Dr. Stefan Forcey is a Professor in the Department of Mathematics at the University of Akron, part of the College of Engineering and Polymer Science. His research focuses on combinatorial geometry, algebraic structures (such as operads and Hopf algebras), and their applications to phylogenetics and network analysis. He has collaborated on projects involving polytopes (e.g., associahedra, multiplihedra), electrical networks, and phylogenetic networks. His work bridges algebra, combinatorics, and geometry, with applications in evolutionary biology and circuit theory. Key projects include studies on balanced minimal evolution polytopes, split systems, and the relationship between phylogenetic networks and electrical circuits. He has advised numerous graduate students and co-authored papers on topics ranging from stochastic methods in phylogenetics to categorical structures in iterated monoidal categories. His research outputs include contributions to convex polytopes, algebraic frameworks for tree-like structures, and interdisciplinary applications in biomathematics. Dr. Forcey’s work often emphasizes geometric and combinatorial interpretations of algebraic constructs, with a focus on practical computational tools for phylogenetic analysis and network modeling.
Ruben van Beesten is an Assistant Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. He also holds an adjunct professorship at the Norwegian University of Science and Technology since September 2023, reflecting his international academic engagement and collaborative research. His research lies at the intersection of operations research, mathematical optimization, and applied economics. His primary interests include distributionally robust optimization, stochastic programming with integer recourse, error bounds in convex approximations, and energy system modeling. His work combines theoretical rigor with practical applications, particularly in energy markets and decision-making under uncertainty. The recent publications of Ruben van Beesten demonstrate a consistent focus on improving the tractability and accuracy of two-stage mixed-integer recourse models. His work spans theoretical developments in error bounds and parametric analysis, algorithmic improvements for energy system design, and market-level applications such as interconnector capacity incentives. The research trends highlight a strong emphasis on computational optimization, risk-averse decision frameworks, and the integration of robust methods in real-world systems. His scientific contributions are recognized through publications in top-tier journals such as SIAM Journal on Optimization , Computational Optimization and Applications , and Energy . While no formal awards are listed in the provided text, his active publication record and external collaborations indicate a growing academic impact. Ruben van Beesten collaborates with prominent researchers such as Ward Romeijnders, Kees Jan Roodbergen, and Asgeir Tomasgard. His research involves both theoretical grants in optimization methodology and applied projects in energy systems, suggesting involvement in funded interdisciplinary initiatives, though specific grants are not detailed. He has co-authored multiple peer-reviewed articles and a doctoral thesis, indicating a strong research trajectory. He is actively involved in a research network focused on optimization under uncertainty, with strong ties to institutions in Norway and the Netherlands. His work contributes to both academic and practical advancements in energy modeling and stochastic programming, positioning him within a dynamic and impactful research community.
Nikolaos Diaggelakis serves as an Assistant Professor in the Department of Process Development, Analysis & Design (II) at the School of Chemical Engineering, Technical University of Crete, where his work bridges theoretical optimization and industrial process applications. Education: Ph.D. in Chemical Engineering, Imperial College London, 2017 M.Sc. in Chemical Engineering, Imperial College London, 2012 Diploma in Chemical Engineering, National Technical University of Athens, 2011 Visiting Doctoral Researcher, Texas A&M Energy Institute, 2017 His research pioneers process systems engineering through mathematical optimization with moving time horizons, simultaneous design-control integration, and multi-parametric programming frameworks. Key applications span chemical process intensification, energy-efficient microgrids, and environmental systems, emphasizing worst-case and nonlinear optimization robustness. His theoretical contributions directly enable industrial implementations in energy systems and manufacturing. Publications from 2015-2022 reveal a concentrated evolution toward real-time optimization and decentralized control architectures , with increasing focus on data-driven surrogate models and robust explicit MPC strategies. The work consistently bridges fundamental algorithm development (e.g., multi-parametric quadratic programming solutions) with tangible industrial applications like air separation units and combined heat-power systems. Scientific Awards: Excellence Award for Outstanding PhD Thesis in Computer Aided Process Engineering (CAPE), Third Place, 2017 Invited Speaker, Distinguished Young Researchers Seminar Series, Northwestern University, 2016 Best Poster Award, CPSE Autumn Industrial Consortium Meeting, Imperial College London, 2014 Dr. Diaggelakis actively secures competitive funding, including U.S. Department of Energy grants (2018-2020) for smart manufacturing in chemical processing and National Science Foundation projects (2015-2016) on integrated process design-control frameworks. His leadership in the PAROC research group drives software development (POP toolbox) and industry collaborations through consortia like CPSE. Current projects emphasize uncertainty-aware scheduling for process industries and energy-efficient microgrid operation. He co-develops the industry-standard PAROC framework and POP toolbox, enabling multi-parametric optimization for complex process systems, and maintains active partnerships with Texas A&M and Imperial College London through ongoing research consortia.
