جستجوی استاد راهنمای دانشگاهی

Silvia Cascianelliمشاهده پروفایل

پژوهشگر

• Computer Vision
• Document Analysis
• Handwritten Text Recognition
• +۴ مورد دیگر

Silvia Cascianelli is an AI and Computer Vision Researcher at the University of Modena and Reggio Emilia (UNIMORE). She actively contributes to the computer vision and document analysis communities through research, conference organization, and academic mentorship. She serves as Area Chair for major computer vision conferences including CVPR2025, BMVC2025, and ECCV2024, demonstrating her standing in the field. Her research focuses on several key areas within computer vision and document analysis: Image Generation : Developing efficient and lightweight methods for image generation with desired characteristics, particularly using diffusion models Handwriting Imitation : Creating algorithms for generating images of text with specific content and handwriting styles, along with evaluation methods Document Understanding : Extracting information from 2D and 3D document images, ranging from modern documents to historical artifacts like carbonized Roman papyri Dr. Cascianelli's work shows a clear progression toward more sophisticated generative models and evaluation frameworks, with recent publications focusing on diffusion models for handwritten text generation, efficient token reduction for multimodal tasks, and innovative approaches to historical document analysis. Her research bridges theoretical advancements with practical applications across diverse document types. Her scientific contributions have been recognized through invitations to serve as Area Chair for top-tier computer vision conferences (CVPR, ECCV, BMVC) and opportunities to organize specialized workshops including VisionDocs at ICCV, AI4DH at ECCV, and ADAPDA at ICDAR. Area Chair at CVPR2025 Area Chair at BMVC2025 Area Chair at ECCV2024 Organizer of VisionDocs Workshop at ICCV2025 Organizer of AI for Digital Humanities Workshop at ECCV2024 Organizer of ADAPDA Workshop at ICDAR2024 Dr. Cascianelli actively mentors the next generation of researchers: Vittorio Pippi - PhD Student at UniMoRe (National PhD program in AI) Fabio Quattrini - PhD Student at UniMoRe (ICT program) Carmine Zaccagnino - Research Intern at UniMoRe (formerly MSc student) Kostantina Nikolaidou - PhD Student at Luleå University of Technology Pau Torras Coloma - PhD Student at Computer Vision Center, Universitat Autònoma de Barcelona Bram Vanherle - CV Engineer at Colruyt Group Smart Innovation (formerly PhD student) She is actively involved in several research initiatives including the AI Governance Lab where she serves as a lecturer, and collaborates with institutions worldwide. Her current projects focus on advancing diffusion models for image generation, improving handwritten text recognition systems, and developing novel methods for document understanding across historical and contemporary contexts.

Grzegorz Chrupałaمشاهده پروفایل

دانشیار

• Computational Linguistics
• Artificial Intelligence
• Cognitive Science
• +۳ مورد دیگر

Grzegorz Chrupała is an Associate Professor at the Department of Cognitive Science and Artificial Intelligence , Tilburg University, where he leads research in computational approaches to multimodal communication. Previously, he was a postdoctoral researcher at Saarland University's Spoken Language Systems group and earned his PhD from Dublin City University's School of Computing. His research bridges biological and artificial computation , focusing on enabling machines to learn language from multimodal data (speech, gestures, visual-auditory stimuli) as children do naturally. This involves developing and interpreting deep learning architectures, analyzing emergent representations, and advancing speech technology for under-resourced languages. Key themes include Visually grounded speech modeling Feature attribution and model interpretability Human-inspired learning paradigms BlackboxNLP workshop leadership His recent publications examine speech model reliability , lexical tone encoding , and contextual dependencies in NLP systems. He mentors a team of PhD candidates and alumni working on topics like user-centric interpretability, bioacoustics, and disentangled speech representations. He also serves on the board of the Dutch Open Speech Technology Foundation, chairs Interspeech 2025 tutorials, and contributes as an Action Editor for TACL.

