جست‌وجوی استاد و پژوهشگر

Anke Gerberمشاهده پروفایل

استاد

• Intertemporal Choice
• Coalition Formation
• Bargaining Theory
• +۶ مورد دیگر

Anke Gerber is a Full Professor of Economics (W3) at the University of Hamburg , specializing in Microeconomic Theory and Experiments . She serves as Spokesperson of the DFG Graduate Program 'Collective Decision-Making' and Director of the Master's Program in Politics, Economics and Philosophy (PEP). PhD in Economics, Bielefeld University (1998) Previous appointments at Universities of Heidelberg (2006-2007) and Zurich (2002-2007) Member of Social Choice and Welfare Society Council Editorial roles for Economic Theory , Games , and Lecture Notes in Economics and Mathematical Systems Research Interests span Game Theory , Social Choice , Public Economics , and Intertemporal Decision-Making . Her work explores strategic behavior in collective decisions, preference reversals in time-dependent choices, and institutional design for public goods provision. Key themes include coalition formation, bargaining theory, and political economy applications. Scientific Contributions reflect in publications across top-tier journals like Journal of Theoretical Politics , International Economic Review , and Social Choice and Welfare . Recent works (2023-2024) focus on endogenous agenda manipulation, electoral campaign strategies, and temporal utility modeling. DFG-funded Graduate Program leadership Extensive peer-review activity for journals like Econometrica , American Economic Review , and Journal of Public Economics

• Microeconomic Theory
• Information Economics
• Mechanism Design
• +۳ مورد دیگر

Steven Kivinen is an Assistant Professor in the Department of Economics at the University of Graz, where he has held his position since September 2022. His research focuses on microeconomic theory, particularly game theory, information economics, and mechanism design, with applications to political economy, matching, and social networks. He has contributed to understanding voting rules, strategic behavior in networks, and persuasion mechanisms. His recent work includes groundbreaking studies on equitable voting systems' manipulability, robust group strategy-proofness, and consensus formation in social networks. Key publications include 'On the Manipulability of Equitable Voting Rules' (2023, Games and Economic Behavior ) and 'Robust Group Strategy-Proofness' (2021, Theoretical Economics ). Dr. Kivinen has received the Yu Chen Prize 2023 for his work on equitable voting rules. His ongoing research explores topics like renegotiation-proof communication, data persuasion strategies, and procedural fairness in voting mechanisms. He collaborates with scholars such as Christoph Kuzmics, Norovsambuu Tumennasan, and Tatiana Mayskaya. His academic contributions span theoretical economics, applied game theory, and network analysis, with a focus on practical applications of abstract economic models.

• Deep Learning
• Reinforcement Learning
• Robotics
• +۷ مورد دیگر

Glen Berseth is an Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute. He holds the prestigious Canada CIFAR AI Chair position and co-directs the Robotics and Embodied AI Lab (REAL). Previously, he was a postdoctoral researcher at Berkeley Artificial Intelligence Research (BAIR) working with Sergey Levine. Dr. Berseth's research focuses on solving sequential decision-making problems for real-world autonomous learning systems, with particular emphasis on human-robot collaboration, reinforcement learning, and various advanced learning paradigms including continual, meta, multi-agent, and hierarchical learning. His work bridges theoretical machine learning with practical robotics applications, aiming to create more capable and adaptable robotic systems. He has published extensively in top venues for robotics, machine learning, and computer animation. His recent publications demonstrate a strong focus on exploration strategies, representation learning, and efficient reinforcement learning algorithms. These works address critical challenges in scaling RL to real-world applications, improving generalization across different robot morphologies, and enabling autonomous learning without extensive human supervision. His research has practical implications for robotics, power grid control, and other complex systems requiring adaptive decision-making. Canada CIFAR AI Chair Dr. Berseth mentors a large group of graduate students across multiple institutions, supervising PhD and Master's candidates working on diverse aspects of robot learning and reinforcement learning. His lab receives significant research funding through his CIFAR AI Chair position and likely other grants supporting his robotics and AI research. The Robotics and Embodied AI Lab (REAL) he co-directs serves as a hub for interdisciplinary research at the intersection of machine learning and physical systems, fostering collaboration between computer scientists, roboticists, and domain experts.

