جست‌وجوی استاد و پژوهشگر

Matthias Baitschمشاهده پروفایل

استاد

• Construction Informatics
• Numerical Methods
• Structural Optimization
• +۳ مورد دیگر

Matthias Baitsch serves as Professor of Construction Informatics and Numerical Methods in the Department of Civil and Environmental Engineering at Bochum University of Applied Sciences, where he concurrently heads the BIM Institute. His academic trajectory includes research assistant and senior engineer roles at Ruhr-University Bochum (2000-2009), academic coordination at the Vietnamese-German University (2009-2012), and an acting professorship at the University of Kassel (2012-2014). His educational foundation comprises: Civil Engineering studies at the University of Dortmund (1991-1997) under the interdisciplinary "Dortmund Model" Doctorate from Ruhr-University Bochum (2003) on geometric imperfection-based optimization of compressive beam structures Professor Baitsch's research integrates computational mechanics with civil engineering practice, specializing in construction informatics, numerical optimization, and high-order finite element methods. His work pioneers distributed optimization frameworks, structural health monitoring for wind energy infrastructure, and BIM-based construction informatics. Key methodological contributions include hp-FEM implementations, parallel optimization algorithms, and mobile structural analysis tools. Analysis of his recent publications reveals three dominant research trajectories: (1) Advanced numerical methods for structural optimization under uncertainty, (2) Health monitoring-driven lifetime prediction for wind turbine systems, and (3) Computational modeling of tunnel environments using viscoacoustic inversion techniques. These threads demonstrate consistent focus on robust numerical implementations and real-world civil engineering applications. As Head of the BIM Institute, he leads institutional efforts in digital construction technologies, fostering industry-academia collaboration on building information modeling standards and applications. His teaching portfolio spans foundational mathematics, numerical methods, and computer science for civil engineering students, emphasizing practical computational skills.

Louis Trudelمشاهده پروفایل

دانشیار

• Science Education
• Science Didactics
• Educational Technology
• +۴ مورد دیگر

Louis Trudel serves as an Associate Professor within the Faculty of Education at the University of Ottawa, where he instructs science didactics courses in teacher training programs and quantitative research methodology courses in the Master's in Education program. His scholarly work focuses on advancing science education through innovative pedagogical approaches, particularly in minority language contexts and technology-integrated learning environments. Education Background: Doctorate in Education, Université du Québec à Montréal Research Focus: Dr. Trudel's research program centers on identifying and addressing student misconceptions in physics concepts (electricity, heat, motion), developing diagnostic assessment tools like two-tier tests, and designing technology-enhanced learning sequences. His work emphasizes science education in French-speaking minority communities, exploring how formal and informal educational settings interact to strengthen science pedagogy. He actively investigates the application of ICT in science teaching, distance learning models for science educators, and conditions for effective implementation of science curricula. Publication Analysis: Examination of his 15 most recent publications reveals a consistent focus on physics education research, with 80% addressing student misconceptions in core concepts. His methodology prominently features diagnostic testing (particularly two-tier instruments) and technology-mediated learning environments. A distinctive thread throughout his work is the adaptation of science pedagogy for minority-language contexts, especially Francophone communities in Canada, demonstrating his commitment to equitable science education across diverse settings. Scientific Recognition: No specific awards or fellowships were documented in the provided materials. Academic Mentorship: As an active instructor in both teacher certification and graduate programs, Dr. Trudel undoubtedly supervises student research, though particular advisees and grant-funded projects remain unspecified in the available documentation. His leadership in organizing international conferences and editing scholarly volumes indicates significant contribution to academic community building. Research Infrastructure: While no dedicated laboratory facilities are mentioned, his publications referencing computer-assisted investigations and hybrid learning environments suggest utilization of technology-enhanced research spaces, potentially within the Faculty of Education's instructional technology resources.

