جست‌وجوی استاد و پژوهشگر

• Human-Robot Interaction
• Rehabilitation Robotics
• Socially Assistive Robotics
• +۷ مورد دیگر

Carlos Cifuentes is an Associate Professor in Human-Robot Interaction at the Bristol Robotics Laboratory (BRL) , part of the University of the West of England (UWE Bristol) . He also serves as the Deputy Director of the VIVO Hub , a £13.4M UK-funded research initiative (2024-2030) focused on robotics for rehabilitation and healthcare. His research spans Human-Robot Interaction , Rehabilitation Robotics , Healthcare Robotics , and Socially Assistive Robotics , with applications for conditions such as cardiac diseases , post-stroke recovery , spinal cord injuries , cerebral palsy , Parkinson's disease , musculoskeletal disorders , and autism spectrum disorder (ASD) . Carlos has over 15 years of experience and 200+ publications in robotics for rehabilitation and assistive technologies. His recent work explores smart walkers with multimodal feedback, soft prosthetics using polymeric optical fiber sensors , and machine learning models for fatigue and stress estimation. He also investigates inclusive design for social robots in global contexts , including a CASTOR Robot for ASD therapy and collaborative design processes with Colombian communities. Carlos serves as an Associate Editor for IEEE Robotics and Automation Magazine (since 2021), ICRA , and IROS (since 2023). He leads projects integrating wearable sensors , deep learning , and haptic feedback to enhance mobility, autonomy, and quality of life for individuals with disabilities. As Deputy Director of the VIVO Hub, he focuses on long-term deployment of robotic systems in real-world healthcare settings, emphasizing collaboration with clinicians , caregivers , and neurodiverse communities . His work bridges robotics , biomedical engineering , and human-centered design .

Abbas Khaliliمشاهده پروفایل

استاد

• High-dimensional statistics
• Distributed learning in big data
• Neural Networks
• +۵ مورد دیگر

Abbas Khalili is a Professor in the Department of Mathematics and Statistics at McGill University, Montreal. His research focuses on statistical methodology in data science, particularly high-dimensional statistics, distributed learning in big data, latent variable models (e.g., finite mixtures, hidden Markov models), and time series analysis. He holds a PhD from the University of Waterloo and BSc/MSc degrees from Isfahan University of Technology. Education: PhD in Statistics, University of Waterloo (supervised by Jiahua Chen) MSc in Mathematical Sciences, Isfahan University of Technology BSc in Mathematical Sciences, Isfahan University of Technology His research interests include post-selection inference, neural networks, sparse network analysis, and change point detection. His work has been funded by NSERC (Canada) and Fonds de recherche du Québec-Nature et technologies. Recent articles focus on mixture models, regularization in autoregressive systems, and hub structure analysis in networks. His work bridges theoretical advancements with practical applications in drug discovery and network science. Dr. Khalili advises students in PhD, MSc, and undergraduate projects in his research areas. He can be contacted at abbas.khalili@mcgill.ca .

Mahir Arzokyمشاهده پروفایل

مدرس

• Artificial Intelligence
• Search Based Software Engineering
• Intelligent Data Analysis
• +۴ مورد دیگر

Dr Mahir Arzoky is a Lecturer in the Department of Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. He holds a PhD from Brunel University London (2015) and has extensive research experience in artificial intelligence and software engineering. His research focuses on: Artificial Intelligence and Intelligent Data Analysis Search Based Software Engineering (SBSE) Clustering algorithms and heuristic search methods Software refactoring and quality assessment Data mining applications in healthcare and education Analysis of his 15 most recent publications (2018-2022) reveals strong interdisciplinary work bridging computer science with healthcare (diabetes patient modeling, medical imaging) and education (chatbot design, algorithm visualization). His technical focus centers on clustering optimization, refactoring impact analysis, and explainable AI, with frequent use of empirical validation methods. Key collaborations include researchers like Stephen Swift, Steve Counsell, and Giuseppe Destefanis. Dr Arzoky has secured significant research funding through EPSRC grants including: AQUATIC project (EP/M024083/1): Assessing Test Suite Quality in Industrial Code FIAR-NET (EP/N011627/1): Fault Analyses in Industry and Academic Research Network His professional network includes active collaborations across computer science, healthcare informatics, and educational technology domains, with recent work extending into transformer models for healthcare SQL conversion and graph partitioning for software modularization.

