جست‌وجوی استاد و پژوهشگر

Thomas Chaffeyمشاهده پروفایل

مدرس

• Nonlinear Control Theory
• Convex Optimization
• Circuit Theory
• +۳ مورد دیگر

Dr. Thomas Chaffey is a Lecturer in the School of Electrical and Computer Engineering at The University of Sydney. His research focuses on nonlinear control theory, convex optimization, and neuromorphic systems. He obtained his PhD from the University of Cambridge (2022) and held the Maudslay-Butler Fellowship at Pembroke College, Cambridge (2022–2025). Education: PhD in Control Theory, University of Cambridge (2022) Maudslay-Butler Fellowship in Engineering, Pembroke College, Cambridge (2022–2025) Master's in Mechanical Engineering, University of Sydney (Australia) Bachelor's in Mathematics and Computer Science, University of Sydney (Australia) Research Interests: Nonlinear control theory and its intersections with optimization and circuit theory Development of neuromorphic systems and analog hardware simulation Monotone operator methods and large-scale optimization algorithms Key Research Trends in Articles: Advances in graphical methods for nonlinear system analysis (e.g., scaled relative graphs) Analysis of neuromorphic circuits using convex optimization frameworks Exploration of symmetry properties in physical systems Awards: Best Student Paper Award, 2021 European Control Conference Outstanding Student Paper Award, 2021 IEEE Conference on Decision and Control Labs/Teams: Leading projects on learning in physical systems and monotone circuits Collaborations with institutions like Lund University and University of British Columbia

Nitisha Jainمشاهده پروفایل

پژوهشگر

• Knowledge Graphs
• Neuro-Symbolic AI
• Ethical AI
• +۳ مورد دیگر

Nitisha Jain is a Postdoctoral Researcher at King's College London's Department of Informatics, part of the Faculty of Natural, Mathematical & Engineering Sciences. She holds a PhD in Knowledge Graphs from the Hasso Plattner Institute (University of Potsdam) and a Master's in Research from the Indian Institute of Science (IISc). Her research focuses on neuro-symbolic AI, ethical AI standards, multimodal knowledge graphs, and knowledge engineering using large language models. Her work includes contributions to the Croissant metadata standard for machine-readable datasets and the development of interpretable embeddings aligned with semantic aspects. She has published extensively in venues like NeurIPS, ACL, and ISWC, and serves on program committees for conferences such as ESWC and DEEM. Key achievements include a spotlight paper at NeurIPS 2024 and a best paper award at DEEM 2024. She supervises students in areas like knowledge graph embeddings and neuro-symbolic methods, and has taught courses on network data analysis and knowledge engineering at both bachelor’s and master’s levels.

Jaakko Timo Henrik Järviمشاهده پروفایل

استاد

• Generic Programming
• Programming Language Design
• Graphical User Interfaces (GUI)
• +۷ مورد دیگر

Jaakko Timo Henrik Järvi is a Professor in the Department of Informatics at the University of Bergen, Norway, with additional affiliations at the University of Turku, Finland. His research focuses on programming language design, generic programming, and human-computer interaction, particularly in GUI frameworks and software reuse. His research interests include generic programming, programming language design (especially the Magnolia language), high-performance computing, array programming, and GUI engineering. He emphasizes formal methods and algebraic specifications to build reusable and efficient software systems. His work bridges theoretical foundations with practical applications in software development and education. The recent publications highlight a strong trend in declarative GUI frameworks, multi-selection models, and generic programming. His work explores domain-specific languages for GUI structure manipulation, reusable selection semantics across platforms, and optimizing array computations using the Mathematics of Arrays. These efforts reflect a consistent focus on software abstraction, correctness, and reusability. Jaakko Järvi has supervised doctoral students, including Tetiana Yarygina, whose dissertation explored microservice security. While no specific grants are detailed, his work on VisAST was supported by the Research Council of Norway (Project 250683), indicating active external funding. He frequently collaborates with researchers like Magne Haveraaen, Knut Anders Stokke, and Sean Parent. He contributes to tools and frameworks such as the MultiselectJS library and the VisAST educational tool. These are outcomes of collaborative research teams focused on improving software development practices and computer science education.

