جست‌وجوی استاد و پژوهشگر

• Traumatic Brain Injury
• Pediatrics
• Balance
• +۵ مورد دیگر

Dr. Isabelle Gagnon is a Senior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) and a Professor at the School of Physical and Occupational Therapy, Faculty of Medicine and Health Sciences, McGill University. She is affiliated with the Department of Pediatrics, Division of Emergency Medicine at McGill University Health Centre (MUHC), contributing to the Child Health and Human Development Program. Senior Scientist, RI-MUHC Professor, School of Physical and Occupational Therapy, McGill University Department of Pediatrics, MUHC Her research focuses on pediatric traumatic brain injury (TBI) and concussion , with emphasis on functional outcomes, rehabilitation interventions, and healthcare system impacts. Key domains include: Balanced assessment of motor and psychosocial domains Integration of imaging and virtual reality technologies Development of innovative rehabilitation approaches for slow recoverers Analysis of emergency department service organization on patient outcomes Recent publications (2024-2025) address quality indicators for trauma care , vestibulo-ocular reflex dynamics , direct-access physiotherapy models , and social determinants of health disparities . Her work spans clinical practice guidelines , EEG-based concussion biomarkers , and virtual pedestrian collision avoidance paradigms . Dr. Gagnon collaborates with interdisciplinary teams to advance pediatric concussion management , focusing on return-to-activity protocols and neurological data integration . She emphasizes patient-centered outcomes and healthcare accessibility .

Susan Schraderمشاهده پروفایل

دانشیار

• Petroleum Engineering
• Flow Modeling
• Data Analytics and Machine Learning
• +۳ مورد دیگر

Susan Schrader serves as an Associate Professor in the Department of Petroleum Engineering within Montana Technological University's Lance College of Mines & Engineering, where she has held faculty positions since 2008 after previously teaching at the University of Texas Permian Basin. Her academic foundation includes a PhD in Petroleum Engineering from the New Mexico Institute of Mining and Technology and an MA in Applied Mathematics from the University of New Mexico, with additional doctoral coursework in Mathematics at Montana State University. Her educational qualifications comprise: PhD in Petroleum Engineering, New Mexico Institute of Mining and Technology MA in Applied Mathematics, University of New Mexico ABD toward PhD in Mathematics, Montana State University Schrader's research program centers on petroleum engineering with dual emphases on technical innovation and educational advancement. She develops computational models for fluid flow in low-permeability reservoirs while pioneering data analytics and machine learning applications for reservoir simulation. Concurrently, she investigates climate change mitigation through carbon sequestration and examines societal dimensions of energy industry-climate relationships. Her educational scholarship focuses on accreditation, program assessment, and promoting diversity within STEM disciplines, reflecting commitment to both technical excellence and inclusive pedagogy. Publication analysis reveals an evolutionary trajectory from early fuzzy logic applications in drilling risk assessment toward contemporary integration of neural networks in reservoir engineering and socio-technical climate research. This progression demonstrates increasing sophistication in computational methodologies while expanding into interdisciplinary energy-environment interfaces, particularly evident in her recent low-permeability flow modeling and climate change studies. No scientific awards are documented in available sources. Schrader's instructional responsibilities span advanced computational courses including Data Modeling for Petroleum Engineers, Data Science, and Advanced Reservoir Engineering, indicating substantial curriculum development contributions. While specific graduate student mentorship details are absent from provided materials, her research areas suggest supervision of projects combining petroleum engineering with data science methodologies. Grant funding specifics remain undisclosed in the source documentation. Her computational research likely leverages Montana Tech's High Performance Computing Cluster infrastructure, though no dedicated laboratory facilities are explicitly attributed to her in the available information.