Dr. Joan Duran Grimalt is an Associate Professor of Applied Mathematics in the Department of Mathematics and Computer Science at the University of the Balearic Islands (UIB). He serves as a member of the Mathematical Image Processing (TAMI) research group and the Institute of Applied Computing and Community Code (IAC3). From July 2021 to June 2024, he held the position of deputy director of the Higher Polytechnic School and head of studies for the Degree in Mathematics program. His academic career at UIB began in 2015 following the completion of his PhD. Dr. Duran Grimalt earned his academic credentials at prestigious institutions: BSc in Mathematics (2010) from the University of the Balearic Islands (UIB) MSc in Advanced Mathematics and Mathematical Engineering (2011) from the Polytechnic University of Catalonia PhD in Mathematics (2016) from UIB with thesis on variational models for ill-posed inverse problems in digital imaging His research spans the intersection of mathematical theory and practical applications in imaging science. Dr. Duran Grimalt specializes in nonlinear analysis, calculus of variations, partial differential equations, and deep unfolding architectures. His work focuses on developing mathematical frameworks that bridge traditional variational methods with modern deep learning approaches, particularly for image processing and computer vision applications. This hybrid methodology allows for both the interpretability of model-based approaches and the performance benefits of data-driven techniques. Analysis of his recent publications reveals a clear research trajectory toward integrating classical mathematical models with deep learning architectures, particularly through the technique of deep unfolding. His work consistently addresses challenging problems in satellite image processing, pansharpening, hypersharpening, and low-light image enhancement. The publications demonstrate a progression from purely variational approaches to increasingly sophisticated hybrid models that incorporate attention mechanisms, nonlocal operations, and specialized network architectures designed specifically for imaging problems. His notable scientific recognition includes: Fellowship from the Govern de les Illes Balears for PhD research (2011-2015) Dr. Duran Grimalt has secured research funding for multiple projects, leading two major initiatives. He has established significant international collaborations with the National Centre for Space Studies (CNES) in France, where he contributed to the image restoration chain for Earth observation satellites, and with the Oceanographic Centre of the Balearic Islands, focusing on deep unfolding architectures for remote sensing data fusion and marine object detection. His academic mentorship includes supervising PhD candidates M. Francesc Alcover (working on nonlocal theory for variational problems) and Daniel Torres (researching the combination of variational models and deep learning for image processing). He has also been a visiting researcher at leading institutions including the Technical University of Munich, ENS Paris-Saclay, and New York University. His research is conducted through the Mathematics, Imaging and Learning (MIA) Consolidated R+D+I Group, where he is an active member, and leverages resources from the Institute of Applied Computing and Community Code (IAC3). These research structures provide the computational infrastructure and collaborative environment necessary for his work on advanced image processing algorithms and their applications in satellite imaging and computer vision.
Yifan Hu is a Professor of Practice at Northeastern University, specializing in AI/ML/NLP and Information Visualization. He held senior roles at Amazon (Senior Manager of Applied Science, 2023-2024) and Yahoo! Research (Senior Director, 2014-2023), leading teams in AI-driven projects. Earlier, he contributed to AT&T Labs, Wolfram Research, and Daresbury Laboratory. His research focuses on graph visualization, machine learning, and data science applications. Research interests include advanced visualization techniques (e.g., SmartGD framework), AI moderation systems, graph algorithms, and federated learning fairness. He has pioneered methods for graph layout optimization, dynamic data visualization, and adversarial NLP defenses. Notable achievements include winning the 2017 IEEE ICDM 10-Year Highest-Impact Paper Award for work on collaborative filtering. He currently teaches Data Mining (CS 6220) and Machine Learning (CS 6140) at Northeastern, emphasizing practical applications of AI. Labs/Projects: Actively develops visualization tools like SmartGD and DeepGD , exploring interdisciplinary applications in healthcare, cybersecurity, and large-scale data analysis. His work bridges theoretical advancements with industrial-scale implementations.
