جست‌وجوی استاد و پژوهشگر

• Geometry
• Mathematical Physics
• Complex Systems
• +۴ مورد دیگر

Jürgen Jost is an Honorary Professor in the Department of Mathematics at the University of Leipzig and a retired Director at the Max Planck Institute for Mathematics in the Sciences. He is a member of multiple prestigious academies including the Leopoldina and the Santa Fe Institute for the Sciences of Complexity. His research focuses on geometric analysis, complex systems, network science, and applications in chemistry and neuroscience. Key areas include Ricci curvature in networks, Dirac-harmonic maps, and the evolution of chemical knowledge systems. Jost has received major awards such as the Leibniz Prize (1993) and an ERC Advanced Grant (2010). His work bridges pure mathematics with interdisciplinary applications, including topological data analysis and mathematical biology. He leads the Max Planck School of Cognition and has contributed to foundational studies on minimal surfaces, geometric flows, and information geometry.

• Stochastic partial differential equations (SPDEs)
• Nonlinear partial differential equations
• Stochastic dynamics
• +۴ مورد دیگر

Benjamin Gess is a Professor of Mathematics at the Technische Universität Berlin (since 2024) and a Research Group Leader at the Max Planck Institute for Mathematics in the Sciences (MIS) in Leipzig. His primary research focuses on stochastic partial differential equations (SPDEs), nonlinear PDEs, stochastic dynamics, and their applications in machine learning, fluid dynamics, and interacting particle systems. He holds a PhD from the University of Bielefeld (2011) and has held academic positions at institutions including the University of Chicago, TU Berlin, and Bielefeld University. Gess leads the ERC Consolidator Grant “Fluco” (since 2023), exploring fluctuations in conservative SPDEs and their continuum models. Key educational milestones include studies at the University of Bonn (2004–2007) and the University of Warwick (2007–2008), where he earned an MSc in Mathematics with distinction. His research emphasizes regularity theory, stochastic processes, and large deviations, with contributions to regularization by noise and synchronization phenomena in SPDEs. Recent work includes stochastic kinetic models of chemotaxis, conservative SPDEs as limits of SGD, and large deviations in porous medium equations. His articles bridge theoretical analysis with applications, such as machine learning dynamics and fluid mechanics. Gess is an associate editor for journals like Probability Theory and Related Fields and Stochastic Processes and their Applications . His ERC-funded research investigates universal fluctuating models in conservative systems, aiming to uncover universal behaviors in complex stochastic systems. Teaching focuses on stochastic analysis, machine learning mathematics, and advanced PDE theory.

Sathiya Venkatesan Rameshمشاهده پروفایل

عضو هیئت علمی

• Probabilistic Program Verification
• Termination Analysis
• Formal Methods

Sathiya Venkatesan Ramesh is a doctoral student at the Max Planck Institute for Software Systems, researching probabilistic program verification under Professor Rupak Majumdar. His work establishes sound and complete proof rules for probabilistic termination and reachability. He develops formal methods to certify termination properties in probabilistic systems, with applications in randomized algorithms and safety-critical systems.

• Computer Science
• Bioinformatics
• Remote Sensing
• +۷ مورد دیگر

Ying Liang is a multidisciplinary researcher with significant contributions across computer science, bioinformatics, and remote sensing. Their work spans AI-driven medical diagnostics, environmental monitoring, and computational biology, with recent focus on precision nutrition, skin lesion recognition, and urban heat island analysis. Research Interests : Machine learning for biomedical applications (e.g., skin cancer detection, pancreatic segmentation), remote sensing (landslide mapping, urban structure dynamics), and computational biology (RNA-protein interactions, microRNA regulation). Notable Article Trends : 2025 publications highlight self-supervised remote sensing classification, fractal reservoir modeling, and biomedical AI. 2024 works emphasize cross-domain gait recognition, panoptic segmentation, and health informatics via Wi-Fi sensing. Grants & Collaborations : Co-authored studies with institutions like Open University and Chinese universities, focusing on thermal cycling reliability, multi-omics data fusion, and wireless health monitoring. Labs & Teams : Collaborated on interdisciplinary projects involving neural network architectures, hybrid AI models for environmental and biomedical applications, and biomedical signal processing systems.

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +۷ مورد دیگر

Kai Wang is a researcher affiliated with Nankai University's Institute of Machine Intelligence under the College of Computer and Control Engineering. His work spans interdisciplinary domains including artificial intelligence, machine learning, and engineering, with a focus on optimization algorithms, neural networks, and biomedical applications. Key research areas: AI, machine learning, biomedical imaging, robotics, environmental science, and optimization. Recent publications address UAV surveillance, medical diagnostics, and tensor-based computational methods. Collaborations include institutions across China, France, Japan, and the USA. His 2025–2026 publications highlight advancements in diffusion models, transformer architectures, and IoT-driven solutions for healthcare and infrastructure. No scientific awards or student advisories are explicitly mentioned in the provided records.

