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Luana RuizView profile

Assistant Professor

• Large-scale Graph Machine Learning
• Graph Neural Networks
• Signal Processing
• +6 more

Luana Ruiz is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), affiliated with the Mathematical Institute for Data Science (MINDS) and the Data Science and Artificial Intelligence Institute (DSAI). She earned her Ph.D. in Electrical Engineering from the University of Pennsylvania (2022), and dual B.Sc. and M.Eng. degrees in Electrical Engineering from the University of São Paulo (Brazil) and École Supérieure d’Electricité (France, now CentraleSupélec) in 2017. Her research focuses on machine learning, signal processing, and network science, particularly scalable algorithms for non-Euclidean domains like graphs and data manifolds. She has pioneered work on graph neural networks (GNNs), graph sampling, and theoretical limits of transferability and generalization in graph-based learning. Notably, she has developed methods for efficient GNN training on large graphs and stability analysis of neural networks on manifolds. Affiliations: Johns Hopkins University, MINDS, DSAI Education: Ph.D., University of Pennsylvania (2022); B.Sc./M.Eng., University of São Paulo & CentraleSupélec (2017) Her research interests include large-scale graph machine learning, manifold learning, physics-informed ML, and combinatorial optimization. She has received awards such as the iREDEFINE Fellowship (2019), MIT EECS Rising Star (2021), and Best Student Paper Awards at EUSIPCO (2019, 2021). Her work bridges theoretical foundations and practical applications, with contributions to GNN architecture design, signal processing on graphs, and stability guarantees for neural networks on manifolds. Recent publications highlight advances in graph sampling for scalable GNNs, stability of manifold neural networks, and theoretical analysis of GNN expressivity. She collaborates with institutions like MIT and the Simons Institute, and her interdisciplinary approach addresses challenges in graph data analysis and AI efficiency. She advises on grants related to graph signal processing and has contributed to the development of graphon-based frameworks for large-scale graph analysis. Awards: Eiffel Excellence Scholarship (2013–2015), Best Paper Awards at EUSIPCO (2019, 2021) Grants/Advising: METEOR and FODSI postdoctoral fellowships, Google Research Fellowships

Angelina AnaniView profile

Associate Professor

• Mining Systems Optimization
• Mine Planning and Production Scheduling
• Sustainable Mining Systems
• +12 more

Dr. Angelina Anani is an Associate Professor in the Department of Mining & Geological Engineering at the University of Arizona, College of Engineering. She is also a member of the Graduate Faculty and actively contributes to research and teaching in mining systems optimization, mine planning, and sustainable mining practices. Education: PhD in Mining Engineering, Missouri University of Science and Technology, Rolla, Missouri, United States BS in Mining Engineering (Summa cum laude), Missouri University of Science and Technology, Rolla, Missouri, United States Research Interests: Dr. Anani's research spans a broad spectrum of mining engineering challenges, focusing on modeling and optimization of mining systems , mine planning and production scheduling , and sustainable mining system design . She investigates mine equipment reliability , tunneling and underground works , and energy and water efficiency . A significant portion of her recent work integrates machine learning and data-driven approaches into mine safety and planning, including 3D/4D/VR applications and digital twin systems . Her interdisciplinary approach also includes ethnographic research in mining communities and supply chain management in the mining sector. Publications Trends: Her recent publications reflect a strong shift toward intelligent systems in mining, with increasing focus on machine learning for safety, process mining for maintenance, and digital twin deployment. She combines traditional optimization techniques like discrete event simulation with modern AI to solve complex mining challenges, particularly in underground and transition mines. Scientific Awards: Freeport-McMoRan, Inc. Career Development Grant Society for Mining, Metallurgy and Exploration, Fall 2022 Faculty Core Advising and Grants: Dr. Anani supervises graduate research through MNE 900 (Research), MNE 910 (Thesis), and MNE 920 (Dissertation) courses. She has secured external funding such as the Freeport-McMoRan Career Development Grant, supporting her innovative work in mine optimization and safety. While current students are not listed, her active supervision load indicates ongoing mentorship of master’s and PhD candidates. Labs and Teams: She is actively involved with the San Xavier Underground Mine Laboratory, where she contributes to monitoring systems and digital twin development. Her collaborative work with researchers from Chile and Ghana highlights her international engagement. She is also affiliated with professional societies including the Society of Mining, Metallurgy and Exploration (SME), Society of Mining Professors, and Women in Mining (WIM), contributing to both technical and diversity initiatives in the field.

