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Miguel L. PardalView profile

Assistant Professor

• Distributed Systems
• Cybersecurity
• Internet of Things
• +2 more

Miguel L. Pardal is an Assistant Professor at Instituto Superior Técnico, Universidade de Lisboa, and a researcher at INESC-ID's Distributed Systems Group (GSD). His work focuses on Cybersecurity, Distributed Systems, Internet of Things (IoT), and Cloud Computing. He has led projects like the EU-funded Safe Cloud initiative and contributed to intrusion recovery systems (e.g., MIRES, μVerum) and blockchain-based solutions (e.g., ChainGuard, EvoChain). His research addresses challenges in secure communication, resilient distributed architectures, and location-based systems. Since 2002, he has taught courses on Network Security, Distributed Systems, and Operating Systems. His projects often intersect academia and industry, emphasizing practical applications in critical infrastructure and smart technologies. Collaborations include developing systems for vehicle-pedestrian safety (SafeWalk) and decentralized identity management. He has authored over 30 publications, with recent work covering blockchain resilience, intrusion detection in industrial processes (IM-DISCO), and generative AI for distributed algorithms. His contributions span academic conferences (DSN, EuroSys) and journals (IEEE Transactions).

Wong, Su LeenView profile

Researcher

• Materials Science
• Crystal Plasticity Modeling
• Twinning-Induced Plasticity (TWIP)
• +4 more

Dr. Wong Su Leen is a researcher at the Max-Planck-Institut für Eisenforschung GmbH under the Max Planck Society . Her work focuses on crystal plasticity modeling of advanced high-strength steels and aluminum alloys, particularly investigating TRIP (transformation-induced plasticity) and TWIP (twinning-induced plasticity) effects. Key research areas: Multi-scale material simulation, ab-initio guided flow stress models, and microchemistry-dependent strengthening mechanisms. Co-developer of the DAMASK simulation toolkit , a widely used software for crystal plasticity and damage modeling in polycrystals. Her publications emphasize computational approaches to understanding deformation mechanisms, stacking fault energy effects, and stress distribution in face-centered cubic metals. She has presented at international conferences including Thermec , ICAA , and COMPLAS . While no students or awards are explicitly listed, her collaborative work with institutions like National Institute for Materials Science (Japan) and University of Düsseldorf highlights her interdisciplinary impact.

• Cosmology
• Gravitational Waves
• Dark Energy
• +5 more

Dr. Miguel Zumalacarregui is the Group Leader of the Cosmology group within the Astrophysical and Cosmological Relativity Division at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Potsdam, Germany. He is a senior co-chair of the LISA Cosmology Working Group and deputy lead for the Euclid Gravitational Waves Science Working Group, highlighting his leadership in major international astrophysics collaborations. PhD in Physics, Autonomous University of Madrid (2012) Graduate Student, University of Barcelona (2008–2012) MA in Theoretical Physics, Autonomous University of Madrid (2009) His research focuses on cosmology, gravitational waves, and fundamental physics, particularly the nature of dark energy and dark matter, testing gravity beyond Einstein, and the cosmological implications of gravitational wave observations. He is the main developer of the hi_class code, a public tool for cosmological predictions in generalized dark energy models. His theoretical work is tightly coupled with observational data from missions like Euclid and LISA. His recent publications span topics such as dark energy reconstruction, gravitational wave lensing, multi-messenger astronomy, modified gravity, and machine learning applications in strong lensing detection. These works, appearing in top journals like Physical Review D and Astronomy & Astrophysics , reflect a strong trend toward combining theoretical modeling with cutting-edge observational and computational techniques to address cosmological tensions and probe new physics. Editor's suggestion, Physical Review D Marie Curie Global Fellow Dr. Zumalacarregui leads a research group and is deeply involved in large collaborative projects like Euclid and LISA, contributing to science working groups and data analysis efforts. His work is supported by significant institutional and collaborative funding, and he plays a key role in shaping the cosmological science case for future gravitational wave observatories. He has no listed formal advisees, but his leadership position implies mentorship of junior researchers. He is a key member of the Cosmology group at the Albert Einstein Institute, contributing to the institute’s focus on astrophysical and cosmological relativity. His team collaborates extensively with the Euclid and LISA consortia, integrating theoretical cosmology with observational data analysis.

