جست‌وجوی استاد و پژوهشگر

David A. Baderمشاهده پروفایل

استاد

• Data Science
• Parallel Computing
• High-Performance Computing
• +۳ مورد دیگر

David A. Bader is a Distinguished Professor and founder of the Department of Data Science at NJIT's Ying Wu College of Computing, and Director of the Institute for Data Science. He holds a Ph.D. in Electrical Engineering from the University of Maryland (1996), an M.S. in Electrical Engineering from Lehigh University (1991), and a B.S. in Computer Engineering from Lehigh University (1990). His research focuses on large-scale graph analytics, parallel computing, and high-performance computing frameworks like Arachne and Arkouda. Dr. Bader has received prestigious awards including the IEEE Sidney Fernbach Award, ACM Fellowship, SIAM Fellowship, and AAAS Fellowship. His work emphasizes scalable algorithms for big data, quantum computing applications, and real-time graph processing systems. He leads research on dynamic graph algorithms, GPU optimization, and interdisciplinary data science projects. Education: Ph.D., Electrical Engineering, University of Maryland, 1996 M.S., Electrical Engineering, Lehigh University, 1991 B.S., Computer Engineering, Lehigh University, 1990 His research interests span parallel computing paradigms, graph theory applications, and high-performance computational systems. Recent work includes quantum interior point methods, scalable graph analytics frameworks, and efficient string processing algorithms. He actively contributes to open-source projects like GraphBLAS and LAGraph. Key Awards: 2022 Innovation Hall of Fame, University of Maryland 2021 ACM Fellow 2021 IEEE Sidney Fernbach Award 2010 IEEE Fellow Bader’s lab focuses on advancing big data infrastructure and has developed tools for large-scale graph analysis, including Arachne and Arkouda. His collaborations bridge academia and industry, addressing challenges in cybersecurity, climate modeling, and bioinformatics.

Steven Ruuthمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Analysis
• Partial Differential Equations
• +۴ مورد دیگر

Steven Ruuth is a Professor in the Department of Mathematics at Simon Fraser University (SFU), within the Faculty of Science. He specializes in numerical methods for partial differential equations (PDEs), particularly those involving complex geometries and discontinuous solutions. His work bridges applied mathematics and computational science, focusing on interface dynamics, geometric numerical methods, and efficient algorithm development. Ph.D. in Applied Mathematics, University of British Columbia (1996) NSERC Postdoctoral Fellow and Visiting Assistant Professor at UCLA (1996–1999) His research emphasizes robust numerical techniques for evolving networks of interfaces, implicit-explicit time integration methods, and the closest point method for PDEs on manifolds. Applications span computational fluid dynamics, materials science, and computer vision. He has contributed to advancements in meshfree methods, RBF-FD discretizations, and parallel domain decomposition algorithms. Recipient of the Germund Dahlquist Prize (2011) and CAIMS Research Prize (2020) , he is also an editorial board member for SIAM Journal on Scientific Computing and Numerical Mathematics: Theory, Methods and Applications. Teaching responsibilities include courses such as MATH 260 (Introduction to Ordinary Differential Equations). His research group actively explores cutting-edge computational methods with applications to geometry processing and scientific computing challenges.