Michael ShelleyView profile

Professor

• Fluid Dynamics
• Active Matter
• Biophysics
• +3 more

Michael Shelley is the Lilian and George Lyttle Professor of Applied Mathematics at New York University's Courant Institute of Mathematical Sciences. He holds additional roles as Professor of Mathematics, Neural Science, and Mechanical Engineering. His research focuses on fluid dynamics, active matter, and biophysical systems, with notable contributions to microswimmer dynamics, cytoplasmic flows, and fluid-structure interactions. Shelley leads the Applied Mathematics Laboratory at Courant and directs the Center for Computational Biology at the Flatiron Institute. His work bridges theoretical, computational, and experimental approaches to understand complex biological and physical phenomena. Research interests include nonlinear dynamics of fluids, collective behavior in active matter systems, and biomechanical processes such as mitosis and cellular transport. Recent studies involve modeling microtubule networks, cytoplasmic stirring, and spindle positioning in cells. His publications span topics like fluid-structure interactions, viscoelastic flows, and the mechanics of swimming organisms. Key projects include the dynamics of erodible bodies in fluid flows, optimization of microswimmer designs, and the rheology of active suspensions. Shelley collaborates across disciplines, integrating applied mathematics with biology, physics, and engineering. His work has advanced understanding of self-organization in living systems and fluid-driven morphological changes.

Dan AbramovichView profile

Professor

• Algebraic Geometry
• Resolution of Singularities
• Logarithmic Geometry
• +3 more

**Dan Abramovich** is a Professor in the Department of Mathematics at Brown University, affiliated with the College of Arts and Sciences. His research focuses on algebraic geometry, particularly in resolution of singularities, logarithmic geometry, moduli spaces, and birational geometry. He has authored numerous papers and contributed to foundational work in these areas, supported by NSF and BSF grants. **Teaching**: He teaches courses like Math 1540 (Galois theory and representations of finite groups) and has extensive past teaching records dating back to 2015. His seminars include Algebraic Geometry and Topology. **Research**: Abramovich's work bridges algebraic structures and geometric applications, with notable contributions to logarithmic geometry and moduli theory. His recent articles address weighted blow-ups, dynamical systems in resolution, and functorial monomialization. **Grants & Collaboration**: He collaborates internationally, organizing conferences like AGNES and BATMoBYle. His work is disseminated via arXiv, with over 50 publications since the 1990s.

Craig S. KaplanView profile

Professor

• Computer Graphics
• Computational Geometry
• Mathematical Art
• +4 more

Craig S. Kaplan is a Professor at the University of Waterloo's David R. Cheriton School of Computer Science within the Faculty of Mathematics. His research bridges computer science and mathematical art, focusing on computational geometry, geometric pattern design, and algorithmic art. He holds degrees including a Ph.D. and M.Sc. from the University of Washington (2002, 1998) and a B.Math. from the University of Waterloo (1996). His work spans applications of mathematics in art and design, including Islamic geometric patterns, computer graphics, and computational geometry. Notable contributions include developing methods for generating Islamic geometric patterns, exploring aperiodic tilings (including the discovery of an aperiodic monotile in 2023), and creating tools for artistic visualization like RepulsionPak and FlowPak. He also engages with digital art forms such as generative algorithms, stereoscopic 3D rendering, and interactive applications for mindfulness and VR. His publications reflect a focus on geometric algorithms, pattern generation, and interdisciplinary art-science projects. Though no explicit awards are listed, his prolific output and contributions to mathematical art suggest significant recognition in his field. His work often emphasizes computational methods for artistic expression, combining rigorous mathematics with creative outcomes.

