جست‌وجوی استاد و پژوهشگر

Laetitia Chapelمشاهده پروفایل

استاد

• Machine Learning
• Remote Sensing
• Time Series Analysis
• +۳ مورد دیگر

Laetitia Chapel is a full professor in computer science at Institut Agro Rennes-Angers , conducting research within the OBELIX team at IRISA , a mixed research unit in computer science, signal/image processing, and robotics. Her work focuses on machine learning for structured data (time series, graphs, hierarchical representations) with applications in remote sensing, and she obtained her HDR in 2022. Research Interests center on advancing optimal transport theory for structured datasets, including time series alignment, hyperbolic embeddings, and Riemannian neural networks. She actively participates in international workshops and conferences, delivering invited talks at venues like NeurIPS and IEEE MLSP. Ambroise Odonnat (2024, Huawei-funded PhD) Léo Buecher (2024, supervised with Nicolas Courty) Sergio Osson (2025, supervised with Charlotte Pelletier) Guillaume Mahey (2024, with Gilles Gasso) Manal Hamzaoui (2023, with Sébastien Lefèvre and Minh-Tan Pham) Titouan Vayer (2020, now at INRIA) Pierre Gloaguen (2017, now assistant professor at UBS) Scientific Awards include the Best paper award at MajecSTIC 2005 and Best paper award at ICPRAI 2022 . Her research projects span from theoretical foundations (e.g., hyperbolic metric learning) to applied remote sensing tasks (e.g., cropland mapping in the Brazilian Amazon).

• Data Science
• Artificial Intelligence
• Knowledge Engineering
• +۹ مورد دیگر

Christoph Lange-Bever (publishing as Christoph Lange) is a senior researcher in computer science, specializing in data science and artificial intelligence, affiliated with the Fraunhofer Institute for Applied Information Technology FIT and the Chair for Information Systems at RWTH Aachen University since 2019. He leads the Data Science and Artificial Intelligence department at Fraunhofer FIT and previously headed the Knowledge Pipelines department from 2019–2021. His career spans roles at the University of Bonn, Fraunhofer IAIS, University of Birmingham (as an honorary research fellow until 2017), and University of Bremen. PhD from Jacobs University Bremen (2011) on semiformal mathematical knowledge collaboration Focus areas include knowledge engineering, semantic web, data infrastructures , and FAIR principles Applications span enterprise data integration , research infrastructures , and cross-organization data spaces His recent publications (2023–2025) emphasize FAIR data spaces , LLM integration , semantic interoperability , and knowledge graph tools . While no specific scientific awards are listed, he has co-supervised 10 PhD students and contributed to international standards (OMG, W3C). He teaches courses like Knowledge Graph Lab and Sovereign Data Exchange , blending research with practical applications for global enterprises.

• Tensor Decompositions
• Quantum Computing
• Fredholm Integral Equations
• +۷ مورد دیگر

Patrick Gelß is a Postdoctoral Researcher at Zuse Institute Berlin, working in the AI in Society, Science, and Technology department. He leads the iol.QUANT research group focused on quantum-inspired and tensor-based methods, with affiliations to Freie Universität Berlin and collaborative projects like MATH+ Cluster of Excellence. Diploma in Mathematics/Physics (2013, Freie Universität Berlin) PhD in Mathematics (2017, summa cum laude, Freie Universität Berlin) Postdoctoral roles at Zuse Institute Berlin (2020-present) and CRC 1114 (2017-2022) His research bridges tensor decompositions, quantum computing, and dynamical systems, with notable contributions to: Graph Isomorphism: Continuous optimization approaches via doubly stochastic matrices and Frank-Wolfe algorithms Quantum Simulation: Tensor network representations for quantum circuits and the open-source WaveTrain package KvN Mechanics: Mathematical analysis of existence/uniqueness solutions for bounded domains Fredholm Networks: Novel training paradigms for neural networks using integral equations Scientific achievements include: Developing Scikit-TT for tensor-train computations Organizing major workshops (QML@SC2024, TMQS 2024) Leading the Thematic Einstein Semester 2024 on Mathematics for Quantum Technologies His work spans chemical kinetics, quantum dynamics, and quantum machine learning, with applications to CO oxidation models, exciton-phonon systems, and mutational hierarchies in medicine.

