جست‌وجوی استاد و پژوهشگر

Matt Koslovskyمشاهده پروفایل

استادیار

• Bayesian modeling
• Variable selection
• Graphical models
• +۱۰ مورد دیگر

Matt Koslovsky is an Assistant Professor of Statistics at Colorado State University. He completed his PhD in Biostatistics at The University of Texas Health Science Center School of Public Health (UTHealth) in 2016 and served as a Post-Doctoral Research Associate at Rice University's Marina Vannucci lab from 2018-2020. Prior to joining CSU in 2020, he worked as a statistical consultant at Johnson Space Center's Biostatistics Lab. PhD, Biostatistics (2016), UTHealth School of Public Health Post-Doctoral Research Associate (2018-2020), Rice University Assistant Professor (2020-Present), Colorado State University His research spans Bayesian methodology and its applications across diverse domains: Theory: Bayesian modeling, variable selection, graphical models, nonparametric Bayes Applications: Cancer prevention, mental health, microbiome analysis, space health, ecological momentary assessment Recent publications demonstrate methodological advancements in: Bayesian variable selection for rare variants Integrated population modeling Compositional data analysis Continuous-time hidden Markov models mHealth data processing Microbiome mediation effects Current advisees include: Hyungjoon Kim (PhD candidate) Brody Erlandson (PhD candidate) Suppapat Korsurat (PhD candidate)

• Time series analysis
• Changepoint analysis
• Signal processing
• +۶ مورد دیگر

Xueheng Shi is an Assistant Professor in the Department of Statistics at the University of Nebraska-Lincoln (UNL), with a joint appointment in Biological System Engineering . He joined UNL in August 2022 after postdoctoral positions at UC Santa Cruz (2020–2021) and UC Davis (2021–2022) under mentors Robert Lund, Alexander Aue, and Thomas Lee. Dr. Shi teaches graduate courses including mathematical statistics, asymptotic theory, probability, stochastic processes, and time series analysis. Research Interests: Dr. Shi's work focuses on developing statistical methodologies for time series analysis , changepoint detection , and high-dimensional data . Key applications include climate science, signal processing, and machine learning. His research emphasizes computational algorithm development and theoretical foundations across domains like climate modeling and stochastic optimization. Publications: Recent articles (2019–2024) concentrate on climate change detection , particularly global warming trends and temperature series analysis. Methodological innovations include autocovariance estimation around changepoints, comparative studies of detection algorithms, and reviews of best practices in climatological statistics. Dominant themes are changepoint techniques, environmental data diagnostics, and statistical computing. Advising: Dr. Shi actively mentors graduate students in statistics and invites prospective applicants interested in his research areas to contact him directly.

Stefan Marrمشاهده پروفایل

پژوهشگر

• Concurrent Programming
• Language Implementation
• Virtual Machines
• +۶ مورد دیگر

Stefan Marr is a faculty member at the University of Kent, United Kingdom, focusing on Concurrent Programming , Language Implementation , and Virtual Machines . His research spans projects like optimizing interpreters, improving concurrency models, and enhancing profiling tools for dynamic languages. Projects : Royal Society Industry Fellowship (FastStart), EPSRC-funded CaMELot, Oracle Labs-funded Automatic Superinstructions. Collaborations : With students and researchers across institutions, including Shopify, Vrije Universiteit Brussel, and Johannes Kepler University Linz. His recent work emphasizes profiling reliability for Java, interpreter performance , and tooling for complex concurrent systems . Projects like SOM (Simple Object Machine) and its variants (SOM++, TruffleSOM, PySOM) highlight his commitment to language implementation research and education. Scientific Awards include fellowships and grants from the Royal Society, EPSRC, and Oracle Labs. His contributions to conferences as program committee member , steering committee , and workshop organizer further underscore his academic leadership.

• Game Theory
• Epistemic Game Theory
• Quantum Game Theory
• +۷ مورد دیگر

Adam M. Brandenburger is the J.P. Valles Professor at the Leonard N. Stern School of Business, New York University, with additional appointments as Distinguished Professor at the Tandon School of Engineering, Faculty Director of the NYU Shanghai Program on Creativity + Innovation, and Global Network Professor. He previously served as a professor at Harvard Business School from 1987 to 2002. His academic foundation includes a B.A., M.Phil., and Ph.D. from the University of Cambridge. His research focuses on game theory , epistemic game theory , quantum game theory , and business strategy . He has made foundational contributions to the understanding of rationality, belief hierarchies, and the intersection of quantum mechanics with decision theory. His work spans economics, philosophy, computer science, and cognitive neuroscience, reflecting a deeply interdisciplinary approach. The recent publications show a strong trend toward integrating quantum foundations , information theory , and behavioral economics . Themes include epistemic reasoning in non-classical systems, the role of symmetry in strategic interactions, and the neural basis of decision-making. His work increasingly explores the limits of classical probability and cognition, using tools from quantum information and sheaf theory. While no formal scientific awards are listed, his publications in Nature Communications , Econometrica , and Philosophical Transactions of the Royal Society indicate high scholarly impact. His collaborations with leading researchers across disciplines suggest a central role in advancing interdisciplinary science. He advises and collaborates extensively, though specific students are not listed. His leadership in the NYU Shanghai Program on Creativity + Innovation highlights his commitment to global education and innovation. He has not received any grants explicitly mentioned, but his sustained publication output suggests active funding support. He is associated with research initiatives bridging game theory, quantum information, and cognitive science. His recent unpublished work on quantum-assisted observatories and large language models points to forward-looking research at the intersection of AI, quantum technology, and strategic reasoning.

• 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 .

• Quantum Optics
• Quantum Information
• Coherence Optics
• +۴ مورد دیگر

Dr. Xiaofeng Qian is an Assistant Professor of Physics at Stevens Institute of Technology’s Charles V. Schaefer, Jr. School of Engineering and Science. He leads the Quantum Inspired Coherence Optics Research Lab, focusing on quantum-classical connections in optical systems, including wave-particle duality, entanglement, and coherence. His work bridges theoretical and experimental research, with notable contributions to quantum-inspired classical optics and coherence theory. Dr. Qian earned his Ph.D. in Quantum Optics from the University of Rochester (2014) and held roles as a Research Associate and Visiting Scientist there before joining Stevens in 2019. His research has been recognized by prestigious awards, including Physics World’s Top 10 Breakthroughs of 2021 and its 10 most popular physics stories of 2023. Research Interests: Dr. Qian’s work explores foundational quantum phenomena using classical optics analogs. Key areas include: Quantum-classical connections in cavity QED and magnonics Entanglement and coherence quantification Super-resolution imaging enhanced by entanglement Quantum duality and its experimental emulation Professional Contributions: He serves on academic committees at Stevens and is a Photonics Topical Editor for IEEE. Notable advising includes mentoring students like Erin, Pawan, and Guogan Zhao. His lab has published in journals like Physical Review Research , Optica , and Advances in Optics and Photonics . Labs/Teams: The Quantum Inspired Coherence Optics Lab focuses on experimental and theoretical advancements at the quantum-classical interface, emphasizing practical applications in quantum information and optics.

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.