Philip Tarantoمشاهده پروفایل

مدرس

• Quantum Information Theory
• Open Quantum Systems
• Quantum Thermodynamics
• +۷ مورد دیگر

Philip Taranto is a Lecturer (Assistant Professor) at The University of Manchester's Physics & Astronomy department, where he leads the Quantum Information & Spatiotemporal Phenomena (QuISP) research group. He also serves as an editor for the Quantum journal. Originally from Melbourne, Australia, Taranto completed his undergraduate studies and Masters at Monash University under Dr. Kavan Modi and Dr. Felix A. Pollock, focusing on memory effects in open quantum systems. He then earned his PhD at the University of Vienna under Dr. Marcus Huber, studying quantum thermodynamics and complex temporal correlations. Following this, he held a JSPS Postdoctoral Fellowship at the University of Tokyo in Dr. Mio Murao's group before joining the University of Manchester. Taranto's research centers on quantum complexity, exploring how quantum systems' intricate behaviors can be harnessed for computational advantages. His primary focus areas include quantum information theory, open quantum dynamics, quantum thermodynamics, quantum foundations, correlations & entanglement, stochastic & complex processes, and quantum computation & simulation. His methodological approach heavily relies on the framework of higher-order quantum operations—transformations that act upon transformations themselves—which has proven valuable for developing optimal quantum interactive strategies, clarifying memory effects in open quantum processes, and analyzing foundational notions like causality. He also employs tensor networks, graphical calculus, and semidefinite programming in his research. His recent publications reveal a strong focus on quantum thermodynamics, higher-order quantum operations, and quantum memory effects. Taranto has made significant contributions to understanding the relationship between Landauer's principle and Nernst's unattainability principle in quantum cooling, developing protocols for efficient quantum system cooling with finite resources, and characterizing multi-time quantum processes with classical memory. His work on the quantum switch and higher-order quantum operations has advanced our understanding of quantum causality and indefinite causal order. JSPS Postdoctoral Fellowship (2022-2025) Editor of Quantum Journal (since June 2024) Taranto actively collaborates with multiple research groups globally, including the Murao group at the University of Tokyo, the Huber group at TU Wien, and the Modi group at SUTD Singapore and Monash University. He has worked with prominent researchers such as Simon Milz, Jessica Bavaresco, Marco Túlio Quintino, Felix Binder, Martí Perarnau-Llobet, Patryk Lipka-Bartosik, and Andrea Smirne. He is currently accepting PhD students and encourages collaboration with researchers sharing similar interests. Taranto is also committed to social responsibility, advocating for open science, climate justice, and empowering historically excluded and marginalized groups.

• Programming Languages
• Formal Verification
• Weak Memory Models
• +۴ مورد دیگر

Azalea Raad is a Reader (Associate Professor) in the Department of Computing at Imperial College London, leading the Veritas Lab. She holds a PhD in Computer Science from Imperial College London and is a UKRI Future Leader Fellow since 2021. Her research focuses on programming languages, formal verification, and persistent memory systems, particularly addressing challenges in weak memory models, concurrency, and software correctness. Affiliations: Co-director of the UK Research Institute on Verified, Trustworthy Software Systems Adjunct roles at Facebook (2020–2022) and Bloomberg (2022–present) Education: MEng from Imperial College London PhD in Computer Science from Imperial College London Research Interests: Azalea’s work bridges theoretical foundations and practical system implementation, emphasizing: Formal semantics of persistent and weak memory systems Verification frameworks for concurrent and persistent programs Bug detection in libraries and binaries Transactional memory and system validation Publications: Her recent work explores compositional bug detection, persistent programming principles, and scalable non-termination analysis. Key contributions include frameworks like IsaBIL and Memento, and foundational papers on RDMA robustness and C/C++ memory model extensions. Awards: UKRI Future Leader Fellowship (2021–present). Research recognized through collaborations with industry (e.g., Intel PMDK validation). Labs/Teams: Leads the Veritas Lab, advancing formal methods for trustworthy software systems.

• Quantum Theory
• Information and Communication Theory
• Algebraic Geometry
• +۳ مورد دیگر

Helena Martín Cruz is an Assistant Professor at the Faculty of Mathematics, Jaume I University . Her research focuses on Quantum Information Processing and Algebraic Geometry applications to error-correcting codes. As of 2024, she has published 4 works since her doctoral graduation from Jaume I University in 2024. PhD in Mathematics (2024), Jaume I University Thesis: Quantum codes and locally recoverable codes from evaluation codes Her work explores Quantum Stabilizer Codes , Hermitian Dual-Containing Codes , and Monomial-Cartesian Code Constructions for quantum computing applications. Collaborators include Dr. Carlos Galindo Pastor and Dr. Fernando Hernando Carrillo. Recent publications analyze quantum code optimization against the Gilbert-Varshamov Bound and develop Locally Repairable Codes for quantum systems. She has contributed to advancements in Quantum Error-Correcting Codes through algebraic structures and finite field cryptography.

