جست‌وجوی استاد و پژوهشگر

• Database Systems
• Query Optimization
• Index Tuning
• +۷ مورد دیگر

Surajit Chaudhuri is a Researcher at Microsoft , with a career spanning decades in database systems and data management . He has received the prestigious SIGMOD Edgar F. Codd Innovations Award (2011) for his contributions to query optimization , index tuning , and data lakes . Research Interests : His work focuses on database tuning , approximate query processing , fuzzy similarity joins , automated data transformations , and machine learning integration for scalable data systems. Recent Publications : In 2025, his research includes Auto-Test for unsupervised error detection in tables, Esc for budget-aware index tuning, and MMTU for multi-task table understanding benchmarks. Earlier works in 2024–2023 address spreadsheet formula recommendation , low-overhead index filtering , and time-series pattern recognition . Scientific Impact : He has co-authored influential papers in SIGMOD , VLDB , and IEEE Transactions , shaping practices in cloud databases , query optimization , and self-service BI . His collaborations span institutions like Microsoft, MIT, and ETH Zurich.

Julia Schnabelمشاهده پروفایل

استاد

• Medical Image Processing
• Machine Learning
• Image Reconstruction
• +۴ مورد دیگر

Prof. Dr. Julia Schnabel is the TUM Liesel Beckmann Distinguished Professor and Helmholtz Distinguished Professor at TUM's TUM School of Computation, Information and Technology. Her research focuses on computational imaging and AI in medicine, including medical image processing, machine learning, motion modeling, and quantitative imaging. She holds IEEE, Ellis, and MICCAI Society fellowships, and has pioneered work in image reconstruction, artifact correction, and AI-based diagnostics. Educations: Bachelor/Master from TU Berlin (1993) PhD from University College London (1998) Postdocs at UMC Utrecht, King's College London, and UCL Her research interests span medical AI, deep learning for medical imaging, and clinical evaluation methodologies. Key contributions include frameworks for motion artifact correction in MRI, physics-informed neural networks, and benchmark datasets like NOVA for anomaly detection in brain MRI. She has authored over 100 publications, with recent work advancing unsupervised anomaly detection and federated learning in healthcare. Prof. Schnabel leads interdisciplinary projects at TUM and Helmholtz Zentrum München, focusing on AI-driven solutions for diagnostic and therapeutic challenges. Her labs develop tools for real-time cardiac imaging, histopathology segmentation, and trustworthy AI guidelines (FUTURE-AI initiative).

• Neural Network Dynamics
• Computational Neuroscience
• Theoretical Physics
• +۳ مورد دیگر

Prof. Raoul-Martin Memmesheimer is a Professor at the University of Bonn's Institute of Genetics , contributing to the Transdisciplinary Research Area (TRA) - Life and Health . His research focuses on understanding neural network dynamics across microscopic, mesoscopic, and large-scale phenomena, integrating mathematical approaches with neurophysiological insights. Key interests include the dynamics of precise spiking activity, collective network behavior, and computational principles underlying neural systems. Education & Background : While specific educational details are not listed, his position as a Professor indicates advanced academic qualifications in theoretical neuroscience or related fields. Research Interests : Memmesheimer's work bridges theoretical physics and computational neuroscience, addressing topics like spiking neuron models, learning in neural networks, and the emergence of complex behaviors. His group employs methods from computer science and applied mathematics to study how neural systems perform computations through their dynamic properties. Publications : Recent work explores topics such as gradient descent learning in spiking networks, STDP-based assembly dynamics, and oscillatory phenomena in hippocampal regions. These publications highlight his focus on both foundational theory and applications to biological systems. Awards & Collaborations : While no specific awards are listed, his participation in TRA Life and Health underscores collaborative efforts in transdisciplinary health-related research. His work is supported by the University of Bonn's strong focus on transdisciplinary innovation. Labs & Teams : His research group operates within the Institute of Genetics, leveraging interdisciplinary resources at the University of Bonn to advance theoretical neuroscience and computational biology.

