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Qihang LinView profile

Professor

• Continuous Optimization
• First-Order Methods
• Distributed Optimization
• +9 more

Qihang Lin is a Professor and the Gary C. Fethke Research Professor in Business Analytics at the Tippie College of Business, University of Iowa, where he also serves as Faculty Director of the part-time Master of Science in Business Analytics (MSBA-PT) program. His academic leadership and research excellence position him at the forefront of optimization and data science. Dr. Lin holds a PhD in Algorithms, Combinatorics, and Optimization from Carnegie Mellon University and a BS in Mathematical Science from Tsinghua University. His research spans continuous optimization, machine learning, and fairness in AI, with a focus on developing efficient and provable algorithms for complex decision-making problems. Continuous Optimization First-Order Methods Distributed Optimization Error Bound Conditions Machine Learning and Predictive Analytics Fairness in AI Markov Decision Processes His recent publications, appearing in venues like Mathematics of Operations Research , NeurIPS , and Management Science , emphasize theoretical advances in optimization under constraints, fairness-aware learning, and scalable methods for large-scale data. The work consistently bridges theory and application, particularly in healthcare and AI ethics. Dr. Lin has been recognized with multiple awards, including: Best Paper Award - INFORMS Workshop on Data Science (2017) Runner-up, Best Paper Award - INFORMS Workshop on Data Science (2019) Early Career Faculty Research Award, Tippie College of Business (2018) MBA Business Analytics Professor of the Year (2018–2019) He has secured significant research funding from the National Science Foundation and the University of Iowa, focusing on fairness-aware machine learning and federated learning in healthcare. His grants demonstrate strong collaboration with interdisciplinary teams and a commitment to solving real-world problems through algorithmic innovation. He has also contributed to the academic community as a reviewer for top journals such as Mathematical Programming , SIAM Journal on Optimization , and IEEE Transactions on Signal Processing . Dr. Lin has been involved in research initiatives related to federated learning for medical imaging (ImagiQ) and fairness in AI systems, often working within collaborative frameworks involving data science and healthcare equity. His role as Faculty Director and research fellow underscores his leadership in shaping analytics education and research at the University of Iowa.

• Database Theory
• Formal Languages
• Logic in Computer Science
• +3 more

Claire David is an Associate Professor in the Department of Computer Science at the University of Paris-Est Marne-la-Vallée, where she has held a full-time academic position since September 2010 as a core member of the Algorithmics team within the Computer Science Laboratory of the Gaspard Monge Institute (UMR 8049). Her academic journey includes a PhD completed between 2004-2008 at LIAFA under the supervision of Anca Muscholl (LABRI) and Luc Segoufin (Inria Futur), followed by a postdoctoral position (2008-2010) in the database group at the University of Edinburgh with Leonid Libkin. Her foundational work centers on formalisms for describing word languages and infinite-alphabet "data" trees, with direct applications to schema specification in semi-structured XML databases. Dr. David's research program bridges theoretical computer science and practical database systems, specializing in logical and automata-theoretic approaches to data representation. Her expertise in Database Theory, Formal Languages, and Logic in Computer Science has been applied through active participation in the European FoX project during her Edinburgh postdoc, developing theoretical tools for semi-structured data management. She maintains significant academic service commitments including membership in CNU section 27 since November 2015 and program committee roles for premier conferences: ICDT (2020, 2018, 2014, 2012), PODS (2016, 2014, 2015 PhD Symposium), Datalog 2.0 (2019), and BDA (2013, 2011). Her institutional leadership includes heading UPEM's third-year computer science degree since 2012 and serving on the LIGM laboratory board and teaching components IGM (2015-present) and ESIPE (2015-2018). Based at Cité Descartes (Room 4B132), she operates within the interdisciplinary framework of the Gaspard Monge Institute, which fosters collaboration between computer science and mathematics research units. Her administrative portfolio demonstrates sustained commitment to curriculum development and laboratory governance within UPEM's computer science ecosystem.

• Continuous Optimization
• Robust Optimization
• Mixed-integer nonlinear optimization
• +7 more

Prof. Dr. Oliver Stein is a faculty member at the Karlsruhe Institute of Technology within the School of Business , specifically the Department of Operations Research . His research focuses on Continuous Optimization , Non-smooth Optimization , and Multiobjective Optimization , with applications in Operations Research , Game Theory , and Engineering Design . Stein has contributed extensively to semi-infinite programming , bilevel optimization , and mixed-integer nonlinear optimization . His work includes theoretical advancements in constraint qualifications , projected gradient flows , and epigraph reformulations , alongside practical applications in gemstone cutting and modular system design . His 15 most recent publications span topics such as non-convex Nash equilibrium problems , granularity in polynomial optimization , and branch-and-bound algorithms , reflecting a blend of theoretical rigor and real-world impact. Stein has received prestigious awards including the Heisenberg fellowship (2005-2006) and Feodor Lynen fellowship (1999-2000). He serves on editorial boards of journals like the Journal of Global Optimization and Optimization .