Eric Ruppertمشاهده پروفایل

دانشیار

• Distributed Computing
• Shared-Memory Algorithms
• Data Structures
• +۳ مورد دیگر

Eric Ruppert is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University, where he has taught computer science since 2000. He is a member of the Theory of Computing Group and teaches foundational computer science courses including algorithms and theory of computation. He has served on program committees for major conferences including PODC 2025 and WADS 2025. Ruppert received his education at the University of Toronto. He has held postdoctoral positions at Brown University and served as a visiting professor at the École Polytechnique Fédérale de Lausanne. His research focuses on the theory of distributed computing, with particular emphasis on algorithm design and lower bound proofs. His recent work has centered on shared-memory data structures for multi-core architectures. His research interests span distributed algorithms, computational complexity, and the theoretical foundations of concurrent systems. He has made significant contributions to understanding the computational power of population protocols, consensus problems, and non-blocking data structures. His publication record shows a consistent focus on distributed computing theory, with recent work emphasizing non-blocking and wait-free data structures. Over the past decade, his research has increasingly addressed challenges in concurrent programming for multi-core systems, developing efficient algorithms for queues, deques, and binary search trees that provide strong progress guarantees. Ruppert has been actively involved in the distributed computing research community, serving on program committees for major conferences including PODC and WADS. His work has been published in top-tier venues such as Distributed Computing, Journal of the ACM, and proceedings of ACM SIGACT/SIGOPS conferences. At York University, Ruppert teaches courses on algorithms, theory of computation, and computational complexity. He has supervised numerous graduate students through project courses and thesis work. He is known for his dedication to teaching, including coaching students for the annual ACM programming contest. Ruppert is a member of the Theory of Computing Group at York University, which focuses on foundational aspects of computer science including algorithms, complexity theory, and distributed systems. His work bridges theoretical computer science with practical applications in concurrent and parallel computing.

Alex Lombardiمشاهده پروفایل

استادیار

• Cryptography
• Quantum Cryptography
• Theoretical Computer Science
• +۳ مورد دیگر

Alex Lombardi is an Assistant Professor of Computer Science at Princeton University, specializing in cryptography and theoretical computer science. He joined Princeton in 2023 after a Simons-Berkeley postdoctoral fellowship hosted by Shafi Goldwasser. His PhD in computer science from MIT (2022) was advised by Vinod Vaikuntanathan. His research focuses on the theoretical foundations of classical and quantum cryptography, with a strong emphasis on cryptographic protocols, complexity theory, and post-quantum security. Education: PhD in Computer Science, MIT (2022); Postdoctoral Fellowship at Simons Institute for the Theory of Computing (Berkeley). Prior academic roles include teaching at MIT (2018–2019) and serving as a teaching assistant for Cryptography and Cryptanalysis courses. Research interests span cryptographic proof systems (e.g., succinct arguments, zero-knowledge proofs), lattice-based cryptography, quantum-resistant algorithms, and complexity-theoretic hardness assumptions. His work often bridges cryptography with computational complexity, exploring topics like PPAD hardness, indistinguishability obfuscation, and the Fiat-Shamir heuristic. Recent publications highlight contributions to SNARGs (succinct non-interactive arguments), post-quantum security analyses, and foundational studies of cryptographic protocols under advanced assumptions (e.g., LWE, Decisional Diffie-Hellman). Key trends include advancing succinct verification techniques, analyzing quantum computational threats, and formalizing cryptographic assumptions via complexity reductions. Awards: No explicit awards listed, though his prolific publication record in top venues (STOC, CRYPTO, EUROCRYPT) reflects academic recognition. Teaching: Taught COS 533 (Advanced Cryptography) in 2023 and COS 433/533 (Cryptography) in 2024. Previously, led courses at MIT on cryptographic proofs and foundations. Service: Served on program committees for STOC 2025, EUROCRYPT 2025, and multiple CRYPTO and TCC events. Active in reviewing for leading cryptography and theoretical computer science conferences.