Tim Schwartzمشاهده پروفایل

استاد پژوهشی

• Human-Robot Interaction
• Multi-modal Interaction
• Industry 4.0
• +۳ مورد دیگر

Dr. Tim Schwartz is an Associated Member at the German Research Center for Artificial Intelligence (DFKI) located at the Saarland Informatics Campus in Saarbrücken, Germany. He is affiliated with the Ubiquitous Media Technology Lab (UMTL) where he conducts research at the intersection of human-robot interaction, multimodal interfaces, and industrial applications. His work spans over two decades with significant contributions to the fields of robotics, augmented reality, and Industry 4.0 implementations. Dr. Schwartz's research interests focus primarily on Human-Robot Interaction , Multi-modal Interaction , and Industry 4.0 applications. His work explores how humans and robots can effectively collaborate in industrial settings, with particular attention to communication modalities, task division, and intuitive interfaces. Recent projects include human-robot collaboration in assembly cells, social cognitive robots for warehouse environments, and augmented reality applications for aircraft manufacturing. Analysis of his publication history reveals a clear trajectory from foundational work in multimodal interfaces and context-aware computing (2005-2015) toward increasingly applied research in industrial robotics and human-robot collaboration (2016-2024). His most recent publications demonstrate a strong emphasis on practical implementations in manufacturing and warehouse environments, with a particular focus on optimizing work dynamics between humans and robots. Dr. Schwartz actively engages in academic supervision, offering thesis opportunities such as the user-study on optimal work dynamics in human-robot collaboration at the Power4Production Hall in Saarbrücken. His collaborations extend to institutions including ZeMA (Zentrum für Mechatronik und Automatisierungstechnik gGmbH), indicating strong industry-academia partnerships. At DFKI, he works within a vibrant research community that includes numerous colleagues in the Ubiquitous Media Technology Lab, contributing to a collaborative environment focused on cutting-edge research in human-robot interaction, multimodal systems, and industrial applications of artificial intelligence.

• Wireless Communication
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

Iman Nematollahiمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Robot Learning
• Reinforcement Learning
• +۵ مورد دیگر

Iman Nematollahi is a PostDoc in Robot Learning at the University of Freiburg under Prof. Dr. Abhinav Valada, having completed his PhD under Prof. Dr. Wolfram Burgard. He holds a MSc in Embedded Systems from the University of Freiburg and a BSc in Electrical Engineering from Shahid Beheshti University. His research focuses on robot learning, world models, and reinforcement learning, particularly in enabling robots to understand physics through world models and adapt skills in unstructured environments. Education: PostDoc in Robot Learning, University of Freiburg (2025–Present) PhD in Robot Learning, University of Freiburg (2019–2024) MSc in Embedded Systems, University of Freiburg (2015–2018) BSc in Electrical Engineering, Shahid Beheshti University (2010–2015) Research Interests: Robot manipulation, world models, reinforcement learning, computer vision, and self-supervised learning. His work emphasizes intuitive physics understanding, skill generalization, and sample-efficient policy improvement. Key Articles: Recent work includes LUMOS (language-conditioned imitation learning), Bayesian optimization for policy refinement, and 3D video prediction (T3VIP). These contributions bridge theory and real-world robotic applications, emphasizing long-horizon tasks and cross-environment adaptation. Awards & Grants: No explicit awards mentioned. His research has been supported through projects like OML (Organic Machine Learning). Teaching: Taught Deep Learning Lab (2020–2022) and Introduction to Mobile Robotics (2019).

• Indoor Localization
• Wireless Networks
• Pervasive Computing
• +۱۱ مورد دیگر

Shueng-Han Gary Chan is a faculty member in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), within the College of Engineering. He is actively engaged in research and mentoring, with a strong publication record in mobile computing, indoor localization, and AI for pervasive systems. His research focuses on indoor localization using Wi-Fi, geomagnetic, and inertial signals , sensor fusion , crowd counting with deep learning , domain adaptation , and efficient mobile AI systems . His work bridges theoretical innovation with real-world deployment, as seen in systems for missing person search and indoor navigation. Recent publications (2023–2025) show a consistent trend toward self-supervised and domain-agnostic learning , efficient model design for mobile devices , and robust signal fusion in noisy environments . His team leverages transformer architectures, graph neural networks, and novel optimization techniques to solve real-world challenges in urban and indoor spaces. He has advised numerous graduate students, including Jierun Chen, Zhuoxuan Peng, and Tianlang He, who have contributed as first authors to joint publications. His collaborations span institutions and include work on large-scale system deployments and mobile AI. He leads a research group focused on mobile and pervasive computing , with projects involving IoT-based contact tracing, indoor navigation (e.g., DeepNavi, SiFu), and real-time localization systems. The team emphasizes practical deployment and system robustness.