Malcolm Heywoodمشاهده پروفایل

استاد

• Genetic Programming
• Coevolution
• Reinforcement Learning
• +۱۲ مورد دیگر

Dr. Malcolm Heywood is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the Network Information Management and Security (NIMS) Lab and is actively involved in research on genetic programming, coevolution, reinforcement learning, and big data analytics. His research interests span: Genetic Programming and Evolutionary Computation Coevolution and Competitive Learning Problem Decomposition and Hierarchical Models Streaming Data Analysis and Anomaly Detection Network Security and Insider Threat Detection Reinforcement Learning in Games (Atari, ViZDoom, Dota 2) Dr. Heywood's recent publications focus on emergent behaviors in reinforcement learning using Tangled Program Graphs (TPG), benchmarking genetic programming for streaming data, and applications in cybersecurity and computational finance. His work demonstrates a strong trend toward scalable, efficient evolutionary models for complex, real-world problems. His scientific awards include: Silver placed at Human-Competitive (Humies) Competition (2018) Best Paper at EuroGP (2017) Best Paper at DETA track, ACM GECCO (2017) Best Paper at RWA track, ACM GECCO (2018) Nomination for Best Paper at DETA track, ACM GECCO (2019) He has supervised numerous graduate students, including PhD and Master's candidates, many of whom have continued research in evolutionary computation. His lab has developed open-source code distributions for Tangled Program Graphs and Symbiotic Bid-Based GP. Dr. Heywood teaches courses in Computer Organization, Introduction to AI with Gaming Applications, and Genetic Algorithms and Programming.

Yue Liمشاهده پروفایل

دانشیار

• Program Analysis
• Programming Languages
• Static Program Analysis
• +۳ مورد دیگر

Yue Li is an Associate Professor at the School of Computer Science, Nanjing University, where they co-run the PASCAL Research Group with Tian Tan. Their work focuses on static program analysis techniques and tools for programming languages, software engineering, security, and hardware verification. PhD in Computer Science from UNSW Sydney (2016) Postdoctoral research at Aarhus University (Denmark) and UNSW Sydney B.Eng and M.Eng from Northwestern Polytechnical University (2010, 2012) Research interests center on Program Analysis and Programming Languages , with a focus on: Pointer analysis for database-backed applications Context sensitivity optimization Reflection analysis in Java/Android Operational semantics for hardware languages Distributed dataflow analysis frameworks Developer-friendly static analysis tools Key publication trends (2016-2025) span static analysis , pointer precision , reflection handling , and tool frameworks across conferences like OOPSLA, PLDI, ICSE, ISSTA, and journals including TOPLAS and IEEE TSE. Notable artifacts include Tai-e and Chianina systems. 2025: ICSE Best Artifact & Distinguished Paper Awards 2024: IEEE TSE Publication on Generic Sensitivity 2023: OOPSLA Distinguished Artifact, SPLASH/ECOOP committees 2021: National Youth Talent Support Program, ZiJin Scholar 2016: ECOOP Distinguished Paper, CGO Best Paper As co-PI of PASCAL Research Group, they lead projects on precision-guided analysis, microservice systems, and cloud-based dataflow frameworks, with teaching awards for SICP and Software Analysis courses.

Catia Pesquitaمشاهده پروفایل

دانشیار

• Semantic Web
• Biomedical Ontologies
• Knowledge Graphs
• +۷ مورد دیگر

Catia Pesquita is an Associate Professor in Computer Science at the Faculty of Sciences of the University of Lisbon , where she is also a Senior Researcher at LASIGE and leads the Health and Biomedical Informatics Research Line . With a multidisciplinary background in Biology and Computer Science, she focuses on Artificial Intelligence and Data Science applications in life and health sciences . Her research spans Semantic Web , Biomedical Ontologies , Knowledge Graphs , and Explainable AI , with significant contributions to ontology matching and semantic similarity . Education: PhD in Computer Science - Bioinformatics (2012) MSc in Bioinformatics (2008) Degree in Cell Biology and Biotechnology (2005) Current Projects: KATY (2021-2024): AI-Empowered Personalized Medicine for cancer treatments. BRAINTEASER (2021-2024): AI for ALS and MS disease progression models. Research Outputs: Developed tools like AgreementMakerLight (AML) , KGsim-benchmark , and the Epidemiology Ontology . Over 133 publications with significant citations (32,909 reads, 3,889 citations). Teaching: Lectures advanced topics in Databases , Data Integration , Bioinformatics , and Big Data . Advocacy: Vice-president of Biodata.pt , promoting biological data valorization in Portugal. Actively involved in initiatives to promote computer science careers to young women .

