جستجوی استاد راهنمای دانشگاهی

John C. W. Lamمشاهده پروفایل

دانشیار

• Power Electronics
• High Frequency Power Converters
• Renewable Energy Systems
• +۳ مورد دیگر

John C. W. Lam is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. He serves as the Graduate Program Director for MASc and PhD programs and is a Professional Engineer (P.Eng). His research focuses on power electronics, high-frequency converter topologies, renewable energy systems, and energy storage technologies. Lam directs the Advanced Power Electronics Laboratory for Sustainable Energy Research (PELSER). He is an IEEE Senior Member and an Associate Editor for multiple IEEE journals, including Transactions on Power Electronics and Transactions on Industry Applications. Education: B.Sc. (Hons.) in Electrical Engineering and BA in Economics from Queen’s University (2003), M.Sc. and Ph.D. in Electrical Engineering from Queen’s University (2006, 2010). He completed a Postdoctoral Fellowship at Queen’s ePOWER from 2010–2014 before joining York University in 2014. Research emphasizes high-frequency power converters for renewable energy integration, medium-voltage DC grids, and electric vehicle charging systems. Key contributions include modular resonant converter designs, soft-switching techniques, and fault-tolerant power electronics architectures. His publications (15 most recent listed) explore topics like MVDC distributed PV systems, high-voltage EV chargers, and grid-connected inverter control. He has developed novel topologies for electrolytic capacitor-less converters and modular power balancing techniques. Lam’s lab (PELSER) focuses on sustainable energy research, advancing efficient power conversion solutions for grid, renewable, and transportation applications. He has edited special issues in IEEE/IET journals and holds leadership roles in professional organizations.

Ronald de Wolfمشاهده پروفایل

استاد

• Quantum Computing
• Complexity Theory
• Error-Correcting Codes
• +۳ مورد دیگر

Ronald de Wolf is a part-time Full Professor at the Institute for Logic, Language and Computation (ILLC), University of Amsterdam, and a researcher/group leader at the Algorithms and Complexity group of CWI (Dutch Centre for Mathematics and Computer Science). He is an active member of QuSoft and the Amsterdam Theoretical Computer Science ecosystem. His PhD was completed at CWI and ILLC, followed by postdoctoral research at UC Berkeley. Research Focus: De Wolf specializes in quantum computing, complexity theory, and algorithm design. His work explores quantum advantages in computation, communication, and learning, with applications in optimization, machine learning, and information theory. Recent investigations include quantum algorithms for linear algebra, error correction, and communication complexity. Publication Trends: His recent articles (2020-2025) predominantly focus on quantum algorithmic advantages, complexity bounds, and practical applications in machine learning and optimization. Key themes include quantum speedups for linear algebra, error-resilient quantum protocols, and theoretical limits of quantum computation. Awards & Honors: ERCIM Cor Baayen Award (2003) STOC Best Paper Award (2012) STOC Test-of-Time Award (2022) Gödel Prize (2023) Academic Leadership: He currently advises PhD student Lynn Engelberts and has graduated 10 doctoral students. As coordinator of the NWO Gravitation program Quantum Software Consortium , he oversees major research initiatives. He secured participation in EU projects (QAIP, RESQ, QAP, QCS, QALGO, QuantAlgo) and leads research teams at CWI and QuSoft.

Susan Blessingمشاهده پروفایل

استاد

• High Energy Physics
• Particle Physics
• Electroweak Interactions
• +۱ مورد دیگر

Susan Blessing is a Professor of Physics at Florida State University's College of Arts and Sciences, Department of Physics. She specializes in experimental high-energy particle physics with research focusing on electroweak interactions, top quark studies, and quantum chromodynamics. Her work primarily utilizes proton-antiproton collision data from the DØ experiment at Fermilab. Her research examines fundamental particle interactions including W boson asymmetry, quark structure analysis, and searches for beyond-Standard-Model physics. Recent investigations explore exotic hadronic states and precision measurements of electroweak parameters. Honors include the First Year Assistant Professor Award (1995), Dr. Martha Roberts Award (2000), and Developing Scholar Award (2001). She actively contributes to Fermilab's DØ Collaboration and mentors students in particle physics research methodologies.

