جستجوی استاد راهنمای دانشگاهی

Sean Andersonمشاهده پروفایل

مدرس ارشد

• Bioinspired Robotics
• Adaptive and Optimal Control
• System Identification
• +۳ مورد دیگر

Dr Sean Anderson is a Senior Lecturer at the Department of Automatic Control and Systems Engineering , University of Sheffield , with over 15 years of experience in interdisciplinary research spanning robotics, control systems, and computational biology. He earned his MEng and PhD from the University of Sheffield, focusing on control systems and chemical engineering. Education: MEng in Control Systems Engineering, University of Sheffield (2001) PhD in Chemical and Process Engineering, University of Sheffield (2005) Research Interests include: Bioinspired robotics Adaptive and optimal control in biological systems Nonlinear system identification Computational neuroscience Acoustic and visual sensor fusion for localization His recent publications highlight innovations in robotic localization in hazardous environments, interpretable deep learning for control systems, acoustic sensing technologies, and data-driven modeling of complex systems. Key projects involve autonomous navigation in pipe networks, turbulence modeling, and biomedical signal processing. Grants and Funding: He has secured major grants from EU H2020 (£4M), EU FP7 (£2.9M), and EPSRC (£5.7M), focusing on bioinspired control algorithms, robotic safety, and infrastructure assessment. Teaching: He leads the ACS61011 Deep Learning module, emphasizing practical applications in robotics and signal processing.

Tobias Neckelمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +۴ مورد دیگر

Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.

• Sustainable Finance
• Ecological Economics
• Post-Keynesian Economics
• +۷ مورد دیگر

Katie Kedward is a Senior Research Fellow at the University College London Institute for Innovation and Public Purpose (IIPP), specializing in sustainable finance and ecological economics. With prior experience in capital markets at Royal Bank of Canada and green banking at ShareAction, she combines post-Keynesian, political economy, and ecological economics perspectives in her research. Her work focuses on aligning financial systems with environmental goals, particularly addressing biodiversity loss and climate change. Education: MSc in Ecological Economics (University of Leeds, 2019), BA in Economics (University of Cambridge, First Class) Advisory Roles: Member of Positive Money UK’s advisory panel Collaborations: Network for Greening the Financial System (NGFS), Dasgupta Review Katie’s research examines financial stability risks from biodiversity loss, green credit policy reforms, and systemic uncertainty in climate-related financial regulation. Her work has been cited by central banks (Banque de France, Bank Negara Malaysia) and featured in major outlets like The Guardian and BBC News. Recent publications focus on nature-economy modeling, biodiversity financing gaps, and financial policy coordination for climate action. She advocates for increased public investment as a complement to private finance in achieving conservation goals. Scientific Awards Sally Macgill Memorial Prize for Best Masters Thesis (2019) Katie contributes to policy discussions through publications in FT Sustainable Views, LSE Business Review, and Nature Ecology & Evolution. She actively reviews for journals including New Political Economy and Conservation Letters.

Jingrui Heمشاهده پروفایل

استاد

• heterogeneous machine learning
• active learning
• neural bandits
• +۹ مورد دیگر

Jingrui He is a Professor and MSIM Program Director at the School of Information Sciences, University of Illinois Urbana-Champaign. She holds multiple faculty affiliate positions including with the Department of Computer Science, National Center for Supercomputing Applications (NCSA), Illinois Informatics, Center for Digital Agriculture (CDA), and Mayo Clinic Arizona. Her research spans machine learning with applications in diverse domains including healthcare, agriculture, security, and finance. Dr. He received her PhD in Machine Learning from Carnegie Mellon University in 2010. Her research focuses on heterogeneous machine learning, active learning, neural bandits, and self-supervised learning. She addresses complex data challenges where multiple types of heterogeneity coexist, developing methods for exploring, understanding, characterizing, and predicting real-world data through statistical machine learning techniques. Her recent publications demonstrate a strong focus on graph learning, federated learning, fairness in AI, and neural bandit algorithms. She has developed innovative approaches for class-imbalanced graph learning, Byzantine-robust federated learning, and privacy-preserving graph machine learning. Her work bridges theoretical foundations with practical applications across multiple domains. Her scientific awards include the Amazon Research Award (2025), ACM Distinguished Member (2023), AAAI Senior Member (2023), FAccT Distinguished Paper Award (2022), NSF CAREER award (2016), and multiple IBM Faculty Awards. She has been recognized as an excellent teacher and received Best Paper awards at major conferences including ICDM and SDM. Dr. He directs the iSAIL Lab and leads several major research projects including the AI Institute for Future Agricultural Resilience Management and Sustainability (AIFARMS). She has successfully mentored numerous doctoral students who have become co-authors on her publications. Her research has been funded through prestigious grants including the NSF CAREER award and IBM Faculty Awards.

