جست‌وجوی استاد و پژوهشگر

Thomas Kesselheimمشاهده پروفایل

استاد

• Online Algorithms
• Algorithmic Game Theory
• Algorithms and Uncertainty
• +۳ مورد دیگر

Thomas Kesselheim is a Professor for Theoretical Computer Science at the University of Bonn, Department of Computer Science (Institute V). He is based in room 2.058 at Friedrich-Hirzebruch-Allee 8, D-53115 Bonn, Germany. His contact email is thomas.kesselheim@uni-bonn.de and he holds virtual office hours via Zoom (Meeting ID: 685 2407 0922, Passcode: 305903). Professor Kesselheim's research focuses on Online Algorithms , Algorithmic Game Theory , and Algorithms and Uncertainty . His work examines how algorithms perform in environments with strategic participants such as internet service providers, cloud infrastructure markets, and advertising platforms. Key research areas include congestion games, price of anarchy, mechanism design, revenue maximization, and optimization under incomplete information. His teaching portfolio spans multiple semesters with courses including Randomized Algorithms & Probabilistic Analysis, Algorithmic Game Theory, and Algorithms and Uncertainty. He regularly offers specialized seminars on Online Algorithms and Optimization under Uncertainty where students explore cutting-edge topics like online stochastic matching and combinatorial auctions via posted prices. Professor Kesselheim's career trajectory shows steady progression from PhD studies at RWTH Aachen University through postdoctoral positions at Cornell University and Max Planck Institute for Informatics, a fellowship at the Simons Institute for the Theory of Computing, a Junior Professorship at TU Dortmund, and his current professorship at the University of Bonn since April 2018. He actively supervises labs and seminars where students implement and evaluate algorithms for optimization under uncertainty, focusing on both theoretical guarantees and practical performance. His research group participates in joint research seminars with other groups at the University of Bonn, discussing topics in computational geometry and algorithm design.

Stefania Degaetano-Ortliebمشاهده پروفایل

دانشیار

• Computational Linguistics
• Corpus Linguistics
• Sociolinguistics
• +۶ مورد دیگر

Stefania Degaetano-Ortlieb is an Associate Professor of English Linguistics and Corpus Linguistics at Saarland University's Department of Language Science and Technology. She serves as Principal Investigator for the Collaborative Research Center (SFB 1102) 'Information Density and Linguistic Encoding,' leading Project B1 on diachronic information density in English scientific writing (17th century-present). Her interdisciplinary work bridges computational methods with sociolinguistics, focusing on register variation, language change, and digital humanities. Research interests center on text mining, data analytics, and probabilistic modeling of language variation. Key areas include: Diachronic evolution of scientific registers and linguistic densification Information-theoretic approaches to language efficiency Computational sociolinguistics and register diversification AI applications in humanities education (e.g., ChatGPT integration) Her publications show a strong trend toward quantitative diachronic analysis, with recent work emphasizing: interpretable AI models for linguistic change detection; propagandistic narrative analysis in conflict zones; and multi-word expression dynamics in scientific discourse. Cross-disciplinary collaborations frequently intersect with history, psychology, and media studies. Awards include the Fellowship Excellence Program for Young Female Scientists (2015-2018). Current grants: EU Horizon MSCA Doctoral Network 'CASCADE' (€521K to UdS, 2024-2027) Data-Pin Project for AI in education (€50K, 2023-2024) SFB 1102 Project B1 (€595K, 2022-2026) Advises PhD candidates in the EU CASCADE project on computational semantic change. Leads a research team exploring Russian media narratives, personality modeling in LLMs, and multi-word expressions. Directs teaching modules integrating AI tools for humanities students.