Helen Barronمشاهده پروفایل

دانشیار

• Memory
• Neural Circuits
• Decision Making
• +۳ مورد دیگر

Associate Professor Helen Barron is a UKRI Future Leaders Fellow at the University of Oxford, leading the Barron Group at the MRC Brain Network Dynamics Unit (BNDU) and the Wellcome Centre for Integrative Neuroimaging. Her research bridges cellular mechanisms with human cognition through cross-species approaches to decode memory computations. Education: Natural Sciences, University of Cambridge PhD, University College London (UCL) Helen Barron's work centers on memory circuitry , specifically defining hippocampal-neocortical interactions during learning/sleep, characterizing inhibitory interneuron roles in memory separation/integration, and linking memory pathophysiology to psychiatric symptoms. Her lab employs multi-scale techniques including electrophysiology, calcium imaging, optogenetics, and neuroimaging to reveal how neural computations enable adaptive behavior while identifying disruptions in disease. Analysis of her 2019-2024 publications reveals a trajectory toward integrative neuroscience : early work established cross-species frameworks for inferential reasoning (2020 Cell paper), while recent studies combine fMRS/fMRI with computational modeling to decode neurotransmitter dynamics during memory recall (2021-2023). Current research emphasizes translational bridges between rodent circuit manipulations and human memory disorders. Scientific Awards: UKRI Future Leaders Fellowship (2022) Junior Research Fellowship at University of Oxford (2015) Barron directs a thriving research program mentoring PhD students (including Rawson, Nalluru, Tang) and postdocs, with group projects funded by her UKRI fellowship focusing on memory circuit perturbations in psychiatric disease. Her lab actively shares datasets and code via the MRC BNDU Data Platform, supporting open science in neuroscience. The Barron Group operates within Oxford's MRC BNDU, utilizing specialized facilities for cross-species experimentation . Team members apply electrophysiology, calcium imaging, and optogenetics in rodents alongside human fMRI/MRS, with strong emphasis on equality/diversity and clinical translation pathways for memory-related disorders.

Chris Sagovacمشاهده پروفایل

دانشیار

• Animation Production
• Surrealism
• Comic Book Production
• +۵ مورد دیگر

Chris Sagovac serves as Associate Professor of Animation within the Electronic & Photographic Media Department at Webster University's School of Communications. His dual appointment bridges fine arts and digital media production, with a studio practice spanning animation, painting, mail art, and independent comic creation. His research centers on experimental animation techniques, particularly rotoscoping with found footage manipulated through mathematical structures like triangular numbers. Key interests include surrealism as methodology rather than genre, digital degradation aesthetics mimicking analog television reception, and cross-dimensional media theory inspired by childhood experiences with 1950s monster movies. Sagovac explicitly rejects animation as a children's genre, advocating for its recognition as a versatile artistic medium. His 2013 film newscaster/dragon/maggots —featured in the St. Louis Filmmakers Showcase—exemplifies his approach through randomly selected public domain footage edited with intentional horizontal hold distortion. The work explores television reception in Hell, blending Big Media imagery with grotesque organic forms influenced by John Carpenter's horror aesthetics. Sagovac's creative process involves intuitive decision-making grounded in mathematical frameworks, often using noise music (Merzbow, Sunn O))) as compositional guides. His Deep Space Transmissions series investigates pure imagery conveyance, while the Hell 99 experimental works examine mythological narratives through sequential art. As a practicing artist, his work has been exhibited globally in galleries, film festivals, and street art exhibitions. He holds both Bachelor's and Master's degrees from Webster University in Media Communications and Studio Art respectively, maintaining strong institutional ties while operating at the intersection of commercial and fine art practices.