Andrew Goodwinمشاهده پروفایل

استاد

• Materials Chemistry
• Correlated Disorder
• Structural Flexibility
• +۷ مورد دیگر

Andrew Goodwin is a Professor of Materials Chemistry and Associate Head of Department (Research) at the University of Oxford, leading a research group focused on structural flexibility and disorder in functional materials. His work bridges fundamental understanding with practical applications in energy storage and smart materials. Education: University of Sydney, University of Cambridge Research in the Goodwin Group revolves around four themes: fundamentals of disorder, advanced characterization techniques, disorder-by-design synthesis, and disorder-property relationships. They specialize in total scattering, PDF, and 3D-ΔPDF methods using X-rays, neutrons, and electrons, with applications in thermoelectrics, magnetocalorics, and adaptive sensing. Recent publications highlight his group's exploration of metal-organic frameworks (MOFs), Jahn-Teller distortions, and disorder-driven properties. Key trends include leveraging correlated disorder for functional materials, developing novel characterization algorithms, and synthesizing hybrid frameworks with unconventional magnetic/electronic states. Royal Society of Chemistry Harrison-Meldola Prize RSC Marlow Prize RSC Corday-Morgan Prize RSC Peter Day Prize Inaugural UK Blavatnik Laureate in Chemistry Andrew has secured both ERC Starting and Advanced Grants, advising roles at the Leverhulme Trust and Max-Planck-Gesellschaft. His group at the Inorganic Chemistry Laboratory collaborates globally on materials synthesis and characterization challenges.

Dylan Bumfordمشاهده پروفایل

دانشیار

• Formal Semantics
• Pragmatics
• Theoretical Computer Science
• +۳ مورد دیگر

Dylan Bumford is an Associate Professor in the Department of Linguistics at the University of California, Los Angeles (UCLA). His research focuses on formal semantics and pragmatics, with a particular emphasis on their interfaces with theoretical computer science. He has developed techniques integrating functional programming and type theory into linguistic analysis, particularly leveraging algebraic structures like monads and functors to model natural language composition. His work addresses phenomena such as split-scope definites, distributivity, superlative adjectives, and the dynamics of quantification. Key contributions include analyses of definiteness and its interactions with presupposition, as well as pragmatic models incorporating rational inference over dynamic denotations. Bumford has also contributed to computational linguistics through semantic parsers and calculators, including collaborative work on a type-driven parser using Haskell and Purescript. His teaching spans advanced graduate courses (e.g., Mathematical Structures in Language , Semantic Theory ) and undergraduate courses in semantics and computational linguistics. Education: NYU Dissertation (2017) on split-scope definites Key Projects: Collaborative work with Simon Charlow on 'Effect-driven interpretation' and dynamic semantics frameworks His presentations include invited talks at institutions like Stanford, Penn, and USC, addressing topics ranging from monadic semantics to cross-linguistic studies of exhaustive inferences. Bumford has also participated in experimental research, including a grammar learning project exploring expectation-based learning mechanisms.

Azita Mayeliمشاهده پروفایل

استاد

• Harmonic Analysis
• Fourier Analysis
• Representation Theory
• +۶ مورد دیگر

Azita Mayeli is a Professor of Mathematics and Faculty Deputy Executive Officer at the City University of New York's Graduate Center. Her academic leadership and research contributions position her at the forefront of mathematical analysis within the CUNY system, with a focus on theoretical and applied harmonic analysis. Her research spans abstract and classical harmonic analysis, Fourier analysis, representation theory, functional analysis, approximation theory, sampling theory, interpolation theory, and signal processing. She investigates fundamental connections between uncertainty principles, eigenvalue distributions, and signal recovery mechanisms across diverse mathematical structures including finite abelian groups, Riemannian manifolds, and fractal geometries. Her work bridges pure mathematics with practical applications in data science and signal processing. Analysis of her 15 most recent publications (2022-2025) reveals a dominant focus on eigenvalue distributions of spatio-spectral operators, uncertainty principles in novel settings, and sparse signal recovery techniques. Her research demonstrates increasing interdisciplinary reach, connecting harmonic analysis with machine learning, geometric measure theory, and time-series analysis while maintaining deep theoretical foundations. Scientific Awards: Feliks Gross Endowment Award (2015) - CUNY's highest honor for assistant professors, recognizing exceptional early-career contributions While specific advising records and grant details are not documented in available sources, her extensive publication record and leadership position suggest active mentorship of graduate students and involvement in significant research funding. Her work on signal recovery mechanisms and mathematical foundations has clear implications for data science applications and computational mathematics. Dr. Mayeli maintains active research laboratories focused on harmonic analysis applications, with particular emphasis on developing mathematical frameworks for signal processing in constrained environments. Her team explores connections between abstract function spaces and practical data recovery problems, contributing to both theoretical advances and algorithmic innovations.