Sebastiaan Breedveldمشاهده پروفایل

دانشیار

• Radiation Therapy
• Applied Mathematics
• Decision-Making
• +۳ مورد دیگر

Dr. Sebastiaan Breedveld is an Associate Professor in the Department of Radiation Oncology at Erasmus University Medical Center in Rotterdam, The Netherlands. His research focuses on improving radiation therapy through applied mathematics, multi-criteria optimization, and automated treatment planning. He developed the clinically implemented Erasmus-iCycle algorithm and leads projects like TROTS (Radiotherapy Optimisation Test Set). Education includes a PhD in Medical Physics (cum laude) from Erasmus University Rotterdam and an MSc in Applied Mathematics from Delft University of Technology. Research interests center on developing computational methods for real-time, personalized radiotherapy. Key areas include: Large-scale optimization for treatment planning Deep learning for instantaneous dose prediction Multi-criteria decision analysis for balancing treatment trade-offs Proton therapy and VMAT techniques High-performance computing applications Recent publications demonstrate strong focus on AI-driven automation in brachytherapy, IMPT robustness, and FLASH proton therapy. Work consistently addresses clinical translation of mathematical optimization. Significant scientific awards include: NWO Vidi Grant (2021) for INSTORAD project NWO Veni Grant (2016) MCDM Doctoral Dissertation Award (2015) Erasmus MC Efficiency Grant (2015) PhD with honors (2013) Leads research group supervising 2 post-docs, 11 PhD students, and numerous master/bachelor projects. Major grants support work in proton therapy optimization and automated planning systems. Developed TROTS dataset for benchmarking radiotherapy optimization algorithms and collaborates internationally through projects like BiCycle for automated brachytherapy planning.

• Optimization
• Combinatorics
• Tropical Geometry
• +۳ مورد دیگر

Xavier Allamigeon is a researcher at INRIA and CMAP (Centre de Mathématiques Appliquées) at École Polytechnique, where he also serves as a part-time associate professor in the Applied Mathematics Department. His research focuses on optimization, combinatorics, tropical geometry, game theory, and formalization of mathematics in proof assistants, with a particular emphasis on computational aspects of these fields. He earned his Ph.D. in 2009 from École Polytechnique with a thesis on static analysis of memory manipulations and tropical polyhedra. His work bridges theoretical mathematics and practical applications, including contributions to emergency call center modeling and epidemic monitoring during the COVID-19 pandemic. Key awards include the 2022 Prix Inria–Académie des sciences, the 2021 SIAM SIGEST award, and the 2010 Gilles Kahn Prize for Best Dissertation in Computer Science. His research has led to the development of tools like the Tropical Polyhedra Library (TPLib) and formal proofs in proof assistants such as Coq. He has supervised multiple Ph.D. students and contributed to funded projects such as URGE (2023–2026) and CAPPS (2018–2021). His interdisciplinary work spans optimization, formal methods, and applied mathematics, with applications in healthcare systems and computational geometry.

• Operational Research
• Mathematical Optimization
• Simplex Algorithms
• +۲ مورد دیگر

Richard J. Caron is a Professor at the University of Windsor's College of Engineering, specializing in operational research and mathematical optimization. He has supervised students including Nooshin Nekoiemehr, Qiqi Zhang, and Anqi Chen. His team won the first practice prize at the national conference of the Canadian Operational Research Society. His research focuses on mathematical programming, constraint analysis, and optimization algorithms. Recent publications examine simplex algorithms, matrix inequalities, and probabilistic methods for extreme point identification. Dr. Caron's awards include the First Practice Prize from the Canadian Operational Research Society for developing cargo optimization tools for Air Canada. His work demonstrates consistent contributions to optimization theory and computational mathematics throughout his career.

Christopher Jazwaمشاهده پروفایل

دانشیار

• Human-environmental dynamics
• Human behavioral ecology
• Island and coastal archaeology
• +۵ مورد دیگر