Panagiotis Christofidesمشاهده پروفایل

استاد

• Control Systems Engineering
• Process Control
• Distributed Control
• +۷ مورد دیگر

Panagiotis Christofides is a Distinguished Professor in the Department of Chemical and Biomolecular Engineering at the University of California, Los Angeles (UCLA), with a secondary affiliation in Electrical and Computer Engineering. He serves as Department Chair and holds the William D. Van Vorst Chair in Chemical Engineering Education. Education: Diploma in Chemical Engineering (1992), University of Patras M.S. in Electrical Engineering (1995), University of Minnesota M.S. in Mathematics (1996), University of Minnesota Ph.D. in Chemical Engineering (1996), University of Minnesota Research Focus: Christofides specializes in control systems engineering for complex industrial processes, including nonlinear/hybrid systems, economic model predictive control, fault-tolerant control, and multiscale modeling. His work bridges chemical engineering with advanced computational methods, particularly in water/energy systems and solar-cell technologies. Scientific Leadership: He has authored over a dozen influential books and special issues on control theory and its industrial applications. Awards include AIChE Fellow, IEEE/IFAC/AAAS Fellowships, the Donald P. Eckman Award, and multiple Schuck Best Paper Awards. His research has been recognized through the NSF CAREER Award and ONR Young Investigator Award.

Tracy Beckerمشاهده پروفایل

استادیار مدعو

• Earthquake Engineering
• Structural Optimization
• Seismic Isolation
• +۳ مورد دیگر

Tracy Becker is an Adjunct Assistant Professor in the Department of Civil Engineering at McMaster University, where she has been since 2014. Her expertise lies in the design, modeling, and experimental testing of high-performance structural systems with a focus on seismic isolation. Education: BS in Structural Engineering, University of California, San Diego MS and PhD in Structural Engineering, Mechanics and Materials, University of California, Berkeley Post-doctoral research at Kyoto University's Disaster Prevention Research Institute Research Interests: Becker specializes in seismic isolation systems, hybrid simulation methods, and structural performance under extreme events. Her work spans bridge engineering, nuclear infrastructure protection, and innovative materials for earthquake resilience. She integrates computational modeling with experimental validation to address challenges in: Nonlinear system behavior in isolated structures Multi-hazard optimization for seismic and wind loads Bridge management using data-driven and fuzzy logic frameworks Advanced gusset plate design for seismic retrofit Adaptive isolation systems for nuclear facilities Probabilistic lifetime demand predictions for infrastructure Teaching: She has instructed courses in Seismic Design (CIVENG 4ED4), Structural Mechanics (CIVENG 2C04), and Earthquake Engineering (CIVENG 730) at McMaster University.

• Offshore Renewable Energy Systems
• Wave Power Farm Modeling
• Extreme Weather Resilience
• +۳ مورد دیگر

Malin Göteman is an Associate Professor at the Department of Electrical Engineering, Uppsala University. Her research focuses on offshore renewable energy systems, particularly modeling and optimizing large-scale wave power farms and analyzing their resilience to extreme weather conditions. Deputy Director, Center for Natural Disaster Studies (CNDS), Sweden Specialized in wave energy converter dynamics and hybrid offshore energy systems Collaborates on SPH-based numerical wave-current tanks and CFD validation Research Interests: She investigates wave energy farm interactions, hydrodynamic performance of floating platforms, extreme wave load modeling, and survivability strategies using machine learning. Her work spans renewable energy integration, coastal protection, and power system stability under extreme conditions. Recent Publications: Her 2025 articles address resilience of offshore energy systems to metocean extremes and reduced-order modeling via Bayesian design. Earlier works (2023-2024) cover SPH validations for floating wind-wave systems, neural network survivability approaches, and hybrid energy-water supply solutions. Collaborations: She works with international teams on projects like Lysekil wave energy test sites and DeepCwind floating platforms. Key areas include grid-connected wave parks, multi-fidelity surrogate modeling, and comparative studies on offshore wind dependencies.

Jochen Garckeمشاهده پروفایل

استاد

• Numerical Methods in Science and Technology
• Machine Learning
• Sparse Grids
• +۵ مورد دیگر

Prof. Dr. Jochen Garcke is a faculty member at the Institute for Numerical Simulation, University of Bonn, with a dual affiliation at Fraunhofer SCAI's Department of Numerical Data-Based Prediction. His work bridges numerical simulation and machine learning, focusing on high-dimensional problems, sparse grids, and optimal control. Key research themes: Sparse grids, machine learning for simulations, reinforcement learning, uncertainty quantification Teaching includes courses on Numerical Methods in Science and Technology and Scientific Computing , emphasizing practical machine learning applications. Recent publications explore hybrid models combining data-driven and physics-based approaches in automotive engineering, wind turbines, and geoscientific modeling. His group employs adaptive sparse grids, graph algorithms, and spectral methods to tackle challenges in crash simulations, fluctuating renewable energy systems, and turbulent flow analysis. Collaborations span Fraunhofer SCAI and industry 4.0 initiatives.

