جست‌وجوی استاد و پژوهشگر

Michele Piscitelliمشاهده پروفایل

مدرس

• Classical Reception
• Comparative Literature
• Creative Multilingualism
• +۹ مورد دیگر

Dr. Michele Piscitelli is a Lecturer in Italian Language at the University of Oxford's Department of Italian, within the Faculty of Medieval and Modern Languages. He holds a Ph.D. from the University of Birmingham, where his research focused on English learning of Italian during the Tudor period, and an MA in History, Archival Studies, and Librarianship from the Università degli Studi di Genova. His academic career includes teaching Italian and English in the UK, Australia, Honduras, and Spain. Education: Ph.D., University of Birmingham (M4C-funded) MA in History, Archival Studies, and Librarianship, University of Genoa His research interests bridge classical reception, comparative literature, and early modern cultural studies, including utopian/dystopian literature, Renaissance art of memory, and globalization. His work also intersects with interdisciplinary quantum physics research, as evidenced by his publications on superconducting qubits, dark matter detection, and quantum sensor technologies. Recent articles explore cutting-edge topics like scalable quantum lattice designs, self-driving laboratory systems, and axion dark matter searches, reflecting his dual engagement with humanities and experimental physics. Though no scientific awards are explicitly mentioned, his publications highlight contributions to both language pedagogy and quantum engineering. No advising or grant details are provided. His work spans teaching, cultural history research, and quantum physics, underscoring a unique interdisciplinary profile.

Magdalena Larforsمشاهده پروفایل

استاد

• String Theory
• Calabi-Yau Manifolds
• Quantum Gravity
• +۲ مورد دیگر

Magdalena Larfors is Professor of Theoretical Physics at Uppsala University, where she leads a research group studying string theory and M-theory compactifications. Her work focuses on how geometry and topology of extra dimensions determine observable physics, using methods from algebraic and complex geometry complemented by machine learning approaches. Research areas include: Mathematical aspects of string theory compactifications Geometry of Calabi-Yau manifolds Physics-inspired machine learning applications Classification of stable string vacua She is principal investigator for multiple funded projects including 'Exploring the Structure of Stable String Vacua' and 'Realistic Ground States of String Theory'. Her recent publications demonstrate innovative applications of computational methods to problems in high-dimensional geometry.

Joshua Pulsipherمشاهده پروفایل

استادیار

• Optimization under Uncertainty
• Data Science
• Machine Learning
• +۷ مورد دیگر

Joshua Pulsipher is an Assistant Professor of Chemical Engineering at the University of Waterloo, Canada. He joined in 2023 and leads a research group focused on interdisciplinary solutions at the intersection of chemical engineering, computer science, mathematics, and statistics. His work addresses challenges in sustainability, energy systems, and data-driven decision-making under uncertainty. He holds a B.Sc. from Brigham Young University and a Ph.D. from the University of Wisconsin-Madison, with postdoctoral training at Carnegie Mellon University. Education: B.Sc. in Chemical Engineering, Brigham Young University (2017) Ph.D. in Chemical & Biological Engineering, University of Wisconsin-Madison (2022) Postdoctoral Research, Carnegie Mellon University (2022–2023) Research Interests: Optimization under uncertainty, machine learning, sustainable systems, infinite-dimensional optimization, energy systems, wildfire mitigation, and software development for accessibility in research. His projects span wildfire management frameworks, resilient infrastructure, and software tools like InfiniteOpt.jl and FlexibilityAnalysis.jl. Awards: Best Presentation Award, JuMP-dev 2024 Travel Award, FOCAPO/CPC (2023) Plenary Speaker at AICHE Annual Meeting (2022) Teaching: Courses include CHE 322 (Numerical Methods), CHE 341 (Process Control), and CHE 521 (Process Optimization). His philosophy emphasizes active learning and inclusivity. Labs/Software: Develops open-source software frameworks such as InfiniteOpt.jl for infinite-dimensional optimization and SAFE-OCC for computer vision sensor reliability. Active in promoting reproducibility and software-driven research.

