جست‌وجوی استاد و پژوهشگر

Jeff Linderothمشاهده پروفایل

استاد

• Large-Scale Numerical Optimization
• Mixed-Integer Nonlinear Programming
• Operations Research
• +۵ مورد دیگر

Jeff Linderoth is the Harvey D. Spangler Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on large-scale numerical optimization, mixed-integer nonlinear programming, and stochastic programming, with applications in energy systems, global routing, and industrial processes. Education: BS in General Engineering (highest honors) from University of Illinois at Urbana-Champaign, MS in Operations Research from Georgia Institute of Technology, PhD in Industrial Engineering from Georgia Institute of Technology. Linderoth's work addresses theoretical and applied challenges in optimization, including developing algorithms for mixed-integer programming, analyzing knapsack polytopes, and creating tools like the Minotaur optimization toolkit. His recent publications explore integer programming techniques for subspace clustering, complementarity constraints, and customized coverage instrumentation. Selected trends in his research include advancements in stochastic programming, orbital branching for symmetric integer programs, and congestion analysis in power systems. His group contributes to optimization software and data-driven libraries like MIPLIB. Scientific Award: Harvey D. Spangler Professor.

Armin Nurkanovićمشاهده پروفایل

استاد

• Optimal Control
• Numerical Methods
• Hybrid Dynamical Systems
• +۴ مورد دیگر

Dr. Armin Nurkanović is an interim professor at the Technical University of Braunschweig's Department of Mathematical Optimization, where he teaches courses on dynamic optimization and numerical methods. Previously, he completed his PhD at the University of Freiburg under Prof. Moritz Diehl, focusing on optimal control of nonsmooth dynamical systems. His research emphasizes numerical methods for hybrid systems, real-time optimization, and applications in robotics and renewable energy systems. He has received the IEEE Control Systems Letters Outstanding Paper Award (2022) and was a finalist for the 2024 European Systems & Control PhD Thesis Award. Education: Bachelor's in Electrical Engineering (University of Tuzla, 2015) Master's in Electrical Engineering and Information Technology (Technical University of Munich, 2018) PhD in Control (University of Freiburg, 2023) Research Interests: Optimal control of hybrid and nonsmooth systems (e.g., Filippov systems, switched systems) Real-time optimization for model predictive control (MPC) Robust control theory and stochastic optimization Applications in robotics and renewable energy systems Teaching & Software: Developed open-source tools nosnoc and nosnoc_py for optimal control Teaching courses on numerical optimization and optimal control at TU Braunschweig Collaborations & Students: Open to academic and industry collaborations Supervises Bachelor's/Master's theses in mathematics, engineering, and computer science

• Process Systems Engineering
• Mathematical Optimisation
• Sustainable Energy Systems
• +۱۰ مورد دیگر

Dr Michael Short is an Associate Professor of Process Systems Engineering at the University of Surrey , affiliated with the School of Chemistry and Chemical Engineering and the Surrey Institute for Sustainability . His research focuses on mathematical optimisation tools for sustainable process design, bioenergy systems, renewable energy integration, and pandemic risk modelling. Current projects include AI-driven biogas production optimisation and carbon-negative chemical synthesis. EPSRC-funded £1.7M project on AI for biogas production Co-I in £5M Supergen Bioenergy Impact Hub Developed open-source DECO2 software for ASEAN decarbonisation His team applies mixed-integer nonlinear programming (MINLP) and machine learning to industrial challenges in pharmaceuticals, aquaculture, and microbreweries. Collaborations span Eli Lilly, Pfizer, and universities in Japan, Malaysia, and Brazil. Supervised projects address grid-scale energy storage, CO2 utilisation, and rural electrification. Scientific Awards : 2021 EPSRC Impact Acceleration Account Commercialisation Fellow Best Speaker Award at 2020 Sustainable Process Integration Lab Conference Editorial Board Member of Journal of Water Process Engineering Recent publications examine direct air capture integration, waste-to-energy brewing processes, and whole-system energy models. Supervises students in distributed energy systems, catalytic processes, and sustainable design.