Andreas Ingerlمشاهده پروفایل

مدرس

• New Media Design
• Applied Media Art
• Audiovisual Media
• +۲ مورد دیگر

Andreas Ingerl serves as a University Lecturer within the Department of Design and Culture at HTW Berlin - University of Applied Sciences, primarily affiliated with the Communication Design program. He holds key administrative roles including Department Council member, Deputy Chair of the Selection Committee, Chair of the Examination Board, and BAföG Representative, while managing the Physical and Ubiquitous Computing laboratory from his office at Wilhelminenhof Campus WH Building A, 419. His research centers on the intersection of technology and artistic expression, specializing in audiovisual media, new media design, and applied media art. He actively explores creative computing methodologies and ubiquitous computing applications to transform physical environments into interactive experiences, with strong emphasis on virtual reality systems, sensory design, and AI integration for immersive media installations that challenge conventional perception and interaction paradigms. Analysis of his recent publications reveals a cohesive trajectory toward experimental media forms and intelligent systems, exemplified by projects like CINEMIRACLE (public-space VR), Lucid Dream (consciousness exploration), and CFI – Craving Future Intelligence (AI-driven art). These works collectively demonstrate his commitment to bridging technical innovation with critical artistic inquiry, particularly in addressing societal implications of emerging media technologies and redefining educational frameworks through digital experimentation. As Laboratory Manager for Physical and Ubiquitous Computing, Ingerl directs a multidisciplinary research environment focused on developing novel interfaces for immersive storytelling and sensor network integration in urban contexts. The lab functions as a nexus for the Creative Computing research cluster, facilitating collaborations between design, art, and engineering disciplines to pioneer tangible media interactions that dissolve boundaries between digital and physical realms.

• Privacy-preserving machine learning
• Adversarial robustness
• Differential privacy
• +۵ مورد دیگر

Saeed Mahloujifar is a researcher in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. He has established himself as a prominent researcher in machine learning security, privacy, and robustness, with extensive collaborations with leading researchers including Prateek Mittal, Mohammad Mahmoody, and Somesh Jha. His research primarily focuses on security and privacy challenges in machine learning systems. Mahloujifar has made significant contributions to understanding and mitigating membership inference attacks, developing defenses against poisoning and backdoor attacks, advancing differential privacy techniques, and exploring the fundamental limits of robust machine learning. His work bridges theoretical foundations with practical applications, often developing novel algorithms and frameworks that have been widely adopted in the security and privacy communities. The analysis of his recent publications reveals a strong research trajectory centered on privacy-preserving machine learning, with particular emphasis on large language models, differential privacy guarantees, and security vulnerabilities. His work spans theoretical foundations of privacy through practical security mechanisms like watermarking and robustness benchmarking. A notable trend is his increasing focus on large language models and their security implications, alongside continued contributions to foundational privacy theory. Mahloujifar has co-authored over 90 publications since 2017, with a significant acceleration in output since 2020. His work appears consistently in top-tier venues including NeurIPS, ICML, IEEE S&P, and USENIX Security, demonstrating both breadth and depth in his research contributions.

Stefanie Donnerمشاهده پروفایل

پژوهشگر

• Seismology
• Rotational Seismology
• Earthquake Source Analysis
• +۳ مورد دیگر

Dr. Stefanie Donner is a Researcher in Seismology affiliated with the Department of Earth System Sciences at the University of Hamburg (Faculty of Mathematics, Informatics and Natural Sciences, MIN). She serves as a seismologist for the Federal Seismological Survey and National Data Center at the Federal Institute for Geosciences and Natural Resources (BGR) in Hannover, Germany, where she leads the Monitoring and Verification unit (B4.3) . Research Focus: Observational seismology, earthquake source dynamics, rotational seismology, and applications of machine learning in geophysical contexts. Contact: Office at Geomatikum Room 1304, Bundesstrasse 55, 20146 Hamburg; Phone: +49 40 42838 4921; Email: stefanie.donner@bgr.de .