Christopher Jazwa, Ph.D., is Associate Professor and Undergraduate Program Director in the Department of Anthropology at the University of Nevada, Reno, where he also co-directs the Human Paleoecology and Archaeometry Laboratory. His research integrates human behavioral ecology and archaeometry to understand long-term human-environment interactions, focusing on island and coastal settings. Education Ph.D., Anthropology, The Pennsylvania State University, 2015 Research Interests Jazwa’s work centers on how environmental change and cultural factors shape human settlement and subsistence strategies. He employs ecological models—especially the Ideal Free Distribution and Ideal Despotic Distribution—to analyze settlement expansion, territoriality, and resource competition. Methodologically, he specializes in zooarchaeology, stable-isotope analysis of marine shells, and radiocarbon dating. Current projects include: California’s Channel Islands (since 2004): reconstructing 13,000 years of maritime hunter-gatherer settlement and paleoenvironmental change. Cabo Pulmo, Baja California Sur, Mexico (since 2018): documenting chronology and human use of an understudied coastal desert. Loukkos Valley, Morocco (since 2016): applying ecological models to Classical-period settlement dynamics. Recent Publication Themes Jazwa’s 2020–2022 articles emphasize (1) refined radiocarbon protocols for early American human remains, (2) isotopic approaches to seasonality and paleoclimate using California mussel shells, (3) large-scale settlement modeling in Bronze Age Greece and Roman Morocco, and (4) archaeological evidence for ritual ingestion of hallucinogens. Collectively, these works advance methodological rigor in paleoenvironmental reconstruction and demonstrate the explanatory power of behavioral-ecological theory across diverse cultural and temporal contexts. Laboratory & Team As co-director of the Human Paleoecology and Archaeometry Laboratory (with Dr Christopher Morgan), Jazwa maintains facilities for zooarchaeological, isotopic, and GIS analyses and welcomes graduate and undergraduate collaborators interested in similar questions.

• Architectural Engineering
• Construction Management
• 3D Modeling
• +۳ مورد دیگر

Dr. Kosei Ishida is an Associate Professor at the School of Creative Science and Engineering, Waseda University, specializing in architectural and construction engineering. With a Doctor of Engineering degree from Waseda University, he focuses on improving construction workflow efficiency through 3D modeling, BIM integration, and motion analysis techniques. Faculty of Science and Engineering (2012-2014) Graduate School of Creative Science and Engineering (2014) School of Science and Engineering (2009) His research spans architectural planning, city planning, and information-based construction methods, with particular emphasis on work efficiency analysis, 3D laser scanning for structural assessment, and BIM technology adoption in construction companies. He has developed factor analysis frameworks for construction time studies and pioneered pre-cut component design methodologies using 3D scanning. Research trends show strong focus on: 3D modeling and point cloud analysis Construction process optimization Building lifecycle management systems Work environment assessment Material usage efficiency Digital fabrication techniques Scientific awards include: ISARC Best Paper Award (2012) Ono Azusa Memorial Award (2013) Multiple Building Construction Symposium Outstanding Presentation Awards (2014-2015) He teaches courses in construction measurement, building systems, architectural design, and construction management, while maintaining active involvement in industry committees and research projects including digital fabrication methodologies for building repairs.

Felix Haehlمشاهده پروفایل

پژوهشگر ارشد

• Theoretical Physics
• Quantum Gravity
• String Theory
• +۶ مورد دیگر

Felix Haehl is a UK Research and Innovation (UKRI)/ERC Frontier Principal Research Fellow at the University of Southampton, leading the research team for the ERC-funded project 'Chaos and Coarse-Graining in Holography' since 2022. He is also Director of the Southampton Theory, Astrophysics and Gravity (STAG) Research Centre since 2023. His academic background includes a PhD from Durham University (2016), and MSc/BSc degrees from ETH Zurich (2012/2010), with research visits at UC Santa Barbara, Perimeter Institute, and UC Davis. His research focuses on theoretical physics, particularly string theory, AdS/CFT duality, quantum gravity, and non-equilibrium systems. Central themes include thermality, entropy, real-time dynamics, and chaos. He actively supervises PhD students in Physics and Mathematical Sciences. His work spans topics such as black hole formation, quantum chaos in conformal field theories, and holographic methods applied to condensed matter systems. Key research projects include exploring chaotic conformal field theories via spectral statistics and arithmetic chaos, as well as studying Euclidean wormholes through quantum chaos and number theory. He has published extensively in high-impact journals like Journal of High Energy Physics and Physical Review D , with recent work on operator growth in black holes and spin liquid-glass crossovers. Felix’s research integrates advanced mathematical techniques, including modular invariance, random matrix theory, and Schwinger-Keldysh formalism, to address fundamental questions in quantum gravity and holography. His contributions bridge theoretical frameworks with computational models, such as quantum circuits and collision dynamics in black hole interiors.