Mehmet Gumusمشاهده پروفایل

استاد آموزشی

• Linear Algebra
• Matrix Computations
• Machine Learning
• +۳ مورد دیگر

Dr. Mehmet Gumus is a Teaching Assistant Professor in the Department of Mathematics and Statistics at the University of Nevada, Reno (UNR). He joined UNR in 2019 after serving as a Visiting Assistant Professor at Auburn University's Department of Mathematics and Statistics. His research focuses on linear algebra, matrix computations, and machine learning applications, with notable contributions to stability analysis, matrix inequalities, and data-driven predictive models in materials science. Education: Ph.D. in Mathematics, Southern Illinois University Carbondale (2017) M.S. in Mathematics, Koç University, Turkey (2011) B.S. in Mathematics, Koç University, Turkey (2009) Research Interests: Dr. Gumus explores theoretical and computational aspects of linear algebra, including Lyapunov stability, matrix inequalities, and block matrix structures. His work bridges pure mathematics with applied domains like machine learning and quantum information theory. Recent studies apply hybrid data analytics to material science challenges, such as predicting concrete compressive strength. Grants & Advising: No grants or advisees explicitly listed in available texts. Courses taught include Calculus, Linear Algebra, and Statistics at both undergraduate and graduate levels (e.g., Math 126, 181, 330; Stat 152, 352).

Luca Magriمشاهده پروفایل

استاد

• Scientific Machine Learning
• Data Assimilation
• Fluid Mechanics
• +۸ مورد دیگر

Luca Magri is a Professor of Scientific Machine Learning at Imperial College London's Department of Aeronautics (Faculty of Engineering), leading the MagriLab . He holds dual roles as Director of Research in Aeronautics and Director of the Research Centre in Data-Driven Engineering. He is also a Professor in Fluid Mechanics at Politecnico di Torino under the PNRR project on Twin Real-time digital twins. His research bridges physics-aware machine learning, data assimilation, and fluid mechanics, with applications in quantum computing, turbulence modeling, and thermoacoustics. Magri completed his PhD in Engineering at the University of Cambridge (2012-2015) and held postdoctoral roles at Stanford University (2015-2016). He has been a Royal Academy of Engineering Research Fellow (2016-2021), Simons Fellow (2022-2023), and Hans Fischer Fellow at TUM (2018-2021). He leads the Alan Turing Institute's Scientific Machine Learning group and collaborates with institutions like Pembroke College and the Isaac Newton Institute. His research interests span quantum reservoir computing, data-driven stability analysis, and real-time digital twins for combustion systems. Key projects include optimizing wind farm layouts and suppressing extreme events in chaotic flows. He has pioneered methods like Proper Latent Decomposition (PLD) and physics-constrained neural networks for turbulence reconstruction. Magri has secured funding from EPSRC, Royal Academy of Engineering, and EU initiatives. His group includes engineers, physicists, and computer scientists addressing net-zero challenges in aerospace propulsion and energy systems. Recent work focuses on quantum computing for nonlinear PDEs and AI-driven process design in manufacturing.

• STEM Education
• Educational Evaluation
• Skills Development
• +۳ مورد دیگر

Dr Helen Cramman is an Associate Professor in the School of Education at Durham University and Head of the Evaluation and Innovation in STEM Education (EISE) national research group. She holds roles as Department Representative in the Durham Research Methods Centre and Fellow of both the Durham Research Methods Centre and the Higher Education Academy. Her expertise lies in evaluating education programs, particularly in STEM, focusing on skills development, inclusive environments, and equity, diversity, and inclusion (EDI). She has 20 years of experience leading research and education projects, including evaluations of national initiatives like the Levelling Up Widening Participation Programme and the EPSRC-funded Northern Power Inclusion Matters Programme. Her research interests emphasize the impact of education environments on learners and educators, blended/online teaching during the pandemic, and transdisciplinary approaches to quantum literacy. She has supervised numerous PhD and Master’s students in STEM education and EDI. Notable publications include analyses of pandemic-era practical science education and studies on early language interventions. She has also contributed to influential reports on creativity in education and STEM access programs. Dr Cramman’s awards include Fellowships from the Durham Research Methods Centre (2019) and the Higher Education Academy (2016). Her work integrates policy evaluation with practical pedagogy, aiming to enhance educational equity and innovation across sectors. She leads initiatives that bridge research and practice, fostering inclusive and effective educational practices.

