Xiuyuan Cheng is an Associate Professor of Mathematics at Duke University, affiliated with the Trinity College of Arts & Sciences. Her expertise lies in applied analysis, focusing on developing theoretical and computational techniques for high-dimensional data analysis, signal processing, and machine learning. She holds a Ph.D. from Princeton University (2013). Her research emphasizes graph-based methods, kernel techniques, and deep learning applications in manifold data analysis. Notable contributions include work on graph Laplacian convergence, optimal transport for single-cell data, and rotation-equivariant neural networks. She has received grants from the National Science Foundation, including a CAREER award (2023–2028) for learning graph diffusion from high-dimensional data. Recent publications explore topics such as bi-stochastic graph normalization, neural tangent kernels, and adversarial defense using basis transformations. Teaching includes advanced courses like Measure and Integration and High-Dimensional Data Analysis. She collaborates extensively in interdisciplinary projects, integrating mathematical theory with computational tools for real-world applications.
Prof Ian M. Wanless is a Professor in the School of Mathematics at Monash University, Melbourne, Australia. He has held academic positions at institutions including the Australian National University (ANU), University of Melbourne, Christ Church Oxford, and Charles Darwin University. His research primarily focuses on combinatorics, with specializations in Latin squares, matrix permanents, graph theory, and algebraic structures. He has made significant contributions to the enumeration and properties of Latin squares, including groundbreaking work on transversals, orthogonality, and symmetry. Wanless has also explored connections between Latin squares and algebraic structures like quasigroups and loops. His education includes a PhD from ANU (supervised by Brendan McKay) and postdoctoral fellowships at Oxford and ANU. He has been awarded an Australian Research Council Future Fellowship (2011) and has led major research initiatives, including organizing international conferences (e.g., 5ICC in 2017). His work spans theoretical results and computational methods, with over 100 publications in top journals like Journal of Combinatorial Theory and SIAM Journal on Discrete Mathematics . Wanless’s research interests extend to design theory, hypergraphs, and permutation polynomials. He co-edits the Electronic Journal of Combinatorics and has held leadership roles in professional societies, including president of the Combinatorial Mathematics Society of Australasia. His current projects include studies on perfect 1-factorizations, covering radii of permutation sets, and algebraic properties of Latin squares.
James Forbes is an Associate Professor in the Department of Mechanical Engineering at McGill University. He holds the title of William Dawson Scholar and is affiliated with the Dynamics Estimation & Control of Aerospace & Robotics Systems research group. His primary research focus is on Dynamics and Control, with emphasis on navigation, guidance, and control (GNC) techniques for robotic systems. He teaches courses such as MECH 309 (Numerical Methods), MECH 412 (System Dynamics), and advanced topics in control systems. Forbes earned his Ph.D. in Aerospace Science and Engineering from the University of Toronto, following an M.A.Sc. from the same institution and a B.A.Sc. in Mechanical Engineering from the University of Waterloo. His research interests include nonlinear state estimation (batch methods, filtering), control synthesis via optimization (LQR, LMI approaches), and data-driven modeling using Koopman operator techniques. Applications span unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), and SLAM systems. He has developed the navlie Python package for state estimation on Lie groups. Notable awards include the William Dawson Scholar distinction. His recent work focuses on multi-UAV localization, robust control algorithms, and sensor fusion techniques. He collaborates on projects involving UWB-based positioning and inertial navigation systems.
