جست‌وجوی استاد و پژوهشگر

Marius Lindauerمشاهده پروفایل

استاد

• AutoML
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Marius Lindauer is a Professor of Machine Learning at the Department of Artificial Intelligence , Leibniz University Hannover , and Deputy Head of the Institute since 2025. Previously, he served as Spokesperson of Computer Science Professors (2023-2025) and Head of the Institute (2022-2024). PhD (Dr. rer. nat, 2010-2015), Master (2008-2010), and Bachelor (2005-2008) in Computer Science from University of Potsdam His research focuses on democratizing AI through AutoML innovations, including: Green AutoML for sustainable deep learning Human-Centered AutoML for user-centric optimization Dynamic Algorithm Configuration in reinforcement learning Generalization techniques for production and health applications Recent publications show strong multi-objective optimization trends across medical imaging , protein design , and time series forecasting , with 15+ papers in 2024-2025 at venues like NeurIPS, AAAI, and IEEE TPAMI. Key scientific awards : ERC Starting Grant (2022), NeurIPS BBO-Challenge winner (2020), multiple AutoML/ML competition victories Advisory role in 140+ publications and leadership of LUHAI Institute

• Machine Learning
• Deep Learning
• Cognitive AI
• +۱۱ مورد دیگر

Volker Tresp is a Professor at the Ludwig-Maximilians-Universität München (LMU) and a leading researcher in machine learning for relational structured domains . His work bridges cognitive AI , knowledge graphs , and quantum computing . He is a PI in the Munich Center for Machine Learning (MCML) and co-director of the ELLIS program on Semantic, Symbolic, and Interpretable Machine Learning . His research interests focus on temporal knowledge graphs , foundation models , multimodal learning , and quantum machine learning . Recent projects include WebPilot (multi-agent web task execution) and FedBiP (federated learning with diffusion models). His work on PyKEEN and RESCAL has advanced knowledge graph embeddings . Volker Tresp's scientific contributions are recognized through ELLIS Fellowship (2020) , Siemens Inventor of the Year (1996) , and Best Paper Awards at ISWC 2021 and IEEE ICHI 2020 . His students have published extensively at top AI venues like AAAI , CVPR , and ECCV . Awards and Honors: ELLIS Fellow (2020) Siemens Inventor of the Year (1996) Best Paper Award, ISWC 2021 Student Best Paper Award, ISWC 2017 Best Paper Runner-up, PKDD 2005

Jörn Heesمشاهده پروفایل

استاد

• Deep Learning
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Jörn Hees is a Professor at the German Research Center for Artificial Intelligence (DFKI), affiliated with the Smart Data & Knowledge Services department. His work bridges deep learning, linked data, and knowledge graphs, with projects like TreeSatAI (AI for environmental monitoring) and DeFuseNN (deep network fusion). Research Interests : Deep learning, machine learning, data mining, knowledge graphs, semantic web, linked data, and association modeling. Projects : TreeSatAI (remote sensing AI), MInD (machine intelligence for digital transformation), DeFuseNN (neural network fusion), MOM (multimedia opinion mining). Recent publications focus on outlier detection for tabular data (Fin-Fed-OD, RECol), super-resolution techniques, and transformer-based models. He actively develops open-source tools like the RDFLib Python library and Graph Pattern Learner for SPARQL query generation. No scientific awards are explicitly mentioned in the available data.

• Data Management
• Database Systems
• Information Retrieval
• +۵ مورد دیگر

Slava Novgorodov is an Associate Professor in the Department of Computer Science at Tel Aviv University's Faculty of Engineering. His research focuses on data management systems with applications in e-commerce, information retrieval, and machine learning. He leads research projects at the intersection of database systems and artificial intelligence, particularly exploring how large language models can enhance traditional data management tasks. Novgorodov's research interests center around data management, database systems, information retrieval, and e-commerce applications. His work addresses practical challenges in data cleaning, category tree construction, and product information extraction. Recent research has increasingly focused on leveraging large language models for cost-effective data processing, with applications in visual search, review analysis, and personalized recommendation systems. His approach combines theoretical algorithm design with practical system implementation, often addressing resource constraints in real-world applications. His publication record shows a clear progression from foundational data management research to increasingly sophisticated applications of machine learning and LLMs. Early work focused on data cleaning, fraud detection, and collaborative filtering, while more recent publications demonstrate a strategic shift toward LLM applications in e-commerce contexts. This evolution reflects broader trends in the field while maintaining his core expertise in database systems and information management. Novgorodov has established productive collaborations with researchers including Tova Milo, Ido Guy, and Kira Radinsky, resulting in numerous publications at premier venues like SIGMOD, VLDB, and WWW. His research has practical applications in e-commerce systems, particularly in product information management, review analysis, and visual search capabilities.