Daniele De Martiniمشاهده پروفایل

مدرس

• Mobile Robotics
• Autonomous Systems
• Robot Navigation
• +۵ مورد دیگر

Daniele De Martini is a Departmental Lecturer in Mobile Robotics at the Oxford Robotics Institute (ORI) and a College Lecturer in Engineering Science at Pembroke College, University of Oxford. He co-leads the Mobile Robotics Group (MRG) with Professor Paul Newman. His academic journey began in Pavia, Italy, where he earned his BSc in Mechanical Engineering, followed by an MSc in Mechatronic Engineering from Politecnico di Torino, and completed his PhD in Robotics at Università degli Studi di Pavia under the supervision of Prof. Tullio Facchinetti. De Martini's research focuses on robust navigation and scene understanding for mobile robots, with particular emphasis on enabling robot operation in challenging weather conditions and diverse scenarios. His work spans from odometry and localization to detection and segmentation, utilizing various sensing technologies including vision, laser, and radar systems. A significant portion of his research explores FMCW scanning radar technology as a more robust alternative to traditional visual perception systems, especially in adverse weather conditions. He also investigates techniques to enhance the training of perception modules using machine learning approaches, believing that learning and adaptation are paramount for achieving true robot autonomy. His publication record demonstrates a clear progression from early work on energy management and scheduling algorithms to his current focus on mobile robotics and perception. The analysis of his recent publications reveals strong emphasis on radar-based perception, robust localization methods, scene generation for autonomous driving simulation, and long-term autonomy systems. His work bridges the gap between theoretical robotics and practical field deployment, as evidenced by projects like The Hulk vehicle platform designed for weather-proof outdoor operations. As an educator, De Martini teaches Structures and Mechanics at Pembroke College and contributes to the academic mission of the Department of Engineering Science. His research group actively develops solutions for real-world robotic challenges, with applications ranging from urban mobility safety assessment to long-term autonomous inspection systems.

Cezara Drăgoiمشاهده پروفایل

پژوهشگر

• Static Analysis
• Formal Verification
• Distributed Systems
• +۳ مورد دیگر

Cezara Drăgoi serves as a Researcher (Chargé de Recherche) at INRIA with dual affiliation at École Normale Supérieure's Department of Computer Science and CNRS. She maintains active roles in major programming language conferences including PLDI, POPL, and SPLASH where she contributes to program committees and steering groups, demonstrating her standing in the formal methods community. Her research program focuses on automated formal verification through abstraction techniques, specifically targeting data structures across the spectrum from sequential implementations to distributed systems. Drăgoi develops logic-based frameworks that enable Hoare-style reasoning about content and consistency properties, with particular expertise in identifying suitable fragments of first-order and separation logic for static analysis. Her work bridges theoretical foundations with practical verification challenges in both single-threaded and concurrent environments. Publication trends reveal a clear evolution from foundational work on shape analysis for sequential data structures toward increasingly complex distributed systems verification. This progression culminated in her development of PSync - a domain-specific language for fault-tolerant distributed algorithms with integrated verification capabilities - demonstrating her ability to create practical abstractions that make verification tractable for real-world systems. Steering Committee Member, Static Analysis Symposium (SAS 2025) Program Committee Co-Chair, VMCAI 2023 Multiple program committee roles at PLDI (2017, 2020), POPL (2016-2023), ECOOP (2019), and SPLASH (2020-2024) Session chair for SAS 2021 and VMCAI 2019 Drăgoi actively mentors emerging researchers through internship and PhD opportunities in specialized areas including Byzantine consensus verification (SABC project) and refinement techniques for fault-tolerant asynchronous code (RFTA project). She has developed two influential verification tools: CELIA (a Frama-C plugin for C program verification handling both data and structural properties) and PSync (a language with verification engine for distributed algorithms). Her teaching includes graduate-level courses on abstract interpretation at Université Paris Diderot. She leads verification research within the INRIA/ENS collaboration, focusing on making static analysis tractable for complex distributed systems through innovative synchronous programming abstractions. Current work targets verification challenges in consensus algorithms and communication-closed protocols, with emphasis on bridging theoretical soundness with practical applicability.

