جست‌وجوی استاد و پژوهشگر

Sarat Mokaمشاهده پروفایل

مدرس

• Probability Theory
• Data Science
• Statistics
• +۲ مورد دیگر

Sarat Moka is a Lecturer in the School of Mathematics & Statistics at The University of New South Wales and holds an Honorary Lecturer position at Macquarie University. His research spans Probability Theory, Data Science, Statistics, Deep Learning, and Monte Carlo Simulation. Education: PhD (Tata Institute of Fundamental Research, 2017), Master of Engineering (Indian Institute of Science, 2008), Bachelor of Engineering (Andhra University, 2006) Previous Roles: Research Fellow at Macquarie University (2021–2023), ACEMS Postdoctoral Research Fellow at The University of Queensland (2017–2021), Scientist at Indian Space Research Organization (2008–2010) His research integrates theoretical and applied approaches, focusing on rare event simulation, optimization algorithms, and statistical modeling. Recent publications emphasize continuous optimization for machine learning tasks (e.g., best subset selection, change point detection) and Monte Carlo methods. Scientific awards include the prestigious ACEMS Postdoctoral Research Fellowship. His collaborative work involves institutions like UNSW, Macquarie University, and The University of Queensland.

• Biophotonics
• Imaging
• Nanophotonics
• +۷ مورد دیگر

Kevin J. Webb is a Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University (West Lafayette campus). He has been on the faculty since 1990 and specializes in interdisciplinary research spanning physics, mathematics, and engineering applications. His research interests include Biophotonics Nanophotonics Metamaterials Imaging in Scattering Media Optomechanics Electromagnetics Solid State Devices Information Theory Webb's recent publications focus on electromagnetic field control, imaging through scattering media, and biomedical optics. His work combines theoretical and applied approaches to solve complex problems in optical systems. Scientific honors include Fellow of the American Physical Society (APS) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Optical Society of America (OSA) Visiting professorships at MIT, University of Melbourne, and KTH Royal Institute of Technology He founded the Purdue Karate Club in 1990 and remains its chief instructor. His academic affiliations include being an Honorary Professor at the University of Melbourne's School of Physics.

Caroline Fossatiمشاهده پروفایل

استاد

• Signal Processing
• Hyperspectral Imaging
• Medical Imaging
• +۲ مورد دیگر

Caroline Fossati is a Full Professor at Ecole Centrale de Marseille and a member of the Institut Fresnel - GSM Signal Processing lab. Her work bridges signal processing, hyperspectral imaging, and medical diagnostics, with a focus on Alzheimer's disease detection using PET imaging and tensor-based noise reduction techniques. Current Affiliations: Ecole Centrale de Marseille, Institut Fresnel Past Affiliations: Université Paul Cézanne (1996-1997) Her research spans: Medical Imaging: Developing advanced algorithms for Alzheimer's diagnosis through 18F-FDG PET scans and multi-level feature extraction. Hyperspectral Processing: Innovating tensor-based methods for noise reduction, dimensionality reduction, and target detection in complex imaging environments. Optical Engineering: Investigating CMOS sensor design, photolithography simulations, and defect characterization in optical materials. Publications demonstrate expertise in multidimensional signal processing, array modeling, and biomedical applications. While no specific awards are documented in the provided text, her extensive co-authorship network and technical contributions across disciplines underscore her academic impact.

Markus Reiherمشاهده پروفایل

استاد

• Theoretical Chemistry
• Quantum Chemistry
• Electronic Structure Calculation
• +۷ مورد دیگر

Markus Reiher is a Full Professor at the Department of Chemistry and Applied Biosciences, ETH Zurich, where he leads a research group focused on theoretical chemistry since 2006. His work bridges quantum mechanics and computational chemistry, emphasizing electronic structure theory and algorithm development for chemical reaction exploration. BSc in Chemistry (University of Bielefeld, 1995) PhD in Chemistry (University of Bielefeld, 1998) Habilitation in Chemistry (University of Erlangen, 2002) His research spans quantum computing applications in chemistry, machine learning potentials, and advanced methods for reaction networks. Recent publications highlight quantum-classical hybrid models, vibronic structure simulations, and solvent effects in chemical processes. Scientific awards include the ADUC Annual Prize (2004), Emmy Noether Habilitation Award (2003), Golden Owl Award for Teaching (2010), Löwdin Lecture (2018), and Heilbronn-Hückel Lectureship (2023). He was elected to the International Academy of Quantum Molecular Science in 2022.