• Logistics Optimization
• Transportation Planning
• Operations Research
• +3 more

Prof. Michael Schneider is Universitätsprofessor and Chair of Computational Logistics at RWTH Aachen University since 2016. Previously, he held positions at TU Darmstadt (2013-2016) and earned his doctoral degree from TU Kaiserslautern (2012). His research focuses on logistics optimization, including transportation routing, warehouse management, and metaheuristic methods for solving complex operational challenges. He leads the Global Challenges Lab and serves as communication chair of VeRoLog (EURO's vehicle routing group). Research interests include: quantitative modeling for supply chains, heuristic/exact optimization methods, electric vehicle routing, territory design, and production planning. Notable achievements include the INFORMS Journal on Computing Meritorious Paper Award (2021) and editorial roles in journals like Applied Mathematical Modelling. Publications span topics like vehicle routing with time windows, electric logistics networks, and warehouse automation strategies. His work integrates advanced algorithms with real-world industry applications (e.g., DHL, Picnic). The Computational Logistics group collaborates on projects addressing sustainability, autonomous systems, and modern warehousing challenges. Education: Business Admin & Computer Science (University of Mannheim), PhD (TU Kaiserslautern) Professional Roles: VeRoLog Communication Chair, EURO Working Group Key Projects: Electric vehicle routing optimization, warehouse automation, and sustainable logistics networks

• Theoretical Computer Science
• Algorithmic Information Theory
• Computational Complexity
• +3 more

Neil Lutz is a Visiting Assistant Professor at Swarthmore College and an Affiliate Assistant Professor at Iowa State University, specializing in Theoretical Foundations. His research focuses on algorithmic information theory, computational complexity, and fractal geometry. Key contributions include work on algorithmic dimensions, point-to-set principles, and fairness in facility location. Lutz has published extensively in top venues such as STACS, TOCT, and SODA. His work bridges theoretical computer science with applications in molecular programming and distributed computing.

Reza ZadehView profile

Adjunct Professor

• Machine Learning
• Distributed Computing
• Discrete Mathematics
• +7 more

Reza Zadeh is a Computational Mathematics professor at Stanford University's School of Engineering and Founder & CEO of Matroid . He previously served as a Technical Advisory Board member for Databricks and leads the Spark Tutorial at Stanford. Research Interests: Specializing in Machine Learning and Distributed Computing , his work bridges theoretical mathematics with practical implementations in big data systems. Key focus areas include Optimization of Apache Spark 3D Convolutional Neural Networks Discrete Mathematics and Graph Theory Medical Imaging Applications Academic Contributions: His publications reveal trends across multiple disciplines: Adapting machine learning for medical diagnostics (2019-2022) Advancing distributed computing frameworks (2014-2016) Developing mathematical foundations for social networks (2009-2013) Creating scalable optimization algorithms (2014-2016) Scientific Awards: Best Paper Award runner-up at KDD 2016 Academic Leadership: He has taught SMACC Consulting and designed courses including CME 323: Distributed Algorithms and Optimization (2015-2024) and CME 305: Discrete Mathematics and Algorithms (2010-2017). His lectures cover graph theory, approximation algorithms, and spectral sparsification. Labs & Teams: Organized Spark Summit workshops and leads Scaled Machine Learning Conference . Collaborates with Stanford's ICME computational consulting services.

Stephen J. WrightView profile

Professor

• Computational Optimization
• Mathematical Optimization
• Optimization Algorithms
• +3 more

Stephen J. Wright holds the George B. Dantzig Professorship, Sheldon Lubar Chair, and Amar and Balinder Sohi Professorship as a Professor of Computer Sciences at the University of Wisconsin-Madison. His research focuses on computational optimization with broad applications across scientific and engineering disciplines. His expertise spans computational optimization, mathematical optimization, and optimization algorithms, with significant contributions through widely adopted textbooks like Primal Dual Interior-Point Methods and Numerical Optimization . His work bridges theoretical foundations with practical implementations for complex scientific computing challenges. Notable honors include: W.R.G. Baker award from IEEE (2014) SIAM Fellow designation Wright serves as editor-in-chief of the SIAM Journal on Optimization and has held editorial leadership roles for Mathematical Programming (Series A/B), SIAM Review , and SIAM Journal on Scientific Computing . His academic career includes prior positions at North Carolina State University (1986-1990), Argonne National Laboratory (1990-2001), and the University of Chicago (2000-2001), followed by continuous service at UW-Madison since 2001. He has chaired the Mathematical Optimization Society and served as SIAM Trustee, with recent program participation at the Simons Institute including Fall 2021's Geometric Methods in Optimization and Sampling.