• AI Safety
• Generative Modeling
• Reinforcement Learning
• +۳ مورد دیگر

Sangwoong Yoon is a Research Fellow in the Department of Electronic and Electrical Engineering at University College London (UCL), working under Professor Ilija Bogunovic on Reliable AI Alignment. His role focuses on developing algorithmic foundations for AI systems that operate safely and risk-awarely in real-world environments, contributing to UCL's leadership in trustworthy artificial intelligence research. Dr. Yoon's research centers on the intersection of generative modeling and reinforcement learning, with core objectives including building safe AI systems through energy-based models, diffusion processes, and inverse reinforcement learning. His work spans theoretical advances in anomaly detection, kernel density estimation, and sequential decision-making, while maintaining strong application relevance in robotics, biomedical systems, and fluid dynamics. This dual focus on theoretical rigor and practical impact defines his approach to reliable AI development. Analysis of his publication trajectory reveals consistent innovation in generative AI safety mechanisms, with increasing emphasis on medical and industrial applications since 2022. His recent work demonstrates sophisticated integration of reinforcement learning with diffusion models for robust decision-making, while maintaining cross-disciplinary relevance through collaborations in liver fibrosis research and microfluidics. The high citation counts (e.g., 83 for Deep-Hurricane-Tracker) reflect significant community impact. As an active member of Professor Bogunovic's research group, Dr. Yoon contributes to UCL's AI ecosystem through NeurIPS 2025 Area Chair duties and collaborative projects advancing reliable AI alignment. His work environment fosters interdisciplinary innovation at the hardware-software-safety interface within UCL's Electronic and Electrical Engineering department.

Minhyuk Sungمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Generative AI
• +۳ مورد دیگر

Minhyuk Sung is an Associate Professor in the School of Computing at KAIST, where he leads the KAIST Visual AI Group. He is also affiliated with the Graduate School of AI and the Metaverse Program. Previously, he worked as a research scientist at Adobe Research. He received his Ph.D. from Stanford University under the supervision of Leonidas Guibas and completed his M.S. and B.S. degrees at KAIST. Dr. Sung's research focuses on generating, manipulating, and analyzing various visual data, including images, videos, and 3D data. His work primarily centers around diffusion models, flow models, and other generative AI techniques applied to visual content. He has made significant contributions to the fields of 3D vision, text-to-image generation, and multi-modal AI systems. His recent publications demonstrate a strong trend toward improving the quality, efficiency, and controllability of generative models, with particular emphasis on 3D-aware generation, synchronization techniques, and grounding visual content with linguistic descriptions. His work spans multiple top-tier conferences including NeurIPS, CVPR, ICCV, and SIGGRAPH. Asiapgraphics Researcher Award (2024) Dr. Sung actively mentors students and recruits new members for his research group. He serves as an area chair for conferences like 3DV 2026 and frequently gives talks at major institutions including NVIDIA, Google, Stanford, and various universities across Asia. His research has attracted significant attention in both academic and industrial circles. He teaches advanced courses including Diffusion and Flow Models (CS492(C)), Machine Learning for 3D Data (CS479), and Diffusion Models and Their Applications (CS492(D)), demonstrating his commitment to training the next generation of AI researchers.

• Artificial Intelligence
• Reinforcement Learning
• Machine Learning
• +۴ مورد دیگر

Linjie Xu is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on artificial intelligence, particularly in reinforcement learning, machine learning, and game-playing algorithms. Key interests include state abstraction techniques, multi-agent systems, Monte Carlo tree search, and adversarial defense mechanisms for large language models. His academic work spans strategy game AI, optimization algorithms, and computational game theory. Xu has contributed to advancing sample-efficient methods in multi-agent reinforcement learning and developing elastic Monte Carlo tree search frameworks. His recent publications emphasize practical applications of state abstraction and cross-domain decision transformers. No scientific awards or grants are explicitly mentioned in the provided materials. He currently holds no listed advisees or doctoral students. Contact him at linjie.xu@qmul.ac.uk for collaboration opportunities.