Richard Meyesمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr.-Ing. Richard Meyes is a Scientific Researcher and Head of the Research Field 'Interpretable Learning Models' at the Institute for Technologies and Management of Digital Transformation (TMDT), University of Wuppertal. His work bridges artificial intelligence and industrial applications, with a focus on interpretable and practical machine learning systems. His research interests lie at the intersection of Artificial Intelligence, Machine Learning, and Industrial Digital Transformation . He focuses on developing interpretable neural networks , analyzing time series data from industrial sensors , and creating structured representations in deep learning models . These efforts support predictive maintenance, automated inspection, and intelligent decision-making in manufacturing and automotive domains. The recent publications highlight a strong trend in applying deep learning to industrial computer vision , sensor data analytics , and ethical AI . His work spans both technical innovation (e.g., BEV perception, CTR prediction) and societal implications (e.g., AI ethics in research). There is a consistent emphasis on real-world deployment and interpretability. Scientific Awards: No awards listed in the provided text. Advising and Grants: While no specific students or grant projects are listed, Richard Meyes leads a dedicated research field and actively collaborates on high-impact publications, suggesting involvement in funded research initiatives and potential mentorship of junior researchers within the TMDT institute. Labs and Teams: He leads the research field 'Interpretable Learning Models' at the Institute for Technologies and Management of Digital Transformation. This group is part of a larger interdisciplinary team working on digital transformation technologies, likely involving collaborations across AI, engineering, and industrial application domains.

Susanne Draheimمشاهده پروفایل

پژوهشگر

• Affective Computing
• Smart Systems
• Ambient Displays
• +۸ مورد دیگر

Dr. Susanne Draheim is the Managing Director of the Research and Transfer Center 'Smart Systems' at the Hamburg University of Applied Sciences , within the Faculty of Engineering and Computer Science and the Department of Computer Science . Her work bridges cutting-edge technology with ethical considerations, focusing on smart environments and interdisciplinary research. Managing Director, Research and Transfer Center 'Smart Systems' Department of Computer Science, Hamburg University of Applied Sciences Laboratory: 'Living Place' and 'Creative Space for Technical Innovations' Her research spans Affective Computing , Ambient Displays , Ethical AI , and Hybrid Work Technologies . She explores how technology integrates into human contexts, emphasizing ethics, small data challenges, and environmental awareness. Recent studies include the 'EmotionBike Project' and frameworks for long-term field research. Her work demonstrates a strong focus on Human-Computer Interaction in agile environments, leveraging Machine Learning for real-time health monitoring and stress detection. She investigates the sociotechnical dynamics of ambient displays in professional settings, aiming to enhance workplace efficiency and user-driven innovation. Key trends in her publications include Smart Systems for ethical agency, Environmental Monitoring via interactive interfaces, and Digital Ethics in academic labs. Her interdisciplinary approach merges Computer Science Education with Sustainability and Public Engagement strategies.

Felix Süttmannمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Bayesian Statistics
• Meta-Research
• +۷ مورد دیگر

Felix Süttmann is an applied statistician affiliated with Georg-August-University Göttingen and works at the German Institute for Economic Research (DIW Berlin) since 2022. He focuses on survey methodology, including nonresponse modeling, mixed-mode effects, and complex weighting, while also engaging with Bayesian statistics and machine learning applications. Doctoral candidate at Berlin Graduate School of Social Sciences Previously worked as a sampling statistician at the International Association for the Evaluation of Educational Achievement (IEA) for TALIS Research interests span natural language processing, Bayesian modeling, and sustainability, with a strong emphasis on methodological rigor in survey research and econometrics. His work at DIW involves improving nonresponse adjustment factors using ML tools and developing stand-alone weights for refugee samples. Recent publications address sampling techniques in SOEP, nonresponse correction, and weighting methodologies for migration studies, alongside his 2020 work on deep learning for NLP. His methodological contributions include copula-based Bayesian hierarchical models for sample selection. Lectures and presentations cover topics like nationwide household sampling, survey mode preferences, and record linkage weighting adjustments at conferences such as the European Survey Research Association (ESRA) and the Society for Longitudinal and Life Course Studies (SLLS).

