جست‌وجوی استاد و پژوهشگر

Tracy Beckerمشاهده پروفایل

استادیار مدعو

• Earthquake Engineering
• Structural Optimization
• Seismic Isolation
• +۳ مورد دیگر

Tracy Becker is an Adjunct Assistant Professor in the Department of Civil Engineering at McMaster University, where she has been since 2014. Her expertise lies in the design, modeling, and experimental testing of high-performance structural systems with a focus on seismic isolation. Education: BS in Structural Engineering, University of California, San Diego MS and PhD in Structural Engineering, Mechanics and Materials, University of California, Berkeley Post-doctoral research at Kyoto University's Disaster Prevention Research Institute Research Interests: Becker specializes in seismic isolation systems, hybrid simulation methods, and structural performance under extreme events. Her work spans bridge engineering, nuclear infrastructure protection, and innovative materials for earthquake resilience. She integrates computational modeling with experimental validation to address challenges in: Nonlinear system behavior in isolated structures Multi-hazard optimization for seismic and wind loads Bridge management using data-driven and fuzzy logic frameworks Advanced gusset plate design for seismic retrofit Adaptive isolation systems for nuclear facilities Probabilistic lifetime demand predictions for infrastructure Teaching: She has instructed courses in Seismic Design (CIVENG 4ED4), Structural Mechanics (CIVENG 2C04), and Earthquake Engineering (CIVENG 730) at McMaster University.

Jan Draismaمشاهده پروفایل

استاد

• Algebraic Geometry
• Combinatorics
• Algebraic Statistics
• +۸ مورد دیگر

Jan Draisma is a full professor of Mathematics at the University of Bern and a part-time full professor of Applied Algebra and Geometry at Eindhoven University of Technology (TU/e), where he is affiliated with the Department of Mathematics and Computer Science, specifically in Discrete Algebra and Geometry and Coding Theory and Cryptology. He obtained his Master's and Ph.D. degrees from TU/e cum laude and held a postdoctoral position at the University of Basel (2002–2005). He returned to TU/e as an assistant professor, later advancing to associate professor (2011–2016), and served as a part-time full professor at VU Amsterdam (2015–2016). His research focuses on the interplay between combinatorics, statistics, and algebraic geometry. Key areas include tropical geometry, algebraic statistics, symmetric systems of polynomial equations in infinitely many variables, and representation stability. His work often explores the structure of infinite-dimensional algebraic objects and their finite approximations. The most recent publications highlight trends in polynomial functors, topological Noetherianity, amoebas of linear spaces, and the geometry of tensor representations. These works reflect a deep integration of algebraic geometry with combinatorics and category theory, emphasizing stabilization phenomena and symmetry in algebraic structures. NWO Vici Award: Stabilisation in Algebra and Geometry (2015) NWO Vidi Award: Finite thanks to symmetry (2010) Draisma has received significant research funding, including the NWO Vidi and Vici grants, and two NWO Free Competition grants (2008, 2012). He has supervised 16 students and is actively involved in the academic community as an associate editor for Experimental Mathematics, SIAM Journal on Applied Algebra and Geometry, and Linear and Multilinear Algebra. He has held leadership roles in major conferences such as MEGA 2015 and SIAM AG 19. He is affiliated with the research groups in Discrete Algebra and Geometry and Coding Theory and Cryptology at TU/e and leads research activities centered on algebraic methods in discrete mathematics and statistics.

Alex Scottمشاهده پروفایل

استاد

• Combinatorics
• Graph Theory
• Probability

Alex Scott is a Professor of Mathematics at the Mathematical Institute, University of Oxford, with active research contributions evidenced by publications through 2025. His work is centered within the university's Mathematical Institute, a key hub for theoretical mathematics research. Scott's research specializes in combinatorics, particularly graph theory and probabilistic methods. His investigations span structural graph theory, extremal combinatorics, and random graph models, addressing fundamental questions about graph minors, forbidden substructures, and combinatorial optimization. This focus reflects deep engagement with discrete mathematical structures and their theoretical applications. Analysis of his publication timeline (2014-2025) reveals consistent contributions to graph theory, with recent work emphasizing subdivisions, stable sets, and graph reconstruction algorithms. Earlier research examined Erdős-Hajnal properties, deviations in random graphs, and dimensional properties of posets, demonstrating both depth and evolution in his scholarly focus. No scientific awards were documented in the source material. The available information does not specify any advised students or research grants, though his extensive publication record suggests significant collaborative research activity. Scott is affiliated with the Combinatorics research group at Oxford's Mathematical Institute, contributing to one of the world's leading centers for discrete mathematics research through theoretical investigations and academic collaboration.

