جست‌وجوی استاد و پژوهشگر

• Computational Biochemistry
• Protein Design
• Machine Learning in Biochemistry
• +۶ مورد دیگر

Dr. Mehdi D. Davari is Head of the Computational Chemistry Research Group at the Leibniz Institute of Plant Biochemistry (IPB) in Halle, Germany, a position he has held since April 2021. Previously, he served as Head of Division Computational Biology at RWTH Aachen University (2013-2021) and as a Postdoc Research Fellow at the Max Planck Society (2010-2013). Dr. Davari's research focuses on computational (bio)chemistry and protein design, with emphasis on developing and applying advanced computational methods including QM/MM simulations, structure-activity relationship analyses, and AI-based approaches. His group specializes in machine learning and deep learning techniques to predict properties of proteins, enzymes, and small molecules, with applications in sustainable bio-based technologies. Research areas include enzyme engineering, protein stability prediction, and computational design of biocatalysts. His recent publications (2023-2025) demonstrate a strong trend toward integrating machine learning with traditional biochemical approaches, particularly in protein engineering, enzyme design, and natural product chemistry. The work spans computational prediction methods, experimental validation, and applications in biocatalysis and drug discovery, with emphasis on minimizing experimental effort through data-driven approaches. Notable scientific achievements include: DAAD PhD Research Fellowship (2009) Multiple significant grants from major funding bodies including the Federal Ministry of Education and Research, Deutsche Forschungsgemeinschaft, and Agence Nationale de la Recherche Dr. Davari leads an active research program with substantial grant funding, including multiple projects running through 2028 such as BioCasNavi (BMBF), LeFOS (BMBF), and PhosFect (BMBF). His research group collaborates extensively with internal partners including Computational Plant Biochemistry, Receptor Biochemistry, and Phenylpropanoid Metabolism groups, as well as external partners at Martin Luther University Halle-Wittenberg and Max Planck Institutes. The Computational Chemistry group maintains strong technical capabilities in molecular simulations, machine learning, and bioinformatics, with research directly contributing to sustainable biotechnology applications and fundamental understanding of protein structure-function relationships.

Günter Neumannمشاهده پروفایل

استاد

• Computational Linguistics
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Günter Neumann is a Professor of Computational Linguistics at Saarland University and a Research Fellow at the German Research Center for Artificial Intelligence (DFKI). His work spans computational linguistics, artificial intelligence, and biomedical informatics, with a focus on information extraction, question answering systems, and knowledge graph reasoning. Education: PhD in Computer Science (1994) and Computational Linguistics (2004), both from Saarland University. Neumann's research interests include multilingual natural language processing, low-resource language modeling, and neural architectures for information retrieval and biomedical relation extraction. He pioneered techniques in dense passage retrieval for Urdu, feature textualization for BERT interpretability, and low-rank temporal knowledge graph embeddings. Recent publications focus on cross-lingual transfer learning, code-mixed clinical text de-identification, and robust biomedical benchmarks. He has led EU/national projects in language technology and served on program committees for ACL, EMNLP, and LREC. His work with the EXCITEMENT Open Platform and DOMLIN system demonstrates expertise in textual entailment and fact verification, achieving top rankings in competitions like CLEF and TAC.

• Machine Learning
• Medical Imaging
• Computer Vision
• +۳ مورد دیگر

Domenec Puig is a Professor at the Department of Computer Science and Mathematics, Rovira i Virgili University, Spain. His research focuses on machine learning, medical imaging, and computer vision, with applications in healthcare and biomedical informatics. He collaborates extensively with researchers in AI-driven medical diagnostics, including fundus image analysis, MRI segmentation, and neonatal birth weight prediction using multimodal data. Key research areas include deep learning architectures for semantic segmentation, generative adversarial networks (GANs), and interpretable AI models. His work bridges theoretical advancements in neural networks with practical solutions for challenges in medical image analysis, such as crack detection in materials, tumor classification, and survival prediction in oncology. Recent publications highlight contributions to transformer-based models, hybrid architectures (e.g., CoAtUNet), and domain adaptation techniques for cross-site generalization in MRI segmentation. His methodologies often emphasize explainability and efficiency in resource-constrained settings.