• Hydraulic Energy
• Hydroelectric Power
• CFD Simulation
• +۶ مورد دیگر

Cécile Münch-Alligné is a Professor in Hydraulic Energy at the University of Applied Sciences and Arts Western Switzerland (HES-SO) in Sion, where she serves as the Head of the Hydroelectricity Research Group and the Renewable Energy Program. She leads the Hydro Alps Lab, which conducts applied research in hydropower combining experimental and numerical approaches. Her work focuses on enhancing the flexibility of both small and large hydropower plants, with particular emphasis on adapting these systems to the evolving energy landscape and integration of renewable energy sources. Her educational background includes a BSc in Energy and Environmental Techniques, an MSc in Engineering, and a BSc in Industrial Systems, all from HES-SO Valais-Wallis. Her research spans multiple domains within hydraulic engineering and renewable energy systems, with particular expertise in CFD simulation, numerical methods, and hydraulic machine design. Münch-Alligné's research interests primarily center around improving hydropower flexibility through innovative approaches such as hydraulic short-circuit operating modes, variable speed operation, and energy recovery systems in water networks. She investigates both large-scale pumped storage power plants and micro-hydropower systems for urban water networks, with a strong focus on practical implementation and commercialization of research findings. Her work bridges theoretical modeling with experimental validation to address real-world challenges in the energy transition. Her research has been published extensively in leading journals, covering topics from Pelton turbine dynamics and Francis turbine vortex analysis to micro-turbine implementations in drinking water networks. The publications reveal a clear trend toward enhancing operational flexibility of hydropower systems to better integrate with intermittent renewable energy sources, with increasing emphasis on practical demonstration projects and commercial applications. As Principal Investigator, she has led multiple significant research projects including the SCCER 4 WP 3.2.0 2017-2020 (Supply of Electricity), Hydrolienne pour canaux artificiels Centrale de Lavey, and SOLUTION DE TRANSFERT D'ENERGIE PAR POMPAGE-TURBINAGE A PETITE ECHELLE. These projects, totaling over 2 million CHF in funding from sources including CTI, OFEN, and industrial partners, demonstrate her ability to secure substantial research funding and collaborate effectively with both academic and industry partners. Münch-Alligné leads the Hydro Alps Lab research team, which includes numerous researchers such as Steiner Amandus, Walpen Olivier, Vaccari Aldo, and others. Her collaborative approach extends to partnerships with institutions like Stahleinbau GmbH and The Ark Energy, facilitating the transfer of knowledge from research to industry application. The lab's work spans from fundamental fluid dynamics research to full-scale demonstration projects, creating a comprehensive pipeline from theory to practical implementation.

Marco A.R. Ferreiraمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Multi-Scale Modeling
• Spatial-Temporal Models
• +۴ مورد دیگر

Marco A.R. Ferreira is an Associate Professor in the Department of Statistics at Virginia Polytechnic Institute and State University (Virginia Tech), affiliated with the College of Science. He holds a Ph.D. in Statistics from Duke University (2002), with a dissertation on Bayesian multi-scale modeling under M. West. He also earned an M.Sc. (1994) and B.Sc. (1993) in Statistics from the Federal University of Rio de Janeiro. Research Interests: Ferreira specializes in Bayesian statistics, multi-scale modeling, spatial-temporal models, computational methods (e.g., MCMC), and applications in environmental science, genomics, and epidemiology. His work emphasizes hierarchical models, inverse problems, and high-dimensional data analysis. Publications Trends: His research spans advanced statistical methodologies for environmental monitoring, civil unrest modeling, and genomic data analysis. Key themes include Bayesian hierarchical models, spatiotemporal fusion, and computational algorithms for optimal experimental design. Awards & Honors: OBAYES Poster Prize (2009) CNPq Fellowship (2003–2006) WNAR/COBAL 2 Award (2005) Springer Poster Prize (2003) Finalist, Savage Award (2003) Best Contributed Paper (JSM 2000) Professional Activities: He serves as an Associate Editor for Bayesian Analysis and is a member of the American Statistical Association and the International Society for Bayesian Analysis.

