جست‌وجوی استاد و پژوهشگر

• Scientific Visualization
• Computer Graphics
• Visual Analytics
• +۵ مورد دیگر

Christoph Müller is a Doctoral Researcher at the Visualization Research Center (VISUS) at the University of Stuttgart, working within the Weiskopf Group and Sedlmair Group. With over 19 years of research experience documented through publications from 2006 to 2025, he has established himself as a significant contributor to the scientific visualization community. Dr. Müller's research spans multiple domains of visualization including scientific visualization, computer graphics, visual analytics, cybersecurity visualization, molecular graphics, and energy-efficient visualization techniques. His recent work demonstrates a strong focus on practical applications, particularly in cybersecurity analysis through projects like AlertSets for exploratory analysis of cybersecurity alerts and VITALflow for network traffic analysis. He has also made important contributions to understanding and reducing the energy consumption of visualization systems, with recent papers on foveated volume visualization and comprehensive energy measurement approaches. His publication record shows consistent contributions to top visualization venues, with recent work appearing in IEEE Transactions on Visualization and Computer Graphics, Journal of Visualization, and proceedings of major conferences including IEEE VIS, Eurographics, and VizSec. His research often involves interdisciplinary collaboration, particularly with cybersecurity experts and domain scientists. Active research in cybersecurity visualization for security operations Pioneering work on energy measurement and reduction in visualization systems Contributions to scientific visualization for cosmic evolution and manufacturing Development of visualization frameworks like MegaMol Exploration of novel platforms including gaming consoles for scientific visualization Dr. Müller's work bridges theoretical visualization research with practical applications, making significant contributions to both the academic community and real-world visualization challenges across multiple domains.

Christof Moslerمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence
• IT Management
• +۴ مورد دیگر

Christof Mosler serves as Professor of Business Informatics at Stuttgart University of Applied Sciences since 2011, teaching Programming, Software Engineering, AI, IT Management, and Entrepreneurship within the Computer Science department. He actively contributes to the Competence Center for Digitalization in research, teaching and business, and maintains industry connections through advanced training semesters at companies like Börse Stuttgart GmbH and Robert Bosch GmbH. His educational background includes: Dipl.-Informatiker (Computer Science) from RWTH Aachen (1999-2004) Dipl.-Volkswirt (Economics) from FernUni Hagen (2003-2007) Dr. rer. nat. in Software Engineering from RWTH Aachen (2004-2009) His research bridges computer science and business domains with emphasis on practical applications . Key focus areas include enterprise software systems, AI-driven financial modeling, security solutions for SMEs, and innovation in traditional industries. His work consistently addresses real-world business challenges through technological implementation. Publication trends reveal a strategic evolution from foundational project management studies (2012-2016) toward cutting-edge applications in financial technology and security (2019-2024). Recent work demonstrates increasing specialization in algorithmic trading systems and SME-focused security architectures, reflecting industry's digital transformation demands. His scientific recognition includes: National winner of the 17th Federal Computer Science Competition (1999) Prof. Mosler maintains strong industry-academia integration through consultancy roles at major corporations and startup ventures. He directs the Enterprise Software Laboratory, which facilitates hands-on student training in enterprise system development and serves as a hub for applied research projects with industry partners.

Nuttida Rungratsameetaweemanaمشاهده پروفایل

استادیار

• Neural computations
• Cognitive neuroscience
• Computational neuroscience
• +۶ مورد دیگر