Carl R Schmidtمشاهده پروفایل

دانشیار

• Particle Physics
• Theoretical High Energy Physics
• Quantum Chromodynamics
• +۴ مورد دیگر

Carl R Schmidt is an Associate Professor in the Department of Physics & Astronomy at Michigan State University, where he conducts theoretical research in high-energy particle physics with a focus on quantum chromodynamics and proton structure. His work is central to advancing precision predictions for collider experiments worldwide. Education: Ph.D. in Physics, Harvard University (1990) Dr. Schmidt's research program revolves around parton distribution functions (PDFs) and their applications in high-energy collisions. As a key member of the CTEQ collaboration, he develops global QCD analyses (including CT10, CT14, and CT18 PDF sets) that incorporate data from the LHC, HERA, and fixed-target experiments. His expertise spans Higgs boson production mechanisms, electroweak symmetry breaking in beyond-Standard-Model scenarios (particularly little Higgs models), top quark physics, and photon-induced processes. His theoretical frameworks directly enable precision tests of the Standard Model and searches for new physics at energy frontiers. Analysis of his 15 most recent publications (2019-2024) reveals a dominant focus on reducing PDF uncertainties through novel methodologies and incorporation of high-precision LHC data. Key themes include the determination of photon content within the proton, NNLO corrections to global fits, and applications to critical measurements like the weak mixing angle and Higgs cross-sections. His work bridges theoretical developments with experimental requirements, particularly for ATLAS and CMS collaborations. Scientific awards: No awards or fellowships were documented in available sources While specific student mentorship details are absent from current records, his active role in the CTEQ collaboration—which involves extensive international collaboration and training—suggests significant contribution to graduate education. Research funding is inferred through CTEQ's institutional support from the U.S. Department of Energy and National Science Foundation, though specific grants aren't itemized in the source material. Dr. Schmidt operates within the CTEQ framework, a major international consortium connecting theorists and experimentalists to refine QCD understanding. This collaboration maintains vital links with LHC experiments and drives community-wide efforts in PDF development through regular workshops and shared computational frameworks.

• Quantum Chromodynamics
• Beyond Standard Model Phenomenology
• Machine Learning in Physics
• +۲ مورد دیگر

Maria Ubiali is a Professor of Theoretical Particle Physics and Phenomenology at the University of Cambridge, affiliated with the Department of Applied Mathematics and Theoretical Physics and the Cavendish Laboratory's Cambridge SUSY Working Group. Her research focuses on Quantum Chromodynamics, Beyond Standard Model phenomenology, and precision proton structure determination using machine learning techniques. Education & Career: PhD in particle physics, followed by postdoctoral roles at RWTH Aachen and University of Cambridge (2010-2017), Senior Research Fellow at Magdalene College (2014-2017), University Lecturer (2017-2022), and Royal Society Dorothy Hodgkin Fellow (2016-2023). Research: Combines collider physics with data science, specializing in parton distribution functions (PDFs), SMEFT analyses, and heavy quark/higgs production mechanisms. Leads the ERC-funded project 'Physics Beyond the Standard Proton' (2020-). Scientific Awards: ERC Starting Grant (2020) Royal Society Dorothy Hodgkin Research Fellowship (2016-2023) Notable Contributions: Pioneered Monte Carlo sampling for PDF uncertainties, developed open-source machine learning frameworks for global PDF analyses, and advanced understanding of new physics hiding in proton structure. Collaborations: Key contributor to NNPDF/TNNPDF collaborations, DiRAC conferences, and LHC precision studies. Her recent publications (2023-2025) showcase innovations in PDF determination, axion-like particle interactions, HL-LHC new physics searches, and symbolic regression applications. Co-author network spans 15+ international institutions, reflecting multidisciplinary impact.

Samaneh Farokhiradمشاهده پروفایل

استادیار

• Fluid Mechanics
• Soft Matter Physics
• Computational Modeling of Soft Materials
• +۴ مورد دیگر