• 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.

Danial Hamedi Jamaliمشاهده پروفایل

پژوهشگر

• Hydrogen Infrastructure
• Renewable Energy Systems
• MINLP Optimization
• +۳ مورد دیگر

Danial Hamedi Jamali is a doctoral researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany, affiliated with the Process Systems Engineering group under Prof. Kai Sundmacher. He holds an M.Sc. from the University of Tehran and was awarded scholarships for his bachelor’s and master’s studies, along with the Dean’s Scholarship from the University of Saskatchewan. His research focuses on topology optimization of pipeline networks using mixed-integer nonlinear programming (MINLP), with recent emphasis on hydrogen infrastructure design and retrofitting of natural gas grids. His expertise spans MINLP optimization, energy-exergy analysis, and sustainable system design. He has authored over ten peer-reviewed publications (over 1,000 citations) and serves as a reviewer for Elsevier and Springer journals. Collaborations include international research teams addressing renewable energy systems and low-temperature solar technologies. Research highlights include work on hydrogen pipeline networks, biogas utilization frameworks, and solar-based multi-generation systems. Awards include scholarships for academic excellence and the prestigious Dean’s Scholarship. His contributions bridge theoretical optimization with practical applications in energy transition and infrastructure sustainability.

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

Bjarne André Grimstad is an Associate Professor at the Norwegian University of Science and Technology (NTNU). His work focuses on optimization, surrogate modeling, and data-driven approaches for complex systems in chemical and petroleum engineering. Key contributions include advancements in B-spline models, hybrid gray-box systems, and neural network applications for virtual flow metering. Research interests span optimization algorithms, machine learning integration with physical models, and industrial process control. Notable publications address multi-task learning frameworks, identifiability in hybrid models, and the efficacy of gray-box modeling in nonstationary environments. Publications (2024–2015) highlight interdisciplinary work combining mathematical programming with real-world applications in oil & gas production and flow measurement. His 2015 doctoral dissertation explored daily production optimization using surrogate modeling techniques. Outreach activities include academic lectures at international conferences (e.g., IFAC OOGP 2015) and poster presentations on hybrid modeling at Geilo Winter School 2021. Collaborations with industry partners like SINTEF and TU Delft underscore his applied research focus.

Juan Ruizمشاهده پروفایل

مدرس

• Process Systems Engineering
• Optimization
• Global Optimization
• +۳ مورد دیگر

Juan Ruiz serves as a Lecturer in the Department of Chemical Engineering at the University of Texas at Austin, specializing in computational methods for complex process systems optimization. His work bridges theoretical mathematics with practical engineering applications in energy and chemical production systems. Educational Background: Ph.D., Chemical Engineering, Carnegie Mellon University (2011) B.S., Chemical Engineering, Universidad Tecnologia Nacional, Argentina (2003) Research Focus: Dr. Ruiz develops advanced optimization techniques for process systems engineering, with particular expertise in stochastic programming , generalized disjunctive programming , and global optimization algorithms . His research addresses critical challenges in energy dispatch systems and chemical process design through mathematically rigorous computational frameworks that enhance solution efficiency while maintaining theoretical soundness. Publication Trends: Analysis of his 2010-2013 publications reveals a concentrated research trajectory centered on strengthening relaxation methods for non-convex optimization problems. His work consistently appears in high-impact journals like Computers and Chemical Engineering and Applied Energy , demonstrating significant contributions to mathematical programming techniques applicable to power systems and chemical process networks. Scientific Recognition: Mark Dennis Karl Outstanding Graduate Teaching Award, Carnegie Mellon University (2008) Best National Engineering Graduates Award, Argentine National Academy of Engineering (2004) Argentine Chemistry Association Award, Argentine Chemistry Association (2004) Instructional Role: He teaches graduate-level courses including ChE 356/384 Optimization: Theory and Practice and ChE 381P Advanced Analysis for Chemical Engineers, where he integrates his research on computational optimization methods into advanced engineering education.