• Database Systems
• Data Management
• Data Visualization
• +۳ مورد دیگر

Eugene Wu is a Professor affiliated with Columbia University, specializing in database systems, data management, and visualization. His work bridges theoretical foundations with practical applications, focusing on systems that empower users through interactive analysis and human-in-the-loop processes. Key contributions include Smoke (lineage tracking), AlphaClean (automated data cleaning), and PI2 (interactive visualization generation). He has also explored privacy-preserving data systems (Saibot) and leveraging large language models (LLMs) for tasks like table profiling (Cocoon) and prompt development. Education: PhD from MIT in Computer Science Research spans database theory, visualization frameworks, and machine learning integration. Recent work emphasizes data quality (SPADE), privacy in distributed markets, and scalable training data debugging. He actively contributes to conferences like SIGMOD, CIDR, and VLDB, often chairing workshops on human-in-the-loop analytics. Articles focus on system design, theoretical advancements, and LLM applications in data management. Awards and recognition include sustained innovation in data systems and visualization. Advises on projects blending databases with emerging technologies like LLMs and decentralized data markets.

• Cloud Computing
• Distributed Systems
• Cybersecurity
• +۳ مورد دیگر

Ramin Yahyapour is a researcher affiliated with the University of Göttingen and GWDG (Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen). His work focuses on cloud computing, distributed systems, cybersecurity, and high-performance computing. Key research areas include virtual machine migration optimization, data center energy efficiency, federated identity management, and parallel computing frameworks. He has collaborated extensively with institutions like the University of Göttingen and researchers such as Philipp Wieder, Faraz Fatemi Moghaddam, and Andrei Tchernykh. Research Interests: Cloud resource optimization Cybersecurity for distributed systems Workflow management in data-intensive environments Network function virtualization Machine learning for system scheduling Identity and access management Publications span topics like VM placement algorithms, data center power reduction, and federated identity systems, demonstrating expertise in both theoretical and applied computer science. His work often bridges academia and industry, addressing real-world challenges in cloud infrastructure and digital systems.

Varish Mulwadمشاهده پروفایل

پژوهشگر

• Information Extraction
• Knowledge Graph Population
• Semantic Web
• +۴ مورد دیگر

Varish Mulwad is a Senior Scientist at GE Research with 14 years of experience in algorithm development and knowledge graph construction from structured/unstructured data. He holds a Ph.D. and M.S. in Computer Science from the University of Maryland, Baltimore County (UMBC), where he worked under Prof. Tim Finin and collaborated with Prof. Anupam Joshi, and a B.E. in Computer Engineering from University of Mumbai. Ph.D. & M.S. in Computer Science (UMBC) B.E. in Computer Engineering (University of Mumbai) His research focuses on semantic interpretation of tabular data through linked data frameworks, information extraction from unstructured text, and knowledge graph population using probabilistic reasoning and graphical models. He has pioneered domain-independent systems for table interpretation and developed novel methods for cloud SLA automation and cybersecurity threat detection via social media analysis. Recent work trends include: Context-aware web table annotation Relational table representation learning Linked data generation from spreadsheets Pre-training/fine-tuning paradigms for web tables Application of semantic web standards to cybersecurity He has led 3-4 member project teams in developing production-ready solutions, contributed to 19 peer-reviewed publications, and secured 7 patents with 900+ citations. During his academic tenure at UMBC's Ebiquity Research Lab, he co-developed the TABEL framework for table semantics inference and produced the first interactive system for meta-analysis report generation from linked data.

Markus Wolfienمشاهده پروفایل

پژوهشگر

• RNA-Seq Data Analysis
• Workflow Development
• Omics Data Integration
• +۳ مورد دیگر

Dr. Markus Wolfien is a Researcher in Systems Biology and Bioinformatics at the University of Rostock's Faculty of Computer Science and Electrical Engineering. His research develops computational workflows for analyzing biological data, particularly RNA-Seq datasets. Research focuses on integrating omics data through customizable analysis pipelines using tools like Galaxy, Conda, and Docker. Key applications include: Stem cell research and cardiac regeneration Machine learning for biomedical data Workflow optimization for NGS data Epigenomic analysis in cancer Recent publications demonstrate cross-disciplinary approaches combining bioinformatics, machine learning, and systems biology. Current projects include developing deep learning methods for sequencing data and creating accessible tools for cancer genomics research.