Sven Mayerمشاهده پروفایل

استاد

• Human-AI Interaction
• Human-Robot Interaction
• Machine Learning
• +۷ مورد دیگر

Sven Mayer is a Professor at TU Dortmund University’s Faculty of Computer Science and leads the Chair for Human-AI Interaction. He previously held a postdoctoral position at LMU Munich’s Media Informatics group and completed his PhD at Carnegie Mellon University (2014–2018). His research focuses on Human-AI/Robot Interaction , machine learning for touch systems , and sensor fusion . Recent work includes super-resolution capacitive touchscreens, pose estimation via smartphones, and city-scale sensing using retroreflective markers. The 15 most recent publications highlight trends in AI-driven interaction design , robot expressions , and smartphone-based sensing , with keywords spanning robotics, machine learning, and smart cities. Subdomains include contextual awareness, ergonomic constraints, and novel touch input paradigms. Scientific contributions recognized with Honorable Mention for 'Flyables' (2022) Honorable Mention for ISS piano learning paper (2022) . He has supervised projects in augmented reality and human-robot interaction courses , and frequently contributes to conferences like CHI, UIST, and MobileHCI as both author and organizational leader (e.g., Publications Chair at CHI 2022).

Margot Kaminskiمشاهده پروفایل

استاد

• Privacy Law
• Artificial Intelligence Law
• First Amendment Law
• +۶ مورد دیگر

Margot Kaminski is a Professor of Law and Director of the Privacy Initiative at Silicon Flatirons Center for Law, Technology and Entrepreneurship at the University of Colorado Law School. Her work focuses on AI law, privacy, and First Amendment issues in technology contexts. Current affiliations: University of Colorado Law School, European University Institute (Fernand Braudel Senior Fellow), Berkman Klein Center at Harvard (Faculty Associate) Past roles: Assistant Professor at Ohio State University Moritz College of Law, Executive Director of Yale's Information Society Project Research Interests: AI and algorithmic accountability Data privacy and GDPR analysis First Amendment in digital contexts Copyright law and technological authorship Surveillance law and civil liberties Scientific Awards: 2022 Jules Milstein Scholarship Award 2020 Future of Privacy Forum Privacy Papers for Policymakers Award 2019 University of Colorado Provost's Faculty Achievement Award 2016 Junior Scholar Award (Privacy Law Scholars Conference) Grants & Fellowships: Fulbright-Schuman Innovation Grant (2018) Fulbright-Schuman Grant and Fernand Braudel Senior Fellowship (2024, European University Institute) Key contributor to legal scholarship in Yale Law Journal , Columbia Law Review , and Harvard Law Review , with a focus on the interplay between technology and constitutional rights. Director of the Privacy Initiative at Silicon Flatirons, a research center addressing law and technology intersections. Regularly publishes on AI governance and privacy law in leading journals.

Mathis Richterمشاهده پروفایل

پژوهشگر

• Higher cognition
• Concept representation
• Dynamic Field Theory
• +۹ مورد دیگر

Dr. Mathis Richter is a Postdoctoral Researcher at the Institute of Neuroinformatics (INI), part of the Faculty of Computer Science at Ruhr University Bochum, Germany. He has been affiliated with the INI since 2008, progressing from Research Assistant to Research Associate, and currently serves as a Postdoctoral Researcher since July 2018. At the INI, he contributes to both the Embodied Cognition group and the Autonomous Robotics group, led by Prof. Dr. Gregor Schöner. Dr. Richter earned his Dr.-Ing. (Ph.D. equivalent) in Engineering from Ruhr-Universität Bochum between 2011 and 2018, following an M.Sc. and B.Sc. in Applied Computer Science from the same institution. His academic journey includes an exchange year at the University of Birmingham, UK. His research centers on higher cognition, specifically concept representation, how concepts combine to form complex mental scenes, and the neural mechanisms organizing cognitive operations in time. Using Dynamic Field Theory as his primary framework, he develops mathematical models explaining how neural populations represent objects and concepts. His work demonstrates how these cognitive models connect to sensory-motor systems, often implemented on robotic platforms to validate their autonomy and functionality. Analysis of Dr. Richter's publications reveals a consistent focus on neural dynamic modeling of cognitive processes, with particular emphasis on spatial relations, language grounding, and embodied cognition. His research trajectory shows increasing sophistication in modeling complex cognitive phenomena while maintaining strong connections to robotic implementations. As an educator, Dr. Richter has taught Lab courses in Autonomous Robotics across multiple terms since Winter 2015/2016 and has delivered Lectures in Computational Neuroscience: Neural Dynamics since Winter 2018/2019. His teaching directly reflects his research expertise in neural dynamics and cognitive systems. Dr. Richter actively participates in interdisciplinary research that bridges cognitive science, neuroscience, computer science, and robotics, contributing to the INI's mission of understanding how organisms generate behavior and cognition through interaction with their environments.

