جست‌وجوی استاد و پژوهشگر

Vladimir Kazeevمشاهده پروفایل

استادیار

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +۳ مورد دیگر

Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.

Dinusha Mendisمشاهده پروفایل

استاد

• Intellectual Property Law
• Copyright Law
• 3D Printing and Emerging Technologies
• +۴ مورد دیگر

Professor Dinusha Mendis is a leading academic in Intellectual Property and Innovation Law at Bournemouth University, where she serves as Director of the Centre for Intellectual Property Policy and Management (CIPPM). Her expertise bridges copyright law with emerging technologies like 3D printing, AI, blockchain, and the Metaverse, informed by extensive research and consultation with entities such as the European Parliament, UKIPO, and corporations like HP and Chanel. She holds a PhD from Edinburgh University and has authored seminal works on the intersection of technology and IP. BSc in Law from Aberdeen University LLM and PhD from Edinburgh University Called to the Bar of England and Wales Her research focuses on copyright challenges in immersive environments , AI-generated content regulation , and blockchain/NFTs . She led major studies for the European Commission and UKIPO , including the first peer-reviewed work on 3D printing and IP. Her 2019 co-edited book with Stanford and QUT scholars established foundational frameworks for additive manufacturing legal analysis. Recent publications highlight AI copyright disputes (2024 The Conversation ), NFT legal ambiguities (2022), and pandemic-era 3D printing (2020). Trends show increasing emphasis on decentralized IP systems and machine learning's impact on creative industries . Scientific Awards & Grants JSPS Visiting Professorship (2023) Daiwa Anglo-Japanese Foundation Grant (2024) EU Commission Research Funding (2020) AHRC Grant for 3D Jewellery Study (2017) As a PhD supervisor, she mentors researchers on AI copyright (Benjamin White), crypto-art (Bahar Dagli), and music creation law (Elizabeth Bailey). She contributes to global IP policy through the World Economic Forum's Metaverse Governance Team and EU IPR Enforcement Projects .

James C. Gumbartمشاهده پروفایل

استاد مدعو

• Computational Biochemistry and Biophysics
• Membrane proteins
• Free-energy calculations
• +۷ مورد دیگر

James C. Gumbart is an Adjunct Professor in the School of Physics at Georgia Institute of Technology, with additional affiliation to the School of Chemistry and the Institute for Bioengineering and Bioscience . His research leverages molecular dynamics simulations to decode the atomic-level mechanisms of bacterial proteins and cellular structures. B.S., Physics and Mathematics, Western Illinois University, 2003 Ph.D., Physics, University of Illinois at Urbana Champaign, 2009 Dr. Gumbart's work bridges computational biophysics and biochemistry to understand: Mechanisms of bacterial membrane protein insertion and nutrient import Structural dynamics of cell wall mechanics SARS-CoV-2 spike protein interactions with ACE2 Free-energy calculations for protein-ligand binding Applications of machine learning in biomolecular simulations His publications reflect trends in membrane protein biophysics , viral dynamics , and computational drug design , with a strong emphasis on interdisciplinary techniques. Awards include multiple fellowships and grants from NSF , DOE , and NIAID . He has mentored numerous PhD students, including Zijian Zhang , David Ryoo , and Andrew Pang , whose work has advanced understanding of bacterial systems and viral proteins. The Gumbart Lab integrates high-powered supercomputing and advanced software to model biomolecular processes, fostering collaborations with institutions like the National Institutes of Health and Argonne National Laboratory .

Arthur A. Danielyanمشاهده پروفایل

استاد

• Complex analysis
• Approximation theory
• Functional analysis

Arthur A. Danielyan is Professor in the Department of Mathematics and Statistics at the University of South Florida. His research focuses on complex analysis and approximation theory, particularly boundary behavior of analytic functions, polynomial and rational approximation, and functional analysis methods. Danielyan earned his PhD from the Armenian Academy of Sciences (1987) under S. N. Mergelyan. Research solves longstanding problems including Rubel's bounded analytic functions problem (2016) and von Renteln's boundary uniqueness problem. Recent work addresses Fatou's theorem extensions and interpolation in Hardy spaces. He has supervised multiple PhD students and organized international conferences including the Southeastern Analysis Meeting (2016). Funded by Simons Foundation and DAAD, Danielyan has published over 35 scholarly papers resolving problems from Hayman's list. Articles demonstrate consistent focus on boundary properties of analytic functions, interpolation theorems, and polynomial approximation in complex domains. Recent publications increasingly address Blaschke products and Baire classification problems. Honors and Grants Simons Foundation collaborative grant (2017-2022) DAAD Visiting Research Professorship (1996-1997) Henri Hecaen Award (1989)

