جست‌وجوی استاد و پژوهشگر

• Computational Biology
• Bioinformatics
• Systems Biology
• +۳ مورد دیگر

Dr. Sebastian Falkner is a Research Fellow in Computational Biology at the University of Augsburg, affiliated with the Institute of Physics under the Faculty of Mathematics, Natural Sciences, and Materials Engineering. He holds a postdoctoral position focused on interdisciplinary research at the intersection of physics and biology. His contact information includes the email sebastian.falkner@uni-a.de and office 3016 at BioInnovationScience (Universitätsstraße 1 b, Augsburg). While specific educational details are not provided, his research interests center on computational methods applied to biological systems, including genomics, biophysics, and data-driven approaches in biology. He contributes to the Computational Biology group within the Institute of Physics, collaborating with faculty such as Prof. Dr. Nadine Schwierz and colleagues like Dr. Christian Wiebeler. No specific scientific awards or grants are mentioned in the provided text. His current affiliations and roles emphasize research and academic collaboration within the university's interdisciplinary framework.

Philipp Jungمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Cleaning
• Artificial Intelligence
• +۴ مورد دیگر

Philipp Jung is a researcher affiliated with the Calgo Lab and the TU Berlin's Data Science +X Graduate School. He holds a physics degree from TU Berlin and an industrial engineering background from BHT Berlin. His work focuses on applied machine learning for data cleaning, with interdisciplinary research in AI-driven healthcare, industrial processes, biodiversity, and human-computer interaction. Key Research Projects: APPL-FM (DFG): AI in health and cell biology KIP-SDM (BMBF): Data science in medical applications HUMAN.VR.LAB (BMBF): VR interface development GCA (BMUV): Green AI for biodiversity He collaborates with tech startups like QualiFiction and contributes to initiatives like the Berlin Big Data Center. His work is funded by DFG, BMBF, and EU programs.

Christian Gawronمشاهده پروفایل

استاد

• Internet Technologies
• Software Engineering
• Natural Language Processing
• +۳ مورد دیگر

Prof. Dr. Christian Gawron is a Professor of Internet Technologies at the University of Applied Sciences Südwestfalen since 2019. He leads the Master's program in Applied Computer Science and has held roles as Department Head and Practice Group Leader at IBM Global Business Services (2014–2018), focusing on Watson AI, chatbots, and text mining. Previously, he was a Senior IT Architect at IBM (1999–2014), specializing in business process digitalization for the insurance sector. He earned his Dr. rer. nat. in 1999 with a dissertation on Simulation-Based Traffic Assignment , and held research roles at the Center for Applied Computer Science (ZAIK) and the Universities of Cologne and Bonn. His research spans internet technologies, software engineering, natural language processing (NLP), and traffic simulation. His academic journey includes a physics degree from the Universities of Cologne and Bonn (1989–1995). His work bridges theoretical computer science and practical AI applications, including projects like BERT-DE-NER for NLP and traffic simulation models for large networks. He advises students on theses and projects, emphasizing real-world applications of technology. Prof. Gawron’s research trends reflect a shift from foundational traffic simulation (1990s) to modern AI-driven solutions (2020s), with notable contributions to transformer models and toxic comment detection. His IBM experience underscores practical IT architecture and AI implementation. Consultations are arranged via Calendly or email. He is a prolific researcher, with key contributions in traffic flow theory (e.g., dynamic user equilibrium algorithms) and NLP (e.g., BERT-based models). His work frequently addresses interdisciplinary challenges, such as environmental impact modeling in traffic simulations (FVU cooperative, 1998). Advising & Grants: Supervised numerous theses in internet technologies and AI. Prior IBM projects included grants for AI-driven insurance process optimization and real-time traffic simulation infrastructure. Labs/Teams: Leads the Internet Technologies research group at FH Südwestfalen, collaborating with industry partners on AI and NLP applications.

Veit Dominik Kunzمشاهده پروفایل

استاد

• Electrical Engineering
• Renewable Energy
• Wind Energy
• +۴ مورد دیگر

Prof. Veit Dominik Kunz is a Professor of Electrical Engineering and Renewable Energy at the Department of Process Engineering, Hamburg University of Applied Sciences. His academic role includes teaching and research in advanced electrical systems, power electronics, and renewable energy technologies. He is affiliated with the Faculty of Engineering and Computer Science and leads research projects such as FLEDERWIND (focusing on bat detection in wind parks) and Drones4Bats (wildlife impact assessment). His expertise spans semiconductor device design, nanotechnology, and strategic product development. Research interests include optimizing renewable energy systems, mitigating environmental impacts of wind energy, and advancing semiconductor technologies for high-performance electronics. His work bridges academic research with practical applications, particularly in energy management and sustainable technologies. Publications highlight contributions to vertical MOSFET design, renewable energy systems, and product development strategies. He is also involved in academic governance as head of the examination committee for his department.