• Natural Language Processing
• Explainable AI
• Fact-Checking
• +۴ مورد دیگر

Isabelle Augenstein is a Professor at the University of Copenhagen, Department of Computer Science (DIKU), where she heads the Copenhagen Natural Language Understanding (CopeNLU) research group and the Natural Language Processing section. She is also a co-lead of the Danish Pioneer Centre for Artificial Intelligence, Denmark's largest research center initiated by the Danish Ministry of Higher Education and Science. In October 2022, she became Denmark's youngest ever female full professor. Dr. Augenstein earned her undergraduate degree in Computational Linguistics and Psychology from Heidelberg University, followed by a Master's in Computational Linguistics. She completed her PhD in Computer Science at the University of Sheffield under the supervision of Dr. Diana Maynard and Prof. Fabio Ciravegna. In 2021, she earned a Habilitation at the University of Copenhagen in Explainable Fact-checking. Professor Augenstein's primary research focuses on fair and accountable Natural Language Processing, with particular emphasis on explainability, factuality, and bias detection. Her work spans multiple subfields including automated fact-checking, stance detection, gender bias analysis, and cultural bias in language models. She has pioneered research in explainable fact-checking, developing methods that not only predict claim veracity but also provide meaningful explanations of the decision-making process. Her research group has produced numerous influential papers on measuring model fragility, quantifying gender biases, and developing robust fact-checking systems that account for distribution shifts. Her significant contributions have been recognized with several prestigious awards: ERC Starting Grant on 'Explainable and Robust Automatic Fact Checking' DFF Sapere Aude Research Leader fellowship on 'Learning to Explain Attitudes on Social Media' Karen Spärck Jones Award from the British Computing Society and Bloomberg Hartmann Diploma Prize from the Hartmann Foundation Member of the Royal Danish Academy of Sciences and Letters since 2024 Professor Augenstein has secured significant research funding including her ERC Starting Grant supporting five years of blue-sky research. She actively mentors PhD students and postdoctoral researchers through her 'ExplainYourself' project. She served as President of SIGDAT (which organizes the EMNLP conference series), having previously held leadership roles as Vice President and Vice President-Elect. She is a co-founder of Widening NLP (WiNLP), an initiative to increase diversity in the NLP community, and maintains the BIG Directory of underrepresented groups in NLP. She leads the Copenhagen Natural Language Understanding (CopeNLU) research group, which relocated to the historic Østervold Observatory in Copenhagen's Botanical Gardens in 2023. The group focuses on developing methods for explainable and robust natural language understanding, with applications in fact-checking, bias detection, and social media analysis. Professor Augenstein also co-leads the Speech and Language collaboratory at the Pioneer Centre for Artificial Intelligence, where her team investigates how language models can better serve diverse populations while maintaining accountability and transparency.

Katharina Eggenspergerمشاهده پروفایل

پژوهشگر

• Automated Machine Learning (AutoML)
• Hyperparameter Optimization (HPO)
• Tabular Machine Learning
• +۱ مورد دیگر

Katharina Eggensperger is an Early Career Research Group Leader at the University of Tübingen , leading the AutoML for Science group within the Cluster of Excellence Machine Learning for Science . She previously completed her Ph.D. at the University of Freiburg under Frank Hutter and Marius Lindauer (2022), and actively contributes to the AutoML community through open-source tool development and competition leadership. Co-developer of AutoML.org tools Faculty member of IMPRS-IS Chair for multiple AutoML workshops/conferences (2019-2025) Her research focuses on automated machine learning (AutoML) with specific attention to: AutoML Systems Hyperparameter Optimization Tabular Machine Learning Scientific Applications of ML She has organized multiple AutoML schools and conferences, including serving as Program Chair for AutoML 2024 and Non-archival Track Chair for AutoML 2025. Her work emphasizes making machine learning accessible through automation while maintaining scientific rigor and interpretability, particularly for tabular data applications. Katharina actively recruits PhD students through IMPRS-IS and collaborates with institutions like the University of Freiburg and Cyber Valley .