Nuttida Rungratsameetaweemana is an Assistant Professor in the Department of Biomedical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science. She also serves as a Provost Research Fellow at Columbia University, demonstrating her significant research contributions early in her academic career. NuttidaLab, her research group, focuses on integrating computational and experimental approaches to investigate the neural computations underlying complex cognitive functions in both health and disease. The lab's research spans several key areas including learning mechanisms, decision-making processes, and social behavior, with particular emphasis on understanding how these cognitive functions become disrupted in neurological and neuropsychiatric disorders. The lab has developed several important computational neuroscience tools including NeuralDecoder for representational similarity analysis, state_space_analysis for modeling neural dynamics, MARLAX for multi-agent reinforcement learning, and dynamax for state space modeling. These tools reflect the lab's commitment to developing robust analytical frameworks that can be widely adopted by the neuroscience community. NuttidaLab maintains an active GitHub presence with multiple repositories that demonstrate the lab's focus on open science and reproducible research. The lab's work combines cutting-edge computational approaches with experimental neuroscience to develop a deeper understanding of brain function. The lab actively seeks talented students and researchers interested in interdisciplinary work at the intersection of engineering, neuroscience, and computational science. Professor Rungratsameetaweemana's research represents an important bridge between theoretical computational models and experimental neuroscience, with significant potential for advancing our understanding of both healthy brain function and neurological disorders.

Jan Bohlenمشاهده پروفایل

استاد پژوهشی

• Materials Science
• Magnesium Alloys
• Mechanical Properties
• +۳ مورد دیگر

Dr. Jan Bohlen is a Research Professor at Helmholtz-Zentrum Hereon, serving as a Senior Scientist in the Magnesium Innovations Centre (MagIC). His research spans magnesium alloys, corrosion science, and materials processing, with a strong focus on biomedical and lightweight structural applications. He investigates alloy design, microstructure-property relationships, and advanced manufacturing techniques like extrusion and rolling. Bohlen's recent work emphasizes machine learning-driven modeling for material behavior prediction and process optimization. His publications highlight trends in biodegradable Mg/Zn alloys, corrosion mitigation, and thermomechanical processing, aiming to enhance performance for medical implants and industrial components. He leads research at MagIC, collaborating on projects involving alloy development, dieless wire drawing, and texture control. No awards or student advisories are documented in the provided text.

Urte Schlüterمشاهده پروفایل

پژوهشگر

• Plant Physiology
• Photosynthesis
• Plant Biochemistry
• +۳ مورد دیگر

Dr. Urte Schlüter is a Researcher at the Institute of Plant Biochemistry, Heinrich Heine University Düsseldorf, specializing in plant photosynthesis mechanisms. Her work focuses on C 3 -C 4 intermediate species within the Brassicaceae family, investigating biochemical, anatomical, and developmental aspects of photosynthetic evolution. Her research spans plant physiology, photosynthesis biochemistry, and computational biology, with emphasis on how leaf anatomy and biochemical pathways reorganize during C 4 evolution. She examines intermediate CO 2 compensation points and centrifugal organelle arrangements in bundle sheath cells, bridging theoretical plant biology with metabolic engineering. Publications reveal a strong trend toward integrating machine learning with traditional plant science, particularly using leaf reflectance spectra to predict photosynthetic capacity. This interdisciplinary approach combines high-throughput phenotyping with evolutionary biochemistry to optimize plant performance traits. Dr. Schlüter operates within CEPLAS (Cluster of Excellence on Plant Sciences), utilizing specialized facilities including the Plant Metabolism and Metabolomics Platform and advanced imaging resources for metabolic network analysis and edaphic adaptation studies.

• Machine Learning
• Drug Discovery
• Privacy-Preserving AI
• +۴ مورد دیگر

Dr. Matthias Becker serves as a Group Leader within the Career Development Fellow Programme at the German Center for Neurodegenerative Diseases (DZNE) in Bonn, Germany. His research integrates advanced machine learning techniques with biomedical applications, focusing on drug discovery and privacy-preserving health data analysis. His primary research interests include: Development of generative AI models for drug molecule design (DrugDiff) Privacy-preserving synthetic data generation for medical research (PriSyn project) Swarm learning applications for high-dimensional biomedical data Energy-efficient computing for molecular modeling His work bridges computational methods with neurodegenerative disease research, emphasizing practical implementations that balance innovation with ethical data handling. Becker's recent publication analyzes autoencoder architectures for molecular data, demonstrating significant reductions in data requirements (97%) and energy consumption (36%) while maintaining performance. His research shows particular strength in optimizing latent space utility for chemical applications. Becker actively collaborates with major institutions including CISPA Helmholtz Center for Information Security, QuantPi startup, and Hewlett Packard Enterprise. His technical expertise spans HPC/GPU cluster utilization, containerization for reproducibility, and emerging technologies like FPGAs for energy-efficient computing. He contributes to DZNE's computational infrastructure while advancing research in synthetic data generation for genetic and clinical applications.