Rob Voigt Assistant Professor of Linguistics and courtesy faculty in Computer Science at Northwestern University, affiliated with the Institute for Policy Research and Cognitive Science Program. Specializes in computational linguistics, focusing on natural language processing (NLP) applied to social science questions including policing, mental health, and immigration. Director of the Linguistic Mechanisms Lab, exploring how language reflects and shapes societal structures. Education PhD in Linguistics (2019), Stanford University Postdoctoral Scholar (2019-2020), Stanford University M.A. in East Asian Studies (2013), Stanford University B.A. in Chinese (2008), Vassar College Research Interests Combines computational methods with sociolinguistics to study: Police-community interaction through body-worn camera data Linguistic markers of procedural justice and racial disparities Language in marginalized communities and social movements LLM applications to clinical language analysis (autism spectrum disorders) Historical discourse analysis of immigration policies Recent Work Recent projects include analyzing 140 years of political immigration rhetoric (PNAS 2022), evaluating officer communication training via bodycam NLP (PNAS Nexus 2024), and developing stereotype analysis pipelines (EMNLP 2024). Active in policy-relevant research through grants on police training (NIJ) and mental health language analysis (NIH). Awards & Recognition Holds Cozzarelli Prize (2017) for groundbreaking policing research and Cialdini Prize for field methods innovation. Recipient of interdisciplinary fellowships from Stanford and NIH grants totaling $8M+. Teaching Current courses include Text Processing for Linguists . Known for ungrading pedagogy emphasizing intrinsic motivation and collaborative learning. Develops open-access computational linguistics curricula using Unix/Python. Labs & Collaborations Linguistic Mechanisms Lab focuses on socially responsible NLP. Collaborates with criminologists, cognitive scientists, and sociologists on projects like the Media Accountability Project (racial disparities in gun violence reporting).
George Dasoulas is a Postdoctoral Researcher at Harvard University's Department of Biomedical Informatics, affiliated with the Zitnik Lab. He holds a PhD in Computer Science from École polytechnique in Paris, France, and previously worked at Huawei Technologies France. His research focuses on graph machine learning, particularly in biomedical applications and telecommunications, with contributions to graph neural networks (GNNs), attention mechanisms, and topological deep learning. Education: Ph.D., Computer Science (DaSciM group, LIX, École polytechnique); Diploma in Electrical & Computer Engineering (National Technical University of Athens). His work includes developing Lipschitz-normalized attention layers, parametrized graph shift operators, and modularity-aware graph autoencoders. He has been recognized with the 2022 Wojcicki and Troper Fellowship from Harvard's Data Science Initiative. Key Research Themes: Graph Representation Learning, Topological Neural Networks, Equivariant Learning, Multimodal Learning Applications: Biomedical Informatics, Telecommunications, Sustainable AI His articles emphasize scalable GNN architectures, graph-based unlearning strategies, and multimodal protein phenotyping. He has contributed to open-source projects like LipschitzNorm and PGSO, and actively publishes in top conferences (ICML, ICLR, NeurIPS).
Bernd Sturmfels is a leading mathematician serving as Director of the Max Planck Institute for Mathematics in the Sciences in Leipzig since 2017. He is also Professor Emeritus of Mathematics, Statistics, and Computer Science at the University of California, Berkeley, and holds honorary professorships at the Technical University of Berlin and the University of Leipzig. His research bridges pure and applied mathematics, with foundational contributions to algebraic geometry, combinatorics, and computational biology. Education: Sturmfels earned dual Ph.D. degrees in 1987 from the University of Washington and Technische Universität Darmstadt, followed by an honorary doctorate from Goethe University Frankfurt in 2015 and additional honorary doctorates from the University of Bern (2023) and the University of Chicago (2024). Research Interests: His work spans algebraic geometry , combinatorics , commutative algebra , algebraic statistics , convex optimization , and computational biology . He explores deep connections between abstract algebraic structures and practical applications in statistics, optimization, and the life sciences. Publications and Trends: With over 300 research articles and 11 books, his recent work (2022–2025) focuses on advanced topics like Grassmannian geometry, tropical implicitization, quantum chemistry applications, and algebraic statistics. His research increasingly integrates computational methods with theoretical insights, addressing problems in machine learning, phylogenetics, and optimization. Awards and Honors: Sturmfels has received numerous prestigious awards, including: George David Birkhoff Prize in Applied Mathematics (2018) SIAM von Neumann Lecturership (2010) Humboldt Senior Research Prize (2007–2008) David and Lucile Packard Fellowship (1992–1997) Fellowships of the AMS and SIAM Membership in the Berlin-Brandenburg Academy of Sciences and Humanities Mentoring and Grants: He has supervised 60 doctoral students and numerous postdocs, with many securing positions at leading institutions. His mentoring philosophy emphasizes diversity and excellence, as highlighted in his Notices of the AMS article. Funding sources include the NSF, DARPA, and the German National Science Foundation (DFG). Labs and Teams: At the Max Planck Institute, he leads the Nonlinear Algebra group, fostering interdisciplinary collaboration between mathematics and the sciences. His team focuses on developing algebraic methods for data analysis, optimization, and theoretical physics.