• Algorithms
• Randomized Data Structures
• Divide-and-Conquer Algorithms
• +۵ مورد دیگر

Salvador Roura Ferret is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the Department of Computer Sciences. He is an active researcher in the ALBCOM group, focusing on algorithmics, bioinformatics, complexity, and formal methods. His research interests span algorithms , randomized data structures , divide-and-conquer methods , and computational complexity . His work often bridges theoretical computer science with practical applications in voting systems, game theory, and educational technologies. The recent publications (2013–2025) reflect a strong trend in theoretical computer science, particularly in algorithm design, data structures (e.g., K-d trees, binary search trees), and game-theoretic models. There is a notable emphasis on mathematical rigor, complexity analysis, and formal modeling of computational problems. Best student paper award Distinció Jaume Vicenç Vives a la qualitat docent universitària (modalitat individual) 18è Premi UPC a la Qualitat en la Docència Universitària Roura has contributed to teaching innovation through projects like Eines per a la Transformació de la Docència en Programació and the development of Jutge.org , an online programming judge platform. He has participated in numerous competitive R+D+i projects, often in collaboration with researchers such as Conrado Martinez, Xavier Molinero, and Maria Serna. His work has been supported by national and regional funding programs. While no direct mention of grants or student advising is made, his extensive publication and project history suggest significant research leadership.

Holger Kabothمشاهده پروفایل

مدرس

• Sports Pedagogy
• Digital Education
• Teacher Training
• +۴ مورد دیگر

Holger Kaboth is a Lecturer at the Institute of Sports Science, Goethe University Frankfurt. His work focuses on integrating digital media into teacher training and physical education, managing practical semesters, and advancing snow sports in educational institutions. Current roles: Management of practical semesters, Head of Snow Sports, and Goethe Lab coordination. Primary research areas: Sports pedagogy, digital education, and curriculum development. Research Interests He specializes in leveraging digital tools like iPads and BYOD (Bring Your Own Device) strategies to enhance teaching workflows, particularly in teacher training and secondary school contexts. His work bridges technology and pedagogy in sports and mathematics education. Scientific Recognition Holger holds certifications from Apple, including Apple Professional Learning Specialist (2020), and has contributed to discussions on systemic action and thinking in schools. Advising & Contributions He has led recurring workshops on digital media, presented at educational conferences, and developed training programs for secondary school teachers.

Michael Minockمشاهده پروفایل

دانشیار

• Natural Language Interfaces
• Knowledge Representation
• Databases
• +۴ مورد دیگر

Michael Minock is an Associate Professor in Computing Science at Umeå University. His research focuses on the intersection of AI and relational databases, particularly in natural language interfaces, semantic tractability, and knowledge representation. He has led significant projects such as the VR grant (2015-04953) and EU STREP SpaceBook (2011–2014). Minock also co-founded C-Phrase Technologies AB and teaches courses on databases and large language models. B.S. in Computer Science (Honors), University of Michigan Ph.D. in Computer Science, UCLA His research explores the logical foundations of database query languages, emphasizing higher-order logic in natural language interfaces. Recent work integrates LLMs into database management systems. Key publication trends include query containment analysis, cooperative question answering, and context-aware navigation systems. Subfields span natural language processing, spatial databases, and semantic reasoning. Scientific Awards: Pell Grants Michigan Competitive Scholarship DARPA AASERT Award Minock's teaching includes courses on database management and LLM applications in IT. He actively contributes to open-access publications and maintains a strong interdisciplinary focus across AI, logic, and real-world database challenges.

• Image processing
• Inverse problems
• Radio interferometric imaging
• +۷ مورد دیگر

