Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML
Professor Jelena Grbic is a distinguished mathematician serving as Professor of Mathematics within the School of Mathematical Sciences at the University of Southampton since 2012. Her academic journey began with a B.Sc. in Mathematics from the University of Belgrade, Serbia in 1997, followed by a Ph.D. in Algebraic Topology from the University of Aberdeen in 2004. Prior to her current position, she held academic appointments at the University of Manchester (2007-2012) as Lecturer and Senior Lecturer, and at the University of Aberdeen (2004-2006) as Lecturer. Professor Grbic's research spans multiple interconnected domains of pure mathematics, with a primary focus on modern homotopy theory, particularly unstable homotopy theory, and its applications across topology, algebra, and geometry. Her work centers on decompositions and exponent problems in homotopy theory, homotopy aspects of Toric Topology, Hopf algebras, and geometric problems related to cobordisms and string topology. This research bridges abstract mathematical theory with potential applications in data science and computational topology. Analysis of her recent publications (2020-2025) reveals a consistent research trajectory in algebraic topology with increasing interdisciplinary connections. Her work demonstrates sophisticated mathematical techniques applied to complex topological structures, particularly moment-angle complexes and polyhedral products. Notably, her 2022 paper 'Aspects of topological approaches for data science' indicates growing interest in applying topological methods to contemporary data analysis problems, suggesting an expanding research horizon beyond pure mathematics. Professor Grbic actively supervises PhD students, including current student Salvatore Elia in Mathematical Sciences, and teaches modules covering algebraic topology and homotopy theory. She serves as a reviewer for prestigious journals including Homology, Homotopy and Application (2019), Transactions of the London Mathematical Society (2021), and The LMS Newsletter (2017), contributing to the scholarly community through peer review and academic service.
Caterina Consani is a Professor of Mathematics at Johns Hopkins University's Krieger School of Arts & Sciences. She holds a PhD from the University of Chicago (1996) and a Dottore di Ricerca in Matematica from the Universities of Genoa and Turin (1993). Her research focuses on arithmetic geometry, non-commutative geometry, and the development of absolute geometry over the 'absolute point.' She has contributed to foundational work on the BC-system, the Arithmetic Site, and the Scaling Site, linking number theory with geometric frameworks. Education: PhD in Mathematics, University of Chicago (1996); Dottore di Ricerca in Matematica, Universities of Genoa & Turin (1993). Prior to Johns Hopkins, she taught at MIT (1996–1999) and the University of Toronto (1999–2005). Research Interests: Arithmetic geometry, non-commutative geometry, absolute geometry in characteristic one, connections to number theory and the Riemann Hypothesis. Collaborations include work with Alain Connes on adele class spaces and non-commutative algebraic geometry. Awards: Fellow of the American Mathematical Society (2024); 2025 Best Paper AOFA Award (Annals of Functional Analysis). Editorial roles include the Journal of Number Theory and Journal of Noncommutative Geometry. Teaching: Offers advanced courses in algebraic geometry and number theory, including topics like étale cohomology and the yoga of weights in 'Weil II.' Grants: Supported by the Simons Foundation. Organized major conferences such as the JAMI Conference on Riemann-Roch in Characteristic One (2019).
Liming Feng is an Associate Professor at the Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, and has served as Director of the Master of Science in Financial Engineering (MSFE) program since 2022. His academic career at the university spans from Assistant Professor (2006-2012) to his current role. He earned his Ph.D. in Industrial Engineering and Management Sciences from Northwestern University (2006), an M.S. in Mathematics from Northwestern University (2000), and a B.S. in Mathematics from Beijing Normal University (1997). Ph.D., Industrial Engineering and Management Sciences, Northwestern University, 2006 M.S., Mathematics, Northwestern University, 2000 B.S., Mathematics, Beijing Normal University, 1997 Feng’s research focuses on Financial Engineering, Stochastic Modeling, and Computational Methods. He has contributed extensively to quantitative finance, particularly in options pricing, portfolio optimization, and market impact models. His work leverages advanced numerical methods, Fourier transforms, and stochastic calculus to solve complex financial problems. The trends in his publications highlight expertise in Levy processes, jump diffusion models, and numerical algorithms for financial derivatives. He has developed innovative techniques for Bermudan options pricing, discretely monitored barrier options, and portfolio deleveraging strategies. His articles often intersect Operations Research with Financial Engineering, emphasizing computational efficiency and mathematical rigor. ISE Faculty Fellow (2025) INFORMS Financial Services Section Best Student Research Paper (2013) First runner-up of the 2012 Morgan Stanley Prize for Excellence in Financial Markets Feng has served on editorial boards for Operations Research Letters and Mathematical Finance . He has been recognized repeatedly for teaching excellence, including the Sharp Outstanding Teaching Award (2011, 2022) and multiple entries in the List of Teachers Ranked as Excellent by Their Students (2007-2024). He currently leads the MSFE program and contributes to curriculum development through courses like IE 522 (Statistical Methods in Finance) and IE 527 (MSFE Professional Development).
