جست‌وجوی استاد و پژوهشگر

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.

• Study of Risks in operations
• Methods of Financial Engineering
• Interior Point Methods for Linear and Convex Programming
• +۵ مورد دیگر

Romesh Saigal is a Professor at the Department of Industrial and Operations Engineering in the College of Engineering at the University of Michigan , where he has been employed since 1986. His career spans multiple prestigious institutions, including Northwestern University, the University of California, Berkeley, and Bell Telephone Labs. Education: B.Tech (Hons), I.I.T. Kharagpur, India, 1961 M.Tech, I.I.T. Kharagpur, India, 1963 Ph.D., University of California, Berkeley, 1968 Romesh Saigal’s research focuses on stochastic programming , financial engineering , interior point methods , Kalman filtering , continuous optimization , and game theory applications . His work bridges theoretical advancements with practical applications in transportation systems and risk analysis. His recent publications highlight the application of stochastic modeling to traffic flow prediction, dynamic pricing in toll lanes, and combinatorial auction mechanisms for traffic allocation. These works demonstrate a strong emphasis on integrating optimization algorithms and data-driven approaches in operations research. Romesh Saigal has supervised several PhD students, including Li Yang , Jie Ning , Hao Zhou , and Katherina Best , whose dissertations explore topics like risk management , traffic pricing , and higher education value analysis . He is also known for authoring the book Linear Programming: A Modern Integrated Analysis and co-editing the Handbook on Semidefinite Programming . Additional contributions include foundational work on interior point methods , accessible via collaborations at Argonne National Lab. His research continues to influence both academic and industrial practices in optimization and transportation systems.

• Nonconvex Optimization
• Semidefinite Programming
• Power Engineering
• +۴ مورد دیگر

Richard Y Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois, affiliated with the Coordinated Science Lab. His research focuses on optimization theory, nonconvex optimization, semidefinite programming, and their applications in power systems, machine learning, and control systems. Key topics include low-rank matrix recovery, spurious local minima analysis, and robust optimization methods. He has received the NSF CAREER Award in 2021 for early career achievements. Research Interests: Nonconvex Optimization Semidefinite Programming Power System Analysis Low-Rank Matrix Recovery Machine Learning Algorithms Control Systems Design Recent work emphasizes certified optimization methods with global guarantees, adversarial machine learning robustness, and efficient algorithms for large-scale systems. His publications explore topics like phase synchronization in power systems, preconditioned gradient descent techniques, and sparse semidefinite program optimizations. Scientific Awards: NSF CAREER Award (2021) Advising and Grants: While specific student names aren’t listed, his research group focuses on cutting-edge optimization theory with applications to energy systems and data science. Collaborations include work on power grid stability, neural network certification, and large-scale convex/nonconvex problem solving. Labs/Teams: Active in the Coordinated Science Lab’s optimization and control research clusters.>

Gianfrancesco De Falcoمشاهده پروفایل

مدرس

• Interior Design
• Freehand Drawing
• Perspective Techniques
• +۳ مورد دیگر

Gianfrancesco De Falco is a Lecturer at Chelsea College of Arts, part of the University of the Arts London. He specializes in teaching freehand perspective drawing techniques for designers, offering courses that blend theoretical knowledge with practical skills. His courses cater to both beginners and those seeking to refine their drawing abilities, emphasizing communication of creative ideas through 3D sketches and perspective mastery. While specific educational background details are not provided, his expertise focuses on enhancing visual literacy and problem-solving through sketching. The course structure includes sessions on one and two-point perspectives, scale application, and techniques to boost creativity and professional communication. De Falco highlights the psychological benefits of drawing, such as stress reduction and enhanced focus, alongside practical advantages like faster workflow evaluation and collaborative idea-sharing. No scientific awards or grants are explicitly mentioned, though his work emphasizes pedagogical innovation in art education. Students gain skills in proportion estimation, rendering techniques, and the 'measuring point method,' enabling them to present ideas effectively to clients and manufacturers. No lab affiliations or teams are detailed in the provided text.

• Operations Research
• Multi-Objective Optimization
• Transportation Networks
• +۳ مورد دیگر

Matthias Ehrgott is a Professor in the Department of Management Science at the Lancaster University Management School (LUMS). His research focuses on multi-objective optimization, operations research, transportation systems, and healthcare logistics. He leads the OptiWaSP project (2022–2025), which designs efficient walking school bus routes integrating optimization and instance generation. He is affiliated with the Centre for Transport & Logistics (CENTRAL), STOR-i Centre for Doctoral Training, and Health Systems Optimisation groups. His editorial roles include serving on the editorial boards of EURO Journal on Decision Processes , Optimization , 4OR , and others. Recent work emphasizes applications in railway timetabling, radiation therapy planning, and sustainable urban mobility. He advises PhD students like Malek Almousa, an Associate Lecturer. Key research themes include bi-objective optimization, algorithm design for multi-criteria problems, and real-world implementations in healthcare and transportation. His projects bridge theoretical advancements with practical solutions for complex decision-making environments.

