جستجوی استاد راهنمای دانشگاهی

Tim Baarslagمشاهده پروفایل

استاد

• Automated Negotiation
• Multi-Agent Systems
• Artificial Intelligence
• +۴ مورد دیگر

Tim Baarslag is a Senior Researcher and group leader of the Intelligent and Autonomous Systems group at CWI (The Dutch research institute for Mathematics and Computer Science). He holds the title of Professor of Mathematics of Cooperative AI at Eindhoven University of Technology and serves as a Visiting Associate Professor at Nagoya University of Technology, Visiting Fellow at the University of Southampton, and Visiting Scholar at MIT. His research focuses on automated negotiation systems for collaborative decision-making in smart energy trading, IoT, autonomous vehicles, and digital privacy. Education : MSc (cum laude) and BSc (cum laude) from Utrecht University; PhD (cum laude) from Delft University of Technology Tim pioneered the COMBINE project (NWO Vidi grant) for coordinating multi-deal negotiations and developed the widely-used Genius negotiation environment. His work appears in prestigious venues like Science Magazine , Artificial Intelligence , and MIT Technology Review . He also leads the International Automated Negotiating Agent Competition and contributes to policy through memberships in The Young Academy and Netherlands Academy of Engineering . Recent research trends emphasize multi-deal negotiation protocols (2024), preference uncertainty modeling in privacy negotiations (2022), and scalable algorithms for handling outcome spaces as large as 10²⁵⁰ possibilities. His 2023 work on search algorithms for large negotiation domains has applications in energy trading and supply chain management. Scientific Awards : Cor Baayen Young Researcher Award (2017), Springer Theses Award (2016), multiple Best Paper Awards (AAMAS 2022, WI-IAT 2015, IJCAI 2014), and recognitions as Science Talent (2018), Academic Pioneer (2020), and Young Talent (2019) As a grant recipient , Tim leads NWO Vidi project COMBINE and previously held a Veni grant for preference uncertainty research. He mentors through organizing competitions, serving on conference PCs (AAAI, IJCAI), and reviewing in top journals like Artificial Intelligence . His work bridges theory and practice through the Genius framework and real-world implementations in smart grid and vehicular platooning.

• Fuzzy Systems
• Computational Intelligence
• Nonlinear Control
• +۴ مورد دیگر

Michio Sugeno is a distinguished Professor at Tokyo Institute of Technology's Graduate School of Information Science and Engineering, Department of Computational Intelligence. With a career spanning over four decades, he has established himself as a leading figure in fuzzy systems and computational intelligence. His research interests encompass Fuzzy Systems, Computational Intelligence, Nonlinear Control, Choquet Integral theory, Brain-Computer Interfaces, and Linguistic Computing. Sugeno's work has fundamentally shaped modern fuzzy control theory, particularly through his development of the Takagi-Sugeno fuzzy model which has become a standard approach in industrial applications. Analysis of his recent publications reveals a continued focus on piecewise nonlinear modeling, stability analysis of fuzzy systems, and the application of Choquet calculus to various computational problems. His work demonstrates a consistent trajectory from theoretical foundations to practical implementations in control systems and intelligent computing. IEEE Pioneer Award in Fuzzy Systems IFSA Fellow Emanuel R. Piore Award Sugeno has mentored numerous researchers who have become prominent in their own right, including Tadanari Taniguchi, Luka Eciolaza, and Anh-Tu Nguyen. His laboratory has been instrumental in developing novel approaches to nonlinear control systems using piecewise bilinear models and fuzzy logic. Current research directions include brain-computer interfaces using EEG analysis and the development of everyday language computing systems that enable more natural human-computer interaction.

Gary Edmondمشاهده پروفایل

استاد

• Expert evidence
• Forensic science
• Law and science
• +۴ مورد دیگر

Professor Gary Edmond is a law professor at the University of New South Wales School of Law, directing the Program in Expertise, Evidence and Law. Holding a BA(Hons) from the University of Wollongong, LLB(Hons) from the University of Sydney, and PhD from the University of Cambridge, he bridges legal scholarship with forensic science expertise through extensive research grants and international collaborations. Education: BA(Hons), LLB(Hons), PhD Institutions: University of New South Wales, Australian Academy of Forensic Sciences His research focuses on the intersection of law and forensic science, examining expert evidence reliability, forensic reporting practices, and the adversarial legal system's limitations. With over $1.4 million in research funding since 2007, he leads interdisciplinary projects involving policing agencies and forensic institutions across Australia and international partners. Recent publications analyze judicial handling of expert evidence, cognitive biases in courtroom identification, and forensic science reform. His work has shaped evidence law understanding through the 6th edition of 'Australian Evidence: A principled approach to the common law and the uniform acts' and advisory roles in high-profile inquiries like the Goudge Inquiry. Awards: Fellow of the Royal Society of New South Wales As Chair of the Evidence-based forensics initiative and member of Standards Australia’s forensic science committee, he continues to influence policy while teaching core legal subjects including Courts, Procedure, Evidence and Proof, and Introducing Law and Justice.

