جست‌وجوی استاد و پژوهشگر

George Slotaمشاهده پروفایل

دانشیار

• Graph Mining
• Network Analysis
• Big Data Analytics
• +۵ مورد دیگر

George Slota serves as an Associate Professor in the Computer Science Department within the School of Science at Rensselaer Polytechnic Institute (RPI). Previously, he held positions as a Computer Scientist at Sandia National Labs New Mexico and as a Computer Engineer at the Naval Undersea Warfare Center. His research spans graph and network mining, big data analytics, machine learning, bioinformatics , and their intersections with parallel computing, scientific computing, and high performance computing . His work focuses on scalable algorithms for trillion-edge graphs, graph partitioning, community detection, and GPU-accelerated graph processing. His recent publications reveal strong emphasis on graph coarsening and spectral methods distributed graph algorithms for biconnectivity and coloring random graph generation and validation GPU acceleration for graph neural networks His research has been recognized with a prestigious NSF CAREER award supporting his project "Scalable Graph Processing to a Quadrillion Edges and Beyond." Award: National Science Foundation Faculty Early Career Development (CAREER) Award His work demonstrates consistent funding and collaboration with major national laboratories including Sandia National Labs and involvement in ExaGraph projects targeting exascale computing challenges. He maintains an active research group focused on extreme-scale graph analytics.

Georgi S. Medvedevمشاهده پروفایل

استاد

• Dynamical Systems
• Stochastic Analysis
• Mathematical Neuroscience
• +۳ مورد دیگر

Georgi S. Medvedev is a Professor in the Department of Mathematics at Drexel University. He earned his PhD from Boston University (1999) and was a Veblen Research Instructor at Princeton University and the Institute for Advanced Study before joining Drexel in 2002. His research investigates dynamical systems, stochastic analysis, and mathematical neuroscience. Research develops mathematical frameworks for understanding complex network dynamics. Primary areas include synchronization in coupled oscillator systems, metastability in stochastic networks, and continuum limits of large-scale dynamical systems. Applications span neuroscience, statistical physics, and engineering. Recent publications focus on the Kuramoto model, exploring bifurcations in networked oscillators, stability of twisted states, and continuum approximations for sparse graphs. The work bridges discrete network models with continuum descriptions using graph limit theory. He has developed interdisciplinary courses including Mathematical Neuroscience and Dynamical Networks. His research is supported by multiple NSF grants focusing on network metastability, mean-field analysis, and synchronization phenomena. Service includes editorial roles for Discrete and Continuous Dynamical Systems and Networks and Heterogeneous Media. He mentors undergraduate researchers in projects investigating metastability in oscillator networks.

Zhenyu Cuiمشاهده پروفایل

دانشیار

• Financial Engineering
• Insurance Analytics
• Operations Research
• +۵ مورد دیگر

Zhenyu Cui is an Associate Professor of Financial Engineering at the School of Business, Stevens Institute of Technology. He holds a PhD in Statistics from the University of Waterloo and a BS in Actuarial Science from the University of Hong Kong. His research focuses on financial engineering, insurance analytics, and operations research, with notable contributions to stochastic volatility models, Monte Carlo simulation, and financial systemic risk. He has published in top journals such as Mathematical Finance, SIAM Journal on Financial Mathematics, and European Journal of Operational Research. Education: PhD (2013, Statistics, University of Waterloo); MS (2010, Quantitative Finance, University of Waterloo); BS (2008, Actuarial Science, University of Hong Kong). Research Interests: Financial Systemic Risk, Monte Carlo Methods, Stochastic Volatility, Option Pricing, and Risk Management. His work bridges theoretical finance and practical applications, emphasizing robust pricing techniques and computational methods. Grants include leadership roles in NSF-funded projects on quantum algorithms for financial risk management and collaborations with institutions like Accenture and the Society of Actuaries. Awards include recognition for top-cited articles and editorial excellence.

Hye-Won Kangمشاهده پروفایل

دانشیار

• Stochastic Modeling
• Mathematical Biology
• Biochemical Reaction Networks
• +۷ مورد دیگر

Hye-Won Kang is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). Her research focuses on stochastic modeling of biochemical systems, particularly in understanding complex biological processes such as enzyme clustering, microRNA signaling pathways in lung cancer and COPD, and multiscale reaction-diffusion systems. She holds a Visiting Research Fellowship from Merton College, University of Oxford. Her work integrates mathematical analysis, simulation, and experimental data to address challenges in systems biology. Key research areas include: Multiscale approximations for stochastic chemical networks Stochastic analysis of reaction-diffusion processes Modeling enzyme kinetics in metabolic diseases Gene regulatory networks and pattern formation Recent contributions include studies on glucosome condensate formation (2024), chemical systems with limit cycles (2023), and the role of microRNAs in lung cancer (2013). Her research has been published in high-impact journals such as Physical Biology , Bulletin of Mathematical Biology , and SIAM Multiscale Modeling and Simulation . She has advised PhD student Luan Chip Nguyen and has taught advanced courses in mathematical biology, statistics, and differential equations at UMBC since 2013.