Suchuan Dongمشاهده پروفایل

استاد

• Computational and Applied Mathematics
• High-Order Numerical Methods
• Multiphase Flows
• +۷ مورد دیگر

Suchuan Dong is a Professor in the Department of Mathematics at Purdue University , affiliated with the Center for Computational and Applied Mathematics . His work bridges Computational Mathematics and Machine Learning , focusing on High-Order Numerical Methods and Multiphase Flows . Academic Background Post-Doc in Applied Mathematics, Brown University (2004) Ph.D. in Mechanical Engineering, SUNY Buffalo (2001) M.S. in Physics, Zhejiang University (1995) B.S. in Aerospace Engineering, National University of Defense Technology (1992) His research centers on Neural Network-Based Numerical Methods and Data-Driven Scientific Computing , with applications to Computational Fluid Dynamics , Contact Line Dynamics , and High-Performance Computing . Publications highlight Physics-Informed Neural Networks , Energy-Stable Schemes , and Extreme Learning Machines for PDEs. The articles reflect trends in Neural Network Applications to Dynamic PDEs , Phase Field Modeling , and High-Dimensional Computing , often combining High-Order Numerical Methods with Interfacial Phenomena . His recent work focuses on Exact Time Integration Algorithms and Hidden-Layer Concatenation for stability and efficiency. He leads research in the Center for Computational and Applied Mathematics , emphasizing Thermodynamically Consistent Modeling and Flow-Structure Interactions . His teaching includes MA-36600: Ordinary Differential Equations (Spring 2025).

• Quantum Mechanics
• Quantum Information
• Quantum Computing
• +۳ مورد دیگر

Adán Cabello Quintero is a University Professor at the University of Seville, affiliated with the Department of Applied Physics II at the Higher Technical School of Building Engineering. His research focuses on foundational aspects of quantum mechanics, particularly quantum contextuality, nonlocality, and their applications in quantum information theory. He leads the research group Fundamentos de Mecánica Cuántica and has coordinated multiple projects on quantum communication and quantum foundations. His work explores the boundaries of quantum theory through experiments and theoretical frameworks, such as the exclusivity principle and graph-theoretic approaches. Key contributions include studies on state-independent quantum contextuality, device-independent quantum correlations, and Bell test experiments. He has authored numerous textbooks, including Física I: Mecánica and La curiosidad y el universo , and edited volumes on quantum phenomena. Cabello Quintero has participated in over 100 international conferences and workshops, presenting on topics ranging from loophole-free Bell tests to the thermodynamical cost of contextuality. His research bridges theoretical quantum foundations with experimental implementations, emphasizing the interplay between quantum correlations and classical limits.

• Energy Informatics
• Building Energy Simulation
• Multi-Scale Energy Modelling
• +۴ مورد دیگر

James O'Donnell is an Associate Professor in the School of Mechanical and Materials Engineering at University College Dublin (UCD). He joined UCD in 2013 after working at Lawrence Berkeley National Laboratory. His research focuses on multi-scale energy modeling, interoperable BIM solutions, and sustainable building design. He contributes to buildingSMART’s Energy Modelling Expert Panel and teaches modules on building physics and energy analysis. Education: B.E. Civil and Environmental Engineering, University College Cork Ph.D. in Holistic Building Performance Analysis, University College Cork Professional Diploma in University Teaching & Learning, University College Dublin Research Interests: James specializes in energy informatics, building energy simulation, and semantic web technologies for the built environment. His work bridges BIM integration with energy modeling to optimize urban-scale performance. Grants & Funding: PROBONO (2022–2026): Integrator-centric approach for energy-efficient buildings NexSys (2022–2026): Next-Generation Energy Systems ALIVE (2020–2024): Indoor Environmental Quality in Energy-Efficient Buildings Teaching & Editorial Roles: James coordinates courses like Energy Systems in Buildings and serves as an Associate Editor for Journal of Building Engineering and editorial board member of Advanced Engineering Informatics . Professional Affiliations: Member of Engineers Ireland, International Building Performance Simulation Association (IBPSA), and European Council on Computing in Construction.

• Particle Physics
• Neutrino Physics
• Experimental High Energy Physics
• +۱ مورد دیگر

Sophie S. Berkman is an Assistant Professor in the Department of Physics & Astronomy at Michigan State University. Her research focuses on experimental particle physics, particularly neutrino interactions and the development of liquid argon time projection chamber (LArTPC) detectors. Her work involves precision measurements of neutrino-argon cross sections critical for the Deep Underground Neutrino Experiment (DUNE). She contributes to Fermilab's MicroBooNE and ICARUS detectors within the Short-Baseline Neutrino program, analyzing data to understand neutrino properties and detector performance. Her research spans charged-current and neutral-current interactions, pion production mechanisms, and searches for physics beyond the Standard Model through sterile neutrino and dark sector investigations. Recent publications demonstrate leadership in neutrino interaction vertex reconstruction using deep learning, liquid argon purity monitoring, and supernova neutrino detection capabilities. Her work on trigger systems and software development directly supports DUNE's operational readiness and scientific objectives in neutrino oscillation physics. She actively participates in international collaborations including DUNE, MicroBooNE, and ICARUS, contributing to detector calibration, event reconstruction algorithms, and cross-section measurements essential for next-generation neutrino experiments.

