategic Reasoning in Multi-Agent Systems under Perfect and Imperfect Information, Specification, Verification, and Synthesis of Reactive Systems, Model Checking and Module Checking, Formal Languages, Automata Theory, Real-time Systems, Pushdown and Hierarchical Systems, Temporal and Modal Logics, Logics for Strategic Reasoning in AI, Game Theory, Parity Games, Solution Concepts, and Automated Mechanism Design. His work bridges theoretical computer science with practical applications in AI systems. Murano has published extensively in top-tier venues, with recent publications spanning KR, IJCAI, AAAI, AAMAS, and other premier conferences. His research shows strong trends in strategic reasoning frameworks, verification techniques for multi-agent systems, and applications of game theory to mechanism design. He has made significant contributions to Strategy Logic and its extensions. JPMorgan Faculty Research Award 2022 Best Paper Award PRIMA 2015 Vigevani Research Project Prize 2010 In KR Top 100, best AI-2000 world researchers Italian Scientific Abilitation as Full Professor in Engineering Computer Science (March 2018) Italian Scientific Abilitation as Full Professor in Computer Science (April 2017) Royal Society Award 2016 Murano has supervised numerous PhD students including Silvia Stranieri, Antonio Di Stasio, Vadim Malvone, and Giuseppe Perelli. He has served as Program Chair for major conferences including ECAI 2025 (Program co-chair), KR 2024 (Area chair), and CSL 2024. His research has been supported by significant grants including Horizon 2020 projects, JPMorgan funding, and Italian national research programs. He leads the ASTREA Laboratory focused on automated strategic reasoning and has been instrumental in establishing collaborative research initiatives with institutions worldwide.

Francesco Leofanteمشاهده پروفایل

پژوهشگر ارشد

• Explainable AI
• Counterfactual Explanations
• Robustness
• +۳ مورد دیگر

Francesco Leofante is a Research Fellow at Imperial College London , affiliated with the Centre for Explainable AI . His work focuses on Explainable AI (XAI) , particularly counterfactual explanations with formal robustness guarantees against perturbations. Imperial College Research Fellowship DAAD AINet Fellowship (Safety and Security in AI) Imperial PFDC Supporting Research Staff and Students Award 2023 Research Interests center on Explainable AI , emphasizing counterfactual explanations , robustness , model multiplicity , and formal verification in critical systems like energy and aviation. His work bridges AI , formal methods , and human-AI collaboration . Publications include studies on robust counterfactual explanations , parametric ReLUs for verification, and multi-agent systems with formal guarantees. These appear in top venues like AAAI , KR , IJCAI , and AAMAS , often addressing AI safety and trustworthy systems . Scientific Awards include the Imperial PFDC Supporting Research Staff and Students Award 2023 , DAAD AINet Fellowship , and Imperial College Research Fellowship . He also contributes to workshops and program committees at conferences like AAAI, KR, and IJCAI. Future Work includes expanding robust XAI into critical infrastructure systems (energy, aviation) and developing tools like OMTPlan for AI planning and verification .