Jesse ThalerView profile

Professor

• Theoretical Particle Physics
• Quantum Field Theory
• Machine Learning
• +7 more

Jesse Thaler is a Professor of Physics at the Massachusetts Institute of Technology (MIT), where he has been a faculty member since 2010. He is affiliated with the MIT Center for Theoretical Physics - a Leinweber Institute (CTP-LI), the Laboratory for Nuclear Science (LNS), the Statistics and Data Science Center (SDSC), and the Institute for Data, Systems, and Society (IDSS). Since 2020, he has served as the inaugural Director of the NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI). Thaler received his Sc.B. in Math/Physics from Brown University in 2002, followed by a Ph.D. in Physics from Harvard University in 2006. From 2006 to 2009, he was a Miller Research Fellow at the University of California, Berkeley. His research focuses on theoretical particle physics, particularly on fusing quantum field theory with machine learning techniques to address fundamental physics questions. His work spans three main areas: Data Science and AI/ML applications in particle physics, Collider Physics and QCD, and Beyond the Standard Model physics. He is an expert in jet physics, which involves studying collimated sprays of particles produced at the Large Hadron Collider (LHC), and he investigates jet substructure to enhance searches for new phenomena and understand gauge theory dynamics. His research also explores strategies for probing dark matter at the LHC and theoretical structures of supersymmetry. Data Science and AI/ML: Merging deep learning with physics principles for improved particle physics analysis Collider Physics and QCD: Developing new theoretical frameworks for analyzing collider data Beyond Standard Model: Exploring dark matter detection strategies and theoretical extensions to the Standard Model Thaler has received numerous prestigious awards including being named an American Physical Society (APS) Fellow in 2022 and receiving a Simons Investigator Award in the same year. Other notable honors include the Fermilab Distinguished Scholar (2018-2020), Simons Fellowship in Theoretical Physics (2018), Frank E. Perkins Award for Excellence in Graduate Advising (2017), and the Presidential Early Career Award for Scientists and Engineers (2012). As an advisor, Thaler has mentored numerous Ph.D. students, postdoctoral researchers, and undergraduate students. His research group has produced significant work in jet physics, machine learning applications for particle physics, and beyond the standard model physics. He has also secured substantial research funding through grants from the Department of Energy and other agencies. Thaler leads the Thaler Research Group at MIT and serves as Director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions, which brings together researchers from MIT, Northeastern University, Harvard University, and Tufts University to advance AI research with applications to fundamental physics.

• Non-Euclidean Geometry
• Convex Geometry
• Minkowski Geometry
• +1 more

Ákos G. Horváth is a Professor at the Department of Geometry , Budapest University of Technology and Economics. His academic career focuses on geometric theories and their applications, with a particular emphasis on Minkowski geometries and discrete geometry. Room: H222 Phone: +36-1-463-2645 Email: ghorvath@math.bme.hu Personal Website: https://math.bme.hu/~ghorvath/ His research areas include Non-Euclidean Geometry, Convex Geometry, and Minkowski Geometry. He investigates topics such as generalized Minkowski spaces, Dirichlet-Voronoi cells, and bisectors in normed spaces, contributing to advancements in discrete and computational geometry. Key trends in his publications span over two decades, with significant work on: Unimodular lattices (1996) Bisectors in Minkowski spaces (2000) Semi-indefinite inner products (2010)

Phong NguyenView profile

Research Professor

• Cryptography
• Cryptanalysis
• Post-quantum Cryptography
• +5 more

Phong Nguyen is a Research Professor at Inria (Directeur de recherche) and a part-time professor at the Computer Science Department (DI ENS) of École Normale Supérieure (ENS), PSL University in Paris. He leads the ENS Crypto Team (Inria Equipe Projet Cascade) and serves as the principal investigator for the ERC Advanced Grant PARQ (2020) focused on lattices in parallel and quantum computing. He holds a PhD (1999) and Habilitation (2007) from ENS-Lyon, with an agrégation de mathématiques (1997). His research integrates cryptography, algorithmic number theory, and lattice-based computations, emphasizing: Cryptanalysis : Deconstructing cryptographic protocols, especially lattice-based systems Post-quantum cryptography : Developing quantum-resistant solutions Lattice algorithms : Optimization of reduction, enumeration, and sieving techniques Real-world applications : Bridging theoretical constructs with practical security implementations His publications (spanning Eurocrypt, Asiacrypt, and Journal of Cryptology) demonstrate deep expertise in lattice cryptography, with recurring themes in algorithm efficiency, cryptanalysis of NTRU/GGH systems, and theoretical advancements in lattice reduction. Recent work (2024) continues this trajectory with improved BKZ analysis and hypercubic lattice optimizations. Awards include : ERC Advanced Grant (2020) for PARQ project Best Paper Award at EUROCRYPT 2006 Cor Baayen Award (2001) He advises PhD students (e.g., Henry Bambury, Leo Ducas) and interns from institutions like École Polytechnique and ENS. He directs the ENS Crypto Team and previously held leadership roles as: French Director of the Japanese-French Laboratory for Informatics (2015-2019) European Director of LIAMA (Sino-European Computer Science Lab, 2013-2015) Coordinator of ECRYPT II virtual labs (2008-2012)