• Radiobiology
• Molecular Oncology
• Radiation Therapy
• +۴ مورد دیگر

Dr. Laure Marignol serves as Associate Professor in Radiobiology and Head of Discipline at the Discipline of Radiation Therapy within the School of Medicine . She leads the Translational Radiobiology and Molecular Oncology research laboratory and pioneered the Applied Radiation Therapy Trinity (ARTT) research group. 30+ Total Research Outputs Specializes in hypoxia-targeted therapies and MRI radiomics Focus on improving radiotherapy through molecular oncology

Mariana de Britoمشاهده پروفایل

پژوهشگر

• Hydro-climatic Extremes
• Human-Water Interactions
• Computational Social Sciences
• +۴ مورد دیگر

Dr. Mariana de Brito is a Group Leader at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany, within the Department of Urban and Environmental Sociology. She coordinates the Thirsty Cities Graduate School and the Text in Natural Hazards Research Network, focusing on interdisciplinary research at the intersection of natural and social sciences. Education: PhD in Geography (University of Bonn, 2018), MSc in Environmental Engineering (UFRGS, Brazil, 2014), BSc in Environmental Engineering (Lutheran University of Brazil, 2011) Research Interests: Her work centers on hydro-climatic extremes , synthesizing knowledge via quantitative methods , computational social sciences , and machine learning to analyze drought and flood impacts. She explores human-water interactions and social inequalities using natural language processing and system dynamics modeling . Key projects include Thirsty Cities and GoMo - Governance Monitor . Publications & Projects: Over 2021-2025, her articles address multi-criteria flood risk assessment , maladaptation in water policies , and drought impact cascades . She applies text mining to newspaper archives and stakeholder meetings for real-time risk evaluation. Awards: Arne Richter Award for Outstanding Early Career Scientists (2025) Early Career Short Term Scientific Mission Grant (COST, 2019) Best Oral Presentation Award (Volkswagen Stiftung, 2017) Multiple Postdoc Travel Awards (IAHS, COST, 2018-2023) Advising: Mentors students like J. Sodoge and T.M. Nunes Carvalho on flood vulnerability, drought policy, and computational social methods.

• Theoretical Computer Science
• Property Testing
• Approximation Algorithms
• +۸ مورد دیگر

Yuichi Yoshida is a Professor at the National Institute of Informatics (NII), affiliated with the Principles of Informatics Research Division. He also holds a concurrent position as Senior Researcher at Preferred Networks. His academic career at NII spans from Assistant Professor (2012–2015), Associate Professor (2015–2022), to full Professor since 2022. He serves as Vice Director of the Global Research Center for Big Data Mathematics at NII and has held advisory and research roles at Preferred Infrastructure and the Ministry of Education, Culture, Sports, Science and Technology (MEXT). Ph.D. in Informatics, Kyoto University, 2012 Master of Informatics, Kyoto University, 2009 Bachelor of Engineering, Kyoto University, 2007 His research focuses on theoretical computer science, particularly property testing , approximation algorithms , sublinear-time algorithms , and constraint satisfaction problems . He investigates how theoretical insights can be applied to real-world graphs, with recent emphasis on average sensitivity of algorithms and Lipschitz continuity in combinatorial optimization . His work bridges theory and practical scalability in large network analysis and machine learning. The most recent 15 publications (2022–2025) demonstrate a strong trend in algorithmic stability, spectral methods for hypergraphs, influence propagation in networks, and learning-augmented algorithms. Key venues include FOCS, SODA, ICALP, and NeurIPS, reflecting high impact in theoretical computer science and machine learning. Topics such as Lipschitz continuity, average sensitivity, spectral sparsification, and sublinear algorithms recur, indicating a cohesive research program on robust and efficient computation. Best Paper Award, AISTATS (2018) MEXT Commendation for Science and Technology – Young Scientists’ Prize (2017) Inoue Research Award for Young Scientists (2014) KDDI Foundation Award (2024) Funai Information Technology Award (2024) Yuichi Yoshida advises PhD students and hosts numerous postdocs, RAs, and international interns. He leads major research projects funded by JSPS and JST, including Grants-in-Aid for Scientific Research (S) and PRESTO programs. His service includes program committee roles for ICML, NeurIPS, SODA, and ICALP. He has co-authored a book on Property Testing and contributed to encyclopedias on big data technologies. He leads research teams on algorithm desensitization and large-scale graph algorithms, and has been program co-chair for GRADES-NDA'23. His lab fosters international collaboration, hosting interns from top universities worldwide.

