جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Biometrics
• Deep Learning
• +۱۲ مورد دیگر

Sambit Bakshi is an active academic researcher in the field of computer science, affiliated with the Department of Computer Science and Engineering at the National Institute of Technology Rourkela, India. His research spans computer vision, biometrics, deep learning, and intelligent systems, with a strong emphasis on real-world applications in surveillance, healthcare, agriculture, and IoT. His primary research interests include biometrics (especially ear and periocular recognition), person re-identification , UAV-based image analysis , multimedia forensics , and fog/edge computing . He has contributed significantly to the development of deep learning models for object segmentation, anomaly detection, and secure biometric systems. The recent publications demonstrate a consistent trend in applying deep neural networks , attention mechanisms , and lightweight architectures to solve complex problems in visual recognition and data analytics. His work often appears in high-impact IEEE and ACM journals, reflecting strong technical rigor and relevance. Scientific Contributions: Co-edited special issues in IEEE Access and Image and Vision Computing Contributed to benchmark challenges such as the Unconstrained Ear Recognition Challenge 2019 Developed novel methods in ear biometrics, periocular recognition, and UAV-based segmentation Active in editorial roles and collaborative research across international teams Advising and Collaborative Research: Frequent collaborator with researchers from NIT Rourkela, University of Beira Interior (Portugal), and other international institutions Co-authored over 120 publications, indicating active supervision and mentoring of students Involved in interdisciplinary projects spanning AI, security, and healthcare Labs and Research Groups: Though not explicitly stated, his work suggests involvement in a computer vision and biometrics lab at NIT Rourkela, focusing on AI-driven solutions for societal and industrial applications.

LAU Hoong Chuinمشاهده پروفایل

استاد

• Artificial Intelligence
• Optimization
• Operations Research
• +۵ مورد دیگر

LAU Hoong Chuin is a Full-time Faculty Professor of Computer Science at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). His research focuses on optimization techniques, artificial intelligence, and their applications in urban logistics, healthcare systems, maritime operations, and emergency response. He actively explores quantum computing methods for solving complex combinatorial optimization problems and develops machine learning frameworks for dynamic decision-making systems. Professor Lau’s work spans multiple domains: Quantum Optimization: Applying quantum algorithms to solve knapsack, vehicle routing, and scheduling problems Urban Logistics: Designing collaborative delivery networks and optimizing last-mile services Public Safety: Creating intelligent systems for police patrol scheduling and maritime traffic management Healthcare Operations: Developing nurse staffing models and emergency response frameworks His recent articles emphasize cross-disciplinary applications, combining quantum computing with traditional optimization methods to address real-world challenges such as supply chain disruptions, energy-efficient routing, and dynamic resource allocation. He has advised students on topics ranging from quantum algorithm design to maritime logistics optimization. His research integrates machine learning with large-scale optimization, evident in frameworks like OFFICERS for crime response scheduling and Grand-vision for law enforcement deployment. Current projects explore adaptive algorithms for stochastic systems and AI-driven policy-making in urban environments.

