جست‌وجوی استاد و پژوهشگر

Jens Lechtenbörgerمشاهده پروفایل

مدرس

• Open Educational Resources (OER)
• Data Warehousing
• Business Intelligence
• +۴ مورد دیگر

Dr. Jens Lechtenbörger is a Faculty member and Lecturer at the Chair of Machine Learning and Data Engineering at the University of Münster's School of Business and Economics. He holds a Doctoral degree (2001) and a diploma in Computer Science (1997) from the University of Münster and Oldenburg, respectively. His roles include serving as Akademischer Oberrat since 2006, Acting Professor (2016), and Fellow for innovation in digital university teaching (2016–2017). He leads projects like Tutor.AI and OER infrastructure development, focusing on Open Educational Resources (OER), data warehousing, and educational technology. Research interests span OER creation, data integration, business intelligence, and machine learning applications in education. He has published extensively on data warehouse design, database systems, and educational technologies. Notable awards include the Best Evolutionary & Swarm Computation Paper Award (2019) and the Dissertation Prize 2003. Teaching responsibilities include courses on data structures, operating systems, and management information systems. He supervises theses on topics like AI-driven learning systems, data privacy, and business logic vulnerabilities. Active in academic service, he chairs committees on digital teaching and curriculum reform at the University of Münster.

Yifan Sunمشاهده پروفایل

استادیار

• Optimization Algorithms
• Machine Learning
• Scientific Computing
• +۱۰ مورد دیگر

Yifan Sun is an Assistant Professor in the Department of Computer Science at Stony Brook University. She is also affiliated with the AI Institute and the Institute of Advanced Computational Science (IACS) at Stony Brook. Dr. Sun received her PhD in Electrical Engineering from UCLA in 2015, with research focusing on convex optimization and semidefinite programming. Prior to joining Stony Brook, she worked at Technicolor Research and Innovation on machine learning applications and completed postdoctoral research at the University of British Columbia in Vancouver and INRIA in Paris. Her research centers on the design and analysis of optimization algorithms, particularly those arising in large-scale machine learning and scientific computing. Dr. Sun studies how structural properties like sparsity, curvature, and decomposability can be exploited to design faster, more stable, and more interpretable optimization methods. Her work spans both theoretical foundations and practical applications, covering first-order methods, quasi-Newton techniques, and algorithms for convex programming to nonconvex deep models. She also investigates how optimization theory, particularly insights from linear algebra and geometry, can improve understanding of deep learning models, regularization techniques, and representation learning. Dr. Sun's recent publications demonstrate strong trends in large-scale graph learning, advanced optimization techniques (particularly Frank-Wolfe variants), and applications to natural language processing. Her work often bridges theoretical guarantees with practical implementations, as evidenced by her open-source code repositories. She leads the OptML Research lab at Stony Brook, focusing on optimization methods for machine learning challenges. Her publications appear in top venues including NeurIPS, ICML, CVPR, and various optimization journals, reflecting her interdisciplinary approach that connects computer science, applied mathematics, and engineering disciplines.

• Power Electronics
• RF Engineering
• Nonlinear Dynamics
• +۳ مورد دیگر

Dr. Brendan Hayes is an Assistant Professor at the School of Electronic Engineering, Dublin City University. His research focuses on power electronics, RF engineering, and nonlinear dynamics, with particular emphasis on high-efficiency power amplifiers, DC-DC converter stability, and renewable energy systems. He has contributed to advancements in ultra-wideband amplifier design, fractional element applications in nonlinear systems, and control strategies for PV-fed converters using Filippov methods. His work integrates theoretical analysis with practical circuit design, addressing challenges in broadband signal processing, energy conversion efficiency, and nonlinear system dynamics. Recent projects include optimizing power amplifier performance through novel load design spaces and exploring hybrid differential-algebraic equation models for renewable energy applications.