Agnes Flöelمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Sleep Physiology
• Brain Stimulation
• +۷ مورد دیگر

Prof Dr Agnes Flöel is a Professor at the University of Greifswald Medical School, where she leads research at the intersection of cognitive neuroscience, sleep physiology, and brain stimulation. Her work focuses on understanding neural mechanisms underlying memory formation and cognitive decline in aging, with particular emphasis on sleep-related processes and non-invasive brain stimulation techniques. Her research interests center on sleep physiology , cognitive neuroscience , and neurostimulation . Dr. Flöel investigates how slow oscillations, spindles, and delta waves interact during sleep to support memory consolidation, particularly in older adults and those with cognitive impairment. Her work extends to developing interventions combining cognitive training with transcranial direct current stimulation (tDCS) to enhance cognitive function in aging and early Alzheimer's disease. She also contributes to methodological advancements in sleep staging through deep learning approaches and addresses technical challenges in neuroimaging. Dr. Flöel's publication record demonstrates consistent productivity with multiple high-impact papers annually, primarily focused on sleep neuroscience, brain stimulation, and cognitive aging. Her work shows strong collaborative patterns with researchers across Germany, particularly within the SFB 1315 research consortium. Recent publications (2022-2024) increasingly incorporate computational approaches, deep learning, and multimodal neuroimaging to address fundamental questions about neural plasticity and memory. Her research has been supported through the Collaborative Research Center 1315 (SFB 1315) funded by the German Research Foundation, focusing on neuronal circuits in aging. Dr. Flöel serves as principal investigator for multiple clinical trials examining brain stimulation approaches for cognitive enhancement in older adults and those with mild cognitive impairment. Dr. Flöel leads a research group investigating sleep-brain interactions in aging, collaborating closely with computational neuroscientists, neurologists, and psychologists. Her team utilizes multimodal approaches including EEG, MRI, and behavioral assessments to understand neural mechanisms of cognitive decline and potential interventions.

Dirk Roosمشاهده پروفایل

استاد

• Machine Learning
• Robust Design Optimization
• Stochastic Analysis
• +۱۰ مورد دیگر

Prof. Dr.-Ing. Dirk Roos is a Professor for Computersimulation und Design Optimization at the Department of Mechanical Engineering and Process Engineering, Niederrhein University of Applied Sciences, where he has served since March 2011. He is also the Head of the Institute for Modeling and High Performance Computing (IMH) since December 2016. His academic career includes previous positions as Head of Robust Design Optimization at DYNARDO Dynamic Software and Engineering GmbH (2002-2011) and Technical Solutions Specialist at CADFEM GmbH (2000-2008). Prof. Roos specializes in machine learning, robust design optimization, stochastic analysis, and probabilistic modeling for virtual product development. His research focuses on developing mathematical methods for stochastic structural and fluid simulation, robustness and reliability analysis, multidisciplinary optimization, and software development for complex system development. He has established strong collaborations with academic institutions including RWTH Aachen, Ruhr-Universität Bochum, and industrial partners like Siemens AG and Robert Bosch GmbH. His recent work explores Probabilistic Intelligence, cyber-physical systems, digital twins, and Big Data Analysis, with applications spanning power plant flexibility, renewable energy, turbomachinery, automotive, aerospace, and medical technology. Prof. Roos has successfully secured numerous third-party funded research projects totaling over 1.8 million euros, including collaborations with Siemens AG, Robert Bosch GmbH, and various universities. optiSLang Award 2012 Weimar Optimization and Stochastic Days Gutachter BMBF im Programm FH-Kooperativ since 2019 Mitglied des Scientific Committee International Probabilistic Workshop since 2017 Mitgliedschaft im Graduierteninstitut für angewandte Forschung der Fachhochschule in NRW since 2017 Prof. Roos supervises multiple doctoral students and has successfully completed several cooperative PhD projects. His current research projects include AI-driven optimization for medical care, reinforcement learning for emergency room planning, and machine learning algorithms for power plant component lifetime prediction. The Institute for Modeling and High Performance Computing (IMH) under his leadership develops mathematical methods and software competence in machine learning and CAE-based robust design optimization for virtual product development.