Adrian Vladuمشاهده پروفایل

پژوهشگر

• Optimization
• Convex Geometry
• Continuous Optimization
• +۴ مورد دیگر

Adrian Vladu is a permanent researcher at CNRS, affiliated with the Institute of Research in Fundamental Computer Science (IRIF) at Université Paris Cité. He holds a PhD in Mathematics from MIT (2017), advised by Jonathan Kelner and Aleksander Mądry, followed by postdoctoral work at Boston University. His research focuses on optimization (convex/non-convex) and deep learning, emphasizing algorithm design with provable guarantees. Recent work explores refining interior point methods and scalable training techniques for deep models. Education: PhD in Mathematics (MIT, 2017); Postdoctoral Associate (Boston University). Research interests include combining convex geometry, continuous optimization, and linear algebra to address discrete problems. He also investigates deep learning's theoretical underpinnings, particularly generalization and training efficiency. Current projects aim to develop simple, implementable algorithms with strong theoretical foundations. Grants: Supported by CNRS and the French National Research Agency. Job Market: Actively seeking tenure-track faculty or industry research roles in optimization, algorithms, or machine learning. Notable contributions include STOC/FOCS/SODA publications on optimization algorithms and ICLR/ICML work on adversarial attacks and distributed training.

• Operations Research
• Optimization Theory
• Game Theory
• +۴ مورد دیگر

Immanuel Bomze is a Full Professor of Applied Mathematics and Statistics at the University of Vienna. He holds a PhD from the University of Vienna (1982) and has held academic positions including Assistant Professor (1982–1988), Associate Professor (1988–2002), and Full Professor (since 2004). His research focuses on Operations Research, Optimization Theory, Game Theory, and Dynamical Systems. He has contributed extensively to quadratic programming, copositive optimization, and evolutionary game dynamics. Education: Studied Mathematics and Physics at the University of Vienna (1976–1982), earned a Mag. rer. nat. (1981) and Dr. rer. nat. (1982). Served as a Scholar at the Institute for Advanced Studies (1981–1983) and earned a Venia docendi (1987). Research interests include optimization theory, game theory applications, and statistical modeling. Notable awards include the EurOpt Fellow 2014 and Best Paper Awards (2020, 2021). He has held visiting positions at institutions worldwide, including the University of Melbourne and University of Rome 'La Sapienza'. Professional activities include editorial roles at journals like the European Journal of Operational Research and EURO Journal of Computational Optimization. He is a leader in the optimization and game theory communities, with over 120 refereed publications and contributions to both theoretical and applied research.

Santosh Vempalaمشاهده پروفایل

استاد

• Theory of Algorithms
• Foundations of Data Science
• Theory of Brain
• +۵ مورد دیگر

Roles and Affiliations: Santosh Vempala is the Frederick G. Storey Chair in Computing and Professor in the School of Computer Science at Georgia Institute of Technology. He also holds courtesy appointments in Mathematics and Industrial and Systems Engineering. He directs the Algorithms, Combinatorics, and Optimization (ACO) PhD program and previously led the Algorithms & Randomness Center (ARC). His work bridges theoretical computer science, data science, and computational neuroscience. Education: PhD in Computer Science from Carnegie Mellon University (1997), advised by Avrim Blum. Postdoctoral fellowship at UC Berkeley (Miller Fellow). Research Interests: Focuses on algorithms, high-dimensional geometry, randomized methods, and applications to brain computation and societal challenges (e.g., Computing for Good). Recent work explores sampling/optimization limits and brain modeling via neural assemblies. His interdisciplinary approach merges theoretical rigor with practical impact in healthcare and technology. Publications Trends: Recent work emphasizes algorithmic diffusion for sampling, neural computation models, and AI evaluation. Key themes include logconcave sampling, optimization complexity, and robust learning. Collaborations span computer science, mathematics, and neuroscience. Awards: Sloan, Guggenheim, ACM, and AMS Fellowships. Recognized for contributions to algorithms and computational theory. Advising & Grants: Advises PhD students and postdocs in optimization, machine learning, and neuroscience. Leads projects like C4G (Computing for Good), addressing global health and societal challenges. Labs & Teams: Founder of ARC and core member of ML@GT. Collaborates with interdisciplinary teams on projects like Brain Assembly Models and OMSCS (Online Master of Science in CS).

