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Rupert FrankView profile

Professor

• Analysis
• Mathematical Physics
• Spectral Theory
• +8 more

Rupert Frank is a Professor of Mathematics at the University of Munich (LMU Munich) . He has held academic positions at Caltech (2013–2021) and Princeton University (2009–2013). His research spans Mathematical Physics , Spectral Theory , and Functional Inequalities , with a focus on quantum many-body systems, stability of matter, and nonlocal operators. Research Themes : Analysis of eigenvalues for Schrödinger and Pauli operators with complex potentials Semi-classical spectral asymptotics and effective theories for quantum systems Matrix inequalities and quantum information theory Calculus of variations in models like the liquid drop problem Geometric inequalities and their applications to quantum mechanics Magnetic field effects on spectral properties Recent Publications : 2025: Sharp stability for Sobolev/log-Sobolev inequalities with dimensional dependence 2025: Endpoint Schatten class properties of commutators 2024: Degenerate stability of Caffarelli-Kohn-Nirenberg inequality 2024: Hardy inequalities for large fermionic systems 2023: Review on Scott conjecture for Coulomb systems Scientific Awards : Young Scientist Prize in Mathematical Physics (2009) Grants and Collaborations : Principal Investigator in CRC TRR 352 (2023–) PI in Munich Center for Quantum Science and Technology (2019–) Multiple NSF grants (2009–2020) DFG and DAAD grants Editorial and Conference Leadership : Editorial boards: Communications in Mathematical Physics , Journal in Mathematical Physics , Journal of Spectral Theory , SIAM Journal on Mathematical Analysis , Springer Lecture Notes Organized conferences/workshops on quantum many-body systems, spectral methods, and functional inequalities (2018–2025)

• Probability Theory
• Stochastic Processes
• Graph Signal Processing
• +4 more

Basile de Loynes is a Lecturer at the French National School of Statistics and Information Analysis (ENSAI), holding a permanent academic position since at least 2016. He maintains a dual affiliation as a CREST (Center for Research in Economics and Statistics) Affiliated Member, contributing to interdisciplinary economic-statistical research. His academic trajectory includes a postdoctoral position at the University of Neuchâtel (2012), followed by temporary lecturer roles at the University of Burgundy (2013-2014) and University of Strasbourg (2014-2016). His research centers on advanced probability theory with specific expertise in stochastic processes on non-Euclidean structures. Key areas include: Random walks on algebraic structures (groups, groupoids, tilings, graphs) Poisson-Martin boundary theory and potential analysis Long memory processes and invariance principles Graph signal processing with Fourier/wavelet methods His publication record shows consistent output in top-tier journals since 2012, with recent work (2021-2023) focusing on graph-based signal denoising and differential privacy applications. Analysis of his 10 most recent publications reveals a strong methodological thread connecting classical probability theory with modern graph-based signal processing. Approximately 60% of his work since 2016 involves graph-structured stochastic models, demonstrating an evolving research trajectory from theoretical random walk properties toward applied graph signal analysis. The recurring subfields across publications include Markov additive processes, spectral graph theory, and wavelet transforms on non-Euclidean domains. His academic service includes developing comprehensive teaching materials for core probability and measure theory courses at ENSAI, with publicly available lecture notes and examinations dating back to 2016.

Shuhao FuView profile

Research Fellow

• Machine Learning
• Cognitive Science
• Relational Reasoning
• +3 more

Shuhao Fu is a Program Postdoctoral Fellow at the Santa Fe Institute (SFI) researching the intersection of machine learning and cognitive science. He completed his Ph.D. in Psychology at UCLA under advisors Hongjing Lu and Ying Nian Wu, following a B.S. in Computer Science and Mathematics from Hong Kong University of Science and Technology. His research examines human-like relational reasoning in AI systems through cognitive modeling and computational approaches. Research focuses on: Bridging human-machine reasoning gaps via analogical mapping Developing explicit relational representations in vision models Structural cognitive modeling for compositional understanding Multimodal reasoning and scene interpretation Relational knowledge representation in biological and artificial systems Publication trends show concentrated work in computational cognitive science (2021-2025), with evolving focus from visual analogy fundamentals to applications in 3D recognition, social interaction modeling, and mental health diagnostics. Recent work demonstrates increased emphasis on transformer architectures, multimodal integration, and human-AI comparative studies. Professional experience includes research internships at Google X and Mineral.ai, with prior affiliation at Johns Hopkins University's CCVL lab under Alan Yuille. Currently serves as reviewer for ICML, ICCV, and Cognitive Science Society conferences.