Robert Lewis-Swanمشاهده پروفایل

استاد

• Atomic, Molecular, and Optical Physics
• Quantum Many-Body Systems
• Quantum Information Science
• +۳ مورد دیگر

Robert Lewis-Swan is the Tedd S. Webb Presidential Professor in the Homer L. Dodge Department of Physics and Astronomy at the University of Oklahoma. His research focuses on non-equilibrium many-body physics in atomic, molecular, and optical (AMO) systems, with applications to quantum technologies. He explores quantum phenomena like entanglement and coherence in diverse systems including neutral atoms, polar molecules, and trapped-ion crystals. His work combines analytical and numerical methods to study complex interactions and quantum control. Education: B.Sc., University of Queensland, 2011 Ph.D., University of Queensland, 2015 Research interests include quantum simulation, cavity QED, spinor Bose-Einstein condensates, and quantum-enhanced sensing. Recent work highlights include studies on dynamical phase transitions, quantum chaos, and precision metrology using trapped ions and Rydberg systems. Selected Awards: Tedd S. Webb Presidential Professorship (2025) His lab develops tools for quantum state preparation, noise mitigation, and quantum control, with applications in next-generation quantum devices. Collaborative efforts span theoretical modeling and experimental implementations in AMO systems.

Ryan Shiflerمشاهده پروفایل

دانشیار

• Quantum Schubert Calculus
• Algebraic Geometry
• Quantum Cohomology
• +۳ مورد دیگر

Ryan Shifler is an Associate Professor in the Department of Mathematical Sciences and serves as Associate Dean of the Henson School of Science and Technology at Salisbury University. He holds a PhD in Mathematics from Virginia Tech (2017), with earlier degrees from Salisbury University and Hagerstown Community College. His research focuses on quantum Schubert calculus and algebraic geometry, particularly quantum cohomology of non-homogeneous spaces. Notable work includes studies on symplectic Grassmannians, curve neighborhoods of Schubert varieties, and spectral properties of quantum cohomology. Collaborations with students and colleagues explore topics like isotropic Grassmannians and conjecture validations in algebraic geometry. Shifler has advised numerous students, including co-authors on publications such as studies on minimum quantum degrees and Frobenius-Perron dimensions. His work appears in journals like Annals of Combinatorics, Communications in Algebra, and Mathematische Zeitschrift. He also engages in expository writing on academic writing practices in mathematics.

• Partial Differential Equations
• Waves
• Dispersive PDEs
• +۷ مورد دیگر

Tomas Dohnal is a Professor at the Institute of Mathematics of Martin Luther University Halle-Wittenberg , Germany, since 2018. His research focuses on Nonlinear Partial Differential Equations (PDEs) , Dispersive Waves , Bifurcation Theory , and Wave Propagation in Periodic Structures . He has held academic positions at Technical University Dortmund, Karlsruhe Institute of Technology, ETH Zurich, and University of New Mexico. Research Interests : Nonlinear PDEs, Surface Plasmon Polaritons, Gap Solitons in Photonic Crystals, Spectral Problems, Rigorous Asymptotics, Numerical Analysis Grants : DFG grants on nonlinear wavepacket asymptotics and moving gap solitons in periodic media Students : Supervised PhD students including Maximilian Hanisch, Matthias Ionescu-Tira, and Daniel Tietz; Master students at multiple institutions Software : Co-developer of the PDE2PATH MATLAB package for bifurcation analysis Publications span topics in Maxwell equations with interfaces, PT-symmetric problems, homogenization of periodic media, and nonlinear wave dynamics. His work often bridges rigorous mathematical analysis with numerical methods. Teaching includes courses on Dispersive PDEs, Asymptotic Methods, Nonlinear Analysis, and Wave Propagation. He has taught at TU Dortmund, Karlsruhe Institute of Technology, and Martin Luther University.