Ignacio Grossmannمشاهده پروفایل

استاد

• Process Systems Engineering
• Discrete-Continuous Optimization
• Process Synthesis
• +۳ مورد دیگر

Ignacio E. Grossmann is the R.R. Dean University Professor in the Department of Chemical Engineering at Carnegie Mellon University . He obtained a B.S. from Universidad Iberoamericana (1974), followed by M.S. (1975) and Ph.D. (1977) in Chemical Engineering from Imperial College London. Member & Former Director, Center for Advanced Process Decision-Making (industrial consortium with 20+ companies) Foreign Academic, Royal Academy of Exact, Physical & Natural Sciences of Spain (2025) 700+ publications, textbooks: Advanced Optimization in Process Systems Engineering and Systematic Methods of Chemical Process Design (co-authored with Larry Biegler & Art Westerberg) His research interests span: Mixed-Integer/Nonlinear Programming (MILP/MINLP) General Disjunctive Programming (GDP) Global Optimization & Multiperiod Optimization Applications in chemical process design, energy systems, and decarbonization Honors & Awards : Multiple AIChE awards (Computing, Walker, Warren Lewis, Sustainable Engineering) 2015 Sargent Medal (IChemE) 2025 Foreign Academic of Spanish Royal Academy 2019 Top Cited Scientist (Worldwide #53, National #38 in CS/Electronics) Education & Legacy : 68 Ph.D. and 34 M.S. students mentored. Honorary doctorates from 9 institutions including RWTH Aachen (Germany), University of Maribor (Slovenia), and Universidad Nacional del Sur (Argentina).

Aida Khajaviradمشاهده پروفایل

استادیار

• Mixed Integer Nonlinear Optimization
• Nonconvex Optimization
• Algorithm Development
• +۳ مورد دیگر

Aida Khajavirad is an Assistant Professor in the Department of Industrial and Systems Engineering at Lehigh University. Her research focuses on advancing Mixed-Integer Nonlinear Optimization (MINLP) at theoretical, algorithmic, and software levels, integrating convex analysis, integer programming, and constraint programming. She has recently expanded her work to optimization algorithms for data science and machine learning applications. Education: Ph.D. in Mechanical Engineering from Carnegie Mellon University (2012). Research Interests : Mixed Integer Nonlinear Optimization Nonconvex Optimization Algorithm Development Convex Analysis Combinatorial Optimization Her work emphasizes bridging theoretical foundations with practical algorithm design for real-world challenges. Awards : 2023 INFORMS Computing Society Prize This prestigious award recognizes her contributions to optimization theory and applications. Grants & Funding : Air Force Office of Scientific Research ($1M award, 2023) This funding supports her research on advanced optimization algorithms. Labs and Affiliations: Based in the Harold S. Mohler Laboratory, she leads interdisciplinary efforts in optimization and data science.

Jose Fernandez Hernandezمشاهده پروفایل

استاد

• Location Science
• Competitive Facility Location
• Global Optimization
• +۴ مورد دیگر

José Fernández Hernández is a Full Professor at the Department of Statistics and Operations Research, Faculty of Mathematics, University of Murcia (Spain). His academic career spans over two decades with significant contributions to location science, global optimization, and interval analysis. Ph.D. in Mathematics (1999), University of Murcia (Thesis: "Nuevas técnicas para el diseño y resolución de modelos de localización continua") Member of Operations Research Group (IOMUR), Spanish Society of Statistics and Operations Research (SEIO), and Euro Working Group on Locational Analysis (EWGLA) Research Interests: Specializing in Location Science where he develops models for optimal facility placement considering transportation costs, market share capture, and customer behavior. His work extends to Global Optimization techniques using interval analysis to solve nonlinear problems with multiple local optima, and Multiobjective Optimization frameworks for balancing conflicting objectives in spatial decision-making. Article Trends: His publications demonstrate a progression from geometric decomposition methods (2000s) to advanced competitive facility location models (2010s) parallel evolutionary algorithms (2010-2020) MINLP formulations for firm expansion (2015-2024) probabilistic consumer choice modeling attractiveness adjustment in existing facilities Stackelberg games in facility location Grants & Projects: Over 20 funded projects including national grants (Ministerio de Ciencia e Innovación) 1997-2024 regional projects (Fundación Séneca, Junta de Andalucía) 1998-2022 international cooperation with Hungary 2002-2006 Coordinating research networks on location analysis and computational methods. Collaboration: Regularly works with researchers from University of Almería University of Szeged (Hungary) International Federation of Operational Research Societies with over 50 co-authored publications.