• Numerical Analysis
• Partial Differential Equations
• Finite Element Method
• +۳ مورد دیگر

Dirk Praetorius is a Professor of Numerics of Partial Differential Equations (PDEs) at the Technische Universität Wien (TU Wien) , affiliated with the Institute for Analysis and Scientific Computing (ASC) within the Faculty of Mathematics and Geoinformation . He leads the research group on Numerics of PDEs and has held various leadership roles, including Institute Director (since 2020) and head of the Numerics research area. His work focuses on numerical methods for PDEs, including Finite Element Methods (FEM), Boundary Element Methods (BEM), adaptive algorithms, and computational micromagnetics. Education and Career: Praetorius earned his Diplom in Mathematics (2000) and PhD in Applied Mathematics (2003) from TU Wien, followed by a Habilitation in Numerical Analysis (2005). He has been a faculty member at TU Wien since 2005, progressing from Assistant Professor to full Professor in 2017. He has also held visiting positions at institutions such as the University of Jyväskylä and RICAM (Linz). Research Interests: His research spans numerical analysis, adaptive FEM/BEM, a-posteriori error estimation, matrix compression, and computational micromagnetics. He has contributed to modeling spin dynamics, magnetic skyrmions, and multiscale systems. His work emphasizes efficient algorithms for large-scale problems and optimal computational complexity. Awards and Editorial Roles: Praetorius received the TU Best Teacher Award (2021) and TU Best Lecture Award (2019). He serves as Senior Editor for Computational Methods in Applied Mathematics (CMAM) and on the editorial board of Applied Numerical Mathematics (APNUM) . He co-founded the outreach initiative TUForMath to promote mathematics education. Grants and Projects: He leads or co-leads several research projects funded by the Austrian Science Fund (FWF), including the collaborative SFB "Taming Complexity in Partial Differential Systems" (2017–2025) and international collaborations with Germany. His work addresses topics like functional error estimates, nonlinear PDEs, and computational design of magnetic devices. Labs and Teams: He contributes to the ASC Institute and coordinates interdisciplinary projects involving computational physics and engineering. His team develops software tools like MooAFEM and Commics for micromagnetic simulations.

Süli Endreمشاهده پروفایل

استاد

• Free-discontinuity problems
• Computational modelling of fracture
• Quasi-continuum methods
• +۱۰ مورد دیگر

Endre Süli is a Professor of Numerical Analysis at the University of Oxford, affiliated with Worcester College and Linacre College. He has held various academic roles since 1985, including Fellowships and Tutorships in Mathematics. University Education: B.Sc. in Mathematics, University of Belgrade (1974-1978) M.Sc. in Mathematics, University of Belgrade (1978-1980) Ph.D. in Mathematics, University of Belgrade (1985) M.A., University of Oxford (1985) British Council Visiting Student, Reading University and University of Oxford (1983/84) Süli's research focuses on numerical methods for partial differential equations (PDEs), with expertise in finite element methods, adaptive algorithms, error control, and computational modeling of fractures and non-Newtonian fluids. His work bridges mathematical theory and practical applications in fluid dynamics and material science. His recent publications emphasize finite element approximations, nonlinear PDEs, and stochastic models for polymer dynamics. Themes include multiscale methods, tensor-sparsity for high-dimensional problems, and compressible flow simulations. Scientific Awards: Fellow of the Royal Society (2021) London Mathematical Society Naylor Prize and Lectureship (2021) Pro Urbe Prize, City of Subotica (2021) SIAM Fellow (2016) Member, Academia Europaea (2020) Foreign Member, Serbian National Academy of Sciences and Arts (2009) IMA Service Award (2011) Fellow, European Academy of Sciences (EurASc) (2010) Fellow, Institute of Mathematics and its Applications (2007) London Mathematical Society/New Zealand Mathematical Society Forder Lecturer (2015) Professor Hospitus, Charles University, Prague (2012) Distinguished Visiting Chair Professor, Shanghai Jiao Tong University (2013) Invited Speaker, International Congress of Mathematicians, Madrid (2006) Süli has supervised numerous research projects and held visiting appointments globally. His contributions to numerical analysis span foundational work on error estimation, nonlinear stability, and advanced computational frameworks for complex physical systems.