Laila Girgisمشاهده پروفایل

مدرس

• Mathematical Physics
• Nonlinear Dynamics
• Optical Solitons
• +۲ مورد دیگر

Laila Girgis, PhD, is a Lecturer in the Department of Mathematical Literacy at Delaware State University. She holds a Ph.D. in Mathematical Physics (2012) and two Master’s degrees: M.S. in Applied Mathematics (2010) and M.S. in Physics and Physics Teaching (2002) from Delaware State University, alongside a B.Sc. in Electrical Engineering from Ain Shams University (1985). Her research focuses on nonlinear dynamics, optical solitons, and mathematical physics, with notable contributions to soliton perturbation theory and solitary wave solutions. Her work has been published in journals like Optik , Optica Applicata , and Optics and Laser Technology . Girgis has extensively studied optical Gaussons and soliton behavior in birefringent fibers, intermodal dispersions, and complex media. Her methodologies include variational principles and adiabatic phase analysis. While no scientific awards are explicitly listed, her publications reflect sustained research activity in interdisciplinary physics and applied mathematics. Her teaching and academic roles emphasize mathematical literacy, and she has no listed advisees or grants in the provided profile. Her academic journey spans over three decades, bridging engineering, physics, and advanced mathematical research.

• Partial Differential Equations
• Optimal Control
• Quantum Control
• +۷ مورد دیگر

Prof. Dr. Alfio Borzi holds the Chair of Scientific Computing at the University of Würzburg's Institute of Mathematics. He specializes in computational mathematics, optimal control, and numerical analysis. His research focuses on partial differential equations (PDEs), quantum control, multigrid methods, and stochastic models. He has contributed extensively to the development of numerical methods for solving complex systems such as the Vlasov-Poisson and Kohn-Sham equations. Borzi has led multiple interdisciplinary projects, including collaborations with medical and engineering institutions, and has supervised numerous PhD students. His work spans applications in medical imaging, plasma physics, and ecological modeling. Education: Borzi earned his PhD in Mathematics from SISSA/ISAS (Trieste, Italy) and completed his habilitation at the University of Graz. He has held academic positions at institutions in Italy and Austria before joining Würzburg in 2011. Research Interests: Borzi’s expertise includes optimal control of PDEs, quantum systems, and stochastic processes. He develops numerical schemes for Fokker-Planck equations, kinetic models, and superresolution microscopy. His work integrates computational methods with real-world applications like wine fermentation optimization and MRI reconstruction. Awards and Grants: Although no specific awards are listed, Borzi has secured significant funding, including a BMBF grant for AI-driven MRI advancements and a BayFrance grant for plasma cooling optimization. His research also involves international collaborations, such as with French institutions on plasma control and Italian teams on synchronization models. Teaching and Mentorship: Borzi teaches courses on numerical methods, optimal control, and mathematical modeling. His team includes postdocs and students working on projects like neural network optimization and quantum control algorithms. He actively publishes in top journals and organizes workshops on multigrid methods and computational optimization.

Arthur Kosowskyمشاهده پروفایل

استاد

• Cosmology
• Cosmic Microwave Background Radiation
• Dark Matter
• +۴ مورد دیگر

Arthur Kosowsky is a Professor in the Department of Physics & Astronomy at the University of Pittsburgh, affiliated with the Dietrich School. His research focuses on cosmology, particularly the cosmic microwave background (CMB) radiation, dark matter/dark energy, inflationary universe models, and gravitational waves. He is a key member of the Simons Observatory collaboration, leading efforts to observe the CMB using advanced telescopes in Chile's Atacama Desert. His work addresses fundamental questions about cosmic structure formation, dark energy's nature, and potential deviations from general relativity. Research interests include CMB polarization analysis, detecting primordial gravitational waves, and probing cosmic topology. Notable contributions involve analyzing anomalies in CMB asymmetry and large-scale correlations, simulating gravitational wave backgrounds from early-universe turbulence, and developing methods to study galaxy cluster dynamics. He has mentored numerous graduate students whose thesis topics span CMB lensing, cosmic birefringence, and transient phenomena. Scientific awards include the 2024 Fulbright US Scholar to Chile, APS Fellow (2014), and Cottrell Scholar (2000). His collaborations with the Simons Observatory aim to measure the B-mode polarization signal, neutrino masses, and cosmic magnetic fields through upcoming observations (2024-2025). He also leads efforts to detect transient millimeter-wave sources and study cosmic topology using machine learning techniques. Key projects include analyzing Atacama Cosmology Telescope (ACT) data for cosmological parameters and exploring the moving lens effect. His research bridges theoretical models with observational data, emphasizing precision cosmology and fundamental physics tests. Active in fostering interdisciplinary methods, he advocates for leveraging advanced instrumentation and computational tools to unravel cosmic mysteries.