• Machine Learning
• Medical Imaging
• Computer Vision
• +۳ مورد دیگر

Isack Farady is a researcher specializing in machine learning applications across medical imaging, computer vision, and biomedical signal processing. His work focuses on developing algorithms for anomaly detection in healthcare data, medical diagnosis enhancement, and improving industrial defect detection systems through advanced neural network architectures. Frequently collaborating with institutions like National Taiwan University, his research bridges computer science and medical sciences, addressing challenges in Alzheimer's disease classification, ECG analysis, and seismic layer segmentation for carbon storage studies. Key research interests include generative adversarial networks (GANs) for medical image synthesis, robust adversarial attack detection in speech recognition systems, and optimizing deep learning models for small-scale training datasets. His contributions leverage techniques such as LSTM-autoencoders, channel attention blocks, and multi-level feature hierarchies to address real-world problems in healthcare and industrial automation.

Daniel Straubمشاهده پروفایل

استاد

• Risk Analysis
• Reliability Engineering
• Structural Safety
• +۶ مورد دیگر

Prof. Daniel Straub is a Professor of Engineering Risk Analysis at the Technical University of Munich (TUM), holding a W2 professorship within the TUM School of Engineering and Design. He leads the Engineering Risk Analysis Group, focusing on probabilistic modeling, data analysis, and decision theory for risk assessment in engineering systems. His research emphasizes dynamic information integration (e.g., sensor data) for risk evaluation, with applications in structural design, offshore engineering, geotechnics, natural hazards, automotive, and aerospace systems. Prof. Straub earned his diploma (2000) and PhD (2004) in civil engineering from ETH Zurich. He joined TUM in 2008 after postdoctoral work at UC Berkeley and has held executive roles in international engineering risk organizations. He is also an Honorary Professor at the University of Aberdeen. His industry consulting spans multiple sectors, and he co-founded Eracons and Matrisk to commercialize risk analysis tools. Research interests include Bayesian methods, reliability sensitivity analysis, and infrastructure resilience. Key projects involve probabilistic modeling of deterioration, value of information from monitoring systems, and risk management of autonomous systems. Awards include the SAE Ralph H. Isbrandt Automotive Safety Award (2022), Geotechnical Safety Network Research Award (2022), and ETH Silver Medal (2005). Prof. Straub advises on over 25 students and researchers, including former PhD/Master’s students like Alessandra Altamura and Mario Berk. He serves on editorial boards for journals such as Structural Safety and Reliability Engineering & System Safety , and chairs committees like the Joint Committee on Structural Safety. His team’s current research includes Bayesian networks for infrastructure reliability and AI-driven maintenance optimization.

Christian Rupprechtمشاهده پروفایل

پژوهشگر

• Computer Vision
• Medical Augmented Reality
• Machine Learning for Medical Applications
• +۳ مورد دیگر

Christian Rupprecht is an alumnus of the Technical University of Munich (TUM), where he held the position of Scientific Researcher in the Chair for Computer Aided Medical Procedures & Augmented Reality. He later joined the University of Oxford as a postdoctoral researcher. His research focuses on computer vision, medical augmented reality, and machine learning applications in medical imaging, with specialties in ambiguity handling in predictions, 3D reconstruction, and surgical workflow optimization. He has supervised numerous students and contributed to advancements in segmentation, reconstruction, and robotic grasping algorithms. Research Interests: Medical Computer Vision Deep Learning for Medical Imaging 3D Scene Understanding Human-Robot Interaction Ambiguity Representation in Predictive Models Articles Trends: His work emphasizes addressing uncertainty in computer vision tasks, with notable contributions to multiple hypothesis prediction frameworks (MHP) and interactive deep network guidance. Recent articles explore scene graph-based image manipulation and ambiguity-aware object detection for robotics. Scientific Awards: No awards explicitly listed, but his work has been accepted at top-tier conferences like CVPR, ICCV, and MICCAI. Advising & Grants: Supervised over 15 students in projects ranging from adversarial learning to surgical instrument tracking. Grants include BaCaTeC-funded projects on 3D reconstruction and webly-supervised activity recognition. Labs & Teams: Core member of TUM's CAMP group, collaborating on medical augmented reality systems and robotic vision solutions.