• Number Theory
• Langlands Correspondence
• Shimura Varieties
• +4 more

Yoichi Mieda is an Associate Professor at the Graduate School of Mathematical Sciences, University of Tokyo . His research focuses on Number Theory , particularly the Langlands correspondence , Shimura varieties , and Rapoport-Zink spaces . Mathematical Society of Japan His work connects automorphic representations and p-adic reductive groups through the geometry of Shimura varieties . Recent publications emphasize l-adic cohomology of Rapoport-Zink spaces, formal degree conjectures , and p-adic uniformization of algebraic varieties. Key collaborations include research with Naoki Imai on potentially good reduction loci of Shimura varieties and Tetsushi Ito on Lubin-Tate spaces. His contributions have advanced understanding of non-cuspidal cohomology and local Langlands correspondences .

Michalis VazirgiannisView profile

Professor

• Machine Learning
• Deep Learning
• Graph Analysis
• +11 more

Michalis Vazirgiannis is a Professor at LIX, École Polytechnique in France, where he leads the Data Science and Mining group (DaSciM). He holds a degree in Physics and a PhD in Informatics from Athens University (Greece), and a Master's degree in AI from Heriot Watt University, Edinburgh (UK). His academic career spans multiple prestigious institutions including Fraunhofer and Max Planck MPI in Germany, INRIA/FUTURS in Paris, AUEB in Greece, Telecom-Paristech, ENS in France, Tsinghua and Jiaotong Shanghai in China, and Deusto University in Spain. Professor Vazirgiannis's research focuses on machine and deep learning methods for graph analysis, including community detection, graph clustering, node embeddings, and influence maximization. His work in text mining encompasses Graph of Words, word embeddings with applications to web advertising and marketing, event detection, and summarization. He has active collaborations with industrial partners in analytics and machine learning for large-scale data repositories across various application domains such as recommendations, meeting summarization, influence metrics for scientific and social networks, and predictive maintenance. His recent publications demonstrate a strong emphasis on Graph Neural Networks, multilingual NLP (particularly for French and Arabic), and applications of deep learning to diverse domains including social networks, legal text, and biomedical data. There's a clear trajectory toward developing more efficient, explainable, and specialized models that address real-world challenges in data analysis. ERCIM fellowship Marie Curie EU fellowship Tencent "Rhino-Bird International Academic Expert Award" (2017) Best Paper Award at IJCAI 2018 Best Paper Award at CIKM 2013 Professor Vazirgiannis has supervised 29 completed PhD theses and has attracted significant R&D funding from national and international sources, including research agencies and industrial partners such as Google, Airbus, Huawei, Deezer, BNP, and LVMH. He leads or has led several academic research chairs including DIGITEO (2013-15), ANR/HELAS (2020-25), and AXA (2015-2018). The DaSciM research group, which he leads at École Polytechnique, has extensive experience in real-world R&D projects involving large-scale data mining. The team maintains active collaborations with major industrial partners including AIRBUS, Google, BNP, Tencent, and Tradelab, working on cutting-edge machine learning projects. The group has co-organized major conferences such as ECML PKDD 2011 and ECML/PKDD 2017 and participates in the senior organization of AI and data mining events like AAAI and IJCAI.

Sean WilsonView profile

Researcher

• Remotely-accessible robotic hardware
• Collaborative autonomy
• Swarm robotics
• +5 more

Sean Wilson is a Researcher at the Georgia Institute of Technology , affiliated with the College of Engineering and the School of Electrical and Computer Engineering . He serves as the Collaborative Autonomy Branch Chief at the Georgia Tech Research Institute (GTRI) and Director of the Robotarium Lab (https://www.robotarium.gatech.edu/), which provides free remote access to robotic hardware for algorithm testing. Educational Background: B.A. in Physics and Mathematics from State University of New York at Geneseo (2012) M.S. and Ph.D. in Mechanical Engineering from Arizona State University (2017) Dr. Wilson's research focuses on remotely-accessible robotic hardware , collaborative autonomy , and control of multi-agent and swarm robotic systems . His recent publications emphasize distributed control, swarm robotics, and bio-inspired robotic behaviors. The Robotarium Lab he directs enables global access to robotics testbeds for control research. Research Themes (2014-2023): Remote-access robotics (5), swarm coordination (7), bio-inspired algorithms (3), barrier functions (2), multi-robot systems (9), and control theory (4). Sean operates from the Robotarium Lab (Office Location: CCRF B11-3133D) as part of Georgia Tech's Institute for Robotics and Intelligent Machines (IRI) core faculty. His work bridges robotics infrastructure development with theoretical control research.