• Ontologies
• Artificial Intelligence
• Semantic Web
• +۳ مورد دیگر

Dr. Valentina Tamma is a Lecturer in the Department of Computer Science at the University of Liverpool. Her research focuses on ontologies in open and distributed environments, including Semantic Web, Multi-Agent Systems, and Knowledge Graphs. She leads the Knowledge-Based Agents research group and serves as Area Editor for the Transactions on Graph Data and Knowledge journal. Her recent work explores the intersection of Generative AI and Semantic Web technologies, including competency question engineering, ontology alignment, and knowledge evaluation frameworks. She has co-authored 15+ publications (2023-2025) on topics like ontology reuse, question difficulty prediction, and autonomous agent governance. Dr. Tamma has held significant editorial and organisational roles, including Programme Co-Chair for ISWC 2020 and co-chair of the Alan Turing Institute's Knowledge Graphs Interest Group. She teaches modules such as Ontologies and Semantic Web (COMP598) and Planning Your Career (COMP221). Scientific contributions include: Ontology modularization techniques Negotiation protocols for semantic alignment Knowledge evaluation frameworks AI-driven competency question generation Knowledge Graph applications in life sciences

Tobias Dalbergمشاهده پروفایل

مدرس ارشد

• sociology of education
• social stratification
• higher education policy
• +۳ مورد دیگر

Tobias Dalberg is a Senior Lecturer at Uppsala University's Department of Pedagogy, Didactics and Educational Sociology, with research focusing on sociology of education, social stratification, and higher education policy. His work examines gender differentiation in course selection, curricular blind spots, and historical transformations in academic disciplines. Contact: tobias.dalberg@edu.uu.se | Personal Website Research: Utilizes graph theory, natural language processing, and geometric data analysis for educational pathway studies Current research areas include: Gendered patterns in major selection and course enrollment Educational policy analysis (1950-2020) Academic field institutionalization processes Disciplinary legitimacy struggles (1935-1980) Recent publications (2021-2024) reveal trends in: Higher education marketization and gender segregation Curriculum structure impacts on student trajectories Disciplinary evolution in social sciences and humanities Assessment consistency in educational evaluation As member of international research collaborations, Dalberg works with networks studying educational stratification and transnational academic markets. His methodological contributions include applications of network analysis for studying epidemic spread in educational contexts.

Jill Dunhamمشاهده پروفایل

دانشیار

• Graph Theory
• Computational Mathematics
• Recreational Mathematics
• +۳ مورد دیگر

Jill Dunham is an Associate Professor in the Department of Mathematics at Schmid College of Science and Technology , Chapman University. Her research spans topological graph theory , computational problems in discrete mathematics , recreational mathematics , and pedagogy , focusing on tactile learning and course assessment. She earned a B.Sc. from Iowa State University and M.Sc. and Ph.D. from George Mason University. Education: B.Sc. in Mathematics, Iowa State University M.Sc. in Mathematics, George Mason University Ph.D. in Mathematics, George Mason University Her research projects include finding the maximum touching number for coin graphs , enumerating solutions to paper-folding problems , and applying graph theory to commutative algebra structures . She also contributes to pedagogical innovations through hands-on activities in undergraduate mathematics education. Recent publications include work on round-robin tournaments , edge colorings in Hamiltonian cycles , and coin graph representations . Her collaborations appear in volumes like The Mathematics of Various Entertaining Subjects and Numery .

