جست‌وجوی استاد و پژوهشگر

• Computing Education
• Computer Science Education
• Programming Education
• +۵ مورد دیگر

Quintin I. Cutts is a Professor in the School of Computing Science within the College of Science and Engineering at the University of Glasgow. He serves as a leading researcher in computing education with extensive contributions to programming pedagogy, educational frameworks development, and assessment methodologies across multiple educational levels from primary school through undergraduate programs. His research interests focus on computing education , particularly the development of educational frameworks like the ABC Framework for orienting learners in programming classes, spatial skills development in computational thinking, code comprehension strategies, and early-years computing education. His work bridges cognitive psychology with practical educational interventions, examining how spatial reasoning, argumentative reasoning, and physical activities impact programming learning outcomes. Analysis of his recent publications reveals a strong emphasis on developing structured educational approaches for novice programmers, with particular attention to how spatial skills correlate with computational thinking development, how to design effective early programming experiences, and how to address participation inequalities in computer science education. His work increasingly focuses on practical frameworks that can be implemented in real classroom settings across different educational stages. Cutts has established significant collaborative networks through organizing UKICER (United Kingdom and Ireland Computing Education Research) conferences and participating in international computing education research communities. His mentoring approach is evident through frequent co-authorship with junior researchers including Jack Parkinson, Maria Kallia, and Joseph Maguire, who have become established researchers in the computing education field themselves. He has contributed to building research infrastructure through initiatives like Works-in-Progress workshops that support emerging scholars.

Sami Domischمشاهده پروفایل

پژوهشگر

• Freshwater Biodiversity
• Biogeography
• Conservation Biology
• +۵ مورد دیگر

Dr. Sami Domisch is a Research Group Leader at the Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB) in Berlin, leading the Global Freshwater Biodiversity, Biogeography and Conservation (GLOWABIO) research group since 2024. He previously held a Leibniz Junior Research Group Leader position (2019–2024) and completed a postdoc at IGB (2016–2019). His academic journey includes a PhD at Senckenberg Research Institutes (2009–2012) and postdoctoral work at Yale University (2013–2016). Domisch specializes in freshwater biodiversity, climate change impacts, and hydrological modeling, with a focus on macroinvertebrates and ecosystem services. Education: PhD in Biodiversity and Climate Research (2012), Senckenberg Research Institutes Postdoc at Yale University (2013–2016) Advanced studies in biology at Carl von Ossietzky Universität Oldenburg (2005–2007) Basic studies in biology at Technische Universität Kaiserslautern (2001–2005) His research interests span freshwater biogeography, conservation strategies, and the integration of geospatial tools for environmental data analysis. Domisch has co-developed influential tools like the hydrographr R package and the GeoFRESH platform. He is an Associate Editor for Scientific Data and Journal of Biogeography , and a member of multiple editorial boards. Key grants include DFG-funded projects on freshwater fish diversity and NFDI4Earth initiatives for geospatial data interoperability. His work addresses global challenges like dam impacts, climate change effects, and invasive species dynamics. Domisch has received the EFFS-Award (2011–2012) for his PhD work and hosts international researchers through DAAD programs. Notable collaborations include the AquaINFRA and SOS-Water Horizon Europe projects, focusing on infrastructure for water research and safe operating spaces for water systems. His lab develops geospatial solutions to bridge data gaps in freshwater science.

• Software Engineering Education
• Programming Skills Assessment
• Curriculum Development
• +۳ مورد دیگر

W. Michael McCracken was a prominent figure in software engineering education research at Georgia Institute of Technology, with a career spanning nearly three decades from the late 1980s through 2015. His work primarily focused on curriculum development, programming skills assessment, and understanding how students conceptualize software design. McCracken's research interests centered on improving software engineering education through empirical studies of student learning. He investigated misconceptions in software design, the effectiveness of different teaching approaches, and the integration of industry practices like Agile methodologies into academic settings. His work often took a multi-institutional and sometimes international perspective, collaborating with researchers across multiple universities to gather comprehensive data on programming education. His publications demonstrate a consistent focus on practical applications of educational theory to software engineering curriculum. The progression of his work shows movement from foundational studies of programming skills assessment in the late 1990s to more sophisticated investigations of hybrid learning approaches by 2015. His research has significantly influenced how software engineering concepts are taught in academic settings. Among his notable contributions was the development of frameworks for undergraduate software engineering degree programs and studies examining why certain educational interventions succeed or fail. His work often addressed the gap between academic preparation and industry expectations for software engineering graduates. McCracken was an active participant in the computer science education community, regularly publishing in major venues including SIGCSE, ITiCSE, and CSEE&T conferences, as well as journals like IEEE Software and Computer Science Education. His collaborative approach is evident in his extensive co-authorship network across institutions.