• Machine Learning
• Computer Vision
• Signal Processing
• +۶ مورد دیگر

Dongwook Kim is affiliated with the Korea Advanced Institute of Science & Technology (KAIST) as a faculty member in the Department of Business and Technology Management under the College of Business. His research spans multiple domains including machine learning, robotics, signal processing, and biomedical engineering. Key contributions in Computer Vision (CNN-based semantic segmentation, 3D point cloud analysis) Significant work in Hardware Design (energy-efficient processors, neuromorphic computing) Interdisciplinary expertise in Medical Imaging (bone age assessment, retinal biomarkers) and Cybersecurity (attack detection, network analytics) Publications since 2015 demonstrate sustained innovation in AI applications , Signal Processing , and Smart City Governance . His work often integrates theoretical advances with practical implementations in real-world systems. No scientific awards or student mentorship details are explicitly documented in the provided records.

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +۵ مورد دیگر

Dr. Kevin G. Jamieson is a faculty member at the University of Washington , School of Computer Science , with prior affiliations at the University of California, Berkeley (Department of Electrical Engineering and Computer Sciences) and the University of Wisconsin-Madison (Department of Electrical and Computer Engineering). His work spans machine learning, reinforcement learning, bandit algorithms, and robotics. Current university: University of Washington Academic rank: Professor His research focuses on: Bandit algorithms and sequential decision-making Optimization in non-stationary environments Reinforcement learning with real-world applications Multi-agent systems and game theory Efficient data selection for multimodal learning Human-in-the-loop AI systems Recent publications highlight his expertise in pure exploration strategies, robotic manipulation, and bridging simulation-to-reality gaps in RL. He has mentored numerous collaborators, though formal student advising details are not explicitly listed here. No scientific awards are mentioned in the provided data.

Asaf Shapiraمشاهده پروفایل

استاد

• Extremal Combinatorics
• Graph Theory
• Property Testing
• +۴ مورد دیگر

Professor Asaf Shapira is a faculty member in the Department of Theoretical Mathematics at Tel Aviv University's School of Mathematical Sciences. He has been actively contributing to combinatorics and graph theory research for over a decade, with numerous publications in top journals including Journal of the ACM, Advances in Mathematics, and Geometric and Functional Analysis. Professor Shapira's research focuses on extremal combinatorics, graph theory, and property testing. His work explores fundamental questions in Ramsey theory, hypergraph theory, and probabilistic methods in combinatorics. He has made significant contributions to the study of graph regularity, removal lemmas, and extremal problems in dense and sparse graphs. His recent publications demonstrate a consistent focus on theoretical aspects of combinatorics with connections to theoretical computer science. A notable trend is his work on developing polynomial bounds for various combinatorial theorems and exploring connections between combinatorial structures and computational complexity. His research often bridges pure mathematics with theoretical computer science applications. Professor Shapira teaches advanced courses at Tel Aviv University including Extremal Graph Theory, Basic Combinatorics, and seminars on specialized topics in combinatorics. His teaching spans undergraduate and graduate levels, reflecting his commitment to educating the next generation of mathematicians.

• Computer Architecture
• Hardware Acceleration
• Approximate Computing
• +۳ مورد دیگر

Akash Kumar is a Professor at Ruhr University Bochum, Germany, with affiliations to multiple institutions including TU Dresden and National University of Singapore. His research focuses on computer architecture, hardware acceleration, and approximate computing, with a strong emphasis on FPGA-based systems and neural network optimization. He has contributed extensively to embedded systems, mixed-criticality systems, and security in emerging technologies like reconfigurable nanodevices. His work spans cross-layer approximation methodologies, graph neural networks, and energy-efficient processing for edge AI. Collaborations with industry and academia highlight his leadership in VLSI design and secure hardware implementations. Notable contributions include frameworks for bounding time in mixed-criticality systems and resilient logic locking techniques. Research interests include hardware-software co-design, real-time systems, and efficient neural network architectures. His publications in top-tier conferences (DAC, DATE, FCCM) and journals (IEEE Trans. CAD, ACM TECS) underscore his impact in the field.

