جست‌وجوی استاد و پژوهشگر

• Civil Engineering
• Materials Engineering
• Structural Engineering
• +۵ مورد دیگر

Kamiran Abdouka is a Senior Lecturer in Civil Engineering at Swinburne University's School of Engineering. His research focuses on structural engineering, materials science, and sustainable construction practices. He specializes in concrete materials, anchor performance, FRP composites, and innovative construction techniques like void-former systems. His work addresses challenges in early-age concrete behavior, carbon capture utilization in construction, and structural strengthening methods. Key research areas include: Civil engineering with an emphasis on concrete structures and material behavior Materials engineering, particularly supplementary cementitious materials and early-age properties Structural strengthening using FRP composites and anchorage systems Sustainable construction through CO2 capture and utilization in building materials Publications highlight advancements in anchor design for thin concrete panels, FRP applications in bridge and building systems, and probabilistic assessment of underground infrastructure. He has supervised multiple PhD students in topics ranging from carbon sequestration to post-earthquake structural retrofits. His contributions bridge theoretical models with experimental validation, often involving finite element analysis and hybrid testing techniques. Collaborations include industry and academic partners focused on advancing construction materials and structural safety. His work aligns with global sustainability goals and resilient infrastructure development.

Salah Bazziمشاهده پروفایل

پژوهشگر

• Robotics
• Biomechanics
• Neuroscience
• +۳ مورد دیگر

Salah Bazzi is an Associate Research Scientist in the Biology Department at Northeastern University's College of Engineering. His research focuses on understanding human control of complex objects and translating insights into robotics, emphasizing biomechanics, optimal control, and human-machine interaction. He is affiliated with the Action Lab, exploring topics like robotic assistance systems and ergonomics in collaborative environments. His work spans probabilistic motion models for off-road robotics, multi-modal perception in human-robot collaboration, and the application of dynamic primitives to enhance robotic dexterity. Recent studies address posture variability in human-robot handovers and initial condition optimization for complex object manipulation. Bazzi's research bridges neuroscience and engineering, with applications in medical robotics and autonomous systems. He has contributed to over 20 peer-reviewed articles since 2013, addressing topics ranging from nonholonomic constraints in robotics to biomechanical modeling of motor tasks.

• Machine Learning
• Probabilistic Models
• Bayesian Inference
• +۵ مورد دیگر

Michael Hughes is an Assistant Professor of Computer Science at Tufts University's School of Engineering. His research focuses on statistical machine learning and its applications in healthcare, including probabilistic models, Bayesian inference, and semi-supervised learning. He leads projects such as automated cardiovascular disease diagnosis via echocardiograms and mortality risk prediction from electronic health records. Education: PhD, Computer Science, Brown University (2016) MS, Computer Science, Brown University (2012) BS, Computer Science, Franklin W. Olin College of Engineering (2010) Research Interests: Bayesian hierarchical models, variational inference, clinical informatics, and interpretable AI. His work bridges theoretical advancements with high-impact applications in healthcare. Awards & Activities: Recipient of multiple reviewer awards at top conferences (NeurIPS, AISTATS) Organizer of workshops on Bayesian methods and health informatics Principal investigator on grants from NSF, NIH, and the US Army Teaching: Courses include Intro to Machine Learning, Bayesian Deep Learning, and Statistical Pattern Recognition. Labs & Teams: Leads the Tufts Machine Learning research group and collaborates with clinicians at Tufts Medical Center on healthcare AI projects.