Audrey Repetti is an Associate Professor in the Department of Actuarial Mathematics and Statistics within the School of Mathematical and Computer Sciences at Heriot-Watt University in Edinburgh, UK. She also holds a dual affiliation with the Institute of Sensors, Signals, and Systems in the School of Engineering and Physical Sciences, and is part of the Maxwell Institute for Mathematical Sciences - Edinburgh. Her research spans mathematical imaging, optimization, and computational methods with applications across astronomy, medical imaging, and optical engineering. Dr. Repetti's research focuses on developing advanced mathematical frameworks for solving imaging inverse problems. Her work centers on optimization algorithms, Bayesian uncertainty quantification, and the integration of machine learning with traditional mathematical approaches. She has made significant contributions to radio interferometric imaging, computational optical imaging with photonic lanterns, and uncertainty quantification in medical imaging. Her research bridges theoretical mathematics with practical applications in astronomy, healthcare, and engineering. Analysis of her recent publications reveals a clear trajectory toward integrating traditional mathematical imaging approaches with modern machine learning techniques. Her work increasingly focuses on 'hybrid' methodologies that combine data-driven models with optimization frameworks. Key themes include plug-and-play algorithms, uncertainty quantification in imaging, and the development of efficient computational methods for high-dimensional inverse problems. Her research demonstrates strong interdisciplinary connections between mathematics, signal processing, astronomy, and medical imaging. Dr. Repetti is actively involved in academic service, including co-organizing the 2026 ICMS Workshop on Imaging inverse problems and generating models. She has received research funding supporting her work in computational imaging and inverse problems, though specific grant details aren't listed in the provided materials. Her teaching portfolio includes advanced courses in scalable inference, deep learning, and statistics for sciences. She leads several research projects with associated software toolboxes including BUQO (Bayesian Uncertainty Quantification by Optimization), SARA-COIL (Compressive optical imaging with a photonic lantern), and CALIM (Self direction-dependent effect calibration and imaging in radio-interferometry). These projects demonstrate her commitment to developing practical computational tools that advance both theoretical understanding and real-world applications in imaging science.

• Socio-Technological Change
• Design Futuring
• Sustainable Transitions
• +۷ مورد دیگر

Dr. Thomas Lee is a Senior Lecturer in Design Studies within the Faculty of Design and Society at the University of Technology Sydney (UTS). With a background in the humanities, his academic work bridges design, technology, and cultural studies with a focus on sustainable transitions, particularly in agricultural contexts. His interdisciplinary approach spans design futuring, narrative theory, and socio-technological change. Dr. Lee's research interests center on understanding how cultural factors interact with technology, with recent work focusing on the cultural dimensions of industries involving living organisms and technology, such as livestock farming, synthetic biology, and biodiversity conservation. His current emphasis is on socio-technological change for sustainable transitions in the farming sector, drawing on his personal history growing up on a mixed farming business in regional NSW. This includes leading the design research team for Foragecaster (Food Agility CRC), one of Australia's largest AgTech projects, and developing tools for biodiversity conservation on agricultural land. His publication portfolio demonstrates remarkable breadth across design theory, sustainable technologies, and narrative approaches. The 15 most recent articles reveal a consistent pattern of interdisciplinary work connecting design with agricultural technology, sustainability transitions, and speculative futures. His research frequently appears in high-impact journals including Journal of Cleaner Production, Futures Journal, and People and Nature Journal, showing strong citation impact particularly in sustainable design and agricultural technology fields. UTS Teaching and Learning Citation (2015) Postgraduate Futures Associate Award (2015) Dr. Lee actively supervises research students, currently guiding one PhD candidate and having previously supervised another to completion. His funded research projects include significant collaborations with Food Agility CRC on Foragecaster (2023), Innovation Connections Grants for agritech animal husbandry (2022), and biodiversity conservation initiatives with Landcare Australia. Beyond his research, he serves as the School of Design Indigenous Liaison Officer since 2021, faculty assessor of Non-Traditional Research outputs since 2023, and convenor of the Design for Societal Transitions research cluster.

Aamir Younis Rajaمشاهده پروفایل

استادیار

• Medical Physics
• Radiation Physics
• Medical Imaging Physics
• +۸ مورد دیگر

Dr. Aamir Younis Raja is an Assistant Professor in the Physics Department at Khalifa University, UAE, where he co-founded the Medical Physics wing and co-developed the accredited M.Sc program in medical physics. He has held academic roles including Senior Research Fellow at the University of Otago, Visiting Research Fellow at the University of Canterbury, and Visiting Academic Teaching Staff at ARA Institute of Canterbury. His research focuses on radiation physics , medical imaging physics , and spectral photon-counting CT applications in bone/cartilage health , metal implant characterization , cancer imaging , atherosclerosis , and arthritis . Education: PhD in Medical Physics (University of Canterbury, 2013), M.Sc in Applied Physics (UET Pakistan, 2006), B.Sc in Physics & Mathematics (University of the Punjab, 2004) Dr. Raja’s work combines nanoparticle technology with low-dose multi-energy CT to identify non-toxic contrast agents, develops machine learning-based radiation monitoring tools , and pioneers AI-driven artefact reduction in CT imaging. His projects include collaborations with international institutions on material decomposition algorithms and biomedical applications of spectral CT. Notable scientific recognition includes being a Fellow of the Union for International Cancer Control and securing the Khalifa University Faculty Startup Grant . He has supervised over 20 thesis students across University of Otago, University of Canterbury, and Khalifa University, including PhD candidates working on material reconstruction software , parametric color imaging , and machine learning for CT artefact reduction .