Gal Elidanمشاهده پروفایل

دانشیار

• Probabilistic Graphical Models
• Machine Learning
• Copula Models
• +۳ مورد دیگر

Gal Elidan is an associate professor in the Department of Statistics at the Hebrew University of Jerusalem, Faculty of Social Sciences. His work bridges statistical theory with practical applications in machine learning and artificial intelligence. Dr. Elidan's research focuses on Probabilistic Graphical Models , with emphasis on fundamental representations and methods for inference and learning in large-scale domains. His work addresses structure learning, discovery of hidden variables, transfer of knowledge between related tasks, and nonlinear high-dimensional representations of continuous or hybrid distributions. He also applies these techniques to real-life applications including computational biology, machine vision, and medical informatics, with recent focus on principled techniques for medical diagnosis. His publication record shows consistent contributions to top machine learning venues with a recent emphasis on copula models for high-dimensional data. His work demonstrates a progression from foundational graphical model research to specialized applications in medical informatics and time series analysis. Scientific recognition includes: Runner-up for Best Student Paper Award at UAI 2004 Best Paper Award at ISMB 2001 Dr. Elidan serves on the editorial board of the Journal of Artificial Intelligence Research (JAIR) and has contributed to the development of important software tools including FastInf, a library for large-scale inference of graphical models, and LibB, a package for Bayesian network inference and learning. He has organized significant academic events including the NIPS 2011 Workshop on Copulas in Machine Learning and co-organized the PASCAL2 Probabilistic Inference Challenge and UAI 2010 Approximate Inference Challenge.

Gian-Gabriel Garciaمشاهده پروفایل

استادیار

• Health Policy
• Personalized Medicine
• Medical Decision-Making
• +۵ مورد دیگر

Gian-Gabriel Garcia is an Assistant Professor and Harold E. Smalley Early Career Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, with a dual role as Pillar 1 Co-lead in Data Science, Machine Learning, and Artificial Intelligence at the Children's Healthcare of Atlanta Pediatric Technology Center. His work bridges predictive analytics and decision-making frameworks in healthcare contexts. Education : PhD and MS in Industrial and Operations Engineering (University of Michigan), BS in Industrial Engineering (University of Pittsburgh) Research focuses on responsible AI for healthcare, emphasizing health equity , stochastic optimization , and machine learning applied to concussion management, opioids, maternal health, and cardiovascular disease. His methodologies integrate dynamic programming and simulation to address stakeholder acceptability. Key article themes include concussion diagnostics , responsible AI in diabetes care, and interpretable policies for chronic disease management. His work frequently leverages Markov decision processes and stochastic modeling . Scientific Awards National Science Foundation Graduate Research Fellowship INFORMS Bonder Scholarship for Applied Operations Research in Health Services SMDM Lee B. Lusted Prize INFORMS Minority Issues Forum Paper Competition First Prize INFORMS Minority Issues Forum Poster Competition First Prize Garcia also contributes to undergraduate mentorship and serves as Vice President of Communications (co-chair) for the INFORMS Minority Issues Forum, alongside council membership in the INFORMS Health Applications Society.

Santiago Figueiraمشاهده پروفایل

استاد

• Theoretical computer science
• Computability theory
• Algorithmic randomness
• +۸ مورد دیگر

Santiago Figueira is a Professor at the Department of Computer Science, Faculty of Exact and Natural Sciences, University of Buenos Aires, and a Researcher at the Institute of Computer Science (ICC), CONICET (Argentine National Council for Scientific and Technical Research) in Buenos Aires, Argentina. He leads the Logic, Language and Computability Research Group (GLyC) and co-directs the Argentinian-French Laboratory SINFIN, a collaborative initiative between Université Paris-CNRS and Universidad de Buenos Aires-CONICET. Additionally, he is a researcher in the Quantum Information, Computation, and Communication team (QuICC). Dr. Figueira's research spans theoretical computer science with a strong emphasis on computability theory, algorithmic randomness, Kolmogorov complexity, and mathematical logic. His work extends to applications in database theory, quantum information, and cognitive modeling. He has made significant contributions to modal logics, model theory, and the theory of databases, particularly in the context of data trees and XPath query languages. His interdisciplinary approach bridges formal methods with practical applications in quantum computing and human cognition. His publication record demonstrates a consistent trajectory of high-impact research, with recent work focusing on modal logic extensions, quantum information theory, cognitive modeling of concept learning, and database query languages. His collaborations span international institutions, particularly with French researchers through the SINFIN laboratory, and with cognitive scientists studying human memory and concept formation. Dr. Figueira has supervised numerous PhD students and postdoctoral researchers, including Gabriel Goren-Roig, Santiago Cifuentes, Edwin Pin Baque, Sergio Romano, Gabriel Senno, and Sergio Abriola. His former postdocs and CONICET researchers include Guido Bellomo, María Emilia Descotte, and Ariel Bendersky, indicating an active and productive research group. He has developed lecture notes in Spanish on Logic and Computability (2020) and Computability Theory (2021), contributing to academic education in his field. His research group GLyC serves as a hub for theoretical computer science research in Argentina, fostering collaborations between Argentine and international researchers.