Tadashi Hashimotoمشاهده پروفایل

دانشیار

• Economics
• Game Theory
• Market Design
• +۵ مورد دیگر

Tadashi Hashimoto is an Associate Professor of Economics (tenured) at Yeshiva University, holding dual office locations at the Beren and Wilf campuses. He previously served as a Postdoctoral Researcher at the Toulouse School of Economics (2013) and a Research Intern at Yahoo! Labs (2010). His education includes a PhD in Business Administration from Stanford University (2013), MA in Economics from the University of Tokyo (2008), and BA in Economics from the University of Tokyo (2006). Hashimoto’s research focuses on game theory, market design, financial markets, and corporate governance. He explores topics such as strategic behavior in auctions, information asymmetry in financial systems, and managerial decision-making dynamics. His work bridges theoretical models with practical applications, addressing challenges like board effectiveness, CEO turnover mechanisms, and market inefficiencies arising from information delays. His publications span top journals including Journal of Financial Economics , Journal of Economic Theory , and Theoretical Economics . Recent work examines managerial myopia’s impact on corporate investment strategies and the role of aggressive boards in executive turnover. He maintains active collaborations with researchers like Cyrus Aghamolla, producing interdisciplinary studies on market mechanisms and equilibrium dynamics. Hashimoto teaches advanced courses such as Game Theory, Microeconomics II, and Market Design. His academic contributions include pioneering analyses of probabilistic allocation mechanisms and axiomatic fairness criteria. Ongoing research explores dynamic freeriding in financial markets and endogenous offer dynamics in imperfect information settings.

• Data Mining
• Machine Learning
• Computational Sustainability
• +۵ مورد دیگر

Stefan Kramer is a Full Professor at the Institute of Computer Science, Johannes Gutenberg University Mainz. He holds leadership roles including Spokesperson of the DeCoDeML network and involvement in initiatives like the Plattform Lernende Systeme. His research focuses on data mining, machine learning, computational sustainability, and bioinformatics, with over 200 peer-reviewed publications. Kramer has supervised numerous theses and secured significant funding from EU, DFG, BMBF, and industry. Awards include Best Paper recognitions at KDD, ICDM, and ICBK conferences. He actively contributes to academic governance and interdisciplinary collaborations, leading the Institute of Computer Science from 2016–2018. His work emphasizes privacy-preserving techniques, interpretable models, and applications in social good. Education: PhD in Technical Sciences (Dr. techn.) from TU Wien (1999) Positions at Austrian Research Institute for Artificial Intelligence, Siemens AG, and TU München (2003–2011). Research Interests: Kramer’s work spans structured data mining (sequences, graphs), privacy-aware algorithms, computational sustainability, and bioinformatics applications. Notable projects include CheS-Mapper for chemical data visualization and enviPath for biodegradation pathway prediction. Grants & Collaborations: Over 30 projects funded by EU, DFG, BMBF, and industry. Key collaborations include OpenTox Euro, the Predictive Toxicology Challenge, and interdisciplinary networks like the Rhein-Main Universities’ DeCoDeML initiative. Labs/Teams: Leads the Data Mining Group at Mainz, contributing to tools like CheS-Mapper and enviPath. Active in organizing conferences (ECML/PKDD 2021) and editorial roles for Machine Learning journals.

Salvador Barberàمشاهده پروفایل

استاد

• Social Choice Theory
• Mechanism Design
• Game Theory
• +۷ مورد دیگر

Salvador Barberà is a Professor of Economics at the Autonomous University of Barcelona since 1986, Director of the MOVE research consortium, and a Research Professor at the Barcelona Graduate School of Economics. His academic contributions span multiple roles in research institutions and government agencies, including leadership positions at ICREA and the Spanish Ministry of Science and Education. Research Focus: Social Choice Theory, Mechanism Design, Game Theory, and Public Economics. Leadership Roles: Past-president of the Social Choice and Welfare Society and ASSET; served on journal editorial boards (Econometrica, Games and Economic Behavior). His research investigates strategy-proof rules, constitutional design, domain restrictions, coalition formation, and decision-making under uncertainty. Key themes include preference aggregation, institutional design, and the interplay between individual and collective rationality. Recent publications address foundational topics in social choice and mechanism design, with a focus on voting systems, committee structures, and incentive-compatible allocation rules. These works bridge theoretical rigor with applications in political economy and public policy. Scientific Awards 2011: Fellow of the Society for the Advancement of Economic Theory 2008: Corresponding member of the Academia Nacional Argentina de Ciencias Económicas 2008: Rey Jaime I Award 2007: Doctor Honoris Causa, Universidad Pablo de Olavide de Sevilla 2006: Fellow of the Spanish Economic Association 1988: Fellow of the Econometric Society 2010: Spanish National Research Prize “Pascual Madoz” in Social Sciences and Economics 2007: Fundació Catalana per a la Recerca i la Innovació Prize (FCRI) 1996: Rey Juan Carlos Award 2007: Encomienda con Placa de la Orden Civil de Alfonso X “El Sabio” 2006: Commandeur de l’Ordre National Du Mérite Barberà has held visiting positions at Caltech, Stanford University, and the Université des Sciences Sociales de Toulouse. He has influenced economic policy through roles like Secretary General for Scientific and Technological Policies in Spain’s Ministry of Science and Education.

