Eric Kuennen is a Professor at the University of Wisconsin Oshkosh based at the Oshkosh Campus, with office Swart 111 and contact details kuennene@uwosh.edu/(920) 424-1059. His research centers on Mathematics Education and Mathematical Sophistication, particularly instrument development for measuring pre-service teacher knowledge and curriculum design for elementary/middle grades educators. Secondary interests include Statistics Education and computational physics modeling. Publication analysis reveals two dominant research streams: (1) pedagogical frameworks for teacher preparation spanning numbers & operations to modern algebra, and (2) cluster growth modeling in physics. His work consistently bridges theoretical constructs with classroom applications in mathematics education.
Jennifer Szydlik is a Professor at the University of Wisconsin Oshkosh, Department of Mathematics. She specializes in mathematics education with a focus on preparing elementary teachers. Her research emphasizes mathematical practices, pedagogical strategies, and educator development. Her work explores how to foster mathematical reasoning and conceptual understanding in pre-service teachers through inquiry-based learning and classroom dialogue. Key areas include Common Core Standards implementation, algebra/geometry pedagogy, and assessing mathematical sophistication. Her publications span over two decades, from foundational calculus education studies (1995) to contemporary work on transforming elementary teacher preparation (2020). Current research trends focus on improving preservice teachers' mathematical beliefs and content knowledge through innovative curricula. No scientific awards are listed, though her CV indicates sustained contributions to mathematics education scholarship. She advises no listed students but actively engages in curriculum development for teacher training programs. No specific grants or labs are documented in the provided text.
Adam Kurpisz is a Tenure Track Professor at Bern Business School (BFH) and a senior researcher at the Institute for Operations Research (IFOR) at ETH Zürich. He holds a PhD from Wrocław University of Science and Technology and has held postdoctoral and visiting researcher positions at IDSIA and the Max-Planck-Institut für Informatik. His research bridges theoretical computer science and applied optimization, focusing on combinatorial optimization, semi-algebraic proof systems, and robust optimization. He has secured over CHF 600,000 in grants, including leadership in the Ambizione Junior Research Group. His teaching spans convex optimization, polynomial optimization, and discrete mathematics at ETH Zürich and BFH. He co-founded startups Silencions and Deeptale, advancing to international competitions and securing EU grants. Research interests include leveraging algebraic geometry and Fourier analysis to develop algorithms for optimization problems. He has published extensively on sum-of-squares hierarchies, approximation algorithms, and scheduling under uncertainty. His work has been presented at venues like ISSAC, ICALP, and SIAM Conferences.
Dr. Jemimah Young is an Associate Professor in the Department of Teaching, Learning & Culture at Texas A&M University. She specializes in multicultural and urban education, focusing on academic outcomes for historically marginalized populations, particularly Black women and girls. Her work bridges research with practice through teaching undergraduate and graduate courses on culture, identity, diversity, social justice, educational foundations, and research methodology. Additionally, she has extensive experience in K-12 education as a teacher and consultant in urban school systems across the U.S. Dr. Young serves as program chair for the AERA Critical Examinations of Race, Class, and Gender SIG and co-founder/co-editor of the Journal of African American Women and Girls in Education. She also chairs the Multicultural Education program in her department. Her research emphasizes equity in STEM, gifted education, and curriculum design, often employing critical race theory and QuantCrit methodologies to analyze systemic disparities. Her scholarly contributions span topics such as Black girls’ STEM identity formation, the impact of teacher diversity on student belonging, and reimagining multicultural education. She critiques policies like school tracking and advocates for culturally informed pedagogical practices through frameworks like the Urban Education Typology and the equity paradox typology. Dr. Young’s advisory and editorial roles reflect her commitment to amplifying marginalized voices in education. She has co-edited influential works like Cultivating Achievement, Respect, and Empowerment (CARE) for African American Girls in PreK-12 Settings (2017). Her work frequently intersects with policy analysis, content analysis, and mixed-methods research to address racial and gender inequities in educational systems.