Mariana Hill CruzView profile

Researcher

• Ecosystem Modelling
• Marine Biogeochemistry
• Biogeochemical Modelling
• +7 more

Mariana Hill Cruz is a Researcher at the GEOMAR Helmholtz Centre for Ocean Research Kiel , within the FB2: Marine Biogeochemistry division and the FE Biogeochemical Modelling department. She has been actively contributing to marine ecosystem research since 2022, focusing on modeling higher trophic levels and their interactions with environmental dynamics in key upwelling systems such as the Humboldt Current and the Western Baltic Sea. She holds a PhD in Biological Oceanography from Christian-Albrechts-Universität zu Kiel (2022), an MSc in Biological Oceanography (2018), and a BSc in Marine Science from The University of Western Australia (2015). Her research interests center on ecosystem modelling , particularly using the OSMOSE model coupled with biogeochemical frameworks. She investigates fish population dynamics, mesopelagic biomass, habitat suitability, and the impacts of climate and fishing pressures. Her work integrates ecological, biogeochemical, and machine learning approaches to understand complex marine systems. The recent publications highlight a strong trend in end-to-end ecosystem modelling , with emphasis on the Northern Humboldt Current System. Her work spans sensitivity analyses of biogeochemical models, comparisons of fish biomass estimation techniques, and the application of machine learning to forecast species habitats under climate change. These studies reflect interdisciplinary efforts combining oceanography, fisheries science, and computational modelling. BMT Oceanica Consulting Marine Science Prize She has served as Principal Investigator for the BMBF-funded Humboldt Tipping (Phase 2) project and was a doctoral researcher in both Humboldt Tipping and CUSCO projects. She has supervised multiple bachelor's and master's interns and is open to advising students in ecosystem modelling. She is an active member of the ICES Working Group on Integrative, Physical-biological and Ecosystem Modelling . Her work is disseminated through peer-reviewed journals, conference presentations, workshops, and preprints. She has contributed to scientific capacity building by teaching courses on ocean upwelling systems, ecosystem modelling with OSMOSE, and scientific communication. Her research is conducted within collaborative international teams, as evidenced by her co-authorship in multi-institutional studies.

Peter UhrigView profile

Professor

• Digital Linguistics
• Big Data in Linguistics
• Corpus Linguistics
• +12 more

Peter Uhrig is a Professor at the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), holding the Chair of Digital Linguistics with a Focus on Big Data within the Institute of English and American Studies. He is based in Erlangen, Germany, and actively contributes to research and teaching in computational and cognitive linguistics. His research interests lie at the intersection of linguistics, computation, and cognition, with a strong emphasis on corpus-based analysis , construction grammar , multimodal communication , and digital methods in language research . He investigates phenomena such as collocation, argument structure, creativity in language, and spoken word comprehension using large-scale datasets and advanced computational models. His recent publications reveal a clear trend toward interdisciplinary, data-intensive research. He frequently collaborates on projects involving multimodal corpora , gesture analysis , spoken language processing , and computational modeling , often in collaboration with researchers from computer science and cognitive neuroscience. His work is published in leading journals in linguistics and cognitive science. How Collostructional Analysis Contributes to the Description of Argument Structure Constructions with Slots for that-and Infinitive Clauses (2024) The pluralization palette: unveiling semantic clusters in English nominal pluralization through distributional semantics (2024) World Futures through RT’s Eyes: Multimodal Dataset and Methodology (2024) Prof. Uhrig has co-edited several academic volumes and plays a key role in developing research infrastructure, such as the Red Hen Protocol for anonymizing audiovisual data and tools for multimodal analysis. While specific grant funding is not detailed, his involvement in large collaborative projects indicates active grant-supported research. He also contributes to academic service through editorial work and conference organization. He is involved in building digital infrastructure for multimodal research, including work on gesture retrieval systems and data anonymization protocols. His lab or research group appears to focus on integrating computational tools with linguistic theory, particularly within the framework of construction grammar and cognitive linguistics.