• Robotics
• Human-Robot Interaction (HRI)
• Artificial Intelligence
• +۳ مورد دیگر

Marc Hanheide is a Professor of Intelligent Robotics and Interactive Systems at the University of Lincoln 's School of Computer Science. With a career spanning EU projects like VAMPIRE, COGNIRON, CogX, and STRANDS, his work focuses on long-term robotic behavior, human-robot spatial interaction, and cognitive system architectures. He has secured over 12 major grants from organizations including EPSRC, BBSRC, and the European Commission. Key Research Areas : Autonomous robotics, HRI, AI, cognitive systems, agricultural robotics Current Projects : STRANDS (long-term behavior), AgriFoRwArdS (robotics training), NCNR (nuclear robotics) Major Contributions : Human-aware navigation modules, topology optimization for robot fleets, causal analysis frameworks Scientific Awards: While no specific awards are listed, his numerous EPSRC grants and leadership in multi-institutional projects highlight his impact. He has over 172 publications and collaborates with institutions like CoR-Lab and CITEC.

Hannah Spitzerمشاهده پروفایل

استادیار

• Computational Biology
• Machine Learning
• Spatial Omics
• +۴ مورد دیگر

Hannah Spitzer is a Research Group Leader at the Institute for Stroke and Dementia Research (ISD) at Ludwig Maximilian University of Munich and an associated Research Group Leader at Helmholtz Munich's Computational Health Center. She leads the Spitzer Lab, focusing on computational analysis of multimodal brain datasets to advance understanding of neurovascular and neurodegenerative diseases. Her educational background includes: PhD in Computer Science from Heinrich-Heine University Düsseldorf and Research Center Jülich (2015-2020) Master's in Computer Science from RWTH Aachen (2013-2015) Bachelor's in Computer Science from RWTH Aachen (2009-2013) Dr. Spitzer's research integrates computational biology and machine learning to decode brain complexity, with emphasis on spatial omics analysis , interpretable image representation learning , and cross-modal data integration . Her group develops tools like squidpy and campa for spatial omics while applying graph neural networks to epilepsy lesion detection through the international MELD project, prioritizing biological interpretability in AI models. Recent publications reveal strong trends in leveraging graph neural networks for subtle brain lesion detection and creating computational frameworks for spatial omics integration. Her work consistently bridges advanced machine learning with clinical neuroscience to uncover disease mechanisms in neurodegeneration and vascular disorders. Dr. Spitzer actively mentors students including current PhD candidate Beatrice Guastella and alumni Deniz Fettahoglu (MSc) and Katia Berr (PhD). Her lab operates through major collaborations including the MELD epilepsy consortium and Helmholtz Imaging Project, with funding supporting computational pipeline development for small-vessel disease prediction and multimodal brain atlasing. The Spitzer Lab comprises postdoc Wasim Aftab and PhD student Beatrice Guastella, working on computational pipelines that integrate histology, spatial omics, and neuroimaging data to decode brain disease mechanisms through interpretable AI approaches.

