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Dan MikulincerView profile

Assistant Professor

• High-Dimensional Geometry
• Probability Theory
• Statistics
• +10 more

Dan Mikulincer is the Brian and Tiffinie Pang Assistant Professor at the University of Washington in the Department of Mathematics, College of Arts and Sciences. He previously held a postdoctoral Instructor position at MIT Mathematics and earned his Ph.D. from the Weizmann Institute of Science under Ronen Eldan. He completed his B.Sc. in Mathematics and Computer Science at Ben-Gurion University, where he also studied Cognitive Neuroscience. B.Sc.: Ben-Gurion University (Mathematics, Computer Science, Cognitive Neuroscience) Ph.D.: Weizmann Institute of Science, Faculty of Mathematics Postdoc: MIT Mathematics Current: Assistant Professor, University of Washington, Department of Mathematics His research lies at the intersection of high-dimensional geometry, probability, statistics, information theory, and data science. He is particularly focused on normal approximations, Stein's method, stochastic analysis, and dimension-free phenomena. His work explores foundational aspects of learning theory, random matrices, transportation inequalities, and neural networks, often using probabilistic and analytic tools to derive sharp, robust results in high dimensions. The recent publications reflect a consistent focus on probabilistic methods in high-dimensional settings. Key themes include normal approximation via Stein's method, optimal transport, concentration and anti-concentration inequalities, random graph models, and theoretical aspects of machine learning such as learnability and neural network expressivity. The work spans both pure mathematics (e.g., GAFA, PTRF) and top-tier computer science venues (e.g., COLT, STOC, NeurIPS), highlighting interdisciplinary impact. Although no formal scientific awards are listed in the provided text, his publications in premier journals and conferences (Annals of Probability, STOC, NeurIPS, COLT) indicate significant recognition in the theoretical community. Dan Mikulincer has advised or collaborated with several researchers including Yair Shenfeld, Max Fathi, Ronen Eldan, and Sébastien Bubeck. He has served as a TA for 18.650: Statistics for Applications at MIT and taught programming courses (Java, Python, JavaScript) at the Interdisciplinary Center Herzliya. He is also a senior lecturer at WeCode, a nonprofit providing free programming education to underrepresented youth in Israel, indicating a strong commitment to education and outreach. He has been affiliated with research groups at MIT Mathematics, Weizmann Institute, and Microsoft Research AI, where he spent the summer of 2019 hosted by Sébastien Bubeck. These collaborations span theoretical machine learning, stochastic processes, and algorithmic foundations.

• Geometric Function Theory
• Complex Analysis
• Harmonic Analysis
• +3 more

Arturo Nicolau Nos is a Professor in the Department of Mathematics at the Autonomous University of Barcelona (UAB). His research focuses on Geometric Function Theory, Complex Analysis, Harmonic Analysis, and Functional Analysis. He has held various academic roles since 1984, including Editorial Leadership in Publicacions Matemàtiques (2007–2011) and coordinated several research projects at the Centre de Recerca Matemàtica (CRM). Education: Ph.D. in Mathematical Sciences (UAB, 1990) and a Degree in Mathematics (UAB, 1984). Research Projects STOCHASTIC PROPERTIES IN GEOMETRIC FUNCTION THEORY (2022–2026) Aspectos probabilísticos y geométricos de la teoría de funciones (2018–2022) TEORIA GEOMETRICA DE FUNCIONES (2015–2018) He has advised 10 theses, including notable work on interpolation problems in complex analysis. His grants include funding from the European Regional Development Fund, Spanish Ministry of Economy, and international collaborations. He has held visiting positions at institutions like the University of Washington (2009), Washington University School of Medicine (2008), and Scuola Matematica Interuniversitaria (2002).

