جست‌وجوی استاد و پژوهشگر

Lucas Schmittمشاهده پروفایل

پژوهشگر

• Machine Learning
• Partial Differential Equations
• Numerical Analysis
• +۳ مورد دیگر

Lucas Schmitt is a PhD student in the Professorship for Mathematics III (Mathematics of Machine Learning) at the University of Würzburg's Institute of Mathematics. His research focuses on adversarial robustness in machine learning using variational methods and geometric approaches, particularly through the DFG-funded GeoMAR project. He also investigates data-driven analysis of young handball players' development through the Handball Metastats initiative. Schmitt contributes to open-source projects, including a reinforcement learning agent for the Bomberman game, demonstrated in his GitHub repository. His work bridges theoretical mathematics with practical applications in machine learning and sports analytics. Department: Institute of Mathematics Supervisor: Prof. Dr. Leon Bungert Location: Würzburg, Germany Research interests include partial differential equations, numerical analysis, and calculus of variations applied to machine learning challenges. His GitHub contributions highlight practical implementations of reinforcement learning algorithms.

Jonas Kucklingمشاهده پروفایل

پژوهشگر ارشد

• Artificial Life
• Swarm Robotics
• Artificial Intelligence
• +۳ مورد دیگر

Jonas Kuckling is a postdoctoral researcher at the Department of Computer and Information Science, University of Konstanz, and a fellow at the Zukunftskolleg. His research focuses on artificial life and swarm robotics, particularly open-endedness in embodied collectives, integrating disciplines including biology, chemistry, physics, computer science, philosophy, and art. Dr. Kuckling obtained his doctoral degree in April 2023 from the Artificial Intelligence research laboratory (IRIDIA) at the Université Libre de Bruxelles, Belgium. His doctoral work established foundations for his current investigations into emergent collective behaviors in robotic systems. His primary research interests center on artificial life and swarm robotics, with emphasis on open-ended evolution in embodied collectives. He explores how interdisciplinary approaches can generate autonomous systems capable of continuous novelty without external direction, bridging computational models with biological principles of self-organization and adaptation. This work addresses fundamental challenges in scalability, robustness, and emergent complexity in multi-robot systems. Analysis of his recent publications reveals a concentrated focus on automatic modular design of robot swarm control software, particularly through behavior trees and optimization techniques like simulated annealing. His work progresses from theoretical frameworks (e.g., manifestos on off-line design) to practical implementations (AutoMoDe tools), with increasing integration of learning, communication, and edge computing in large-scale swarm deployments. No scientific awards are documented in available sources. Dr. Kuckling currently leads an interdisciplinary research project at the Zukunftskolleg, collaborating with experts in complex systems, artificial life, AI, and robotics to advance open-endedness in embodied collectives. No information is available regarding student advising or specific grant funding beyond his fellowship appointment.

• Recommender Systems
• Educational Technology
• Multi-Criteria Decision Making
• +۳ مورد دیگر

Yong Zheng is a Professor in the College of Computing at Illinois Institute of Technology in Chicago, with additional affiliations at DePaul University. His research focuses on intelligent systems with specialization in recommender systems, multi-criteria decision frameworks, and educational technologies. He develops algorithms for context-aware recommendations and explores applications in sustainable investments, educational personalization, and AI-enhanced learning. His primary research interests include: Design of transparent multi-stakeholder recommendation frameworks Context-aware modeling using neural networks and matrix factorization Educational group recommendations with personality-aware adaptations Multi-objective optimization for financial and learning systems Analysis of recent publications (2023-2025) shows strong focus on: Hybrid recommendation approaches combining multi-criteria decision making Open educational datasets and tools for recommender system development Applications of generative AI in data science pedagogy Sustainable investment modeling through portfolio optimization

• Additive Manufacturing in Construction
• Structural Design
• Concrete 3D Printing
• +۴ مورد دیگر