• AI Ethics
• Fairness in AI
• Text-to-Image Generation
• +۵ مورد دیگر

Felix Friedrich is a researcher at the Technical University of Darmstadt, Department of Computer Science, focusing on AI ethics, fairness, and safety in generative models. He collaborates extensively with Professor Kristian Kersting and other researchers in the AI field, contributing significantly to understanding and mitigating biases in text-to-image generation systems and multilingual AI models. His research interests center on ethical AI development, with particular emphasis on identifying and addressing gender stereotypes, cultural biases, and safety concerns in generative models. Friedrich's work bridges technical AI development with societal implications, examining how language and cultural contexts influence model behavior across different populations. His approach combines rigorous empirical evaluation with practical mitigation strategies to create more responsible AI systems. Friedrich's publication record shows a strong trend toward addressing critical challenges in AI safety and fairness, particularly in multimodal and multilingual contexts. His recent work develops benchmarks and evaluation frameworks to systematically assess issues like cross-linguistic safety gaps, cultural bias magnification, and emotional recognition accuracy in synthetic data. These contributions provide valuable tools for the research community to build more equitable and reliable AI systems. His work has contributed to several collaborative research initiatives focused on AI safety and ethics, working with international teams to develop comprehensive evaluation frameworks and mitigation strategies for emerging AI technologies. Friedrich's research has practical implications for AI developers seeking to create more responsible systems that account for diverse cultural contexts and user needs.

Shakoor Poosehمشاهده پروفایل

پژوهشگر

• Mathematical Psychology
• Computational Neuroscience
• Value-based Decision-making
• +۵ مورد دیگر

Shakoor Pooseh is a Postdoctoral Research Fellow at the Freiburg Center for Data Analysis and Modeling (FDM) , affiliated with the Institute of Physics at the University of Freiburg. His research focuses on computational approaches to decision-making, resilience dynamics, and neuropsychopharmacology. He leads the Value-Based Decision-Making Battery project, a Bayesian adaptive assessment tool for impulsive and risky behavior, and contributes to the EU Horizon 2020 DynaMORE initiative modeling psychological resilience. Education: Completed a PhD in Computational Methods in the Fractional Calculus of Variations at the University of Aveiro, Portugal (2013), with prior work in fractional optimal control problems. His interdisciplinary background bridges mathematics, neuroscience, and clinical psychology. Research Interests: Explores mathematical psychology, computational neuroscience, and statistical modeling of decision processes. Key topics include serotonin/dopamine modulation of impulsivity, resilience dynamics under stress, and translational research in addictive disorders. He develops software tools for behavioral experiments and machine learning models. Grants/Projects: Principal investigator for the DynaM-OBS longitudinal study on resilience, and collaborates on interventions like DynaM-INT . His work integrates clinical data with computational models to understand mental health outcomes. Labs/Teams: Active in the FDM interdisciplinary group and the Institute of Physics’ neuroscience unit. Collaborates with psychiatric research teams at the University Medical Center Freiburg.

Tingting Hanمشاهده پروفایل

مدرس

• Formal Verification
• Probabilistic Systems
• Machine Learning in Verification
• +۳ مورد دیگر

Tingting Han is a Lecturer in Computer Science at Birkbeck, University of London since October 2013. She holds a BSc and MEng in Computer Science from Nanjing University (2003, 2006) and a PhD from RWTH Aachen University and University of Twente (2009), supervised by Joost-Pieter Katoen. Prior to her current role, she was a postdoc at RWTH Aachen University (2009–2011) and a research assistant at the University of Oxford (2011–2013) under the VERIWARE project. Her research focuses on formal verification of probabilistic systems, machine learning applications in verification, and software model checking. She has contributed to advancements in adversarial robustness, code generation, and automated analysis of probabilistic programs. Her work integrates formal methods with machine learning to enhance software correctness and security. Han has taught courses such as 'Problem Solving for Programming', 'Big Data Analytics using R', and 'Introduction to Programming'. She actively participates in academic service, including PC memberships in QAPL 2014 and SAC SVT 2015–2017.