Dr. Samaneh Farokhirad is an Assistant Professor in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT), affiliated with the Center for Applied Mathematics and Statistics. Her research focuses on fluid mechanics, soft matter physics, and biophysical interactions, employing computational and theoretical models to study soft materials at interfaces. She holds a Ph.D. and M.Phil. from City College of New York (CCNY), an M.S. from Iran University of Science and Technology, and a B.S. from Sharif University of Technology. Her academic journey includes postdoctoral roles at the University of Pennsylvania and CCNY, along with a visiting scholar appointment at the University of Wisconsin-Madison. Dr. Farokhirad has secured significant funding from the National Science Foundation and Oracle, and serves on editorial boards for Computational Physiology and Medicine, Frontiers in Physiology, and Frontiers in Bioengineering and Biotechnology. Research interests span computational modeling of droplet dynamics in microfluidic systems, nanoparticle adhesion to biological membranes, and electromechanical coupling in biological systems. Her work bridges fluid mechanics, soft matter physics, and biomedical applications, with recent focus on optimizing nanocarrier targeting for therapeutic delivery. Key Awards: DOE’s Director’s Discretionary Allocation Award (2025), Oracle for Research Project Award (2023), NIH Early Career Reviewer (2021). Grants: NSF funding for nanocarrier targeting research, Oracle computational infrastructure support. Labs/Teams: Leads the Farokhirad Research Group at NJIT, collaborating on multiphase flow dynamics and biophysical modeling.

• Multiresolution Methods
• Numerical Analysis
• Computational Mathematics
• +۴ مورد دیگر

Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.

• Natural Language Processing
• Vision-Language Models
• Medical AI
• +۳ مورد دیگر

Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۷ مورد دیگر

Elisabet Golobardes Ribé is a Full Professor at La Salle Digital Engineering School, Ramon Llull University, where she is affiliated with the Engineering Department. She leads and participates in the Research Group on Smart Society and is actively involved in major international research collaborations, particularly with CERN's LHCb experiment. Her research spans artificial intelligence, machine learning, data science, and high-energy physics, focusing on analyzing particle physics data and detecting disinformation in digital societies. She applies computational techniques to fundamental physics problems, including B-meson decays and searches for physics beyond the Standard Model. The body of her recent publications demonstrates a strong trend in experimental particle physics, with numerous contributions to high-impact journals such as the Journal of High Energy Physics and Physical Review D. These works involve amplitude analyses, angular distributions, branching fraction measurements, and constraints on new physics, primarily through the LHCb and ATLAS collaborations. She has secured competitive research funding, including EU grants such as FLAIR and national grants from AGAUR, and serves as Principal Investigator on projects related to AI readiness and disinformation detection. Her work bridges engineering, computer science, and fundamental physics. Notable research projects include: FLAIR (Fostering Learners' AI Readiness) – European Commission-funded project (2024–2026) LHCb-RTA-URL – CERN data challenges and new physics searches (2023–2026) Rapid response for detecting disinformation flows – Internal grant (2021) Research Group on Smart Society – AGAUR-funded network (2022–2025) She is a member of the LHCb collaboration and contributes to large-scale data analysis in high-energy physics experiments. Her work emphasizes computational methods in science and societal applications of AI.

Laura Elisa Marcucciمشاهده پروفایل

استاد

• Theoretical Nuclear Physics
• Nuclear Astrophysics
• Few-nucleon structure and dynamics
• +۳ مورد دیگر

Laura Elisa Marcucci is a Full Professor of Theoretical Nuclear Physics at the Department of Physics, University of Pisa. She serves as Deputy Director of the Department of Physics and as Delegate for Orientation Activities at the University of Pisa. Since 2000, she has been a research associate at the Pisa Section of the National Institute for Nuclear Physics (INFN). Her academic journey includes: Graduated with honors in Physics from the University of Pisa (1995) PhD from Old Dominion University in Norfolk and Jefferson Laboratory in Newport News (2000) Research fellow (2000) University researcher (2002) Associate professor (2016) Full professor in Theoretical Physics of Fundamental Interactions (2020) Professor Marcucci's research focuses on theoretical nuclear physics, particularly on nuclear interaction modeling and the description of light nuclear systems and reactions. Her work spans from fundamental few-nucleon structure to astrophysically relevant reactions involved in Big Bang nucleosynthesis and stellar evolution. More recently, she has expanded her research to nuclear reactions relevant for clean energy production through fusion reactors, serving as the scientific responsible for a PON project in this area. Her publication record shows a consistent focus on few-body nuclear systems, chiral effective field theory applications, and nuclear reactions of astrophysical significance. The most recent publications reveal growing interest in the PTOLEMY experiment for cosmic neutrino background detection, X17 boson theoretical analyses, and advanced modeling of few-nucleon correlation functions using hyperspherical harmonics methods. Professor Marcucci's scientific recognition includes: Fellow of the American Physical Society (2014) Ordinary member of the Italian Physical Society She has supervised numerous bachelor's, master's, and doctoral theses throughout her career. Her research has been supported by various grants, including her current role as scientific responsible for a PON project on fusion energy. She has established productive collaborations with major experimental groups worldwide, including Hall A at Jefferson Laboratory, LUNA and PTOLEMY collaborations at Gran Sasso National Laboratories, and ALICE collaboration at the Large Hadron Collider. Professor Marcucci founded and directed (2018-2022) the nuclear physics section of Frontiers in Physics journal. She has organized numerous conferences and given invited talks at over 50 national and international conferences, demonstrating her active engagement with the global nuclear physics community.