Mahir Arzokyمشاهده پروفایل

مدرس

• Artificial Intelligence
• Search Based Software Engineering
• Intelligent Data Analysis
• +۴ مورد دیگر

Dr Mahir Arzoky is a Lecturer in the Department of Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. He holds a PhD from Brunel University London (2015) and has extensive research experience in artificial intelligence and software engineering. His research focuses on: Artificial Intelligence and Intelligent Data Analysis Search Based Software Engineering (SBSE) Clustering algorithms and heuristic search methods Software refactoring and quality assessment Data mining applications in healthcare and education Analysis of his 15 most recent publications (2018-2022) reveals strong interdisciplinary work bridging computer science with healthcare (diabetes patient modeling, medical imaging) and education (chatbot design, algorithm visualization). His technical focus centers on clustering optimization, refactoring impact analysis, and explainable AI, with frequent use of empirical validation methods. Key collaborations include researchers like Stephen Swift, Steve Counsell, and Giuseppe Destefanis. Dr Arzoky has secured significant research funding through EPSRC grants including: AQUATIC project (EP/M024083/1): Assessing Test Suite Quality in Industrial Code FIAR-NET (EP/N011627/1): Fault Analyses in Industry and Academic Research Network His professional network includes active collaborations across computer science, healthcare informatics, and educational technology domains, with recent work extending into transformer models for healthcare SQL conversion and graph partitioning for software modularization.

Mauricio Bustamanteمشاهده پروفایل

استادیار

• Astroparticle physics
• Neutrino physics
• Ultra-high-energy astrophysical neutrinos and cosmic rays
• +۵ مورد دیگر

Mauricio Bustamante is an Assistant Professor at the Niels Bohr Institute , University of Copenhagen, specializing in theoretical high-energy astrophysics, astroparticle physics, and neutrino phenomenology. His research bridges cosmic phenomena with fundamental particle physics, focusing on ultra-high-energy neutrinos, cosmic rays, gamma-ray bursts, and new physics beyond the Standard Model. PhD in Physics (2012-2014) M.Sc. in Physics (2007-2010) B.Sc. in Physics (2001-2006) His work explores neutrino oscillations, self-interactions, and decay in extreme astrophysical environments. He contributes to major international collaborations like GRAND (Giant Radio Array for Neutrino Detection) and IceCube-Gen2, developing simulation pipelines and forecasting detection methods for EeV-scale neutrinos. Recent publications highlight energy-dependent flavor transitions, Lorentz invariance testing, and constraints on long-range neutrino interactions via DUNE and T2HK experiments. He actively participates in peer review for journals such as Physical Review D , Physical Review Letters , and Astrophysical Journal , and has attended conferences like TeV Particle Astrophysics (2017). His research emphasizes detector design, cosmic ray reconstruction via graph neural networks, and multi-messenger astronomy.

Rolf Wankaمشاهده پروفایل

استاد

• Algorithms
• Combinatorial Optimization
• Swarm Intelligence
• +۳ مورد دیگر

Prof. Dr. Rolf Wanka is a Professor at the Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), specializing in efficient algorithms and combinatorial optimization. His research focuses on swarm intelligence, discrete optimization algorithms, and scheduling problems, particularly in timetabling and robotics applications. Education : Sc.D. (Dr. rer. nat.) in Computer Science His work includes theoretical and experimental analyses of particle swarm optimization (PSO) algorithms, addressing runtime complexity, stagnation behavior, and convergence properties. He has developed novel heuristics for timetabling and sorting problems, with applications in multi-robot systems and medical imaging. Notable collaborations include studies on Markov chain-based PSO and fairness in academic scheduling. Key trends in his recent publications span swarm intelligence , discrete optimization , and scheduling heuristics , with a focus on robust timetabling , runtime analysis , and stochastic algorithm behavior . While no explicit scientific awards are listed, his mentorship in the Max Weber-Programm highlights his advisory role in academia. His publications demonstrate interdisciplinary applications of algorithms in robotics , medical imaging , and parallel computing , leveraging both theoretical rigor and practical experimentation. The full description below provides exhaustive details on his academic contributions and affiliations.