Hongyu Zhangمشاهده پروفایل

استاد

• Intelligent software engineering
• Software analytics
• Data-driven software engineering
• +۴ مورد دیگر

Hongyu Zhang is a Professor and Dean of the School of Big Data and Software Engineering at Chongqing University, China, and an Honorary Professor at The University of Newcastle, Australia. Previously, he served as a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD from the National University of Singapore in 2003. His academic journey spans prestigious institutions, combining industry research experience with academic leadership. Dr. Zhang's research interests focus on intelligent software engineering, software analytics, data-driven software engineering, software fault management, testing and debugging, and software maintenance and reuse. His work centers on improving software quality and productivity by mining and analyzing vast amounts of software data. Over the years, he has developed innovative methods that apply data mining, machine learning (including deep learning), and information retrieval techniques to extract knowledge from software data and solve complex software engineering problems. His research spans three major areas: intelligent programming (code search, code summarization, code generation), intelligent quality prediction (defect prediction, cloud failure prediction, performance prediction), and intelligent fault detection and diagnosis (log-based fault detection, crash-based fault localization, bug report analytics). His recent publications demonstrate a clear trend toward integrating large language models and deep learning techniques with traditional software engineering practices. The research spans intelligent programming assistance, code security, UI automation, distributed systems optimization, and performance analysis. His work increasingly focuses on practical applications of AI in software engineering, with emphasis on real-world impact in industrial settings, particularly in microservices, cloud systems, and large-scale software development environments. 8 ACM Distinguished Paper Awards Best Paper Award: How Long Will it Take to Mitigate this Incident for Online Service Systems? David Lorge Parnis Fellowship Senior Member of IEEE Distinguished Member of ACM Distinguished Member of CCF Fellow of Engineers Australia (FIEAust) Recognized in The Australian's Top Researchers special edition as leading researcher in Software Systems World's Top 2% Scientists (career-long) Dr. Zhang has successfully advised numerous PhD and Master's students who have gone on to prominent positions at leading technology companies and academic institutions worldwide. His research has been supported by significant grants including Australian Research Council Discovery Projects (as Lead CI) and multiple National Science Foundation of China projects. His work has made tangible impacts in industry, most notably through the Microsoft Developer Assistant project which received over 450K downloads in 2016. He leads research groups focused on intelligent software engineering and software analytics, with strong collaborations between Chongqing University, The University of Newcastle, and Microsoft Research. His teams develop practical tools for code intelligence, log analysis, and fault diagnosis that are deployed in real-world online service systems.

Fabian Schönfeldمشاهده پروفایل

پژوهشگر ارشد

• Theoretical neuroscience
• Computational neuroscience
• Machine learning
• +۵ مورد دیگر