Michael Raskinمشاهده پروفایل

مدرس

• Theoretical Computer Science
• Distributed Systems
• Algorithms
• +۶ مورد دیگر

Michael Raskin is a Lecturer (maître de conferences) at LaBRI, University of Bordeaux, where he conducts research in theoretical computer science with a focus on distributed systems and formal methods. His work bridges theoretical foundations with practical implementations, particularly in population protocols, temporal graphs, and Petri nets. His research interests span Theoretical Computer Science , Distributed Systems , Algorithms , and Formal Methods , with specific expertise in population protocols, temporal graph analysis, vector addition systems, and verification techniques for parameterized systems. His publications demonstrate a consistent focus on computational complexity, state complexity, and threshold phenomena in distributed models. Recent publication trends show a strong emphasis on temporal graph theory (with multiple papers on random temporal graphs and giant components), population protocol analysis (including modular protocols and leaderless rendez-vous systems), and verification techniques for parameterized systems. His work often combines mathematical rigor with practical implementation insights. Raskin has developed significant software projects including: Agnostic Lizard - A portable code walker for Common Lisp QueryFS - A filesystem defined through compiled queries His technical contributions span both theoretical advances and practical systems implementation, with a recurring theme of applying programming language techniques to systems problems. His work on Common Lisp tools demonstrates a commitment to practical language implementation alongside theoretical research.

• High performance computing
• Computational Science and Engineering
• Scientific computing
• +۷ مورد دیگر

Olaf Schenk is a Professor at the Institute of Computing within the Faculty of Informatics at Università della Svizzera italiana (USI), Switzerland. He serves as Director of the Institute of Computing and Co-Director of the Master in Computational Science. He is also an adjunct member of the Computer Systems Institute at USI. PhD in Information Technology and Electrical Engineering, ETH Zurich (2001) Venia Legendi in Mathematics and Computer Science, University of Basel (2009) Applied Mathematics, Karlsruhe Institute of Technology (KIT), Germany His research focuses on high-performance computing , computational science and engineering , and applied algorithms for extreme-scale simulations. He bridges computer science with scientific computing needs, particularly in parallel algorithms , sparse solvers , graph analytics , and manycore architectures . His work emphasizes scalable software tools and programming models for emerging HPC systems. The 15 most recent publications reflect a consistent focus on sparse matrix computations , parallel and task-based algorithms , graph partitioning , and performance optimization for heterogeneous and manycore systems. Keywords span high-performance computing, numerical linear algebra, and large-scale data analysis, showing strong integration of theoretical algorithm design with practical implementation. Olaf Schenk has received several prestigious honors: Elected Fellow, Society for Industrial and Applied Mathematics (SIAM) Senior Member, IEEE and ACM SIAM Supercomputing Prize 2023 IBM Faculty Award Two Leadership Computing Awards from the U.S. Department of Energy He has held leadership roles as Chair, Vice Chair, and Program Director of the SIAM Activity Group on Supercomputing. He serves as Associate Editor for ACM Transactions on Mathematical Software and on the editorial board of SIAM Journal on Scientific Computing . He has participated in over 60 international program committees, including top-tier conferences such as SC, IPDPS, and IEEE CSE. He advises PhD and Master’s students in computational science and leads research projects funded by national and international agencies. He is also the Founder & Director of Panua Technologies Sagl, focusing on high-end software for simulation and optimization. His research group at USI works on next-generation computing tools for extreme-scale scientific simulations, with ongoing work in adaptive algorithms, resilience, and hybrid CPU-GPU computing. He leads collaborative projects with institutions in Europe and the U.S., aiming to develop scalable, robust, and efficient software for future exascale systems.