John R. Morris is a Professor of Chemistry and Associate Dean for Research at the College of Science, Virginia Tech. He holds the Dr. A.C. Lilly Jr. Faculty Fellowship in Nanoscience. His research focuses on interfacial chemistry, with emphasis on gas-surface reactions, catalysis, and chemical sensing. Key projects include chemical warfare agent detection, environmental pollutant interactions, and energy materials. Education: B.S., Aquinas College, 1991 Ph.D., University of Notre Dame, 1996 Postdoctoral Associate, University of Wisconsin-Madison, 1996–1999 Research Interests: Dr. Morris explores molecular mechanisms in gas-surface interactions using advanced techniques like molecular beam scattering and ultrahigh vacuum spectroscopy. His work addresses environmental chemistry (e.g., atmospheric pollutants), energy storage (e.g., MOFs for diborane), and defense applications (e.g., nerve agent decomposition). Recent studies include photocatalytic oxidation mechanisms on Au/TiO₂ and the development of novel sensors for volatile organic compounds. Awards: National Science Foundation CAREER Award (2001) Army Research Office Young Investigator Award (2001) Schug Research Award (2012) Advising & Grants: Morris has mentored over 10 students and led projects funded by NSF, DoD, and industry. His lab (005 Hahn Hall South) focuses on interdisciplinary collaborations in materials science and environmental chemistry. Labs/Teams: The Morris Group operates a state-of-the-art facility for studying gas-surface reactions, including custom-built chambers for CRDS and molecular beam experiments.
Lamine M. Mili is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His expertise spans power systems, signal processing, and robust estimation theory. He holds an IEEE Fellowship (2016) for contributions to robust state estimation in power systems. Mili's research focuses on advancing methodologies for power system reliability, control, and integration of renewable energy sources. His work includes studies on dynamic state estimation, nonlinear dynamics, bifurcation theory, and quantum computing applications. He has contributed extensively to resilience engineering and computational social science in power systems. Mili’s recent articles address challenges in smart grids, quantum circuit error prediction, and multifractal signal analysis in EEG. His research often combines advanced statistical techniques with real-world grid data, emphasizing robustness and adaptability in dynamic environments. Education: Ph.D., University of Liège, 1987 M.S., University of Tunis, 1983 B.S., Swiss Federal Institute of Technology, Lausanne, 1976 Research Interests: Power system stability and control State estimation and robust filtering Quantum computing for power systems Resilience and cyber-physical-social systems Nonlinear dynamics and bifurcation analysis His recent publications reflect a focus on hybrid power systems, probabilistic methods, and data-driven approaches for grid optimization. The 2025 articles highlight advancements in photovoltaic state estimation, quantum error prediction, and robust modulation techniques. Mili’s work often bridges theoretical models with practical grid applications, emphasizing uncertainty quantification and real-time monitoring.