Dr. Fabian Schönfeld is a postdoctoral researcher at the Institute of Neural Computation (INI), which is part of the Faculty of Computer Science at Ruhr-Universität Bochum. He works in the Theory of Neural Systems group, focusing on computational modeling of the hippocampus using Slow Feature Analysis and other machine learning techniques. His educational background includes: Diploma in Computer Science from FAU Erlangen (2004-2009) PhD in Neuroscience from the International Graduate School of Neuroscience (2010-2016) Dr. Schönfeld's research primarily focuses on theoretical neuroscience, particularly on modeling the hippocampus and spatial cognition. His work combines computational approaches with neuroscience to understand how the brain processes spatial information. He has extensively used Slow Feature Analysis to model place cell behavior in the rat hippocampus, arguing for its feasibility as a fundamental principle of cognitive data processing. His research interests also extend to deep learning, artificial intelligence, and the intersection of these fields with neuroscience. His publications demonstrate a consistent focus on hippocampal function and spatial representation, with an increasing sophistication in modeling approaches over time. The research shows how computational models can help understand neural mechanisms of spatial representation, navigation, and memory formation, with applications in both neuroscience and artificial intelligence. Dr. Schönfeld has supervised several bachelor's theses on topics related to robot navigation using Slow Feature Analysis and has taught courses on Scientific Computing with Python. His academic service includes: Supervising bachelor's theses (2012-2013) Teaching Scientific Computing with Python (2015-2017) Tutoring high school students in mathematics and physics (2007-2010)

Raghavan B. Sunojمشاهده پروفایل

استاد

• Computational Chemistry
• Organic Chemistry
• Homogeneous Catalysis
• +۵ مورد دیگر

Raghavan B. Sunoj serves as Professor and Class of 1998 Chair in Quantum Computing at the Department of Chemistry, Indian Institute of Technology Bombay, while also holding the position of convener for supercomputing. His academic journey includes: Early education in Kerala, India Ph.D. from Indian Institute of Science (IISc) Bangalore under Professor J. Chandrasekhar Postdoctoral research at Ohio State University with Professor Christopher Hadad Professor Sunoj's research bridges computational and experimental organic chemistry with emphasis on homogeneous catalysis. Key focus areas include: Reaction mechanism elucidation Weak noncovalent interactions in asymmetric catalysis In silico catalyst design through computational chemistry and machine intelligence His collaborative work with experimental groups has successfully validated computational predictions and rationalized experimental observations. His distinguished recognition includes: Charles Coulson Lecture (2017) at University of Georgia, Athens IIT Bombay Excellence in Teaching Award Shanti Swarup Bhatnagar Prize in Chemical Science (2019) While specific student advising details are not provided, his research program demonstrates significant collaborative impact through experimental validation of computational models. As convener for supercomputing, he leads high-performance computing initiatives supporting quantum computing and catalyst design research at IIT Bombay.

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Prof. Dr. Dieter Landes serves as a Professor in the Faculty of Electrical Engineering and Computer Science at Coburg University of Applied Sciences, Germany. His research bridges theoretical AI advancements with practical cybersecurity and energy solutions, particularly targeting small and medium enterprises (SMEs) through federally funded initiatives. Landes' core research interests include: Cybersecurity applications of Large Language Models for Blue/Red Teaming operations AI-driven predictive maintenance in photovoltaic energy systems Realistic data generation for security system training and testing Intelligent educational technologies for personalized learning navigation High Performance Computing infrastructure for applied AI workloads His current grant portfolio demonstrates sustained research leadership: Blue-and Red-Agent-based Cyber Security (2025-2028): Federal Office for Information Security project developing LLM-powered defenses against SME-targeted cyberattacks Kick-PV (2023-2026): AI characterization methods for remote photovoltaic system diagnostics GENESIS (2022-2025): Framework for generating realistic data flows to train insider threat detection systems VoLL-AI (2021-2025): Multi-university collaboration (Erlangen/Bamberg) on AI-driven learning navigation HPC4AAI (2021-2024): Infrastructure development for applied artificial intelligence computing Landes operates within interdisciplinary research collectives at Coburg University, frequently partnering with Friedrich-Alexander University of Erlangen-Nuremberg and Otto-Friedrich University of Bamberg. His project pipeline extending to 2028 confirms active research trajectory in applied computer science.