• Differential-Algebraic Equations (DAEs)
• Interval Methods
• Structural Analysis
• +۷ مورد دیگر

Nedialko Nedialkov is a Professor in the Department of Computing and Software at McMaster University, Faculty of Engineering. His research centers on differential-algebraic equations (DAEs) , interval methods , and computational science , with a focus on rigorous numerical solutions and structural analysis. Contact: nedialk@mcmaster.ca Teaching: Courses include Scientific Computing, Advanced Topics in Computing and Software, and Principles of Programming. Research Highlights: Nedialkov has developed symbolic-numeric methods for DAEs, improved verified ODE integration, and created tools like DAETS for structural analysis. His work spans applications in hybrid systems , control theory , and biomedical computing . Publications Trends: Over the past 15 years, his scholarship emphasizes DAEs, validated numerical methods, and computational modeling. Key subfields include Port-Hamiltonian systems , block triangularization , interval constraints , and automatic differentiation . Labs & Teams: Collaborates with McMaster Experts and contributes to open-source projects for validated numerical computing.

Robert J. Vanderbeiمشاهده پروفایل

استاد

• Optimization
• Linear Programming
• Semidefinite Programming
• +۹ مورد دیگر

Robert J. Vanderbei is a full Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University. He also holds courtesy appointments in Mathematics, Astrophysics, Computer Science, Mechanical and Aerospace Engineering, and Applied Mathematics, and is a member of the Bendheim Center for Finance. He served as department chair from 2005 to 2011. Education: BS in Chemistry and MS in Operations Research and Statistics from Rensselaer Polytechnic Institute (1976) PhD in Applied Mathematics from Cornell University (1981) Vanderbei's research spans optimization, applied mathematics, and astrophysics. He is a pioneer in linear and semidefinite programming, having co-developed the influential HKM algorithm and contributed to robust optimization. His work at Bell Labs led to patented algorithmic enhancements. He has also made significant contributions to space telescope design for exoplanet detection and is an accomplished astrophotographer. His interdisciplinary work bridges theoretical research and real-world applications in finance, engineering, and astronomy. His recent publications reflect a strong trend in interdisciplinary research, combining mathematical optimization with astrophysical applications, data visualization, and educational outreach. Key themes include interior-point methods, robust optimization, space imaging, and 3D astronomical visualization. His work continues to influence both theoretical and applied domains. Author of a widely adopted textbook on Linear Programming Developer of the LOQO nonlinear optimization software Co-author of popular science books: Sizing Up The Universe and Welcome To The Universe in 3D Creator of the 'Purple America' election visualization Vanderbei has been actively involved in academic leadership and public engagement. His research has been supported by various grants, particularly in optimization and space science. He has mentored students and collaborated widely across disciplines, though specific advisees are not listed. He led the ORFE department for six years and continues to contribute to curriculum development and interdisciplinary initiatives. He maintains a strong presence in scientific and public communities through his astrophotography gallery and educational outreach. His Fitzrandolph Observatory supports both personal and educational imaging projects. He was formerly a glider pilot and flight instructor, demonstrating a lifelong passion for aerospace and observational science.