• Causal Analysis
• Econometrics
• Machine Learning
• +۷ مورد دیگر

Martin Huber is Professor of Applied Econometrics and Policy Evaluation at the University of Fribourg, Switzerland, within the Faculty of Management, Economics and Social Sciences, Department of Economics. He leads the Chair of Applied Econometrics and maintains an active research profile with numerous publications in top economics and statistics journals. His work bridges theoretical econometrics with practical policy applications across multiple domains including labor, health, and education economics. Professor Huber earned his Ph.D. in Economics and Finance in 2010 and served as Assistant Professor at the University of St. Gallen until 2014. He has conducted research stays at Harvard University (2011/2012) and the University of Sydney (2014 and 2019), establishing an international research network. His academic affiliations include the Committee for Econometrics of the Verein für Socialpolitik, Global Labor Organization, Soda Labs (Monash Business School), and Centre for European Economic Research (ZEW) Mannheim. Huber's research focuses on data-based causal analysis , machine learning applications in economics , and policy evaluation methods . He specializes in developing and applying statistical and econometric methods for measuring causal effects, with particular emphasis on semi- and nonparametric microeconometrics. His work spans labor economics (gender occupational segregation, maternal labor supply), health economics, education policy, and competition policy (bid-rigging cartels detection). His recent publications (2023-2025) demonstrate a clear trajectory toward integrating machine learning techniques with traditional econometric methods for causal inference. This includes developing frameworks for causal discovery, improving difference-in-differences methods with machine learning, and creating novel approaches for detecting collusion in markets. His 2023 book "Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R" (MIT Press) has become a key reference in the field. As an active researcher, Professor Huber directs several research projects including experimental evaluations of gender occupational segregation in the Swiss apprenticeship market. His work combines theoretical rigor with practical policy relevance, often employing experimental and quasi-experimental methods to address questions of causal mechanisms in social and economic phenomena. Through his Chair of Applied Econometrics, Huber supervises Ph.D. students and maintains an active research group focused on advancing causal inference methodologies. His work has significant implications for evidence-based policymaking across multiple sectors, particularly in evaluating the effectiveness of social programs and economic policies.

Oskari Ville Pakariمشاهده پروفایل

مدرس

• Nuclear Engineering
• Reactor Physics
• Radiation Detection
• +۳ مورد دیگر

Oskari Ville Pakari is a Lecturer at the School of Basic Sciences, École polytechnique fédérale de Lausanne (EPFL), affiliated with both the Institute of Physics (IPHYS) and the Swiss Plasma Center (SPH-ENS). He contributes to teaching and research, particularly in reactor physics and radiation detection. His research focuses on nuclear reactor diagnostics , gamma noise analysis , and neutron spectroscopy . He actively develops mixed reality visualization tools for radiation detection data and participates in the European CORTEX project for reactor monitoring. Selected publications highlight his work in gamma-ray imaging , neutron noise simulations , and detector system validation using advanced statistical methods like bootstrapping and Welch's technique. Teaching activities include courses on Radiation biology, protection, and applications Radiation and reactor experiments He advises PhD student Saliba Michel and collaborates with international institutions such as CEA, KIT, and LRS (Laboratory of Reactor Physics and Systems Behaviour) at EPFL.

• Economic Development
• Regional Economics
• Applied Econometrics
• +۵ مورد دیگر

Marco Letta is a Tenure-Track Assistant Professor at the Department of Social and Economic Sciences , Sapienza University of Rome. His research focuses on economic development , regional economics , policy evaluation , and applied econometrics , with a strong emphasis on climate change impacts, food security, and machine learning applications in economic policy. University: Sapienza University of Rome Department: Department of Social and Economic Sciences Email: marco.letta@uniroma1.it His recent work explores the climate migration nexus , household resilience , and policy targeting , often leveraging machine learning and empirical econometric methods . Publications span topics such as local inequalities during the COVID-19 crisis , temperature shocks in rural Tanzania , and machine learning applications in state aid regulation . Notable trends in his research include: Integration of machine learning with traditional econometric techniques Focus on climate resilience and migration patterns Analysis of policy impacts in developing economies Investigation of local mortality estimates during global crises Development of cross-country empirical frameworks Current projects include assessing agrifood system vulnerabilities and refining counterfactual policy evaluation methodologies.