Holger Schwarzمشاهده پروفایل

دانشیار

• Data Management
• Database Systems
• Data Lakes and Lakehouses
• +۶ مورد دیگر

Holger Schwarz is an Associate Professor (Apl. Professor) at the Institute for Parallel and Distributed Systems (IPVS) within the Faculty of Computer Science, Electrical Engineering and Information Technology at the University of Stuttgart. He serves as Head of the Infrastructure Department and is actively involved in research and teaching in the areas of data management, database systems, and data analytics. Professor Schwarz earned his doctorate (Dr. rer. nat.) from the University of Stuttgart in 2003 with a dissertation on "Integration of Data Mining and Online Analytical Processing." He later completed his habilitation (Dr. rer. nat. habil.), qualifying him as a university professor in Germany. His primary research interests focus on data management systems , particularly in the domains of data lakes, lakehouses, enterprise data platforms, and metadata management. Professor Schwarz investigates how to design efficient and scalable data architectures that support modern analytical workloads while addressing challenges in data integration, governance, and democratization. His work bridges theoretical database concepts with practical industrial applications, as evidenced by numerous collaborations with industry partners. Professor Schwarz's recent publications demonstrate a clear trend toward enterprise data management solutions, particularly focusing on data lakehouse architectures, enterprise data marketplaces, and advanced clustering techniques. His research shows a consistent pattern of addressing real-world data management challenges through innovative architectural patterns and algorithmic improvements, with strong emphasis on practical industrial implementation. Professor Schwarz supervises numerous research projects including MetaMan (metadata management in complex data landscapes), DLArchitecture (design of comprehensive data lake architecture), INTERACT (interactive rapid analytic concepts), and VALID-Partition (improving prediction quality using domain knowledge). He also coordinates the University of Stuttgart's projects within the Software Campus initiative and serves as Managing Director of the Technology Partnership Lab and as a Member of the Board of Directors of the Industrial Data Lab. His teaching portfolio includes courses on Advanced Information Management, Database Systems, and Data Science projects across multiple semesters, demonstrating his commitment to educating the next generation of data management professionals.

Yuhong Nanمشاهده پروفایل

دانشیار

• Software Security
• Privacy Leakage Analysis
• IoT Security
• +۳ مورد دیگر

Yuhong Nan is an Associate Professor in the School of Software Engineering at Sun Yat-sen University, China, specializing in software security and privacy leakage analysis for emerging platforms including IoT, mobile systems, and blockchain. Previously a Post-doctoral Research Associate at Purdue University under Prof. Dongyan Xu, she builds practical security tools to detect and mitigate vulnerabilities in real-world systems. Dr. Nan earned her PhD from Fudan University in 2018 supervised by Prof. Min Yang. Her academic journey spans rigorous research in security engineering with emphasis on empirical validation and tool development for complex platform ecosystems. Her research program focuses on uncovering systemic security flaws through innovative analysis techniques. Key contributions include vulnerability detection in smart contracts (e.g., state dependencies, reentrancy), privacy leakage analysis in mobile/IoT ecosystems, and countermeasures against deceptive UI patterns. She employs hybrid approaches combining static/dynamic analysis, machine learning, and large-scale empirical studies to develop deployable security solutions. Analysis of her 15 most recent publications (2023-2025) reveals dominant themes in blockchain security (60%), particularly smart contract/DApp vulnerabilities, with significant work in mobile privacy (30%) and cross-platform threats (10%). Her methodology consistently leverages fine-grained static analysis, semantic enrichment, and feedback-driven fuzzing, yielding tools like SmartAxe and Midas that have influenced industry practices. Dr. Nan actively mentors graduate researchers with 17 advisees including Tencent-employed graduates, and serves as a trusted reviewer for premier journals (IEEE TDSC, TMC, TOPS) and conference committees (ASIACCS, ICICS). Her leadership in security communities bridges academic research with practical defense mechanisms. At Sun Yat-sen University, she directs a high-output research group that collaborates with industry partners to address evolving threats in decentralized systems, maintaining her position among top publishing authors in USENIX Security, CCS, and NDSS venues through rigorous technical innovation.

Christian Winklerمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• User Experience
• +۴ مورد دیگر

Christian Winkler is Professor for AI-based UX optimization and general business administration at Nuremberg Institute of Technology since 2022. With over 25 years of experience spanning entrepreneurship, enterprise architecture, and academia, he brings substantial industry expertise to his academic role. His career includes founding multiple technology companies including an internet service provider (WWL Internet AG) that went public in 1999, Querplex GmbH through management buyout in 2003, and datanizing GmbH as an NLP SaaS provider in 2017. Professor Winkler's research focuses on practical applications of artificial intelligence in business contexts, with particular expertise in natural language processing, user experience optimization, and data-driven marketing strategies. His work bridges theoretical AI research with real-world business applications, emphasizing accessibility of complex technologies. He has published extensively on language models (including BERT and LLaMA implementations), text analysis techniques, and social media data analysis for business insights. Recent publications reveal a strong trend toward optimizing large language models for practical deployment, with significant focus on analyzing user-generated content from social platforms like Instagram and WallStreetBets. His work demonstrates how NLP can extract valuable business intelligence from unstructured data sources while making advanced AI techniques accessible to non-technical business professionals. Winkler teaches E-Commerce, International Marketing Tools - Quantitative Methods, Applied User Experience, and Communication Management, reflecting his interdisciplinary approach that combines technical AI knowledge with business administration expertise. He is an active contributor to the data science community through conference presentations at events like m3 Konferenz, MLsummit, and data2day, as well as educational content for Heise Academy on Python and NLP topics.