• Catalysis
• Low-temperature catalysis
• Chemical looping
• +۷ مورد دیگر

Takuma Higo is an Assistant Professor at the Faculty of Science and Engineering, Waseda Research Institute for Science and Engineering, Waseda University. His research focuses on developing innovative catalytic processes for energy conversion and environmental protection, with particular expertise in low-temperature catalysis, chemical looping, and electric field-assisted reactions. He has made significant contributions to the fields of CO 2 conversion, hydrogen production, ammonia synthesis, and methane reforming, with 51 publications, 1,233 citations, and an h-index of 23 as of September 2025. Dr. Higo's research interests center on catalyst design and reaction engineering for sustainable energy systems. His work explores how electric fields can dramatically enhance catalytic activity at low temperatures (373-473 K), enabling energy-efficient chemical processes. He investigates surface protonics mechanisms, oxygen carrier materials for chemical looping, and bimetallic catalysts with synergistic effects. His research has important applications for CO 2 utilization, hydrogen economy, and automotive emissions control, with significant potential to reduce industrial energy consumption. Analysis of his publication trends shows a consistent focus on overcoming thermodynamic limitations through innovative approaches like chemical looping and electric field assistance. His work demonstrates how these methods enable high conversion rates at significantly lower temperatures than conventional processes, particularly for CO 2 conversion, methane reforming, and ammonia synthesis. This represents a paradigm shift in catalytic process design toward more sustainable energy systems. Dr. Higo has established himself as a leading researcher in low-temperature catalysis, with publications in high-impact journals including Chemical Science, ACS Catalysis, and Catalysis Science and Technology. His research combines experimental and computational approaches to understand fundamental reaction mechanisms at the molecular level. He collaborates extensively with Professor Yasushi Sekine and other researchers at Waseda University, focusing on advancing catalytic technologies for a sustainable future. His work spans from fundamental surface science to practical applications in energy and environmental technologies, with strong potential for industrial implementation.

Jorge Nocedalمشاهده پروفایل

استاد

• Optimization
• Machine Learning
• Engineering Design
• +۳ مورد دیگر

Jorge Nocedal is the Walter P. Murphy Professor of Industrial Engineering and Management Sciences at Northwestern University, with a courtesy appointment in Engineering Sciences and Applied Mathematics. He directs the Center for Optimization and Statistical Learning. His research focuses on optimization methods, particularly in large-scale, noisy, and uncertain environments, with applications in machine learning, engineering design, and physical sciences. Nocedal co-authored the influential textbook Numerical Optimization and developed widely used optimization software including L-BFGS and KNITRO. Education: B.S. in Physics from the National University of Mexico (UNAM) and Ph.D. in Mathematical Sciences from Rice University. Research emphasizes creating efficient algorithms for complex optimization problems, including derivative-free methods, constrained optimization under noise, and parallel computing. His work bridges theory, algorithm design, and software implementation, aiming to reduce computational costs in machine learning and AI. Scientific awards include the 2012 George B. Dantzig Prize and 2017 Von Neumann Theory Prize, alongside being a SIAM Fellow and US National Academy of Engineering member. Advising: Nocedal has mentored numerous students, including recent advisees Shigeng Sun and Yuchen Lou. His former students hold prominent roles in academia and industry. Research grants and collaborations are extensive but not detailed here. Labs/Teams: Leads the Center for Optimization and Statistical Learning, fostering interdisciplinary research in optimization and data science.

Shuyang Lingمشاهده پروفایل

استادیار

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

Shuyang Ling is an Assistant Professor of Data Science at NYU Shanghai and a Global Network Assistant Professor at the Tandon School of Engineering, New York University. He joined NYU Shanghai in September 2019 as an Assistant Professor Faculty Fellow of Data Science, after serving as a Courant Instructor/Assistant Professor at the Courant Institute of Mathematical Sciences and Center for Data Science at NYU from 2017-2019. Dr. Ling received his PhD in Applied Mathematics from the University of California, Davis in 2017 under the supervision of Thomas Strohmer. He also earned an MS in Statistics from UC Davis in 2016 and completed his undergraduate studies in Mathematics and Applied Mathematics at Fudan University in Shanghai, China in 2012. Dr. Ling's research focuses broadly on the mathematics of data science, with particular interest in tackling inverse problems from engineering applications and extracting meaningful information from large-scale and heterogeneous datasets. His work spans a broad spectrum of subjects including: Optimization (convex and non-convex) Probability and statistics Computational harmonic analysis Numerical linear algebra Signal processing and machine learning theory His recent publications demonstrate a strong focus on synchronization problems, neural collapse phenomena, and optimization landscapes. He has made significant contributions to understanding the theoretical foundations of spectral methods, non-convex optimization, and convex relaxations in data science applications. His work often bridges theoretical guarantees with practical algorithms for problems in signal processing and machine learning. Dr. Ling has received several prestigious awards and grants including the SIAM Student Paper Prize in 2017, Shanghai Eastern Scholar for Young Professionals (2019), Shanghai Rising Star Program (A-type) (2024), and grants from the Natural Science Foundation of Shanghai and National Key R&D Program of China. He is actively involved in mentoring the next generation of researchers, currently advising three Ph.D. students at NYU Shanghai: Jiayang Yin (co-advised with Prof. Mathieu Lauriere), Wanli Hong, and Ziliang Samuel Zhong. He has also supervised numerous undergraduate thesis projects at NYU Shanghai since 2020. Dr. Ling is part of the Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning at NYU Shanghai, where he organizes seminars and reading groups on data science and machine learning.