Winnifried Wollnerمشاهده پروفایل

استاد

• Optimization
• Applied Mathematics
• PDE-Constrained Optimization
• +۷ مورد دیگر

Prof. Dr. Winnifried Wollner is a Professor of Optimization at the University of Hamburg's Department of Mathematics, within the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on optimization with partial differential equations (PDEs), numerical methods for PDEs, and computational mechanics, particularly in the context of shape optimization and fracture mechanics. He holds a PhD from Heidelberg University (2010) and has held academic positions at TU Darmstadt and the University of Hamburg. He is actively involved in editorial boards of journals like the SIAM Journal on Control and Optimization and the OPTPDE Problem Collection. His work emphasizes adaptive numerical methods, phase-field fracture modeling, and optimization under constraints. Awards include the 2014 Teacher of the Summer Term award at Hamburg. He teaches courses on optimization, numerical methods, and PDE-constrained problems, and leads research projects funded by DFG and the Excellence Cluster CLICCS. Professional roles include membership in the MIN Faculty Council, the Department's Equal Opportunities Team, and leadership of the GAMM Student Chapter. His research spans interdisciplinary applications in engineering and computational science, with over 66 publications in top journals. Current projects include the DFG Research Training Group 2583 on fluid dynamics and optimization, and the SPP 1962 project on phase-field fracture simulation.

Luis Ricardez-Sandovalمشاهده پروفایل

دانشیار

• Process Systems Engineering
• Multiscale Modelling
• Computer-Aided Catalyst and Materials Design
• +۷ مورد دیگر

Luis Ricardez-Sandoval is an Associate Professor in the Department of Chemical Engineering at the University of Waterloo, Canada. He holds the Tier II Canada Research Chair in Multiscale Modelling and Process Systems. His research focuses on developing theoretical and computational tools for optimizing chemical processes, with a strong emphasis on CO2 capture technologies, energy systems, and heterogeneous catalysis. His work integrates multiscale modeling, machine learning, and advanced control strategies to enhance process efficiency and sustainability. Education : Doctorate in Chemical Engineering, University of Waterloo (2008) Master of Science in Chemical Engineering, Instituto Tecnologico de Celaya (2000) Bachelor of Science in Chemical Engineering, Instituto Tecnologico de Orizaba (1997) Research Interests : Process Systems Engineering and Multiscale Modelling Computer-Aided Material Design for CO2 Utilization Advanced Process Control and Uncertainty Management Optimal Integration of Process Design, Control, and Scheduling Energy Systems and CO2 Capture Technologies Selected Awards : 2024 Canadian Society of Chemical Engineering D.G. Fisher Award 2017 Canada Research Chair (Tier II) 2015 Early Researchers Award (Ontario) Teaching : CHE 341: Introduction to Process Control CHE 521: Process Optimization CHE 522: Advanced Process Dynamics and Control Labs & Collaborations : Ricardez-Sandoval collaborates with industry and governments to advance carbon reduction technologies. His research group has partnerships with institutions like CONACyT (Mexico), China Scholarship Council, and Tampere University (Finland). Key projects include optimizing aquaculture systems, chemical looping combustion, and catalyst design.