Ai Taniguchiمشاهده پروفایل

استادیار

• Formal Semantics
• Formal Pragmatics
• Public Linguistics
• +۸ مورد دیگر

Ai Taniguchi is an Assistant Professor (Teaching Stream) in the Department of Language Studies at the University of Toronto Mississauga, specializing in English Language Linguistics and Online Teaching. Her academic journey includes a PhD in Linguistics from Michigan State University, where she worked under Marcin Morzycki, an MA in Linguistics from the same institution, and a BA (Highest Honours) in Linguistics and French from the University of Georgia. Her research expertise spans formal semantics and pragmatics, with particular focus on non-at-issue meaning, social meaning, and dynamic semantics. She explores what kinds of meaning exist in language beyond literal, at-issue meanings and how they can be formally modeled. Her work includes investigations of Japanese linguistic phenomena such as sentence-final particles, role language ( yakuwarigo ), and pejorative language use. Taniguchi's pedagogical work is equally innovative, combining linguistics with visual arts to create accessible educational content. She uses comics and other visual media to communicate complex linguistic concepts to broader audiences through her LingComics projects. Her teaching interests include equity, diversity, inclusion, and decolonization in linguistics education, public outreach, and innovative online teaching methods for large introductory courses. Her publications and research demonstrate a consistent pattern connecting formal linguistic theory with practical applications in education and public communication. She has co-authored Essentials of Linguistics (2nd edition), an open educational resource that makes linguistics accessible to wider audiences, reflecting her commitment to public linguistics and educational equity. LSA 2019: 5-Minute Linguist (winner) University of Toronto International Student Experience Fund (2022-2023) eCampusOntario Virtual Learning Strategy grant (2021-2022) As a teaching-focused faculty member, Taniguchi has developed numerous courses including LIN204 (English Grammar I), LIN205 (English Grammar II), LIN337 (Lexical Semantics), LIN338 (Pragmatics), and LIN452 (Communicating English Language Linguistics). Her pedagogical approach emphasizes authentic assessment, justice-centered curriculum development, and the integration of visual arts into linguistic instruction. Taniguchi maintains an active presence in both academic and public spheres, with media appearances, public lectures, and high school outreach programs in the Greater Toronto Area. Her work bridges the gap between specialized linguistic research and public understanding through creative communication methods.

• Cybersecurity
• IoT Security
• Digital Forensics

Mohamed Hamdy Khalil Eldefrawy is a Senior Lecturer at Halmstad University's School of Information Technology , specializing in cybersecurity and IoT-related domains. His research focuses on securing emerging technologies through advanced authentication mechanisms, threat detection, and forensic analysis. Cybersecurity IoT Security Digital Forensics His recent publications (2025-2019) highlight expertise in lightweight authentication for autonomous vehicles, deep reinforcement learning for intrusion mitigation, RFID protocol analysis , and side-channel forensic techniques . Key research trends involve industrial IoT security, formal verification of protocols, and AI-driven threat detection. Current affiliations include Halmstad University. No specific scientific awards or student advising details are mentioned in the provided texts.

Nada Aminمشاهده پروفایل

استادیار

• Programming Languages
• Artificial Intelligence
• Metaprogramming
• +۴ مورد دیگر

Nada Amin is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), where she leads the metareflection lab . Her research combines programming languages (PL) and artificial intelligence (AI), focusing on neuro-symbolic systems that are correct by construction, with applications in program synthesis, verification, and precision medicine. Harvard John A. Paulson School of Engineering and Applied Sciences (2019–Present) University Lecturer in Programming Languages at University of Cambridge (2017–2019) Doctoral and postdoctoral work at EPFL (2011–2017) Her research spans three core themes: Safer : Type systems and formal verification (Coq, Dafny, Frama-C) Faster : Multi-stage programming and interpreter collapsing techniques Easier : Neuro-symbolic AI for program manipulation and biological reasoning Key publications include: 2025: Modular Imperative with LLMs (LMPL), Multi-stage Relational Programming (PLDI) 2024: Persimmon for extensible variant types (OOPSLA) 2023: LURK for recursive knowledge (ICFP), Dolorem language growth pattern (ECOOP) Scientific honors include: Teaching Assistant Team Award (EPFL, 2015) Michigan Cambridge Research Initiative Grant (2018) ArsDigita Prize (1999) She has served on program committees for GPCE (co-chair), ICFP, PLDI, and organized workshops in metaprogramming and logic programming. Her teaching portfolio includes graduate courses on neurosymbolic programming, program synthesis, and advanced PL theory.

• Bayesian Statistics
• Parametric Inference
• Information Theory
• +۷ مورد دیگر

Professor Camila Caiado is affiliated with Durham University as a Professor in the Faculty of Science, Department of Mathematical Sciences . Her research spans Bayesian Statistics, Parametric Inference, Information Theory , and Stochastic Processes , with interdisciplinary applications in Health Informatics and Geophysics . Key Research Themes : Uncertainty Quantification, Computational Statistics, Decision Theory, and Applications of Bayesian Methods in Complex Systems Notable Collaborations : Atom Bank, Healthcare Institutions, Petroleum Engineering Projects Recent Publications focus on digital twin banking models , healthcare utilization during pandemics , and geochronological uncertainty analysis . She employs agent-based modeling and machine learning for pandemic planning and medical diagnostics. Academic Leadership : Active in Durham Research Methods Centre and Institute for Data Science. No formal awards or supervisees are explicitly mentioned in available data.