Valentyn Panchenkoمشاهده پروفایل

استاد

• Financial Econometrics
• Time Series Analysis
• Dependence Measures and Copulae
• +۱۱ مورد دیگر

Professor Valentyn Panchenko is a leading academic in Economics at the UNSW Business School, specializing in advanced econometric methodologies and financial modeling. Holding a PhD from the University of Amsterdam and an MPhil from the Tinbergen Institute, his research bridges theoretical econometrics with real-world financial applications, emphasizing big data analysis, network structures, and dependence modeling in economic systems. His expertise spans financial econometrics, time series analysis, non-parametric statistics, and agent-based economic simulations. He focuses on Granger causality, model evaluation, structural economic modeling, and bounded rationality with heterogeneous agents. His work has secured significant grants including ARC Discovery Projects and DECRA fellowships, enabling cutting-edge research on market dynamics and economic interactions. Professor Panchenko's publications appear in top-tier journals like the Journal of Econometric Theory, AEJ: Micro, Journal of Economic Dynamics & Control, and Journal of Banking & Finance. His methodological contributions include novel approaches to copula-based forecasting, nonlinear causality testing, and evolutionary learning models in strategic economic environments. While specific student advising details aren't provided, his research leadership demonstrates sustained impact across econometric theory, financial markets, and experimental economics.

• Network Tomography
• Software-Defined Networking
• Cyber-physical Systems
• +۶ مورد دیگر

Ting He is a Professor in the Department of Computer Science and Engineering, specializing in interdisciplinary research at the intersection of network sciences, energy systems, and cybersecurity. Their work addresses critical challenges in network tomography, software-defined networking, and cyber-physical systems, with a strong emphasis on advancing edge computing and decentralized learning paradigms. NSF-funded research on Distributed Edge Intelligence (2024–2025) Collaborative projects on Overlay Networks and Adversarial Reconnaissance in SDN Recent publications analyze network topology inference, energy-efficient decentralized learning, and secure cloud file systems. Their research aligns with UN SDGs through contributions to sustainable energy systems and secure IT infrastructure. Key collaborations with Silvestri, La Porta, and Chaudhuri Active in Smart Grid resilience and cascading failure mitigation

• Medical Imaging
• Deep Learning Applications
• Ultrasound Imaging
• +۳ مورد دیگر

Mehrdad Salehi is a researcher at the Chair of Computer Science Applications in Medicine at the Technical University of Munich (TUM) . His work focuses on the intersection of computer science and medical imaging, with expertise in ultrasound technology, deep learning, and surgical navigation systems. Key research areas include sonification of medical data, 3D ultrasound reconstruction, and machine learning-based segmentation. He has contributed to innovative projects like PRO-TIP calibration phantoms and ColibriDoc autonomous docking systems. His publications highlight trends in acoustic feedback mechanisms, neural radiance fields for medical imaging, and real-time image analysis. He can be reached at mehrdad.salehi@tum.de .

Per Enqvistمشاهده پروفایل

دانشیار

• Optimization
• Operations Research
• Systems Engineering
• +۶ مورد دیگر

Per Enqvist is an Associate Professor in the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology, Stockholm, Sweden. He has held this position since 2009 after progressing from Assistant Professor (2006-2009) and post-doctoral roles at INRIA France and CNR Italy. His academic background includes: Ph.D. in Optimization and Systems Theory from KTH (2001), supervised by Professor Anders Lindquist M.Sc. in Engineering Physics (Civilingenjör) from KTH (1994) with Applied Mathematics focus Post-doctoral studies at INRIA Sophia-Antipolis (2003-2004) and CNR Padova (2001-2003) Enqvist's research centers on mathematical modeling of stochastic processes, scheduling, and queueing theory with applications across operations research, systems engineering, and signal processing. His principal interests span Optimization, Operations Research, Systems Engineering, Signal Processing, Mathematical Systems Theory, and Modeling and Simulation. He has made significant contributions to spectral estimation, covariance interpolation, and resource allocation frameworks. Publication analysis reveals an evolution from foundational systems theory work (2000s) on spectral estimation and minimal realization toward applied optimization in healthcare operations (2010s-2020s). Recent articles address radiation therapy scheduling and contact center modeling using queueing theory with risk-sensitive measures like CVaR, while earlier work established theoretical frameworks for covariance interpolation and passive system synthesis. No scientific awards are documented in the provided information. He has received funding from Vetenskapsrådet (Swedish Research Council) and led the ACCESS seed project on "Robust Spectral Estimation". Enqvist is course responsible for multiple master's program tracks including Aerospace systems and Industrial Engineering, and oversees the Optimization and Systems Theory seminar series. No student advisement details are provided. He maintains affiliations with the ACCESS Linnaeus centre, Center for Industrial and Applied Mathematics (CIAM), and serves on the Swedish Operations Research Society (SOAF) board.