Jana Shenمشاهده پروفایل

استاد

• Molecular Simulation
• Data Science
• Kinases
• +۱۲ مورد دیگر

Jana Shen is a Professor in the Department of Pharmaceutical Sciences at the University of Maryland School of Pharmacy, where she leads an interdisciplinary research group at the intersection of chemistry, biology, physics, and computer science. Her lab develops and applies advanced simulation and data science tools to understand biomolecular mechanisms and accelerate drug discovery. Education: Postdoc, The Scripps Research Institute (2003–2007) PhD, University of Minnesota at Twin Cities (1999–2003) MS, University of Calgary, Canada (1996–1999) Diplom-Chemie, Bergische Universität Wuppertal, Germany (1991–1995) Her research focuses on molecular simulation , data science , and computational biophysics , with applications in kinases , GPCRs , transmembrane transporters , and pH-responsive materials . She has pioneered the development of continuous constant pH molecular dynamics (CpHMD) methods and their applications in drug design and biomolecular mechanisms. The recent publications highlight a strong trend in computational drug discovery , particularly in covalent inhibitors , opioid receptor mechanisms , antiviral design , and the integration of machine learning with molecular dynamics . These works span high-impact journals such as eLife , JACS , Nature Communications , and ACS journals. Scientific Awards: National Science Foundation CAREER Award American Chemical Society HP Outstanding Junior Faculty Award Junior Faculty Research Award (University of Oklahoma, 2008, 2009) Phi Kappa Phi, University of Minnesota Louise T. Dosdall Graduate Fellowship Nova Graduate Fellowship Dr. Shen has mentored numerous PhD students and postdoctoral fellows, many of whom have gone on to successful careers in academia and industry. Her research is supported by major agencies including the National Institutes of Health , National Science Foundation , and FDA . She leads the Shen Lab, which actively develops open-source tools such as DeepCys , CpHMD , and PKAD-3 , and maintains databases for covalent ligandability and pKa predictions.

Ryan Giordanoمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Variational Methods
• +۴ مورد دیگر

Ryan Giordano is an Assistant Professor in the Department of Statistics at the University of California, Berkeley. He holds a PhD in Statistics from UC Berkeley (2019), advised by Michael Jordan, Tamara Broderick, and Jon McAuliffe, an MSc in Econometrics and Mathematical Economics from the London School of Economics (2009), and undergraduate degrees in Mathematics and Theoretical/Applied Mechanics from the University of Illinois at Urbana-Champaign. Prior to academia, he worked as an engineer at Google and HP and served as a Peace Corps volunteer in Kazakhstan. His research focuses on variational methods , Bayesian robustness , sensitivity analysis , and statistical computing , with applications in machine learning, environmental science, and astronomy. He is particularly known for developing scalable Bayesian inference techniques and quantifying the robustness of statistical models to data perturbations. Giordano’s recent work includes studies on Laplace approximation accuracy, MCMC sensitivity to data removal, and robustness metrics for differential expression analysis. He has contributed to open-source statistical software and collaborates with Tamara Broderick’s group at MIT on postdoctoral work (pre-2019 position). His academic trajectory combines theoretical innovation with practical applications, emphasizing reproducibility and computational efficiency in statistical methodology.

• Machine Learning
• Coding Theory
• Optimization
• +۵ مورد دیگر

Arya Mazumdar is a tenured Professor of Data Science and Computer Science at the Halıcıoğlu Data Science Institute (HDSI) , part of the School of Computing, Information and Data Sciences at the University of California San Diego (UCSD). He also holds affiliations with the Computer Science and Engineering and Electrical and Computer Engineering departments at UCSD. Previously, he was an Assistant and Associate Professor at the University of Massachusetts Amherst (2015–2021), and held a postdoctoral position at MIT (2011–2012). He is an IEEE Distinguished Lecturer (2023–2024) and recipient of the NSF CAREER Award (2015–2020). Education : PhD in 2011 from the University of Maryland, College Park (advisor: Alexander Barg) Postdoctoral scholar at MIT (2011–2012, advisor: Greg Wornell) Internships at IBM Almaden (2010) and HP Labs (2008) Research Interests : Focus on algorithmic and statistical aspects of machine learning, error-correcting codes, optimization, signal processing, and distributed systems. Key areas include clustering algorithms, compressed sensing, federated learning, and information-theoretic foundations of data science. He has contributed to theoretical guarantees for learning mixtures, distributed optimization, and robust coding schemes. Recent Articles Trends : Recent work emphasizes distributed optimization (e.g., vqSGD, Byzantine-resilient algorithms), sparse recovery in high-dimensional models, and theoretical foundations of learning (e.g., support recovery, parameter estimation). His research bridges information theory and machine learning, with applications in storage systems and large-scale data processing. Awards & Roles : 2020 EURASIP JASP Best Paper Award Co-PI and co-leader of the NSF AI Institute for Learning-Enabled Optimization at Scale Editorships: IEEE Transactions on Information Theory and Foundations and Trends in Communications Grants & Labs : Leads the EnCORE Institute as UCSD Site Lead, focusing on theoretical perspectives of large language models and computational-statistical gaps. Active in organizing workshops on topics like LLMs, clustering, and distributed optimization. His research is funded by NSF and industry collaborations. Teaching : Courses include Algorithms for Data Science , Probability and Statistics for Data Science , and Coding Theory , emphasizing foundational theory and scalable methods.