Mingda Xuمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Computer Vision
• Robotics
• +۷ مورد دیگر

Mingda Xu is a Research Fellow at the Australian National University (ANU), working in Prof. Stephen Gould's group in collaboration with Seeing Machines Ltd. His research focuses on autonomous systems, combining machine learning, computer vision, and robotics. He holds a PhD in Robotics from Queensland University of Technology (2019–2022) and a Master's in Mathematics from the University of New South Wales (2016–2018). His research interests include unsupervised learning, optimal control, differentiable optimization, and visual localization. Notable projects involve feature-shaping methods for anomaly detection, diffusion models for pose estimation, and model-based control techniques like differentiable dynamic programming. He has also explored applications in visual place recognition and SLAM (Simultaneous Localization and Mapping). Key projects: Unsupervised learning for long-form videos, differentiable DTW for visual place recognition, and discrete-time optimal control. His work has been recognized with a CVPR 2024 Best Paper Award nomination. He collaborates actively with industry and academia, contributing to the development of scalable and robust autonomous systems. He is registered to supervise research students and has contributed to multiple grants focused on advancing robotics and AI technologies. Mingda's lab work integrates robotics, computer vision, and machine learning, emphasizing end-to-end solutions for real-world applications in autonomous systems.

Chuan Heمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Data Science
• +۱۲ مورد دیگر

Chuan He is an Assistant Professor in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA) and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research bridges continuous optimization and machine learning, focusing on algorithmic efficiency and theoretical foundations. Education: Ph.D. in Industrial and Systems Engineering, University of Minnesota, USA (2019–2023) B.S. in School of Mathematical Sciences, Xiamen University, China (2015–2019) His research interests include deep learning, decentralized and large-scale optimization, high-order methods, and applications in healthcare, scientific computing, and engineering. He develops algorithms with strong theoretical guarantees, particularly in nonconvex optimization settings. His work emphasizes improving the speed, reliability, and scalability of machine learning training processes. His recent publications focus on Newton-CG based methods, augmented Lagrangian techniques, and federated learning under constraints. These contributions span top journals in operations research, optimization, and machine learning, reflecting a strong trend toward integrating second-order optimization with practical machine learning challenges. Scientific Awards: No awards explicitly mentioned in the text. Chuan He advises and collaborates within the WASP Mathematics research environment, particularly in the 'Optimisation for machine learning' group. He previously held a postdoctoral position at the University of Minnesota under Professor Ju Sun. His research is supported through institutional affiliations with WASP and Linköping University. He actively contributes to the academic community through conference presentations at INFORMS, SIAM, and NeurIPS workshops. He is involved in the 'Optimisation for machine learning' research group at MAI, which aims to develop more efficient and theoretically sound algorithms for machine learning. The group focuses on replacing heuristic methods with principled approaches, reducing computational costs in training models.

Gregory D Cookمشاهده پروفایل

دانشیار

• Maritime Archaeology
• Shipwreck Archaeology
• Remote Sensing Techniques
• +۷ مورد دیگر

Dr. Gregory D. Cook is an Associate Professor in the Department of Anthropology at the University of West Florida, part of the College of Arts, Social Sciences and Humanities. His research and teaching focus on maritime archaeology, remote sensing, and field methodologies related to historic shipwrecks. Bachelor’s in Anthropology – Indiana University Master’s in Anthropology – Texas A&M University Doctorate in Anthropology – Syracuse University Dr. Cook specializes in maritime archaeology with a focus on shipwreck investigations, underwater excavation, and remote sensing technologies. His work spans colonial-era Spanish fleets, trans-Atlantic trade in West Africa, and 20th-century military maritime operations. He has led significant field projects including the excavation of the Emanuel Point II shipwreck and the search for the HMS Mentor. His recent publications reflect a strong trend in historical maritime archaeology, combining archival research with geophysical surveying to uncover submerged cultural heritage across the Atlantic World and Gulf of Mexico. His work bridges colonial history, African archaeology, and military maritime events. Scientific Awards: U.S. Department of the Interior Secretary's Partners in Conservation Award (2011) Dr. Cook actively mentors students through fieldwork, particularly in underwater archaeological excavations. He has secured competitive grants, including from the National Geographic Society, to support his research. His projects often involve interdisciplinary collaboration and student participation in real-world survey and excavation missions. He leads research initiatives such as the Emanuel Point II excavations and the search for the HMS Mentor, engaging students and contributing to national heritage preservation. These projects are supported by advanced sonar mapping and remote sensing technologies.