Matthias Schlottbomمشاهده پروفایل

دانشیار

• Numerical Analysis
• Computational Mathematics
• Radiative Transfer
• +۴ مورد دیگر

Matthias Schlottbom is an Associate Professor specializing in Mathematics of Computational Science, with a focus on numerical methods and their applications in physics, biology, and engineering. His research integrates advanced computational techniques with interdisciplinary problems, including radiative transfer, photonic crystals, and chemotaxis modeling. Research Interests: Schlottbom’s work spans numerical analysis, finite element methods, and machine learning. He develops high-order discretization schemes, iterative solvers for anisotropic transport, and mathematical frameworks for biological network formation. Publications: Recent articles highlight his contributions to accelerating radiative transfer simulations, extending component mode synthesis for Helmholtz equations, and analyzing diffusion limits in kinetic models. His work often bridges computational mathematics with practical applications in photonics and multiscale systems. Collaborations: He actively collaborates on datasets for optical simulations, radiative transfer algorithms, and photonic crystal modeling, contributing to open-access repositories like 4TU.Centre and Zenodo. Activities: Schlottbom has organized workshops such as the Kinetic Theory Workshop in the Netherlands and delivered keynotes on residual minimization and data-driven methods for transport equations. Scientific Awards: No specific awards or fellowships are mentioned in the provided materials. Advising & Grants: Details about students, advising roles, or grant funding are not included in the available data.

Xieyuanli Chenمشاهده پروفایل

دانشیار

• Robotics
• Autonomous Systems
• Computer Vision
• +۳ مورد دیگر

Xieyuanli Chen is an Associate Professor at the National University of Defense Technology (NUDT), China. He holds a Dr.-Ing. (summa cum laude) from the University of Bonn (2022), a Master's in Robotics from NUDT (2017), and a Bachelor's in Electrical Engineering from Hunan University (2015). His research focuses on robot learning, perception, and navigation, with an emphasis on LiDAR-based SLAM, autonomous systems, and semantic perception. Education: PhD: University of Bonn, 2018-2022 (supervised by Prof. Cyrill Stachniss) Master's: NUDT, 2015-2017 Bachelor's: Hunan University, 2011-2015 Research interests include robotics, autonomous systems, computer vision, and LiDAR perception. He has authored over 90 papers in top venues like TRO, RSS, ICRA, and CVPR. He serves as an Associate Editor for IEEE RA-L, ICRA, and IROS, and is a member of the RoboCup Rescue Robot League Technical Committee. Awards include the RSS Pioneer Award (2021), Best-in-Class RoboCup awards, and recognition as a World’s Top 2% Scientist (2024). His work spans LiDAR localization, moving object segmentation, and efficient semantic mapping. He advises students in robotics and autonomous systems. Labs/Teams: Active in the PRBonn group (University of Bonn) and leads research at NUDT on LiDAR-based perception systems.

Cheung Ngai-Manمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Augmented Intelligence
• Data Science
• +۴ مورد دیگر