Marcus Schröterمشاهده پروفایل

استاد

• Sustainable Development
• Logistics Management
• Supply Chain Management
• +۵ مورد دیگر

Marcus Schröter is a Professor of Business Administration, especially Logistics , at Bochum University of Applied Sciences . He leads the Bachelor's and Master's programs in Sustainable Development and Applied Sustainability, while also serving as Chairman of the Sustainable Development Committee and a voting member of the Senate. His work focuses on integrating sustainability into logistics, supply chain management, and industrial systems through teaching, research, and applied projects. Education : Diplom-Ökonom (University of Bremen, 1997); Dr. rer. pol. (PhD in Economics, TU Braunschweig, 2005) Research interests span sustainable logistics , closed-loop supply chains , system dynamics modeling , energy efficiency in manufacturing , technology assessment , and service-based business models . He applies these concepts to real-world challenges like sustainable value chains, alternative transport systems, and resource-efficient production. Publications focus on sustainability metrics, industrial ecology, and practical implementations of circular economy principles. His work examines energy efficiency strategies, material flow optimization, and multi-criteria decision-making frameworks in European manufacturing contexts. Projects include leading THALES (Transfer Hub for Advancement, Livability and Efficacy of Sustainability Transformations) and collaborating on Climate Interactive initiatives. He has contributed to EU-funded research and German federal studies on sustainable production systems.

• Probabilistic Forecasting
• Statistical Evaluation
• Climate Science
• +۳ مورد دیگر

Prof. Tilmann Gneiting is a Professor of Computational Statistics at Karlsruhe Institute of Technology (KIT) and Head of the Computational Statistics Working Group at the Heidelberg Institute for Theoretical Studies (HITS). His research focuses on probabilistic forecasting, statistical evaluation of predictive models, and applications in environmental science, epidemiology, and renewable energy. He has held academic roles in mathematics and statistics departments across multiple institutions. His work emphasizes rigorous statistical methodology for forecasting systems, including ensemble models, uncertainty quantification, and validation frameworks. Notable contributions include developing scoring rules for probabilistic predictions, Bayesian model averaging techniques, and geostatistical methods for weather and climate modeling. Courses taught include Forecasting: Theory and Practice and Time Series Analysis . Prof. Gneiting’s research spans diverse areas such as earthquake forecasting, solar energy prediction, and pandemic modeling. He has contributed to R packages like ensembleMOS and ProbForecastGOP , advancing computational tools for statistical analysis. His work bridges theoretical statistics with practical applications in environmental and public health domains. Professional awards and recognitions are not explicitly listed in the provided texts. His advising and grants involve collaborations in interdisciplinary projects, though specific student names or grant details are not mentioned. He leads computational statistics teams at HITS and actively publishes in top-tier statistical and applied journals.

Jan Wilhelmمشاهده پروفایل

پژوهشگر

• Ultrafast Processes
• Electronic Structure Theory
• Quantum Technologies
• +۳ مورد دیگر

Jan Wilhelm is a Researcher and leader of the Emmy Noether Independent Junior Research Group at the University of Regensburg's Institute for Theoretical Physics. His work focuses on ultrafast electron dynamics and computational methods for electronic structure theory, addressing gaps between experimental capabilities and theoretical predictions in ultrafast processes. He develops low-scaling algorithms for GW calculations to simulate systems with thousands of atoms, enabling studies of materials like 2D heterobilayers and moiré structures. His research intersects quantum technologies, photovoltaics, and nonlinear optics, with collaborations on high-harmonic generation and ultrafast microscopy. Funded by the German Research Foundation (DFG), his work bridges theory and experiment, aiming to understand femtosecond-scale phenomena in materials. Education: Studied physics and mathematics at Karlsruhe Institute of Technology, with a doctorate in theoretical chemistry from the University of Zurich. Prior industry experience in chemical optimization provided insights into applied vs. fundamental research. Research highlights include the development of CUED software for ultrafast dynamics simulations and contributions to the CP2K package. Key projects include ultrafast laser-driven electron dynamics, topological insulator studies, and collaborations with experimental groups at RUN. Future directions involve leveraging the Regensburg Center for Ultrafast Nanoscopy (RUN) to explore uncharted phenomena at atomic scales. Teaching: Lectures on computational methods for nanoscience and condensed matter excitations. Supervised students like Max Graml, who received the Brigitta and Oskar Braumandl Prize.