Krzysztof Podgórskiمشاهده پروفایل

استاد

• Stochastic Processes
• Random Spatio-temporal Fields
• Non-Gaussian Stochastic Models
• +۵ مورد دیگر

Krzysztof Podgórski is a Professor and Head of the Department of Statistics at Lund University School of Economics and Management (LUSEM). His research spans applied probability, statistics, and interdisciplinary applications in engineering, finance, and environmental sciences. Key Research Areas: Multivariate non-Gaussian stochastic models Statistical analysis of spatio-temporal random fields Distributions at random crossing events Applications: Mechanical engineering (road modeling) Ocean engineering (wave/ship reliability) Financial econometrics (market linkages, risk analysis) Actuarial sciences (non-Gaussian claims) Methodological Contributions: Include ergodic theory of stochastic processes, extreme value theory, and computational statistics. His recent work explores functional data analysis with periodic splines and matrix variate distributions. Collaborative Networks: Extensive partnerships across theoretical and applied disciplines, with projects involving road dynamics, stochastic fields, and uncertainty quantification.

• Heterogeneous computing systems
• High-level synthesis (HLS)
• Hardware specialization for machine learning
• +۳ مورد دیگر

Zhiru Zhang is a Professor at Cornell University in the School of Electrical and Computer Engineering , leading research at the Computer Systems Laboratory . His work focuses on algorithms, methodologies, and design automation tools for heterogeneous computing systems. Recent publications emphasize high-level synthesis (HLS), hardware specialization for machine learning, and programming models for software-defined FPGAs. Education: Ph.D. in Computer Science, UCLA B.S. in Computer Science, Peking University M.S. in Computer Science, UCLA Research Interests: Heterogeneous computing systems High-level synthesis (HLS) optimization FPGA-based hardware acceleration Sparse data format compilers Machine learning for electronic design automation Scientific Awards: IEEE Fellow Intel Outstanding Researcher Award NSF CAREER Award DARPA Young Faculty Award Multiple Best Paper Awards at ASPLOS, ISPD, FPGA, AutoML, FCCM Research Group: Mentors 12 current students including Jordan Dotzel, Jie Liu, and Grace Dinh, with 14 alumni now at institutions like AWS AI, NVIDIA, Google DeepMind, and Microsoft. His lab develops tools like UniSparse for sparse format customization, presented at OOPSLA'24 and IEEE CAL.

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +۱۰ مورد دیگر

Robert Krauthgamer holds The Harry Weinrebe Professorial Chair of Computer Science at The Weizmann Institute of Science, where he also serves as Department Head of the Department of Computer Science & Applied Mathematics within the Faculty of Mathematics and Computer Science. His distinguished career includes editorial leadership as Editor-in-Chief of SIAM Journal on Computing (2019-2025) and significant service on numerous program committees for major theoretical computer science conferences. Dr. Krauthgamer's primary research focuses on Analysis of Algorithms , with particular expertise in Data Analysis and Massive Data Sets, Combinatorial Optimization, Approximation Algorithms and Hardness of Approximation, Average-case Analysis and Heuristics, Embeddings of Finite Metrics, and Routing and Peer to Peer networks. His work also spans broader areas of Discrete Mathematics and High-Dimensional Geometry, reflecting his deep mathematical approach to algorithmic problems. His recent publications reveal a strong emphasis on sparsification techniques, high-dimensional computational geometry, streaming algorithms, and graph-theoretic problems. Throughout his career, Krauthgamer has supervised numerous graduate students at both MSc and PhD levels, with recent theses addressing cutting-edge topics in algorithm design and analysis. His service contributions include steering committee roles for major conferences like SODA, ESA, and HALG, as well as committee membership for the prestigious Gödel Prize (2019-2021). As an educator, he regularly teaches advanced courses including Randomized Algorithms and Sublinear Time and Space Algorithms, often in collaboration with other leading researchers. He also organizes Weizmann's TheoryLunch and has coordinated the Foundations of Computer Science seminar for many years, demonstrating his commitment to fostering academic community and intellectual exchange.