• Microelectronics
• Nanotechnology
• Semiconductor Device Reliability
• +۷ مورد دیگر

Daniel M. Dankovic is a Full Professor at the Faculty of Electronics, University of Niš, Serbia, where he also obtained his B.Sc., M.Sc., and Ph.D. degrees. He is affiliated with the Department of Microelectronics and heads both the department and the Laboratory for Practical Teaching in Electronic Components and Microsystems. He is actively involved in national and international research projects and contributes to academic publishing as an assistant editor of Microelectronics Reliability and editor of Facta Universitatis, Series: Electronics and Energetics . Ph.D. in Nanotechnology and Microsystems, Faculty of Electronics, University of Niš (2009) M.Sc. in Microelectronics, Faculty of Electronics, University of Niš (2006) B.Sc. in Electronics and Telecommunications, Faculty of Electronics, University of Niš (2001) His research focuses on the reliability of semiconductor devices, particularly Negative Bias Temperature Instability (NBTI) in p-channel power VDMOSFETs, radiation effects, degradation mechanisms, and microsystem design. He also contributes to engineering education, especially in PCB design and hands-on training. His work bridges theoretical modeling, experimental validation, and practical teaching methodologies. The recent publications (2012–2020) reflect a strong thematic consistency in semiconductor device reliability, with emphasis on NBTI, radiation interactions, and measurement techniques. Additionally, he explores IoT-based smart monitoring systems and innovative approaches in engineering education, demonstrating interdisciplinary engagement across microelectronics, applied physics, and pedagogy. Daniel M. Dankovic holds no explicitly listed scientific awards in the provided text. He advises students through research collaboration, particularly evident in co-authored publications with junior researchers, though no formal list of advisees is provided. He is currently involved in 4 national and 1 international research project, indicating active grant funding and collaborative research. His leadership roles include heading key academic units and organizing major IEEE conferences in the region. He leads the Laboratory for the implementation of practical teaching in the module Electronic Components and Microsystems and the Master's program in Electronics and Microsystems, ensuring integration of research and education in advanced electronics.

• Theoretical Phonology
• Morphology
• Historical Linguistics
• +۴ مورد دیگر

Gunnar Ólafur Hansson is an Associate Professor at the Department of Linguistics, Faculty of Arts, University of British Columbia (UBC). His research focuses on theoretical phonology, the morphology-phonology interface, phonological typology, historical linguistics, and locality relations, particularly in Icelandic. He holds an M.A. from the University of Iceland (1997) and a Ph.D. from the University of California, Berkeley (2001). Research Interests: Theoretical phonology, consonant harmony, long-distance interactions in phonology, phonologization, morphologization, morphological paradigms, inflection classes, allomorphy, syncretism, and paradigm gaps. Languages Studied: Icelandic (native), Faroese, North Sámi, Turkish, Tsilhqot’in (Chilcotin), Yowlumne (Yawelmani), and other Athapaskan/Dene languages. Publications Trends: His work spans phonology, historical linguistics, and computational models of phonotactics. Notable themes include consonant harmony, tier-based strict locality, sound pattern evolution, and interactions between phonetics and phonology.

• Computer Science
• Networking
• Operating Systems
• +۴ مورد دیگر

Eric Brewer is a Professor in the Department of Computer Science at the University of California, Berkeley, part-time since July 2014 while also serving as a leader at Google. He is renowned for his work in operating systems, networking, and technology for developing regions. His academic journey includes a B.S. from UC Berkeley (1989) and a Ph.D. from MIT (1994), both in EECS. Brewer's research focuses on scalable systems, security, and energy-efficient technologies for underserved areas. He co-founded Inktomi Corporation and pioneered projects like FirstGov.gov (now USA.gov). His awards include the ACM Prize in Computing (2009) and membership in the National Academy of Engineering. Education: B.S. in EECS from UC Berkeley (1989), M.S. and Ph.D. in EECS from MIT (1994). Research interests span developing region technologies, programming languages, and networking. His work on DC microgrids and rural cellular networks highlights efforts to bridge the digital divide. Collaborative projects in Cambodia, India, Ghana, and other regions emphasize practical, sustainable solutions. Recent publications address energy systems, cloud computing, and low-cost connectivity. Awards include ACM Fellow, SIGOPS Mark Weiser Award, and Global Leader for Tomorrow recognition. His contributions to education include courses like CS169 (Software Engineering) and CS262A (Advanced Topics in Computer Systems).