Tibor Szabóمشاهده پروفایل

استاد

• Combinatorics
• Extremal Graph Theory
• Random Structures and Algorithms
• +۵ مورد دیگر

Tibor Szabó is a Professor in the Combinatorics and Graph Theory group at the Department of Mathematics, Freie Universität Berlin. He holds a PhD from The Ohio State University, advised by Ákos Seress. Prior to his current position, he held roles at McGill University, ETH Zürich, the Institute for Advanced Study (Princeton), and the University of Illinois (UIUC) as a J.L. Doob Research Assistant Professor. Research Interests: His work focuses on combinatorics and combinatorial optimization, including extremal problems, random structures and algorithms, pseudorandom graphs, positional games, and the combinatorics of linear programming. He explores tools from algebra, probability theory, and topology applied to combinatorics. Teaching: He teaches courses such as Algorithmic Combinatorics, Extremal Combinatorics, and runs the Combinatorics Seminar. His lecture notes include works on positional games and explicit constructions in extremal combinatorics. Students & Postdocs: Notable PhD advisees include Yamaan Attwa, Silas Rathke, Simona Boyadzhiyska, and Patrick Morris. Postdoctoral fellows include Olaf Parczyk and Anurag Bishnoi. His research has involved collaborations with over 50 co-authors. Funding & Grants: Supported by grants from the Swiss National Science Foundation (SNF) and German Research Foundation (DFG), focusing on topics like positional games and extremal graph theory.

Marco Cuturiمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Optimization
• +۴ مورد دیگر

Marco Cuturi is a Research Scientist at Apple ML Research in Paris and Professor of Statistics at CREST-ENSAE, Institut Polytechnique de Paris. His work bridges machine learning , optimal transport , and optimization , with applications in time-series analysis , kernels , and multiresolution methods . He has held academic roles at Kyoto University and Princeton University, and previously worked in the financial industry. Research Interests: Optimal transport theory and computational methods Kernel design for structured data and histograms Time-series alignment and soft-DTW Entropic regularization in optimization Applications to computer vision and genomics Teaching: Cuturi has taught courses on linear optimization at Princeton, geometric methods in machine learning at Kyoto, and scientific English. He has also organized machine learning summer schools in Kyoto, Les Houches, and other international venues. Recent Trends: His 2024-2025 publications focus on entropic optimal transport solvers, disentangled representation learning via Gromov-Monge gaps, and applications to text-to-image diffusion models. Collaborative work with institutions like Google Research, MIT, and University of Tokyo highlights his interdisciplinary impact.

• Hyperdimensional Computing
• Neuromorphic Hardware
• In-Memory Computing
• +۳ مورد دیگر

Paul R. Genssler is a Dr.-Ing. researcher at the Chair of AI Processor Design (AI-Pro) within the Technical University of Munich (TUM), actively advancing hardware solutions for artificial intelligence under Prof. Hussam Amrouch. His work bridges computer engineering and emerging technologies, focusing on overcoming fundamental limitations in conventional computing architectures through brain-inspired paradigms. His research spans critical domains in next-generation computing: Hyperdimensional Computing for robust pattern recognition and bioinformatics applications Neuromorphic and In-Memory Computing architectures for energy efficiency Reliability engineering for emerging memory technologies (FeFET, etc.) Quantum computing support systems including cryogenic embedded electronics Machine learning-driven transistor aging prediction and mitigation Analysis of his 15 most recent publications (2023-2024) reveals a dominant trend toward hyperdimensional computing as a unifying framework for addressing reliability challenges in emerging technologies. His work consistently integrates in-memory computing techniques to bypass von Neumann bottlenecks while targeting real-world applications like genome matching and unsupervised learning. A significant portion focuses on error-resilient implementations for unreliable nanoscale devices, demonstrating exceptional cross-stack expertise from transistor physics to algorithm design. As a core member of TUM's AI Processor Design group affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI), Genssler collaborates extensively on projects spanning cryogenic quantum control systems, FPGA-based AI resilience, and monolithic 3D integration. The team operates at the intersection of semiconductor physics, computer architecture, and machine learning, with strong industry connections evident through publications at DATE, ASP-DAC, and ICCAD.