Pavlo Krokhmalمشاهده پروفایل

استاد

• Stochastic Optimization
• Decision Making Under Uncertainty
• Risk Analysis
• +۱۲ مورد دیگر

Pavlo Krokhmal is a Professor in the Department of Systems and Industrial Engineering at the College of Engineering, University of Arizona. He serves as the Director of Industrial Engineering and is a member of the Graduate Faculty. He has previously held academic positions at the University of Iowa and the University of Florida. Education: PhD in Operations Research, University of Florida, Gainesville, Florida, United States PhD in Mechanics of Solids and Applied Mathematics, Kyiv National Taras Shevchenko University, Kyiv, Ukraine MS in Applied Mathematics and Mechanics, Kyiv National Taras Shevchenko University, Kyiv, Ukraine His research focuses on stochastic optimization, risk analysis, and decision-making under uncertainty, with applications in financial engineering, network resilience, and renewable energy systems. He also contributes to multidisciplinary optimization and cooperative control. His work bridges applied mathematics, engineering, and operations research. The most recent publications reflect a strong trend in risk-averse optimization under uncertainty, especially in network structures, energy systems, and combinatorial problems. His work integrates advanced mathematical modeling, stochastic programming, and computational algorithms. Topics frequently include risk measures like CVaR, p-cone programming, and PDE-constrained optimization with stochastic inputs. Scientific Awards and Honors: Diploma in the Competition of Young Scientists and Students for the Best Research Project, National Academy of Sciences of Ukraine, Spring 1997 Soros Student Award, International Soros Science and Education Program, Fall 1994 Scholarship for scientific and academic achievements, National Academy of Sciences of Ukraine, Spring 1994 Air Force Summer Faculty Fellowship Award (multiple years: 2011, 2012, 2014, 2018, 2019) NRC Senior Research Associateship Award, National Research Council, Spring 2015 Donald E. Bently Faculty Fellowship of Engineering, University of Iowa, Fall 2013 Recognition for Excellence in Teaching, College of Engineering, University of Iowa (2010, 2013) Dr. Krokhmal has been actively involved in advising graduate students and leading research projects funded by agencies such as the Air Force Office of Scientific Research. His collaborations span across institutions and disciplines, including work with researchers at the University of Florida, University of Iowa, and military research labs. He has served on editorial boards and contributed to academic leadership through journal editorials and peer review. His research is conducted within interdisciplinary teams focusing on optimization, risk modeling, and complex systems. These teams often involve mathematical modeling, algorithm development, and simulation for real-world applications in defense, energy, and infrastructure resilience.

• Optimization
• Energy Systems
• Stochastic Programming
• +۴ مورد دیگر

Devon Sigler is a Researcher IV in Applied Mathematics and Computational Science at the National Renewable Energy Laboratory (NREL). His expertise lies in optimization, with a focus on mixed integer optimization, convex optimization, and stochastic optimization. He applies these techniques to complex energy systems, leveraging decomposition methods and high-performance computing (HPC) to solve large-scale problems in parallel. PhD in Mathematics (University of Colorado Denver) Master in Applied Mathematics (University of Colorado Denver) Bachelor in Mathematics and Philosophy (University of Colorado Boulder) Devon has worked as a Postdoctoral Researcher at NREL (2016–2018) and as a Graduate Teaching Assistant at University of Colorado Denver (2013–2017). His research spans energy system planning under uncertainty, AC optimal power flow, and water distribution network optimization, contributing to 46 research outputs. Current projects include electrified rental car centers with behind-the-meter storage and advanced probability modeling for grid reliability.