• Bayesian Statistics
• Probabilistic Programming
• Machine Learning
• +۴ مورد دیگر

Aki Vehtari is a Professor in the Department of Computer Science at Aalto University’s School of Science. His academic roles include leadership in probabilistic machine learning, Bayesian statistics, and computational inference. He is a Principal Investigator for projects like the Finnish Center for Artificial Intelligence and the Iterative Bayesian Model Building initiative. Vehtari holds a Doctoral degree (2001) and Master’s degree (1997) in Engineering and Technology from Helsinki University of Technology. Research Interests: Vehtari specializes in integrating Bayesian methods with computer science, focusing on workflow optimization, probabilistic programming, Gaussian processes, and model diagnostics. His work emphasizes practical inference techniques and their application to real-world problems like aging analysis and RNA sequencing. Recent Research Trends: Recent publications highlight advancements in Bayesian cross-validation, projection predictive inference, and active learning for molecular data. His work often bridges computational efficiency with statistical rigor, addressing challenges in model selection and uncertainty quantification. Grants & Projects: Leading the Finnish Center for Artificial Intelligence (2023–2026) and the Safe Iterative Bayesian Model Building project (2021–2025). Awards: 2016 De Groot Prize, Youden Award (2020), and recognition for contributions to time series prediction (2004). Advising: Supervises doctoral researchers including Guangzhao Cheng and Lu Cheng, advancing topics in RNA analysis and probabilistic modeling. Labs & Teams: Involved in probabilistic AI initiatives, contributing to tools like ArviZ and Stan, and collaborates globally on projects in computational statistics and machine learning.

• Probabilistic Machine Learning
• Preference Learning
• Decision Utility
• +۳ مورد دیگر

Shibei Zhu is a Doctoral Student and Visitor (Faculty) in the Department of Computer Science at the School of Science. Their research focuses on Probabilistic Machine Learning, Preference Learning, Decision Utility, Interactive Visualization, Human Behavior, and Cognitive Science. They actively collaborate on international research projects, contributing to advancements in human-AI interaction and autonomous systems. Research interests include developing human-like models for preferential choice, interactive preference elicitation techniques, and multimodal policy generation from diverse behavioral data. Their work bridges machine learning with cognitive science to enhance decision-making systems. Publications span topics like latent decision utilities, reward tuning in robotics, and data-driven autonomous systems, reflecting a strong emphasis on practical applications of AI. No awards are listed, but their doctoral research involves significant contributions to conference proceedings in top venues. Shibei advises no formal students but collaborates with research groups such as the Kaski Samuel Professorship. Their work aligns with labs focused on probabilistic machine learning and human-centered AI.

Gabriele Durrantمشاهده پروفایل

استاد

• Survey Methodology
• Statistical Modelling
• Survey Data Collection
• +۳ مورد دیگر

Professor Gabriele Durrant is a Professor of Social Statistics & Survey Methodology at the University of Southampton, part of the Faculty of Social Sciences. She specializes in survey methodology, statistical modelling, and data collection strategies. Her research focuses on improving survey quality through innovations in paradata utilization, mixed-mode methodologies, and longitudinal study designs. She has led major projects funded by the Economic and Social Research Council (ESRC), including the National Centre for Research Methods (NCRM) and the UK Survey Data Collection Methods Collaboration. Her work addresses challenges in non-response bias, interviewer effects, and adaptive survey designs. Currently supervising PhD student Atika Nashirah Hasyyati, she collaborates with institutions like the Administrative Data Research Centre for England and contributes to policy through methodological advancements. Key projects include evaluating smartphone survey impacts and optimizing data collection during the pandemic. Research projects span topics such as digital skills development, attrition in longitudinal surveys, and the efficacy of propensity score matching. She is affiliated with the Statistical Sciences Research Institute (S3RI) and the Methodological Innovation: Survey Methods and Official Statistics group. Her publications emphasize practical applications of statistical techniques to enhance survey methodologies and policy relevance.

