Dr. Min Sun is a Professor in the Department of Educational Policy, Organization and Leadership at the University of Washington's College of Education. Her research focuses on teacher learning, AI/ML integration in education, and policy-driven educational reforms. She leads interdisciplinary teams developing AI tools like the NSF-funded Colleague lesson planning platform and the IES-funded AmplifyGAIN Center. Her work addresses inequities in education through policy analysis and partnerships with K-12 schools and EdTech industries. Dr. Sun holds a Ph.D. in Educational Policy and Measurement from Michigan State University. She teaches courses such as EDLPS 302: Intro to Educational Policy and EDLPS 564: Economics of Education. Her research spans four key areas: AI/ML method development, AI-powered educational tools, data science training programs, and policy research with multi-sector collaborations. Notable grants include a $10 million IES grant for the AmplifyGAIN Center and a $1.5 million NSF grant for AI-driven math lesson planning. Her policy work emphasizes equitable education access and data-driven solutions. She directs the Education Policy Analytics Lab (EPAL) and collaborates with stakeholders to translate research into actionable strategies.
Dr. Ding Zhou is an Assistant Professor at the School of System Design and Intelligent Manufacturing (SDIM) at Southern University of Science and Technology (SUSTech). He holds a PhD in Industrial Design from Queensland University of Technology (QUT), Australia (commenced 2018), and prior academic roles include Associate Professor at Nanjing University of the Arts (2009–2018). His research focuses on cross-disciplinary innovation at the intersection of Industrial Design and STEM Education, emphasizing human-centric technology integration. He has conducted visiting research at the University of Virginia (2012) and Victoria University of Wellington (2014–2015). Education: PhD in Industrial Design, Queensland University of Technology (QUT), Australia (ongoing since 2018) M.A. and B.A. in Industrial Design (exact institutions unspecified) Research Interests: Dr. Zhou’s work bridges design science and education, prioritizing how technology enhances human experiences. Key areas include 3D printing applications in STEM education, exoskeleton robotics, computational design aesthetics, and user-centric design processes. He advocates for pedagogical frameworks that cultivate students’ transdisciplinary problem-solving skills through integrated STEM tasks. Articles Trends (2022–2025): Recent publications highlight advancements in 3D printing for education and manufacturing, human-robot interaction, and smart classroom technologies. Notable themes include scalable computer vision systems for posture detection, topology optimization in additive manufacturing, and BIM-enabled construction processes. Labs & Teams: He leads the Design Science and Educational Innovation Lab (DSEIL), focusing on translating theoretical research into practical educational tools and technologies.
Prof. Dr. Elena Mäder-Baumdicker holds a Tenure Track position in Geometry at TU Darmstadt since 2019. She was previously a postdoc at KIT and conducted research at Princeton University. Currently, she is organizing conferences such as the BIRS workshop on Non-linear Critical Point Theory and the Women in Geometry 3 conference. Her research focuses on global and local questions in differential geometry, particularly geometric variational problems involving minimal surfaces and Willmore surfaces. She has supervised two active PhD students (Nils Neumann and Jona Seidel), along with several Master’s and Bachelor’s theses. She is a member of the SPP 2026 'Geometry at infinity' and actively engages in academic outreach, including the Meet&Math initiative and public lectures for school students. Her teaching includes courses on differential geometry and seminars on geometric analysis. Education: PhD in Mathematics (2014, Freiburg), Diploma in Mathematics (2010, Freiburg) Research Interests: Minimal Surfaces, Willmore Surfaces, Geometric Flows (e.g., Mean Curvature Flow), Alexandrov Spaces, Variational Problems in Geometry Recent Conferences: Co-organizer of the Ernst Kuwert 60th Birthday Conference (2021), Geometric Analysis meets Geometric Topology (2019), and multiple BIRS events (2023–2025) Grants & Fellowships: DFG-funded research position (2017–2019), Leopoldina Fellowship (2018–2019) Labs/Teams: Leads the 'Variational Problems in Geometry' team at Women in Geometry 3, co-runs the RMU Seminar 'Solution Concepts in PDEs' Her work emphasizes the interplay between geometric analysis and topology, with a focus on singularities in geometric flows and applications of variational methods. She actively participates in promoting gender equality in academia through her role in the AK-Gleichstellung committee.
Colva Roney-Dougal is a Professor of Pure Mathematics at the University of St Andrews, affiliated with the School of Mathematics and Statistics. She holds roles in the Centre for Interdisciplinary Research in Computational Algebra and the St Andrews GAP Centre. Her research focuses on group theory, computational algebra, and combinatorics, with particular emphasis on symmetric groups, permutation groups, and algorithmic aspects of group theory. She has advised PhD students including Jung Won Cho, Coen Del Valle, and Peiran Wu. Her work includes editing conference proceedings such as 'Groups St Andrews 2022' and contributing to foundational studies in group structure and relational complexity. Awards include an OBE for services to science and mathematics, and recognition for impactful research outputs. Her publications span theoretical advances in group actions, graph symmetry, and computational methods, often involving collaborations with leading mathematicians. She actively participates in international conferences, delivering plenary lectures and organizing workshops on group theory and its applications.
