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Mark Haskins is a Professor of Mathematics at Duke University, affiliated with the Trinity College of Arts & Sciences. He holds a Ph.D. from the University of Texas at Austin (2000) and has held academic positions at institutions including the University of Bath and Imperial College London. His research focuses on differential geometry, special holonomy metrics, and geometric flows, particularly G₂-holonomy manifolds and Laplacian flow solitons. He is a Fellow of the Learned Society of Wales (2014). Research interests include Riemannian geometry, Einstein manifolds, and geometric analysis. Notable contributions involve constructing G₂-manifolds from asymptotically conical Calabi-Yau 3-folds and studying solitons in Laplacian flow. He has led grants from the Simons Foundation (2016–2024) and organized programs like the 2024 Special Geometric Structures and Analysis at MSRI. Teaching includes courses like Real Analysis II and Smooth Manifolds. He mentors students, including Yijia Liu and Anuk Dayaprema, and collaborates with researchers like Nordström and Foscolo. Professional activities include roles as Director of Graduate Studies at Duke and service in academic leadership.

• Differential Geometry
• Partial Differential Equations
• Holonomy Groups
• +5 more

Alexandre Cabot is a Professor at the Institute of Mathematics of Burgundy (IMB - UMR 5584 CNRS) , affiliated with the University of Burgundy and the Faculty of Sciences Mirande . His research focuses on optimization , nonlinear analysis , dynamical systems , and non-regular mechanics . He is part of the Statistics, Probability, Optimization and Control team, contributing to applied mathematics and theoretical analysis. Education : Doctoral thesis (2002) on dissipative dynamical systems and optimization at University of Montpellier II under Hédy Attouch. Habilitation à Diriger des Recherches (2006) on nonlinear and multi-speech analysis at University of Avignon. DEA in Mathematics (1998-99), Bachelor’s and Master’s in Mathematics (1992-94), and Agrégation (1994-95) at University of Paris VI. Research interests include gradient systems , proximal methods , non-smooth analysis , differential inclusions , and nonlinear oscillators . His work explores finite-time stabilization, curvature-dependent optimization, and vibro-impact problems. Recent publications span topics like inertial forward-backward algorithms, asymptotic behavior of damped equations, and regularization techniques. Collaborations include H. Attouch, P. Frankel, L. Thibault, and others. Teaching includes courses in mathematical economics, linear regression, differential calculus, and domain decomposition. He has supervised PhD students such as B. Baji, P. Frankel, and E. Vilches, focusing on optimization, non-regular mechanics, and variational analysis.

• Optimization
• Nonlinear Analysis
• Dynamical Systems
• +5 more

Dr. Li Chen is a Lecturer in the Discipline of Business Analytics at the University of Sydney Business School. Prior to this role, he served as a Research Fellow at the Institute of Operations Research and Analytics (IORA) at the National University of Singapore (NUS), where he also earned his PhD in Operations Research and Analytics. He holds a bachelor’s degree in Computational Mathematics from the University of Science and Technology of China (USTC). His research focuses on developing optimization methodologies and algorithms to address decision-making challenges under uncertainty, with applications in logistics, finance, and environmental policy. Key areas include robust optimization, risk management, and algorithmic frameworks for transforming data into actionable insights. Dr. Chen has been recognized for his work with the Runner-up award in the INFORMS Computing Society Harvey Greenberg Research Award (2023) and the Emerging Scholar Research Fellow title from the University of Sydney Business School (2024). He teaches QBUS2310 Management Science and supervises students such as Taigo Chapman (Honours, 2024). His publications span journals like Operations Research and Production and Operations Management , addressing topics like emission compliance in maritime transport and electric vehicle charging optimization.

• Optimization under uncertainty
• Decision-making under uncertainty
• Data-driven decision models
• +3 more