Alex Ferrer FerreView profile

Associate Professor

• Materials Design
• Aeronautical Engineering
• Computational Mechanics
• +4 more

Àlex Ferrer Ferre is a Serra-Hunter Associate Professor at the Department of Physics, Universitat Politècnica de Catalunya (UPC) in Barcelona, and an associate researcher at the International Center for Numerical Methods in Engineering (CIMNE). His research focuses on topology optimization, computational mechanics, and advanced material design. He holds a PhD in Computational Mechanics from UPC (2017) and has conducted postdoctoral research at the University of Oxford and École Polytechnique. Key roles include teaching courses like Flight Mechanics and leading the Swan project for topology optimization software. His work integrates academic and industrial collaborations with entities like ESA and INEGI. Research interests span multiscale design, topology optimization for additive manufacturing, and fracture mechanics. Major projects include the TopSat CubeSat chassis project (ESA-funded) and the Flexion project on flexible material design. Awards include the Marie Curie Fellowship (2019) and an Excellent Cum Laude PhD. His labs and teams collaborate globally, emphasizing open-source tools and collaborative research.

Malvin GattingerView profile

Researcher

• Dynamic Epistemic Logic
• Model Checking
• Gossip Protocols
• +4 more

Dr. Malvin Gattinger is a researcher at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam, specializing in Theoretical Computer Science. His work focuses on applying formal logic to computational problems, with particular emphasis on dynamic epistemic logic and model checking techniques. He maintains an active research profile with numerous publications in leading venues, including recent work extending into 2025. Dr. Gattinger's research centers on the intersection of logic and computer science, particularly exploring how knowledge and information propagate in multi-agent systems. His work on gossip protocols has significantly advanced our understanding of information dissemination in distributed systems, while his contributions to symbolic model checking have made complex epistemic logic verification more computationally feasible. His research demonstrates both theoretical depth and practical application potential, especially in areas requiring formal verification of knowledge-based systems. Analysis of Dr. Gattinger's publication history reveals a consistent focus on epistemic logic and its computational applications. His work shows a clear trajectory from foundational theoretical work to increasingly sophisticated implementation techniques, with a notable emphasis on symbolic methods using Binary Decision Diagrams and Zero-suppressed Decision Diagrams. The recent inclusion of topics like topological evidence models and perspective shifts indicates expanding research horizons while maintaining core methodological approaches. His publications appear primarily in specialized logic and theoretical computer science venues, reflecting deep expertise in these domains. While no specific scientific awards are listed in the available information, Dr. Gattinger's research has clearly made significant contributions to the field of dynamic epistemic logic. His work on model checking implementation (SMCDEL) and GoMoChe represents practical tools that have likely advanced research capabilities in the community. The inclusion of Logic4Peace demonstrates engagement with broader social applications of logical methods. Dr. Gattinger appears to be actively involved in research supervision and collaboration within the ILLC environment. His work often involves computational implementations, suggesting involvement with research groups focused on logic-based computation. The consistent publication record spanning over a decade indicates sustained research productivity and likely involvement in multiple research projects and collaborations within the Theoretical Computer Science group at ILLC. Based on his research focus and publications, Dr. Gattinger is likely associated with research groups or labs working on formal methods, multi-agent systems, and logic-based computation at the University of Amsterdam. His work on SMCDEL and GoMoChe suggests involvement with teams developing computational tools for epistemic logic verification.

Roland PulchView profile

Professor

• Uncertainty Quantification
• Model Order Reduction
• Multirate Methods
• +5 more

Prof. Dr. Roland Pulch is a faculty member at the Institute of Mathematics and Computer Science , University of Greifswald. His academic role and extensive teaching duties across multiple semesters (2021/22–2025/SS) highlight his commitment to education and research. Current courses: Numerics I (Lecture), Stochastic Differential Equations (Special Lecture), and Simulation of Randomness (Seminar) in SS 2025. Recent courses include Numerics II , Interpolation Methods , and Differential Equations in Biology . His research interests span Uncertainty Quantification , Model Order Reduction , Multirate Methods , and Coupled DAE-PDE Problems . Contact information: Institut für Mathematik und Informatik, Walther-Rathenau-Straße 47, 17489 Greifswald. Office hours by appointment. Phone: +49 3834 420 4635.