• Precision Medicine
• Rare Disease Diagnosis
• Data Science
• +۷ مورد دیگر

Matthew Might is a Professor of Internal Medicine and Computer Science at the University of Alabama at Birmingham's Heersink School of Medicine, where he serves as Director of the Hugh Kaul Precision Medicine Institute and Co-Director of the Center for Precision Animal Modeling. He also holds a position as Senior Lecturer in the Department of Biomedical Informatics at Harvard Medical School. His career spans academia, government service at The White House (2016-2018), and industry as co-founder of Pairnomix, LLC and Q State Biosciences, Inc. Might's research program uniquely bridges computer science and precision medicine, born from his personal quest to diagnose his son Bertrand who was the first identified case of a novel genetic disorder. His work applies data science, artificial intelligence, machine learning, and formal reasoning to accelerate biomedical science in service of patients. His broader interests include oligonucleotide therapeutics, model organisms, drug screening, and medicinal chemistry. As Director of the Precision Medicine Institute, he oversees a comprehensive research portfolio that includes significant bench science targeted at tailoring therapeutics to individual patients. Analysis of Might's recent publications reveals a strong focus on rare genetic disorders, particularly NGLY1 deficiency, with extensive work on disease modeling using induced pluripotent stem cells (iPSCs). His research spans multiple disciplines including genomics, proteomics, neurology, and drug discovery, with a notable emphasis on the Undiagnosed Diseases Network. Recent work has also addressed the COVID-19 pandemic through computational drug discovery and investigations into viral pathogenesis. Throughout his career, Might has maintained a dual focus on theoretical computer science and practical medical applications. His early work included significant contributions to programming language theory, abstract interpretation, and control-flow analysis, which he has successfully translated into biomedical applications. He has been instrumental in developing computational approaches to rare disease diagnosis and matchmaking patients with similar conditions. Might has been actively involved with the Undiagnosed Diseases Network, contributing to numerous collaborative studies that have advanced understanding of rare genetic conditions. His work emphasizes patient-centered approaches to precision medicine, with projects focused on genomic healthcare empowerment and family communication of genetic results. He has also developed innovative computational methods for RNA-seq analysis and drug screening. His leadership extends to directing research teams working on multiple fronts of precision medicine, including laboratory-based investigations of genetic disorders, computational approaches to drug discovery, and patient matchmaking strategies for rare disease communities. Might's interdisciplinary approach has created bridges between computer science theory and practical clinical applications, demonstrating how computational methods can directly impact patient care for those with rare and undiagnosed conditions.