Glenn Marionمشاهده پروفایل

استاد

• Mathematical Modeling
• Epidemiology
• Statistical Inference
• +۷ مورد دیگر

Professor Glenn Marion serves as Head of Research at Biomathematics and Statistics Scotland (BioSS), formally part of the James Hutton Institute. His work applies mathematical modeling to address societal challenges through real-world applications and methodological advancements across three core research themes: statistics, bioinformatics, and process & systems modelling. Marion's research spans complex systems modeling and statistical inference for process models. In complex systems, he develops tools to understand emergent properties critical to addressing unintended consequences in wildlife disease control, pesticide application, and livestock disease management. His statistical inference work has advanced model choice, assessment, and data augmentation Markov Chain Monte Carlo techniques, with applications in phylodynamics, spatial disease risk, and epidemic data analysis. Ongoing work focuses on software applications, improved particle filtering, and novel epidemic inference methods. Analysis of Marion's recent publications (2021-2024) reveals a strong focus on epidemiological modeling, particularly related to livestock diseases and pandemic response. His work integrates mathematical modeling, statistical inference, and genetic approaches to address disease transmission dynamics. Key themes include network analysis of livestock trading, age-stratified contact patterns during pandemics, and genetic aspects of disease resistance. Marion frequently develops practical tools like the SIRE 2.0 software for estimating host genetic variation in disease transmission. As Head of Research at BioSS, Marion oversees a vibrant PhD studentship program and fosters collaborations with biological sciences, mathematics, statistics, and informatics communities. His leadership strengthens BioSS's research environment while deepening interactions with applied challenges in plant science, animal health, ecology, human health, and environmental management. Marion's work is applied across diverse domains including plant science, animal health and welfare, ecology and environment, human health and nutrition, offshore renewables, and wastewater surveillance of diseases like COVID-19. His research addresses immediate real-world challenges through close collaboration with scientists working in these application areas, demonstrating the practical impact of quantitative methods in biological sciences.

Azalea Raadمشاهده پروفایل

پژوهشگر

• Program Logics
• Separation Logic
• Weak Memory Concurrency
• +۱۲ مورد دیگر

Azalea Raad is a researcher at Imperial College London, actively contributing to the fields of programming languages, formal methods, and concurrency. She has a strong presence in top-tier academic conferences such as POPL, PLDI, SPLASH, and ICFP, serving in key roles including program committee member, session chair, and organizing committee member across multiple tracks and co-located events. Her research interests center on weak memory concurrency, non-volatile memory, program logics, separation logic, concurrent reasoning, and verification . She has pioneered work in incorrectness logic and under-approximate reasoning , enabling scalable bug detection in concurrent and persistent systems. Her work bridges formal theory with practical systems challenges, particularly in memory models and semantics for C/C++ and assembly-level concurrency. The recent publications highlight a strong trend toward formalizing memory persistency , extending memory models , and developing logical frameworks for bug detection . The keywords across her work include concurrency, verification, program logics, and systems correctness, with sub-fields spanning separation logic, incorrectness logic, TSO, RDMA, and crash consistency. Her research increasingly focuses on scalable and compositional methods for analyzing unsafe libraries and binaries. She has contributed to academic service through organizing workshops such as O'Hearn Fest , The Future of Weak Memory , and Incorrectness , and has co-chaired the Student Research Competition at POPL. While no grants are explicitly mentioned, her leadership in multiple conference tracks suggests active involvement in funded research and student mentorship. Azalea Raad leads or contributes to collaborative research teams focused on formal semantics and verification tools, often working within frameworks like Isabelle/HOL and developing new logical systems for program analysis. Her personal website, https://www.SoundAndComplete.org , serves as a hub for her research outputs and projects.