Laura Balzanoمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Signal Processing
• +۷ مورد دیگر

Laura Balzano is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan, with a courtesy appointment in the Department of Statistics within the College of Literature, Science, and the Arts. Her research focuses on optimization, machine learning, and signal processing, particularly in high-dimensional data analysis, subspace learning, and low-rank structures. She holds affiliations with the Department of Statistics and is located at 323 West Hall, Ann Arbor, MI. Contact information includes girasole@umich.edu . Her work bridges theoretical foundations and practical applications, addressing challenges in heterogeneous data, online learning, and robust algorithms. Key research areas include subspace clustering, tensor decomposition, and model compression for deep learning systems. Recent contributions explore dynamic networks, control theory integration, and energy-efficient algorithms for large-scale models. Laura Balzano's publications span topics like PCA variants for noisy data, optimization on Riemannian manifolds, and neural collapse analysis. Her methods emphasize scalable solutions for real-time applications, such as energy disaggregation and medical imaging. While no explicit awards are listed, her extensive publication record reflects recognition in her field. Advising and grant details are not provided in the available text.

Jessica Linمشاهده پروفایل

دانشیار

• Partial Differential Equations
• Probability Theory
• Stochastic Homogenization
• +۳ مورد دیگر

Jessica Lin is an Associate Professor in the Department of Mathematics and Statistics at McGill University, holding the Canada Research Chair in Partial Differential Equations and Probability (Tier 2). She specializes in stochastic homogenization, elliptic and parabolic equations, and reaction-diffusion systems. Prior to McGill, she was a Van Vleck Visiting Assistant Professor at the University of Wisconsin-Madison, and earned her Ph.D. from the University of Chicago in 2014 with undergraduate studies at NYU. Her research focuses on partial differential equations and probability, particularly in nonlinear phenomena, stochastic processes, and their applications to mathematical physics and materials science. Key areas include front propagation in stochastic environments, nondivergence form equations, and phase transition models in periodic media. Dr. Lin has contributed to foundational work on quantitative homogenization estimates for elliptic and parabolic equations, asymmetric cooperative motion, and barycentric Brownian systems. Her recent articles explore generalized front propagation, anisotropic surface tensions, and KPZ universality in random growth models. She has been recognized with the Canada Research Chair (Tier 2), and her work spans over 15 peer-reviewed articles since 2013. Her research combines analytical techniques with probabilistic methods, addressing multiscale problems in applied mathematics.

Tze Meng Lowمشاهده پروفایل

استاد پژوهشی

• Parallel and High Performance Computing
• Formal Methods
• Graph Algorithms
• +۳ مورد دیگر

Tze Meng Low is an Associate Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. His research focuses on high-performance algorithms, formal methods, and hardware-software co-design, with an emphasis on achieving performance portability across architectures. He holds a Ph.D. and M.S. in Computer Science and dual B.A./B.S. degrees in Economics and Computer Science from the University of Texas at Austin. His research interests span parallel computing, graph algorithms, machine learning, and cyber-physical systems. He has contributed to projects like the DARPA BRASS initiative, collaborating on adaptive software systems for resource-challenged environments. His work also involves developing tools such as SMaLL and qLD, which address challenges in machine learning library instantiation and genomic analysis. Low has received the Dean’s Early Career Fellowship (2022) and led the $2.7M DARPA-funded BRASS project (2016–2020), supported by SpiralGen, Inc. and academic collaborators. His contributions include code generation frameworks like SPIRAL and advancements in linear algebra-based graph algorithms, emphasizing analytical models and automated code optimization. His research bridges theoretical formal methods with practical implementations, aiming to enhance software reliability and scalability in emerging domains. Collaborations include work on fault-tolerant coded computing and high-assurance systems for cyber-physical applications.

Lawrence Blumeمشاهده پروفایل

استاد

• Economic Theory
• Game Theory
• Inequality
• +۲ مورد دیگر

Lawrence Blume is Distinguished Professor of Arts and Sciences in Economics at Cornell University, specializing in economic theory and game theory. His research examines evolutionary processes in markets using biological analogies, equilibrium refinements in game theory, and mathematical foundations of economic behavior. Seminal contributions include algebraic geometry approaches to perfect and sequential equilibrium, statistical mechanics models of strategic interaction, and evolutionary market dynamics. Recent work investigates optimality principles in market-based natural selection and learning mechanisms in rational expectation formation. He has held visiting positions at Cornell's Center for Advanced Study and Stanford's Center for the Study of Language and Information. Despite extensive theoretical contributions, no major scientific awards are documented in the provided materials.