Jose Almeida Da Silvaمشاهده پروفایل

دانشیار

• Aviation Safety
• Safety Management Systems
• Aircraft Structural Integrity
• +۷ مورد دیگر

Associate Professor Jose Almeida Da Silva holds a position at RMIT University's School of Engineering in Melbourne, Australia. With over 20 years of academic experience, he specializes in aviation safety, aerospace materials, and structural integrity. His work bridges academia with industry through collaborations on projects involving accident investigation and safety management systems. He earned a 5-year Aeronautical Engineering degree from the University of Beira Interior (1991–1996), where he was recognized as the top graduate. Later, he received the Santander Scientific Merit Prize (2010–2013) as the best researcher in aerospace sciences. Professional credentials include a Remote Pilot Licence from CASA and memberships in the Royal Aeronautical Society and AIAA. Research focuses on aviation safety systems, composite materials, and passenger anthropometry's impact on aircraft performance. Recent work explores augmented reality in accident analysis and Bayesian networks for unmanned aircraft safety assessments. He supervises PhD/Masters students on topics like safety culture in military aviation and composite material maintenance strategies. Awards: Santander Merit Prize, Portuguese Air Force Merit Award, University of Beira Interior's Top Graduate Professional Affiliations: AIAA Senior Member, Royal Aeronautical Society Member Key Projects: Aviation Safety Programs, UAV Risk Modeling, Structural Fatigue Monitoring

• Pharmacometrics
• Mathematical Modeling
• Pharmacokinetics
• +۳ مورد دیگر

Professeure titulaire at the Université de Montréal's Faculty of Pharmacy, Fahima Nekka holds the NSERC Industrial Research Chair in Pharmacometrics. She leads research in pharmacometrics, mathematical modeling, and quantitative systems pharmacology. Her affiliations include the CRM (Centre de recherches mathématiques), PhysNum (Laboratoire de physique numérique), GRUM (Groupe de recherche universitaire sur le médicament), and RQRM (Réseau québécois de recherche sur le médicament). Research focuses on drug fate and action, integrating pharmacokinetics, pharmacodynamics, and complex data analysis. Notable projects include modeling dopamine systems for Parkinson’s and ADHD, optimizing chemotherapy regimens, and developing probabilistic dosing strategies. She has supervised over 15 PhD and Master's students since 2000, addressing topics like vancomycin monitoring, G-CSF use in chemotherapy, and medication adherence modeling. Active in grants, she has led projects funded by NSERC, FRQNT, and industry partners like Pfizer. Her work bridges mathematics, pharmacology, and clinical applications, with a focus on translational research to improve therapeutic outcomes.

• Safe AI
• Formal Methods
• Reinforcement Learning
• +۵ مورد دیگر

Francesco Belardinelli is a Senior Lecturer in the Department of Computing at Imperial College London, part of the Faculty of Engineering. He co-directs the UKRI Centre for Doctoral Training (CDT) in Safe and Trusted AI and leads the EPSRC-funded project 'An Abstraction-based Technique for Safe Reinforcement Learning'. His research focuses on Safe AI, formal methods, and strategic reasoning in multi-agent systems under imperfect information. He holds a PhD and undergraduate degrees from Scuola Normale Superiore, Italy. Belardinelli's work bridges formal verification techniques with AI systems, particularly in ensuring safety in reinforcement learning and multi-agent dynamics. He has secured grants including EPSRC NIA, ANR projects (e.g., AGAPE, SVeDaS), and Marie Curie Fellowships. He supervises multiple PhD and postdoctoral researchers in areas like safe RL, formal methods, and multi-agent systems. His educational contributions include courses on logical aspects of AI and scientific research methodologies. He organizes events like the FMAI Seminar series and chairs international conferences (e.g., AAMAS 2021 workshop). His outreach activities include invited talks on topics like safety-aware RL and epistemic logic.

