Linear Programming Relaxations Professors and Supervisors | ApplySharif

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David B. BrownView profile

Professor

Duke UniversityUnited States

David B. Brown is the Snow Family Business Distinguished Professor in Decision Sciences and Faculty Director of the Center for Energy, Development and Global Environment (EDGE) at Duke University's Fuqua School of Business. He holds a Ph.D. from MIT (2006). His research focuses on algorithm design for decision problems under uncertainty, with applications in energy systems, sustainability, and stochastic modeling. Key contributions include dynamic resource allocation frameworks, stochastic dynamic programming relaxations, and robust optimization techniques. Professor Brown leads the GRACE project (funded by the Department of Energy, 2020–2025), exploring risk-aware grid management for clean energy systems. His work bridges theoretical advancements with practical applications in energy transition, climate mitigation, and mathematical optimization. He has received recognition including the Snow Family Distinguished Professorship (2024). Recent publications emphasize dynamic frameworks for integrated energy systems, fluid policies in resource allocation, and sequential decision-making under uncertainty. His research also addresses pricing strategies in shared resource networks and asymptotic optimization techniques. Education: Ph.D., Massachusetts Institute of Technology (2006) Grants: DOE-funded GRACE Project, Duke EDGE Faculty Seed Grant (2024) Affiliations: Center for Energy, Development and Global Environment (EDGE)

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Sara GrundelView profile

Researcher

Max Planck Institute for Dynamics of Complex Technical SystemsGermany

Sara Grundel is a leading researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Her work focuses on computational methods in systems and control theory, particularly in model order reduction, gas network simulation, and optimization of energy systems. Education: Diplom in Mathematics, ETH Zurich (2005) PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University (2011) Research Interests: Sara’s research encompasses mathematical control theory, stability analysis, and numerical methods for differential-algebraic equations. She applies these techniques to gas and energy networks, epidemic modeling, and multi-agent systems. Her interdisciplinary work bridges computational mathematics with real-world engineering and public health challenges. Recent Publications: Her 15 most recent articles (2024–2012) demonstrate expertise in parametrized PDEs, model reduction for coupled systems, and control strategies for SARS-CoV-2 containment. Key subtopics include adaptive meshing, stability-preserving algorithms, and optimization of nonlinear network dynamics. Scientific Contributions: Developed clustering-based model reduction techniques for networked systems Investigated hyperbolic discretization methods using Riemann invariants Advanced polynomial root radius optimization with affine constraints Collaborations: Sara frequently collaborates with researchers like Peter Benner and Martin Gersen on energy grid simulations and control theory. She participates in international conferences (GAMM, IEEE CDC, MTNS) and contributes to edited volumes in applied mathematics.

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James R. LeeView profile

Professor

University of WashingtonUnited States

James R. Lee is a Professor in the Department of Computer Science at the University of Washington, with a focus on algorithms, complexity, and the theory of computation. He is affiliated with the UW Theory Group and currently on leave at Microsoft Research, which may delay responses to UW emails. Research Interests: Algorithms, complexity, geometry/discrete-continuous interfaces, probability, stochastic processes, metric embeddings, spectral graph theory, convex optimization. Recent Work Trends: Sparsification of generalized linear models and norms, spectral hypergraph methods, entropic regularization for metrical task systems, and analysis of scaling exponents in random graphs. His papers address sparsifier existence, lower bounds for SDP/LP relaxations, and geometric random walk properties. Scientific Awards: Best Paper Award at STOC 2015. Students: Farzam Ebrahimnejad, Ewin Tang, Yichuan Deng (co-advised with Shayan Oveis-Gharan, Shirshendu Ganguly, and others). Email: jrl@cs.washington.edu

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Robbert KrebbersView profile