• Computational Linguistics
• Historical Linguistics
• Phylogenetic Linguistics
• +۸ مورد دیگر

Prof. Gerhard Jäger holds the Chair of General Linguistics at the Faculty of Humanities, University of Tübingen . He serves as a Principal Investigator (PI) in the Clusters of Excellence Human Origins and Machine Learning for Science , and leads projects like Phylomilia (funded by Volkswagen Foundation) and CrossLingference (ERC Advanced Grant). His career spans multiple institutions, including Bielefeld University (2004-2009) and Stanford University (visiting scholar, 2004). Habilitation (2002) at Humboldt University Berlin with thesis on Anaphora and Type Logical Grammar PhD (1996) at Humboldt University Berlin on Dynamic Semantics His research bridges computational linguistics , phylogenetic analysis , and game theory , focusing on Bayesian models , language evolution , and cross-linguistic typology . Recent work explores phylogenetic inference from acoustic speech data and geographic influences on language trees . Key contributions include 15+ recent publications on topics spanning phylogenetic typology , cognate detection , and Bayesian language modeling . These works employ machine learning , statistical inference , and evolutionary game theory to analyze language change , typological variation , and linguistic stability . Honors include ERC Advanced Grant , Volkswagen Foundation funding , and DFG-Humanities Centre for Advanced Studies participation. He has taught courses in Computational Historical Linguistics , Phylogenetic Methods , and Bayesian Data Analysis across institutions like Tübingen, Bielefeld, and Stanford. He actively contributes to academic communities through workshop organization (e.g., Quantitative Theoretical Linguistics , Game Theory in Pragmatics ) and serves on the faculty council at Tübingen. His team collaborates with institutions like Max Planck Institute for Evolutionary Anthropology , University of Pennsylvania , and LMU Munich .

• Artificial Intelligence
• Cognitive Science
• Human-Agent Interaction
• +۵ مورد دیگر

Prof. Dr.-Ing. Stefan Kopp is a faculty member at Bielefeld University's Faculty of Engineering and serves as Research Group Leader of the Cognitive Systems and Social Interaction Group . He also holds administrative roles as Vice Dean and Deputy CITEC Coordinator . His work focuses on Artificial Intelligence , Cognitive Systems , and Socio-Technical World research areas. Research Group Leader: Cognitive Systems and Social Interaction Group Vice Dean: Faculty of Engineering Deputy Coordinator: Center for Cognitive Interaction Technology (CITEC) Project Manager: TRR 318 "Constructing Explainability" subprojects His research explores human-agent interaction , multimodal conversational agents , and social AI through projects like 39-Inf-11 Human-Machine Interaction and 39-M-Inf-VKI Virtual Humans and Conversational Agents . Publications address topics including adaptive explanation generation , gesture synthesis , and social cognition in dynamic environments. Current research topics span cooperative AI , explainable decision-making , and sensorimotor grounding in artificial systems.

James Urquhart Allinghamمشاهده پروفایل

پژوهشگر

• Bayesian Deep Learning
• Deep Generative Models
• Probabilistic Methods in Machine Learning
• +۷ مورد دیگر