Carolyn R. Abbott is an Assistant Professor in the Department of Mathematics at Brandeis University, where she holds the Landsman Career Development Chair in the Sciences. She is currently on sabbatical in Spring 2025. Her research is centered in geometric group theory, with a focus on group actions on metric spaces, particularly hyperbolic and hierarchically hyperbolic structures. Ph.D., University of Wisconsin–Madison (2017), advised by Tullia Dymarz NSF Postdoctoral Fellow, Columbia University (2019–2021) NSF Postdoctoral Fellow, UC Berkeley (2018–2019) Morrey Visiting Assistant Professor, UC Berkeley (2017–2018) Her research interests include geometric group theory , acylindrically hyperbolic groups , hierarchically hyperbolic groups , mapping class groups , and group actions on hyperbolic spaces . She has made significant contributions to the understanding of acylindrical actions, hyperbolic structures on groups, and the geometry of infinite-type surfaces. She co-organizes the Brandeis Topology Seminar and serves as the Undergraduate Advising Head II. The most recent publications reflect a sustained focus on the interplay between large-scale geometry and algebraic structure in groups. Her work frequently explores classification of group actions, invariants under quasi-isometry, and structural properties of hierarchically hyperbolic and acylindrically hyperbolic groups. Several recent papers investigate the dynamics of big mapping class groups and the geometry of 3-manifold groups. Landsman Career Development Chair in the Sciences NSF Postdoctoral Fellow (twice) Morrey Visiting Assistant Professor She advises multiple graduate students and has taught a wide range of courses, from calculus to advanced topology. She has previously taught at Columbia University, UC Berkeley, and the University of Wisconsin–Madison. Her advising includes both current and graduated students, and she is actively involved in mentoring and academic leadership. She is a co-organizer of the Brandeis Topology Seminar, contributing to a vibrant research community. Dr. Abbott maintains an active research program with recent publications in top journals such as Algebraic & Geometric Topology , Mathematische Zeitschrift , and Transactions of the AMS . Her collaborative work spans multiple institutions and reflects deep engagement with the geometric group theory community.

• Geometric Group Theory
• Hyperbolic Groups
• Hierarchically Hyperbolic Groups
• +7 more

Yangfang (Helen) Zhou is a Full-time Associate Professor of Operations Management at Singapore Management University's Lee Kong Chian School of Business. She is also an Urban Fellow at the SMU Urban Institute and MPA Research Fellow. Ph.D. in Operations Management, Carnegie Mellon University (2012) Master in Industrial Administration, Carnegie Mellon University (2009) Master in Industrial Engineering, Tsinghua University (2007) Bachelor in Mechanical Engineering, Huazhong University of Science and Technology (2005) Zhou's research focuses on sustainable operations management with emphasis on renewable energy systems, electric vehicle operations, and agricultural sustainability. Her methodological expertise includes stochastic dynamic programming, financial engineering, and exponential cone programming applications. Her work has been recognized with multiple awards, including two Best Publication Awards in Environment-Sustainability from INFORMS (2023 and 2020), Runner-up for Harvey Greenberg Research Award (2023), and Urban Fellow status (2023-2024). She has received teaching honors annually from 2014-2023 through the Lee Kong Chian School of Business Dean's Teaching Honor List. Research trends show her work spans energy storage optimization, smart charging infrastructure, and agricultural operations modeling. She has developed innovative pricing models for renewable energy markets and sustainable business frameworks for utility companies.

• Sustainable Operations Management
• Renewable Energy Operations
• Electric Vehicle Adoption
• +6 more

Dr. Scott Lindstrom is a Lecturer at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences, within the Faculty of Science and Engineering. He holds an office in the Office of the Provost and is based at the Curtin Perth campus. His research focuses on advanced mathematical optimization, convex analysis, and algorithm design, with applications in numerical methods and feasibility problems. Key research interests include error bounds in optimization, convex programming, and the Douglas-Rachford algorithm. He has contributed to theoretical developments in proximal algorithms, projection methods, and Bregman distances. Lindstrom's work often bridges pure and applied mathematics, addressing challenges in optimization theory and numerical analysis. His publications span journals like Mathematical Programming and SIAM Journal on Optimization , with a focus on algorithmic innovation and rigorous mathematical analysis. Recent work includes studies on log-determinant cones, p-cones, and ADMM applications in signal processing. Lindstrom has also explored historical perspectives on mathematical practices and algorithmic evolution. Notable contributions include developing centering methods for spiraling algorithms and advancing understanding of quasi relative interiors in convex programming. His interdisciplinary approach integrates optimization theory with practical computational techniques, addressing both theoretical and applied challenges.