Ichiro Hasuoمشاهده پروفایل

استاد

• Formal Verification
• Mathematical Structures
• Cyber-Physical Systems
• +۳ مورد دیگر

Ichiro Hasuo is a Professor at the National Institute of Informatics (NII) in Tokyo, Japan, where he serves as Director of the Research Center for Mathematical Trust in Software and Systems. He holds a joint appointment at The Graduate University for Advanced Studies (SOKENDAI). Since 2016, he has been the Research Director of the JST ERATO Metamathematics for Systems Design Project, and founded Imiron Co., Ltd. in 2024. Education: PhD in Computer Science (cum laude) from Radboud University Nijmegen (2008) MSc in Mathematical and Computing Sciences from Tokyo Institute of Technology (2004) BSc in Mathematics from University of Tokyo (2002) His research focuses on foundational aspects of software science, particularly formal verification techniques using mathematical structures from category theory and coalgebra. He develops methods for ensuring reliability in cyber-physical systems and systems incorporating machine learning components. Current work emphasizes logical frameworks for autonomous vehicle safety and mathematical trust in complex systems. Hasuo's publications demonstrate consistent focus on theoretical foundations with practical applications. His recent work spans coalgebraic verification methods, temporal logic for hybrid systems, quantum programming semantics, and applications to autonomous driving systems. Key themes include compositional reasoning, probabilistic modeling, and the integration of discrete and continuous system verification. Awards and Honors: Best Paper Award at ICTAC 2024 Minister of Education, Culture, Sports, Science and Technology Commendation (2024) Distinguished Paper Award at CAV 2023 Outstanding Reviewer Award at EMSOFT 2022 Best Paper Award at ICECCS 2018 Best Paper Award at CONCUR 2014 Hiroshi Fujiwara Encouragement Prize (2012) PhD cum laude (2008) He leads multiple major research grants including: JST ASPIRE (2024-2029) for international collaboration on software trust JST START (2022-2025) for autonomous driving verification JST ERATO Metamathematics for Systems Design (2016-2025) Several JSPS KAKENHI grants As head of the MMM laboratory (Hasuo-Lab) at NII, he supervises PhD students and postdoctoral researchers in formal methods and mathematical systems design.

Caroline Lemieuxمشاهده پروفایل

استادیار

• Fuzz testing
• Program synthesis
• Property-based testing
• +۴ مورد دیگر

Caroline Lemieux is an Assistant Professor at the University of British Columbia, specializing in automated software testing and reliability. Her research develops methods for testing, debugging, and improving software correctness through techniques like fuzz testing and program synthesis. She holds a Ph.D. from UC Berkeley advised by Koushik Sen and a B.Sc. in Computer Science and Mathematics from UBC. Research Interests: Dr. Lemieux's work focuses on: Fuzz testing for vulnerability detection Program synthesis using AI/ML approaches Property-based testing frameworks Automated debugging and specification mining Applications of reinforcement learning in test generation Publication Trends: Her recent papers explore large language models for test generation, fuzzing strategies in CI/CD environments, and neural-backed program synthesis. Work consistently bridges theoretical computer science with practical software engineering challenges. Awards: ACM SIGSOFT Distinguished Paper Award Google PhD Fellowship Best Paper Award (Industry Track) Berkeley Fellowship for Graduate Study

Robin Purshouseمشاهده پروفایل

استاد

• Computational Modelling of Complex Social Systems
• Agent-Based Modelling
• Machine Learning
• +۶ مورد دیگر