James Urquhart Allingham is a Research Scientist at Google DeepMind , working on the Gemini project. He completed his PhD in the Machine Learning Group at the University of Cambridge under the supervision of José Miguel Hernández-Lobato, with funding from EPSRC, the Michael E. Fisher Studentship in Machine Learning, and the Qualcomm Innovation Fellowship. He was also part of the ELLIS PhD program, advised by Eric Nalisnick at AMLab UvA. Current affiliation: Google DeepMind (Research Scientist) PhD: University of Cambridge (Machine Learning Group) Academic networks: ELLIS PhD program, Darwin College His research focuses on the intersection of Bayesian deep learning and probabilistic methods in deep learning. Key areas include deep generative models , zero-shot classification , prompt engineering , Monte Carlo gradient estimation , and applications to sustainability and climate change . His work has explored energy-based models , neural architecture search , and equivariance in convolutional networks . Selected scientific awards and grants include the Michael E. Fisher Studentship , Qualcomm Innovation Fellowship , and MPhil in Advanced Computer Science with Distinction . He has collaborated with institutions such as the Amsterdam Machine Learning Group (AMLAB) and University of the Witwatersrand .

Zhen Liuمشاهده پروفایل

استادیار

• Generative Models
• 3D Representation Learning
• Computer Vision
• +۲ مورد دیگر

Zhen Liu is an Assistant Professor at the School of Data Science, CUHK-Shenzhen. His research focuses on generative models, 3D representations, and the synergy of spatial and semantic understanding in AI systems. With a PhD from Mila and Université de Montréal, he develops foundational methods for physics simulation, 3D assembly, and semantic reasoning in neural networks. His work bridges machine learning with applications in computer vision and graphics, emphasizing: Generative architectures for 3D content creation Diffusion model alignment techniques Efficient parameter finetuning strategies Dr. Liu mentors students in AI research and contributes to advancing 3D generative modeling paradigms.

Constantin A. Rothkopfمشاهده پروفایل

استاد

• Perception and Action
• Computational Neuroscience
• Machine Learning
• +۳ مورد دیگر

Prof. Constantin A. Rothkopf is a W3 Professor at the Department of Psychology, Technische Universität Darmstadt, and a secondary member of the Department of Computer Science. He serves as Founding Director of the Centre for Cognitive Science and founding member of the Hessisches Zentrum für Künstliche Intelligenz (hessian.ai). He is also part of the European Laboratory for Learning and Intelligent Systems (ELLIS) and the DAAD Konrad Zuse Schools of Excellence in Artificial Intelligence (ELIZA). His research focuses on the interplay between perception and action, using computational models and experimental studies in humans. Current work includes eye-tracking studies in naturalistic environments, inverse optimal control models, and developing algorithms for virtual agents. Education: Ph.D. in Neuroscience and Informatics from the University of Rochester, followed by postdoctoral research at Frankfurt Institute for Advanced Studies (FIAS). He has held visiting professorships at Central European University (2017) and Columbia University (2023). Awards include an ERC Consolidator Grant (2022) and SCENE Project Funding (2025). Research Interests: Active vision, decision-making under uncertainty, sensorimotor control, and computational modeling. Key themes include how humans use sensory input to form beliefs, make decisions, and act in dynamic environments. Grants/Awards: ERC Consolidator Grant (2022), SCENE Funding (2025) Labs/Teams: Centre for Cognitive Science, hessian.ai, ELLIS Unit Darmstadt

• Biometrics
• Face Recognition
• Fairness in AI
• +۳ مورد دیگر

Ana Filipa Sequeira is a researcher affiliated with INESC TEC, Porto, Portugal, and the University of Porto. Her work spans biometrics, fairness in AI, and explainable artificial intelligence. She has contributed to advancements in face recognition, synthetic data applications, and bias mitigation through techniques like knowledge distillation and model compression. Institution: INESC TEC (Porto, Portugal) Research Themes: Face recognition, fairness, explainability, synthetic data, biometric security Her recent publications focus on addressing demographic biases in face recognition systems, developing privacy-preserving explainable methods, and evaluating synthetic data's impact. She collaborates extensively with researchers like Pedro C. Neto, Jaime S. Cardoso, and Naser Damer. Sequeira has participated in organizing and analyzing competitions such as FRCSyn, BIOSIG, and SYN-MAD, emphasizing robust evaluation frameworks. Her work intersects technical innovation with ethical considerations, advocating for responsible AI applications in biometrics.