Magda Peligradمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Limit Theorems
• +۲ مورد دیگر

Magda Peligrad is the Charles Phelps Taft Professor in the Department of Mathematical Sciences at the University of Cincinnati. She specializes in probability theory and stochastic processes, particularly limit theorems for dependent structures. Her research has been continuously supported by NSF and NSA grants since 1996. Education: Ph.D. from Center of Statistics of the Roumanian Academy of Science (1981) Her research focuses on modeling dependence structures through martingale approximation techniques and spectral analysis of stochastic processes. This work bridges probability theory, statistics, and ergodic theory, with applications to random matrix theory and Gaussian approximations. Her publications demonstrate consistent focus on limit theorems, Markov processes, and dependence structures across random fields. Recent work emphasizes non-stationary systems and refined convergence rates for complex stochastic systems. Major Awards: Fellow of Institute of Mathematical Statistics (1995) Distinguished Research Taft Professor (2003) University of Cincinnati Graduate Fellow (2017) As primary advisor, she has guided 8 PhD students and hosted 2 postdoctoral researchers. Current grant support includes NSF funding for 'Asymptotic results for stochastic processes via new projective methods' (2021-2024).

Nageswari Shanmugalingamمشاهده پروفایل

استاد

• Geometric Analysis
• Metric Measure Spaces
• Potential Theory
• +۴ مورد دیگر

Nageswari Shanmugalingam is a Professor and Distinguished Research Professor (STEMM) in the Department of Mathematical Sciences at the University of Cincinnati's College of Arts and Sciences. She specializes in geometric analysis, focusing on metric measure spaces, potential theory, and quasiconformal maps. Her research explores connections between analytic inequalities and geometric properties, with contributions to functions of bounded variation (BV) and Besov spaces. Education: PhD from the University of Michigan (1999), BSc from the University of Rochester (1994). Research interests include geometric implications of Poincaré inequalities, BV functions, and boundary theories in potential analysis. She has secured significant grants from NSF and Simons Foundation, totaling over $1.2M, supporting projects on potential theory, metric geometry, and hyperbolicity. Her publications span over 100 peer-reviewed articles, including works on Mazurkiewicz distance, BV trace theorems, and Besov spaces. Awards include the 2008 McMicken Dean's Award and 2017 Graduate School Fellowship. Grants include NSF funding for 'Notions of curvature and potential theory' (2024–2027) and 'Role of Gromov hyperbolicity' (2021–2024). She actively contributes to editorial boards of journals like Revista Matemática Complutense and serves on international conference committees.

Antonio Calomarde Palominoمشاهده پروفایل

دانشیار

• Low Power Digital Circuits
• Soft Error Resilient Circuits
• Integrated Circuit Design
• +۴ مورد دیگر

Antonio Calomarde Palomino is an Associate Professor at the Department of Electronic Engineering, Polytechnic University of Catalonia (UPC), located at the Higher Technical School of Industrial Engineering of Barcelona. He holds a B.S. in Telecommunications Engineering (UPC, 1986), an M.S. in Electronic Engineering (Universitat Autònoma de Barcelona, 1993), and a Ph.D. in Electronic Engineering (UPC, 2007) with honors. His research focuses on low-power/high-performance digital circuits and soft-error resilient designs, addressing challenges in semiconductor technology and radiation-hardened electronics. Key research areas include FinFET-based memory cells, RRAM modeling, and radiation mitigation strategies for advanced nanoelectronics. He contributes to the High Performance Integrated Circuits and Systems Design (HIPICS) Group, advancing circuit design methodologies and educational initiatives such as integrating autonomous vehicle systems into undergraduate curricula. His work bridges theoretical advancements with practical applications in automotive electronics and embedded systems. Publications span topics from memristor-based associative memory to approximation techniques in object detection systems, reflecting a balance between fundamental research and applied engineering. His recent work emphasizes variability-aware design and energy-efficient architectures, as seen in 2023-2024 studies on RRAM models and automotive case studies. Calomarde actively organizes academic events like the 2022 ETS conference and develops educational materials for digital electronics and audiovisual systems courses. His contributions to both technical research and pedagogical innovation solidify his role as a key figure in electronic engineering education and innovation.