• Biomedical Signal Processing
• Machine Learning
• Image Processing
• +۴ مورد دیگر

Jie Luo is a researcher affiliated with Harvard Medical School's Radiology Department, Brigham and Women's Hospital, and collaborates with institutions like Colorado State University and Beihang University. Their work spans biomedical signal processing, machine learning, computer vision, and wireless communication. Key research interests include neuroscience applications such as epileptic zone localization, AI-driven medical imaging with transformers, and telecommunications advancements in hollow-core fiber transmission. Publications reflect interdisciplinary expertise in robot evolution , steganography , and 3D reconstruction . Recent articles focus on integrating high-frequency EEG analysis robust point cloud merging quantum-classical signal co-transmission lightweight AI for industrial quality control across 2025 journals like Biomedical Signal Processing and Control and IEEE Transactions . No awards or student advising data are currently available.

Anne Sentenacمشاهده پروفایل

استاد

• Electromagnetism
• Optics
• Nanophotonics
• +۲ مورد دیگر

Anne Sentenac is Professor at Institut Fresnel (CNRS/Aix-Marseille University) specializing in electromagnetic theory, optical component design, and high-resolution imaging. Her research develops innovative approaches to light scattering, resonant gratings, and diffraction tomography. Research advances include super-resolution microscopy techniques surpassing classical resolution limits and computational methods for complex electromagnetic environments. International collaborations span European and US institutions.

Thomas Schierlمشاهده پروفایل

پژوهشگر

• Video Coding
• Wireless Communication Protocols
• Multimedia Systems
• +۳ مورد دیگر

Dr. Thomas Schierl is the Head of the Video Communication and Applications Department at Fraunhofer Heinrich Hertz Institute (HHI) in Berlin. Since 2010, he has led research groups in multimedia communications and video coding, co-developing key video coding standards such as H.264 SVC and HEVC. He currently heads the Video Coding & Analytics department since 2015, focusing on video compression, wireless transmission, and standardization. Education: Diplom-Ingenieur (Computer Engineering) from Berlin University of Technology, 2003 Dr.-Ing. in Electrical Engineering and Computer Science, Berlin University of Technology, 2010 Research interests span video over wireless networks, system integration of video codecs, and cellular network protocols. He contributed to MPEG-2 Transport Stream standards and co-authored IETF RFCs for video payload formats. In 2014, he received the Emmy Award for MPEG-2 Transport Stream development. Active in standardization bodies: JCT-VC, MPEG, IETF, 3GPP, and DVB. His work includes high-level syntax for HEVC parallelism and V2X resource pooling for 5G NR. Labs/Teams: Leads the Video Coding & Analytics team at HHI, specializing in cutting-edge video compression and communication technologies.

• Networking
• Communications
• Cybersecurity
• +۵ مورد دیگر

Martin Reisslein is a Professor at Arizona State University's School of Electrical, Computer and Energy Engineering. His research focuses on advanced networking technologies, cybersecurity, and edge computing, with a specialization in Time-Sensitive Networking (TSN), 5G, and network coding. He collaborates closely with institutions like the National Research Data Infrastructure (NFDI) and has published extensively in top-tier journals and conferences. His work spans theoretical and applied domains, including hardware-software co-design for low-latency systems, cybersecurity for satellite and IoT networks, and AI-driven resource management in transportation networks. Recent projects highlight contributions to TSN testbeds, molecular communication simulations, and unified threat management systems for home networks. Reisslein has co-authored over 270 publications, with notable contributions in IEEE Transactions on Networking, Communications Surveys & Tutorials, and the Journal of Signal Processing Systems. His research emphasizes practical implementations, such as hardware-aligned TSN simulations and flexible measurement frameworks for edge computing.

• Computer Science
• Artificial Intelligence
• Cybersecurity
• +۵ مورد دیگر

Kaushik Roy is an academic researcher affiliated with multiple institutions including North Carolina State University, Purdue University, and North Carolina A&T State University. His primary focus is on computer science, cybersecurity, and AI-driven solutions for real-world challenges. He has extensive experience in machine learning applications for intrusion detection, deepfake detection, biometrics, and federated learning frameworks. His work often bridges theoretical advancements with practical implementations in domains like IoT security, healthcare analytics, and cyber-physical systems. Affiliations: North Carolina State University, Purdue University, North Carolina A&T State University, and others Research Interests: Cybersecurity, Deep Learning, Biometric Authentication, Federated Learning, and AI Ethics He has co-authored over 105 publications, focusing on topics like anomaly-based intrusion detection, malware traffic classification, and privacy-preserving techniques. His recent work emphasizes federated learning architectures for healthcare and transportation IoT systems. Roy collaborates with industry partners to translate research into deployable solutions, particularly in edge computing and sensor networks.