• Energy Economics
• Geosciences
• Hydrocarbon Resource Assessment
• +۴ مورد دیگر

Sofia Berdysheva is a Research Fellow at the Bureau of Economic Geology, Jackson School of Geosciences, University of Texas at Austin. Her research focuses on integrating geoscience with energy economics, addressing challenges in resource assessment, climate impacts, and energy transition strategies. She specializes in analyzing hydrocarbon productivity in unconventional basins like the Permian and Midland, evaluating hydropower vulnerability in Sub-Saharan Africa, and exploring the economic implications of global energy shifts. Her work spans interdisciplinary topics, including well performance optimization, geomechanical influences on production, and the interplay between emission policies and corporate asset strategies. Notable contributions include frameworks for hydrogen trade analysis and frameworks assessing energy transition dynamics across firms and global markets. She also examines energy security through the lens of interfuel substitution and international trade patterns. While no scientific awards are explicitly mentioned, her research demonstrates significant engagement with pressing issues in energy systems and geosciences. Her studies on investment decisions, production dynamics, and energy policy provide actionable insights for firms and policymakers navigating the energy transition landscape. Sofia’s work is characterized by rigorous data analysis and a focus on practical, scalable solutions for sustainable energy futures.

• Quantum Information
• Quantum Computation
• Mathematical Physics
• +۳ مورد دیگر

Kaifeng Bu is a tenure-track Assistant Professor in the Department of Mathematics at The Ohio State University, affiliated with the College of Arts and Sciences. His research focuses on quantum information and computation, mathematical physics, quantum Fourier analysis, quantum machine learning, and quantum resource theory. He previously held a postdoctoral position at Harvard University under Arthur Jaffe. Education: - PhD in Mathematics from Zhejiang University (2019) - Postdoctoral Research at Harvard University Research Interests: Kaifeng Bu's work bridges quantum computing and theoretical physics. He explores foundational aspects of quantum systems, including error correction codes, quantum advantage, and resource quantification. His studies often involve mathematical frameworks like harmonic analysis and probability theory to address challenges in quantum technologies. He actively seeks graduate students and postdocs to join his research group. Recent Publications (2025): His latest research advances include developing quantum codes with intersecting recovery sets and quantifying 'magic' resources in quantum systems. These contributions highlight his focus on enhancing quantum channel capacities and improving fault-tolerant computing through resource optimization. Advising & Grants: Currently, Kaifeng Bu advises no listed students. While specific grant details are not provided, his research aligns with cutting-edge quantum technologies. He encourages prospective students to contact him via bu.115@osu.edu . Labs & Teams: Kaifeng Bu leads a research group within the Department of Mathematics at OSU, focusing on interdisciplinary quantum studies. His work integrates mathematical rigor with computational and experimental quantum physics.

John Ewerمشاهده پروفایل

دانشیار

• Fire Safety Engineering
• Computational Fluid Dynamics (CFD)
• Building Information Modelling (BIM)
• +۵ مورد دیگر

Dr John Ewer is an Associate Professor in Applied Fire Safety Engineering at the University of Greenwich's School of Computing and Mathematical Sciences, part of the Faculty of Engineering and Science. He leads the Fire Research Team for fire modelling and SMARTFIRE software development in the Fire Safety Engineering Group (FSEG). His work focuses on fire dynamics, CFD simulations, BIM integration, and public health risk modelling. He holds a Gold/HEA Fellowship (2021) and has contributed to impactful research in fire safety and maritime safety. Education: PhD (University of Greenwich, 2020) in re-engineering CFD codes; MSc (Scientific and Engineering Software Technology, 1991); BSc (Physics, Reading University, 1989). Research interests include: CFD-based fire and aerosol dispersion modelling Fire safety in maritime and aviation environments BIM standards for fire engineering data sharing Public health risk assessment during disease outbreaks Naval platform survivability and damage control Evacuation simulation for high-risk environments Key activities: Co-ordinator of the annual 'Principles and Practice of Fire Modelling' CPD course Consultancy reviewer for third-party fire strategy submissions Led software engineering module COMP1714 and undergraduate/postgraduate project supervision Contributed to three successful FSEG REF submissions (2008, 2014, 2021) Current projects include: EU Horizon Healthy Sailing Project (2022–ongoing) ISSAC naval survivability tool for Australian DSTG Covid-related indoor aerosol dispersion modelling