Nikolaos TzirakisView profile

Professor

• Harmonic Analysis
• Partial Differential Equations
• Dispersive Equations
• +3 more

Nikolaos Tzirakis is a Professor of Mathematics at the University of Illinois at Urbana-Champaign, affiliated with the Department of Mathematics within the College of Liberal Arts & Sciences. He holds a Ph.D. in Mathematics from the University of Massachusetts, Amherst (2004), and has held roles including Postdoctoral Fellow at the University of Toronto (2005-2007) and Membership at the Institute for Advanced Study in Princeton (2004). Research Focus Dr. Tzirakis specializes in Harmonic Analysis and Partial Differential Equations, particularly the long-time behavior and regularity properties of dispersive PDEs. His work addresses equations like the Schrödinger equation, Korteweg-de Vries (KdV) systems, and nonlinear wave phenomena. Key areas include boundary value problems on semi-infinite intervals, low-regularity solutions, and well-posedness thresholds. Publications Recent works (2020–2024) focus on smoothing estimates for oceanic internal waves, well-posedness for Schrödinger-KdV systems on half-lines, and sharp regularity criteria for dispersive equations. His research bridges theoretical analysis and applications in fluid dynamics and mathematical physics. Affiliations & Teaching He maintains an office in CAB 327 and collaborates internationally. His teaching and advising activities are integral to the department’s graduate and undergraduate programs.

Alexander RazborovView profile

Professor

• Theoretical Computer Science
• Combinatorics
• Proof Complexity
• +4 more

Alexander Razborov is the Andrew McLeish Distinguished Service Professor at the University of Chicago's Department of Mathematics. He holds a B.S. from Moscow State University (1985) and a PhD (1987) and Doctoral Degree (1991) from the Steklov Mathematical Institute. His research spans logic, theoretical computer science (TCS), and combinatorics, with major contributions to proof complexity, continuous combinatorics (flag algebras), and quantum computing. Notable achievements include the Nevanlinna Prize (1990), Gödel Prize (for foundational work on 'Natural Proofs'), and election to the American Academy of Arts and Sciences (2020). His work on flag algebras revolutionized extremal combinatorics, while his research on proof complexity established fundamental limits of propositional reasoning systems. Razborov's recent focus includes continuous combinatorics (studying infinite graph limits) and refining proof complexity trade-offs. His collaborative projects with Leonardo Coregliano and others explore topics like Sidorenko's conjecture and neural network convergence guarantees. He is affiliated with the university's computational theory group and actively publishes across top journals like the Annals of Mathematics and Journal of the ACM.

Konstantinos TyrosView profile

Associate Professor

• Ramsey Theory
• Density Combinatorics
• Banach Space Geometry
• +2 more

Konstantinos Tyros is Associate Professor in Mathematics at the University of Athens, specializing in combinatorial analysis and Ramsey theory. His research connects density theorems in combinatorics with problems in Banach space geometry and probabilistic methods. Key contributions include density versions of combinatorial theorems (Carlson-Simpson, Hales-Jewett), structure theorems for stochastic processes on discrete cubes, and concentration inequalities for high-dimensional random arrays. His work on spreading models in Banach spaces reveals new structures in functional analysis. He has developed novel approaches to nonlinear spectral gaps and dual Ramsey theory for trees, while advancing the Moser-Tardos algorithmic framework. Honors include technical excellence awards from Greek academic institutions.