• Internet of Things (IoT)
• Smart Grids
• Renewable Energy Systems
• +3 more

Mohamed A. Ahmed is a researcher affiliated with Central Michigan University, School of Engineering & Technology , with additional collaborations across institutions like Ain Shams University, Memorial University of Newfoundland, and Prince Sultan University. His work spans interdisciplinary domains including Internet of Things (IoT) Smart Grids Renewable Energy Systems Machine Learning Recent research focuses on IoT platforms for smart energy management , including electric vehicle charging infrastructure, remote elderly care systems, and large-scale wind turbine monitoring. He has contributed to wireless network architectures for cyber-physical energy systems and developed fuzzy logic algorithms for EV charging prioritization. Publications highlight expertise in time-series forecasting for energy demand, clustering algorithms for smart grid planning, and data aggregation techniques in wind energy applications. Collaborations frequently involve researchers such as Young-Chon Kim, Ali Mohamed Eltamaly, and José Luis Gallardo.

Michael MuehlebachView profile

Researcher

• Machine Learning
• Dynamical Systems
• Control Theory
• +3 more

Michael Muehlebach is a Research Group Leader at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany, leading the Learning and Dynamical Systems group. He holds a B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich (2010, 2013) and a Ph.D. from the Institute for Dynamic Systems and Control (2018), advised by Prof. R. D'Andrea. He later pursued postdoctoral research under Prof. Michael I. Jordan at UC Berkeley. Research Interests: Machine Learning, Dynamical Systems, Control Theory, Optimization, Physics-Informed Learning for Cyber-Physical Systems. Scientific Awards: Outstanding D-MAVT Bachelor Award, Willi-Studer Prize, ETH Medal, HILTI Prize, Branco Weiss Fellowship (2018), Emmy Noether Fellowship (2020), Amazon Fellowship (2024). Students: Supervised Julien Kohler (Master's thesis on Quadrotor Control). Projects: Developed Floaty (wind-energy robot), Flying Platform (ducted fan actuation testbed), One-Wheel Cubli (3D inverted pendulum), and electromagnetic navigation systems.

• Blockchain Technology
• Smart Grids and Energy Systems
• Artificial Intelligence
• +5 more

Tudor Cioara is a Professor at the Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University in Romania. With over 100 publications spanning from 2008 to 2025, his research establishes him as a leading academic in the intersection of computer science and energy systems. His research interests focus on blockchain technology applications in energy systems, smart grid optimization, distributed computing, and AI-driven energy management. Cioara's work bridges theoretical computer science with practical energy applications, developing novel approaches for peer-to-peer energy trading, virtual power plants, and data center energy optimization. His recent work has expanded into federated learning, edge computing, and large language models applied to energy systems. Cioara's publication portfolio shows a consistent trajectory of increasing impact, with significant contributions in 2023-2025 focusing on AI-driven energy solutions, blockchain applications, and healthcare technology integration. His work demonstrates strong interdisciplinary connections between computer science, electrical engineering, and healthcare domains. He has received recognition through numerous collaborations with international researchers and institutions, particularly in European energy and computing projects. His work on blockchain-based energy trading systems and AI optimization for smart grids represents some of the most influential contributions in his field. Cioara leads a research group that has produced significant work on data center optimization, energy flexibility management, and digital twin applications for energy systems. His team has developed innovative approaches for integrating renewable energy sources, managing electric vehicle charging, and creating secure energy trading platforms using blockchain technology.

• Galaxy Evolution
• Computational Astrophysics
• Cosmological Simulations
• +5 more