James B D Joshi is a Professor in the Department of Informatics and Networked Systems (DINS) at the University of Pittsburgh’s School of Computing and Information (SCI). He joined Pitt in 2003 after earning his PhD from Purdue University. From 2019 to 2023, he served as a Program Director for the NSF’s Secure and Trustworthy Cyberspace (SaTC) program. Currently, he is an intermittent Expert in NSF’s TIP Directorate and led the NSF PDaSP program. His research focuses on cybersecurity and privacy, including access control, AI/ML security, cloud/edge security, and privacy-preserving techniques. Education: PhD, Computer Engineering, Purdue University (2003) MS, Computer Science, Purdue University (1998) BE, Computer Science & Engineering, Motilal Nehru NIT, India (1993) Research Interests: Privacy, security, trust, attribute-based encryption, cloud security, insider threat detection, and privacy-preserving AI/ML. His work emphasizes trustworthy systems and secure distributed computing environments. Recent Contributions: His articles span privacy-preserving federated learning, insider threat mitigation, and secure edge computing. Recent projects include PPFL-RDSN (2025), TAPFed (2024), and blockchain-based transparency frameworks (2022). Awards & Honors: NSF Director’s Award (2023), IEEE Fellow (2023), ACM Distinguished Member (2017), NSF CAREER Award (2006), and SIRI Research Leadership Award (2018). Service & Leadership: Founding director of LERSAIS lab, co-chair of IEEE conferences (TPS, CIC, CogMI), and former Editor-in-Chief of IEEE Transactions on Services Computing. Active in NSF and NITRD policy initiatives, including privacy and digital assets R&D strategies.
Fedor Fomin is a Professor in the Department of Informatics at the University of Bergen. His research focuses on Theoretical Computer Science, including Graph Algorithms, Parameterized Complexity, Combinatorics, and Combinatorial Games. He is affiliated with the Norwegian Academy of Science and Letters, the Norwegian Academy of Technological Sciences, and the Academia Europaea, and holds fellowships from ACM and EATCS. His work has been recognized with the EATCS Nerode Prize in 2015 and 2017. Dr. Fomin has authored influential books such as Kernelization: Theory of Parameterized Preprocessing and Parameterized Algorithms , which are foundational in the field of algorithm design. His recent publications explore cutting-edge topics in parameterized complexity, graph theory, and distributed computing, with contributions to approximation algorithms, kernelization, and combinatorial optimization. His awards and honors reflect his significant impact on theoretical computer science. Fomin has been awarded an ERC Advanced Grant and has mentored numerous researchers, contributing to the advancement of algorithmic techniques and their applications.
Neil Nicholson is a Professor of the Practice in the Department of Mathematics at the University of Notre Dame, part of the College of Science. He holds a B.A. from Lake Forest College (2002) and a Ph.D. from the University of Iowa (2007). His research focuses on collaborative undergraduate projects across diverse mathematical fields, with primary interests in topology (knot theory, knot invariants, and piecewise linear knots) and secondary interests in algorithmic music writing, graph theory, computational geometry, and math education. He has published works in mathematics education and interdisciplinary journals, including a textbook on proof techniques and studies on service-learning impacts. Nicholson advises undergraduate research in areas bridging pure mathematics and applied computational methods. His office is located in 146 Hayes-Healy Bldg.
Joe D. Warren is a Professor of Computer Science at Rice University, where he has served since 1986. His research focuses on computer graphics, geometric modeling, and computational geometry, with notable contributions to subdivision surfaces, as detailed in his book Subdivision Methods for Geometric Design . He also explores bioinformatics applications, including 3D modeling of mouse brain gene expression and lung motion analysis from 4D CT scans. Additionally, Warren teaches courses in computer game design and introductory programming, co-developing award-winning Coursera specializations. He earned his Ph.D. from Cornell University and previously served as Department Chair (2008-2013). Education: B.Sc. (Rice University, 1983), Ph.D. (Cornell University, 1986) Research Collaborators: Baylor College of Medicine, MD Anderson Cancer Center, Texas A&M Teaching: COMP460 (Game Design), COMP110 (Introductory Computing) His work on the GRACE tool (Graphical Ruler and Compass Editor) won the 1998 Quest for Java award. He has advised PhD students now at Washington University and Texas A&M. Current projects include game prototyping with Pi Studios and lung motion modeling for medical diagnostics.