Associate Professor

Max Planck Institute for Software SystemsGermany

Robbert Krebbers is an associate professor at the Department of Software Science at Radboud University Nijmegen, Netherlands. He is a leading researcher in program verification, specializing in separation logic and the Coq proof assistant. Krebbers is a core contributor to the Iris framework, a higher-order concurrent separation logic framework implemented in Coq. His research spans theoretical foundations of programming languages and practical applications to real-world languages including C, Rust, and Scala. His research interests focus on scaling program verification techniques to challenging programming paradigms like concurrency, higher-order functions, and modules. Krebbers' work bridges formal methods with practical language implementation, particularly in verifying memory safety and concurrency properties. He has made significant contributions to Rust verification through the RustBelt project and has pioneered techniques for verifying concurrent data structures and message-passing systems. Krebbers' recent publications demonstrate a strong trend toward verifying complex concurrency patterns, developing automated proof techniques, and applying separation logic to practical programming language features. His work frequently appears in top-tier programming languages conferences including POPL, PLDI, and ICFP, with several papers receiving distinguished paper awards. The research spans from foundational logic development to practical verification tools for real-world programming languages. As a principal investigator in the Iris project, Krebbers has secured significant research funding including the ERC Consolidator Grant for the RustBelt project. His work has influenced both academic research and industrial practice, particularly in the Rust programming language ecosystem. The Iris framework he helped develop has been adopted by numerous verification projects worldwide. Krebbers has advised PhD students including Ike Mulder (focusing on proof automation for concurrent separation logic) and Jules Jacobs (working on guarantees by construction). He has been actively involved in the programming languages research community, serving on program committees for major conferences and organizing workshops on formal methods and verification. He leads research in the Department of Software Science at Radboud University, where his group focuses on developing foundational techniques for program verification. The group collaborates closely with the Logic and Semantics Group and Foundations of Programming Group across institutions, contributing to a vibrant research ecosystem around formal methods and programming languages.

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Carlos CardonhaView profile

Assistant Professor

University of ConnecticutUnited States

Carlos Cardonha is an Assistant Professor in the Department of Operations and Information Management at the University of Connecticut School of Business since 2019. He holds a Ph.D. in Mathematics from Technische Universität Berlin (2011) and degrees in Computer Science from the University of São Paulo (B.Sc. 2004, M.Sc. 2006). Previously, he worked as a Research Staff Member at IBM Research – Brazil (2012–2019). His research focuses on discrete optimization, approximation algorithms, and applications of machine learning and mathematical programming to operations research problems. He teaches courses such as OPIM 5641 (Business Decision Modeling) and OPIM 3511 (Business Data Analytics II), supported by DataCamp. Education: Ph.D. in Mathematics, Technische Universität Berlin, Germany (2011) M.Sc. in Computer Science, University of São Paulo, Brazil (2006) Bachelor in Computer Science, University of São Paulo, Brazil (2004) Research Interests: His work spans analytics, optimization, and theoretical computer science, with a focus on mixed-integer linear programming, combinatorial optimization, and algorithm design. He applies these techniques to real-world problems in scheduling, resource allocation, and machine learning model optimization. Grants & Advising: No specific grants or advisees are listed in the provided materials. Labs/Teams: Not explicitly mentioned in the text.

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Laura BalzanoView profile

Associate Professor

University of Michigan-Ann ArborUnited States

Laura Balzano is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan, with a courtesy appointment in the Department of Statistics within the College of Literature, Science, and the Arts. Her research focuses on optimization, machine learning, and signal processing, particularly in high-dimensional data analysis, subspace learning, and low-rank structures. She holds affiliations with the Department of Statistics and is located at 323 West Hall, Ann Arbor, MI. Contact information includes girasole@umich.edu . Her work bridges theoretical foundations and practical applications, addressing challenges in heterogeneous data, online learning, and robust algorithms. Key research areas include subspace clustering, tensor decomposition, and model compression for deep learning systems. Recent contributions explore dynamic networks, control theory integration, and energy-efficient algorithms for large-scale models. Laura Balzano's publications span topics like PCA variants for noisy data, optimization on Riemannian manifolds, and neural collapse analysis. Her methods emphasize scalable solutions for real-time applications, such as energy disaggregation and medical imaging. While no explicit awards are listed, her extensive publication record reflects recognition in her field. Advising and grant details are not provided in the available text.