Roberto Tronمشاهده پروفایل

استادیار

• Control Theory
• Robotics
• Computer Vision
• +۴ مورد دیگر

Roberto Tron is an Assistant Professor in the Mechanical Engineering and Systems Engineering departments at the Boston University College of Engineering , with his office located at 110 Cummington Mall. His research integrates control theory, robotics, and computer vision to solve complex multi-agent coordination problems. His primary research interests focus on Riemannian geometry applications , distributed multi-agent systems , and safety-critical control . Key methodologies include Control Barrier Functions (CBFs), Riemannian optimization, and distributed consensus algorithms, with applications spanning autonomous aerial vehicles, robotic manipulation, and multi-robot security systems. Analysis of his recent publications reveals a strong emphasis on safety verification and real-time optimization for autonomous systems. His work consistently bridges theoretical foundations in nonlinear control with practical implementations in robotics, particularly addressing challenges in limited sensor fields of view, distributed task allocation, and noise-robust navigation. The research shows increasing integration of formal methods like Signal Temporal Logic with learning-based approaches. Tron received his Ph.D. from The John Hopkins University and previously conducted post-doctoral research at the GRASP Lab, University of Pennsylvania. His work demonstrates significant contributions to provably safe autonomous systems through frameworks like the Control Barrier Function Toolbox.

• Machine Learning
• Artificial Intelligence
• Data Analysis
• +۷ مورد دیگر

Jonathan Voersaa Wenshøj is an academic researcher at the Department of Computer Science, University of Copenhagen. He contributes to the Machine Learning section's activities spanning theoretical foundations and applications in diverse domains like information retrieval, medical data analysis, remote sensing, sustainability, and biological modeling. The section participates in the SCIENCE AI Centre and collaborates with initiatives like TreeSense for global tree resource analysis. His research intersects machine learning with quantum computing, medical informatics, and sustainability. Recent publications highlight applications in environmental monitoring, healthcare diagnostics, and energy-efficient AI systems. The department provides advanced compute resources including a powerful cluster for intensive machine learning tasks. This researcher's work appears in diverse machine learning domains, with recent publications addressing quantum-inspired architectures, explainable AI in medical imaging, and sustainable computing practices. The section actively hosts events including seminars, conferences, and PhD defences related to machine learning advancements.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۵ مورد دیگر

Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.

• Natural Language Processing
• Legal Informatics
• Multilingual NLP
• +۳ مورد دیگر

Ilias Chalkidis is an Assistant Professor specializing in Natural Language Processing at the Department of Computer Science, University of Copenhagen. He is actively affiliated with the Natural Language Processing research section, contributing to both theoretical and applied advancements in the field. His research spans multiple high-impact domains with particular emphasis on: Legal natural language processing and multilingual legal reasoning Large language model applications in political and social contexts Fairness-explainability trade-offs in AI systems Innovative representation learning techniques for textual data Analysis of his recent publications reveals a strong focus on bridging legal informatics with cutting-edge NLP methodologies. His work on multilingual legal corpora (including the 689GB MultiLegalPile dataset) and legal decision influence prediction demonstrates practical applications for judicial systems. Simultaneously, his investigations into LLMs as voting assistants and European political spectrum analysis showcase innovative intersections between computational social science and language technology. His technical contributions to contrastive learning and hyperbolic embeddings provide foundational advances for document representation. Chalkidis actively participates in the research community through workshop organization (Natural Legal Language Processing Workshop 2023-2024) and conference presentations. His research has been published in top-tier venues including ACL, EMNLP, and ECAI, with significant citations reflecting community impact. While specific advising relationships aren't documented in the provided materials, his collaborative work patterns suggest active mentorship within the NLP research ecosystem.