Arto Anttilaمشاهده پروفایل

دانشیار

• Phonology
• Morphology
• Syntax
• +۲ مورد دیگر

Arto Anttila is an Associate Professor in the Department of Linguistics at Stanford University and holds an Adjunct Professor (dosentti) position in General Linguistics at the University of Helsinki. His research spans multiple linguistic subfields with particular focus on the interfaces between phonology, syntax, and prosody. Dr. Anttila's research interests include phonology, morphology, syntax, metrics, and language variation. His work often explores how phonological constraints interact with syntactic structures and how variation manifests across different linguistic domains. He has made significant contributions to Optimality Theory, MaxEnt grammar, and probabilistic approaches to phonology. His recent publications demonstrate a strong focus on metrical patterns, stress systems, syllable structure, and the relationship between prosody and syntax. Anttila frequently collaborates with researchers like Giorgio Magri, Adams Bodomo, and Ryan Heuser, examining phenomena across diverse languages including English, Finnish, and Dagaare (an African language). Anttila has developed several computational tools for linguistic research, including CoGeTo (Convex Geometry Tools for constraint-based phonology), MetricalTree (for English phrasal stress prediction), Prosodic (for automatic metrical scansion), T-Order Generator, and OTOrder. These tools reflect his interest in the mathematical and computational aspects of linguistic theory.

Gregory F. Lawlerمشاهده پروفایل

استاد

• Probability Theory
• Random Walks
• Schramm-Loewner Evolution
• +۳ مورد دیگر

Gregory F. Lawler is the George Wells Beadle Distinguished Service Professor in the Department of Mathematics at the University of Chicago. He also maintains appointments in the Department of Statistics and has affiliations with Computational and Applied Mathematics and Financial Mathematics programs. His academic background includes: B.A. (1976) from the University of Virginia Ph.D. (1979) from Princeton University under Edward Nelson Professor Lawler is a leading researcher in probability theory with a focus on conformally invariant processes , particularly the Schramm-Loewner Evolution (SLE) and various forms of random walks. His work bridges pure mathematics and statistical physics, establishing rigorous connections between discrete models and their continuous scaling limits. He has made fundamental contributions to understanding loop-erased random walks, Brownian motion, and the geometric properties of random curves, with applications in statistical mechanics for understanding two-dimensional critical phenomena. Analysis of his recent publications reveals a sustained focus on the mathematical foundations of SLE, with particular attention to natural parametrization, loop measures, convergence questions, and the relationship between discrete models and their continuous limits. His work consistently combines complex analysis with probabilistic methods to establish rigorous results about random curves. Professor Lawler is actively involved in mentoring through the University of Chicago's REU program and has developed extensive educational materials for students at all levels. His notes on probability theory and stochastic calculus are widely used resources within the mathematics community. He has authored several influential books including "Random Walk and the Heat Equation," "Conformally Invariant Processes in the Plane," and "Random Walk: A Modern Introduction" (with Vlada Limic), which have become standard references in probability theory.