Jeffrey Lagariasمشاهده پروفایل

استاد

• Number Theory
• Mathematical Physics
• Discrete Geometry
• +۵ مورد دیگر

Jeffrey C. Lagarias is the Harold Mead Stark Distinguished University Professor of Mathematics at the University of Michigan. His research spans diverse areas including number theory, mathematical physics, discrete geometry, harmonic analysis, optimization, and theoretical computer science. He holds a PhD from MIT (1974) and has contributed to groundbreaking work in fields such as quasicrystals, interior point methods, and knot complexity. Notable awards include election to the National Academy of Sciences (2024) and a Simons Fellowship (2018). Education: Ph.D., MIT (1974) Research Interests: Number theory, mathematical physics, discrete geometry, dynamical systems, optimization, and theoretical computer science. Recent work includes studies on Ray class groups , supernorm partition statistics , and floor quotient partial orders . Awards: Elected to the National Academy of Sciences (2024) Simons Fellow in Mathematics (2018) Harold Mead Stark Distinguished University Professor (2018) Professional Contributions: Advised PhD students on topics ranging from integer factorial ratios to discrete geometry challenges like regular tetrahedron packings. Active in the University of Michigan’s number theory group and has engaged with interdisciplinary projects, including collaborations on the Inter-universal Teichmüller Theory and ABC Conjecture .

Jung-Han Kimnمشاهده پروفایل

دانشیار

• Domain decomposition methods
• Parallel algorithms
• Numerical PDE/ODE
• +۵ مورد دیگر

Jung-Han Kimn is an Associate Professor in the Department of Mathematics and Statistics at the University of South Dakota. His research focuses on numerical methods for partial differential equations, domain decomposition techniques, parallel algorithms, and computational physics. He holds a Ph.D. in Mathematics from the Courant Institute (NYU) and has extensive academic experience in applied functional analysis and numerical linear algebra. Education: B.A. in Mathematics, Yonsei University (1992) M.S. in Mathematics, Seoul National University (1994) Ph.D. in Mathematics, New York University (2001) Research Interests: Dr. Kimn specializes in large-scale simulations using domain decomposition methods for engineering and physics problems. His work emphasizes numerical solutions to PDEs, spacetime finite element methods, and high-performance computing applications. Notable projects include analyzing scramjet engine dynamics, ghost field stabilization in quantum systems, and solar energy forecasting models. Teaching Responsibilities: He instructs advanced courses such as Math 321 (Differential Equations), Math 331 (Advanced Engineering Mathematics), Math 751 (Applied Functional Analysis), and Math 770 (Numerical Linear Algebra). He also contributes to academic governance via the Harding Distinguished Lecture Committee and Professional Development Committee. Research Contributions: Recent work spans computational fluid dynamics (impinging jets, biofilm flows), quantum mechanics (Dirac equation solutions), and energy systems (optimal power flow optimization). His methodologies prioritize parallel computing scalability and algorithmic innovation. Professional Activities: Organized the Holistic Summer Undergraduate Research Program in High Performance Computing (2019) and developed frameworks for seismic fragility analysis in civil engineering systems.

Madeleine Udellمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Data Analysis
• +۴ مورد دیگر

Madeleine Udell is an Assistant Professor of Management Science and Engineering at Stanford University, with affiliations at the Institute for Computational and Mathematical Engineering (ICME) and a courtesy appointment in Electrical Engineering. She holds a PhD from Stanford University in Computational and Mathematical Engineering and completed a postdoc at Caltech. Her research focuses on accelerating optimization and machine learning, with applications in healthcare, finance, and engineering. Udell's work bridges algorithmic efficiency, interpretability, and practical deployment in data-driven systems. Education: BS in Mathematics and Physics, Yale University (2009) PhD in Computational and Mathematical Engineering, Stanford University (2015) Research Interests: Her research emphasizes optimization algorithms, scalable machine learning techniques, and their applications in real-world domains. Key areas include low-rank matrix methods, automated machine learning (AutoML), and interpretable models for healthcare. She develops tools like OptiMUS for natural language-based optimization modeling and explores second-order methods for improving deep learning efficiency. Awards: Kavli Fellowship (2023) Alfred P. Sloan Research Fellowship (2021) NSF CAREER Award (2020) ONR Young Investigator Award (2020) Grants & Advising: Udell leads projects funded by ONR and NSF, focusing on randomized numerical linear algebra and automated machine learning. She advises PhD students in optimization modeling, deep learning, and causal inference. Her group collaborates on tools like PINNs for scientific computing and OpenReviewer for bias-aware academic reviews. Labs & Teams: She directs the Udell Research Group at Stanford, emphasizing interdisciplinary work in optimization, machine learning, and data science. Collaborations span academia and industry, including work with Aurora Solar on photovoltaic array design and Google Brain on scalable AutoML.