• Machine Learning
• Control Systems
• Reinforcement Learning
• +3 more

Benjamin Recht is a Professor at the California Institute of Technology , affiliated with the Center for the Mathematics of Information . His work spans Machine Learning , Control Systems , Reinforcement Learning , and Optimization , with a focus on theoretical guarantees, adaptive algorithms, and real-world applications. His research includes: Control Systems : Certainty equivalence, adaptive control, LQR, and robustness in dynamic environments. Machine Learning : Generalization bounds, interpolation in classifiers, test set overuse, and ethical frameworks for systemic harm detection. Neural Rendering : K-Planes for explicit radiance fields in space-time-appearance modeling. Recent publications (2025-2018) highlight trends in automating adaptive control , ethical machine learning , distributed computing , and 3D reconstruction . No student lists, awards, or lab details are explicitly mentioned.

Wenying JiView profile

Assistant Professor

• Construction Engineering
• Infrastructure Systems
• Disaster Management
• +4 more

Wenying Ji is an Assistant Professor in the Sid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering at George Mason University's Volgenau School of Engineering. His research focuses on the integration of advanced data analytics, complex system simulation, and construction management to enhance decision-support processes in the Architecture, Engineering, and Construction (AEC) industry. Dr. Ji received his PhD in construction engineering and management from the University of Alberta, a master's degree from Texas A&M University, and a bachelor's degree from Southeast University. His research interests span several interconnected domains including construction engineering, infrastructure systems, disaster management, data analytics, and complex system simulation. Dr. Ji has developed innovative approaches that apply Bayesian methods and machine learning to solve critical problems in infrastructure resilience, particularly during and after natural disasters. His work emphasizes the integration of real-time data from social media and other sources to improve infrastructure restoration processes following extreme events. A significant portion of his research focuses on highway systems, flood management, and emergency response planning, with particular attention to equity considerations in infrastructure restoration. Analysis of Dr. Ji's recent publications reveals a clear progression toward increasingly sophisticated integration of data analytics with infrastructure engineering problems. His work shows a strong emphasis on Bayesian methods, machine learning applications, and spatiotemporal analysis for disaster management and infrastructure restoration. The research demonstrates practical applications for improving decision-making in construction management, particularly in contexts of uncertainty and emergency response. Dr. Ji's notable awards include: 2017 Outstanding Reviewer Award from ASCE's Journal of Computing in Civil Engineering 2018 ASCE outstanding reviewer award for ASCE Journal of Construction Engineering and Management Jeffress Trust Awards Program in Interdisciplinary Research (2019) WSC Outstanding Reviewer Award (2019, 16 out of 746 reviewers) Dr. Ji actively mentors PhD students including Yitong Li, Yudi Chen, Minjie Xia, and Yuzheng Xie, with several receiving awards for their research. His research group has secured significant funding, including an NSF grant in 2022 on 'Strengthening American Electricity Infrastructure for an Electric Vehicle Future.' He serves as Assistant Specialty Editor for the ASCE Journal of Construction Engineering and Management and regularly reviews for top journals in his field. Dr. Ji's research team collaborates with multiple institutions and participates in conferences such as the Winter Simulation Conference and ASCE International Conference on Computing in Civil Engineering.

Hamid RoshanView profile

Professor

• THMC Experimental and Computational Modelling
• Carbon Capture, Utilization and Storage
• Coal Seam Gas Engineering
• +7 more