David Landriault is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, Canada, and a Canada Research Chair in Risk Theory. His research focuses on Actuarial Science, Quantitative Risk Management, Applied Probability, and Stochastic Processes, particularly in ruin theory, drawdown analysis, and stochastic control for insurance and finance applications. Education: PhD in Mathematics (2005), MSc in Mathematics (2003), BSc in Actuarial Science (2002) from Laval University. Affiliations: University of Waterloo (postdoctoral fellowship, 2006); Canada Research Chair in Risk Theory. Research Interests Risk and Ruin Theory Stochastic Control in Insurance and Finance Drawdown and Occupation Time Analysis Regime-Switching Models Reinsurance Design and Optimization Time-Dependent Risk Models Scientific Awards Fellow of the Canadian Institute of Actuaries (F.C.I.A.), 2009 Fellow of the Society of Actuaries (F.S.A.), 2006
Jeroen Tromp serves as the Blair Professor of Geology and Professor of Geosciences and Applied and Computational Mathematics at Princeton University, where he also directs the Princeton Institute for Computational Science and Engineering (PICSciE). His work centers on theoretical and computational seismology with applications across Earth and planetary sciences. His research interests focus on imaging Earth's interior through advanced computational techniques. Key areas include surface waves, free oscillations, body waves, seismic tomography, numerical simulations of 3-D wave propagation, and seismic hazard assessment. His group develops open-source software for acoustic, elastic and poroelastic wave propagation, addressing problems in exploration geophysics, regional and global seismology, and helioseismology. Current research trends show strong emphasis on Mars seismology (InSight mission), iron spin crossover in the lower mantle, tilted transverse isotropy in Earth's inner core, and crosstalk-free waveform inversion techniques across multiple scales. Tromp actively mentors graduate students and leads collaborative projects involving seismic wavefield imaging across planetary bodies. His group maintains strong connections with NASA's InSight mission and develops computational frameworks for global centroid moment tensor inversions. The research team operates within the Department of Geosciences, leveraging high-performance computing resources through PICSciE to tackle large-scale inverse problems in seismology.
Prof. dr. Steven Hoekstra is an Associate Professor of Atomic and Molecular Physics at the University of Groningen's Faculty of Science and Engineering, within the Van Swinderen Institute. His research focuses on precision measurements using cold molecules to explore fundamental physics, including Stark deceleration, laser cooling, and searches for physics beyond the Standard Model. He leads the NL-eEDM program at Nikhef, investigating the electron's electric dipole moment. Hoekstra is also involved in educational innovation, having received the Teacher of the Year award (2020) and a Senior Teacher Qualification (2023). He has supervised over 11 PhD theses and currently mentors 5 students. His work combines experimental techniques with theoretical insights, addressing questions like symmetry violations and quantum dynamics. Key projects include manipulating BaF molecules with electrostatic fields and exploring levitated nanoparticles as sensors. Hoekstra has secured major grants, including NWO VICI (2022) and VIDI (2013), and collaborates internationally on projects like the European Strategy for particle physics. Recent articles highlight advancements in molecular beam control, spin-precession methods for EDM searches, and opportunities in radioactive molecules. He actively participates in the Physics Olympiad Netherlands as chair, contributing to science outreach and education.
Carolin Pflueger is an Associate Professor at the Harris School of Public Policy , University of Chicago, and holds affiliations as a NBER Faculty Research Fellow and CEPR Research Affiliate . Her work bridges macroeconomics and finance, focusing on inflation dynamics, monetary policy impacts, and financial market risk perception. University: University of Chicago School: Harris School of Public Policy Affiliations: NBER, CEPR Role: Associate Professor Her research explores how inflation and monetary policy influence financial markets, including models connecting Treasury bond risk to stagflation drivers and analyzing economic agents' perceptions of policy uncertainty. Recent work leverages cross-sectional data of stock prices and economic forecasts to quantify macrofinancial linkages. Notable scientific recognitions include the Fama DFA Prize (2023), AQR Insight Award Finalist (2018), and the Arthur Warga Award (2014). She has received NSF Grant 2149193 for macrofinance research. Contact: cpflueger@uchicago.edu | GitHub Code Repositories
Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .
Vladimir Spokoiny is a Professor at the Departments of Mathematics and Economics of the Humboldt University of Berlin and Head of the Research Group "Stochastic Algorithms and Nonparametric Statistics" at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. His research spans multiple areas of statistics, machine learning, and financial mathematics, with significant contributions to nonparametric statistics, high-dimensional data analysis, and statistical methods in finance. Spokoiny received his M.Sc. in applied mathematics from the Moscow Institute of Railway Engineering in 1981 and his Ph.D. in mathematics from Lomonosov Moscow State University in 1988. He completed his Habilitation at Humboldt University in 1996. His academic career includes positions at the All-Union Institute of Railway Transport in Moscow, the Institute for Information Transmission Problems in Moscow, and the Institute for Applied Analysis and Statistics in Berlin before joining the Weierstrass Institute and Humboldt University where he has been a professor since 2002. Spokoiny's research focuses on adaptive nonparametric smoothing and hypothesis testing, high dimensional data analysis, statistical methods in finance, image analysis with applications to medicine, classification, and nonlinear time series. His work often addresses the challenges of nonstationarity in time series data and develops innovative methods for volatility estimation and risk management. He has made significant contributions to the development of adaptive weights smoothing procedures, which have applications in image processing, community detection, and manifold learning. His recent work has expanded into high-dimensional statistics, Bayesian inference, and optimization methods for machine learning, with publications demonstrating novel approaches to Gaussian approximation, Laplace methods, and statistical inference in non-Euclidean spaces. Spokoiny has supervised numerous PhD students including Oliver Reiss, Danilo Mercurio, Ying Chen, Elmar Diederichs, and Mstislav Elagin, whose research has focused on mathematical finance, time series analysis, and statistical methods. He serves as an Associate Editor for The Annals of Statistics (since 2004) and Statistics and Decisions (since 2002), and has previously served on the editorial board of the Journal of Statistical Planning and Inference. His professional activities include reviewing for major statistical journals including Annals of Statistics, Bernoulli, Econometrica, and Journal of American Statistical Association, as well as reviewing grant proposals for the National Science Foundation (USA), German Research Foundation, and Netherlands Organisation for Scientific Research. Spokoiny is a member of several professional societies including the International Statistical Institute, American Statistical Association, Institute of Mathematical Statistics, and Bernoulli Society. He is fluent in Russian (mother tongue), English, and German, and has good knowledge of French. His research group at WIAS focuses on developing novel statistical methodologies with applications across various scientific domains, particularly emphasizing adaptivity and robustness in complex data environments. The group's work has significant implications for financial risk management, medical imaging, and machine learning applications, with recent publications addressing fundamental questions in high-dimensional statistics and nonparametric inference.
Amit Singer is a Professor of Mathematics at Princeton University, specializing in computational methods for structural biology and cryo-electron microscopy (cryo-EM). His work focuses on developing mathematical frameworks and algorithms for analyzing large-scale microscopy datasets, particularly in 3D reconstruction and heterogeneity analysis of molecular structures. He leads research in manifold learning, optimal transport, and harmonic analysis, with applications to cryo-EM, signal processing, and inverse problems. Research interests include: (1) Mathematical methods for cryo-EM, including particle alignment, density map analysis, and subspace-based reconstruction techniques; (2) Development of rotation-invariant representations for imaging problems; (3) Application of machine learning and optimization to biomedical imaging challenges. His contributions bridge pure mathematics (e.g., harmonic analysis, manifold theory) with applied computational techniques for real-world microscopy data. Key trends in his recent articles (2023–2025) include advancements in multi-reference alignment methods, Wasserstein distance-based image registration, and overcoming particle detection limitations in cryo-EM. He also explores sparsity constraints, autocorrelation analysis, and novel algorithms for handling heterogeneous datasets. These methods improve resolution and reduce computational costs in analyzing molecular structures at atomic scales. Notable contributions include the ASPiRE software package for steerable PCA, and foundational work on synchronization problems in cryo-EM orientation estimation. His research often addresses algorithmic scalability and robustness to noise in experimental setups.