Ingo Steinwartمشاهده پروفایل

استاد

• Statistical Learning Theory
• Kernel-based learning algorithms
• Cluster Analysis
• +۶ مورد دیگر

Ingo Steinwart is a Full Professor and Head of the Institute for Stochastics and Applications at the University of Stuttgart, within the Faculty of Mathematics and Physics. He holds a Chair for Stochastics and has held academic positions since 2010, including roles at Los Alamos National Laboratory and the University of California, Santa Cruz. His research focuses on statistical learning theory, kernel-based methods, cluster analysis, and neural networks, with significant contributions to the theoretical foundations of machine learning. Education: He earned a Doctorate (Dr. rer. nat.) in Mathematics from Friedrich-Schiller University, Jena (2000) and a Diploma in Mathematics from Carl-von-Ossietzky University, Oldenburg (1997). Research Interests: Steinwart's work emphasizes rigorous mathematical analysis of learning algorithms, including kernel methods, support vector machines (SVMs), and density estimation. His research explores topics like the capacity of function classes, generalization bounds, and applications to large-scale data. Notable contributions include advancements in SVM theory, reproducing kernel Hilbert spaces, and cluster analysis techniques. Publications: His work spans foundational papers on SVMs, kernel methods, and learning theory, with a focus on theoretical guarantees and algorithmic efficiency. Recent contributions address topics like adaptive learning rates, neural network initialization, and the theoretical limits of kernel-based methods. Grants & Editorial Roles: Steinwart serves as Associate Editor for journals such as the Journal of Complexity and the Annals of Statistics. He has organized conferences like COLT and contributed to software tools like liquidSVM for SVM implementation. Labs & Software: He leads development of software packages like liquidSVM (for SVMs) and liquidCluster (for cluster analysis), emphasizing computational efficiency and automated hyperparameter selection.

Stefan Conradمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Natural Language Processing
• +۵ مورد دیگر

Prof. Stefan Conrad is the Chair of Databases and Information Systems at Heinrich Heine University Düsseldorf's Department of Computer Science, Faculty of Mathematics and Natural Sciences. He has held this position since 2002 and serves as Deputy Spokesperson of the DIID (Digital Innovation Initiative Düsseldorf) since 2023. His research focuses on data mining, machine learning, and natural language processing, with applications in image retrieval, time series analysis, bias mitigation, and fairness in AI. He collaborates with industry partners on projects like ZIM-funded opinion mining and automated text summarization. He has contributed to multiple funded projects, including the NRW-Fortschrittskolleg on Online Participation and VolkswagenStiftung's Mediating Machines. His work spans technical and interdisciplinary domains, including database design, federated systems, and semantic web technologies. Research interests include automated topic recognition, argument structure analysis, and emotion detection in text. He leads the RAPP project on responsible academic performance prediction and is involved in advancing AI ethics and bias-aware methodologies. Publications span conferences like DaWaK, ECIR, and ECAI, emphasizing fairness, bias mitigation, and AI applications. His work bridges theoretical contributions with practical implementations in healthcare, environmental monitoring, and education.

Eirini Ntoutsiمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Responsible AI
• +۷ مورد دیگر

Prof. Eirini Ntoutsi is a Professor of Open Source Intelligence at the CODE Research Institute for Cybersecurity and Smart Data , Bundeswehr University Munich . She leads the Artificial Intelligence & Machine Learning (AIML) research group , focusing on adaptive learning, responsible AI, and generative AI. Research Interests: Developing intelligent algorithms for real-world data challenges, addressing fairness-aware machine learning, explainable AI, and generative models. Projects: Co-leads the EU-funded MAMMOth (Multimodal AI for Trustworthy Human-Centric Applications) and STELAR (Spatio-Temporal Linked Data for Agri-food) initiatives. Applications: Deploying AI solutions in education, social networks, banking, agriculture, manufacturing, and engineering. Key Contributions: Developed the MMM-Fair open-source toolkit for fairness analysis with no-code interface. Actively contributes to conferences like ECML PKDD , FAccT , IJCNN , and WWW .

Michael N. Arbelمشاهده پروفایل

پژوهشگر

• Bilevel optimization
• Generative modelling
• Learning Theory
• +۱ مورد دیگر

Michael N. Arbel is a Researcher at INRIA Grenoble - Rhône-Alpes, where he has been a core member of the THOTH research team since October 2022, following a Starting Research Fellowship position under Julien Mairal at the same institution. His academic foundation includes: Ph.D. in Computational Neuroscience from University College London (2021), supervised by Arthur Gretton at the Gatsby Unit Master's Degree in Mathematics, Machine Learning and Computer Vision (MVA) from ENS Paris-Saclay Applied Mathematics specialization at École polytechnique Dr. Arbel's research program integrates theoretical and applied machine learning, with significant contributions to bilevel optimization frameworks and generative modeling. Recent work includes foundational models for tabular data (February 2025), kernel bilevel optimization theory (December 2024), and NeurIPS 2024 spotlighted research on Functional Bilevel optimization. His methodological innovations bridge theoretical learning guarantees with practical representation learning applications. He directs the ANR JCJC-funded BONSAI project, launched in April 2024, focusing on advanced optimization techniques for machine learning systems. As an integral member of the THOTH team at INRIA Grenoble, he collaborates on cutting-edge machine learning research within France's national digital science institute.