Giulia Ferrandiمشاهده پروفایل

پژوهشگر

• Statistics
• Linear Algebra
• Optimization
• +۳ مورد دیگر

Giulia Ferrandi is a Researcher and Guest Researcher at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). She completed her PhD under the supervision of Prof. Michiel Hochstenbach (TU/e) and Prof. Rosário Oliveira (IST, Lisbon) as part of the European BIGMATH project. Her research focuses on Statistics, Linear Algebra, and Optimization applied to statistical problems, with contributions to gradient methods, trace ratio problems, and Markov chain analysis. She holds a master’s degree from Università degli Studi di Milano (Italy), specializing in Probability and Statistics. Prior to her academic roles, she briefly taught in high school and worked in industry. Research Interests : Her work bridges numerical linear algebra and optimization, with emphasis on: Development and analysis of gradient methods (e.g., limited memory, harmonic frameworks) Applications of Rayleigh quotients in optimization and eigenvalue problems Robust multigroup classification via trace ratio techniques Statistical analysis of Markov chains for farmland transitions and non-stationary processes Publications : Her recent work demonstrates contributions to: Subspace methods for large-scale trace ratio problems Advancements in unconstrained optimization via limited-memory gradient approaches Integration of Rayleigh quotients into gradient-based algorithms Harmonic frameworks for optimizing stepsizes in numerical methods Collaborations & Grants : Her PhD was funded by the EU’s BIGMATH project. Collaborators include Prof. Michiel Hochstenbach (TU/e) and Prof. Nataša Krejić (University of Novi Sad). No explicit advising roles or grants are listed beyond her doctoral funding. Technical Expertise : Expertise in numerical analysis, statistical modeling, and linear algebra applications.

Luis F. Zuluagaمشاهده پروفایل

دانشیار

• Polynomial Optimization
• Financial Engineering
• Large-scale Optimization
• +۳ مورد دیگر

Luis F. Zuluaga is an Associate Professor in the Department of Industrial and Systems Engineering at Lehigh University, where he also serves as Co-Director of the Financial Engineering Program and is affiliated with the Healthcare Systems Engineering initiative. Previously, he held an Associate Professor position in the Faculty of Business Administration at the University of New Brunswick (Canada). His research focuses on polynomial optimization, large-scale optimization, quantum computing, financial engineering, and truss topology design. He has authored influential papers in top journals like Operations Research , SIAM Journal on Optimization , and Mathematical Programming . Education: Ph.D. in Operations Research, Carnegie Mellon University (2004) M.S. in Industrial Administration, Carnegie Mellon University (2000) M.S. in Industrial Engineering, University of Los Andes (Colombia, 1998) B.S. in Physics and Electrical Engineering, University of Los Andes (Colombia, 1996) Research Interests: Dr. Zuluaga's work spans polynomial optimization frameworks, quantum computing applications (e.g., QUBO formulation for combinatorial problems), and financial engineering. His recent projects include quantum interior point methods for semidefinite optimization and truss topology design for structural stability. His research bridges theoretical advances with real-world applications in energy systems, industrial networks, and risk management. Recent Article Trends: His 2023–2025 publications emphasize quantum computing (e.g., QUBO formulations, Hamiltonian-based algorithms) and optimization under uncertainty (e.g., semidefinite relaxations, nonnegativity reduction techniques). Earlier work includes contributions to truss design optimization and carbon pricing mechanisms. Awards: 2019 Journal of Global Optimization Best Paper Award (with student Xiaolong Kuang) Grants & Impact: His team secured a $2.1M DARPA grant for quantum computing research and a U.S. Army grant for optimization algorithms. Lehigh ISE's $500K+ impact on Pennsylvania’s critical sectors is partly attributed to his work. He advises students like Xiaolong Kuang and collaborates with the Quantum Computing and Optimization Laboratory (QCOL). Labs/Teams: Director of the Quantum Computing and Optimization Laboratory (QCOL), fostering interdisciplinary research in quantum optimization and its industrial applications.