Dieter Meillerمشاهده پروفایل

استاد

• Web Design
• Web Engineering
• Mobile Applications
• +۱۳ مورد دیگر

Prof. Dr. Dieter Meiller is a Professor at OTH Regensburg, specializing in the intersection of design and computer science. His research focuses on information visualization, human-computer interaction, and multimedia systems. Key areas include web engineering, mobile applications, aesthetics in computing, and 3D visualization. His work emphasizes making information accessible through engaging visual and interactive methods. Recent research explores topics like automated code evaluation, user response measurement, and virtual reality applications. He has contributed to Flutter-based app development frameworks and industrial data analysis through visual analytics. Dieter Meiller actively collaborates on projects involving digital education tools and immersive technologies. He maintains professional networks via LinkedIn and Xing, reflecting his commitment to interdisciplinary innovation.

• Explainable Machine Learning
• Computer Vision
• Natural Language Processing
• +۳ مورد دیگر

Zeynep Akata is a Professor of Computer Science at Technical University of Munich and Director of the Institute of Explainable Machine Learning (EML) at Helmholtz Munich. She holds a PhD from INRIA Rhone Alpes and has held academic positions including Senior Researcher at Max Planck Institutes, Professor at University of Tübingen, and Tenure Track Assistant Professor at University of Amsterdam where she co-directed the UvA-Bosch Delta Lab. Her research focuses on explainable AI, multimodal learning, zero-shot learning, and ethical implications of AI systems. Key areas include improving transparency of AI models while maintaining GDPR compliance, and applying machine learning to societal challenges through computer vision and natural language processing. Notable awards include the Alfried Krupp Award (2023), ECVA Young Researcher Award (2022), and ERC Starting Grant (2019). Her work emphasizes both theoretical advancements and practical applications such as gender bias mitigation in generative models and cross-modal understanding through vision-language integration. Current research trends in her publications emphasize model merging, lifelong learning, and ethical AI practices. She explores techniques like concept-guided interpretability, sparse autoencoders, and low-rank adaptation for efficient model updates. Her work bridges theoretical machine learning foundations with real-world deployments in healthcare and environmental applications. Zeynep has contributed to major conferences such as NeurIPS, ICLR, and CVPR. Her group actively develops open-source tools for explainable AI and maintains partnerships with industrial collaborators including Bosch and leading research institutes.

• Social Computing Systems
• Algorithmic Fairness
• Privacy and Security in Social Media
• +۳ مورد دیگر

Krishna P. Gummadi is a Professor and Scientific Director at the Max Planck Institute for Software Systems (MPI-SWS) , leading the Networked Systems Research Group. His work bridges computer science and social science, focusing on social computing systems that network humans and computers at societal scale. Academic Leadership: Faculty (Scientific Director), Head of Networked Systems Research Group Key Research Areas: Algorithmic fairness, privacy in social media, distributed systems, human-centered machine learning His research methodology combines user-centric studies through large-scale observational analysis, data-centric studies using statistical learning and NLP, and systems-centric approaches for practical deployments like information diet tools and fraud detection services . Current work explores crowdsourcing systems , peer-to-peer networks , and trusted cloud computing . Scientific Awards & Recognitions: CNIL-INRIA Privacy Runners-Up Award (2018) Casper Bowden PET Runners-Up Award (2018) SIGCOMM Test of Time Award (2007) Students & Postdocs: Current: Junaid Ali, Reza Babaei, Nina Grgić-Hlača, Preethi Lahoti, Johnnatan Messias, Till Speicher Former: Asia J. Biega (Microsoft Research), Przemyslaw Grabowicz (UMass Amherst), Muhammad Bilal Zafar (Bosch AI), Juhi Kulshrestha (GESIS), Mainack Mondal (University of Chicago), Rijurekha Sen (IIT Delhi)

Simone Paolo Ponzettoمشاهده پروفایل

استاد

• Natural Language Processing
• Information Retrieval
• Knowledge Graphs
• +۷ مورد دیگر