Cheung Ngai-Man is an Associate Professor and Associate Head of Pillar (Education) at Singapore University of Technology and Design (SUTD), part of the Information Systems Technology and Design (ISTD) pillar. He holds a Ph.D. in Electrical Engineering from the University of Southern California (2008) and has held research positions at Stanford University, Texas Instruments, IBM, and others. His research focuses on image and signal processing, computer vision, machine learning, and artificial intelligence. Education: Ph.D., Electrical Engineering, University of Southern California (2008); Postdoctoral research at Stanford University (2009–2011). Research Interests: Develops algorithms for multimedia data processing, explores interdisciplinary applications of signal processing and AI, and addresses challenges in computer vision and generative models. Recent work includes fairness in generative models, few-shot image generation, and adversarial robustness. Publications: Over 100+ peer-reviewed papers in top venues (CVPR, NeurIPS, IEEE TIP, TPAMI) focusing on computer vision, generative models, and AI security. Notable 2023 work includes studies on label-only model inversion attacks and fairness metrics in generative systems. Awards: Best Paper Finalist (CVPR 2019), SAIL Award Finalist (WAIC 2019), Outstanding Associate Editor (IEEE T-MM), Croucher Foundation Fellowship. Students: Supervised postdocs (Hossein Nejati, Fang Lu), research assistants (Mohammad Rostami), and visiting students (Ma Rui). Labs/Teams: Leads research groups in AI, computer vision, and multimedia systems at SUTD. Has spun off AI initiatives for wound care and contributed to Singapore’s National AI Strategy.

Benjamin Eysenbachمشاهده پروفایل

استاد

• Reinforcement Learning
• Self-Supervised Learning
• Machine Learning
• +۲ مورد دیگر

Benjamin Eysenbach leads the Princeton Reinforcement Learning Lab, where he designs algorithms that enable artificial intelligence systems to learn intelligent behaviors through trial-and-error, specializing in self-supervised methods that eliminate the need for human labels. He joined Princeton after completing his PhD in machine learning at Carnegie Mellon University under Ruslan Salakhutdinov and Sergey Levine, supported by the NSF Graduate Research Fellowship and Hertz Fellowship. His research bridges fundamental machine learning principles with practical applications in robotics and decision-making systems. Eysenbach's research focuses on developing self-supervised reinforcement learning algorithms that enable autonomous skill acquisition without external rewards. His investigations span contrastive learning methods, temporal abstraction techniques, and scalable architectures for goal-conditioned behaviors. These innovations aim to create more efficient and generalizable learning systems that can discover useful behaviors from unlabeled experience. Eysenbach's publications demonstrate consistent advancement in self-supervised RL methodologies, with recent work focusing increasingly on temporal abstraction and representation learning theory. His research shows progression from foundational contrastive RL frameworks toward more sophisticated analyses of generalization properties and uncertainty quantification. The 2025 works indicate expanding investigation into hierarchical control, probabilistic alignment, and hyper-deep network architectures. Eysenbach has been recognized with prestigious awards including the Hertz Fellowship and NSF Graduate Research Fellowship, supporting his doctoral research in self-supervised RL methodologies. His work has been presented at top machine learning conferences including NeurIPS, ICML, and ICLR. As director of the Princeton Reinforcement Learning Lab, Eysenbach oversees research initiatives in self-supervised RL, including projects on intention-conditioned modeling, horizon generalization, and contrastive learning frameworks. He has secured funding from the Princeton AI Lab to study neural correlates of temporal contrast in decision-making. Eysenbach teaches courses in reinforcement learning and has developed new benchmarks like JaxGCRL to accelerate research in goal-conditioned RL.

• Mesh generation
• Numerical simulation
• Finite element methods
• +۵ مورد دیگر

Houman BOROUCHAKI is a Professor at the University of Technology of Troyes (UTT), France, with over 20 years of academic leadership. He has served as Head of the Automatic Mesh Generation and Advanced Methods (GAMMA3) project team since 2008 and previously led the Laboratory of Mechanical Systems and Concurrent Engineering (LASMIS) (2005-2007). His work bridges academic research and industrial applications through collaborations with INRIA , French Petroleum Institute (IFPEN) , Dassault Aviation , and others. Research Interests: A pioneer in adaptive meshing , he focuses on finite element methods , geometric modeling , and numerical simulations . His innovations underpin mesh generation algorithms , 3D triangulation software , and industrial applications in metal forming, composite simulation, and subterranean modeling. Scientific Trends: His recent work emphasizes metric-based meshing , high-order geometric validity , and parallel processing for mesh generation , with applications in petroleum reservoirs, aviation surfaces, and nanomaterials. His Google Scholar profile reflects 25+ years of contributions to meshing and simulation. Teaching: With 22 years of experience, he teaches courses on meshing , numerical analysis , geometric modeling , and computer graphics at UTT, covering undergraduate to PhD levels. Labs & Teams: He leads the interdisciplinary GAMMA3 team and has contributed to LASMIS (mechanical engineering), L2n (CNRS-UMR 7076) (nanomaterials), and LIST3N (computer science).