Eric Rohr is a postdoctoral researcher at the Max Planck Institute for Astronomy (MPIA) in Heidelberg, Germany, specializing in computational astrophysics and cosmological simulations. He recently completed his Ph.D. at MPIA under Dr. Annalisa Pillepich as part of the International Max Planck Research School for Astronomy and Cosmic Physics (IMPRS), with upcoming transition to a postdoc fellowship at the Max Planck Institute for Astrophysics (MPA) in Garching, Germany, in Prof. Volker Springel's Computational Astrophysics department. His research focuses on galaxy evolution , hydrodynamic simulations , and the cosmic baryon cycle , particularly through projects like the IllustrisTNG and TNG-Cluster simulations. Key topics include ram pressure stripping , AGN feedback , and the intracluster medium (ICM) dynamics in galaxy clusters. He has developed novel tools for tracer particle analysis and general relativistic photon propagation in simulation codes. Eric's recent TNG-Cluster publications investigate (1) the evolution of cool ICM gas since redshift 7, (2) observational signatures of satellite galaxy CGM , and (3) machine learning applications for X-ray cluster populations . His work bridges cosmological theory with observational predictions for missions like Euclid, JWST, and XRISM. He actively presents at international conferences such as Building Galaxies from Scratch (Vienna) and Epoch of Galaxy Quenching (Cambridge), with expertise in AREPO simulations and Athena++ module development . Eric's personal interests include mountain running , gravel cycling , and triathlon training , alongside learning German and enjoying vegetarian cuisine.

• Decision Making
• Transformative Life Decisions
• Radical Uncertainty
• +6 more

Shahar Hechtlinger is a Predoctoral Fellow at the Max Planck Institute for Human Development in Berlin, Germany, affiliated with the Adaptive Rationality research group. His work investigates human decision processes in high-stakes transformative life events and under radical uncertainty, addressing real-world challenges from migration to AI-driven societal shifts. His core research spans: Transformative life decisions (emigration, career choices, trauma resolution) Decision making with limited experience (AI societal impact, extreme weather) Social learning strategies Natural language processing and classification algorithms Adaptive/maladaptive beliefs and philosophy of science in cognitive sciences Publication analysis reveals an evolving focus from career counseling and pandemic misinformation toward cutting-edge intersections of large language models and collective intelligence. This trajectory demonstrates interdisciplinary rigor, blending cognitive theory with empirical methods to decode complex human behavior in uncertain environments. No scientific awards were documented in source materials. Similarly, advising activities and grant details remain unspecified. He actively contributes to the Adaptive Rationality team's mission of unraveling decision mechanisms through collaborative projects spanning behavioral experiments, computational modeling, and real-world applications.

• Natural Language Processing
• Computer Vision
• Artificial Intelligence
• +3 more

Aditya Sharma is a researcher at the University of California Santa Barbara , focusing on Natural Language Processing , Computer Vision , and Artificial Intelligence . His work bridges vision-language models, medical AI evaluation, and knowledge graph reasoning. PhD : Advancing AI Understanding in Language & Vision (UCSB, 2024) Research Interests: • Vision-Language Models for geometric reasoning and mixed reality • Medical LLM evaluation and hallucination reduction • Temporal knowledge graph reasoning and multimodal systems Article Trends: Recent publications emphasize vision-language integration (e.g., GeoCoder, OCTO+), LLM reliability in medical contexts (MedHELM), and temporal reasoning (TwiRGCN). He explores applications in dark web security , healthcare diagnostics , and energy-efficient computing .

• Machine Learning
• Computer Vision
• Signal Processing
• +3 more

Chenguang Liu is a researcher at Institut Polytechnique de Paris , France, with a focus on computational methods, machine learning, and control systems. His work bridges theoretical and applied research across multiple domains including computer vision, signal processing, and operations research. Key research areas: Machine Learning, Computer Vision, Computational Physics, Control Theory Notable publication trends: Develops novel algorithms for parallel computing, peridynamic modeling, and real-time systems Recent contributions include Bayesian neural networks for gas-bearing prediction, multi-agent reinforcement learning for UAV swarms, and domain adaptation techniques in object detection. He collaborates extensively with researchers in electrical engineering and applied mathematics disciplines.