Prof. Simone Paolo Ponzetto is the Chair of Information Systems III (Enterprise Data Analysis) at the University of Mannheim, leading the Natural Language Processing and Information Retrieval (NLP and IR) group within the Data and Web Science Group since 2013. His research bridges computational methods with applications in Social Sciences and Humanities. Full Professor (W3) since February 2016 Based at School of Business Informatics and Mathematics Contact: simone@informatik.uni-mannheim.de | ponzetto@uni-mannheim.de His work spans knowledge acquisition , multimodal NLP , and LLM applications in: GUI prototyping, scientific text analysis, political discourse modeling, and social science data integration. Recent projects explore zero-shot synthesis , cross-lingual knowledge editing , and ethical analysis of language models . Key research trends in his publications include LLM-driven interface design , multimodal summarization , and robust cross-lingual methods for NLP tasks. His group's work combines symbolic approaches with deep learning , leveraging knowledge graphs and distributional semantics across disciplines. Research grants include DFG-funded projects JOIN-T 2 , SFB 884 , and UNCOVER , alongside MWFK Baden-Württemberg programs for junior professors and part-time master's education in Data Science. The Data and Web Science Group under his leadership focuses on empirical research in computational social science and digital humanities, with applications in political text analysis, survey data integration, and surgical language modeling through projects like SurgicBERTa and FrameASt .

Alexander Fraserمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing (NLP)
• Multilingual Systems
• +۵ مورد دیگر

Prof. Alexander Fraser is a faculty member at the Technical University of Munich (TUM), holding the Chair of Data Analytics & Statistics within the TUM School of Computation, Information and Technology. He specializes in machine learning for natural language processing, particularly focusing on multilingual systems, data sparsity, and cross-lingual alignment. His research bridges theoretical advancements and practical applications, including projects on hate speech detection, low-resource language support, and ethical AI. Fraser earned his PhD in Computer Science from the University of Southern California (2007). Prior to TUM, he held professorial roles at LMU Munich (2013–2024) and the University of Stuttgart (2007–2013). He has led significant research initiatives, including ERC grants and EU-funded projects like Health in my Language (HimL). His work has been recognized with awards such as the ERC Advanced Grant (2023) and ERC Proof of Concept Grant (2022). Teaching responsibilities include courses on large language models, generative models, and machine learning at TUM. Fraser is also a Principal Investigator at the Munich Center for Machine Learning (MCML) and an active member of the ELLIS Unit Munich. His contributions span academic research, industry collaboration, and language revitalization efforts for minority languages like Sorbian. Key publications highlight innovations in multilingual benchmarks, cross-lingual alignment, and ethical NLP applications. His research addresses challenges in data scarcity, model bias, and responsible AI deployment across diverse linguistic landscapes.

Zeynep Akataمشاهده پروفایل

استاد

• Explainable Machine Learning
• Artificial Intelligence
• Machine Vision
• +۳ مورد دیگر

Prof. Zeynep Akata is a distinguished professor of computer science at the Technical University of Munich (TUM) and director of the Institute for Explainable Machine Learning at Helmholtz Munich. Her research focuses on developing transparent AI systems that can interact reliably with humans, integrating methods from machine vision, natural language processing, and learning. She holds the Liesel Beckmann Distinguished Professorship and has held academic positions at institutions including the University of Tübingen, University of Amsterdam, and UC Berkeley. Education: PhD from INRIA Rhone Alpes (2014), followed by postdoctoral research at the Max Planck Institute for Informatics and UC Berkeley. Career highlights include ERC Starting Grant (2019) and the European Computer Vision Young Researcher Award (2022). Research interests span explainable AI, zero-shot learning, vision-language models, and mitigating biases in AI systems. Her work emphasizes ethical AI deployment and human-centered AI design. Awards: Alfried Krupp Promotion Prize (2023), German Pattern Recognition Award (2021), and Werner von Siemens Ring Young Researcher Award (2019). Her laboratory at TUM and Helmholtz Munich focuses on advancing interpretable machine learning frameworks, with projects addressing societal impacts and technical challenges in AI transparency.