Yin Tat Leeمشاهده پروفایل

دانشیار

• Convex Optimization
• Convex Geometry
• Spectral Graph Theory
• +۵ مورد دیگر

Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he has been faculty since 2017 (initially as Assistant Professor until 2022). He also holds a position as Senior Principal Researcher at Microsoft AI since 2024, having previously served as Principal Researcher (2022-2024) and Visiting Researcher (2018-2022) at Microsoft Research. His academic career spans prestigious institutions including MIT, where he completed his PhD in Mathematics. 2024-Now: Member of Technical staff / Senior Principal Researcher in Microsoft AI 2022-2024: Principal Researcher in Microsoft Research 2022-Now: Associate Professor in University of Washington 2017-2022: Assistant Professor in University of Washington 2018-2022: Visiting Researcher in Microsoft Research 2016-2017: Postdoc in Microsoft Research Dr. Lee received his PhD in Mathematics from MIT (2012-2016) and his undergraduate degree in Mathematics from the Chinese University of Hong Kong (2008-2012). His exceptional academic journey was recognized with the MIT Presidential Fellowship and the Charles W. and Jennifer C. Johnson Prize. Lee's research fundamentally advances algorithms across multiple domains, particularly in convex optimization, convex geometry, spectral graph theory, and online algorithms. His work bridges continuous and discrete mathematics to develop state-of-the-art algorithms for fundamental problems in computer science and optimization. Notably, he has developed breakthrough approaches for linear programming, maximum flow problems, and optimization in high-dimensional spaces. His research has evolved from foundational theoretical work to more applied areas including differential privacy and connections to machine learning. Analysis of his recent publications reveals a strong trajectory toward practical applications of theoretical optimization, with significant contributions to differentially private machine learning, efficient sampling methods, and connections between optimization theory and deep learning. His work consistently demonstrates how deep theoretical insights can yield practical algorithmic improvements across computer science. Lee's exceptional contributions have been recognized with numerous prestigious awards including the Packard Fellowship, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, A.W. Tucker Prize, and multiple Best Paper Awards at top theoretical computer science conferences (FOCS, SODA, NeurIPS). He has also received the NSF CAREER Award and MIT's Sprowls Award for his doctoral thesis. Packard Fellowship (2020) Sloan Research Fellowship (2020) Microsoft Research Faculty Fellowship (2019) Best Paper Awards at FOCS, SODA, and NeurIPS A.W. Tucker Prize NSF CAREER Award As an advisor, Lee has mentored PhD students including Haotian Jiang, whose work earned a Best Student Paper award at SODA. His research has been supported by significant grants from NSF and Microsoft Research. Lee actively contributes to the academic community through service on program committees for FOCS, SODA, and other major conferences, as well as organizing workshops on continuous approaches to discrete optimization. He has also taught graduate courses including Theory of Optimization and Continuous Algorithms and undergraduate courses on algorithms. Lee's work bridges theoretical computer science and practical applications, with his recent research expanding into differential privacy for machine learning and connections between optimization theory and deep learning. His collaborative work spans institutions including MIT, Microsoft Research, and the University of Washington, reflecting his position at the intersection of theoretical and applied computer science.

Jean-Rodolphe Rocheمشاهده پروفایل

استاد

• Numerical Analysis
• Domain Decomposition
• Nonlinear Equations
• +۴ مورد دیگر

Professor Jean-Rodolphe Roche is a Full Professor at Université de Lorraine , affiliated with the engineering college Polytech Nancy and the Institute Élie Cartan de Lorraine (IECL). His research focuses on numerical methods for partial differential equations, domain decomposition, nonlinear optimization, and scientific computing. Research Fields : Numerical Analysis, Domain Decomposition, Nonlinear Equations, Scientific Computing, Shape Optimization His recent publications emphasize adaptive numerical techniques for nonlinear boundary value problems, domain decomposition in electromagnetic casting, and radiative-conductive heat transfer systems. Collaborations span institutions in Brazil, France, and international conferences.