David Brown is the Snow Family Business Professor in Decision Sciences at Duke University's Fuqua School of Business, where he has been on faculty since completing his Ph.D. in Electrical Engineering and Computer Science at MIT. He also serves as Faculty Director of the Center for Energy, Development, and Global Environment (EDGE). His research focuses on developing algorithms for decision problems under uncertainty, with applications in energy systems, stochastic scheduling, and dynamic pricing. Brown has held teaching roles in Decision Models, Data Analytics, and Convex Optimization, earning multiple teaching awards. Education: B.S. and M.S. in Electrical Engineering from Stanford University, Ph.D. in Electrical Engineering and Computer Science from MIT. Research Interests: Operations Research, stochastic models, dynamic programming, energy sustainability, and optimization. Current work includes improving electricity grid efficiency under renewable energy uncertainty (via the GRACE project funded by the U.S. Department of Energy) and developing sequential search and dynamic resource allocation algorithms. Publications: Brown has authored influential papers in Operations Research , Management Science , and other top journals, focusing on stochastic dynamic programming, duality theory, and applications in energy systems. Recent work explores fluid policies for resource allocation and dynamic frameworks for integrated energy systems. Awards: Second place, 2023 INFORMS Manufacturing and Service Operations Management Student Paper Competition First prize, 2015 INFORMS Decision Analysis Society Best Paper Award Teaching awards across multiple Fuqua programs Advising & Grants: Lead researcher on the GRACE project (DOE-funded); advisor to graduate students and researchers in energy systems and optimization. His work bridges theoretical advancements and practical applications in energy grid management and resource allocation. Labs/Teams: Directs the Center for Energy, Development, and Global Environment (EDGE) at Fuqua, fostering cross-disciplinary research on energy systems and sustainability.
Prof. Giulia Codenotti is a Junior Professor in the Discrete Geometry and Topological Combinatorics Group at the Institute of Mathematics, Freie Universität Berlin. Her research focuses on lattice polytopes, convex geometry, and simplicial complexes, with an emphasis on unimodular covers, triangulations, and algebraic-topological invariants. She holds an office at Arnimallee 2, Room 105/103, and can be reached at giulia.codenotti@fu-berlin.de. Her academic roles include teaching courses like "Discrete Geometry 1" and supervising research in combinatorial and convex geometry. Prior to her position at FU Berlin, she taught at Goethe University Frankfurt, leading seminars and exercise sessions in discrete mathematics and geometric optimization. Research Interests: Discrete and combinatorial geometry Lattice polytopes and their subdivisions Triangulations and unimodular covers Algebraic and topological invariants of simplicial complexes Covering minima and convex body geometry Outreach Activities: Soapbox Science Berlin (2019) - Public engagement on higher-dimensional geometry Girls' Day initiatives for promoting STEM among schoolgirls Co-creator of Polytopia, a project showcasing polyhedrons to the public Teaching Philosophy: Emphasizes foundational concepts in discrete geometry through problem-solving, with active participation in exercise sessions and rigorous assessment criteria including coursework and exams.
Prof. Dr. Christian Haase is a Professor at the Department of Mathematics within the School of Mathematics and Computer Science at Freie Universität Berlin. He leads the Discrete Algebraic Geometry & Math for Teaching Post workgroup. His research focuses on lattice polytopes, toric algebra, tropical geometry, combinatorial commutative algebra, algorithmic algebra, and geometric/topological combinatorics. He has held a permanent academic position since at least 2008, as evidenced by his extensive publication record from that period onward. His work includes collaborations on foundational topics like Ehrhart theory, integer decomposition properties, and convex polytope decompositions. His research contributions span theoretical advances in algebraic geometry and discrete mathematics, with applications to computational algebra and geometric combinatorics. Notable works include studies on the finiteness threshold width of lattice polytopes and the exploration of Gaussian mixture models' mode counts. He maintains an active role in mentoring PhD students and postdoctoral researchers in his workgroup, though specific advisee names are not listed here. His research has been published in top journals such as Advances in Mathematics, Mathematische Zeitschrift, and the Annals of Combinatorics. Haase's academic activities include organizing research projects and conferences, and he serves as a key contributor to the mathematical community through his editorial work and collaborative research initiatives.