• Chemical Engineering
• Energy Systems
• Global Optimization
• +3 more

Prof. Alexander Mitsos is a Universitätsprofessor for Process Systems Engineering at RWTH Aachen University's Faculty of Mechanical Engineering since 2012. He leads the Systemverfahrenstechnik group and directs the Institute for Energy and Climate Research (IEK-10) at Forschungszentrum Jülich. His research focuses on deterministic global optimization, energy systems, and sustainable chemical processes. Mitsos' academic lineage traces back to Prof. Roger W. H. Sargent via his PhD advisor Paul I. Barton. Education: 1994–1999: Diploma in Chemical Engineering from Universität Karlsruhe (KIT), specializing in Thermodynamics and Mechanics 2001–2006: PhD in Process Systems Engineering at MIT, advised by Paul I. Barton Research Interests: His work integrates advanced mathematical methods with engineering challenges. Key areas include: Optimal design and operation of chemical processes and energy systems Development of global optimization algorithms for nonlinear problems Integration of machine learning in process design and control Energy transition technologies, including power-to-X processes and CO2 utilization Publications: Over 200 articles across 2025–2024 highlight innovations in optimization, electrochemical systems, and sustainable fuels. Recent focus areas include reinforcement learning for control, graph neural networks for molecular design, and energy market scenario modeling. Labs & Teams: Leads the Process Systems Engineering Lab at RWTH and directs IEK-10’s energy systems modeling initiatives. Collaborates internationally on projects like COMANDO (energy systems optimization framework).

• Operations Research
• Mathematical Programming
• Fuzzy Optimization
• +8 more

Maria Teresa León Mendoza is an Associate Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, Universitat de València. She is affiliated with the IARM (Image Analysis, Modelling and Retrieval) research group, reflecting her interdisciplinary work in data analysis and image processing. Doctorate: Universitat de València, 1992 Thesis: Un algoritmo primal para el problema continuo de programación semi-infinita lineal Supervisor: Dr. Enriqueta Vercher González Her research focuses on Operations Research , Mathematical Programming , and Fuzzy Optimization , with significant contributions to semi-infinite programming, data envelopment analysis (DEA), and portfolio selection under uncertainty. She has also applied mathematical methods to diverse areas such as image retrieval and musical tuning systems, demonstrating a broad interdisciplinary reach. Her publication record since 1992 shows a consistent focus on optimization under uncertainty, fuzzy methods for decision-making, and numerical algorithms. She frequently collaborates with researchers like Vicente Liern and Enriqueta Vercher, and her work is published in top journals such as European Journal of Operational Research and Fuzzy Sets and Systems . There is no mention of scientific awards in the provided sources. Maria Teresa León Mendoza has supervised at least one PhD student (Dr. Enriqueta Vercher González, who later became her supervisor, suggesting a long-standing academic collaboration), and she has been involved in multiple research projects, though specific grant details are not listed. Her work bridges theoretical optimization and practical applications in economics, finance, and engineering. She is actively involved in the IARM research group, which specializes in image analysis and retrieval, indicating ongoing research in machine learning and pattern recognition applications.

Jan-Joachim RückmannView profile

Professor

• Non-linear Programming
• Mathematical Optimization
• Semi-infinite Programming
• +12 more

Jan-Joachim Rückmann is a Professor in the Department of Informatics at the University of Bergen, Norway. His research lies at the intersection of mathematical optimization, nonlinear programming, and their applications in finance and engineering. He is actively engaged in theoretical and applied research, with a focus on generalized semi-infinite programming and complementarity constraints. His research interests include non-linear programming, parametric optimization, robust optimization, data envelopment analysis, and the application of topological and differential geometric methods to optimization problems. He has made significant contributions to the theory of stability and stationarity in mathematical programs with complementarity constraints (MPCC) and semi-infinite programming. The recent publications highlight a strong trend in theoretical optimization, particularly in MPCC, stability of stationary points, and generalized semi-infinite programming. Several works explore duality, constraint qualifications, and convexification techniques. There is also interdisciplinary work applying optimization to mathematical finance, gas explosion modeling, and computational social choice using machine learning. While no scientific awards are listed, his extensive publication record in top journals such as Mathematical Programming , SIAM Journal on Optimization , and Optimization underscores his scholarly impact. He has served as guest editor for multiple special issues on parametric optimization, reflecting his leadership in the field. Though no students or grants are explicitly mentioned, his long-standing collaboration with researchers such as H. Günzel, D. Hernandez Escobar, F. Guerra Vazquez, and O. Stein indicates active research advising and team involvement. His work with the European Space Agency on trajectory analysis and space applications also points to applied research collaborations. Dr. Rückmann is affiliated with the Department of Informatics, contributing to both theoretical advances and practical applications of optimization methods. His work integrates deep mathematical analysis with real-world problem solving in engineering and economics.