Andrew S. Gordonمشاهده پروفایل

عضو هیئت علمی

• Interactive Narrative
• Commonsense Reasoning
• Computational Models of Narrative
• +۶ مورد دیگر

Andrew S. Gordon is a Research Associate Professor of Computer Science at the University of Southern California and Director of Interactive Narrative Research at the Institute for Creative Technologies. His work integrates artificial intelligence, cognitive science, and interactive storytelling to create systems that automatically interpret and generate narrative structures, with a special focus on commonsense reasoning and abductive inference. Education Ph.D. in Computer Science, Northwestern University, 1999 Research Interests Gordon’s research converges on computational narrative intelligence . He develops formal models of commonsense psychology that enable machines to reason about human intentions, beliefs, and emotions. His group designs interactive narrative systems for training and education, builds large-scale story corpora, and pioneers abductive reasoning techniques that combine symbolic logic and statistical learning to interpret temporal data. Recent work explores how large language models can be guided to co-create interactive fiction and how vision–language models can be benchmarked for causal understanding. Publication Trends Across more than two decades, his publications reveal consistent themes: (1) foundational theories of commonsense psychology and strategy representation, (2) narrative technologies that blend AI planning with human creativity, and (3) practical training simulations for defense and education. The 2024-2025 papers highlight a pivot toward evaluating and steering large language models for narrative tasks, while earlier work established abductive reasoning frameworks such as “Etcetera Abduction.” Awards & Honors Best Paper Award, System Lifecycle and Technologies Track, Simulation Interoperability Standards Organization (SIW 2021) Advising & Grants He has successfully mentored three PhD students—Reid Swanson (2010), Christopher Wienberg (2017), and Melissa Roemmele (2018)—whose dissertations span computational narrative, commonsense reasoning, and interactive fiction. His research has been continuously supported by agencies including the U.S. Army, DARPA, and NSF for projects on virtual training environments, narrative-centered learning, and large-scale commonsense knowledge acquisition. Labs & Teams Gordon directs the Interactive Narrative Research Group at USC’s Institute for Creative Technologies, where interdisciplinary teams of computer scientists, cognitive psychologists, and interactive media designers collaborate on systems such as the Rapid Integration & Development Environment (RIDE) for embodied conversational agents and story-driven training simulations.

• Engineering Education
• Technology-Enhanced Learning
• AI in Education
• +۶ مورد دیگر

Christian Stöhr is a Professor at Chalmers University of Technology's Department of Communication and Learning in Science. His research focuses on technology-enhanced learning in engineering education, including flipped and hybrid teaching methods, AI in education, and Massive Open Online Courses (MOOCs). He also investigates science communication and the role of knowledge in governance contexts. Current projects include Navigating Large Language Models , 4S , and ENHANCE collaborations. He supervises two PhD students and a post-doc, and serves as Director of Studies for the Graduate School 'Communication and Learning in STEM'. Research interests span engineering pedagogy, digital learning environments, interdisciplinary groupwork, and AI integration in education. His work bridges formal and informal learning, emphasizing sustainable educational practices. The 15 most recent articles (2025–2023) reflect trends in hybrid learning, AI chatbot usage, social regulation of learning, and MOOC development. Themes include pedagogical innovation, cross-cultural integration, and ethical implications of AI tools. Teaching includes CLS905 'Supervising Research Students' and leadership in interdisciplinary PBL across Nordic institutions. His projects involve collaborations with European Commission, VINNOVA, and EIT, focusing on mechanical engineering labs, nuclear safety courses, and teacher development in AI/MOOC design.