• Artificial Intelligence
• Machine Learning
• Computer Science
• +۶ مورد دیگر

Fredrik Heintz is a professor at Linköping University's Department of Computer and Information Science within the Faculty of Science & Engineering. His research bridges artificial intelligence, education, and healthcare, focusing on AI literacy, synthetic data generation, and autonomous systems. Key affiliations: Linköping University (Faculty of Science & Engineering, Department of Computer and Information Science) Research Interests: Heintz's work spans multiple domains: Developing frameworks for AI literacy in K-12 education Creating fair synthetic healthcare data using GANs and bias-transforming techniques Advancing autonomous 3D exploration algorithms for dynamic environments Benchmarking tools for fairness, utility, and explainability in AI models Stream reasoning for real-time data analytics and knowledge extraction Evaluating ethical implications of AI in teacher education Scientific Contributions: His publications highlight collaborations with international researchers and significant grants from the Swedish Research Council, Knut and Alice Wallenberg Foundation, and VINNOVA. Notable projects include TransFusion for time-series generation, Bt-GAN for fair healthcare data, and DAEP for dynamic exploration planning. Funded by Wallenberg AI, Autonomous Systems and Software Program (WASP) ELLIIT Excellence Center at Linköping-Lund Mistra Geopolitics research program

Lingjia Liuمشاهده پروفایل

استاد

• 5G/6G Networks
• Deep Learning in Communications
• Internet of Things (IoT)
• +۴ مورد دیگر

Lingjia Liu is a Professor and Bradley Senior Faculty Fellow at Virginia Tech's Bradley Department of Electrical and Computer Engineering. Her research focuses on enabling technologies for 5G/6G networks, including massive MIMO systems, dynamic spectrum access, and AI-driven communication networks. She holds a Ph.D. from Texas A&M University (2008). Research Interests : 5G/6G Network Architectures (3D MIMO, cloud-RAN, ultra-low latency) AI in Communications (Reservoir Computing, federated learning) IoT & Cyber-Physical Systems (energy harvesting, privacy protection) Non-Terrestrial Networks (satellite-based connectivity) Recent work emphasizes generative AI for network simulation, explainable AI in communication systems, and secure dynamic spectrum sharing. Her research spans theoretical foundations (e.g., OTFS modulation analysis) and practical implementations (e.g., FPGA-based reservoir computing). Awards : Bradley Senior Faculty Fellow (Virginia Tech). Her contributions bridge communication theory and AI, addressing 6G challenges through innovative algorithmic and architectural solutions. Current projects explore agentic protocol learning, federated multi-agent RL for spectrum access, and resilient ML under adversarial conditions.

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

Prof. Frank-Peter Schilling is a Senior Lecturer at Zurich University of Applied Sciences (ZHAW) School of Engineering and Deputy Director of the Centre for Artificial Intelligence (CAI). He leads the Intelligent Vision Systems group and coordinates the PhD Programme in Data Science with the University of Zurich. As an Adjunct Professor at Victoria University of Wellington, he specializes in AI, Machine Learning, and applications in healthcare and physical sciences. His research focuses on deep learning-based computer vision, MLOps, and trustworthy AI certification frameworks. Education: PhD in Physics (University of Heidelberg, 2001) Dipl.-Phys. (MSc equivalent in Physics, University of Heidelberg, 1998) CAS University Didactics (PH Zurich, 2024) Research Interests: Developing AI systems for medical imaging (e.g., CBCT artifact reduction) Certification schemes for AI trustworthiness (e.g., certAInty project) Applications of deep learning in particle physics and industrial vision Achievements: Recipient of the EPS HEP Prize (2013) for contributions to the Higgs boson discovery at CERN Lead author of over 20 peer-reviewed articles on AI, MLOps, and medical imaging Principal investigator for projects like AI-BRIDGE (responsible AI development) and GenAI4SKA (Square Kilometre Array simulations) Teaching: Courses in MLOps, Machine Learning Operations, and Computer Vision at BSc and MSc levels. Developed the CAS Advanced Machine Learning program. Labs & Networks: Active in ELLIS (European Lab for Learning and Intelligent Systems), CLAIRE (AI research), and ZHAW’s Digital Health/Datalab initiatives.