Torbjörn Larsson is a Professor in the Department of Mathematics at Linköping University, affiliated with the Division of Applied Mathematics (TIMA). His work bridges theoretical and applied optimization with significant impact in healthcare, logistics, and finance. His research interests include Mathematical Optimization , Operations Research , Brachytherapy Treatment Planning , Vehicle Routing , and Portfolio Optimization . He develops advanced algorithms such as Lagrangian heuristics, metaheuristics, and feasible direction methods to solve complex decision problems. The recent publications indicate a strong focus on developing bounding techniques and heuristic frameworks for discrete and multi-objective optimization, with applications ranging from radiation therapy to transportation logistics. His work emphasizes both theoretical rigor and practical implementation. Scientific Contributions: Development of novel optimization methods for brachytherapy treatment planning Advancement of Lagrangian and metaheuristic frameworks Application of optimization in finance (portfolio selection) and scheduling He collaborates extensively on research projects involving mathematical modeling and algorithm design. While specific advising roles are not listed, his co-authorship with junior researchers suggests mentorship activity. He has contributed to projects on decision support systems for scheduling and large-scale optimization in finance. Laboratories and Research Groups: Applied Mathematics (TIMA), Department of Mathematics, Linköping University Research environment focused on optimization and its applications in medicine and logistics
Robert Raussendorf is a Professor at the Institute of Theoretical Physics, part of the Faculty of Mathematics and Physics at Leibniz University Hannover. He leads the research group focusing on quantum information, particularly measurement-based quantum computation (MBQC) and quantum fault-tolerance. His work includes the invention of the one-way quantum computer (QCc), a paradigm where quantum computations are performed via local measurements on entangled cluster states. Raussendorf’s research bridges foundational quantum mechanics with practical applications, emphasizing the role of contextuality and resource states in quantum advantage. Research Interests: His primary areas include quantum computation models, quantum cellular automata, topological error correction, and the foundational aspects of quantum mechanics. He explores how quantum principles like entanglement and contextuality enable computational power, with recent focus on symmetry-protected systems and efficient quantum architectures. Articles Trends: Recent publications highlight advancements in measurement-based computation, error correction strategies, and theoretical frameworks like contextuality and cohomology. Notable work includes high-error-threshold architectures and the application of dual-unitary circuits in one-dimensional systems. Lab/Team: His team includes postdocs (e.g., Markus Frembs, Martin Plávala) and doctoral candidates (e.g., Arnab Adhikary, Ruben Campos Delgado), collaborating on topics like quantum error tolerance, computational phases of matter, and algorithm optimization. The group also engages in interdisciplinary projects with institutions like the Stewart Blusson Quantum Matter Institute.
Christopher R. Johnson is a Distinguished Professor of Computer Science and Founding Director of the Scientific Computing and Imaging (SCI) Institute at the University of Utah. He holds additional appointments as Research Professor of Bioengineering and Adjunct Professor of Physics. His research focuses on scientific computing, visualization, and biomedical applications, with notable contributions to software tools like SCIRun, ShapeWorks, and FluoRender. He has led the SCI Institute since its founding in 1992, growing it to over 200 members. Johnson has pioneered advancements in cardiac electrophysiology modeling, medical imaging, and high-performance computing. Johnson’s academic journey includes a B.S., M.S., and Ph.D. in Computer Science, though specific institutions are not mentioned. His editorial roles include co-editing The Visualization Handbook and serving on multiple journal boards. He has advised numerous students, including Brian Zenger, Jake Bergquist, and Lindsay Rupp, who have received prestigious awards like the NSF Graduate Research Fellowship. His awards span decades and disciplines, including the NSF Presidential Faculty Fellow Award, IEEE Visualization Career Award, and Utah Cyber Pioneer Award. He is a Fellow of AIMBE, AAAS, SIAM, and IEEE. Johnson’s work emphasizes translating computational methods into clinical tools, such as deep brain stimulation for Parkinson’s and atrial fibrillation treatment. Johnson’s lab, the SCI Institute, collaborates widely with industry and academia, developing open-source software for biomedical computing. Key projects include SCIRun (a biomedical problem-solving environment) and ShapeWorks (statistical shape modeling). He has been instrumental in securing over $6 million in NIH grants for the Center for Integrative Biomedical Computing (CIBC).