• Multi-Physics Simulations
• Crystal Plasticity
• Materials Engineering
• +۳ مورد دیگر

Sharan Roongta is a Researcher at the Max Planck Institute for Sustainable Materials, affiliated with the Department of Microstructure Physics and Alloy Design. His work focuses on computational materials engineering and multi-physics simulations, particularly within the DAMASK framework. He explores chemo-mechanical coupling, crystal plasticity, and machine learning applications in materials science. Research interests include developing simulation tools for advanced materials characterization, optimizing multi-physics models for real-world applications, and addressing challenges in collaborative software development. His contributions span the integration of chemo-mechanical damage modeling, machine learning-enhanced yield surface prediction, and high-performance computing strategies. Notable contributions include the DAMASK Python library for simulation processing and scalable multi-physics frameworks. His work emphasizes bridging theoretical models with practical applications in metallic systems and materials design.

Dirk Johannßenمشاهده پروفایل

استاد

• Artificial Intelligence
• Human-Machine Interaction
• Natural Language Processing
• +۳ مورد دیگر

Prof. Dr. Dirk Johannßen holds the Professorship for Artificial Intelligence in Human-Machine Interaction at the Department of Economics, West Coast University of Applied Sciences. He is Co-Head of Applied AI (starting WS25/26), Scientific Director of the Digital Learning Campus's 'Digitalization & AI' initiative, and Diversity Committee substitute member (2024–2026). His research focuses on AI-driven psychometric diagnostics and human-computer interaction. Education: Dr. rer. nat. in Computer Science, University of Hamburg (2022) M.Sc. in Business Information Systems/IT Management, Nordakademie GS (2015–2017) B.Sc. in Business Information Systems, FH Nordakademie (2011–2015) Research Interests: Developing AI systems that analyze text for psychological traits (e.g., personality, motivation), with applications in education, mental health, and crisis management. He emphasizes ethical AI practices and digital transformation in academia. Professional Experience: Shareholder & IT consultant at effective WEBWORK GmbH (2021–present) Software developer at PPI AG (2015–2021) Grants/Consulting: Led workshops funded by Germany's Federal Ministry for Economic Affairs on AI in HR and usability. Active in Mittelstand 4.0 Competence Center. Labs/Initiatives: Co-founder of the CAPTA Institute for computational psychometrics. Collaborates with institutions like TU Berlin and ETH Zurich on NLP-driven diagnostics.

David Spielerمشاهده پروفایل

استاد

• Machine Learning
• Stochastic Systems
• Biochemical Systems Modeling
• +۳ مورد دیگر

David Spieler is a Professor of Machine Learning at the University of Applied Sciences Munich since September 2018. Previously, he served as a product owner for big data topics at Audi AG (2015–2018), a system developer at Bosch SoftTec (2014–2015), and completed his PhD in Modeling and Simulation at Saarland University (2009–2014). His research focuses on stochastic systems, biochemical modeling, parameter estimation, and oscillatory behavior analysis in Markovian systems. He has contributed to numerical methods for steady-state analysis, formal verification techniques, and applications in systems biology. Research interests include stochastic hybrid systems, sensitivity analysis of biochemical networks, geometric bounds for CTMC steady-state distributions, and oscillatory dynamics in chemical reaction networks. He has published extensively on topics like model checking, parameter estimation, and algorithmic solutions for stochastic processes. His work bridges theoretical computer science with practical applications in bioinformatics and engineering. Spieler has taught courses on data networks, quantitative model checking, and stochastic simulation techniques. He has reviewed manuscripts for over 20 conferences including CAV, CMSB, HSCC, and QEST, contributing to the advancement of formal methods and computational systems biology.