Christena Nippert-Engمشاهده پروفایل

استاد

• Ethnography
• Privacy Studies
• Human-Animal Interaction
• +۳ مورد دیگر

Christena Nippert-Eng is Professor of Informatics at Indiana University's Luddy School with adjunct appointment in Sociology. She directs the Animal Computer Interaction Track and researches cognition, privacy, ethnography, and human-animal interaction. Her work combines sociological frameworks with technology design. Nippert-Eng has authored influential books on boundary negotiation and ethnographic methods. Her current research examines privacy negotiations with domestic robots and human-animal-computer interaction. She has consulted for major technology companies including HP, Motorola, and Steelcase. Her publications demonstrate sustained interdisciplinary examination of privacy practices across contexts including homes, workplaces, online gaming, and social media, increasingly focusing on technology-mediated interactions and ethical considerations.

Diane Guignardمشاهده پروفایل

استادیار

• Numerical Analysis
• Applied Mathematics
• Computational Mathematics
• +۶ مورد دیگر

Diane Guignard is an Assistant Professor in the Department of Mathematics and Statistics at the University of Ottawa. Her research focuses on numerical analysis, partial differential equations, and computational methods with applications to mechanics and stochastic systems. She holds a position in a leading mathematics department and can be contacted at dguignar@uOttawa.ca . Her research interests include finite element methods, model reduction, uncertainty quantification, and optimal transport-based mesh adaptation. She explores nonlinear approximation theories for high-dimensional anisotropic functions and develops computational frameworks for thin structures and colloidal flow simulations. Her work bridges numerical analysis with practical engineering challenges, emphasizing adaptive algorithms and error estimation techniques. Her recent publications (2021-2024) highlight contributions to goal-oriented mesh adaptation, stochastic field approximations on surfaces, and large deformation analyses of prestrained plates. These studies emphasize interdisciplinary approaches combining mathematical rigor with computational innovation. Dr. Guignard has not been explicitly noted for awards in the provided materials. Her advising record is currently unspecified, though her research group likely engages in advanced numerical methods and computational mechanics projects.

Wang Li-Lianمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Analysis
• Spectral Methods
• +۵ مورد دیگر

Professor Wang Li-Lian is a faculty member in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore. He holds the rank of Professor of Applied Mathematics and has been affiliated with NTU since 2006, progressing through roles including Assistant and Associate Professor before his current position. His research focuses on spectral methods, computational acoustics/electromagnetics, and PDE-based image processing. He has supervised multiple PhD and Master's students, including Zhang Jing, Gu Ying, Yang Zhiguo, and others. Education and career highlights include a Postdoctoral Research Associate at Purdue University (2002-2003), followed by a Visiting Assistant Professorship (2003-2005). His academic work spans spectral element methods, fractional differential equations, and high-order numerical techniques for wave scattering problems. Notable contributions include advancements in spectral-Galerkin methods for nonlocal operators and exact nonreflecting boundary conditions for Maxwell's equations. Research interests emphasize high-accuracy numerical schemes, with applications to fractional PDEs, metamaterial simulations, and image processing. His publications (over 100+ articles) reflect expertise in spectral methods, computational physics, and mathematical modeling. Teaching responsibilities include courses on partial differential equations, numerical analysis, and scientific computing.

Lehel Banjaiمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Mathematics
• Boundary Element Methods
• +۱۶ مورد دیگر

Professor Lehel Banjai is a faculty member at the School of Mathematical and Computer Sciences, Heriot-Watt University, where he has held the position of Professor in Mathematics since 2022. Previously, he served as Associate Professor (2015–2022) and Assistant Professor (2012–2015) at the same institution. Education: Habilitation (University of Dusseldorf, 2013), D.Phil. (Oxford, 2003), BA(Hons) in Mathematics and Computation (Oxford, 2000) His research spans numerical analysis and computational mathematics, focusing on boundary and finite element methods, space-time formulations, and convolution quadrature for time-domain problems. Key areas of interest include wave scattering (acoustic and electromagnetic), Schrödinger equations, fractional differential operators, and high-order numerical schemes. Recent publications highlight his work on Runge-Kutta convolution quadrature, fractional diffusion, nonlinear impedance boundary conditions, and efficient solvers for wave equations. He has collaborated with researchers at institutions such as Max Planck Institute, University of Zurich, and University of Dusseldorf. Students supervised include Brian Hennessy (joint with Emmanuil Georgoulis), Ebraheem Aldahham, Katherine Baker, Jeta Molla (joint with Gabriel Lord), and Oluwaseun Lijoka, among others.