Isabel Valeraمشاهده پروفایل

استاد

• Machine Learning
• Robust Machine Learning
• Interpretable AI
• +۱۰ مورد دیگر

Isabel Valera is a full Professor in the Department of Computer Science at Saarland University in Saarbrücken, Germany, and an Adjunct Faculty member at the Max Planck Institute for Software Systems (MPI-SWS). She is also a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & ML (Sam) Unit. Department of Computer Science, Saarland University Adjunct Faculty, MPI for Software Systems ELLIS Fellow, Robust ML Program Sam Unit, Saarbrücken AI & ML She obtained her PhD and MSc from Universidad Carlos III de Madrid, followed by postdoctoral research at the University of Cambridge and MPI for Software Systems. She previously led an independent research group at MPI for Intelligent Systems in Tübingen and held the Humboldt Post-Doctoral Fellowship and Minerva Fast Track Fellowship. PhD in Machine Learning, Universidad Carlos III de Madrid, 2014 MSc in Multimedia and Communications, Universidad Carlos III de Madrid, 2012 Telecommunications Engineering, Technical University of Cartagena, 2009 Her research centers on developing machine learning methods that are flexible, robust, interpretable, and fair, particularly for heterogeneous, temporal, and high-stakes decision-making systems. She emphasizes applications in medicine, psychiatry, and social domains such as hiring, bail, and lending. Her methodological contributions include Bayesian nonparametric models, latent feature modeling, and temporal point processes. Her recent publications reflect a strong focus on fairness, robustness, and interpretability in machine learning. Key themes include latent feature modeling for mixed data types, clustering temporal event streams, source separation, and fair classification. Her work bridges theoretical innovation with practical applications across healthcare, social networks, and policy-relevant domains. Scientific awards and recognitions include: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow She has been actively involved in teaching and dissemination, delivering tutorials at NIPS and MLSS on temporal point processes and social network analysis. She has also supervised research assistants and mentored junior researchers. Her research has been supported through prestigious fellowships and institutional affiliations. She leads the development of open-source tools such as GLFM, HDHP, and iFDM, promoting reproducibility and accessibility in machine learning research. She is affiliated with the following labs and research groups: Max Planck Institute for Intelligent Systems (former group leader) Max Planck Institute for Software Systems (adjunct, postdoctoral) ELLIS Sam Unit (Saarbrücken AI & ML) Robust Machine Learning Program (ELLIS)

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +۱۱ مورد دیگر

Michael Hahn is a Tenure-Track Professor (W2) at Saarland University, based at the Saarland Informatics Campus. He directs the Language, Computation, and Cognition Lab (LaCoCo) and holds joint affiliations with the Department of Language Science and Technology and the Department of Computer Science. He earned his PhD from Stanford University in 2022 under Judith Degen and Dan Jurafsky. His research lies at the intersection of machine learning, cognitive science, and linguistics. Key areas include the expressive power and learning biases of transformer models and state space models, mechanistic interpretability of neural networks, and computational modeling of human cognition and perception. He investigates how information processing constraints shape both artificial and biological systems. The recent publications demonstrate a consistent focus on understanding the theoretical and cognitive foundations of language models, particularly transformers. Themes include length generalization, reasoning limitations, in-context learning mechanisms, and the cognitive plausibility of model behaviors. His work spans from formal mathematical analysis to cognitive modeling and large-scale empirical evaluation. His scientific recognition includes: Best Paper Award at ACL 2024 Second Prize at ICML 2024 Mechanistic Interpretability Workshop Outstanding Area Chair at EMNLP 2024 ELLIS Member ELIZA Fellow Michael Hahn actively advises a team of PhD students and researchers, including Xinting Huang, Yana Veitsman, and Mark Rofin. He is involved in organizing major academic events, such as a NeurIPS 2025 workshop on transformer capabilities. His lab receives significant attention, as evidenced by organizing the ELLIS pre-NeurIPS poster session with 17 posters from across campus. The Language, Computation, and Cognition Lab (LaCoCo) is a vibrant research group investigating machine learning, language, and the human mind. The team includes postdoctoral researchers, PhD students, and research assistants working on interpretability, cognitive modeling, and NLP theory.

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۳ مورد دیگر

Frederic Bechet is a researcher affiliated with Aix-Marseille Université, CNRS, and the LIF UMR 7279 laboratory. His work spans Natural Language Processing, Computational Linguistics, and Machine Learning, focusing on task structure analysis, factual knowledge robustness, and semantic-driven evaluation methodologies. Research Interests: Semantic Parsing, Question Answering, Text Summarization, and Multitask Learning. Recent Trends: Empirical studies on task inclusion via statistical deficiency, POS-driven specialization in Mixture-of-Experts models, distractor-based factual evaluation, and temporal knowledge decay in LLMs. His work on WikiFactDiff introduces a realistic framework for atomic fact updates in causal language models. Collaborations: Co-authored with experts in semantic annotation (Géraldine Damnati), QA systems (Alexis Nasr), and adversarial learning (Gabriel Marzinotto). Labs/Teams: Involved in the DECODA corpus for call-center analysis and the MEDIA corpus for dialogue understanding.