Harald Kloft is a Full Professor of Structural Design and Director of the Institute of Structural Design (ITE) at TU Braunschweig, within the Faculty of Architecture, Civil Engineering and Environmental Sciences. He holds a Dipl.-Ing. in Civil Engineering from TU Darmstadt (1982–1990) and a Doctorate in Civil Engineering from TU Darmstadt (1998). His academic career includes roles as a visiting professor at TU Graz and TU Kaiserslautern, and he founded the engineering practice office for structural design (osd) in 2002. **Research Interests**: Kloft focuses on additive manufacturing in construction, digital fabrication, and sustainable structural design. His work emphasizes innovations like robotic 3D concrete printing, material optimization, and resource-efficient construction techniques. He leads the Collaborative Research Center TRR 277 (Additive Manufacturing in Construction) and has contributed significantly to projects like Shotcrete 3D Printing (SC3DP) and hybrid modular construction systems. **Notable Contributions**: Over 150+ publications, including influential works on robotic concrete fabrication, machine learning in structural assessment, and sustainable building practices. He actively collaborates with industry partners, advancing technologies like wire-and-arc additive manufacturing (WAAM) and non-waste wax formwork. **Awards & Grants**: While no explicit awards are listed, his leadership roles (e.g., spokesperson of TRR 277) and extensive grants (e.g., BMBF-funded projects) reflect his academic and industrial impact. His research infrastructure supports early-career researchers and fosters interdisciplinary collaboration. **Labs & Teams**: Directs the ITE lab, specializing in structural design and digital fabrication. Collaborates with teams on projects like the Capricorn Bridge and adaptive coastline protection structures using additive manufacturing.

• Entity Resolution
• Knowledge Graphs
• Anomaly Detection
• +۴ مورد دیگر

Vassilis Christophides is a Researcher at INRIA Paris, France, specializing in data management and knowledge systems. His research spans entity resolution, knowledge graphs, anomaly detection, and IoT analytics, with recent focus on fairness-aware algorithms and explainable AI. He maintains active collaborations with institutions across Europe and has published extensively in top-tier venues including VLDB, ICDE, and KDD. His core research investigates: Scalable entity resolution techniques for web-scale data Knowledge graph construction and alignment methodologies Real-time anomaly detection in streaming environments Fairness and bias mitigation in data integration pipelines Edge computing optimizations for IoT analytics Recent publications demonstrate a strong trend toward responsible data science, combining foundational data management with emerging concerns in AI ethics. His work frequently develops novel methods for: (1) improving transparency in automated systems through explainable anomaly detection, (2) ensuring fairness in entity resolution workflows, and (3) optimizing resource-constrained edge environments for continuous analytics. Dr. Christophides leads research initiatives at INRIA and collaborates on European projects involving streaming data processing, knowledge representation, and distributed computing infrastructures. His team focuses on bridging theoretical frameworks with practical implementations for web-scale data challenges.

• Database Systems
• Cloud Computing
• Software as a Service (SaaS)
• +۷ مورد دیگر

Dean Jacobs is a prolific computer-science researcher whose career has focused on database systems, cloud computing, and software-as-a-service architectures. While no current institutional affiliation is provided in the dblp source, his continuous publication record in top-tier venues such as SIGMOD, VLDB, CIDR, IEEE CLOUD, and BTW from 1982 to 2013 attests to senior faculty status. Education PhD, Computer Science, Cornell University, USA, 1985 (Dissertation: General Correctness: A Unification of Partial and Total Correctness ) Research Interests Jacobs’ research spans the full lifecycle of data management in cloud and enterprise environments. Early work concentrated on type systems and formal semantics for database programming languages, contributing foundational insights into algebraic specifications, delayed updates, and active-database rule semantics. During the 2000s his focus shifted to multi-tenant architectures and SaaS, pioneering schema-mapping techniques, extensibility models, and performance-prediction methods for large-scale, elastic database clusters. More recently he has investigated operational business intelligence, real-time analytics, and SLA-aware resource management for cloud-hosted transactional and analytical workloads. Publication Trends Across three decades, Jacobs has produced a coherent body of work that moves from theoretical database-language design to applied cloud-data management. The 1990s papers revolve around formal methods—type theory, rule languages, and transaction semantics. From 2003 onward, the emphasis transitions to SaaS and cloud systems: multi-tenancy, elasticity, and service-level guarantees. His 2010–2013 output integrates these themes into practical systems for operational BI and robust tenant placement in in-memory clusters. Collaborations & Mentoring Although advisees are not listed in the dblp data, Jacobs has an extensive co-author network including Alfons Kemper, Stefan Aulbach, Jan Schaffner, Richard Hull, and Michael Seibold, indicating long-standing collaborative relationships and likely supervision of graduate students and post-docs.