• IoT and Sensor Networks
• Quantum Computing
• RFID Systems
• +۴ مورد دیگر

Bartolomeo Montrucchio is a full Professor at the Polytechnic University of Turin's Department of Electrical, Computer and Biomedical Engineering. His research focuses on IoT systems, quantum computing applications, RFID technologies, and sensor networks. He leads interdisciplinary projects involving environmental monitoring, cybersecurity, and embedded systems design. Key research areas include: Development of low-cost air quality monitoring systems leveraging IoT and sensor fusion Quantum algorithm design for financial risk analysis and game theory applications RFID security protocols and anti-collision mechanisms for dense networks Energy-efficient image processing techniques for mobile displays Recent work highlights quantum computing's potential in vulnerability analysis and financial modeling, while maintaining strong contributions to sensor network reliability and environmental applications. His publications span prestigious journals like IEEE Internet of Things Journal and IEEE Transactions on Dependable and Secure Computing. Collaborations with industry partners and academic institutions emphasize practical implementation of theoretical advancements, particularly in smart city technologies and healthcare monitoring systems.

Maarten Buisمشاهده پروفایل

مدرس

• Social Stratification
• Educational Attainment
• Statistics
• +۳ مورد دیگر

Maarten Buis is a Lecturer of Statistics for the Social Sciences at the Department of Sociology, University of Konstanz. Previously, he held positions such as Senior Research Fellow at the WZB Berlin Social Science Center and Research Fellow roles at Eberhard Karls Universität Tübingen and VU University Amsterdam. He earned his PhD from VU University Amsterdam in 2010 with a thesis on educational inequality in the Netherlands. Education: PhD in Social Science (2010), VU University Amsterdam Master's in Social and Institutional Economics (2003), University of Utrecht His research focuses on social stratification, educational attainment, statistical methodologies, and the application of Stata software. He has contributed significantly to statistical techniques in non-linear models and has developed several Stata packages. His publications span peer-reviewed articles in journals like European Sociological Review and The Stata Journal , as well as edited volumes. He frequently presents at international conferences and Stata Users' Group meetings, focusing on topics like agent-based models and statistical software best practices. Labs/Teams: Former member of the Research Department 'Skill Formation and Labor Markets' at WZB Berlin Grants/Advising: No formal student advising listed, but extensive collaborative research engagements

Chinmoy Bhattacharjeeمشاهده پروفایل

استاد

• Stochastic Geometry
• Distributional Approximations
• Stein's Method
• +۴ مورد دیگر

Prof. Chinmoy Bhattacharjee is a Professor of Mathematical Stochastics at the University of Hamburg's Department of Mathematics, part of the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on stochastic geometry, distributional approximations via Stein's method, stochastic analysis, random graphs, continuum percolation, and nonparametric inference. He holds a PhD from the University of Southern California and previously held postdoctoral positions at the University of Luxembourg and University of Bern. His work is supported by the German Research Foundation (DFG) through Project No. 531540467. Education: PhD in Applied Mathematics, University of Southern California (2013–2018) M.Stat. and B.Stat. (Hons.), Indian Statistical Institute, Kolkata (2008–2013) Teaching: Recent courses include Stochastic Processes, Probability and Statistics, and seminars on percolation theory and probabilistic methods. Research Highlights: Prof. Bhattacharjee's work bridges stochastic geometry and Stein's method, with applications in spatial random structures, non-Gaussian limit theory, and noise sensitivity in percolation models. Key contributions include Gaussian and Dickman approximation techniques, spectral analysis of Poisson functionals, and statistical applications in random forests. Upcoming Events: Co-organizing session on Spatial Stochastics at German Probability and Statistics Days (2025) Invited talks at Aarhus University and workshops in Germany/Croatia (2025)

Wolfgang Mulzerمشاهده پروفایل

استاد

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +۳ مورد دیگر

Wolfgang Mulzer is a Professor in the Department of Computer Science at Freie Universität Berlin, within the Faculty of Mathematics and Computer Science. He leads the Theoretical Computer Science research group (AG Theoretische Informatik) and holds a PhD from Princeton University (2010), advised by Bernard Chazelle. His research focuses on computational geometry, algorithms, and discrete mathematics, with notable contributions to geometric algorithms, data structures, and combinatorial optimization. Education: PhD in Computer Science, Princeton University (2010) Advisor: Bernard Chazelle Research Interests: Mulzer's work spans theoretical computer science with an emphasis on computational geometry. Key areas include geometric algorithms (e.g., Voronoi diagrams, Fréchet distance), dynamic graph algorithms (e.g., disk graphs, connectivity problems), and algorithmic complexity. His research also addresses discrete geometry challenges like Tverberg theorems and geometric intersection graphs. Grants & Labs: Leading the Theoretical Computer Science group at FU Berlin Active in collaborative projects on geometric algorithms and algorithmic foundations Professional Activities: Mulzer has authored or co-authored over 100 peer-reviewed papers, with work appearing in top venues like Discrete & Computational Geometry , SIAM Journal on Computing , and Algorithmica . His research bridges theoretical foundations with practical algorithm design.

Ullrich Kötheمشاهده پروفایل

دانشیار

• Machine Learning
• Neural Networks
• Explainable AI
• +۶ مورد دیگر

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .