Find Academic Supervisors

• Machine Learning
• Natural Language Processing
• Neural Networks
• +4 more

Subhabrata Mukherjee is a prominent researcher in the fields of Machine Learning and Natural Language Processing. His work focuses on advancing techniques such as model compression, multimodal learning, and credibility analysis. He holds a Ph.D. from Saarland University (2017), where he explored probabilistic graphical models for credibility analysis in online communities. His research spans topics including large language models (LLMs), knowledge distillation, and ethical AI. Key contributions include the development of retrieval-augmented generation frameworks, cost-efficient LLM architectures, and systems for mitigating misinformation. His work bridges theoretical advancements with practical applications in healthcare, social media, and multilingual systems. Collaborations with institutions like Microsoft and academic networks highlight his interdisciplinary approach to AI challenges. Research Highlights: Developed GraphRAG for integrating graph structures into generative models Pioneered Hybrid LLM architectures balancing efficiency and quality Advanced techniques for hallucination reduction in LLMs Contributed to fake news detection systems like DeClarE Explored experience-aware recommendation models His publications reflect sustained innovation across NLP, ML systems, and ethical AI, with impactful contributions to both academic conferences (ACL, NeurIPS, KDD) and industry research.

• Virtual Reality
• Augmented Reality
• Computer Graphics
• +16 more

Qi Sun is an Assistant Professor at New York University's Tandon School of Engineering, jointly affiliated with the Department of Computer Science and Engineering and the Center for Urban Science + Progress (CUSP). He leads the Immersive Computing Lab, where his research focuses on advancing virtual and augmented reality through perception-aware computing, neural rendering, and energy-efficient XR displays. Research Interests: His work lies at the intersection of computer graphics, computer vision, and computational perception. He investigates how human perception can guide the design of immersive technologies, with research directions including foveated rendering, generative models for VR/AR, bio-physically inspired wearable displays, and multimodal interaction. His lab collaborates with industry leaders such as Meta to develop next-generation XR systems. The recent publications reflect a strong trend in perception-driven optimization for XR, focusing on reducing computational and energy costs while preserving visual quality. Key themes include gaze-contingent rendering, saliency modeling, holographic compression, and AI-assisted AR guidance. These works are published in top venues like ACM SIGGRAPH, IEEE ISMAR, IEEE VR, and IEEE VIS, often earning best paper awards. Scientific Awards: IEEE Virtual Reality Best Dissertation Award Best Paper Award, ACM SAP 2024 Best Paper Honorable Mention, ACM SIGGRAPH 2024 Best Paper Honorable Mention, IEEE VR 2024 Best Paper Honorable Mention, IEEE VIS 2023 Best Paper Award, IEEE ISMAR 2022 Best Paper Honorable Mention, ACM SIGGRAPH 2022 Best Paper Award, ACM SIGGRAPH 2022 Advising and Grants: Dr. Sun actively recruits PhD students, postdoctoral researchers, and interns, particularly those with backgrounds in neural rendering and generative models. He encourages pre-application collaborations and advises through the Tandon CSE PhD program. His research is supported by academic-industry partnerships, including recent work with Meta on XR display optimization. He promotes interdisciplinary research that bridges urban science, AI, and immersive computing. Labs and Teams: He directs the Immersive Computing Lab at NYU Tandon, which conducts cutting-edge research on human-aligned wearable displays and generative AI algorithms for XR. The lab focuses on making XR devices more efficient, immersive, and accessible for real-world applications in urban environments, healthcare, and education.

• Probability Theory
• Machine Learning
• Stochastic Optimization
• +4 more

Stephan Eckstein is a junior professor in the Department of Mathematics at the University of Tübingen and a member of the university's machine learning cluster. His research bridges probability theory and machine learning with particular focus on stochastic optimization and numerical approximation. Research interests include: Optimal transport theory and its computational aspects Regularization techniques for high-dimensional problems Causal models and probabilistic structures Graphical models in machine learning Graph neural networks Recent publications analyze dimensional stability in optimal transport, exponential convergence rates for Sinkhorn algorithms, and causal modeling in financial time series generation. Contact: stephan.eckstein@uni-tuebingen.de

Jovita LukasikView profile

Research Fellow

• Neural Architecture Search
• Model Robustness
• Deep Learning
• +3 more

Jovita Lukasik is a Research Fellow in the Institute for Vision and Graphics at the University of Siegen. Her research focuses on neural architecture search (NAS), model robustness, and efficient deep learning methods, with applications in computer vision and performance prediction. Key Research Areas: Developing zero-cost proxies for efficient neural architecture evaluation Improving CNN robustness via frequency-based regularization Creating benchmark datasets for architecture design and robustness analysis Exploring generative approaches for efficient NAS Her work has been published in TMLR, GCPR, ECCV, and ICLR. She contributed to the organization of the NAS workshop at ICLR 2021 and co-leads the AutoML seminar series. Recent work investigates transferable surrogates for expressive search spaces and texture/shape biases in vision-language models.

Meysam GoodarziView profile

Lecturer

• Artificial Intelligence
• Data Science
• Economics
• +7 more

Meysam Goodarzi is a Lecturer at the Hertie School in Berlin, Germany, specializing in Data Structures & Algorithms within the Master of Data Science for Public Policy program. His research bridges computer science, economics, and probabilistic modeling, focusing on Bayesian inference, probabilistic graphical models, and optimization techniques. PhD in Computer Science (Humboldt University of Berlin) M.Sc. in Economics (Panthéon-Sorbonne University) His work emphasizes applications in data science and economics, particularly within AI-driven network optimization and 5G technology. He has contributed to EU Horizon 2020 projects, advancing synchronization, localization, and machine learning algorithms in telecommunications. Recent publications highlight his expertise in 5G networks , autonomous systems , and probabilistic modeling , with a focus on synchronization, localization, and communication-sensing integration. Articles span from 2024 to 2019, reflecting sustained contributions to 5G/6G research and AI applications. At Hertie School, he contributes to the Data Science Lab , integrating advanced computational techniques into public policy education. His interdisciplinary approach connects technical advancements in telecommunications with economic analysis and public sector applications.

Claus-Eberhard LiedtkeView profile

Professor

• Image Analysis
• Remote Sensing
• Industrial Automation
• +3 more

Claus-Eberhard Liedtke is a Professor of Digital Signal Processing at the Department of Electrical Engineering, Leibniz University Hannover. He has held academic positions at the University of Minnesota (Assistant Professor, 1973–1976) and lectured as a Visiting Professor at Zhejiang University, China (1985). His work focuses on knowledge-based image analysis, 3D scene modeling, and remote sensing applications in industrial automation. Education: Diplom (1968) and Dr.-Ing. (1972) in Electrical Engineering from TU Berlin. Research Interests include automated knowledge acquisition for image systems, semantic networks in remote sensing interpretation, and multisensor fusion for 3D geometry reconstruction. His publications span hierarchical pose estimation, GIS-data integration, and Bayesian reasoning in vision systems. Publications Trends highlight expertise in remote sensing (7/15), 3D modeling (5/15), knowledge-based systems (6/15), and sensor fusion (3/15). Key subfields include topographic dataset verification, multitemporal analysis, and lifelike 3D human modeling. Professional Affiliations include EURASIP, EUROGRAPHICS, GI, ITG, and DAGM. He chairs the ITG working group on Pattern Recognition and serves on the Signal Processing journal’s editorial board, with over 90 publications and 30 grants from DFG, EU, and industry.

Barbara FilaView profile

Associate Professor

• Formal Methods
• Computer Security
• Quantitative Security Analysis
• +3 more

Barbara Fila is an Associate Professor at the Institut National des Sciences Appliquées de Rennes (INSA Rennes) and a researcher at the Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA) in Rennes, France. She is a member of the Security and Privacy (SPICY) project-team. Ph.D. in Computer Science, University of Orléans (2008) Habilitation (HDR), University of Rennes 1 (2021) Her research focuses on the application of formal methods to security analysis, particularly in attack-defense trees, security ceremonies, and quantitative risk assessment. She has developed tools like SPTool for SAND attack tree equivalence checking and contributed to foundational work on security modeling frameworks. Recent publications examine formal verification of security ceremonies, countermeasure optimization in attack trees, and probabilistic reasoning for security scenarios. She has received recognition for her work on attribute decoration in attack-defense trees (Excellence award, IGI Global 2012). Excellence award (IGI Global, 2012) Barbara has supervised numerous Ph.D., Master's, and Bachelor students on topics such as security protocols, attack tree analysis, and risk assessment. She also organizes academic events like the GraMSec workshop series and serves on program committees for security conferences.

Jianhua WuView profile

Researcher

• Computer Graphics
• Mesh Processing
• Surface Reconstruction
• +3 more

Dr. Jianhua Wu is a researcher affiliated with the Department of Computer Science at RWTH Aachen University , focusing on Computer Graphics and Mesh Processing . His work addresses challenges in 3D modeling, surface reconstruction, and digital watermarking. Department: Department of Computer Science, RWTH Aachen University. Email: wu@informatik.rwth-aachen.de Research Interests: Jianhua Wu’s research centers on Computer Graphics , with a focus on surface splatting , variational approximation , and spectral watermarking of 3D models. His methods improve robustness in geometric data processing and reduce computational complexity for large datasets. Publication Trends: His 2002–2005 work emphasizes mesh decimation , splatting algorithms , and digital watermarking , leveraging techniques like radial basis functions and multiple-choice optimization to enhance efficiency and accuracy. Scientific Awards: Best Student Paper Award, Eurographics Symposium on Point-Based Graphics 2005

Nikolai KäferView profile

Researcher

• Computational Logic
• Artificial Intelligence
• Software Engineering
• +3 more

Nikolai Käfer is a doctoral student at the International Center for Computational Logic (ICCL) within the Faculty of Computer Science at Technische Universität Dresden. His research focuses on computational logic, artificial intelligence, and probabilistic reasoning. Algebraic and logical foundations of computer science Specializing in feature diagram sampling and probabilistic argumentation frameworks Contributing to software variability modeling and Bayesian network analysis Nikolai has published extensively in premier venues such as the International Working Conference on Variability Modeling of Software-Intensive Systems (VaMoS) and the ACM International Systems and Software Product Line Conference (SPLC). His research interests include: Variability modeling in software systems Probabilistic knowledge representation Formal methods and verification Bayesian network foundations Abstract argumentation frameworks Decision support under uncertainty His recent publications address challenges in feature diagram sampling, BDD compilation techniques for feature models, and theoretical foundations of probabilistic argumentation. Nikolai collaborates with leading researchers in computational logic and software engineering at TU Dresden. The ICCL team at TU Dresden emphasizes interdisciplinary research in logic-based AI and formal methods. Nikolai's work contributes to advancing these areas through rigorous theoretical analysis and practical tool development.

Tobias SchubertView profile

Researcher

• Multi-Robot Localization
• Optical Motion Capture
• Machine Learning
• +5 more

Tobias Schubert is a Researcher at the Autonomous Intelligent Systems group at Albert-Ludwigs-University Freiburg's Faculty of Engineering. He holds a PhD in Mathematics from the University of Regensburg (2013) and has extensive postdoctoral experience in robotics and computer science. His research focuses on multi-robot localization, optical motion capture, and human-motion analysis with applications in healthcare robotics. Notable projects include the MAKE IT ReaL motion tracking system, the Data Glove project for stroke patient rehabilitation, and the NaRKo compliant service robots for hospital logistics. Education: Diploma in Mathematics (2010), PhD in Mathematics (2013) Key Projects: Motion capture for humans/animals, Parkinson's disease monitoring, compliant robotics His work has been recognized with nominations for Best Student Paper (IROS 2016) and Best Application Paper (IROS 2016). He contributes to both theoretical robotics and applied healthcare solutions through interdisciplinary collaborations.

• Spatial Data Science
• Multivariate Statistical Analysis
• Structured Biomedical Data
• +4 more

Matthias Eckardt is a Professor at the Chair of Statistics within the School of Business and Economics at Humboldt-Universität zu Berlin. His research focuses on complex structured data, spatial statistics, and the integration of deep learning with statistical methods. Key areas of interest include graph-valued data analysis, marked spatial point processes, and topological data analysis. He leads the DesBi research initiative, which bridges biomedical data analysis with advanced statistical techniques. Research Interests: Complex/Graph-valued Data Multivariate Statistical Analysis Spatial Deep Learning Stochastic Geometry Object-valued Marked Processes Probabilistic Reasoning Recent work emphasizes spatial point processes on networks, functional data modeling for epidemiological applications (e.g., Covid-19 incidence curves), and software tools like 'intensitynet' for spatial network analysis. His publications span statistical methodology development and applied projects in public health, urban analytics, and biomedical research. No scientific awards are explicitly mentioned in the provided texts. Advising and grant information is not detailed here, though his research group's activities suggest active collaboration in interdisciplinary projects.