Professor Robin Purshouse is a leading academic at the University of Sheffield , currently serving as Professor of Decision Sciences in the Department of Automatic Control and Systems Engineering within the School of Electrical and Electronic Engineering . With a career spanning academia and industry, his work bridges computational modelling , optimization , and systems science to address complex challenges in public health and engineering. His research has been pivotal in developing mechanisms for agent-based modelling and evolutionary multi-objective optimization . Education: PhD in Control Systems (2004), MEng in Control Systems Engineering (1999) from the University of Sheffield Professor Purshouse's research focuses on computational modelling of complex social systems , decision analytics for population health policy , and Bayesian optimization . He has pioneered the integration of machine learning and uncertainty quantification in social science simulations, with notable projects like the Sheffield Alcohol Policy Model and CASCADE initiative. His work spans interdisciplinary domains, including health economics , policy evaluation , and engineering design . Recent publications highlight his expertise in agent-based modelling for smoking/vaping dynamics , intersectional disparities in alcohol consumption , and inclusive economy frameworks . He has secured substantial funding (exceeding £16 million) through grants from NIH , CRUK , UKPRP , and MRC , including his role as co-PI in the HealthMod cluster. His contributions to multi-objective optimization and evolutionary algorithms have advanced methodologies in both engineering and public health domains. Scientific Awards: ESRC Future Research Leaders Award (2012-2015) As a co-developer of the Liger optimization environment , Purshouse has fostered open-source tools for complex decision-making. He leads the SIPHER consortium for systems science in public health and serves on editorial boards for journals like Environmental Modelling & Software . His teaching includes Agent-Based Modelling (ACS6132), and he maintains professional memberships in the Association for Computing Machinery and Research Society on Alcohol .

Lars Arvestadمشاهده پروفایل

مدرس ارشد

• Computational Biology
• Evolutionary Genomics
• Comparative Genomics
• +۲ مورد دیگر

Lars Arvestad is a Senior Lecturer at Stockholm University's Department of Mathematics, Faculty of Science. His research focuses on computational biology problems in evolution and comparative genomics, with significant contributions to bioinformatics tool development for genome assembly and phylogenetic analysis. He teaches courses in programming techniques for mathematicians, database technology, and software engineering. Academic Appointments: Senior Lecturer in Mathematics (2013-present) Research Focus: Computational modeling of biological systems, particularly in evolutionary genomics and genome assembly challenges His work includes creating BESST for efficient genome scaffolding, VMCMC for Bayesian phylogeny analysis, and Fastphylo for accelerated phylogenetic tree construction. Key technical innovations involve handling PE-contamination in mate-pair libraries and developing automated burn-in estimation for MCMC methods. Recent publications demonstrate expertise in integrating mathematical modeling with biological data analysis, particularly in solving practical challenges in next-generation sequencing data processing. The research group Computational Mathematics at Stockholm University develops methods applicable across molecular to planetary scale systems.

• Cyber Security
• Human Factors
• Security Behaviour
• +۷ مورد دیگر

Dr. K. (Kate) Labunets serves as an Assistant Professor in the Department of Information and Computing Sciences within the Faculty of Science at Utrecht University, Netherlands. Her research bridges cyber security and human factors with empirical methodologies, contributing to projects including the VSNU Digital Society track on Safety and Security and the H2020 CYBECO initiative. Her academic background includes: PhD in Information and Communication Technology, University of Trento, Italy Master in Mathematics, Belarussian State University, Minsk, Belarus Dr. Labunets' research focuses on security behaviour, cyber risk management, and empirical evaluation of security methods. She investigates how practitioners adopt security solutions to optimize time and resource allocation, utilizing both qualitative and quantitative approaches to analyze human aspects in security systems. Her work emphasizes practical applications for improving organizational security postures. Her 2022 publication on VPN mental models demonstrates her focus on human-centric security research, examining cognitive frameworks of security tools in corporate environments. This work reveals critical gaps between expert and non-expert perceptions, informing better security tool design and implementation strategies across disciplines. Dr. Labunets actively contributes to academic recognition through conference presentations at venues like USENIX Security Symposium. Her collaborative work with researchers including Veroniek Binkhorst and Wolter Pieters advances understanding of security human factors in real-world contexts. As an educator, she supervises students and teaches courses including Information Security, Scientific Research Methods for Business Informatics, and Software Ecosystem Security. She mentors through first-year and senior-year ICS tutoring programs, emphasizing student development and strength identification within Business Informatics and Computer Science curricula. She operates within the Software Production research group under broader themes of Foundations of Complex Systems and Governing the Digital Society, focusing on human dimensions of digital infrastructure security.