Charles Burnette is an Assistant Professor of Mathematics at Xavier University of Louisiana. Prior to joining Xavier in 2020, he held postdoctoral positions at the Academia Sinica Institute of Statistical Science (Taipei, Taiwan) and Saint Louis University. He earned his Ph.D. in Mathematics at Drexel University in 2017 under Dr. Eric Schmutz. Education: Ph.D. in Mathematics, Drexel University (2017) Postdoctoral Experience: Academia Sinica Institute of Statistical Science (Taipei, Taiwan) Department of Mathematics & Statistics, Saint Louis University Dr. Burnette's research lies in discrete mathematics, with a focus on analytic combinatorics and probabilistic number theory. His work explores topics such as: Asymptotic enumeration Random permutations and their statistical properties Random polynomials/matrices over finite fields Integer anatomy and geodesic convexity in graphs Applications to biology and epidemiology His recent publications examine permutation statistics, rational function dynamics, and involutions in symmetric groups. Notably, he has collaborated with undergraduate students on projects like the 'Bee Colony Optimization for Traveling Salesperson Problem.' Dr. Burnette is deeply committed to education, teaching courses in discrete structures and combinatorics. He co-organizes mathematics outreach events, including Pi Day Jeopardy! and a high school math fair, where he often hosts competitions in Alex Trebek cosplay. Funding for his research has been provided by the Simons-Laufer Mathematical Sciences Institute's ADJOINT program and internally at Xavier University.
Gabriele Stabile is an Associate Professor at the Department of Methods and Models for Economy, Territory, and Finance of Sapienza University of Rome , specializing in mathematical finance and actuarial sciences. His academic work focuses on stochastic control, risk management, and optimization in financial and insurance contexts. Research Areas : Mathematical Finance, Actuarial Sciences, Stochastic Control, Insurance Modeling, Economic Optimization Recent Publications address topics like ambiguity in reinsurance contracts, optimal annuitization strategies, and stochastic control applications in taxation and commodity procurement. His work frequently appears in journals such as Finance and Stochastics and European Journal of Operational Research . Projects include studies on ambiguity in pension choices and quantitative management of longevity risks in life markets. His methodological approach emphasizes probabilistic modeling and decision theory under uncertainty.
Knarik Tunyan is a Visiting Assistant Professor in the Department of Mathematics/Computer Science at Purchase College, State University of New York, within the School of Natural and Social Sciences. She contributes to undergraduate education through courses in Linear Algebra, Calculus, Differential Equations, and advanced topics in mathematics. Education: PhD in Technology, Tampere University of Technology, Finland MS in Mathematics, Yerevan State University, Armenia Her research integrates applied mathematics with computational science, focusing on Linear Algebra, Numerical Analysis, Linear Programming, and Computational Inverse Problems . She explores mathematical methods in signal and image processing, optimization, network theory, economics, and biostatistics. Recently, she has been investigating the use of technology in mathematics education, including 3D printing and software tools like Mathematica and OpenSCAD. The 15 most recent publications highlight a consistent trajectory in matrix computations, optimization algorithms, and interdisciplinary applications . Early work centered on pivot rules and interior-point methods, particularly their behavior on challenging problems like the Klee-Minty cube. Later contributions expanded into image compression using QkRk factorization, Bayesian metabolic modeling, and educational innovations involving STEAM and 3D visualization. The articles reflect a blend of theoretical rigor and real-world application, spanning fields from computational biology to educational technology. Scientific Contributions and Recognition: Active presenter at international conferences including ICTCM and WCGO Published in reputable journals such as SIAM Journal on Matrix Analysis and Applications , Journal of Theoretical Biology , and Inverse Problems and Imaging Contributions to algorithmic efficiency in least squares and optimization She advises on curriculum development and student learning, particularly in integrating technology into mathematical instruction. Though no formal students are listed, she mentors through senior seminars and research-oriented courses. She has not received explicitly mentioned awards or grants in the provided text. Her work is supported by institutional resources and collaborative networks in computational mathematics and STEM education. Dr. Tunyan is involved in academic outreach and technological experimentation, especially through her presentations on 3D printing and mathematical software. She maintains regular office hours and engages students through digital platforms like Starfish, emphasizing accessibility and active learning.