• Robotics
• Motion Planning
• Multi-robot Systems
• +۳ مورد دیگر

Liding Zhang is a Ph.D. candidate and researcher at the Chair of Robotics, Artificial Intelligence and Real-Time Systems at the Technical University of Munich (TUM), supervised by Prof. Alois Knoll. His work focuses on advanced robotics with emphasis on motion planning and multi-robot systems. His educational background includes: Master’s degree in Mechanical Engineering and Automation Technology from Technical University of Clausthal (2022) Bachelor’s degree in Mechanical Engineering from Rhine-Waal University of Applied Sciences (2020) Zhang's research centers on sampling-based asymptotically optimal motion planning, high-DOF multi-robot manipulation, coordinated control of mobile robot fleets, and real-time performance optimization. His work explores geometric reasoning in complex robotic systems, addressing challenges in manipulation, navigation, and control through innovative algorithmic approaches that balance computational efficiency with solution quality. Key contributions include adaptive sampling techniques and optimization frameworks for dynamic environments. His publication record (2023-2025) demonstrates consistent contributions to top robotics venues including ICRA, IROS, and IEEE Transactions. The body of work reveals a cohesive research trajectory advancing sampling-based planning through innovations in informed search strategies, adaptive batch processing, and physics-inspired optimization. Recurring themes include handling high-dimensional configuration spaces, ensuring real-time performance, and developing robust solutions for multi-robot coordination and deformable object manipulation. Zhang actively contributes to the robotics community as a reviewer for major conferences (ICRA, IROS, Humanoids) and journals (RA-L, T-ASE, T-Mech), and chaired the Robot Motion Planning IV session at IROS 2024. As a researcher, he participates in nationally and EU-funded projects including Bavarian State Project KI.FABRIK. He mentors bachelor's and master's students on topics spanning LLM-based risk mapping, task and motion planning, and multi-robot coordination, with several theses resulting in peer-reviewed publications. He operates within TUM's Robotics, AI and Real-Time Systems group, which pioneers real-time control systems and advanced planning algorithms for industrial and service robotics applications, maintaining strong industry collaborations and state-of-the-art experimental facilities.

• Machine Learning
• Data Privacy
• Computer Vision
• +۴ مورد دیگر

Ji Zhang is a Professor in the Department of Mathematics, Physics and Computing at the University of Southern Queensland. His research focuses on machine learning, data privacy, computer vision, and blockchain technology. He holds an MSc from Singapore and a PhD from Dalhousie University. His work addresses challenges in secure multiparty computation, adversarial machine learning, and deepfake detection. Key research themes include privacy-preserving techniques for data publishing, generative models for image manipulation, and graph neural networks for network analysis. Recent studies explore efficient neural dynamic data valuation and fair algorithmic systems. He has contributed to scalable graph mining frameworks and secure blockchain applications in healthcare and smart contracts. His publications reflect interdisciplinary innovation across cybersecurity, computer vision, and distributed systems. Notable projects include EVRACE for e-bike charging analysis and Tara-Net for takeaway rider accident detection. Despite no listed awards, his work demonstrates significant impact in AI-driven solutions for real-world problems. Collaborative activities span academic-industry partnerships in fraud detection systems and edge computing frameworks for worker safety monitoring (DeepSafety). Current research emphasizes improving transparency in graph neural networks and developing robust anomaly detection methodologies.