Michael L. Madiganمشاهده پروفایل

استاد

• Human Factors Engineering
• Ergonomics
• Occupational Biomechanics
• +۶ مورد دیگر

Michael L. Madigan is a Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. His research focuses on human factors engineering, biomechanics, and ergonomics, particularly addressing issues related to slips, trips, falls, and musculoskeletal disorders. He leads the Madigan Biomechanics Group, which investigates the biomechanics of human motion and neuromuscular control to enhance occupational safety and elderly health. Education: Ph.D., Biomedical Engineering, Virginia Commonwealth University, 2001 M.S., Bioengineering, Texas A&M University, 1996 B.S., Bioengineering, Texas A&M University, 1994 Research Interests: Obesity’s impact on mobility, aging-related biomechanics, workplace ergonomics, and biomechanical interventions for fall prevention. His work combines experimental and computational methods to develop preventive measures like exercise interventions and ergonomic designs. Professional Roles: Editor-in-Chief, Journal of Applied Biomechanics (2017–present) Formerly Editor, Medicine and Science in Sports and Exercise (2013–2017) Secretary, American Society of Biomechanics (2009–2012) Awards: Kevin P. Granata Faculty Fellow (2013–2014) Virginia Tech College of Engineering Certificate of Teaching Excellence (2014) Finalist for International Society of Biomechanics Clinical Award (2005) Advising & Grants: Advised over 20 doctoral and undergraduate students since 2007. His grants support studies on obesity-related falls, exoskeleton design, and biomechanical modeling. Collaborates with the Virginia Tech-Wake Forest School of Biomedical Engineering and Sciences. Labs/Teams: Director of the Madigan Biomechanics Group and core member of the Occupational Ergonomics and Biomechanics Laboratories. Active in developing smart prosthetics and wearable technologies for injury prevention.

• Noise Abatement in Machines and Vehicles
• Numerical Modeling in Mechanics
• Automotive Structures and Dynamics
• +۱۲ مورد دیگر

Prof. Wim Desmet is a full professor at the Faculty of Engineering Science and head of the Department of Mechanical Engineering at KU Leuven . His research focuses on advanced modeling techniques for mechanical systems, including: noise and vibration control in automotive and industrial systems computational acoustics and interval field uncertainty modeling metamaterials for broadband vibroacoustic performance AI-driven diagnostic systems in renewable energy and manufacturing Current research projects address challenges in electric vehicle drivetrains, wind turbine monitoring, and multi-physical digital twin development. He actively contributes to academic governance as: Managing Director of KU Leuven Head of Subdivision HIST Chair of multiple executive committees Member of 15+ academic and administrative councils

Andrea Bonariniمشاهده پروفایل

استاد

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

Andrea Bonarini is a Professor at the Department of Electronics, Information, and Bioengineering (DEIB) within the School of Industrial and Information Engineering at Politecnico di Milano. His research focuses on Artificial Intelligence , Robotics , and Human-Robot Interaction , particularly in assistive technologies for children with disabilities and autism therapy. Key Research Areas: Reinforcement Learning, Social Robotics, Affective Computing, Accessibility in Robot Design Notable Projects: Development of frameworks like Mushroomrl and datasets like POLIMI-ITW-S for robotics research His recent publications highlight advancements in playful robotics for neurodevelopmental disorders, emotion projection in non-bio-inspired robots, and safety experiments for small social robots interacting with children. He also contributes to ethical, social, and psychological challenges in robotics applications.

Alexander Rodríguezمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Time Series Analysis
• +۷ مورد دیگر

Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.

James Mitchellمشاهده پروفایل

استاد

• Public Policy
• Devolution Studies
• Scottish Politics
• +۵ مورد دیگر

James Mitchell is a Professor of Public Policy at the University of Edinburgh’s School of Social and Political Science . He holds a MA (Political Studies) from the University of Aberdeen and a D.Phil. (Oxford). His research focuses on British politics, devolution, public policy, territorial politics, and Scottish nationalism. He has supervised over 30 years of PhDs and Masters in public policy, devolution, and constitutional politics. Key research themes include fiscal accountability in Scotland, party membership surges post-referendum, and multi-level governance. Research Projects: ESRC-funded study on SNP and Scottish Greens’ post-referendum membership growth Investigation of fiscal devolution’s accountability gaps Analysis of Scotland’s constitutional questions and local governance Research Interests: Mitchell explores territorial politics, public service reform, and political behavior in sub-state governments. He has contributed to debates on Scottish independence, EU referendum impacts, and intergovernmental relations. Recent work includes studies on the SNP leadership contest (2023) and Holyrood’s fiscal structures. Grants & Awards: He has secured grants from the Economic and Social Research Council (ESRC) for projects on party membership and constitutional dynamics. No specific scientific awards are listed, but his work is widely cited in political science and public policy. Labs & Teams: He is part of the Territorial Politics Research Group and has collaborated on regional economic forecasting and governance studies. His work often bridges academia and policy, influencing debates on devolution and public finance.