Professor Hamid Roshan is a faculty member at the School of Minerals and Energy Resources Engineering within the Faculty of Engineering at UNSW Sydney. He graduated with a PhD in Petroleum Geomechanics Engineering from UNSW Sydney in 2012, followed by 3.5 years of postdoctoral training in the School of Civil and Environmental Engineering. He was appointed to his current position in 2016 after gaining valuable industry experience with the Underground Gas Storage Company, where he worked on the Sarajeh field gas storage project. Professor Roshan's research spans multiphysics geomechanics and rock characterization, with applications across Mining, Civil, and Petroleum Engineering sectors. His work integrates theoretical, numerical, and experimental approaches to solve complex geomechanical problems. Since 2017, he has developed the advanced GeoEngineering Research Lab at UNSW, where next-generation equipment for coupled geomechanics-rock characterization has been designed and built, offering state-of-the-art services to industry. His research interests include THMC Experimental and Computational Modelling in CCUS, Coal Seam Gas and Shale Gas Engineering, Geophysics and Data-driven Rock Mass Characterization, Borehole Geotechnical Logging, In-situ Stress Measurement and Estimation, and Fundamentals of Rock Mechanics with a focus on Multiphysics Geomechanics. His recent publications demonstrate expertise in ultramafic rock interactions, geothermal energy storage, shale gas reservoirs, and advanced computational modeling techniques. Professor Roshan has developed and patented field-scale downhole logging tools for stress-mechanical property measurements along with software solutions for the mining industry. His work has practical applications in carbon sequestration, underground hydrogen storage, and unconventional gas extraction. Professional Recognition: American Rock Mechanics Association Future Leader Professional Memberships: Australian Geomechanics Society, International Society of Rock Mechanics, Society of Petroleum Engineers Professor Roshan teaches Petrophysics (PTRL2020), Formation Evaluation (PTRL5107), Integrated Oil and Gas Reservoir Evaluation (PTRL4010), and Geomechanics A, while actively seeking students with strong academic backgrounds for research opportunities.

Michael J. LindseyView profile

Assistant Professor

• Computational Methods
• Numerical Linear Algebra
• Optimization
• +19 more

Michael J. Lindsey is an Assistant Professor in the Department of Mathematics at the University of California, Berkeley, and a Faculty Scientist at Lawrence Berkeley National Laboratory. His research focuses on computational methods driven by Numerical Linear Algebra , Optimization , and Randomization , particularly for High-Dimensional Scientific Computing in quantum many-body problems and applied probability. University : UC Berkeley (Assistant Professor since 2022) Lab Affiliation : Mathematics Group at Lawrence Berkeley National Laboratory Email : lindsey@berkeley.edu His work includes Semidefinite Relaxation for quantum and classical problems, Monte Carlo Sampling techniques, and Tensor Networks for high-dimensional functions. He has pioneered Variational Embedding theory with guaranteed energy bounds and scalable solvers for quantum systems. Recent publications span Quantum Chemistry , Machine Learning , and High-Dimensional Probability , with applications to Electronic Structure , Molecular Dynamics , and Optimal Transport . He received the 2024 Hellman Fellowship and the 2019 SIAM Student Paper Prize . Teaching includes graduate and undergraduate courses in numerical analysis and applied mathematics at UC Berkeley and New York University. He also organizes the HDSC Seminar on high-dimensional scientific computing.

• Spatial Statistics
• Spatial Econometrics
• Location-Allocation Problems
• +3 more

BONNEU FLORENT is a faculty member at the University of Avignon and Pays de Vaucluse , affiliated with the Institute of Agrosciences, Environment and Health and the CER Mathematics Department . His research focuses on spatial statistics, spatial econometrics, and location-allocation problems. Specializes in spatio-temporal point processes and spatial structure analysis Develops kriging-based forecasting methods for conditional intensity modeling Investigates biodiversity patterns and economic concentration metrics Recent work includes spatial modeling of wildfire occurrences, grapevine disease patterns, and herbicide impact studies. He contributes to educational planning through simulation approaches and applies mass transportation theory in spatial optimization problems. Bonneu collaborates with the Mathematics Laboratory (UPR 2151 LMA) , focusing on statistical tools for spatial data analysis. His methodological contributions span stochastic modeling, optimization algorithms, and geospatial applications across environmental science, agricultural economics, and emergency management.