• Stochastic Analysis
• Rough Path Theory
• Mathematical Finance
• +۳ مورد دیگر

Dr. Christian Litterer is a Lecturer in the Department of Mathematics at the University of York. He holds a DPhil from the University of Oxford and has held postdoctoral positions at Oxford, Imperial College London, and École Polytechnique. His research bridges pure and applied mathematics, focusing on stochastic analysis, rough path theory, and their applications to mathematical finance and numerical methods. Key interests include Malliavin calculus, non-linear filtering algorithms, and pricing derivatives. He currently supervises Xintao Xie and leads a research group in Mathematical Finance and Stochastic Analysis. Dr. Litterer's work explores the intersection of geometric rough paths and probabilistic applications, with contributions to high-order numerical methods like cubature on Wiener space. He has been involved in organizing conferences (e.g., 2024 Conference on Modern Topics in Stochastic Analysis) and serves on the editorial board of Stochastic Analysis and Applications 2025 . His research projects are supported by grants such as the EPSRC-funded 'Making Cubature on Wiener Space Work.' Dr. Litterer’s academic activities include teaching roles in MSc programs like Financial Engineering and Mathematical Finance. His research collaborations span institutions globally, reflecting his interdisciplinary approach to stochastic analysis and its real-world applications.

Stephen Connorمشاهده پروفایل

مدرس

• Applied Probability
• Coupling Theory
• Perfect Simulation
• +۴ مورد دیگر

Dr. Stephen Connor is a Lecturer in the Department of Mathematics at the University of York. He obtained his undergraduate and PhD degrees from the University of Warwick and joined York in 2008. His research focuses on applied probability, coupling theory, and convergence properties of Markov processes. Research Interests: Specializes in Markov process convergence, stochastic stability, perfect simulation algorithms, and cutoff phenomena in random walks. Key applications include card shuffling, queueing systems, and interacting particle systems. Publication Trends: Recent articles demonstrate theoretical advances in stochastic processes with experimental validation. Works frequently combine mathematical rigor with computational methods to solve complex probabilistic models. Recurring themes include optimal coupling strategies, mixing time analysis, and algorithm development. Professional Activities: Editor for Applied Probability Trust journals Academic Steering Group member - Scottish Mathematical Sciences Training Centre Founder - Probability in the North East research network Research Grants Committee - London Mathematical Society Advising & Outreach: Currently supervises PhD students Georgia Sowerby and Wachiraphong Ratiphaphongthon. Leads school outreach programs on mathematical topics including card shuffling probabilities and cryptography. Served as LMS Holgate Session Leader (2017-2020).

Hans-Andrea Loeligerمشاهده پروفایل

استاد

• Signal Processing
• Information Theory
• Communications
• +۶ مورد دیگر

Prof. Hans-Andrea Loeliger is a Full Professor at ETH Zurich's Department of Information Technology and Electrical Engineering and serves as Deputy Head of the Signal and Information Processing Laboratory. With a career spanning over two decades at ETH Zurich since 2000, he has established himself as a leading researcher in signal processing, information theory, and related fields. His work bridges theoretical foundations with practical applications in communications, electronics, and machine learning. Loeliger's research interests encompass a broad spectrum of topics including signal processing, information theory, communications, system theory, electronics, machine learning, quantum systems, error correcting codes, and neural computation. His work on factor graphs and message passing algorithms has been particularly influential, providing a unifying framework for various signal processing techniques. His recent publications demonstrate continued innovation in areas such as NUV priors, control-bounded analog-to-digital conversion, and neural network applications. His publication record shows consistent high-impact contributions across multiple disciplines, with recent work focusing on the intersection of statistical signal processing, machine learning, and circuit design. The trend in his research demonstrates an evolution from foundational work in factor graphs and information theory toward increasingly sophisticated applications in machine learning, neural computation, and practical circuit implementations. Fellow of the IEEE Loeliger has supervised numerous PhD students and master's candidates through ETH Zurich's Signal and Information Processing Lab, though specific student names aren't listed in the provided text. His teaching responsibilities include courses such as Discrete-Time and Statistical Signal Processing, Electronic Circuits & Signals Exploration Laboratory, and Introduction to Estimation and Machine Learning. His research has been supported by various grants enabling the development of novel signal processing techniques and their implementation in practical systems. The Signal and Information Processing Laboratory under his leadership serves as a hub for interdisciplinary research connecting theoretical signal processing with applications in communications, imaging, and neural systems. The lab maintains strong connections with both academic and industrial partners, facilitating the translation of theoretical advances into practical implementations.