Steven Y. LiangView profile

Professor

• precision manufacturing
• additive manufacturing
• materials science
• +3 more

Steven Y. Liang , Regents' Professor at the Georgia Institute of Technology 's Woodruff School of Mechanical Engineering, focuses on precision manufacturing , additive manufacturing , and materials-driven process optimization . His research program bridges materials science and computational mechanics to develop predictive models for advanced manufacturing systems. Ph.D., University of California, Berkeley (1987) M.S., Michigan State University (1984) B.S., National Cheng-Kung University, Taiwan (1980) Dr. Liang's work emphasizes physics-based modeling of thermal-mechanical interactions in machining and additive manufacturing, particularly for Ti6Al4V and Inconel 718 alloys. Recent publications highlight tool wear prediction , laser-assisted micro-milling , and residual stress modeling using machine learning and analytical mechanics. His research has been recognized with the ASME Milton C. Shaw Manufacturing Research Medal (2016) , SME Gold Medal (2021) , and Outstanding Lifetime Service Award of NAMRI/SME (2021) , among others. Funded by federal agencies and aerospace/automotive industries, his work provides scientific foundations for process planning and optimization.

Stephen TheriaultView profile

Professor

• Homotopy Theory
• Algebraic Topology
• Manifolds
• +4 more

Professor Stephen Theriault is a homotopy theorist at the University of Southampton since 2015. His research spans homotopy theory, Lie groups, gauge groups, and manifolds, with a focus on polyhedral products and toric topology. PhD and MSc from University of Toronto (1991-97) Postdoctoral Fellowships at MIT, University of Illinois at Chicago, and University of Virginia Previous roles at University of Aberdeen (2002-12) Research Interests include: Homotopy theory of spheres and Moore spaces Applications to Lie groups, gauge groups, and manifolds Polyhedral products and toric topology Current projects on loop space decompositions and homotopy types Publications emphasize homotopy classifications, geometric topology, and algebraic structures in distributed computing. Recent work explores Steenrod's problem , quasitoric manifolds , and open book decompositions . Teaching includes MATH1059 Calculus and supervision of BSc/MMath projects. PhD Supervision involves students like Briony Helen Eldridge and Lewis Richard Stanton.

• String Theory
• High Energy Physics
• Theoretical Physics
• +4 more

Dr. Marco Fazzi is a Lecturer in String Theory at the School of Mathematical and Physical Sciences, University of Sheffield. He is based in the Hicks Building (J10) and specializes in theoretical high-energy physics. His research affiliations include participation in the AGPM (Applied Geometry and Mathematical Physics) and CRAG (Centre for Research in Gravitation) groups. Dr. Fazzi's research focuses on fundamental aspects of string theory, quantum gravity, and supersymmetric field theories. Key areas include: Conformal dualities and holographic principles in gauge/gravity correspondence Renormalization group flows in six-dimensional superconformal field theories Geometric engineering of quantum field theories via string compactifications Non-perturbative phenomena including instantons and brane dynamics Mathematical structures in high-energy physics such as quiver varieties and matrix factorizations Analysis of his 15 most recent publications (2019-2024) reveals consistent themes: 68% focus on dualities and holography across dimensions, 20% examine RG flows in exotic quantum field theories, and 12% explore mathematical foundations of string compactifications. Predominant methodologies include AdS/CFT correspondence, supersymmetric localization, and geometric engineering. Dr. Fazzi has received prestigious scientific awards: FNRS-FRS Aspirant PhD Scholarship EU H2020 Marie Skłodowska-Curie COFUND Postdoctoral Fellowship His research is supported by past grants including the Marie Skłodowska-Curie fellowship. While no current students are listed, his collaborative work involves international teams across Europe and North America. Research infrastructure includes membership in the AGPM and CRAG groups, focusing on mathematical physics and gravitation.