Omid Chatrabgounمشاهده پروفایل

استادیار

• Uncertainty Quantification
• Bayesian Machine Learning
• Gaussian Processes
• +۵ مورد دیگر

Dr. Omid Chatrabgoun serves as an Assistant Professor in Data Science and AI at the CEES School of Science, holding a PhD in Data Science from Shahid Chamran University (Iran, 2015). Previously affiliated with Malayer University, he teaches undergraduate and postgraduate courses including Statistical Learning, Data Science, and Machine Learning while actively supervising PhD students. His research focuses on uncertainty quantification and Bayesian machine learning for complex systems. Education: PhD in Data Science, Shahid Chamran University, Iran (2015) Dr. Chatrabgoun specializes in Modelling, Optimization, and Uncertainty Quantification (UQ) using (Deep) Gaussian processes, with applications spanning bioinformatics (Gene Regulatory Networks, Protein-Protein Interactions) and environmental engineering (coastal protection, flood modeling). He develops novel approaches for machine learning of linear operational equations to quantify uncertainty in highly complex systems, emphasizing computational efficiency through sparse approximations and kernel methods. His work bridges theoretical statistics with practical implementations in healthcare, environmental science, and industrial applications. Analysis of his 29 publications (2016-2025) reveals a consistent trajectory in Gaussian process methodologies, evolving from foundational graphical models to advanced deep Gaussian processes and probabilistic surrogate modeling. Key thematic clusters include bioinformatics network inference, environmental risk assessment, and computational optimization techniques, demonstrating interdisciplinary impact across computer science, statistics, and domain-specific applications. Dr. Chatrabgoun has secured research funding and conducted consultancy projects with the Iran National Science Foundation (INSF) and Research Institute for Grapes and Raisin (RIGR). His collaborative network spans international institutions, as evidenced by co-authorships across civil engineering, bioinformatics, and environmental science domains. Current projects involve developing machine learning frameworks for linear differential operators and expanding applications of pair-copula Bayesian models in high-dimensional data analysis. His research group actively explores uncertainty propagation in PDE-based models and spatio-temporal environmental simulations, maintaining strong industry-academia partnerships for knowledge exchange in data-driven decision systems.

Brian Quinnمشاهده پروفایل

استاد

• Nuclear Physics
• Particle Physics
• Quark Interaction Physics
• +۶ مورد دیگر

Brian Quinn is a Professor of Physics at Carnegie Mellon University's Mellon College of Science, where he has served on the faculty since 1988, achieving indefinite tenure in 1996 and full professorship in 2001. His research focuses on the intersection of nuclear and particle physics through quark interaction studies. Education Ph.D. in Physics, Massachusetts Institute of Technology (1984) B.Sc. in Physics, McGill University, Canada (1978) Research Focus Professor Quinn's work explores non-perturbative quantum chromodynamics through precision experiments at Jefferson Laboratory. His research program investigates hadron structure, strangeness content of nucleons, parity violation effects, and neutron radius measurements using electron and photon beams. Key contributions include leadership in the G0 and HAPPEX-III parity violation experiments and development of advanced instrumentation for nucleon form factor studies. Publication Trends His 2006-2016 publications reveal a consistent focus on precision measurements at Jefferson Lab, particularly through parity-violating electron scattering. Major themes include determination of strange quark contributions to the proton, neutron radius measurements in lead-208, and high-precision nucleon form factor studies. Instrumentation development for polarimetry and calorimetry constitutes approximately 40% of his recent work, reflecting his dual expertise in physics analysis and detector design. Scientific Awards No specific awards mentioned in source material Advising and Research Leadership Professor Quinn has advised graduate students including Megan Friend (thesis on Compton polarimetry for HAPPEX-III). His research is conducted through major Jefferson Lab collaborations including G0, HAPPEX, MOLLER, and nucleon form factor experiments. He led CMU's design and prototyping of the hadron calorimeter for high-precision form factor measurements, securing significant experimental resources through these collaborations. Research Group He leads the Carnegie Mellon Quark Interaction group, which has contributed to diverse experiments using anti-protons, kaons, pions, photons, and electrons. The group specializes in detector development, data acquisition systems, and complex data analysis, with CMU graduate students consistently taking leadership roles in experimental execution and interpretation.