Ashutosh TrivediView profile

Associate Professor

• Formal Methods
• Cyber-Physical Systems
• Reinforcement Learning
• +5 more

Ashutosh Trivedi is an Associate Professor of Computer Science at the University of Colorado Boulder, currently on leave from his position as Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay. He is affiliated with multiple research initiatives including the Centre for Formal Design and Verification of Software (CFDVS) at IIT Bombay, Free and Open Source Software for Education (FOSSEE), and the Indo-French project on Algorithmic Verification of Real-Time Systems (AVeRTS). At CU Boulder, he leads the Programming Languages and Verification (CUPLV) research group focusing on trustworthy AI systems. Trivedi's research centers on bridging formal methods with artificial intelligence to create more trustworthy systems. His work spans formal verification of cyber-physical systems, reinforcement learning with formal guarantees, and developing techniques for ensuring software fairness and accountability. He specializes in using formal languages, automata, and logic to transform vague natural-language instructions into precise specifications for AI systems. His recent projects include developing reinforcement learning algorithms for cardiac pacemaker design based on formal safety requirements, using SAT solvers to ground large language model outputs in logical reasoning, and encoding state representations in reinforcement learning using formal languages. His publication trends reveal a strong focus on neurosymbolic approaches that combine neural networks with symbolic reasoning, particularly for safety-critical applications. Recent work demonstrates increasing integration of formal methods with reinforcement learning, with applications spanning medical devices, tax preparation software, and puzzle-solving AI. His research shows a clear trajectory toward making AI systems more explainable, accountable, and verifiable through principled mathematical frameworks. Distinguished Paper Award at CAV for Regular Reinforcement Learning (2024) NeuS 2025 Disruptive Idea Award for Stochastic Neural Simulation Relations for Transferring Control under Uncertainty ACM Senior Member recognition (2024) Royal Society Wolfson Visiting Fellowship (2024) Trivedi has successfully advised multiple PhD students to completion, including Shadi Tasdighi Kalat (2025), Mateo Perez (2025), John Komp (2024), Vishnu Murali (2024), and Taylor Dohmen (2024). His teaching portfolio includes foundational courses in automata theory, digital logic design, and cyber-physical systems at both IIT Bombay and CU Boulder. He has served on program committees for major conferences including FSTTCS, HSCC, and FORMATS, and organized workshops such as ICLA 2015 and ALC 2015. As leader of the CUPLV research group, Trivedi directs projects focused on formal verification of AI systems, reinforcement learning with safety guarantees, and software fairness. His group collaborates with medical researchers on cardiac device verification and with legal scholars on tax software accountability, reflecting his commitment to applying formal methods to real-world problems with significant societal impact.

Christian BenderView profile

Professor

• Backward stochastic differential equations
• Nonlinear option pricing
• Monte Carlo methods for dynamic programming equations
• +3 more

Prof. Dr. Christian Bender is a Full Professor of Applied Mathematics at Saarland University's Department of Mathematics since 2009. His academic career includes positions as Junior Professor for Applied Stochastics at TU Braunschweig (2006-2009) and various research roles at the Weierstrass Institute and University of Konstanz. He leads the Stochastics Group at Saarland University and serves on the Steering Committee of the DMV Specialist Group for Stochastics. His educational background includes: PhD in Mathematics (2003) from University of Konstanz Diploma in Mathematics (2001) from University of Konstanz Prof. Bender's research focuses on advanced stochastic methods with applications in mathematical finance. His work centers on backward stochastic differential equations, nonlinear option pricing, Monte Carlo methods for dynamic programming, and stochastic calculus for non-semimartingales. He has made significant contributions to fractional Brownian motion modeling, particularly in financial contexts where traditional semimartingale assumptions don't hold. His research bridges theoretical probability with practical financial applications, developing novel numerical methods for complex derivative pricing and risk management problems. Analysis of his recent publications reveals a strong trend toward developing computational methods for backward stochastic differential equations (BSDEs) and their applications in finance. His work increasingly integrates machine learning techniques with traditional stochastic methods, particularly in the 'Regression Anytime' approach. There's also a clear focus on non-semimartingale models that better capture market phenomena that standard models miss, along with continued exploration of fractional processes for modeling anomalous diffusion in financial markets. Prof. Bender has received the following professional recognition: Member of the Steering Committee of the DMV Specialist Group for Stochastics eV Associate editor of Mathematical Methods of Operations Research Associate editor of Methodology and Computing in Applied Probability Co-Organiser of the International Seminar on SDEs and Related Topics As an advisor, Prof. Bender has successfully guided seven PhD students to completion, including Matthias Thiel, Steffen Meyer, and Christian Gärtner. His research has been supported through various academic positions and collaborative projects with leading researchers in stochastic analysis and mathematical finance. His teaching portfolio includes advanced courses in stochastics, financial mathematics, and insurance mathematics across multiple semesters. Prof. Bender leads the Stochastics Group at Saarland University, which focuses on theoretical and applied stochastic analysis with particular emphasis on financial applications. The group maintains active collaborations with researchers worldwide and regularly contributes to major conferences in stochastic analysis and mathematical finance.