Manuel Landstorferمشاهده پروفایل

پژوهشگر

• Mathematical Modeling
• Lithium Ion Batteries
• Solid Electrolytes
• +۴ مورد دیگر

Manuel Landstorfer is a researcher at Ulm University's Department of Numerical Mathematics. His work focuses on mathematical modeling of lithium-ion batteries, particularly solid electrolyte systems, and numerical methods like finite element analysis (FEM) and MPI parallelization. He collaborates with the Theoretical Electrochemistry Group led by PD Dr. Timo Jacob and participates in the BMBF-funded LISA project. His research includes ion transport in solid electrolytes, impedance spectroscopy simulations, and free energy functional-based transport equations. Education: Diploma in Mathematics (FH Regensburg, 2007) Professional Experience: Research associate since 2007, PhD scholarship since 2010 Research interests span numerical solutions to Laplace equations via Schwarz-Christoffel mapping, space charge layer effects, and full-cell battery simulations. He has presented at conferences including the ECS Meetings and DPG Spring Meetings, focusing on battery modeling and electrochemical systems. Technical expertise includes parallel computing (C++, MPI), mesh generation, and PDE-based modeling. His work bridges applied mathematics with electrochemical engineering, emphasizing first-principles-derived parameters in battery models.

• Planetary Science
• Astrophysics
• Geophysics
• +۳ مورد دیگر

Wieland Dietrich is a Researcher and Research Coordinator at the Max Planck Institute for Solar System Research (MPS) , affiliated with the Planetary Science Department. His expertise lies in planetary dynamics, particularly the magnetic fields and atmospheric flows of exoplanets like Hot Jupiters, as well as convection processes in planetary cores and gas giants. Education & Employment : PhD in Physics (2009-2012): IMPRS program at MPS, Katlenburg-Lindau Postdoctoral Research Fellow (2013-2019): University of Leeds (School of Applied Mathematics) and MPS Current roles since 2022: Research Coordinator at MPS Research Focus : Investigates dynamo processes, zonal winds, and satellite measurement correlations with planetary interiors. Recent work emphasizes Hot Jupiter atmospheres and gravity dynamics of gas giants like Jupiter. Publications : Focused on exoplanet magnetism, zonal wind mechanisms, and computational models of planetary convection. Key contributions include studies on KELT-9b's magnetic fields and Jupiter's gravity moments. Awards : None explicitly mentioned in the provided texts. Labs & Teams : Active within the Planetary Science Department at MPS, contributing to interdisciplinary research on solar system and exoplanetary dynamics.

Chris Köcherمشاهده پروفایل

پژوهشگر

• Algorithms
• Theory & Logic
• Programming Languages
• +۹ مورد دیگر

Chris Köcher is a Researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany. His work focuses on theoretical computer science, with a strong emphasis on formal methods, automata theory, and verification of complex systems. He leads research in areas such as algorithms, programming languages, cyber-physical systems, and security. His research bridges foundational theory and practical applications, addressing challenges in distributed systems, mobile systems, and privacy-preserving technologies. His research interests span formal language theory, verification of concurrent systems, and the analysis of storage mechanisms in automata. Notably, he has contributed to the study of queue automata, multi-pushdown systems, and the decidability of reachability problems in lossy systems. His work often intersects with theoretical computer science and algebraic structures, exploring topics like monoids and trace languages. Chris’s publications reflect a deep engagement with foundational questions in computer science, including the verification of cooperating multi-pushdown systems, the complexity of semilinear set separability, and the formal analysis of hard attention mechanisms in transformers. His research has implications for improving the reliability and security of software systems through rigorous mathematical foundations. He is affiliated with MPI-SWS, a leading research institution in software systems, and contributes to the institute’s mission to advance theoretical and applied research in computer science. His work is supported by collaborations within the institute and the broader academic community.

• Time Series Analysis
• Blockchain Technologies
• Business Process Management
• +۸ مورد دیگر

Roman Vaculín is a Researcher at IBM Research, focusing on interdisciplinary domains where artificial intelligence, blockchain technologies, and data-centric workflows converge. His work spans automated machine learning, time series analysis, and secure computation via cryptographic methods like homomorphic encryption. Affiliation: IBM Research Key research areas: Time Series Analysis, Blockchain, AI Explainability, Business Process Management Across his publications, Vaculín explores: Time Series Modeling: Developing robust frameworks like TsSHAP and end-to-end architectures for forecasting and imputation. Blockchain Applications: Designing trusted AI systems, secure multi-party computation, and verifiable simulations. Automated Machine Learning: Creating toolkits for industrial AI explainability and automation. Privacy-preserving Techniques: Optimizing encrypted inference and secure decision tree protocols. His methodology often integrates formal verification with practical implementations, emphasizing efficiency and interpretability in complex systems. While no formal awards or students are documented in the provided data, his collaborative publications with institutions like IBM Research and academic partners highlight his role in advancing applied AI research.