Michael C. Ferrisمشاهده پروفایل

استاد

• Optimization
• Mathematical Programming
• Algorithm Development
• +۴ مورد دیگر

Michael C. Ferris is the John P. Morgridge Chair and Jacques-Louis Lions Professor of Computer Sciences at the University of Wisconsin–Madison, with courtesy appointments in Mathematics and Industrial & Systems Engineering. He serves as Director of Hub Central, Wisconsin Institutes for Discovery. PhD in Mathematics from University of Cambridge (1989) Research focuses on optimization algorithms, mathematical programming, and applications spanning energy systems, transportation, environmental engineering, and biomedical domains His scholarly work includes 15+ recent publications on topics such as: Stochastic optimization for energy markets Complementarity problems in ecological systems Power flow formulations with sparse matrices Decision support tools for Great Lakes restoration Game-theoretic models of hydro-thermal electricity systems On-chip network optimization As a SIAM and INFORMS Fellow , he has received prestigious honors including the Beale-Orchard-Hays Prize, NSF Presidential Young Investigator Award, and Guggenheim Fellowship. His methodological contributions include software tools like PATH , NLPEC , and EMP for large-scale optimization problems, with applications ranging from cancer treatment planning to smart grid development .

• Machine Learning
• Image Processing
• Evolution Strategies
• +۲ مورد دیگر

Oswin Krause is an Associate Professor at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning . His research focuses on applying machine learning techniques to diverse domains including quantum computing, medical imaging, and astrophysics. Quantum dot array optimization and Coulomb diamond estimation Medical image analysis for clinical applications Evolutionary optimization algorithms and deep learning Neural networks for astronomical data interpretation Recent publications demonstrate a strong emphasis on quantum device calibration (2025), medical outcome prediction (2024), and algorithmic improvements in optimization (2022-2023). While no specific scientific awards are mentioned in the data, his work appears in journals like Physical Review Applied and Medical Image Analysis . Collaborations extend across physics, medicine, and computer science disciplines.

Jacek Gondzioمشاهده پروفایل

استاد

• Large Scale Optimization
• Interior Point Methods
• Sparse Matrix Methods
• +۳ مورد دیگر

Jacek Gondzio is a Professor in the School of Mathematics at the University of Edinburgh. He received his M.Eng. in Electronics (1983) and PhD in Automatic Control and Robotics (1989) from Warsaw University of Technology. His career includes positions at the Polish Academy of Sciences (1989–1993), University of Geneva (1993–1998), and the University of Edinburgh since 1998, where he progressed from Lecturer to Professor. Research Interests: Gondzio's work spans large-scale optimization techniques, including interior point methods, sparse matrix computations, parallel algorithms, and applications in finance and engineering. Key focus areas include: Development of efficient solvers (HOPDM, OOPS) for linear/quadratic/nonlinear programming Matrix-free methods and preconditioning for massive-scale problems Applications in quantum information, tomography, structural design, and financial planning Publication Trends: His recent articles emphasize scalable algorithms for optimization, including proximal methods for semidefinite programming, interior-point innovations, and applications in medical imaging and transport. Work frequently integrates regularization, decomposition techniques, and structure-exploiting linear algebra. Awards: EUROPT Fellow (2019) for contributions to continuous optimization Grants & Advising: Current projects include EPSRC-funded work on building structure optimization (EP/N019652/1), Google-funded LP solvers, and risk modeling with Standard Life Investments. He has supervised 16+ PhD students on topics ranging from interior point methods to machine learning optimization. Software includes HOPDM, PDCGM, and the parallel solver OOPS. Leadership: Organizes workshops on optimization (e.g., COA, Advances in Preconditioners series) and serves on editorial boards for Mathematical Programming Computation , Computational Optimization and Applications , and other leading journals.