• Orthogonal Polynomials
• Numerical Analysis
• Approximation Theory
• +3 more

Elías Manuel María Berriochoa Esnaola is a full-time professor at the University of Vigo , affiliated with the Faculty of Sciences and the Department of Applied Mathematics I . His research is centered on Orthogonal Polynomials , Numerical Analysis , and Approximation Theory , particularly focusing on interpolation techniques and quadrature formulas. Research Themes: Orthogonal polynomials on the unit circle, Hermite-Fejér interpolation, Sobolev orthogonality, asymptotic behavior of polynomials, and numerical quadrature. Collaborations: Extensive work with Alicia Cachafeiro, José M. García-Amor, and Francisco Marcellán. His publications span from 1998 to 2020, addressing problems in interpolation , quadrature rules , and anti-periodic trigonometric systems . A notable contribution is the characterization of Chebyshev orthogonal families and the study of Gibbs-Wilbraham oscillations in interpolation problems. He can be contacted at esnaola@uvigo.es .

• Partial Differential Equations
• Dynamic Systems
• Applied Analysis
• +3 more

Stefanopoulos Vangelis is a Professor at the University of Patras, affiliated with the Division of Applications and Foundations of Computer Science. His research spans mathematics and computer science, focusing on theoretical and applied problems. University: University of Patras Department: Division of Applications and Foundations of Computer Science Academic Rank: Professor Research Interests: His work centers on Partial Differential Equations, Dynamic Systems, and Applied Analysis, with applications in mathematical physics, algorithmic modeling, and computational theory. Key areas include eigenvalue problems, universal series, and stability analysis. Email: vstefan@ceid.upatras.gr Office Hours: Tuesday (9:00-11:00, 10:00-11:00 when no meetings), Friday (12:00-14:00) or by appointment.

Ishan LevyView profile

Research Fellow

• Homotopy Theory
• Algebraic K-theory
• Stable Homotopy
• +2 more

Ishan Levy is a Research Fellow at the Massachusetts Institute of Technology, set to receive his PhD from MIT in 2024 under the supervision of Professor Michael Hopkins. He is internationally recognized for transformative contributions to homotopy theory, particularly for resolving Ravenel's Telescope Conjecture—a central problem in stable homotopy theory that remained unsolved for over 40 years. His educational background includes: PhD in Mathematics (expected 2024), Massachusetts Institute of Technology, Advisor: Michael Hopkins Levy's research focuses on deep structural problems in homotopy theory, with breakthrough work in algebraic K-theory and stable homotopy. He developed novel techniques that solved Ravenel's Telescope Conjecture by constructing counterexamples with Burklund, Hahn, and Schlank. His collaboration with Burklund established rational convergence of the Waldhausen tower interpolating between K-theory of integers and moduli space A(pt), while joint work with Burklund, Carmeli, Hahn, Schlank, and Yanovski achieved unprecedented estimates for growth rates of stable homotopy groups of spheres. His scientific recognition includes: Clay Research Fellow (2024-2029) Levy collaborates extensively with leading mathematicians including Robert Burklund, Tomer Schlank, Jeremy Hahn, Shaul Barkan, and Yuval Yanovski on foundational problems in algebraic topology. While no formal advising relationships or grant portfolios beyond the Clay Fellowship are documented, his research program represents a significant advance in understanding high-dimensional manifold structures through homotopy-theoretic methods. His current work centers on extending K-theoretic techniques to solve remaining challenges in stable homotopy theory, with potential implications for geometric topology and mathematical physics.