• Structural and Tectonic Geology
• Seismic Hazard Assessment
• Fault Mechanics
• +3 more

Giovanni Camanni is an Associate Professor at the Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia. His work focuses on structural geology, fault mechanics, and seismic hazard assessment, with extensive research in fault segmentation, reactivation processes, and 3D geological modeling. Research Interests: Structural geology, tectonic inheritance, fault kinematics, petrophysical reservoir characterization, and volcano-tectonic processes. Teaching: Courses on Seismic Sources and Microzonation , Structural Geology and Tectonics , and Geological Survey , covering advanced fault mapping, displacement analysis, and field methodologies. Techniques: Utilizes 3D seismic reflection data, photogrammetry, unsupervised learning algorithms (DBSCAN/OPTICS), and U-Pb dating for fault and reservoir studies. Applications: Findings contribute to seismic risk mitigation, fluid flow prediction in fractured reservoirs, and urban subsidence management, particularly in southern Italy and Taiwan. Recent Publications (2020-2025): Advanced understanding of fault zone architecture in dolostones, displacement transfer mechanisms in relay zones, Wilson cycle fault reactivation, and multidisciplinary approaches to ground deformation studies.

Juan Antonio Corrales RamónView profile

Research Fellow

• Robotics
• Human-Robot Interaction
• Robotic Manipulation
• +3 more

Juan Antonio Corrales Ramón is a Research Fellow at the University of Santiago de Compostela's CiTIUS laboratory under the prestigious Beatriz Galindo program. Previously, he served as an Associate Professor at Sigma Clermont Engineering School (2014-2020) and conducted research at UPMC/ISIR in France. His academic credentials include a PhD in Automatic Control and Robotics (2011) and a Computer Engineering degree (2005) from the University of Alicante. Dr. Corrales' research centers on robotics with specialized interests in: Human-robot physical interaction for industrial applications Deformable object manipulation and control theory Tactile sensing technologies and sensor fusion Robotic grasping and in-hand manipulation Multi-robot coordination systems His recent publications demonstrate strong focus on adaptive control systems for deformable objects, tactile sensor development, and human-robot collaboration frameworks. He has participated in significant EU projects including: H2020 SoftManBot (soft material handling) H2020 Bots2Rec (recycling robotics) FUI Aerostrip (aerospace applications) Sudoe Commandia (service robotics) Honors include competitive fellowships: FPU Program Fellowship (Spanish Ministry of Education) Beatriz Galindo Program Fellowship (research excellence) At CiTIUS laboratory, he contributes to advanced robotics research with applications in industrial automation, healthcare, and agricultural robotics.

Eric Christopher ChenView profile

Assistant Professor

• Geometric Analysis
• Geometric Flows
• Partial Differential Equations
• +1 more

Eric Christopher Chen is an Assistant Professor in the Department of Mathematics at the University of Illinois Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. His research focuses on geometric analysis, geometric flows, and partial differential equations. Previously, he was an NSF Postdoctoral Fellow at UC Berkeley and a Ky Fan Visiting Assistant Professor at UC Santa Barbara. Chen’s recent work in geometric flows includes studies on Yamabe flow convergence on asymptotically Euclidean and flat manifolds, Ricci flow smoothing with integral curvature pinching, and sphere theorems for Yamabe metrics. His publications often bridge differential geometry with nonlinear PDE analysis. NSF Postdoctoral Fellowship (2019–2022) Chen is actively involved in the Illinois Geometric Analysis Seminar , organizing talks and presenting his research on Yamabe flow behavior in 2025. His email contact is ecchen@illinois.edu.