Arielle Carrمشاهده پروفایل

استادیار

• Applied Linear Algebra
• Numerical Analysis
• Preconditioners
• +۵ مورد دیگر

Arielle Carr is an Assistant Professor in the Department of Computer Science & Engineering at Lehigh University's Rossin College of Engineering. She holds a Ph.D. in Mathematics from Virginia Tech (2021), an M.S. in Mathematics (2015), and dual B.S. degrees in Mathematics with a Computer Science minor and Sociology with an Education minor from Virginia Tech (2012) and Roanoke College (2008). Her research focuses on applied linear algebra, numerical analysis, and preconditioning techniques for large-scale systems, with applications in quantum computing, weather prediction, and science policy advocacy. Dr. Carr’s work emphasizes preconditioner development, Krylov subspace methods, and recycling techniques to enhance solver efficiency. She is particularly known for integrating machine learning into numerical methods and advancing quantum interior point algorithms. Her recent SIAM Science Policy Fellowship (2024) reflects her commitment to STEM advocacy and policy engagement, including collaborations with institutions like the Da Vinci Science Center to promote women in engineering. Her publications span interdisciplinary topics, blending numerical analysis with emerging technologies like quantum computing and diffusion models for ensemble forecasting. Though not explicitly listed, her research likely involves collaborations with computational science and engineering groups. Awards include the 2024 SIAM Science Policy Fellowship.

Xiu Yangمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Quantum Computing
• Scientific Machine Learning
• +۴ مورد دیگر

Xiu Yang is an Associate Professor in the Department of Industrial and Systems Engineering at Lehigh University's College of Engineering. He previously worked at Pacific Northwest National Laboratory (PNNL) as a scientist and holds a Ph.D. in Applied Mathematics from Brown University, along with degrees from Peking University. His research focuses on modern scientific computing, including uncertainty quantification, quantum computing, physics-informed machine learning, and multi-scale modeling. Yang has applied these methods to fluid dynamics, hydrology, biochemistry, and energy storage systems, with recent emphasis on quantum computing algorithms and their applications in scientific computing. He has received notable awards, including the NSF CAREER Award (2022) and PNNL's Outstanding Performance Awards (2015 and 2016). His work bridges computational mathematics and real-world challenges, such as error modeling in NISQ devices and developing quantum algorithms for linear systems. Yang also contributed to the DOE applied mathematics visioning committee in 2019, reflecting his leadership in advancing computational science. His research outputs span interdisciplinary areas, combining quantum computing with machine learning (e.g., Quantum DeepONet) and enhancing Gaussian process regression techniques with constraints. This work underscores his commitment to advancing computational tools for complex scientific problems, from seismic wave equations to power grid systems.

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.

Jacques Desrosiersمشاهده پروفایل

استاد

• Operations Research
• Transportation
• Optimization
• +۳ مورد دیگر

Jacques Desrosiers is a Full Professor at HEC Montréal in the Department of Management Science since 1989. He has been with the institution since 1978 and is a founding member of the Group for Research in Decision Analysis (GERAD). His research focuses on optimization, transportation systems, and algorithmic methods for solving complex linear and combinatorial problems. He has received prestigious recognitions, including the Grand Prize for Excellence in Research (2015) and Fellowship in the Royal Society of Canada (2001). His work spans contributions to column generation techniques, degenerate linear programming, and vehicle routing problems. Notable achievements include advancing the minimum mean cycle-canceling algorithm, vector space decomposition for large-scale problems, and developing efficient heuristics for logistics challenges. His research bridges theoretical advancements and practical applications, influencing transportation and operations management globally. Desrosiers has authored over 150 peer-reviewed articles, with recent focus on algorithmic optimization, primal degeneracy handling, and decomposition methods. His contributions to software tools like COIN-OR’s CLP further highlight his commitment to advancing computational methods in operations research. Awards: Royal Society of Canada Fellowship (2001), HEC Montréal Grand Prize (2015) Key Research Themes: Column Generation, Network Flow Algorithms, Linear Programming, Vehicle Routing, Degeneracy Resolution Labs/Teams: Co-founder and active member of GERAD, collaborating on optimization projects