Naftali WeinbergerView profile

Research Fellow

• Causation
• Causal Modeling
• Philosophy of Science
• +9 more

Naftali Weinberger is a Postdoctoral Fellow at the Munich Center for Mathematical Philosophy within Ludwig Maximilian University of Munich's Faculty of Philosophy, Philosophy of Science and Religious Studies. His research focuses on causal modeling methods applied to foundational questions in causal inference and explanation across multiple scientific disciplines. Education PhD, University of Wisconsin, Madison (2015) Dr. Weinberger's research addresses the theoretical foundations and practical applications of causal modeling, with particular emphasis on dynamical systems and discrimination. His work spans population genetics, psychometrics, neuroscience, and economics, examining how causal concepts operate in diverse scientific contexts. Current projects investigate causation in dynamical systems and develop causal frameworks for modeling discrimination. His publication record (2011-2022) demonstrates consistent output in leading philosophy of science journals, with evolving focus from evolutionary biology and psychometrics toward dynamical systems and causal frameworks. Key themes include equilibrium models, time-scale relativity, non-factual scientific disagreement, and path-specific causal effects across disciplines. No scientific awards were mentioned in the source materials. No information regarding student advising or research grants was provided in the available documentation. Dr. Weinberger is currently affiliated with the Munich Center for Mathematical Philosophy. Previously, he contributed to the 'Causation, Continuity, and Complexity' project at the University of Pittsburgh's Center for Philosophy of Science and the 'Bridging Causal and Explanatory Reasoning' project at Tilburg University.

• High-Performance Computing
• Parallel I/O Systems
• Scientific Computing
• +3 more

Venkatram Vishwanath is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the College of Engineering. His research focuses on high-performance computing (HPC), parallel I/O systems, machine learning applications, and scientific computing. He has contributed to advancing scalable data management, performance optimization on supercomputers, and interdisciplinary projects such as the HACC cosmological simulation framework. Collaborations with institutions like Argonne National Laboratory highlight his work in bridging computational science with practical applications. Key areas of expertise include topology-aware data aggregation, GPU performance modeling, and accelerating I/O operations for large-scale simulations. His work on frameworks like vl3 and PIDX has improved parallel data handling and visualization. Awards and recognitions are not explicitly listed in the provided data, but his extensive publication record and leadership in HPC projects reflect significant contributions to the field.

Joschka BoedeckerView profile

Professor

• Reinforcement Learning
• Machine Learning
• Probabilistic Models
• +3 more

Professor Joschka Boedecker serves as a faculty member at the Department of Computer Science, University of Freiburg, Germany. His academic profile centers on cutting-edge machine learning research with strong emphasis on practical applications across multiple domains. His primary research interests span: Reinforcement Learning (core focus with multi-semester course offerings) Probabilistic Graphical Models and causal inference Health informatics applications of machine learning World models and hierarchical decision systems Professor Boedecker maintains an active teaching schedule with recurring courses including Reinforcement Learning (taught continuously since WS2018/19 through SS2025), Probabilistic Graphical Models, and specialized seminars on Machine Learning in Health. His course portfolio demonstrates sustained expertise in sequential decision-making systems and modern extensions of Q-learning and policy gradient methods. He leads a research group comprising postdoctoral researchers (including Dr. Maria Kalweit and Dr. Gabriel Kalweit) and numerous graduate students, as evidenced by the extensive team listing on his institutional page. The group's structure indicates active supervision of Master's and PhD candidates in reinforcement learning and related AI subfields.

• Visualization
• Geographic Information Science
• Spatial Analysis
• +4 more

Aidan Slingsby is a Professor in the Department of Computer Science at University College London (UCL), within the Faculty of Engineering Sciences. His research focuses on visualization, geovisualization, and spatial analysis, with applications in environmental science, transportation, epidemiology, and movement ecology. He has contributed to the development of novel visual analytics frameworks and tools for exploring complex spatiotemporal data. His work emphasizes interdisciplinary collaboration, integrating computer science with domains such as geography, ecology, and public health. Key areas of interest include uncertainty visualization, interactive graphics for decision support, and leveraging AI techniques (e.g., large language models) to enhance visualization design. Slingsby's recent projects include developing the VisUnit framework for reproducible visual studies, exploring narrative visualization for qualitative data, and creating glyph-based methods for spatial multicriteria decision analysis. His publications span top venues like IEEE VIS, EuroVis, and Environmental Visualization workshops.