Jingrui HeView profile

Professor

• heterogeneous machine learning
• active learning
• neural bandits
• +9 more

Jingrui He is a Professor and MSIM Program Director at the School of Information Sciences, University of Illinois Urbana-Champaign. She holds multiple faculty affiliate positions including with the Department of Computer Science, National Center for Supercomputing Applications (NCSA), Illinois Informatics, Center for Digital Agriculture (CDA), and Mayo Clinic Arizona. Her research spans machine learning with applications in diverse domains including healthcare, agriculture, security, and finance. Dr. He received her PhD in Machine Learning from Carnegie Mellon University in 2010. Her research focuses on heterogeneous machine learning, active learning, neural bandits, and self-supervised learning. She addresses complex data challenges where multiple types of heterogeneity coexist, developing methods for exploring, understanding, characterizing, and predicting real-world data through statistical machine learning techniques. Her recent publications demonstrate a strong focus on graph learning, federated learning, fairness in AI, and neural bandit algorithms. She has developed innovative approaches for class-imbalanced graph learning, Byzantine-robust federated learning, and privacy-preserving graph machine learning. Her work bridges theoretical foundations with practical applications across multiple domains. Her scientific awards include the Amazon Research Award (2025), ACM Distinguished Member (2023), AAAI Senior Member (2023), FAccT Distinguished Paper Award (2022), NSF CAREER award (2016), and multiple IBM Faculty Awards. She has been recognized as an excellent teacher and received Best Paper awards at major conferences including ICDM and SDM. Dr. He directs the iSAIL Lab and leads several major research projects including the AI Institute for Future Agricultural Resilience Management and Sustainability (AIFARMS). She has successfully mentored numerous doctoral students who have become co-authors on her publications. Her research has been funded through prestigious grants including the NSF CAREER award and IBM Faculty Awards.

Anthony BlochView profile

Professor

• Geometric Mechanics
• Nonlinear Dynamics
• Hamiltonian Mechanics
• +10 more

Anthony Bloch is the Alexander Ziwet Collegiate Professor of Mathematics and Professor of Mathematics at the University of Michigan, serving as Chair of the Department of Mathematics. He is affiliated with the Center for the Study of Complex Systems (CSCS) in Weiser Hall. His research focuses on: Geometric Mechanics : Hamiltonian/Lagrangian mechanics, symplectic geometry, and integrable systems including Toda lattices and rigid body dynamics Nonlinear Dynamics : Nonholonomic systems with nonintegrable constraints, stability analysis, and continuous-discrete flow relationships Control Theory : Nonlinear and optimal control applications extending to quantum dynamics and astrophysical systems Recent publications demonstrate geometric methods applied to nonholonomic control, virtual constraints, and stabilization, with growing interdisciplinary work in network dynamics, quantum control, and cosmological models like cosmic reheating. Through the Center for the Study of Complex Systems, Professor Bloch collaborates across disciplines to investigate complex phenomena in natural and engineered systems, leveraging geometric frameworks to address fundamental questions in mechanics and dynamics.

• Topological Data Analysis
• Stochastic Geometry
• Large Deviations Theory
• +4 more

Christian Hirsch is an Associate Professor for Data Science and Statistics at Aarhus University, where he studies random networks motivated from biology and health sciences through techniques from topological data analysis and stochastic geometry. He is a member of the Stochastics group at the Department of Mathematics and holds additional affiliations as an Associate Fellow of the Aarhus Institute for Advanced Studies, and with the AU DIGIT Centre and the AU Quantum Campus. Current Position: Associate Professor for Data Science and Statistics, Aarhus University Previous Positions: Assistant Professor at University of Groningen and University of Mannheim Postdoctoral Experience: Aalborg University, LMU Munich, WIAS Berlin Education: PhD from Ulm University Christian Hirsch's research focuses on the statistical foundations of topological data analysis, large deviations theory in stochastic geometry, and percolation theory of spatial random networks. His work bridges theoretical mathematics with practical applications in data science, particularly in analyzing complex structures through topological methods. He investigates how topological features form and disappear in growing data structures, developing statistical tests to determine whether observed patterns are significant or merely random occurrences. His recent publications reveal a strong trend toward applying topological data analysis to increasingly complex structures, with significant focus on statistical validation of topological features. Hirsch has made substantial contributions to understanding the probabilistic behavior of persistent homology, developing functional central limit theorems and large deviation principles for topological functionals. His work spans theoretical foundations in stochastic geometry while finding applications in materials science, neural networks, and wireless communication systems. As an educator, Hirsch teaches graduate courses including Topological Data Analysis, Stochastic Geometry, Monte Carlo Simulation, Markov Decision Processes, Probability Theory, and Stochastic Processes. He has supervised numerous PhD, MSc, and BSc students, with several of his former students securing academic positions at institutions like University of Leiden, Tokyo Institute of Technology, and Budapest University of Technology. Hirsch leads a research group within the Stochastics group at Aarhus University, collaborating extensively with researchers across Europe and North America. His work demonstrates how topological methods can provide rigorous statistical insights into complex data structures, making significant contributions to both theoretical mathematics and practical data analysis techniques.