Mirjam DürView profile

Professor

• Mathematical Optimization
• Copositive Programming
• Quadratic Optimization
• +7 more

Mirjam Dür is a Full Professor (W3) at the Department of Discrete Mathematics, Optimization and Operations Research within the Faculty of Mathematics, Natural Sciences and Technology at the University of Augsburg (since 2017). Previously, she held Full Professor positions at the University of Trier (2011-2017) and other academic roles in Groningen, Darmstadt, and Vienna. Her research focuses on mathematical optimization, particularly copositive programming, quadratic optimization, matrix theory, and conic optimization. Born in Vienna, Austria M.Sc. in Mathematics (1996) and PhD in Applied Mathematics (1999) from University of Trier Positive Habilitation evaluation at TU Darmstadt (2005) Research Interests : Global optimization, quadratic and combinatorial optimization, conic optimization and matrix theory, copositive programming, and applications to graph theory and discrete problems. She has pioneered methods like factorization-based approaches for completely positive matrices and cutting plane techniques in copositive programming. Scientific Awards : 2013 Optimization Letters Best Paper Award 2010 VICI Grant (NWO) 2012 GIF Research Grant (German-Israeli Foundation) Key Contributions : Development of algorithms for copositive optimization, theoretical advances in matrix cones, and novel applications to problems like graph stability and discrete optimization. She serves as Senior Editor for Optimization Methods and Software and editorial board member for multiple optimization journals.

Abraham Benito Barragán AmigónView profile

Researcher

• Semi-infinite linear programming
• Optimization
• Mathematical programming
• +2 more

Abraham Benito Barragán Amigón is a faculty researcher at the Universidad Autónoma de Nuevo León (UANL) , specifically within the Faculty of Physical and Mathematical Sciences . He is an active member of the National System of Researchers (SNI) at the candidate level (2019-2021) and holds a doctoral degree with Cum Lauden honorable mention (2017). His research centers on semi-infinite linear programming , with a focus on primal-dual partitions, bounded coefficients, and continuous optimization problems. He has extensively contributed to advancing theoretical frameworks and algorithms in this domain, as evidenced by his numerous publications in high-impact journals and conference proceedings. Barragán Amigón has been a frequent speaker at national and international conferences, including the XIII Balkan Conference on Operational Research (BALCOR 2018) in Serbia and the Sixth International Conference on Mathematics and its Applications at the Benemérita Universidad Autónoma de Puebla (2019). His work bridges mathematical theory and practical applications in operations research. Scientific Awards: Cum Lauden honorable mention in the doctoral degree (2017) Member of the National System of Researchers, candidate level (2019-2021) Institutional Contributions: Active member of the research community at UANL's Faculty of Physical and Mathematical Sciences. Participation in multiple national congresses of the Mexican Mathematical Society and Mexican Society of Operations Research, fostering academic collaboration.

• Machine Learning
• Numerical Linear Algebra

Dr. Paz Fink Shustin serves as a Research Fellow at the Mathematical Institute of the University of Oxford, where she contributes to advanced computational research at the intersection of numerical methods and machine learning. Her work addresses complex challenges in algorithm development for scientific computing and data-driven applications. Her research focuses on Machine Learning and Numerical Linear Algebra, with specific emphasis on regression techniques, uncertainty quantification in high-dimensional systems, and numerical solutions to partial differential equations. She develops innovative algorithms for surrogate modeling, error analysis, and optimization, targeting improved efficiency and accuracy in computational simulations across scientific domains. Analysis of her recent publications reveals a cohesive trajectory integrating deep learning architectures with foundational numerical linear algebra, particularly in regression frameworks and uncertainty propagation. Her work demonstrates consistent innovation in adapting classical numerical methods to modern machine learning challenges, with strong applications in scientific computing and data science. No scientific awards, prizes, or fellowships were mentioned in the available information. Dr. Shustin actively engages in teaching at Oxford as a tutor for advanced courses including 'Theories of Deep Learning' (Michaelmas Term 2023), 'Optimisation for Data Science' (Hilary Term 2024), and 'Continuous Optimisation' (Hilary Term 2024). While her teaching responsibilities are well-documented, details regarding research grants, graduate student supervision, or advising relationships were not provided in the source material. She is an integral member of the Machine Learning and Data Science and Numerical Analysis research groups within the Mathematical Institute, collaborating on projects that bridge theoretical development with practical implementations in computational mathematics and artificial intelligence.