Michael J. Tsatsomeros is a Professor in the Department of Mathematics & Statistics at Washington State University (WSU), affiliated with the College of Arts and Sciences. His academic journey includes positions at the University of Regina (Canada), University of Wisconsin, and the Indian Institute of Technology-Madras. He holds a Ph.D. from the University of Connecticut (1990), an M.Sc. from Concordia University (1986), and a Diploma in Mathematics from the University of Patras, Greece (1984). His research focuses on Linear Algebra and Matrix Analysis , with emphasis on nonnegative matrices, numerical range, and applications in dynamical systems, control theory, and AI. He has supervised numerous Ph.D. and Master’s students, including notable advisees such as Samir Mondal, Hung Le, and Pietro Paparella. His work has been recognized with awards like the 2024 Tom & Julie Lutz Teaching Excellence Award and the 2021 Excellence in Advising Award. Key publications include studies on P-matrices, Cayley transforms, and semimonotone matrices, often appearing in Linear Algebra and its Applications and Electronic Journal of Linear Algebra . He co-authored textbooks like Matrix Positivity (2020) and Linear Algebra and Matrix Methods (2024). Tsatsomeros serves as an Advisory Editor for Electronic Journal of Linear Algebra and Associate Editor for Linear Algebra and its Applications . His grants include the SPARC Collaboration Grant (2019–2021) and NSERC research funding. Beyond academia, his postdoctoral mentorship includes Fei Zhou and Panos Psarrakos, who now hold academic and industry roles. His research bridges theoretical matrix analysis with applied problems in engineering and computer science.
Frank R. Kschischang is a Distinguished Professor of Digital Communication in the Department of Electrical and Computer Engineering at the University of Toronto. He holds the Canada Research Chair in Communications Algorithms and has been a faculty member since 1991. His research focuses on coding theory, digital communications, and information theory, with applications in optical, wireless, and fiber-optic systems. He is a Hans Fischer Senior Fellow at the Institute for Advanced Study (TUM-IAS), Technische Universität München, collaborating on advancing fiber-optic communication capabilities. Education: B.A.Sc. (University of British Columbia, 1985), M.A.Sc. and Ph.D. (University of Toronto, 1988 and 1991). He has held visiting roles at MIT (1997–1998) and ETH Zurich (2005). Academic roles include IEEE Information Theory Society President (2010) and editorial roles for the IEEE Transactions on Information Theory. Research emphasizes error-correcting codes, fiber-optic channel modeling, and nonlinear Fourier transform-based communication. Notable contributions include staircase codes for 100 Gb/s optical networks and rank-metric codes for network coding. His work has led to practical advancements in high-speed optical systems and improved channel capacity understanding. Awards include IEEE Fellow (2006), Killam Research Fellowship (2010), and multiple teaching and service awards. He advises numerous graduate students and collaborates internationally on projects like spectral modulation in fiber optics and machine learning for signal processing. Labs and teams: Leads the Coding and Information Theory group at UofT, focusing on cutting-edge research in communications algorithms and hardware implementation. Collaborates with industry partners like the Communications Research Centre Canada and Inphi Corp.
Nicolas Boumal is an Assistant Professor in Mathematics at EPFL, Switzerland, holding dual affiliations in the Continuous Optimization Chair (OPTIM) within the Institute of Mathematics (MATH) and the SMA Education Group. He leads research in optimization on Riemannian manifolds, non-convex optimization, and statistical estimation. His work bridges geometry, numerical analysis, and applications in cryo-electron microscopy and synchronization problems. Education: PhD in Mathematical Engineering from UCLouvain (2014), postdoctoral research at Inria Paris and Princeton University. Prior to EPFL, he was an Instructor and Assistant Professor at Princeton’s Mathematics Department. Research Interests : Non-convex optimization landscapes, optimization on manifolds, low-rank matrix optimization, synchronization, phase retrieval, and computational methods in cryo-EM. His ERC Starting Grant (GEOSYM, 2022–2027) focuses on geometric and symmetric optimization techniques. Grants & Awards : ERC Starting Grant (2021), SIAM Optimization Prize (2018 for student co-author), and several conference best paper awards. Teaching : Courses include Continuous Optimization (MATH-329), Algebra Linéaire (MATH-111), and graduate-level Optimization on Manifolds (MATH-512). He authored the textbook An Introduction to Optimization on Smooth Manifolds (Cambridge University Press, 2023). Labs & Teams : Heads the OPTIM lab at EPFL, collaborating with global researchers in optimization and applications. His group develops the Manopt toolbox for manifold optimization.