Robert V. Kohn is the Silver Professor of Mathematics at New York University, affiliated with the Courant Institute of Mathematical Sciences (CIMS). He holds academic positions within the Department of Mathematics at the College of Arts & Science and the Graduate School of Arts & Science. His research focuses on nonlinear partial differential equations (PDEs), calculus of variations, and their applications to materials science, thin elastic sheets, and machine learning. Education: Ph.D. in Mathematics from Princeton University (1979), M.Sc. from the University of Warwick (1975), and A.B. from Harvard University (1974). Research interests span elastic energy-driven pattern formation (e.g., wrinkling, folding), PDEs in machine learning (e.g., prediction with expert advice), and continuum mechanics. Recent work includes variational analysis of thin film mechanics and PDE-based approaches for binary sequence prediction. His articles explore topics ranging from metamaterials to stochastic growth models. Notable themes in his publications include energy minimization in materials, optimal control analogies in learning algorithms, and mathematical modeling of physical phenomena. While no formal awards are listed, his contributions to PDE theory and applied mathematics are widely recognized. Advising and grants details are not explicitly documented here.
Luana Ruiz is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), affiliated with the Mathematical Institute for Data Science (MINDS) and the Data Science and Artificial Intelligence Institute (DSAI). She earned her Ph.D. in Electrical Engineering from the University of Pennsylvania (2022), and dual B.Sc. and M.Eng. degrees in Electrical Engineering from the University of São Paulo (Brazil) and École Supérieure d’Electricité (France, now CentraleSupélec) in 2017. Her research focuses on machine learning, signal processing, and network science, particularly scalable algorithms for non-Euclidean domains like graphs and data manifolds. She has pioneered work on graph neural networks (GNNs), graph sampling, and theoretical limits of transferability and generalization in graph-based learning. Notably, she has developed methods for efficient GNN training on large graphs and stability analysis of neural networks on manifolds. Affiliations: Johns Hopkins University, MINDS, DSAI Education: Ph.D., University of Pennsylvania (2022); B.Sc./M.Eng., University of São Paulo & CentraleSupélec (2017) Her research interests include large-scale graph machine learning, manifold learning, physics-informed ML, and combinatorial optimization. She has received awards such as the iREDEFINE Fellowship (2019), MIT EECS Rising Star (2021), and Best Student Paper Awards at EUSIPCO (2019, 2021). Her work bridges theoretical foundations and practical applications, with contributions to GNN architecture design, signal processing on graphs, and stability guarantees for neural networks on manifolds. Recent publications highlight advances in graph sampling for scalable GNNs, stability of manifold neural networks, and theoretical analysis of GNN expressivity. She collaborates with institutions like MIT and the Simons Institute, and her interdisciplinary approach addresses challenges in graph data analysis and AI efficiency. She advises on grants related to graph signal processing and has contributed to the development of graphon-based frameworks for large-scale graph analysis. Awards: Eiffel Excellence Scholarship (2013–2015), Best Paper Awards at EUSIPCO (2019, 2021) Grants/Advising: METEOR and FODSI postdoctoral fellowships, Google Research Fellowships
Dr. Angelina Anani is an Associate Professor in the Department of Mining & Geological Engineering at the University of Arizona, College of Engineering. She is also a member of the Graduate Faculty and actively contributes to research and teaching in mining systems optimization, mine planning, and sustainable mining practices. Education: PhD in Mining Engineering, Missouri University of Science and Technology, Rolla, Missouri, United States BS in Mining Engineering (Summa cum laude), Missouri University of Science and Technology, Rolla, Missouri, United States Research Interests: Dr. Anani's research spans a broad spectrum of mining engineering challenges, focusing on modeling and optimization of mining systems , mine planning and production scheduling , and sustainable mining system design . She investigates mine equipment reliability , tunneling and underground works , and energy and water efficiency . A significant portion of her recent work integrates machine learning and data-driven approaches into mine safety and planning, including 3D/4D/VR applications and digital twin systems . Her interdisciplinary approach also includes ethnographic research in mining communities and supply chain management in the mining sector. Publications Trends: Her recent publications reflect a strong shift toward intelligent systems in mining, with increasing focus on machine learning for safety, process mining for maintenance, and digital twin deployment. She combines traditional optimization techniques like discrete event simulation with modern AI to solve complex mining challenges, particularly in underground and transition mines. Scientific Awards: Freeport-McMoRan, Inc. Career Development Grant Society for Mining, Metallurgy and Exploration, Fall 2022 Faculty Core Advising and Grants: Dr. Anani supervises graduate research through MNE 900 (Research), MNE 910 (Thesis), and MNE 920 (Dissertation) courses. She has secured external funding such as the Freeport-McMoRan Career Development Grant, supporting her innovative work in mine optimization and safety. While current students are not listed, her active supervision load indicates ongoing mentorship of master’s and PhD candidates. Labs and Teams: She is actively involved with the San Xavier Underground Mine Laboratory, where she contributes to monitoring systems and digital twin development. Her collaborative work with researchers from Chile and Ghana highlights her international engagement. She is also affiliated with professional societies including the Society of Mining, Metallurgy and Exploration (SME), Society of Mining Professors, and Women in Mining (WIM), contributing to both technical and diversity initiatives in the field.