Luiz ChamonView profile

Assistant Professor

• Machine Learning
• Reinforcement Learning
• Constrained Optimization
• +3 more

Luiz Chamon is an Assistant Professor (tenure-track) and Hi!PARIS Chair holder at the Center for Applied Mathematics (CMAP) , École polytechnique, Paris, France. His research bridges machine learning with control theory and signal processing, focusing on intelligent systems that extract, process, and act on information under constraints. Research Interests: He specializes in constrained learning frameworks for machine learning, with applications to reinforcement learning, statistical signal processing, and optimization. His work includes adversarial robustness, safe policy design, and duality analysis in constrained settings. Article Trends: Recent publications span differential equation solving (ICLR 2025), primal-dual sampling (NeurIPS 2024), and resilient reinforcement learning (IEEE Trans. Autom. Control 2024). Common themes include constrained optimization, robustness, and bridging theory with practical applications in wireless systems and graph signal processing. Scientific Awards: Hi!PARIS Chair (2025) ELLIS Scholar (2025) Best Student Paper Award (ICASSP 2020) Best Paper Award (ICASSP 2020) Top 50 Most Accessed Articles in IEEE TSP (2019) Grants & Projects: Currently leads a 4-year ANR JCJC project on "Beyond (Robust) Learning: Striking Compromises in Machine Learning" (2024). Previously involved in technical reports on aircraft vibroacoustics and wireless power systems. Labs & Teams: Heads a research group at École polytechnique developing tools for constrained learning, collaborating with institutions like University of Pennsylvania and IMPRS-IS. Organizes tutorials and panels on constrained learning and career development.

• Stochastic Modeling of Communication, Information and Service Systems
• Queueing Theory
• Stochastic Processes
• +9 more

Alexander Stolyar is Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign , where he has taught courses on stochastic processes, queueing systems, and service engineering. Before joining Illinois in January 2017, he was the Timothy J. Wilmott Endowed Chair Professor at Lehigh University (2014–2017) and spent nearly two decades in industrial research at Bell Laboratories , Motorola, and AT&T Labs-Research. Stolyar’s research centers on stochastic modeling of large-scale communication, information, and service systems , including cloud data centers, communication networks, and call/contact centers. He develops mathematical frameworks for dynamic resource allocation, scheduling, congestion control, and distributed algorithms , leveraging tools from queueing theory, stochastic processes, fluid and diffusion limits, large deviations, and mean-field analysis . His recent publications (2021–2025) reveal sustained focus on asymptotic optimality in complex systems: designing lightweight algorithms that become provably optimal as system scale grows. Key themes include greedy and randomized policies for inventory systems with stochastic lead times , parallel-server systems with redundancy and cancel-on-completion , particle systems with mean-field interactions exhibiting traveling-wave behavior , and packing-constrained service systems motivated by cloud data-center operations . Scientific honors include election as INFORMS Fellow (2021) , the INFORMS Applied Probability Society Best Publication Award (2004) , and the ACM SIGMETRICS Best Simulation Paper Award (1996) . He serves or has served on the editorial boards of Queueing Systems (Editor, 2008–present), IEEE Transactions on Information Theory (2019–2021), Operations Research (2006–2011), Advances in Applied Probability / Journal of Applied Probability (2008–2015), and as Technical Program Committee member for leading conferences such as SIGMETRICS and IFIP Performance . Stolyar’s research has generated more than 30 U.S. patents spanning wireless network scheduling, cloud resource allocation, dynamic load balancing, and packet-data network control—many of which have been incorporated into commercial cellular and cloud systems. His work is supported by NSF and industry grants, and he has advised numerous PhD students and postdocs who now hold faculty and research positions worldwide.

Stephen J DisneyView profile

Senior Lecturer

• English Language
• Second Language Acquisition (SLA)
• Phonology
• +4 more

Dr. Stephen J Disney is a Senior Lecturer at the School of Education, University of Marjon, with 15 years of experience in teaching, pedagogical innovation, and international academic program development. He leads the MA TESOL program and oversees English Language courses on campus, while managing degree programs at two Vietnamese universities and developing international initiatives. PhD in Linguistics from Lancaster University TEFLQ certification MA in Modern English Language from UCL BA in English Language and Linguistics from University of Wales Bangor His research focuses on English Language and Second Language Acquisition (SLA), particularly pronunciation challenges for Vietnamese learners. Current projects include a British Council-funded photographic exhibition and ELT materials development partnership with the University of Science & Technology in Hanoi, examining phonology-morphology interactions in Vietnamese English learners. Key research contributions span construction grammar, grammaticalization processes (e.g., BE going to), intercultural communication, and figurative language in healthcare contexts. His publications address topics like Vietnamese English pronunciation patterns, semi-modal constructions, and pain metaphor analysis. Senior Fellow, Higher Education Academy Dr. Disney teaches across linguistics domains including Phonetics, Phonology, Linguistic Analysis, Diversity, Metaphor, and TESOL at undergraduate and MA levels, with particular emphasis on sound, structure, and language in use.