Халил Снопче is a Regular Professor (part-time) at the Faculty of Modern Sciences and Technologies, South East European University (SEEU) in Tetovo, North Macedonia. He holds a Doctorate in Computer Science and Applied Mathematics from SEEU (2011), a Master's in Numerical Analysis from the University of Tirana (2007), and a Bachelor's in Mathematics from Ss. Cyril and Methodius University (1997). His academic roles include Deputy Assembly Member of North Macedonia (2020–present), Associated Professor (2016–2020), and various administrative positions like Pro-Dean for Administrative Issues (2008–2010). His research focuses on applied mathematics, parallel processing, blockchain applications in education, and determinant calculation algorithms. Over 20+ publications since 2016 span topics like blockchain-based academic credential verification, pandemic-era online education assessment, and computational mathematics optimizations. He has contributed to interdisciplinary projects such as a national research ranking platform and an automated job-market-university curriculum matching model. His professional experience includes 15+ years in education and research roles, including teaching at high school and university levels. He has held political roles such as Head of Gostivar Municipal Council (2000–2004). Fluent in Albanian (native), Macedonian (C2), Turkish (C1), and other languages, his work bridges technical innovation with societal challenges.
Natacha Gesquière is a researcher affiliated with the University of Ghent (UGent), focusing on computational thinking, artificial intelligence in education, and interdisciplinary STEM learning. Her work bridges technology and pedagogy, particularly in K-12 contexts. Key research areas: Computational Thinking, Social Robotics, and AI-Driven Education Collaborates with UGent colleagues like Francis wyffels and Tom Neutens Active in developing educational frameworks and tools for teachers Her publications highlight applications of physical computing, social robotics, and universal quadratic forms in educational settings. She contributes to open-access resources and interdisciplinary teacher design teams.
Stein Arnold Berggren is a Senior Lecturer at Østfold University College, affiliated with the Department of Natural Sciences, Practical-Aesthetic, Social and Religious Studies (RES) within the Faculty of Education. He holds a Master of Science in Applied Mathematics and Practical Pedagogical Education (PPU). His academic career includes roles as Assistant Professor since 2013 and extensive high school teaching experience. Berggren specializes in mathematics education, particularly in primary and continuing education. He is a key contributor to the Scottish Storyline Approach in teacher training, emphasizing interdisciplinary learning and narrative-based pedagogy. His research explores topics like sustainability integration in mathematics, collaborative learning through escape rooms, and transitioning algebraic concepts between educational levels. Education: Cand. Scient in Applied Mathematics, PPU certification. Research focuses on early mathematical literacy, teacher education innovation, and curriculum transitions. Notably, he co-authored works on recycling as a pedagogical tool and digital inclusive practices in education. His publications span journals like Applications of Mathematics and Composites Part B: Engineering , with recent emphasis on educational methodologies. Berggren collaborates widely, appearing in over 30 peer-reviewed articles and books since 2001, including contributions to sustainability-focused curricula and narrative-driven teaching strategies.
Olga Kanishcheva is an Associate Professor at the Department of Intellectual Computer Systems, National Technical University "Kharkiv Polytechnical Institute" (NTU "KhPI"). Her academic and research activities span natural language processing, sentiment analysis, and language-technology integration. Education: PhD in Computer Science (2010) and MA in Mathematics (2006) from NTU "KhPI". Her research focuses on applying computational methods to linguistic problems, including authorship attribution, semantic representation, and knowledge modeling. Her recent publications highlight statistical and algebraic approaches to NLP and semantic analysis. She has contributed to international projects like AComIn (Advanced Computing for Innovation) and developed monographs and educational materials in informatics and computational linguistics. Notable achievements include the Valentin Sviridov Scholarship for scientific excellence. Scientific Awards: Valentin Sviridov Scholarship in informatics (2012). Her teaching portfolio includes courses on algorithms, natural language processing, and object-oriented programming at NTU "KhPI".