Yixiang Fangمشاهده پروفایل

دانشیار

• Data Management
• Data Mining
• Artificial Intelligence
• +۸ مورد دیگر

Yixiang Fang is an Associate Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. Previously, he was a Research Associate at the School of Computer Science and Engineering, the University of New South Wales (UNSW) during 2018-2020, working with Prof. Xuemin Lin. He received his PhD from the Department of Computer Science at the University of Hong Kong (HKU) in 2017, advised by Prof. Reynold Cheng, and was a visiting scholar at Nanyang Technological University in 2016. Dr. Fang's research focuses on data management, data mining, and artificial intelligence over big data, particularly big graph data. His work centers on community search, densest subgraph discovery, path/connectivity computation, and LLM + Graph (including graph-based retrieval augmented generation). He has made significant contributions to the field, particularly in cohesive subgraph search and densest subgraph discovery, with his works on community search being well-recognized by leading IT companies like Huawei and ByteDance. His research has resulted in numerous publications in top-tier conferences and journals including SIGMOD, VLDB, ICDE, NeurIPS, WWW, AAAI, IJCAI, TODS, VLDBJ, and TKDE. Notably, his SIGMOD 2020 paper was selected as one of the Best Papers and was awarded the 2021 ACM SIGMOD Research Highlight Award. 2025 CUHK-Shenzhen Teaching Achievement Award (Excellent Prize) 2021 ACM SIGMOD Research Highlight Award Best Paper Award, International Conference on Spatial Data and Intelligence (SpatialDI) 2021 Presidential Young Fellow at CUHK-Shenzhen, 2021 One of the Best Papers, SIGMOD 2020 CCF-Ant Research Fund (2025) Dr. Fang has advised numerous students including PhD candidates, MPhil students, and undergraduate researchers. He has received research funding from Huawei Cloud, CCF-Huawei Populus euphratica Fund, and ByteDance. Currently, he serves as an Editorial Board Member for Information Processing & Management (IPM) and has held leadership roles in major conferences including serving as PC co-chair for LLM+Graph@VLDB2025 workshop. He is also the coach of the CUHK-Shenzhen Programming Contest Team.

• Nonlinear Analysis
• Dynamical Systems
• Applied Mathematics
• +۷ مورد دیگر

Hermen Jan Hupkes is a Professor of Non-linear Analysis at the Mathematical Institute of Leiden University . His research bridges mathematical analysis with dynamical systems , focusing on discrete media and reaction-diffusion equations . Key Research Areas: Nonlinear stability of travelling waves in discrete and stochastic systems Functional differential equations of mixed type (MFDEs) in economics and biology Curvature-driven wave propagation on lattices Numerical algorithms for discrete reaction-diffusion models Recent Publication Trends emphasize nonlinear dynamics across discrete spatial structures , including stochastic stability , economic agent-based models , and biological pattern formation . His work on lattice differential equations and MFDEs has advanced tools like exponential dichotomies and Lin's method in discrete settings. Awards: Vici Grant (2025) for innovative research in non-linear analysis Advising and Collaborations include mentoring PhD candidates such as Christian Hamster and Mia Jukic, as well as interdisciplinary collaborations with computational imaging experts and biophysicists.

Luke Mazurمشاهده پروفایل

پژوهشگر ارشد

• Applied Statistics
• Computational Statistics
• Statistical and Quantitative Genetics
• +۳ مورد دیگر

Dr. Luke Mazur is an active Research Fellow at the School of Mathematics and Applied Statistics, University of Wollongong, Australia. His work focuses on computational methods for statistical modeling in agricultural and financial contexts. Education: Bachelor of Mathematics and Economics (First Class Honours, 2014), PhD in progress Key Affiliation: Centre for Bioinformatics and Biostatistics at University of Wollongong (CBaDSSPI) Research Interests: Mazur specializes in optimizing linear mixed models for datasets from sustainable primary industries. His methodology emphasizes: Exploitation of sparse matrix techniques Multi-threaded computational approaches Integration of efficient third-party libraries Modern coding practices for statistical analysis Publishing Trends: His research bridges computational statistics and practical applications, with recent work covering financial network analysis and data privacy techniques. Earlier publications focus on statistical formulations for agricultural trials and multivariate data analysis. Laboratory Affiliation: Currently works with the Computational Biology and Data Science Solutions for Primary Industries (CBaDSSPI) research team.