Olaf Wolkenhauerمشاهده پروفایل

استاد

• Systems Biology
• Bioinformatics
• Mathematical Modeling
• +۴ مورد دیگر

Prof. Dr. Olaf Wolkenhauer is a faculty member at the University of Rostock , where he holds the Chair of Systems Biology & Bioinformatics within the Institute of Computer Science . He also serves as an Adjunct Professor at institutions including Case Western Reserve University , Chhattisgarh Swami Vivekanand University of Technology , and the University of Cleveland , as well as a Visiting Professor at the Leibniz Institute for Food Systems Biology at the Technical University of Munich. His research focuses on systems biology, bioinformatics, and machine learning applications in medicine. Chair of Systems Biology & Bioinformatics, University of Rostock Adjunct Professor, Case Western Reserve University Adjunct Professor, Chhattisgarh Swami Vivekanand University of Technology Adjunct Professor, University of Cleveland Visiting Professor, Leibniz Institute for Food Systems Biology Wolkenhauer’s research integrates mathematical modeling , data analysis , and machine learning to address complex biological and medical problems. His recent work explores epigenetic instability in cancer , drug repurposing , synthetic data generation , and collaborative filtering for biomedical applications. He has contributed to advancements in AI-driven diagnostics and functional data analysis . His scientific contributions have earned him recognition, including Fellowship at the Stellenbosch Institute for Advanced Studies (STIAS) and membership in the DFG Review Board 201 Fundamentals of Medicine and Biology . His teaching portfolio includes courses on modeling and simulation in life sciences , data science with Python , and seminars on systems biology and scientific communication . Fellow, Stellenbosch Institute for Advanced Studies (STIAS) DFG Review Board Member, Fundamentals of Medicine and Biology

• Deep Learning
• Brain-Computer Interfacing
• Neurotechnology
• +۳ مورد دیگر

Robin Tibor Schirrmeister is a researcher at the University of Freiburg's Faculty of Engineering, specializing in deep learning applications for brain-computer interfacing and neurotechnology. His work bridges engineering and medical domains through affiliations with BrainLinks-BrainTools, the Neurorobotics Lab, and the Department of Presurgical Epilepsy Diagnostics. His doctoral research (2024) focused on EEG signal decoding under advisors Frank Hutter and Tonio Ball. Key research areas include: Deep learning for EEG pathology classification Real-time brain-signal decoding systems Human-robot interaction via BCI Medical diagnostics using neural networks Publication trends since 2018 reveal consistent advancement in EEG decoding methodologies, with recent work (2024-2025) focusing on brain-age dynamics, small-data prediction models, and Riemannian geometry applications. His research demonstrates strong clinical translation potential, particularly in epilepsy diagnostics and assistive robotics. Award: 2018 Poster Prize for µECoG research Collaborative work spans multiple labs including BrainLinks-BrainTools and the Epilepsy Center, with significant contributions to clinical EEG dataset development and intracranial signal processing. Current projects focus on scaling behavior in EEG classification and brain-age modeling. Research infrastructure includes the Neurorobotics Lab for BCI-robot integration and the Epilepsy Center for clinical validation, enabling translational work from algorithm development to medical applications.

• Probabilistic Programming
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam , where he co-directs the AMLab with Max Welling. He holds an Assistant Professor position (on leave) at Northeastern University , continuing to advise students and collaborate remotely. His research focuses on combining probabilistic programming and deep learning to develop models that generalize from limited data. Key areas include inductive biases through physical simulators, causal structures , and symmetries , with applications in robotics , NLP , healthcare , and physical sciences . Recent work includes Variational Flow Matching for graph generation Equivariant neural models for physical systems Entropy coding of complex data structures Goal-contrastive reinforcement learning for robotics Awards NSF CAREER award (2021) Students & Postdocs Robin Walters (Postdoctoral Fellow) Ondrej Biza (Ph.D. Candidate) Babak Esmaeili (Ph.D. Candidate) Sam Stites (Ph.D. Candidate) Hao Wu (Ph.D. Candidate) Xiongyi Zhang (Ph.D. Candidate) Heiko Zimmermann (Ph.D. Candidate) Jered McInerney (Ph.D. Candidate) Eli Sennesh (Ph.D. Candidate)