Johannes Kokeمشاهده پروفایل

استاد

• Mechanical Engineering
• Thermal Engineering
• Fluid Mechanics
• +۴ مورد دیگر

Prof. Dr.-Ing. Johannes Koke is a Professor of Mechanical Engineering, specializing in Process Engineering at Osnabrück University of Applied Sciences, Lingen Campus. He holds a Ph.D. in Engineering from RWTH Aachen University and has extensive experience in research and industry, focusing on thermal systems, fluid mechanics, and renewable energy solutions. Education: Ph.D. in Engineering (2000), RWTH Aachen University Master's in Mechanical Engineering (1995), RWTH Aachen University Bachelor's in Engineering (1994), Dartmouth College, USA Research Interests: His work centers on energy-efficient systems, solar thermal technology, fluid mechanics, and process engineering. Key areas include the design of nearly zero-energy buildings, optimization of solar thermal systems, and modeling of industrial processes. He emphasizes sustainable solutions for energy and material systems, with applications in beverage production, container architecture, and climate-adaptive design. Awards: 2002: Borchers-Plakette for outstanding doctoral thesis (RWTH Aachen) Teaching & Grants: He teaches modules on thermodynamics, fluid mechanics, and solar technology. His research has been supported by projects exploring energy-efficient container buildings and solar thermal integration in industries. Collaborations include institutions in South Africa, Sweden, and Oman. Labs & Teams: Active in the Faculty of Management, Culture, and Technology, he contributes to interdisciplinary projects on sustainable energy systems and process optimization.

Heiko Tapkenمشاهده پروفایل

استاد

• Computer Science
• Agricultural Technology
• Data Science
• +۳ مورد دیگر

Prof. Dr.-Ing. Heiko Tapken is a Professor at the Faculty of Engineering and Computer Science at Osnabrück University of Applied Sciences. His research focuses on databases, software development, Industry 4.0 applications in agriculture, and big data analytics. He leads projects like Agri-Gaia and OPeRAte, aiming to enhance agricultural processes through IoT and resilient process modeling. Research interests include computer vision for livestock monitoring, metadata management in agricultural data ecosystems, and dynamic optimization of cooperative farming processes. He actively contributes to committees such as Agrotech Valley Forum and VDI committees on Big Data and Industry 4.0 in agriculture. His work bridges computer science and agriculture, with notable contributions in AI-driven animal re-identification, hyperspectral image processing, and resilient process execution frameworks. Recent research trends emphasize cross-domain adaptation of AI models and farm automation systems.

Mina Jamshidi Idajiمشاهده پروفایل

پژوهشگر

• AI for Healthcare
• Computational Pathology
• Ophthalmology
• +۳ مورد دیگر

Mina Jamshidi Idaji is a Postdoctoral Researcher at the Machine Learning Group, BIFOLD – Berlin Institute for the Foundations of Learning and Data, Technical University of Berlin. Her research focuses on AI-driven healthcare applications, including computational pathology, ophthalmology, and biomedical sensing. She holds a Dr.-Ing. (PhD) in Machine Learning from TU Berlin and the Max Planck Institute CBS, Leipzig (2022), an M.Sc. in Biomedical Engineering from Sharif University of Technology (2016), and dual B.Sc. degrees in Electrical Engineering and Mathematics from Isfahan University of Technology (2014). Her research interests span AI for healthcare, computational pathology, and biomedical engineering. Recent work includes developing explainable AI frameworks for histopathology (xMIL), EEG/MEG simulation tools (MEEGsim), and methods for nonlinear interaction decomposition (NID). She has contributed to open-source projects like Harmoni and xMIL, addressing challenges in neural data analysis and clinical AI applications. Mina’s publications highlight advancements in brain-computer interfaces (BCI), motor imagery studies, and neuroimaging techniques. Her work bridges machine learning and clinical neuroscience, emphasizing translational research in healthcare AI.