• Computer Science
• Electrical Engineering
• Robotics
• +۷ مورد دیگر

Pengfei Li is a prolific researcher affiliated with multiple academic institutions, including Harbin Medical University, Yale University, Beihang University, Zhejiang University, and others. His work spans interdisciplinary domains such as machine learning, robotics, remote sensing, and biomedical engineering. Research interests focus on Machine learning and deep learning for industrial and medical applications Signal processing and sensor technologies Remote sensing and geospatial data analysis Robotic control systems and exoskeleton design Code search and software engineering optimization His recent publications highlight trends in FPGA-based real-time systems, multimodal machine learning, and AI-driven diagnostics. While awards and student advising details are absent in the provided data, his contributions to IEEE journals and conferences underscore his expertise in algorithm design and applied informatics.

Leonidas Guibasمشاهده پروفایل

استاد

• Computer Vision
• Computer Graphics
• Machine Learning
• +۵ مورد دیگر

Leonidas Guibas is the Paul Pigott Professor of Computer Science at Stanford University, leading the Geometric Computation group. He is a Hans Fischer Senior Fellow at the Technical University of Munich (TUM), associated with the Visual Computing Focus Group under Prof. Matthias Nießner. His research bridges computer vision, graphics, and machine learning, focusing on 3D data processing, geometric modeling, and sensor networks. Guibas holds prestigious awards including the Vannevar Bush Fellowship and ACM Allen Newell Award, and is a member of the US National Academy of Engineering and American Academy of Arts and Sciences. Education: Ph.D. from Stanford University under Donald Knuth, with prior appointments at Xerox PARC, MIT, and international institutions. His work emphasizes algorithms for sensing, reasoning, and acting in physical environments, with contributions to robotics, computational geometry, and discrete algorithms. Research interests span 3D vision (e.g., generative models for shape synthesis), deep learning architectures for spatiotemporal data, and multimodal sensor integration. Recent projects include object pose estimation, functional relationship learning in scenes, and deformation-aware 3D model retrieval. Publications highlight advancements in 3D reconstruction, point cloud analysis, and geometric deep learning. His TUM fellowship supported work on digital twins and visual computing, emphasizing high-fidelity environmental modeling. Awards also include IEEE and ACM Fellowships, underscoring his impact on theoretical and applied computer science.

Benjamin Brossمشاهده پروفایل

مدرس پاره‌وقت

• Video Coding
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Benjamin Bross is a part-time lecturer at HTW University of Applied Sciences Berlin and heads the Video Coding Systems group at Fraunhofer Heinrich Hertz Institute. He specializes in video coding standards, including HEVC (H.265) and VVC (H.266), contributing to their development and standardization. His work emphasizes open-source implementations like VVenC and VVdeC, deployed in broadcast and streaming systems. Education: Dipl.-Ing. in Electrical Engineering (RWTH Aachen University, 2008). Active in ITU-T VCEG and ISO/IEC MPEG since 2010, leading core experiments and editing key standards. Recognized with IEEE Best Paper (2013), SMPTE Merit (2014), and an Emmy (2017) for HEVC contributions. Research focuses on advanced compression techniques, machine learning integration, and real-time encoding. His team develops VVC tools for 4K/UHD, low-latency streaming, and adaptive bitrate systems. Recent work includes optimizing partitioning strategies and reducing encoding complexity in VVC implementations. Awards highlight his impact on video technology: IEEE Consumer Electronics Best Paper (2013), SMPTE Journal Certificate (2014), and an Emmy for HEVC (2017). Teaching emphasizes practical coding standards and their applications in multimedia systems.

• Wireless Communication
• Machine Learning
• Internet of Things (IoT)
• +۳ مورد دیگر

Aderemi Aaron-Anthony Atayero is an academic affiliated with the Department of Electrical Engineering and Information Engineering at Covenant University, Nigeria. His research focuses on telecommunications, wireless communication systems, IoT applications, and machine learning, with a strong emphasis on path loss modeling, signal processing, and smart city technologies. He has contributed to over 30 publications since 2005, collaborating extensively with researchers like Segun I. Popoola, Sanjay Misra, and Simon K. Hinga. His work spans theoretical and applied domains, including 5G mmWave models, botnet detection in IoT networks, and smart city infrastructure. Notable projects include the development of a low-intensity light imaging probe for medical applications and a cellular RSS dataset for roaming analysis. His research has practical implications for urban communication systems, healthcare technology, and cybersecurity.