Bin Dongمشاهده پروفایل

استاد

• Biomedical Imaging
• Machine Learning
• Artificial Intelligence
• +۹ مورد دیگر

Bin Dong is a Professor at the Beijing International Center for Mathematical Research (BICMR) and Deputy Director of the Center for Machine Learning Research (CMLR) at Peking University. He holds a Ph.D. in Mathematics from UCLA (2009), M.Sc. from National University of Singapore (2005), and B.S. from Peking University (2003). Current affiliations: BICMR (2023–present), CMLR (2022–present) Prior: Associate Professor at Peking University (2014–2022), Assistant Professor at University of Arizona (2011–2014), SEW Assistant Professor at UCSD (2009–2011) Research focuses on biomedical imaging (reconstruction/analysis, clinical decision-making), machine learning (PDE-Net, ODE-Net architectures), and AI for Mathematics (AI4M) (formal mathematics digitalization via Lean, AI-assisted theorem proving). Teaching includes Mathematical Image Processing and Learning by Research at Peking University. Key scientific awards: Wang Xuan Outstanding Young Scholar Award (2023) New Cornerstone Investigator (2023) Qiu Shi Outstanding Young Scholar Award (2014) Silver Medal, New World Mathematics Award (2007) Organized multiple workshops including the One World Seminar Series on Mathematics of Machine Learning and co-developed AI tools like Brainiac Buddy for education.

Tue Herlauمشاهده پروفایل

دانشیار

• Bayesian Methods
• Complex Networks
• Brain Imaging Data Analysis
• +۳ مورد دیگر

Tue Herlau is an Associate Professor at the Department of Applied Mathematics and Computer Science, Cognitive Systems, within the College of Engineering at Technical University of Denmark. His research focuses on modeling complex networks using Bayesian methods, particularly applied to brain imaging data. He explores multiscale structures in networks (ranging from individual nodes to large-scale temporal dynamics) and causal inference in social and biological systems. Education : Bachelor in Physics and Mathematics (University of Copenhagen), Masters in Informatics (DTU) Recent publications highlight work on generative AI for educational psychology, causal probability trees, Bayesian neural network training, and moral reinforcement learning. Key collaborations span institutions in Denmark and international partners in AI and neuroscience research. He supervises students in probabilistic methods and machine learning applications, including a 2019-2020 Master project on prognostics for carbon/epoxy composites. His network shows strong engagement with computational science, relational databases, and formal concept analysis.

A.E. Zaidmanمشاهده پروفایل

استاد

• Software Testing
• Test Amplification
• Mutation Testing
• +۶ مورد دیگر

A.E. Zaidman is a Professor in the Software Technology department at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on software testing, code quality, and developer tooling, with recent emphasis on socio-technical factors, environmental impacts of testing, and GitHub Actions workflows. Key Research Areas Test amplification (developer interactions with automated testing) Code smell detection and its impact on software quality Empirical studies in open-source software testing AI/ML applications in test generation and analysis Scientific Awards Best Artifact Award (2022) Best Emerging Results Paper Award (2018) Best ERA paper award (2015) Best tool demo paper award (2017) ICPC 2009 Best Paper Award Recent Publications 2025: Systematic mapping of qualitative software testing methods 2025: Industrial language engineering case studies using Spoofax 2024: Environmental impact analysis of Java testing 2024: GitHub Actions workflow smell investigations

• Electronic Design Automation
• High-Level Synthesis
• Machine Learning Applications
• +۳ مورد دیگر

Kangwei Xu is a researcher at the Chair of Design Automation (Lehrstuhl für Entwurfsautomatisierung) under Prof. Ulf Schlichtmann at the Technical University of Munich (TUM), actively advancing Electronic Design Automation through AI-driven methodologies. His core research interests include: Electronic Design Automation (EDA) High-Level Synthesis Machine Learning for EDA Neural Network Accelerators Timing Analysis Hardware Reliability Analysis of his 2024-2025 publications reveals a decisive trend in leveraging Large Language Models to revolutionize hardware design flows. Key innovations span automated C/C++ code refactoring (HLSRewriter), behavioral discrepancy testing (HLSTester), and neural network logic optimization, demonstrating how AI integration significantly enhances efficiency and accuracy in EDA toolchains while addressing longstanding challenges in synthesis and verification. Scientific Awards: No awards documented in available sources. Advising and Grants: Public records indicate no formal student advisement roles or individually attributed research grants; his work operates within the broader funded projects of TUM's Design Automation chair. Labs and Teams: Xu contributes to TUM's interdisciplinary Design Automation research group, which spans Analog EDA, Electronic System Level design, Emerging Technologies, Microfluidics, Optical NoC, Novel Microfabrication, Timing Analysis, Neural Networks and Accelerators, and Reliability, maintaining strong industry collaborations and cutting-edge experimental facilities.