• Optimization
• Convex Analysis
• Nonlinear Programming
• +3 more

Alexandra Lassota is an Assistant Professor at Eindhoven University of Technology (Netherlands) since 2023. She holds a PhD in Computer Science from CAU (Germany), advised by Klaus Jansen. Her postdoctoral research included stints at EPFL (Switzerland) under Fritz Eisenbrand and at MPI-INF in Saarbrücken (Germany) with Danupon Nanongkai. Her work focuses on theoretical computer science, particularly integer programming, scheduling, and algorithmic complexity. Education: B.Sc./M.Sc. from Lübeck University (Germany), Ph.D. from CAU (Germany). Her research interests include mixed-integer programming , extension complexity , fixed-parameter tractability , and approximation algorithms . Recent work explores lower bounds for block-structured integer programs and the computational hardness of detecting points in integer cones. Her 2024 publications address topics like separable convex optimization, parameterized algorithms for large-scale integer programs, and aggregation of continuous preferences in AI-driven systems. These contributions highlight her expertise in bridging theoretical foundations with practical algorithmic challenges. Grants: Supported by Swiss National Science Foundation (SNSF), GA ČR, and Einstein Foundation Berlin. Lassota is affiliated with the Combinatorial Optimization group at TU/e and actively contributes to research in discrete mathematics and computational complexity.

• Integer Programming
• Scheduling
• Extension Complexity
• +3 more

Professor Timo de Wolff leads the Applied Algebra research group at the Institut für Analysis und Algebra of Technische Universität Braunschweig. He will serve as department head for the mathematics department from 2025 to 2027. His work bridges real algebraic geometry, polynomial optimization, and computational methods, with applications in chemical reaction networks, material design, and theoretical computer science. Education: PhD in Mathematics (2013), TU Braunschweig Master in Mathematics (2008) and Philosophy (2010), Goethe University Research Interests: Nonnegativity of real polynomials and sums of nonnegative circuits (SONC) Amoeba and tropical geometry Polynomial optimization hierarchies Mathematical software development (e.g., POEM, Maximal_Mediated_Sets.sage) Scientific Engagement: Associate Editor, SIAM Journal on Applied Algebra and Geometry Advisory Board, MEGA and ICMS conference series Organized summer schools and public exhibitions like 'Unendlichkeit - Leere - Lebendigkeit' (2025) Awards: DFG Emmy Noether Program member (2017-2023) Elected member (2019) and Chair (2022-2023) of Die Junge Akademie Contact: Primary: t.de-wolff@tu-braunschweig.de Group: applied_algebra@tu-braunschweig.de

• Applied Algebra
• Real Algebraic Geometry
• Polynomial Optimization
• +7 more

Ting Kei Pong is a Professor in the Department of Applied Mathematics at the Hong Kong Polytechnic University, where he has been employed since August 1, 2014. His research focuses on continuous optimization, with particular expertise in convex relaxations and first-order methods for large-scale optimization problems. Dr. Pong received his Bachelor's degree in 2004 and MPhil degree in 2006 from the Department of Mathematics at the Chinese University of Hong Kong. He completed his PhD in 2011 from the Department of Mathematics at the University of Washington under the supervision of Professor Paul Tseng, with co-advisement from Professors Maryam Fazel and Rekha Thomas after Professor Tseng's disappearance. His postdoctoral training included positions at the University of Waterloo (2011-2013) and the University of British Columbia (2013-2014) as a PIMS postdoctoral fellow. His research interests span Continuous Optimization , with current focus on convex relaxations and first-order methods for large-scale problems. He also investigates constraint qualifications for convex optimization, statistical computation, and robust optimization. His work bridges theoretical foundations with practical applications in areas such as compressed sensing, sensor network localization, and machine learning. Dr. Pong's publication record demonstrates consistent high-impact contributions to optimization theory. His recent work (2022-2025) shows a strong focus on convergence analysis, error bounds for conic optimization problems, and development of efficient algorithms for nonconvex optimization. His research often combines theoretical analysis with practical implementation, as evidenced by the availability of code for many of his publications. He serves as Associate Editor for Mathematics of Operations Research (since 2019) and on the editorial boards of Computational Optimization and Applications, Pacific Journal of Optimization, and Open Journal of Mathematical Optimization. Dr. Pong actively mentors students and postdocs, currently supervising PhD students Yanbo Wang and Hao Zhang, and postdoc Jiefeng Xu. His former students have secured positions at institutions including the University of Texas Arlington, University of Hong Kong, and Sun Yat-Sen University. His advising reflects his research expertise, with students working on topics in nonconvex optimization, compressed sensing, and matrix factorization. He maintains an active research schedule, regularly presenting at major optimization conferences including ICCOPT, ISMP, and SIAM Optimization. His upcoming talks in 2025 demonstrate continued research productivity in error bounds for log-determinant cones and single-loop proximal-conditional gradient methods.