Jun Chenمشاهده پروفایل

استاد

• Information Theory
• Machine Learning
• Wireless Communications
• +۲ مورد دیگر

Dr. Jun Chen is Professor and former Barber-Gennum Chair in Information Technology at McMaster University's Faculty of Engineering. His research spans information theory, machine learning, and communications systems. Core research areas: Information theory and coding Machine learning for signal processing Wireless communication systems Multimedia compression Publications address fundamental limits of rate-distortion-perception tradeoffs and computational methods for fluid dynamics. Research shows strong mathematical foundations with applications in communications and engineering. Honors include Early Researcher Award, IBM Faculty Award, and JSPS Invitational Fellowship. Current projects examine quantum information constraints and learned data compression.

Seshasai Srinivasanمشاهده پروفایل

دانشیار

• Nanotechnology
• Electrochemical Sensors
• Health Technology
• +۴ مورد دیگر

Dr. Seshasai Srinivasan is an Associate Professor and holder of the W Booth Chair in Engineering Entrepreneurship and Innovation at the W Booth School of Engineering Practice and Technology, McMaster University. He also serves as Program Chair for the BTech Software Engineering Technology program. His academic affiliations include associate memberships in the Mechanical Engineering Department, McMaster School of Biomedical Engineering, and School of Computational Science and Engineering. His research focuses on nanotechnology-based sensors, electrochemical detection systems, and AI-driven material science. Notable projects include developing MXene-based stretchable sensors, carbon dot-infused hydrogels for biomedical applications, and AI frameworks for predicting battery performance. He has pioneered gradeless residency models in software engineering education and collaborated with industry on projects like roadside drug detection systems with Eye3 Concepts Inc. Dr. Srinivasan teaches courses such as Finite Element Analysis (ENGTECH 3FE3), Senior Engineering Project (ENGTECH 4FD3), and Numerical Linear Algebra (SFWRTECH 4MA3). His work bridges cutting-edge nanomaterials research with practical engineering education innovation.

Brad Darrallمشاهده پروفایل

دانشیار

• Computational Mechanics
• Applied Mechanics
• Finite Element Methods
• +۵ مورد دیگر

Brad Darrall is an Associate Professor of Teaching in the Department of Mechanical and Aerospace Engineering at the University at Buffalo. He serves as the Faculty Advising Coordinator within the School of Engineering and Applied Sciences. His research focuses on computational and applied mechanics, including finite element methods, variational approaches, multphysic continuum dynamics, and size-dependent continuum theories. Education: PhD in Mechanical Engineering, University at Buffalo (2016) MS in Mechanical Engineering, University at Buffalo (2015) BS in Mechanical Engineering, University at Buffalo (2011) Research interests include advanced finite element formulations for anisotropic materials, coupled thermomechanical systems, and electromechanical phenomena in piezoelectric materials. His work emphasizes variational principles and their integration with numerical methods for solving complex engineering problems. Key awards include the SEAS Teaching Faculty of the Year (2022), Vanderhoef Faculty Award for Excellence in Mentorship (2021), and Tau Beta Pi Professor of the Year (2017). His NSF Graduate Research Fellowship (2011–2015) supported foundational work in computational mechanics. Advising and grants: While no specific grants are listed, his mentorship is highlighted through awards. He advises students through his teaching and advising roles but no formal advisees are named in the text.