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Suhan ZhongView profile

Visiting Assistant Professor

Texas A&M UniversityUnited States

Suhan Zhong is a Visiting Assistant Professor in the Department of Mathematics at Texas A&M University, mentored by Prof. Jianxin Zhou. He holds a Ph.D. in Mathematics from UCSD (2022), supervised by Prof. Jiawang Nie. His research focuses on optimization theory, with expertise in operations research, mathematical programming, and their applications to data science and machine learning. He employs techniques from real algebraic geometry, moment problems, and stochastic optimization to tackle complex problems like multilevel optimization, equilibrium models, and tensor analysis. His work includes advancements in generalized Nash equilibrium problems, distributionally robust optimization, and global solution methods for nonconvex stochastic programs. He teaches MATH 311 and contributes to the TAMU Math Circle as an instructor. Current research trends include polynomial optimization under uncertainty, robust constraint handling, and algorithmic development for bilevel systems. His articles reflect contributions to stochastic programming, equilibrium analysis, and tensor decomposition. Presently seeking tenure-track or postdoctoral positions, Zhong emphasizes interdisciplinary approaches to optimization challenges. His work bridges theoretical foundations with practical applications in finance, data science, and engineering systems.

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Albina TropinaView profile

Research Professor

Texas A&M UniversityUnited States

Albina Tropina is a Research Professor in the Department of Aerospace Engineering at Texas A&M University. Her primary affiliation is with the College of Engineering, focusing on advanced plasma physics and combustion engineering. She holds a D.Sc., Ph.D., and M.S. from Ukrainian institutions, including the National Aviation University and V.N. Karazin National University. Education: D.Sc., Mechanics of Liquid, Gas and Plasma, National Aviation University, Kyiv, Ukraine Ph.D., Mechanics of Liquid, Gas and Plasma, V.N. Karazin National University, Kharkiv, Ukraine M.S., School of Mechanics and Mathematics, V.N. Karazin National University, Kharkiv, Ukraine Research Interests: Tropina specializes in plasma-assisted combustion, ignition systems for engines, turbulent flows, and nonequilibrium plasma dynamics. Her work integrates experimental and computational methods to advance ignition technologies and plasma applications in high-speed flows. Key topics include femtosecond laser-induced filaments, dual-pulse ignition systems, and aero-optical effects in hypersonic environments. Awards & Honors: “Honorary Professor of Science,” Ukrainian Ministry of Education and Science (2016) Fulbright Grant, US Department of State (2009–2010) Window on Science Program, Air Force Office of Scientific Research (2011–2014) Advising & Grants: Tropina has led multiple research initiatives funded by agencies like the Air Force Office of Scientific Research and the US Department of State. Her work emphasizes collaborations, including a visiting scientist role at Princeton University’s Applied Physics Lab (2014–2015). She contributes to plasma simulation models and experimental setups for ignition systems. Labs & Teams: Her research is part of the Plasma Simulation Laboratory at Texas A&M, focusing on modeling plasma-assisted ignition, combustion, and detonation processes. She also engages in computational fluid dynamics and high-resolution diagnostics for plasma dynamics.

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Valeria Chiado' PiatView profile

Professor

Polytechnic University of TurinItaly

Valeria Chiado' Piat is a Full Professor at Politecnico di Torino's Department of Mathematical Sciences (DISMA), holding the academic rank of Professor under Italian Law 240. She serves as a member of the Polito Social Center Coordination Committee and coordinates research networks including the Gnampa-INdAM project on variational problems in geometrically complex domains. Her research focuses on Calculus of Variations , Asymptotic Homogenization , and Elliptic Partial Differential Equations , with applications to multi-scale engineering structures, phase transitions, and transport problems. She leads the Nonlinear Analysis and Calculus of Variations research group within DISMA, specializing in theoretical PDE aspects for engineering models. Her recent publications demonstrate expertise in degenerate functionals, spectral gaps in water-wave systems, and stiff Neumann problems, reflecting strong activity in asymptotic methods for multi-scale analysis. The work consistently bridges abstract mathematical theory with concrete engineering applications. Professor Chiado' Piat actively participates in academic governance as a member of Electrical and Energy Engineering and Mathematical Engineering degree program committees. She has organized conferences including "Beyond Elasticity: Advances and Research Challenges" (2022) as program committee member.