Daniel SonntagView profile

Professor

• Intelligent User Interfaces
• Natural Language Processing
• Information Retrieval
• +7 more

Prof. Dr.-Ing. Daniel Sonntag serves as Scientific Director of Interactive Machine Learning at the German Research Center for Artificial Intelligence (DFKI) and holds an Endowed Chair of Applied Artificial Intelligence at University of Oldenburg. He leads multiple significant research initiatives including No-IDLE (Interactive Deep Learning Enterprise), MASTER (Mixed reality ecosystem for teaching robotics), Ophthalmo-AI (intelligent eye care diagnostics), pAItient (AI applications in medicine), and XAINES (explainable AI with narratives). His research spans intelligent user interfaces, natural language processing, information retrieval, dialogue systems, common-sense modeling, and explainable machine learning methods for cognitive computing. His work particularly focuses on multimodal-multisensor interfaces for medical and health systems and Industry 4.0 applications. His research has had industrial impact, with SmartWeb's multimodal interaction design forming the technical infrastructure for Apple's Siri. His recent publications demonstrate strong trends in medical AI applications, particularly in eye tracking, radiology report generation with enhanced interpretability, and interactive federated learning frameworks. His work consistently bridges theoretical AI advances with practical medical and industrial applications. German High Tech Champion Award (2011) As a principal investigator, Sonntag leads national and European projects funded by the Federal Ministry of Education and Research, Federal Ministry for Economic Affairs and Energy, Federal Ministry of Health, and Horizon Europe. He has supervised numerous researchers including Michael Barz, Jan Zacharias, and Alexander Prange across multiple projects. His research group has successfully transitioned technologies to industry, with former researchers joining companies like Apple, Wacom, and Semvox. Sonntag's laboratory work centers on the Interactive Machine Learning department at DFKI, with strong connections to medical institutions and industry partners. His team develops cognitive assistance systems, mixed reality applications for smart factories, and AI-based diagnostic support tools, particularly in ophthalmology and radiology.

Cheng LinView profile

Assistant Professor

• Geometric Modeling
• 3D Vision
• Shape Analysis
• +3 more

Cheng Lin is an Assistant Professor at the Department of Computer Science and Engineering, Macau University of Science and Technology (MUST). He earned his Ph.D. in Computer Science from the University of Hong Kong (HKU) under Prof. Wenping Wang and completed a visiting research period at the Visual Computing Group, Technical University of Munich (TUM), advised by Prof. Matthias Nießner. His B.Eng. degree from Shandong University focused on geometry and graphics. His research focuses on geometric modeling , 3D vision , shape analysis , and computer graphics , with recent contributions in diffusion models for 3D reconstruction, neural surface modeling , and material-aware generation . He has published extensively in top venues like SIGGRAPH, CVPR, and ECCV. Key trends in his publications include advancing single-view 3D generation , multiview consistency , and neural diffusion techniques for geometric and material reconstruction. Notable works include PDT: Point Distribution Transformation with Diffusion Models (SIGGRAPH 2025) and Wonder3D (CVPR 2024). Scientific Awards : CVPR 2024 Most Influential Papers (Corresponding Author) ICLR 2024 Most Influential Papers (First Author) CGF Top Cited Article 2022-2023 Tencent Excellent Contributor [2022] National Scholarship of China [2013-2015] Cheng Lin co-founded the non-profit research group AnySyn3D and has served as a reviewer for journals like TPAMI, TOG, and TVCG, as well as conferences including SIGGRAPH and CVPR.

Christoph WeidenbachView profile

Adjunct Professor

• Automated Deduction
• Decision Procedures
• Theorem Proving
• +5 more

Christoph Weidenbach is an Adjunct Professor in the Computer Science department at Saarland University, Germany, and the head of the research group Automation of Logic at the Max-Planck-Institute for Informatics. His work focuses on automated deduction, decision procedures, and theorem proving, particularly in the context of first-order logic and decidable fragments. He has been actively involved in the development of automated verification tools and techniques, including SAT solvers and SMT frameworks. Education: Diplom (1989), PhD (1996), Habilitation (2000), all in Computer Science at Saarland University. Current Roles: Research Leader (Automation of Logic group, MPI) since 2005; Adjunct Professor (Saarland University) since 2007. Past Roles: Research Coordinator (MPI, 1999-2005); IT Manager (GM Powertrain Europe, 1991-1999). His recent publications highlight trends in automated deduction, formal verification, and constraint solving, with applications in linear arithmetic, network protocols, and semantic web reasoning. He has also contributed to the theoretical foundations of superposition calculi and SCL(FOL) extensions.