• Statistics
• Applied Data Science
• Veterinary Epidemiology
• +۲ مورد دیگر

Hans Vernooij is a Lecturer in Farm Animal Health at the Faculty of Veterinary Medicine, Utrecht University. He specializes in statistical methods and data science applications in veterinary epidemiology and animal health. His areas of expertise include: Statistical methods for veterinary research Applied Data Science in Life Sciences Epidemiological modeling Machine learning applications in animal health Vernooij has extensive experience in developing statistical models for animal health applications. His research focuses on applying advanced statistical techniques and data science methods to solve problems in veterinary epidemiology and farm animal health. He has particular expertise in Random Forest models, as demonstrated during his sabbatical at the Human Sciences Research Council in Pretoria where he developed a model for HIV status prediction based on demographic information and knowledge of HIV prevention from large-scale survey data. His publication record shows consistent contributions across veterinary epidemiology, with recent work emphasizing machine learning applications and big data analytics in animal health surveillance. The research demonstrates a clear trajectory from traditional statistical methods toward more advanced data science approaches. Vernooij is actively involved in teaching and mentoring: Teaches statistics to Bachelor students at the veterinary faculty Supports PhD candidates and Master students during data analysis phases of their research Provides statistics education for the Master of Epidemiology program at the Julius Centre of University Medical Centre

• Reinforcement Learning
• Computational Decision-Making
• Artificial Intelligence
• +۵ مورد دیگر

Carlo D'Eramo is Professor and Head of the Reinforcement Learning and Computational Decision-Making professorship at the Center for Artificial Intelligence and Data Science (CAIDAS) of University of Würzburg. He additionally serves as an independent group leader of hessian.AI, focusing on developing lightweight methods to obtain adaptive autonomous agents that can handle real-world complexity. His academic journey includes: B.Sc. in Computer Engineering from Politecnico di Milano (2011) M.Sc. in Computer Engineering from Politecnico di Milano (2015) Double degree in Computer Science from University of Illinois at Chicago (2015) Ph.D. in Information Technology from Politecnico di Milano (2019) Postdoctoral research at TU Darmstadt's Intelligent Autonomous Systems group (2019-2022) D'Eramo leads the LiteRL research group investigating how agents can efficiently acquire expert skills accounting for real-world complexity. His research spans multiple reinforcement learning domains including multi-task, curriculum, adversarial, options, and multi-agent RL. His work bridges theoretical advances with practical applications, particularly in robotics and decision-making systems. His recent publications (2023-2025) demonstrate significant contributions across exploration strategies, neural network architectures, multi-agent coordination, and physics-informed machine learning. His work frequently appears in top-tier venues including TMLR, RLJ, IEEE PAMI, ICML, and ICLR, with multiple papers receiving spotlight or oral presentation designations. Professional activities include: Senior area chair for RLC Area chair for AAAI, ACML, AISTATS, NeurIPS, and ICLR Reviewer for DFG and ERC proposals Creator of MushroomRL reinforcement learning framework D'Eramo actively contributes to academic community service while mentoring researchers in his group. His work on lightweight methods aims to make reinforcement learning more practical for real-world applications across various domains.

Yves Le Traonمشاهده پروفایل

استاد

• Machine learning systems
• Software testing
• Software security
• +۸ مورد دیگر

Dr. Yves Le Traon is a Full Professor of Computer Science at the University of Luxembourg, where he serves as Vice-Director of the Interdisciplinary Centre for Security, Reliability and Trust (SnT). He leads the 25-member SerVal research group (SEcurity, Reasoning and VALidation), focusing on software testing, security, and data-intensive systems. Previously, he chaired the CSC Research Unit (2013-2016) and pioneered model-driven engineering at INRIA. PhD and engineering degree in Computer Science from Institut National Polytechnique, Grenoble (1997) Former Associate Professor at University of Rennes (1998-2004) His research spans three main areas: innovative software testing and repair , Android security through static analysis and machine learning , and robust machine learning system design . Collaborations include industry leaders like PayPal, CREOS, and Cebi in fintech, smartgrid, and industry 4.0 domains. Awarded IEEE Fellow (2022) and Facebook Testing & Verification Research Award (2019) , he chairs editorial boards for STVR, SoSym, and IEEE Transactions on Reliability. His team has produced 20+ PhD graduates including Li Li (Monash University), Donia El Kateb (European Investment Bank), and Alexandre Bartel (SnT Research Associate). Commercial impact includes co-founding Datathings for runtime AI decision systems.