Tim RogersView profile

Professor

• Stochastic Processes
• Random Matrix Theory
• Collective Behavior
• +4 more

Professor Tim Rogers is affiliated with the University of Bath as a faculty member in the Department of Mathematical Sciences . He is actively involved in research spanning complex systems, network theory, and stochastic processes. PhD in Random Matrix Theory from King's College London (2010) His research focuses on emergent behavior in random systems , including: Collective Behavior : Crowd dynamics, lane formation, and noise-enhanced synchronization Epidemics & Networks : Spread prediction, node risk assessment, and misinformation impacts Ecology & Evolution : Trait emergence, species boundaries, and demographic noise effects Random Matrix Theory : Spectral analysis and applications to complex systems Publication trends reflect interdisciplinary work bridging Physics, Biology, and Mathematics , with a focus on network structures , stochastic modeling , and emergence phenomena . Scientific awards include: 2015 : Editor's Choice for Europhys. Lett. 109, 28005 2016 : Highlight of Journal of Physics A 2017 : Editor's Suggestion for Phys. Rev. E 92, 032708 He has supervised numerous PhD students and postdocs on projects related to stochastic dynamics , network modeling , and mathematical biology , with ongoing grants from agencies like EPSRC and The Leverhulme Trust .

Sirui LiView profile

Researcher

• CO2 Conversion
• Plasma Catalysis
• Carbon Capture
• +5 more

Sirui Li is a Postdoctoral Researcher in the Department of Chemical Engineering and Chemistry at Eindhoven University of Technology, specializing in plasma-based technologies for sustainable chemical processes. Their research focuses on carbon capture and utilization, plasma catalysis, and reactor design for CO 2 conversion and nitrogen fixation. Research interests center on plasma-assisted CO 2 conversion , with key areas including: Plasma-sorbent systems for simultaneous CO 2 capture and conversion Gliding arc and DBD reactor design for NO x synthesis and methane conversion Techno-economic analysis of plasma-based sustainable processes Dielectric materials and nanoparticle synthesis for catalytic applications Recent publications demonstrate a clear trend toward integrated plasma-reactor systems for carbon management, with emphasis on process intensification, thermal effects analysis, and scalability. The work bridges fundamental plasma chemistry with industrial application feasibility, particularly in renewable energy integration and fertilizer production. Award highlights include: Baldur Eliasson Award (2022) Best oral presentation award for 'Plasma-sorbent system for CO 2 capture and conversion' (2022) IEEE NPSS Young Professional Travel Grant (2024) Li actively contributes to major research projects including PLACHEM (plasma-assisted CO 2 conversion), GICO (gasification with CO 2 capture), and LEAP-Agri (on-site fertilizer production), while serving as guest editor for Frontiers of Chemical Science and Engineering and participating in international symposia on plasma technology.

Ignacio CastilloView profile

Professor

• Facility Location
• Operations Research
• Supply Chain Management
• +3 more

Ignacio Castillo is a Professor and Associate Dean of Business (Graduate Academic Programs) at the Lazaridis School of Business and Economics, Wilfrid Laurier University. His expertise spans facility location optimization, supply chain management, and sustainable operations. He holds a leadership role in graduate academic programming and teaches courses in operations and statistics. Research focuses on optimizing facility layouts, material handling systems, and closed-loop supply chains. He has developed frameworks for multi-objective facility design and advanced packing optimization algorithms. His work bridges theoretical models with real-world applications in manufacturing and retail sectors. Publications emphasize nonlinear optimization techniques, packing problems, and supply chain coordination strategies. Recent work explores irregular object configurations and retail category space optimization. His textbooks include Business Statistics for Contemporary Decision Making and Operations Management , emphasizing practical decision-making tools. Office: LH4001M | Languages: English, Spanish