• Biometrics
• Computer Vision
• Pattern Recognition
• +۳ مورد دیگر

Maria De Marsico is an academic affiliated with Sapienza University of Rome, Italy, specializing in biometrics, computer vision, and pattern recognition. She actively contributes to conferences such as ICPRAM, serving as editor for multiple proceedings. Her research focuses on gait and facial recognition, wearable sensor applications, and multimodal interaction systems. She collaborates with institutions like SCITEPRESS and Springer, publishing extensively in journals like Pattern Recognition Letters and IEEE Transactions . Her work addresses challenges in biometric systems, emotion recognition, and accessibility through AI-driven solutions. Maria co-organizes workshops on Games-Human Interaction (GHItaly) and contributes to fields like e-learning accessibility for deaf students via SignWriting tools. Her projects include developing applications such as VQAsk for visually impaired users and exploring gait recognition via smartphone accelerometers. She also investigates robust machine learning models for facial expression analysis and dataset rebalancing to reduce bias in demographic classification. Her research spans from theoretical contributions, such as surveys on image integrity methods, to applied systems like the Biopen-Fusing authentication framework. She collaborates with global researchers on topics ranging from microplastics detection to UAV joystick interfaces, demonstrating interdisciplinary impact.

Ralf Kornمشاهده پروفایل

استاد

• Financial Mathematics
• Stochastic Control
• Risk Management
• +۳ مورد دیگر

Ralf Korn is a Professor in the Department of Stochastic Control and Financial Mathematics at the Rheinland-Pfälzische Technische Universität Kaiserslautern (RPTU). His research focuses on Financial Mathematics, Stochastic Control, and Risk Management, with a particular emphasis on portfolio optimization, computational finance, and actuarial science. He holds leadership roles in academic organizations, including the German Actuarial Association (DAV) and the German Association for Insurance and Financial Mathematics (DGVFM). His work bridges theoretical advancements with practical applications, such as developing models for energy markets, reinsurance strategies, and sustainable investment frameworks. Prof. Korn’s research interests include continuous-time portfolio optimization, risk assessment under uncertainty, and the application of machine learning techniques like GANs in financial and energy contexts. He has contributed to seminal works on worst-case portfolio optimization and the integration of climate scenarios into asset management. His interdisciplinary approach addresses challenges in insurance, energy economics, and regulatory compliance. His recent publications span topics such as optimal trading strategies in electricity markets, robust reinsurance models, and the valuation of complex financial instruments. He is a co-editor of the European Actuarial Journal and has authored books on modern financial mathematics and Monte Carlo methods. His academic service includes roles as Dean of the Faculty of Mathematics (2005–2011) and leadership in research clusters like the Landesexzellenzcluster (2005–2008). Prof. Korn’s collaborative projects often involve industry partnerships, focusing on real-time pricing, computational finance, and the ethical implications of financial modeling. His work on sustainability and taxation in portfolio management reflects current trends in global financial practices.

Maria Bergemannمشاهده پروفایل

پژوهشگر

• Stellar Spectroscopy
• Galactic Structure
• Chemical Evolution
• +۷ مورد دیگر

Maria Bergemann is a Lise Meitner Group Leader and Independent Research Group Lead at the Max Planck Institute for Astronomy in Heidelberg, heading the Stellar Spectroscopy and Populations research group within the Galaxies and Cosmology department. Her research spans theoretical and observational astrophysics with a focus on stellar physics, galactic evolution, and cosmic nucleosynthesis. Her work encompasses non-equilibrium (Non-LTE) radiative transfer modeling and 3D radiation-hydrodynamics simulations of stellar atmospheres, lifting canonical approximations to provide new insights into stellar structure. She is actively involved in major astronomical programs including the Gaia-ESO survey, 4MOST, PLATO, WEAVE, and MSE projects, analyzing multi-million datasets to address fundamental questions about the origins of chemical elements, galactic structure, and star-planet connections. Her group has made significant contributions to understanding galactic chemo-dynamics, including studies of stellar clusters, abundance gradients, and the structure of the Milky Way disk and bulge. Dr. Bergemann's research has received support from the Max Planck Society, European Research Council, and the German Research Foundation. Her team develops sophisticated mathematical and statistical algorithms for robust model-data comparison and pattern recognition in multi-dimensional parameter spaces, including adaptation of the Payne code for Gaia-ESO and 4MOST data analysis and introduction of Bayesian spectroscopy methods. Max Planck Society funding recipient European Research Council funding recipient German Research Foundation funding recipient She has supervised numerous PhD students, postdocs, and master's students who have gone on to faculty positions at institutions worldwide including Purdue University, Frankfurt University, Yunnan University, and the University of Sheffield. Her current research group includes several members working on diverse projects related to stellar atmospheres, galactic structure, and nucleosynthesis. The group maintains the public database http://nlte.mpia.de for 3D Non-LTE models, which are essential for deriving accurate fundamental parameters of stars and their detailed chemical compositions.