Alp Yurtseverمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Data Science
• +۳ مورد دیگر

Alp Yurtsever serves as an Assistant Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden, where his research pioneers end-to-end optimization frameworks bridging theoretical modeling and practical algorithm design for data science challenges. His work fundamentally rethinks traditional black-box system approaches by integrating problem formulation with solution methodologies. His academic journey includes: PhD in Computer and Communication Sciences from École Polytechnique Fédérale de Lausanne (EPFL) under Prof. Volkan Cevher Postdoctoral fellowship at MIT's Laboratory for Information and Decision Systems (LIDS) with Prof. Suvrit Sra Dual BSc in Electrical and Electronics Engineering and Physics from Middle East Technical University Yurtsever's research centers on optimization theory for machine learning, with groundbreaking contributions in federated learning systems, convex programming, and scalable semidefinite solvers. He champions a unified perspective where modeling and algorithmic development inform each other, yielding methods with proven theoretical guarantees and real-world efficiency. His work particularly addresses communication bottlenecks in distributed systems and non-convex landscapes in neural network training, with applications spanning privacy-preserving AI and edge computing. Analysis of his publication trajectory reveals dominant themes in federated optimization and Frank-Wolfe variants, where he consistently develops communication-efficient algorithms for heterogeneous device networks. His recent work demonstrates increasing sophistication in handling multi-tier architectures and personalized learning objectives, while maintaining rigorous convergence guarantees. The integration of quantum-classical hybrid approaches in his 2022 ECCV paper signals expanding methodological boundaries. His scientific recognition includes: Thesis Distinction for PhD dissertation "Scalable Convex Optimization Methods for Semidefinite Programming" (EDIC program committee) Yurtsever actively contributes to Umeå University's Mathematical Programming Group and Statistical Learning for Spatio-Temporal Data initiative, though specific grant awards remain undisclosed in available materials. His collaborative network spans EPFL, MIT, and multiple European institutions as evidenced by co-authorship patterns. While no formal advisees are listed, his publications show mentorship of junior researchers through joint conference presentations. His laboratory operations are embedded within Umeå University's Department of Mathematics and Mathematical Statistics in the MIT-huset building, leveraging institutional resources for high-performance optimization research while maintaining strong international connections through his post-PhD affiliations.

Carlos Cardonhaمشاهده پروفایل

استادیار

• Discrete optimization
• Mixed-integer linear programming
• Combinatorial optimization
• +۶ مورد دیگر

Carlos Cardonha is an Assistant Professor in the Department of Operations and Information Management at the University of Connecticut School of Business since 2019. He holds a Ph.D. in Mathematics from Technische Universität Berlin (2011) and degrees in Computer Science from the University of São Paulo (B.Sc. 2004, M.Sc. 2006). Previously, he worked as a Research Staff Member at IBM Research – Brazil (2012–2019). His research focuses on discrete optimization, approximation algorithms, and applications of machine learning and mathematical programming to operations research problems. He teaches courses such as OPIM 5641 (Business Decision Modeling) and OPIM 3511 (Business Data Analytics II), supported by DataCamp. Education: Ph.D. in Mathematics, Technische Universität Berlin, Germany (2011) M.Sc. in Computer Science, University of São Paulo, Brazil (2006) Bachelor in Computer Science, University of São Paulo, Brazil (2004) Research Interests: His work spans analytics, optimization, and theoretical computer science, with a focus on mixed-integer linear programming, combinatorial optimization, and algorithm design. He applies these techniques to real-world problems in scheduling, resource allocation, and machine learning model optimization. Grants & Advising: No specific grants or advisees are listed in the provided materials. Labs/Teams: Not explicitly mentioned in the text.

David Silvesterمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Science and Engineering
• Incompressible Flow
• +۲ مورد دیگر

Professor David Silvester holds the Chair in Applied Mathematics at the University of Manchester's Department of Mathematics. His research focuses on numerical analysis and computational science, with expertise in finite element methods, incompressible flow, and stochastic PDEs. He leads research groups in Data Science, Numerical Analysis, and Uncertainty Quantification. His educational background includes a BSc in Mathematics (First Class Honours) and PhD in Numerical Analysis from UMIST. He has supervised 14 PhD students to completion and developed widely used computational tools like the IFISS MATLAB toolbox for fluid dynamics. His research contributes to UN Sustainable Development Goals through advanced computational methodologies. Professor Silvester's awards include the prestigious SIAM Fellowship (2023). He serves on multiple EPSRC panels and advises the National Academy for Mathematical Sciences. His open-source software IFISS has been downloaded over 77,000 times and supports teaching globally.