Claudia Klüppelbergمشاهده پروفایل

استاد

• Applied Probability
• Mathematical Statistics
• Financial Risk Management
• +۳ مورد دیگر

Claudia Klüppelberg is a Professor and Chair of Mathematical Statistics at the Center for Mathematical Sciences, Technische Universität München (TUM). Her academic journey includes positions at ETH Zurich, University of Mainz, and TUM since 1997. She holds a Carl von Linde Senior Fellowship at TUM-IAS, focusing on Risk Analysis and Stochastic Modeling. Her research bridges applied probability, statistics, and their applications in finance and insurance, emphasizing extreme value theory, risk processes, and stochastic networks. She has received prestigious awards such as the New Frontiers in Risk Management Award (2007) and Cross of Merit (2001). Her work addresses real-world challenges in financial risk management, including systemic risk in networks and operational risk modeling. Her recent publications explore causal analysis of extreme risks in networks, max-linear models, and Bayesian networks for extreme events. She contributes to academic leadership as an editorial board member and advisor, promoting interdisciplinary stochastic sciences.

Michael Zurelمشاهده پروفایل

پژوهشگر ارشد

• Quantum Information
• Quantum Computation
• Quantum Contextuality
• +۷ مورد دیگر

Michael Zurel is a NSERC Postdoctoral Fellow in the Department of Mathematics at Simon Fraser University, working under Dr. Nadish de Silva, Canada Research Chair in the Mathematics of Quantum Computation. His research focuses on foundational aspects of quantum computation, quantum information, and nonclassical physics. Key interests include quantum contextuality, negativity in quasiprobability representations, and classical simulation algorithms for quantum systems. He holds a PhD, MSc, and BSc in Physics and Mathematics from the University of British Columbia (2024, 2020, 2019), all supervised by Dr. Robert Raussendorf. His doctoral work explored classical descriptions of quantum computations via hidden variable models and quasiprobability representations. His master’s thesis addressed hidden variable models and classical simulation algorithms for quantum computation with magic states on qubits. Research interests emphasize bridging quantum foundations with computational efficiency, particularly how nonclassical features like contextuality enable quantum advantage. Collaborators include prominent figures such as Robert Raussendorf, Juani Bermejo-Vega, and Cihan Okay. His scientific achievements include the NSERC Postdoctoral Fellowship. Advising and grants are not explicitly detailed, but his work is supported by foundational research grants. He collaborates actively within quantum information theory and computational physics communities.

Jonas Kloecknerمشاهده پروفایل

پژوهشگر

• Geostatistics
• Petroleum Engineering
• Mining Engineering
• +۷ مورد دیگر

Jonas Kloeckner is a PhD Candidate at the Federal University of Rio Grande do Sul (UFRGS) in the Mineral & Environmental Technology and Extractive Metallurgy Program, focusing on Geostatistics, and concurrently serves as a Visiting Research Scholar at Stanford University under Professor Jef Caers. His academic journey includes a MSc in Engineering and Mining Engineering from UFRGS, a Bac +5 degree from Ecole des Mines d'Alès (France), and a Visiting Student stint at Columbia University (USA). His research interests span geostatistical modeling of carbonate facies, Bayesian approaches for uncertainty quantification, and applications of machine learning in geological and medical domains. Key technical areas include Markov Chain Random Fields, seismic data integration, and spatial continuity analysis, with a focus on improving sustainability in mining operations and reservoir characterization. Notable contributions include advancements in covariance table methodologies for automated spatial analysis and risk assessment frameworks for bauxite mining. His work bridges geological data interpretation, stochastic simulation techniques, and computational tools for resource optimization. Currently affiliated with Stanford's energy geoscience initiatives, his research emphasizes interdisciplinary applications of geostatistical models.