Rex Leiمشاهده پروفایل

پژوهشگر

• Machine Learning
• Theoretical Computer Science
• Algorithms
• +۳ مورد دیگر

Rex Lei is a Researcher in the Algorithms and Complexity Department (D1) at the Max Planck Institute for Informatics, where he conducts theoretical research on replicability for machine learning algorithms since joining in October 2024. He received his PhD from the University of California San Diego in 2024 under Russell Impagliazzo, with his dissertation "Replicable Learning Algorithms" publicly available online. The introduction is designed for general audiences without mathematical prerequisites. Lei's research centers on theoretical computer science foundations of machine learning, specializing in replicability, reproducibility, and algorithmic stability. His work bridges learning theory and complexity theory while exploring connections to privacy, adaptive generalization, and robustness against noise like Massart errors. This interdisciplinary approach addresses fundamental reliability challenges in modern learning systems. His publication trajectory shows increasing integration of replicability with privacy frameworks and generalization theory, demonstrating how stability properties enable reliable machine learning outcomes across diverse theoretical contexts. Lei actively contributes to the Algorithms and Complexity research group at MPI Informatics, collaborating with leading theorists on foundational problems in computational learning.

István Sárándiمشاهده پروفایل

پژوهشگر

• 3D Human Pose Estimation
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

István Sárándi is a postdoctoral researcher in the Real Virtual Humans group led by Prof. Gerard Pons-Moll at the University of Tübingen, Germany. He holds a PhD in Computer Vision from RWTH Aachen University (2023) and a master’s degree in Computer Science from RWTH Aachen (2015), with earlier studies at the Budapest University of Technology and Economics (2011). His research focuses on robust and efficient 3D human pose estimation, including work on truncation-robust methods (MeTRAbs), synthetic data generation (STAGE), and cross-dataset learning. He has won 3D pose estimation competitions at ECCV 2018 and 2020 and received Outstanding Reviewer Awards at CVPR 2021 and 2022. His PhD was funded by the Bosch Research Foundation. Education: PhD: RWTH Aachen University (2023) MSc: RWTH Aachen (2015) BEng: Budapest University of Technology and Economics (2011) Key Achievements: 1st place in ECCV 2018 and 2020 3D pose challenges Outstanding Reviewer Awards (CVPR 2021/2022) Developed MeTRAbs, STAGE, and other influential methods Publications emphasize advancing 3D human understanding through robust learning techniques, synthetic data, and multi-task frameworks. Supervised theses include work on temporal modeling of multi-person interactions and pose-conditioned human image synthesis. Active in teaching, including deep learning and computer vision courses at RWTH Aachen.

• Computer Vision
• Machine Learning
• Video Analysis
• +۳ مورد دیگر

Laura Sevilla is a Reader (Associate Professor) at the School of Informatics, University of Edinburgh, and an ELLIS Scholar. Her research focuses on understanding the visual world in motion, encompassing video representations, high-level perceptual tasks, and joint learning of video and language. She actively organizes workshops, including 'What is Motion For?' at ECCV 2022 and 'Computer Vision for Global Challenges' at CVPR 2019, aiming to address humanitarian and global issues through vision technology. Her academic journey includes leadership roles such as Program Chair at BMVC 2021 and Area Chair at CVPR 2021 and 2024. She has received the Google Scholar Award 2022. Laura mentors a dynamic team of PhD students, including Dr. Shreyank N Gowda (now at Oxford), Anil, Gen, Davide, and Kiyoon, whose work spans video analysis, temporal dynamics, and efficient learning methods. Her outreach efforts emphasize accessibility, such as developing vision tools for the blind and educational workshops in the Philippines. Key contributions include the ALBA tracking method, FASTER video classification, and the Temporal Dataset benchmark. Her research bridges theoretical advancements with practical applications, fostering collaboration across disciplines within the top-ranked School of Informatics.