Stephen Wrightمشاهده پروفایل

استاد

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

Stephen Wright is a Professor in the Department of Computer Sciences at the University of Wisconsin-Madison, serving as Department Chair from 2023-2025. He previously held roles at Argonne National Laboratory (1990-2001) and the University of Chicago (2000-2001). His research focuses on computational optimization, with applications in data science, machine learning, and engineering. He co-authored seminal books such as Numerical Optimization (with J. Nocedal) and Optimization for Data Analysis (with B. Recht). Wright leads the Wisconsin Institute for Discovery's research initiatives and has developed widely-used optimization software like PCx and SpaRSA . Key awards include the 2024 George B. Dantzig Prize, 2020 Khachiyan Prize, and SIAM Fellow status since 2011. He has served as editor-in-chief of the SIAM Journal on Optimization and Mathematical Programming, Series B . His teaching includes courses on nonlinear optimization (CS726) and introductory optimization (CS524). Wright’s work bridges theory and practice, emphasizing scalable algorithms and interdisciplinary applications.

Dominique Orbanمشاهده پروفایل

استاد

• Numerical Optimization
• Nonlinear Programming
• Numerical Linear Algebra
• +۴ مورد دیگر

Dominique Orban is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a Ph.D. from FUNDP Namur and INP Toulouse and has established himself as a leading researcher in numerical optimization. His academic affiliations include the Institute for Data Valorization (IVADO) and the Decision Analysis Study and Research Group (GERAD). Professor Orban's research focuses on numerical mathematics, particularly continuous nonlinear optimization, nonlinear systems of equations, and numerical linear algebra. His work involves designing specialized numerical algorithms for optimization problems, with particular interest in degeneracy and ill-posed problems. His research spans theoretical development of algorithms, their implementation in software, and applications to real-world problems such as image reconstruction, optimal structure design, and optimization under differential constraints. Analysis of his recent publications (2021-2025) reveals a strong focus on developing practical optimization algorithms with theoretical guarantees. His work spans multiple areas including nonsmooth optimization, iterative methods for linear systems, regularization techniques, and software implementation in Julia. A notable trend is his increasing focus on developing open-source software tools that make advanced optimization methods accessible to practitioners. Over 160 publications including journal articles, conference papers, and technical reports Multiple publications in top optimization journals each year through 2025 Strong emphasis on both theoretical foundations and practical implementation Increasing focus on Julia-based optimization software development Professor Orban has successfully supervised 9 doctoral students and 13 master's students to completion, demonstrating his commitment to mentoring the next generation of researchers. His supervision style appears to balance theoretical depth with practical implementation skills, preparing students for both academic and industry careers. His research has been supported through various institutional and collaborative grants, enabling him to maintain an active research program with multiple ongoing projects. His contributions to the field include significant software developments such as Krylov.jl, JSOSuite.jl, and DCISolver.jl, which have made advanced optimization techniques more accessible to the broader scientific community. These tools reflect his philosophy of bridging theoretical optimization with practical computational implementation.