Jose Israel Rodriguez is an Associate Professor in the Department of Mathematics at the University of Wisconsin-Madison. His research bridges applied algebraic geometry and algebraic statistics, focusing on nonlinear algebra, maximum likelihood estimation, monodromy, and polynomial systems in engineering and science applications. Primary Affiliation: Department of Mathematics , UW-Madison Additional Affiliations: Department of Electrical & Computer Engineering , Institute for Foundations of Data Science Research Interests : Applied algebraic geometry for nonlinear eigenvalue problems and kinematics Algebraic statistics in nearest point problems and likelihood geometry Numerical methods for monodromy, Galois groups, and polynomial optimization Teaching and Mentorship : Co-organized the Collaborative Undergraduate Research Laboratory (CURL) for Spring 2020 Advises PhD students Julia Lindberg and Zinan Wang , with Bernd Sturmfels as his own PhD advisor Developed software tools like Multiregeneration and Decomposable Sparse Polynomial Systems Academic Contributions : Authored over 20 peer-reviewed publications in journals like SIAM Journal on Applied Algebra and Geometry, Foundations of Computational Mathematics, and Journal of Symbolic Computation Organized international conferences including Monodromy and Galois Groups in Enumerative Geometry and SIAM AG19 Active member of the SIAM community and developer of the Matroids Day seminar
Per Christian Hansen is a Professor at the Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark (DTU), where he leads the Section for Scientific Computing. He is a VILLUM Investigator and heads the CUQI (Computational Uncertainty Quantification for Inverse Problems) research initiative, aiming to develop accessible computational platforms for uncertainty quantification in inverse problems. His expertise lies in numerical analysis, numerical linear algebra, iterative reconstruction methods, and computational inverse problems, with applications in tomography, signal analysis, and plasma physics. His research integrates theoretical analysis—such as perturbation and convergence analysis—with the development of robust, adaptive, and efficient computational methods. He has co-authored five books, over 100 scientific papers, and several widely used MATLAB software packages, including IR Tools and Regularization Tools. His recent work (2023–2025) emphasizes uncertainty quantification, Bayesian inversion, and high-dimensional tomography in fusion plasmas, reflecting a strong trend toward probabilistic and robust modeling in inverse problems. He is a SIAM Fellow (2015) for his contributions to computational methods for rank-deficient and discrete ill-posed problems and regularization techniques. His scientific leadership is evident in both theoretical advances and practical software implementations. He actively collaborates across disciplines, particularly in nuclear fusion and medical imaging, and continues to supervise PhD students and publish in top-tier journals such as Inverse Problems , SIAM Journal on Scientific Computing , and Nuclear Fusion . SIAM Fellow (2015) VILLUM Investigator He advises PhD and Master’s students in computational mathematics and inverse problems, and his research is supported by major grants, including the VILLUM Investigator award. He leads the CUQI team, which develops open-source tools for non-experts to apply uncertainty quantification in inverse problems. His lab focuses on creating modeling frameworks that bridge theory, computation, and real-world applications in materials science, imaging, and plasma diagnostics.
Arul Shankar is a Professor of Mathematics at the University of Toronto, with his primary appointment at the Mississauga campus (UTM). He holds an office at 215 Huron Street, Room 1023 and can be reached at ashankar@math.utoronto.ca. His research focuses on Number Theory and Arithmetic Statistics, with particular expertise in elliptic curves, Selmer groups, and class groups. Professor Shankar's research interests center on the arithmetic statistics of number fields and elliptic curves. His work has significantly advanced our understanding of the average rank of elliptic curves, the distribution of Selmer groups, and the statistics of class groups. He has developed innovative applications of the geometry of numbers to arithmetic problems, often in collaboration with Manjul Bhargava and other leading number theorists. His research bridges deep theoretical questions with statistical approaches to understand the behavior of arithmetic objects across families. His publications reveal a consistent focus on the statistical behavior of arithmetic invariants, with particular attention to bounding average ranks of elliptic curves and understanding the distribution of class groups. The work demonstrates sophisticated applications of geometry of numbers techniques to problems in arithmetic statistics, with results published in top journals including the Annals of Mathematics, Inventiones Mathematicae, and Journal of the American Mathematical Society. Scientific Awards: 2018 Sloan Research Fellowship from the Alfred P. Sloan Foundation Professor Shankar is currently on sabbatical as a Simons Fellow. His research has established foundational results in arithmetic statistics, particularly regarding the average rank of elliptic curves and the distribution of class groups across families of number fields. His work with Bhargava on bounding the average rank of elliptic curves represented a major breakthrough in the field. His research group focuses on extending geometry of numbers methods to new contexts in arithmetic statistics, with recent work examining coregular vector spaces and applications to class groups. The lab maintains strong collaborations with number theorists at leading institutions worldwide.
Chan Song Heng is an Associate Professor in the Division of Mathematical Sciences at the School of Physical and Mathematical Sciences, Nanyang Technological University (NTU), Singapore. He has been affiliated with NTU since 2007. His academic journey includes a B.Sc. (Hons) in Mathematics from the National University of Singapore (2001) and a Ph.D. in Mathematics from the University of Illinois at Urbana-Champaign (2005). His research focuses on advanced mathematical topics such as partition theory, q-series, mock theta functions, and number theory. Recent work includes studies on identities analogous to Jacobi, Fermat-Wilson theorems, and applications of Rogers-Fine identities. His publications explore combinatorial, analytic, and algebraic aspects of these fields, with notable contributions to modular forms, theta functions, and partition congruences. Dr. Chan’s articles often intersect with classical problems in mathematics, blending historical insights with modern analytical techniques. Notable themes include exploring identities through modular forms, analyzing partition statistics (ranks/cranks), and studying mock theta functions. Despite his prolific output, no specific scientific awards or student advisees are listed in the provided materials.