• Turfgrass Science
• Cool-season Grasses
• Stress Physiology
• +۴ مورد دیگر

Dominic P Petrella serves as Assistant Professor in the Department of Horticultural Science within the College of Food, Agricultural and Natural Resource Sciences at the University of Minnesota. His research focuses on turfgrass science with specialization in cool-season grasses and stress physiology. Research interests center on turfgrass adaptation to environmental stresses including winterkill, drought, and ice encasement. Key areas include spectral light effects on foliar development, genomic markers for stress tolerance in hard fescue, and intercropping systems involving fine fescues. His work contributes to UN Sustainable Development Goals through sustainable turf management practices. Recent publications demonstrate expertise in turfgrass responses to sequential drought periods, genomic associations in Festuca brevipila, and innovative photoselective filter applications for simulating natural shade conditions. Research output shows consistent contributions to Crop Science and Plant Methods journals. No scientific awards were identified in the provided materials. Dr. Petrella actively collaborates on major research projects including WinterTurf: A Holistic Approach to Understanding Winter Stress on Turfgrasses in Northern Climates (USDA-funded, 2021-2026) and Evaluation of Cold Tolerance in Tall Fescue Cultivars (Ontario Turfgrass Research Foundation, 2021-2023). His grant portfolio includes projects from the US Golf Association and Minnesota Department of Agriculture focused on winterkill recovery and seed production optimization. Research operations involve spectral analysis equipment including chlorophyll fluorescence imaging systems and photoselective gels for light quality manipulation, supporting field studies on turfgrass stress responses.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Deng Cai is a Professor at Zhejiang University's College of Computer Science, working in the State Key Laboratory of CAD&CG in Hangzhou, China. He also maintains an affiliation with Tencent AI Lab, demonstrating his strong connection between academic research and industry applications in artificial intelligence. His academic background includes a PhD from the University of Illinois at Urbana-Champaign, Department of Computer Science (2009). Professor Cai's research spans multiple domains within artificial intelligence, with particular emphasis on computer vision, deep learning, and their applications. His work shows strong focus on 3D object detection, lane detection for autonomous vehicles, and the application of large language models to various vision tasks. He has made significant contributions to traffic forecasting, trajectory prediction, and CAD generation systems. His recent work increasingly integrates large language models with computer vision tasks, demonstrating the evolving nature of his research interests toward multimodal AI systems. The trajectory of Professor Cai's publications reveals a clear progression from foundational computer vision and machine learning research toward increasingly complex and applied systems. His work shows strong emphasis on practical applications in autonomous driving, with numerous papers on 3D object detection, lane detection, and trajectory prediction. More recently, his research has expanded to include generative models for CAD systems and video customization, often leveraging large language models in innovative ways. The consistent publication output across top-tier venues including CVPR, ICCV, AAAI, and NeurIPS demonstrates sustained research productivity and impact. Professor Cai has established significant research collaborations, particularly with Xiaofei He (161 joint publications), Haifeng Liu (50), Zhou Zhao (42), Wenxiao Wang (41), and Binbin Lin (39). His work appears across diverse publication venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and proceedings of major AI conferences. The breadth of his publication venues reflects the interdisciplinary nature of his research spanning theoretical machine learning to applied computer vision systems. Professor Cai leads research activities within Zhejiang University's College of Computer Science, particularly focusing on the State Key Laboratory of CAD&CG. His work bridges academic research with practical industry applications through his affiliation with Tencent AI Lab. The laboratory environment supports research in computer vision, machine learning, and their applications to real-world problems in autonomous systems, content generation, and intelligent transportation.

Paul R. Schraterمشاهده پروفایل

دانشیار

• Machine Learning
• Probabilistic Inference
• Perception
• +۵ مورد دیگر

Paul R. Schrater is an Associate Professor with a joint faculty appointment in the Departments of Psychology and Computer Science at the University of Minnesota. His research bridges computational approaches with cognitive and neural sciences, focusing on understanding human perception, decision-making, and motor control through probabilistic and machine learning frameworks. Dr. Schrater's research interests span across several interconnected domains in cognitive science and artificial intelligence. His work primarily investigates how humans use probabilistic methods to navigate perception and motor control challenges. He approaches these questions through a structured interdisciplinary lens, placing computational theory at the forefront to drive empirical work. His research aims to develop precise, testable theories of brain computations that can be validated through behavioral measures and brain imaging techniques. His publication record reveals consistent work in human perception, decision theory, and motor control, with a strong emphasis on Bayesian modeling approaches. Over the past decade, his research has evolved to incorporate increasingly sophisticated probabilistic models to explain phenomena ranging from perceptual bistability to optimal grasping strategies under uncertainty. His work demonstrates a clear trajectory toward integrating multiple sensory modalities and cognitive processes within unified computational frameworks. Dr. Schrater directs the Computational Perception and Action Laboratory, located in Elliott Hall at the University of Minnesota. The lab is equipped with a Visuo-Haptic workbench for testing visuo-motor behavior with virtual objects, and an adjustable reach laboratory for studying reaching behavior with actual objects. His teaching includes graduate courses such as "Mathematical Models of Human Behavior" (PSY 5018H) and "Artificial Intelligence II" (CSCI 5512), reflecting his interdisciplinary approach that bridges psychology and computer science.