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Caterina VIOLAView profile

Assistant Professor

Catania UniversityItaly

Caterina VIOLA is a theoretical computer scientist and mathematician currently serving as a Fixed-term Assistant Professor (RTDA) at the Department of Mathematics and Computer Science of the University of Catania . Her research focuses on quantum algorithms for text processing, computational complexity, and constraint satisfaction problems within the National Center for HPC, Big Data and Quantum Computing project. Education: Master's in Mathematics from University of Catania (Erasmus exchange at Universidad de Granada); Doctor rerum naturalium in Mathematics from Technische Universitaet Dresden Her research bridges theoretical computer science and mathematics, with a particular emphasis on approximation algorithms, inapproximability, and numerical semigroups. She has previously worked at the University of Oxford as a post-doctoral researcher and tutor, followed by a post-doc position at Charles University in Prague. Recent publications highlight her work on linear programming relaxations for constraint satisfaction problems, submodular functions, and numerical semigroup analysis. Current teaching responsibilities include Laboratorio di Algoritmi for Computer Science undergraduates and Informatica for Communication Sciences and Languages students. As a Senior Associate Post-doctoral Researcher at Oxford and post-doc at Charles University in Prague, she has developed expertise in mathematical foundations of computational optimization and their practical implementations.

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François BouffardView profile

Associate Professor

McGill UniversityCanada

François Bouffard is an Associate Professor at McGill University's Faculty of Engineering, specializing in Power Engineering and Systems Control. He holds the William Dawson Scholar title and serves as Associate Chair (Undergraduate Affairs). His research focuses on smart grids, renewable energy integration, and advanced control systems. Research interests include optimizing power systems through data-driven methods, demand response mechanisms, and energy storage solutions. He contributes to projects like the Group for Research in Decision Analysis (GERAD), emphasizing sustainable energy systems and grid resilience. Recent work explores flexibility in smart grid architectures, cold load management, and multi-agent reinforcement learning for energy systems. His publications highlight optimization techniques, stochastic modeling, and machine learning applications in energy control. Awarded the William Dawson Scholar distinction, his work bridges theoretical advancements with practical grid operations. He collaborates on interdisciplinary projects addressing energy transition challenges, such as hybrid storage systems and distributed energy resource coordination.

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Pauline LafitteView profile

Professor

National Institute of Applied Sciences of RouenFrance

Pauline Lafitte is a Professor at CentraleSupélec since 2011 and a member of the Laboratoire de Mathématiques d’Orsay UMR CNRS 8628 since 2025. She leads the Mathematics Department (3rd year curriculum) and co-heads the Mathematics and Data Sciences program at CentraleSupélec. She has held leadership roles including Director of the CentraleSupélec Mathematics Federation (FR3487) since 2017 and Editor-in-Chief of the Société Mathématique de France’s Gazette since 2023. Education: Habilitation à Diriger des Recherches (HDR) from Université Lille 1 (2010), PhD from ENS Lyon (2001), Agrégation de Mathématiques (1998), and DEA in Numerical Analysis (1997). Research focuses on hyperbolic/parabolic PDEs, kinetic theory, numerical analysis, and applications in physics, biology, and engineering. Key areas include asymptotic-preserving schemes, shock profile analysis, and fluid-particle interactions. Over 27 peer-reviewed articles and contributions to ANR projects like TOP-PRED (2021–2024) and STAB (2013–2016). Teaching includes large undergraduate classes (500–800 students) and specialized M2 courses in applied mathematics, covering PDEs, numerical methods, and functional analysis. Service roles include leadership in academic committees (e.g., CNRS, SMAI), editorial boards, and conference organization (e.g., Franco-Hellenic schools, CEMRACS 2019). Extensive jury participation in PhD defenses and academic recruitment committees.

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Somayeh SojoudiView profile

Associate Professor

University of California, BerkeleyUnited States

Somayeh Sojoudi is an Associate Professor in the Departments of Electrical Engineering & Computer Sciences and Mechanical Engineering at UC Berkeley, affiliated with the Berkeley Institute for Data Science (BIDS). Her research focuses on Artificial Intelligence, Control Systems, Optimization Theory, and Power and Energy systems. She teaches courses like EECS 127 and EECS 227AT on optimization models in engineering. Education: PhD in Control & Dynamical Systems from the California Institute of Technology (2013). Research Interests : AI and Machine Learning, particularly neural network robustness and generative models Optimization methods for non-convex and low-rank problems Control systems, power grids, and distributed energy resources Game-theoretic approaches in dynamic systems Awards : NSF CAREER Award (2021) ONR Young Investigator Award (2021) INFORMS Optimization Society Prize for Young Researchers (2015) IEEE PES Best-of-the-Best Conference Paper Award (2022) Her work bridges theoretical advancements in optimization with practical challenges in energy systems and AI, emphasizing robustness and safety-critical applications.