Michael Shelley is the Lilian and George Lyttle Professor of Applied Mathematics at New York University's Courant Institute of Mathematical Sciences. He holds additional roles as Professor of Mathematics, Neural Science, and Mechanical Engineering. His research focuses on fluid dynamics, active matter, and biophysical systems, with notable contributions to microswimmer dynamics, cytoplasmic flows, and fluid-structure interactions. Shelley leads the Applied Mathematics Laboratory at Courant and directs the Center for Computational Biology at the Flatiron Institute. His work bridges theoretical, computational, and experimental approaches to understand complex biological and physical phenomena. Research interests include nonlinear dynamics of fluids, collective behavior in active matter systems, and biomechanical processes such as mitosis and cellular transport. Recent studies involve modeling microtubule networks, cytoplasmic stirring, and spindle positioning in cells. His publications span topics like fluid-structure interactions, viscoelastic flows, and the mechanics of swimming organisms. Key projects include the dynamics of erodible bodies in fluid flows, optimization of microswimmer designs, and the rheology of active suspensions. Shelley collaborates across disciplines, integrating applied mathematics with biology, physics, and engineering. His work has advanced understanding of self-organization in living systems and fluid-driven morphological changes.
**Dan Abramovich** is a Professor in the Department of Mathematics at Brown University, affiliated with the College of Arts and Sciences. His research focuses on algebraic geometry, particularly in resolution of singularities, logarithmic geometry, moduli spaces, and birational geometry. He has authored numerous papers and contributed to foundational work in these areas, supported by NSF and BSF grants. **Teaching**: He teaches courses like Math 1540 (Galois theory and representations of finite groups) and has extensive past teaching records dating back to 2015. His seminars include Algebraic Geometry and Topology. **Research**: Abramovich's work bridges algebraic structures and geometric applications, with notable contributions to logarithmic geometry and moduli theory. His recent articles address weighted blow-ups, dynamical systems in resolution, and functorial monomialization. **Grants & Collaboration**: He collaborates internationally, organizing conferences like AGNES and BATMoBYle. His work is disseminated via arXiv, with over 50 publications since the 1990s.
Craig S. Kaplan is a Professor at the University of Waterloo's David R. Cheriton School of Computer Science within the Faculty of Mathematics. His research bridges computer science and mathematical art, focusing on computational geometry, geometric pattern design, and algorithmic art. He holds degrees including a Ph.D. and M.Sc. from the University of Washington (2002, 1998) and a B.Math. from the University of Waterloo (1996). His work spans applications of mathematics in art and design, including Islamic geometric patterns, computer graphics, and computational geometry. Notable contributions include developing methods for generating Islamic geometric patterns, exploring aperiodic tilings (including the discovery of an aperiodic monotile in 2023), and creating tools for artistic visualization like RepulsionPak and FlowPak. He also engages with digital art forms such as generative algorithms, stereoscopic 3D rendering, and interactive applications for mindfulness and VR. His publications reflect a focus on geometric algorithms, pattern generation, and interdisciplinary art-science projects. Though no explicit awards are listed, his prolific output and contributions to mathematical art suggest significant recognition in his field. His work often emphasizes computational methods for artistic expression, combining rigorous mathematics with creative outcomes.
Prof. Michael Schneider is Universitätsprofessor and Chair of Computational Logistics at RWTH Aachen University since 2016. Previously, he held positions at TU Darmstadt (2013-2016) and earned his doctoral degree from TU Kaiserslautern (2012). His research focuses on logistics optimization, including transportation routing, warehouse management, and metaheuristic methods for solving complex operational challenges. He leads the Global Challenges Lab and serves as communication chair of VeRoLog (EURO's vehicle routing group). Research interests include: quantitative modeling for supply chains, heuristic/exact optimization methods, electric vehicle routing, territory design, and production planning. Notable achievements include the INFORMS Journal on Computing Meritorious Paper Award (2021) and editorial roles in journals like Applied Mathematical Modelling. Publications span topics like vehicle routing with time windows, electric logistics networks, and warehouse automation strategies. His work integrates advanced algorithms with real-world industry applications (e.g., DHL, Picnic). The Computational Logistics group collaborates on projects addressing sustainability, autonomous systems, and modern warehousing challenges. Education: Business Admin & Computer Science (University of Mannheim), PhD (TU Kaiserslautern) Professional Roles: VeRoLog Communication Chair, EURO Working Group Key Projects: Electric vehicle routing optimization, warehouse automation, and sustainable logistics networks