Marco Salvalaglioمشاهده پروفایل

دانشیار

• Continuum and Mesoscale Modeling
• Phase-Field Crystal Modeling
• Surface Diffusion and Dewetting
• +۵ مورد دیگر

Marco Salvalaglio is an Emmy Noether Group Leader at Technische Universität Dresden, leading the Mesoscale Material Modeling & Simulations Group since March 2021 under the DFG Emmy Noether Programme. He holds an apl. Professorship in Computational Materials Science (awarded 2024) and is affiliated with the Institute of Scientific Computing, Dresden Center for Computational Materials Science (DCMS), and Institute for Scientific Computing (IWR). His educational background includes a Ph.D. in Materials Science (2016), M.Sc. in Physics (2012), and B.Sc. in Physics (2010), all from the University of Milano-Bicocca. He completed an Italian Habilitation as Associate Professor (2020) and Full Professor (2025) in Theoretical Condensed-Matter Physics. Salvalaglio's research centers on continuum and mesoscale modeling of material properties , with expertise in Phase-Field/Phase-Field Crystal (PFC) modeling, surface diffusion, dewetting, heteroepitaxy, defect dynamics, and pattern formation. His work bridges solid-state physics, computational materials science, and applied mathematics, focusing on developing coarse-grained approaches to explain experimental outcomes. Current projects include elasticity in atomistic descriptions, PFC modeling for surface diffusion, solid-state dewetting simulations, and machine learning integration. Analysis of his 15 most recent publications reveals a strong emphasis on hyperuniformity analysis , grain boundary dynamics , and multiscale modeling of crystalline materials. Key trends include topological characterization of nanostructures, disconnection-mediated microstructure evolution, and thermodynamic modeling of non-equilibrium systems. Awards: DFG Heinz Maier-Leibnitz Prize (2025) Richard von Mises Prize - GAMM (2024) Young Academy of Europe Fellowship (2023) MSMSE Emerging Leader Award (2023) TU-Dresden Young Investigator (2021) He mentors students through research projects in computational materials science and applied mathematics, with funding primarily from the DFG Emmy Noether Programme. His group actively recruits for Ph.D. positions focused on mesoscale modeling challenges. Salvalaglio maintains extensive international collaborations, evidenced by invited talks at 25+ global conferences (2021-2026) including TMS Annual Meeting, GAMM, and E-MRS. The Mesoscale Material Modeling & Simulations Group operates within TU Dresden's computational ecosystem, leveraging resources from DCMS and IWR to develop open modeling frameworks for material microstructure evolution.

Rahamatullah Khondokerمشاهده پروفایل

استاد

• Cybersecurity
• Digitalization
• Automotive Security
• +۱۱ مورد دیگر

Prof. Dr.-Ing. Rahamatullah Khondoker is a Professor of Business Information Systems with a focus on IT Security and Digitalization at THM University of Applied Sciences . He leads the CYSECDIGITAL research group, specializing in cybersecurity solutions for digitalization, autonomous mobility, and smart systems. PhD candidates under his supervision include Adnan Kujovic, Kumar Saurabh, Md. Zahirul Islam, and Khandaker Mohammad Mohiuddin. Projects funded by Erasmus+, DAAD, and THM Strategic Fund include ZDayIIoTSec and CyModeB . He has collaborated with institutions like IIT Delhi, BUET, and IIIT Allahabad, and signed MOUs with universities in Bangladesh, India, and Thailand. Research Focus : His work bridges Cybersecurity and Digitalization , with key contributions in SDN/NFV security, automotive cybersecurity (ISO/SAE 21434), IIoT, and AI-driven threat modeling frameworks like STRIDE and TMAP. Recent Publications highlight trends in automotive cybersecurity , IoT security audits , zero-day exploit detection , and AI applications for intrusion detection . His research spans threat intelligence integration, blockchain for supply chains, and machine learning in precision agriculture. Scientific Contributions : Nominated for best paper awards at AINTEC 2014 and IEEE NetSoft 2016. Co-authored foundational works on SDN/NFV security (Springer, 2018) and Industrie 4.0 security (Springer, 2017). Industry Collaboration : Partnerships with Continental, ANSYS, DRASS, and others in automotive, IIoT, and cybersecurity domains. He also leads the Automotive Cybersecurity Academy and advises on digitalization policy.