Francesca Balestrieri is an Assistant Professor in the Department of Computer Science, Mathematics, and Environmental Science at the American University of Paris (AUP). She holds a DPhil in Arithmetic Geometry from the University of Oxford (2017) and a Laurea Magistrale in Scienze Filosofiche from Ca' Foscari University of Venice (2020). Her research focuses on arithmetic geometry, specifically rational points and zero-cycles on algebraic varieties, with interdisciplinary interests in philosophy and artificial intelligence. She has held postdoctoral positions at the Max Planck Institute for Mathematics in Bonn and a Marie Curie Fellowship at IST Austria. She teaches courses in mathematics, data science, and AI at AUP. PhD: DPhil in Arithmetic Geometry, University of Oxford (2013–2017) MSc: Mathematics and Foundations of Computer Science, University of Oxford (2012–2013) BA/MA: Mathematical Tripos, University of Cambridge (2009–2012) Her research bridges pure mathematics with applied fields like technology policy, reflected in her co-authored books *Guerra digitale* (2019) and *Tecnologie dell'impero* (2024), analyzing tech geopolitics. Recent work includes studies on Brauer-Manin obstructions, Campana points, and AI applications. She also explores the philosophy of mathematics, particularly Wittgenstein’s rule-following paradox. Notable awards include the 2-year Marie Curie Individual Fellowship (2019–2021). She actively contributes to teaching, including courses on calculus, statistics, and probability, while leading projects on AI ethics and machine learning applications in speech editing and environmental modeling.
Ralf Zimmermann is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark. His research focuses on Numerical Linear Algebra, Matrix Analysis, and Scientific Computing, with a particular emphasis on Reduced Order Modelling and Manifold Geometry. He has contributed significantly to the development of efficient algorithms for dynamical systems and geometric optimization on matrix manifolds such as the Stiefel and Grassmann manifolds. His work bridges computational mathematics and engineering applications, including aerodynamics and model reduction techniques. Key research interests include the theoretical foundations of manifold geometry, algorithm design for high-dimensional data, and the application of these methods to real-world problems. He has published extensively on topics like Riemannian metrics, injectivity radii, and curvature analysis. His recent work explores adaptive probabilistic reduced-order models and gradient-enhanced Kriging methods for high-dimensional systems. Recipient of the Best Paper Award (2021) and DFG Scholarship (2014) Organized workshops such as the Nordic Numerical Linear Algebra Meeting (2024) Contributed to projects like Optimal Structure-Preserving Model Reduction and the Danish Data Science Academy His teaching includes courses on numerical analysis, computational physics, and differential equations. Zimmermann actively collaborates internationally, contributing to conferences and peer review.
Ling Zhou is a Phillip Griffiths Assistant Research Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. in Mathematics from The Ohio State University (2023), where he was advised by Prof. Facundo Mémoli. His research focuses on applied topology, computational geometry, and topological data analysis, with particular emphasis on persistent homotopy groups, metric space analysis, and stability of filtered chain complexes. Current roles include teaching courses such as Multivariable Calculus (MATH 212) and Probability (MATH 230) at Duke. A recipient of a 2024-2026 grant from the American Mathematical Society for research on persistent invariants, Zhou has collaborated with institutions including Brown University's ICERM and Schloss Dagstuhl (Germany). His professional activities span over 20 conferences since 2020, including upcoming engagements at the Banff International Research Station (2025) and the Mathematical Congress of the Americas (2025). Research highlights include advancing persistent homology techniques, developing ephemeral persistence features, and exploring topological structures in metric spaces. His work bridges pure mathematics with computational applications, addressing challenges in data analysis and geometric modeling.
Shuchen Zhu is a Phillip Griffiths Research Professor of Mathematics at Duke University, affiliated with the Trinity College of Arts & Sciences. They hold a Ph.D. from Rutgers University (2024). Their research focuses on quantum computing, quantum algorithms, and theoretical physics, with notable contributions to quantum gate synthesis, optimization algorithms, and quantum simulation techniques. Recent teaching includes MATH 218D-2: Matrices and Vectors in Spring 2025. Key research areas include quantum circuit design, Trotter error analysis, and applications of quantum mechanics to combinatorial optimization. Publications emphasize advancements in qutrit systems, gluon field digitization, and super-quadratic speedups in quantum algorithms. Their work bridges theoretical foundations with practical implementations in quantum hardware and software. Publications span topics from quantum lookup tables to particle physics simulations, showcasing interdisciplinary expertise. No awards are explicitly listed, but their active publication record reflects sustained academic engagement. Advising details are not provided in available data.