Mario Krennمشاهده پروفایل

استاد

• Artificial Intelligence
• Quantum Physics
• Machine Learning
• +۷ مورد دیگر

Mario Krenn is a Full Professor (W3) of "Machine Learning in Science" at the University of Tübingen since June 2025, leading the Artificial Scientist Lab. His research bridges artificial intelligence , quantum physics , and experimental design , focusing on developing AI systems that act as "artificial muses" to inspire novel scientific discoveries. ERC Starting Grant recipient (2024) for ArtDisQ project Developed PyTheus, a framework for AI-driven quantum experiment design Created XLuminA, a JAX-based simulator for microscopy and photonics Co-inventor of SELFIES, a robust molecular string representation His work has led to experimental implementations of AI-designed quantum protocols (e.g., entanglement without pre-existing resources) and gravitational wave detector concepts. He explores scientific understanding in human-AI collaboration, nonlocal interference phenomena, and the philosophical implications of AI-generated discoveries. Recent projects include predicting research trends via knowledge graphs and developing virtual reality tools to visualize AI-conceived quantum experiments. Scientific awards include the ERC Starting Grant 2024 and International Quantum Technology Emerging Researcher Award (Highly Commended) 2020 . He serves on the editorial board of Machine Learning: Science and Technology and actively promotes open science through GitHub repositories and community-driven initiatives.

• High Performance Computing
• Agent Based Modeling
• Analysis of Large Datasets
• +۴ مورد دیگر

Parantapa Bhattacharya is a Research Scientist at the Network Systems Science and Advanced Computing Division of the Biocomplexity Institute and Initiative at the University of Virginia. His work focuses on developing computational tools and systems for extracting meaningful insights from complex datasets, with applications spanning public health, social networks, and artificial intelligence. Dr. Bhattacharya's research interests center around High Performance Computing, Agent Based Modeling, Analysis of Large Datasets, and Explainable AI with special emphasis on Natural Language Processing models. His work bridges the gap between theoretical computer science and practical applications in epidemiology and social systems, creating scalable frameworks that can handle real-world complexity and data volume. His publication record shows a clear trajectory from social media analysis (2013-2016) toward pandemic modeling and response (2019-2022), reflecting the growing importance of computational epidemiology. His research spans multiple disciplines including computer science, epidemiology, social network analysis, and public health, with a consistent focus on developing scalable computational frameworks. The most recent publications demonstrate significant contributions to pandemic response infrastructure, with several papers related to the PanSim framework for agent-based pandemic modeling. Notable recognitions include being a Finalist for the 2021 ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research and having work published in Nature Computational Science, highlighting the impact and relevance of his research during the global pandemic. Dr. Bhattacharya has developed significant computational infrastructure, most notably PanSim - a distributed pandemic simulator that enables large-scale agent-based modeling of disease transmission. His technical expertise spans distributed systems, high-performance computing, and machine learning, allowing him to tackle complex problems at the intersection of computer science and public health.

Manuel Gomez Rodriguezمشاهده پروفایل

استاد

• Machine Learning
• Social Information Systems
• Counterfactual Reasoning
• +۲ مورد دیگر

Manuel Gomez Rodriguez is a Professor at the Max Planck Institute for Software Systems, specializing in machine learning, social information systems, and human-AI collaboration. His research bridges algorithmic fairness, counterfactual reasoning, and decision-making systems, with applications in public health, refugee resettlement, and language model evaluation. He leads projects developing frameworks for human-aligned AI systems and mitigating counterfactual harms in algorithmic decisions. Research Focus His work spans the development of novel methods for evaluating large language models, designing decision support systems with counterfactual guarantees, and modeling human-AI collaboration dynamics. Key innovations include prediction-powered ranking systems, coupled token evaluation techniques, and computational models of responsibility attribution. Awards and Recognition ERC Consolidator Grant (2024) Best Paper Award at ICML Workshop on AI and HCI (2023) ELLIS Fellow (2019) Outstanding Paper Award at NIPS (2013) Leadership He leads the ERC-funded project on Counterfactuals in Minds and Machines and supervises doctoral students on topics at the intersection of machine learning and social good. His lab maintains collaborations with global institutions to tackle real-world challenges through algorithmic innovations.