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Luigi PreziosiView profile

Professor

Polytechnic University of TurinItaly

Luigi Preziosi is a Full Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at Politecnico di Torino , where he conducts interdisciplinary research at the intersection of mathematics, biology, and engineering. He is a member of the Interdepartmental Center PolitoBIOMed Lab and actively contributes to doctoral programs in Complex Systems for Quantitative Biomedicine at both Politecnico di Torino and the University of Turin. Research Interests: Mathematical modeling of cancer, focusing on tumor growth, cell invasion, and vascular network formation. Mechanics of cell aggregates, tissue growth, and multiphase modeling incorporating adhesion and stress relaxation. Biomechanics of cellular systems, including cell motility, durotaxis, and response to mechanical stretch. Engineering applications such as windblown sand mitigation, avalanche dynamics, soil mechanics, and composite materials manufacturing. His recent publications highlight a strong trend in multiscale and multiphase modeling , particularly in simulating tumor spheroids, cellular reorientation under mechanical stimuli, and the role of the extracellular matrix in cancer progression. These works integrate biomechanics with kinetic and continuum models to understand complex biological behaviors. Scientific Recognition: Fellow, Accademia dei Lincei (2016–present) Corresponding Member, Interuniversity Center for Mathematics Applied to Biology (CIMAB) (2010–present) Editorial and Academic Service: He holds editorial positions in several leading journals, including Mathematics in Engineering (President of Editorial Committee), PLOS ONE , Journal of Theoretical Biology , and Mathematical Medicine and Biology . He has led major research projects funded by the Italian PRIN and EU H2020 programs, such as SMaRT and DERMA. Advising and Research Leadership: He has supervised PhD students including Gabriele Fioretto and Claire Claude Yvonne Alamichel , and leads research groups focused on mathematical physics and biomedical modeling. His work bridges theoretical development with practical applications in oncology and environmental engineering. Laboratories and Research Networks: He is affiliated with the PolitoBIOMed Lab and has participated in international research networks such as M3CS-TU TH, SMaRT, and mcrtn, fostering collaboration in mathematical biosciences.

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Richard Martin LusbyView profile

Associate Professor

Technical University of DenmarkDenmark

Richard Martin Lusby is an Associate Professor in the Department of Technology, Management and Economics at DTU Management, Technical University of Denmark. His academic work focuses on Operations Research with specialization in mathematical optimization applied to transportation and energy systems. His research spans railway scheduling, container logistics, maritime transport, and energy storage optimization. Department: Department of Technology, Management and Economics Section: Section for Operations Research, Division for Management Science Location: Building 358, Room 145A, Academy Road, 2800 Kgs. Lyngby, Denmark Contact: rmlu@dtu.dk, Tel: 45253084 ORCID: 0000-0002-8652-6219 Professor Lusby's research interests center around mathematical optimization methods applied to complex transportation and energy systems. His work spans railway scheduling problems, container logistics optimization, maritime transport operations, and energy storage scheduling. He develops advanced algorithms including Lagrangian Relaxation, Benders decomposition, and stochastic programming approaches to solve challenging real-world problems in these domains. His research has practical applications in improving efficiency and sustainability in transportation networks and energy systems. His recent publications demonstrate a strong focus on optimization techniques for transportation systems, particularly in railway operations and container logistics, as well as energy storage scheduling. The research shows a consistent pattern of developing sophisticated mathematical methods to address complex operational challenges in transportation networks, with increasing attention to sustainability considerations in maritime shipping and energy systems. Professor Lusby has been involved in significant research projects including the SWITCH project (Sustainable Network Design - Electrifying Container Shipping), which received funding from Den Danske Maritime Fond. This project explores how battery-powered ships could replace fossil fuels in global shipping through mathematical modeling and AI-driven optimization. He also supervises PhD students, including Georgios Vassos who completed a thesis on "Procurement Policy Analysis in Intermodal Container Logistics". His work has practical implications for improving efficiency in transportation networks, reducing environmental impact in maritime shipping, and optimizing energy systems operations. Through his research, Professor Lusby contributes to both theoretical advances in optimization methods and their practical applications in critical infrastructure sectors.

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