Abbas Roozbahani is an Associate Professor in the Department of Building and Environmental Technology at the Faculty of Science and Technology, Norwegian University of Life Sciences (NMBU). His academic expertise lies in Water Infrastructure Engineering, where he contributes to research, teaching, and project leadership in sustainable urban water systems. His research interests include: Sustainable water management Urban water transport systems (drinking water, wastewater, stormwater) Risk assessment of water infrastructure Simulation and optimization of water systems Hydroinformatics and artificial intelligence Asset management for urban water infrastructure The analysis of his recent publications (2022–2025) reveals a strong focus on integrating advanced computational methods—such as Bayesian Networks, Fault Tree Analysis, machine learning (e.g., LSTM), and multi-criteria decision-making (MCDM)—into water resources management. His work frequently addresses urban stormwater optimization, drought and climate change risk assessment, groundwater forecasting, and the water-food-energy nexus, demonstrating a consistent trend toward data-driven, risk-informed, and sustainable solutions for complex water systems. Dr. Roozbahani teaches graduate-level courses including: THT301 - Asset Management for Urban Water Infrastructure THT302 - Analysis and Design of Water Distribution Networks THT261 - Introduction to Water and Wastewater Systems (co-instructor) THT313 - Water Management in Changing Conditions (co-instructor) THT390 - Preparations for the Master's Thesis (co-instructor) He has supervised multiple MSc and PhD students and led projects funded by academic and private institutions. His collaborative research spans international institutions, with frequent co-authorship on topics related to risk modeling, AI in hydrology, and sustainable infrastructure planning.
Stanislav Jabinski is a Postdoc at the Leibniz Institute for Baltic Sea Research (IOW) in Rostock, Germany, where he works in the Research Group Microbial Plankton and Fungal Ecology under Dr. Isabell Klawonn. His research focuses on paleoenvironmental reconstruction and microbial ecology using biomarker and stable isotope analyses. Ph.D. in Ecosystem Biology and Ecology, University of South Bohemia (2024) M.Sc. in International Marine Geosciences, University of Bremen (2019) B.Sc. in Geosciences, University of Bremen (2016) His expertise spans microbial membrane biomarker analysis, stable isotope probing (SIP), and method development for ultra-high-resolution mass spectrometry (GC-FID/MS/IRMS, LC-QToF-MS, FTICR-MS). He investigates carbon turnover mechanisms in soil and aquatic ecosystems, integrating δ¹³C and δ²H isotopes to quantify microbial and fungal activity. Jabinski’s publications highlight applications of SIP in fungal metabolism, paleovegetation reconstruction, and soil organic matter dynamics. His work bridges analytical chemistry, biogeochemistry, and microbial ecology, emphasizing lipid biomarker innovation. He is affiliated with the Microbial Plankton and Fungal Ecology working group at IOW, collaborating on biogeochemical cycles and ecosystem fluxes.
Maria Chiara Brambilla is an Associate Professor in the Department of Industrial Engineering and Mathematical Sciences (DIISM) at Università Politecnica delle Marche, Faculty of Engineering. Her research specializes in algebraic geometry, with emphasis on moduli spaces, vector bundles, and secant varieties. She investigates fundamental structures in birational geometry and interpolation theory, contributing to advancements in the classification of geometric objects and their deformations. Research Focus: Her work explores: Birational properties of Mori dream spaces and blowups Defectivity and non-defectivity in Segre-Veronese varieties Terracini loci and their minimality conditions Geometric invariants of flag manifolds and hypersurfaces Algebraic boundaries in tensor rank theory Publication Trends: Recent articles (2021-2025) demonstrate deepening work on Terracini loci, twistor geometry, and movable divisors, frequently employing combinatorial and deformation-theoretic methods to resolve problems in higher-dimensional algebraic geometry.
Dr. Ryan Leduc is an Associate Professor at the Department of Computing and Software within McMaster University's Faculty of Engineering . He holds degrees of B.Eng (Victoria) , M.A.Sc. (Toronto) , and Ph.D. (Toronto) . His research focuses on Discrete-Event Systems (DES) , particularly in Supervisory Control , Hierarchical Structures , Concurrency , and Formal Verification of software and hardware. He has developed the DESpot software tool for hierarchical DES research.