Michael DeWeeseمشاهده پروفایل

دانشیار

• Nonequilibrium Statistical Mechanics
• Machine Learning Theory
• Systems Neuroscience
• +۳ مورد دیگر

Michael DeWeese is an Associate Professor of Physics and Neuroscience at the University of California, Berkeley. His research spans nonequilibrium statistical mechanics, machine learning theory, and systems neuroscience. He holds a BA in Physics from UC Santa Cruz (1988) and a PhD in Physics from Princeton (1995). Before joining UC Berkeley in 2007, he held postdoctoral positions at the Salk Institute and Cold Spring Harbor Laboratory. His work integrates principles from physics, neuroscience, and machine learning to address fundamental questions in theoretical and experimental biology, computation, and statistical mechanics. DeWeese Lab Website provides further details on ongoing projects and collaborations. Education: BA in Physics, UC Santa Cruz (1988) PhD in Physics, Princeton University (1995) Research Interests: Nonequilibrium Statistical Mechanics: Focuses on thermodynamic optimization, active matter, and non-equilibrium processes. Machine Learning Theory: Develops first-principles models to explain neural network performance and efficient algorithms for probabilistic models. Systems Neuroscience: Uses biologically inspired models to understand neural coding, sensory processing, and computational roles of neural networks. Advising & Grants: While no formal student advisees are listed, his lab actively collaborates across disciplines. Funding sources are not explicitly mentioned but likely involve NSF, NIH, or DOE grants based on research themes. His work on quantum control and neural networks suggests potential ties to interdisciplinary funding initiatives. Labs & Teams: Directs the DeWeese Lab, which bridges physics, neuroscience, and machine learning. Collaborations include institutions like the Helen Wills Neuroscience Institute (UC Berkeley).

• Artificial Intelligence (AI)
• Machine Learning Algorithms
• AI Ethics
• +۴ مورد دیگر

Daswin De Silva is a Full Professor of AI and Analytics at La Trobe University, Australia, and Deputy Director of the Centre for Data Analytics and Cognition (CDAC). He also holds an Adjunct Professor position at Lulea University of Technology, Sweden. His expertise spans AI ethics, algorithm development, and applications in healthcare, energy, and education. He leads major initiatives like the La Trobe Energy AI Platform for net-zero emissions and the OptusU AI Micro-credentials program. Education: PhD in AI (Monash University, 2011). Awards include the Australian Awards for University Teaching (2021), Vice-Chancellor’s Teaching Award (2019), and Mid-Career Research Excellence Award (2018). Editor of five journals including IEEE Transactions on Industrial Informatics and Springer Discover AI. Research focuses on generative AI, ethical AI systems, and vector symbolic architectures. Recent work includes AI applications in healthcare diagnostics, energy efficiency, and smart cities. He has secured AU$14M in research funding and supervised 15 PhD completions with 10 current students. Leadership roles include Deputy Chair of La Trobe’s Research & Graduate Studies Committee and chairmanship of IEEE committees on Responsible AI and Web/Information Systems. Keynote speaker at global conferences like IEEE HSI, INDIN, and ETFA. Media engagements include ABC News, Forbes, and The Conversation.

Kuldeep S. Meelمشاهده پروفایل

دانشیار

• Automated Reasoning
• Formal Methods
• Artificial Intelligence
• +۳ مورد دیگر

Kuldeep S. Meel is the Stephen Fleming Early-Career Associate Professor at Georgia Institute of Technology's School of Computer Science and an Associate Professor at the University of Toronto (currently on leave). His research focuses on the intersection of Formal Methods and Artificial Intelligence, emphasizing scalable automated reasoning techniques. He holds prestigious awards including the 2022 ACP Early Career Researcher Award and the 2019 NRF Fellowship for AI. His work has been recognized with multiple best paper awards at conferences like ICLP, CAV, and IJCAI. Meel's research spans automated reasoning, formal methods, and their applications in AI. He has developed influential tools like ApproxMC and UniGen, advancing model counting and uniform sampling. His academic journey includes roles at NUS and collaborations with institutions globally. Teaching excellence is highlighted by NUS Annual Teaching Awards (2022, 2023). Awards include Distinguished Paper Awards at CAV-23 and CAV-24, and 1st place in Model Counting Competitions. His lab has produced notable advisees securing tenure-track positions worldwide. Current projects explore distribution testing, probabilistic reasoning, and AI verification.