Jan Kronqvistمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Convex MINLP
• +۷ مورد دیگر

Jan Kronqvist is an Assistant Professor at KTH Royal Institute of Technology and a faculty member of Digital Futures, a cross-disciplinary research center focused on digital technologies. His research emphasizes optimization methodologies, including convex MINLP, machine learning integration, and algorithm design for complex systems. He actively contributes to projects like SMART (Predictive Maintenance), SimOPT (Process Design), ADAPCOS (Undersea Swarm Optimization), and cybersecurity in power systems. Key research areas include global optimization for clustering problems, robustness in neural networks, GPU-accelerated machine learning, and ensemble model optimization. He has developed algorithms like the Extended Cutting Plane and SHOT solver for convex MINLP, with applications in chemical engineering, power systems, and control systems. His work bridges theoretical optimization and practical industrial challenges, addressing topics such as cyberattack resilience, multimodal mobility systems, and ensemble learning verification. Projects often involve collaboration with industry partners to translate mathematical models into real-world solutions. Jan has advised initiatives like the SEC scholar program for Calvin Tsay and contributed to research on radiation therapy scheduling and distillation process optimization. His research outputs span over 40 peer-reviewed articles, focusing on advancing optimization algorithms and their applications across engineering and data science domains.

Ignasi Palou-Riveraمشاهده پروفایل

استاد مدعو

• Process Intensification
• Chemical Engineering
• Sustainability Analysis
• +۴ مورد دیگر

Dr. Ignasi Palou-Rivera is an Adjunct Professor in the Department of Chemical and Biological Engineering at Illinois Institute of Technology's Armour College of Engineering. He serves as Executive Director and Chief Technology Officer of the RAPID Manufacturing Institute®, a DOE-funded initiative advancing modular chemical process intensification (MCPI). His career spans over 20 years in process development and optimization across industries including oil refining, biofuels, and sustainability analysis. Education includes a Ph.D. in Chemical Engineering from University of Wisconsin-Madison (1998), post-doctoral work at MIT (2001-2002), and a Chemical Engineering degree from Universitat Politècnica de Catalunya (1991). His work integrates computational tools with industrial applications, focusing on techno-economic analysis, lifecycle assessment, and refinery planning. Research interests emphasize sustainable manufacturing systems, process intensification technologies, and bridging innovation to commercialization. He leads RAPID's efforts to accelerate adoption of advanced chemical processing methods through public-private partnerships. His contributions include development of lifecycle analysis frameworks for biofuels and shale gas, as well as refinery optimization algorithms. Key publications span process intensification strategies, biofuel lifecycle assessments, and energy efficiency improvements. He holds patents related to bioreactor systems and wafer edge characterization. Professional service includes leadership roles in AIChE's Computing & Systems Technology Division and Sustainable Engineering Forum.

Laura Palagiمشاهده پروفایل

استاد

• Nonlinear Optimization
• Mixed Integer Nonlinear Programming
• Semidefinite Programming
• +۷ مورد دیگر

Laura Palagi is a Full Professor at Sapienza University of Rome, Department of Computer, Automation, and Management Engineering "Antonio Ruberti". She has been with the institution since 1996, progressing from Researcher to Associate Professor in 2005, and Full Professor since March 2020. She currently serves as Coordinator of the PhD program in Automatic Control, Bioengineering and Operations Research (ABRO) since February 2024 and is the GEP committee representative at the DIAG Department. Her research focuses on developing and analyzing methods for solving nonlinear optimization problems with continuous and discrete variables. Key research themes include decomposition methods for training Support Vector Machines and Deep Networks, methods for Mixed Integer Nonlinear Programming (MINLP), Semidefinite Programming (SDP), standard quadratic problems (StQP), and convex quadratic constrained problems. She also develops predictive machine learning models for healthcare applications and surrogate models for engineering. Her recent publications span optimization methods, machine learning applications in healthcare, sustainability assessment, and network design. Her work demonstrates a strong integration of theoretical optimization with practical applications across multiple domains. Continuous Optimization Awards 2020 Member Best Paper Award from Journal of Global Optimization Honorable Mention from Marguerite Frank Award jury for Best EJCO Paper 2024 EURO Doctoral Dissertation Award Professor Palagi teaches multiple courses including Optimization and Decision Science, Laboratory of Decision Science, Optimization Methods for Machine Learning, and Optimization for Medical programs. She serves as thesis advisor for numerous Bachelor's and Master's students in Management Engineering, Mechanical Engineering, and Data Science programs.