Joshua P. Swansonمشاهده پروفایل

استادیار

• Algebraic Combinatorics
• Representation Theory
• Quantum Groups
• +۵ مورد دیگر

Joshua P. Swanson is an RTPC Assistant Professor of Mathematics at the University of Southern California (USC). Previously, he held a Stefan E. Warshawski Visiting Assistant Professorship at the University of California, San Diego (UCSD). He earned his PhD in Mathematics from the University of Washington under advisor Sara Billey, focusing on major index statistics and asymptotics. His research spans algebraic combinatorics with an emphasis on web bases for quantum group representations, plabic graphs, invariant theory, and analytic combinatorics. Education: PhD in Mathematics, University of Washington (2018) Bachelor's degree (implied, details not specified) Research Interests: Algebraic combinatorics, quantum group representations, plabic graphs, Coxeter groups, q-analogues, limit laws, and supercoinvariant algebras. He co-organizes workshops such as ICERM's Topical Workshop on Webs in Algebra, Geometry, Topology, and Combinatorics (2025). Publications: Over 20 peer-reviewed articles in top journals including Algebraic Combinatorics , European Journal of Combinatorics , and Advances in Mathematics . Recent work focuses on web bases, q-Stirling numbers, and harmonic differential forms. Awards: NSF Grant DMS-2348843 ($180,000) AMS-Simons Travel Grant ($5,000) Personal prize from Doron Zeilberger ($300) McKibben & Merner Fellowship ($10,000) Teaching: Taught over 20 courses at USC and UCSD, including Probability Theory (Math 407), Linear Algebra (Math 225), and Combinatorics (Math 184). Active in mentoring undergraduate researchers via USC's SURF program. Professional Engagement: Co-organizes seminars like the USC Combinatorics Seminar and serves on conference committees for FPSAC and CanaDAM. Maintains active collaborations with researchers such as Christian Gaetz, Oliver Pechenik, and Jessica Striker.

• Artificial Intelligence
• Internet of Things (IoT)
• Wireless Communications
• +۴ مورد دیگر

Professor Wei Xiang holds the Cisco Chair of AI and IoT at La Trobe University, leading the Cisco-La Trobe Centre for AI and IoT and the Australian Centre for AI in Medical Innovation. He previously established Australia's first IoT Engineering degree program at James Cook University, earning recognition in the Pearcy Foundation's Hall of Fame. His expertise spans AI, IoT, wireless communications, and medical AI innovation. As an IEEE Associate Editor for multiple journals, he has published over 450 peer-reviewed papers and books. Key roles include: Director & Chief Scientist: Australian Centre for AI in Medical Innovation Founding Director: Cisco-La Trobe AIoT Centre Adjunct Professor: James Cook University Vice Chair: IEEE Northern Australia Section (2016-2020) Research focuses on AI-driven IoT systems, smart agriculture, and medical applications. Awards include La Trobe Research Excellence Award (2021), Pearcey Entrepreneurship Award (2017), and multiple fellowships. Current grants involve AIoT in smart farming, medical innovation, and satellite IoT. He supervises research students in AIoT and advises on collaborative projects. His labs pioneer technologies like radar-based health monitoring and UAV-enabled environmental sensing. Recent publications highlight advancements in wireless communication systems, deep learning models for remote sensing, and hybrid networks.