Ayush Tewariمشاهده پروفایل

استادیار

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +۳ مورد دیگر

Ayush Tewari is an Assistant Professor at the University of Cambridge. Previously, he was a postdoctoral researcher at MIT CSAIL under Bill Freeman, Josh Tenenbaum, and Vincent Sitzmann, and completed his Ph.D. at the Max Planck Institute for Informatics under Christian Theobalt. His research focuses on visual perception, developing methods to infer 3D structured representations from images and videos, aiming to bridge the gap between human perceptual capabilities and machine learning systems. Key research interests include neural rendering, inverse rendering, 3D reconstruction, and generative models. Notable contributions include advancements in Neural Radiance Fields (NeRF), diffusion models for inverse problems, and human-centric perception studies. His work has been published in top venues such as SIGGRAPH, CVPR, ICCV, and NeurIPS. Recent research trends emphasize ambiguity-aware inverse rendering, stochastic inverse problem solving using diffusion models, and integrating forward models for 3D scene inference. His work on Diffusion with Forward Models (NeurIPS 2023) proposes a novel framework for solving inverse problems without direct supervision. Awards: Best Paper Honorable Mention at BMVC 2022 (VoRF: Volumetric Relightable Faces). Labs/Projects: Core contributor to the DFM (Diffusion with Forward Models) project, advancing 3D scene understanding via probabilistic methods.

Marcel Kolloviehمشاهده پروفایل

پژوهشگر

• Generative models
• Graphs
• Time series
• +۴ مورد دیگر

Marcel Kollovieh is a researcher at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science . He is part of the Data Analytics and Machine Learning (DAML) Lab under the mentorship of Prof. Dr. Stephan Günnemann. Education B.Sc. and M.Sc. in Informatics from TUM Research Interests : Marcel focuses on generative models (including variational autoencoders, diffusion models, and score-based models), graphs , and time series , with additional work on hierarchical structures , robustness , and Bayesian learning . His publications span conferences like ICML , ICLR , and NeurIPS , alongside journals such as TMLR . Themes include token merging , flow matching , and adversarial robustness in temporal data, alongside probabilistic clustering and diffusion models . Lab and Team : Marcel works within the DAML Lab at TUM.

Bashir Kazimiمشاهده پروفایل

پژوهشگر

• Deep Learning
• Computer Vision
• Materials Science
• +۳ مورد دیگر

Dr.-Ing. Bashir Kazimi is a group leader at the Materials Data Science and Informatics (IAS-9) department within the Institute for Advanced Simulation at Forschungszentrum Jülich. His work focuses on advancing deep learning and computer vision techniques for electron microscopy data analysis, enabling efficient material characterization. Expertise: Deep Learning, Computer Vision, Image Analysis Collaboration: Works closely with the Ernst-Ruska-Center (ER-C) for electron microscopy expertise Research Interests: Bashir develops and applies deep learning methods for tasks such as denoising, super-resolution, semantic segmentation, and tracking in electron microscopy. His applications span nanomaterial characterization, crystallographic defect identification, and orientation mapping. Scientific Trends: His recent publications highlight advancements in self-supervised learning, semantic segmentation of TEM images, and applications of deep learning to both materials science and archaeological monument detection in geospatial data. Scientific Achievement: Admitted to the Young Excellent Scientist Program (YESP) in 2024, supporting leadership development and scientific visibility Advising: Supervises Shrindhi Bhat , a PhD student in his group. He is involved in projects like FAST-EMI (Deep-learning assisted fast in situ 4D electron microscope imaging), with a focus on enhancing materials analysis through AI.