Jacob Garcia is a Lecturer in the Mathematical Sciences Department at Smith College, Massachusetts, focusing on geometric group theory and boundary dynamics. He works on studying groups acting on their boundaries at infinity and generalizations of the Gromov boundary. Contact: jgarcia46@smith.edu . Education: Ph.D. (expected Spring 2024), University of California, Riverside (Topic: Stable subgroups and hyperbolic boundary generalizations). M.S., Ball State University (2018) in Mathematics (Topic: Generalized covering space theory). B.S., University of California, Davis (2016) in Mathematics. Jacob’s research bridges geometric group theory with hyperbolic geometry, exploring how finitely generated groups act on metric spaces and their boundaries. His work extends to hierarchically hyperbolic spaces, convex cocompactness, and generalized covering space theory. He has presented extensively at conferences like GAGTA, Young Geometric Group Theory, and MSRI Summer Schools. Scientific Awards: Dissertation Year Program Award (2023). Vernon A. Kramer Memorial Service Award (2022). Jones Junior Fellow in Pure Mathematics (2020). Jacob has supervised undergraduate research projects, including a study on (g,f)-colorability of knots. He actively contributes to outreach, organizing events like WIMIN 2023 and serving as Vice President of the AMS Graduate Student Chapter at UCR.
Prof. Dr. Volker Kaibel is a full-time Professor for Mathematical Optimization at Otto-von-Guericke Universität Magdeburg since 2007. His research focuses on Discrete Optimization , Polyhedral Combinatorics , and Extended Formulations , with applications in combinatorial optimization , quantum computing , and genomic epidemiology . His recent work includes studies on Polytope Extensions with Linear Diameters , Steiner Cut Dominants , and Scale-Free Spanning Trees . Publications span journals like SIAM Journal on Discrete Mathematics , Mathematics of Operations Research , and Journal of Computational Biology , emphasizing theoretical advancements in mathematical programming and polyhedral theory . Key projects include the long-running Mathematical Complexity Reduction (2017–2026) and earlier DFG-funded initiatives on Extended Formulations and Orbitopes . His collaborations extend to institutions like the Zuse-Institute Berlin and DFG Research Center MATHEON .
Dr. Erik Francisco Alvarez Quispe is a Visiting Researcher at the Energy Systems Modeling and Policy Analysis area of the Technological Research Institute (IIT) at Comillas Pontifical University, Madrid. He holds a Bachelor's in Mechanical and Electrical Engineering from the National University of Engineering (Peru) and a Master's in Electrical Engineering from State University of Campinas (Brazil). Bachelor's: Mechanical and Electrical Engineering, National University of Engineering (Peru) Master's: Electrical Engineering, State University of Campinas (Brazil) His research focuses on power system planning, convex relaxations in optimization, and stochastic modeling for energy systems. Recent work includes hydrogen-based virtual power plants and flexibility services in transmission expansion . His publications highlight trends in renewable energy integration , grid modernization , and mathematical optimization applied to energy systems. Projects like DEFINER (2022-2025) and OPEN_ENTRANCE (2019-2023) demonstrate his expertise in low-carbon energy transitions.
Alireza Olama is a Postdoctoral Researcher in the Department of Information Technology at Åbo Akademi University's Faculty of Science and Engineering. His work bridges parallel and distributed computing with machine learning and numerical optimization, contributing to the UN Sustainable Development Goals through algorithmic advancements. Doctoral research: Distributed framework for sparse convex optimization (2019, Universidade Federal de Santa Catarina) Master's thesis: Lyapunov Based Hybrid Model Predictive Control (2017) Research spans: Developing novel algorithms for distributed convex optimization (e.g., ADMM variants, Augmented Lagrangian methods) Creating software tools for sparse convex programming Applying optimization techniques to energy management systems Advancing GPU-accelerated machine learning frameworks Recent publications focus on ℓ0 sparsity, distributed consensus optimization, and hybrid control systems. Active in the academic community through conference presentations and collaborations with institutions like Norwegian University of Science and Technology (NTNU) and KTH Royal Institute of Technology.