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.

Jim Daiمشاهده پروفایل

استاد

• Applied Probability
• Operations Research
• Health Systems
• +۳ مورد دیگر

Jim Dai is the Leon C. Welch Professor of Engineering at Cornell University's School of Operations Research and Information Engineering (ORIE). He previously held the Chandler Family Chair at Georgia Institute of Technology (1990–2012) and serves as a Special Term Professor at Tsinghua University and a Visiting Professor at National University of Singapore. His research focuses on applied probability models for service systems, healthcare operations, and transportation networks. He holds a B.S. and M.S. from Nanjing University and a Ph.D. in Mathematics from Stanford University. Research interests include stochastic models for resource allocation in processing networks, healthcare systems, and ridesharing networks. His work emphasizes fluid and diffusion models, Stein’s method, and reinforcement learning. Awards include the Erlang Prize (1998), ACM SIGMETRICS Achievement Award (2018), and the John von Neumann Theory Prize (2024). He is Editor-in-Chief of Mathematics of Operations Research and a Fellow of INFORMS and the Institute of Mathematical Statistics. Teaching spans Markov chains, stochastic processes (both undergraduate and graduate levels), and optimization. Service roles include editorial positions at journals like Stochastic Systems and advisory roles at Singapore University of Technology and Design.

Jacob Carruthمشاهده پروفایل

پژوهشگر

• Adaptive Control Theory
• Whitney Extension Problems
• Fourier Analysis
• +۳ مورد دیگر

Jacob Carruth is a Postdoctoral Research Associate in the Department of Mathematics at Princeton University. His academic focus includes adaptive control theory, Whitney extension problems, and Fourier analysis. He earned his PhD in 2019 from the University of Texas at Austin under the advisement of Arie Israel. His research explores theoretical control systems and mathematical analysis, with notable contributions to adaptive control strategies for unknown linear dynamics and extensions of Whitney's theorem in functional spaces. Education: PhD in Mathematics, 2019, University of Texas at Austin (Advisor: Arie Israel) Research Interests: Adaptive control theory with applications to uncertain linear systems Whitney extension problems in Sobolev spaces and geometric analysis Fourier analysis methods in extremal problems and approximation theory His work bridges pure mathematics and applied control theory, addressing fundamental questions in system dynamics and function extensions. Recent Research Trends: His publications emphasize regret minimization strategies, optimal control under parameter uncertainty, and functional analysis techniques. Key themes include developing bounded-regret algorithms for linear systems and advancing understanding of extension operators for function spaces. Lab/Team Affiliations: Currently affiliated with Princeton University Mathematics Department's research groups in analysis and control theory.

Kees Jan Roodbergenمشاهده پروفایل

استاد

• E-commerce Logistics
• Warehouse Optimization
• Vehicle Routing
• +۳ مورد دیگر

Prof. dr. Kees Jan Roodbergen is a Professor of Quantitative Logistics at the University of Groningen and Director of the Center for Operational Excellence. He holds a MSc in Econometrics & Operations Research from the University of Groningen and a PhD from Erasmus University Rotterdam. His research focuses on E-commerce Logistics, Warehouse Optimization, Vehicle Routing, and Inventory Management, applying Operations Research techniques to practical challenges. He has led projects like PIONEER (2019), e-global (2017), and 'Towards Virtual Ports' (2015), securing external funding from TKI Dinalog, NWO, and Interreg IVB. He serves on the Scientific Advisory Council of the Global Cold Chain Foundation and has been involved in jury roles for e-commerce awards. His teaching spans courses on Value Chain Management, Facility Design, and Discrete Operations Research. Education: MSc from University of Groningen (Econometrics & OR), PhD from Erasmus University Rotterdam. Ancillary roles include editorial advisory boards for professional magazines and program boards for TRAIL Research School and Graduate Program Operations Management & Logistics. Research contributions emphasize practical applications, such as warehouse optimization methods integrated into management systems. Grants and Projects: Highlighted projects include PIONEER (TKI Dinalog), e-global (NWO), and collaborations with industry. His work addresses sustainable logistics, digital transformation, and operational efficiency. He has published in journals like Operations Research and Transportation Science.

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.