• Continuous optimization
• Convex relaxations
• First-order methods
• +3 more

Bin Zou is an Associate Professor in the Department of Mathematics at the University of Connecticut. His research focuses on actuarial science, financial mathematics, and stochastic control, addressing critical problems in insurance, reinsurance, and investment strategies. Ph.D. in Mathematical Finance (2015), University of Alberta M.S. (2009) and B.S. (2007) in Mathematics, Beijing Institute of Technology Bin Zou's work explores optimal insurance design, reinsurance games, and portfolio management under uncertainty. His recent studies analyze stochastic differential games for reinsurance chains, mean-variance investment strategies in contagious markets, and hedging mechanisms for cryptocurrency futures. His publications span top-tier journals like Insurance: Mathematics and Economics , SIAM Journal on Financial Mathematics , and European Journal of Operational Research . Key themes include moral hazard in insurance contracts, model ambiguity in risk management, and machine learning applications in actuarial contexts. Casualty Actuarial Society 2022 Individual Grant

• Actuarial Science
• Financial Mathematics
• Stochastic Control and Optimization
• +3 more

Bartosz Kołodziejek is an Associate Professor at the Faculty of Mathematics and Information Science, Warsaw University of Technology. His research lies at the intersection of probability theory and mathematical statistics, with a focus on stochastic fixed point equations, free probability, and graphical models. He actively publishes in top-tier journals and maintains a strong collaborative network. His research interests include: Stochastic fixed point equations and their applications Characterizations of probability distributions on symmetric and homogeneous cones Free probability and free convolution Graphical models and high-dimensional statistics Symmetry in Gaussian models and model selection Perpetuities and tail behavior of stochastic recursions The analysis of his recent publications reveals a consistent trend in theoretical probability and statistical methodology, particularly in high-dimensional inference, random matrix theory, and invariant models. His work often involves deep analytical techniques and connections across fields such as convex analysis, exponential families, and stochastic processes. He has no listed scientific awards on his homepage. While no students or grants are explicitly mentioned, his active publication record and software contributions (such as the gips R package) suggest ongoing research leadership. He collaborates with prominent researchers in probability and statistics across Europe. He is involved in the development of statistical software, notably contributing to the theoretical foundations of the gips package for Gaussian models with permutation symmetry, used in high-dimensional data analysis.

• Probability Theory
• Statistics
• Stochastic Fixed Point Equations
• +7 more

Dr. Gaurav Tiwari is an Associate Professor in the Department of Civil Engineering at the Indian Institute of Technology Kanpur . His research focuses on Rock Mechanics , with expertise in Geotechnical Engineering and Rock Engineering . Education : PhD in Geotechnical Engineering (Rock Mechanics) from IISc Bangalore, M.Tech in Geotechnical Engineering from IIT Roorkee, and B.E. in Civil Engineering from M.B.M. Govt. Engineering College, Jodhpur. His work emphasizes Experimental Rock Mechanics , Probabilistic Rock Engineering , and Reliability Analysis for rock structures with limited data. Key applications include rock slope stability , tunnel support design , and dynamic behavior of jointed rocks . He has developed hybrid reliability frameworks and sensitivity analysis methods for spatially varying rock properties and data-deficient scenarios , as evidenced by his recent articles in journals like International Journal of Rock Mechanics and Mining Sciences and Rock Mechanics and Rock Engineering . Dr. Tiwari’s students have contributed to high strain rate rock analysis , grouted flaw dynamics , and probabilistic modeling . His scientific awards include the IGS-Leonard’s Best PhD Thesis Award (2018-2019), ISSMGE scholarship (2017), and multiple IGS-Roorkee YGE Awards for Best Papers. Laboratory Facilities : Computational tools (FLAC-2D/3D, UDEC, Rocscience Academic Bundle) and experimental setups (high-rate direct shear testing, 3D-printed synthetic samples, rock durability tests).

• Geotechnical Engineering
• Rock Engineering
• Experimental Rock Mechanics
• +3 more