Michail Papathomasمشاهده پروفایل

مدرس ارشد

• Bayesian Statistics
• Biostatistics
• Ecological Statistics
• +۷ مورد دیگر

Dr Michail Papathomas is a Senior Lecturer in Statistics at the School of Mathematics and Statistics, University of St Andrews. He is affiliated with the Centre for Research into Ecological & Environmental Modelling. His research focuses on Bayesian statistical methodology, mixture modeling, and applications in ecology, public health, and genomics. He has supervised PhD students including Chao Gao, Thu Nguyen, and Hanyu Wang. His work bridges theoretical developments with practical applications in areas like spatial capture-recapture techniques and antibiotic resistance epidemiology. Recent projects include analysis of multidrug-resistant infections in East Africa and chemical evolution studies of galaxies. Education: PhD in Statistics (inferred from academic role) Research interests span Bayesian nonparametrics, Dirichlet process mixtures, and statistical methods for ecological and biomedical data. He collaborates internationally on projects addressing global health challenges and environmental monitoring. His publications often integrate advanced computational methods with real-world datasets. Key research trends include: (1) Developing flexible statistical frameworks for complex ecological systems; (2) Addressing parameter identifiability in log-linear models; (3) Applying mixture models to genomic and epidemiological datasets. Grants: Received funding for a cancer research studentship from The Melville Trust, and Scottish Funding Council support for blockchain applications in irrigation governance. Led development of spatial capture-recapture methodologies and contributed to tools for galaxy spectral analysis. Active in interdisciplinary collaborations across medicine, ecology, and astrophysics.

Qianqian Yangمشاهده پروفایل

مدرس ارشد

• Fractional Differential Equations
• Numerical Methods
• Medical Imaging (MRI)
• +۳ مورد دیگر

Dr. Qianqian Yang is a Senior Lecturer in applied and computational mathematics at the School of Mathematical Sciences, Queensland University of Technology (QUT). She holds a PhD in computational mathematics from QUT (2010) and has been recognized with prestigious awards such as the QUT Outstanding Doctoral Thesis Award (2010) and an ARC DECRA fellowship (2014). Her research focuses on fractional differential equations, numerical methods, and their applications in medical imaging, particularly diffusion MRI. She has secured major grants, including an ARC DECRA fellowship (2015–2022, part-time) and an ARC Discovery Project (2019–2022). Dr. Yang’s work bridges computational mathematics and biomedical applications, aiming to model brain tissue microstructure using fractional models. She teaches computational mathematics units and has supervised 2 postdocs, 3 PhDs, and numerous Honours and VRES students. Her research is published in top journals like NeuroImage and SIAM Journal on Scientific Computing. Expertise: Fractional PDEs, Numerical Analysis, MRI Modeling Grants: ARC DECRA, ARC Discovery Project

• Probability Theory
• Stochastic Analysis
• Machine Learning
• +۵ مورد دیگر

Xiong Wang is a J.J. Sylvester Assistant Professor in the Department of Mathematics at Johns Hopkins University (non-tenure track). His research focuses on probability theory, stochastic analysis, and their applications to machine learning and complex systems. He holds a Ph.D. from the University of Alberta (2022), where he received the Faculty of Science Dissertation Award. Education: Ph.D. in Mathematics, University of Alberta (2022) Advisor: Professor Yaozhong Hu Research Interests: Probability Theory & Stochastic Processes Machine Learning Algorithms Partial Differential Equations (PDEs) Interacting Particle Systems Inverse Problems in Stochastic Dynamics Mathematical Foundations of Data Science Teaching: Instructor for advanced courses at Johns Hopkins, including Stochastic Differential Equations, Partial Differential Equations, and Real Analysis Prior teaching roles at University of Alberta (Calculus, Linear Algebra) Recent Activities: Speaker at over 30 international conferences/seminars since 2021 Active in SIAM, CMS, and ICERM events Collaborations with institutions globally (e.g., Peking University, Brown University)

Santiago Badiaمشاهده پروفایل

استاد

• Computational Science and Engineering
• Numerical Analysis
• Numerical Linear Algebra
• +۸ مورد دیگر