Efthymios N. Karatzasمشاهده پروفایل

استادیار

• Numerical Analysis
• Computational Mathematics
• Optimal Control
• +۵ مورد دیگر

Efthymios N. Karatzas serves as an Assistant Professor in the Department of Mathematics at Aristotle University of Thessaloniki, Faculty of Sciences, within the Computer Science and Numerical Analysis Section. He maintains an active research profile in computational mathematics with strong institutional affiliations including collaborations with SISSA mathLab and FORTH Institute of Applied and Computational Mathematics. His academic credentials include: PhD in Mathematics, National Technical University of Athens (2015) Master's in Applied Mathematical Sciences – Computational Mathematics, NTUA (2009) Master's in Applied Mathematics, University of Patras (2001) Bachelor's in Mathematics (Computational Mathematics), University of Patras (1999) Dr. Karatzas' research program centers on advanced numerical techniques for partial differential equations , with pioneering work in reduced order modeling , embedded boundary methods , and optimal control systems . His expertise spans computational fluid dynamics, uncertainty quantification, and biomechanical applications, characterized by methodological innovation in handling geometrically complex domains through cut finite element approaches and shifted boundary formulations. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on developing efficient numerical frameworks for parametrized PDE systems. His work consistently bridges theoretical rigor with practical implementation, particularly in advancing reduced basis methods for fluid-structure interaction and biological modeling, demonstrating significant contributions to computational mathematics through high-impact journal publications. No major scientific awards are documented in the available sources. Dr. Karatzas demonstrates research leadership through project management roles including Scientific Manager for the ELIDEK project at NTUA (2019-2021) and Project Manager for the European Social Fund HEaD initiative at SISSA (2017-2019). His grant administration experience encompasses coordinating interdisciplinary teams and securing external funding for computational mathematics research. He maintains active collaborations with the SISSA mathLab in Trieste (particularly with Prof. Gianluigi Rozza's group) and the FORTH Institute in Crete, participating in international workshops including the Reduced Order Methods in CFD Summer School (2019) and SIAM UQ conferences. His research network spans computational mathematics groups across Europe with emphasis on advancing numerical methodologies for real-world engineering and biological applications.

Matthew Collinsonمشاهده پروفایل

مدرس ارشد

• Logic
• Theoretical Computer Science
• Information Security
• +۱۲ مورد دیگر

Matthew Collinson is a Senior Lecturer in Computing Science at the University of Aberdeen, where he also serves as Head of Computing Science and Academic Line Manager. He holds an affiliation with the Scottish Informatics and Computer Science Alliance (SICSA) and leads the EPSRC-funded project SSPEDI (Supporting Security Policy with Effective Digital Intervention). Education: BSc Mathematics, University of Edinburgh (1997) MSc Mathematical Logic, University of Manchester (1998) PhD Computer Science, University of Manchester (2003) Research Interests: His research spans theoretical computer science and cybersecurity , focusing on non-classical logics (intuitionistic, modal, substructural), semantics of computation , concurrency theory , and type theory . He applies these foundations to information security , particularly in modelling security policies, access control, and the economics of cybersecurity decisions. His work integrates formal verification , simulation tools (e.g., Gnosis), and game-theoretic models . Publications Trends: Recent publications (2016–2022) emphasize human-centred security , exploring how persuasion and behavioural interventions can reduce cybersecurity vulnerabilities. Earlier works (2008–2015) concentrate on mathematical systems modelling , layered graph logics , and trust domains , bridging high-level policy and low-level system configurations. Projects & Grants: SSPEDI (2017–2020, EPSRC): Human dimensions of cybersecurity policy compliance. ALPUIS (EPSRC consortium): Algebra and logic for security policy and utility. Trust Domains (RCUK/TSB, 2011–2014): Framework for modelling secure information sharing. Seconomics (EU FP7, 2012–2015): Socio-economic impacts of cybersecurity regulation. PhD Supervision: He has successfully supervised PhD students including Kevin McDonald (2014), Barry Taylor (2015), and Robert (Bob) Duncan (2016), whose theses addressed logic-based security architectures, vulnerability analysis, and cloud stewardship respectively. Labs & Teams: His research is conducted within the Computing Science section of the School of Natural and Computing Sciences, leveraging collaborations with National Grid, HP Labs, and other academic partners.