Andrey Ustyuzhaninمشاهده پروفایل

استاد مدعو

• Machine Learning
• Particle Physics
• Data Science
• +۵ مورد دیگر

Prof. Andrey Ustyuzhanin is an Adjunct Professor of Computer Science at Constructor University's School of Computer Science & Engineering and a Visiting Research Professor at the National University of Singapore (NUS), affiliated with the Institute for Future Intelligent Machines (IFIM). He holds a PhD in Computer Science from the Institute of System Programming (Russian Academy of Sciences) and advanced degrees from Moscow Institute of Physics and Technology (MIPT). His research focuses on developing machine learning methods to address complex scientific challenges in particle physics, materials science, and data-driven discovery. He has contributed to projects like the LHCb experiment at CERN, optimizing online triggers and BDT-based processing, and has pioneered initiatives like the Tracking Machine Learning Challenge and the Code4ML dataset. His work bridges AI and fundamental science, emphasizing interdisciplinary applications. He is also the Director of AI/ML Research at Acronis and a co-organizer of international summer schools in machine learning for particle physics. Education PhD in Computer Science, Institute of System Programming (RAS), 2007 M.Sc. in Applied Mathematics & Physics (Autonomous Control Systems), MIPT, 1994–2000 M.Sc. in Innovative Management, MIPT, 1998–1999 B.Sc. in Applied Mathematics, MIPT, 1994–1998 Mathematics & Physics, Moscow Chemical Lyceum, 1991–1994 Research Interests Prof. Ustyuzhanin specializes in machine learning for scientific discovery, including particle physics (LHCb experiment), materials science (defect analysis in 2D materials), and AI-driven experimental optimization. His work also explores symbolic expression generation, code semantics classification (Code4ML), and cybersecurity frameworks like EAGLEEYE for malicious event detection. He advocates for reproducible science and end-to-end optimization of experimental designs using differentiable programming. Key Projects & Contributions Co-developed the Tracking Machine Learning Challenge to advance high-throughput physics analysis Co-created the Code4ML dataset for annotated machine learning code Designed algorithms for LHCb’s online triggers and scintillator tracking systems Co-founded the annual summer schools on ML in particle physics Labs & Collaborations Director of AI/ML Research at Acronis Head of the LAMBDA Lab at HSE University PI at IFIM, NUS Collaborator on CERN-Yandex research programs

Markus Grüneمشاهده پروفایل

استاد

• Business Information Systems
• E-Commerce
• E-Business
• +۶ مورد دیگر

Prof. Dr. Markus Grüne is a Professor for Business Administration with a focus on Business Information Systems, particularly e-Commerce and e-Business at Frankfurt University of Applied Sciences' Faculty 3: Business and Law. He serves as Coordinator for Business Information Systems and holds office hours in room 4-403. His professional background includes roles at German Bank AG as Lead IT Project Manager and Lead Functional Specialist for regulatory reporting projects. He earned his doctorate (Dr. rer. pol.) from Goethe University, Frankfurt on pricing models in 2006. Teaching focuses include Business Information Systems, e-Business, Big Data, and Data Warehouse modules. Notable courses include 'Big Data – Data Lakes and Crystal Balls' (IBIS program) and 'Process Management' which won an e-Learning Prize for blended learning implementation. Research interests span business process management, agile software development, and IT systems in finance. Key publications explore cloud computing in e-business education, banking IT systems, and workflow-based e-learning. He holds the Best Paper Award (2003) and contributed to major conferences like Bled eConference. Active in professional societies such as the Society for Computer Science, he bridges academic research with practical IT project management expertise.

Sandro Schulzeمشاهده پروفایل

استاد

• Software Engineering
• Software Architecture
• Software Product Lines
• +۵ مورد دیگر

Prof. Dr. Sandro Schulze is a Professor in the Department of Computer Science and Languages at Anhalt University of Applied Sciences. His research focuses on software engineering, particularly in areas such as software architecture analysis, software product line engineering, and variability management. He leads efforts in developing tools for visualizing fork ecosystems and tracking architecture smells. His work bridges theoretical research with practical applications in open-source systems and industrial software. Responsibilities include overseeing the Software Engineering research group within the department. He is actively involved in academic activities, including organizing workshops like WSRE and contributing to initiatives like the Software Reengineering Body of Knowledge (SREBOK). Publications highlight advancements in fork ecosystem visualization (e.g., VisFork toolsuite), empirical studies on architecture smells, and techniques for feature extraction from requirements. His work often emphasizes tool development and empirical validation of software engineering practices.