Santiago Badia is a Professor of Computational Mathematics at Monash University's School of Mathematics since 2019. Previously, he held roles at UPC (Universitat Politècnica de Catalunya) as Professor of Computational Science and Engineering (2009–2019) and was an adjunct researcher at CIMNE (Barcelona), leading the Large Scale Scientific Computing Department. He earned his PhD in 2006 from UPC, with prior work at Politecnico di Milano (2006) and Sandia National Labs (2007–2008). His research focuses on numerical approximation of PDEs using finite element methods, with applications in fluid/solid mechanics, electromagnetics, and multiphysics problems. He leads high-performance computing projects like FEMPAR (scalable PDE solvers) and Gridap (Julia-based PDE framework). His work includes perfect weak scalability on supercomputers and solving PDEs with up to 60 billion unknowns. Research interests span computational science, numerical analysis, parallel computing, and numerical linear algebra. Projects address challenges in fusion energy, additive manufacturing, and nuclear waste repositories. He actively supervises PhD students through funded projects, emphasizing computational modeling and simulation. Collaborations include international teams in geophysical fluid dynamics and stochastic inverse problems. His software frameworks (FEMPAR, Gridap) are widely used for industrial and academic research, emphasizing scalability and usability.

Gregory Markowskyمشاهده پروفایل

مدرس ارشد

• Stochastic processes
• Complex analysis
• Graph theory

Gregory Markowsky serves as a Senior Lecturer in the School of Mathematics at Monash University, maintaining an active research profile with continuous publication output through 2025. His academic appointment shows no indication of part-time status, emeritus designation, or former staff classification. His research centers on three interconnected domains: Stochastic processes, particularly planar Brownian motion and random walk theory Complex analysis with applications to probabilistic models Graph theory focusing on algebraic and combinatorial structures These areas converge in his investigation of probabilistic phenomena with geometric interpretations. Recent publication trends reveal sustained productivity with 61 research outputs cataloged, including 4 articles in 2025 alone. His work demonstrates consistent methodological focus on stochastic analysis and combinatorial structures, with increasing cross-disciplinary applications in forensic science and econometrics as evidenced by 2024-2025 publications. Markowsky has secured three significant research projects funded by the Australian Research Council, including: "Planar Brownian motion and complex analysis" (2014-2017) as Primary Chief Investigator "Finite Markov chains in statistical mechanics and combinatorics" (2014-2017) "AMSI Industry Internship: Melbourne Storm" (2013) His ORCID profile (0000-0003-1656-337X) documents extensive scholarly activity with international collaborations. Professional engagement includes contributions toward UN Sustainable Development Goals through mathematical applications, though specific goal alignments aren't detailed in available materials. His email contact Greg.Markowsky@monash.edu remains active with current research dissemination.

Bogdan Ionمشاهده پروفایل

دانشیار

• Algebra
• Combinatorics
• Geometry
• +۳ مورد دیگر

Bogdan Ion is an Associate Professor in the Department of Mathematics at the University of Pittsburgh, affiliated with the Dietrich School of Arts and Sciences. He holds a PhD from Princeton University. His primary research focuses on Lie theory, representation theory, combinatorics, and non-commutative algebra, with recent work centered on Macdonald theory, affine Kac-Moody groups, and Hecke algebras. Education: PhD in Mathematics, Princeton University Research Interests: Dr. Ion explores advanced topics in algebra and geometry, including the spherical harmonic analysis of real/p-adic reductive groups, affine Grassmannians, and combinatorial structures within Coxeter groups. His work bridges algebraic geometry, representation theory, and combinatorics, with applications to symmetric functions and hypergeometric functions. Professional Profile: His research trends emphasize deep connections between algebraic structures (e.g., DAHA, non-symmetric Macdonald polynomials) and geometric frameworks (e.g., affine flag manifolds). His recent articles reflect contributions to probabilistic renormalization, Weyl modules, and BGG reciprocity. He maintains an active research website and collaborates widely in his field. Advising & Grants: No formal advisees are listed, but his research likely involves graduate students through collaborative projects. No specific grants are mentioned in the provided texts. Labs/Teams: His work is typically individual, though collaborations are indicated through co-authored publications. His office is located at 423 Thackeray Hall.