Chaopeng Shenمشاهده پروفایل

استاد

• Hydrology
• Deep Learning
• Physics-Informed Machine Learning
• +۹ مورد دیگر

Chaopeng Shen is a Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University. His research bridges hydrology with state-of-the-art deep learning and differentiable modeling techniques, focusing on advancing our understanding of hydrologic cycles and their interactions with ecosystems, energy, and carbon cycles. He leads the Multi-scale Hydrology, Processes and Intelligence group (MHPI) and has developed the Process-based Adaptive Watershed Simulator (PAWS) for large-scale hydrologic modeling. Shen's work emphasizes physics-informed machine learning , where deep learning components are integrated with process-based equations through differentiable modeling. This approach enables training neural networks using big data while respecting physical laws, leading to improved generalizability and robustness. His group has demonstrated advantages of differentiable models in rainfall-runoff prediction, routing, ecosystem modeling, and water quality studies. Notably, his team's deepLDB project addresses landslide prediction using AI and big datasets. Recent publications highlight his contributions to global water modeling (grid-LSTM, differentiable Muskingum-Cunge routing), extreme flood forecasting (probabilistic diffusion models), and hydrologic uncertainty quantification . Shen actively engages in interdisciplinary collaborations through the PRISM Cooperative Institute, which aims to integrate multi-domain data for systemic risk assessment. His group has advised students including Dapeng Feng, Wen-Ping Tsai, Kuai Fang, Xinye Ji, and Tasnuva Mahjabin. Shen's research is supported by the National Science Foundation (NSF), Department of Energy (DoE), USGS, Google.org, and the Gates Foundation. He serves as Editor for the Journal of Geophysical Research - Machine Learning & Computation and Chief Editor for Frontiers in Water: Water & AI. His open-source software tools like PAWS and deepLDB are available through dedicated project websites.

Shayan Aziznejadمشاهده پروفایل

پژوهشگر

• Machine Learning
• Acoustic Imaging
• Neural Networks
• +۳ مورد دیگر

Shayan Aziznejad is a Senior ML Scientist at Distran, working on the intersection of machine learning and acoustic imaging. He was previously an ML researcher at Daedalean AI (October 2022–December 2024) and a Ph.D. candidate at Ecole Polytechnique Fédérale de Lausanne (EPFL) , where he focused on mathematical optimization and signal processing under Prof. Michael Unser. His academic background includes dual B.Sc. degrees in Electrical Engineering and Pure Mathematics from Sharif University of Technology . Research Focus: Machine learning, neural network certification, wavelet analysis, Hessian-Schatten regularization, and sparse modeling. Scientific Recognition: Swiss National Science Foundation Postdoc Fellowship (2021) Best Student Paper Award at ICASSP (2019) Gold Medalist at Iranian National Mathematics Olympiad (2011) Academic Contributions: Authored 15+ publications in top-tier journals (SIAM, IEEE, etc.) and conferences (ICASSP, EUSIPCO), with a focus on Lipschitz-regularized models, spline-based optimization, and inverse problems. Advising Experience: Supervised 11+ students across master's theses, summer internships, and semester projects, including Eliana Renzo, Joaquim Campos, and Haojun Zhu. Email: shayan.aziznejad@gmail.com

• Artificial Intelligence
• Logic
• Argumentation
• +۴ مورد دیگر

Dr. Dragan Doder is an Assistant Professor in the Intelligent Systems group within the Faculty of Science at Utrecht University. His office is located in the Buys Ballot Building at Princetonplein 5, Room 5.20, 3584 CC Utrecht, Netherlands. He is actively engaged in research and teaching within the domain of Artificial Intelligence, with a specific focus on logical frameworks for AI systems. Dr. Doder's research expertise spans several interconnected areas of theoretical and applied AI. His primary interests include: Artificial Intelligence with emphasis on logical foundations Argumentation theory and frameworks Probabilistic reasoning and temporal logic Deontic logic for normative reasoning Human-centered AI approaches His work bridges theoretical computer science with practical applications in multi-agent systems and decision-making frameworks. Analysis of Dr. Doder's recent publications (2020-2025) reveals a strong focus on the intersection of logic, probability, and AI. His research trajectory shows increasing sophistication in handling uncertainty through probabilistic temporal logics, while maintaining strong connections to practical applications in multi-agent systems and argumentation frameworks. A notable trend is his work on integrating causal reasoning with probabilistic models, particularly in multi-agent contexts where group responsibility and risk assessment become critical concerns. His publications demonstrate consistent contributions to top AI conferences including IJCAI, AAAI, and ECAI. Dr. Doder actively collaborates with researchers across multiple institutions. His collaborative network includes prominent researchers in AI and logic from institutions worldwide. His research has practical implications for developing AI systems that can reason under uncertainty, handle complex normative constraints, and make responsible decisions in multi-agent environments. Dr. Doder is affiliated with the Intelligent Systems research group at Utrecht University, which focuses on developing theoretically sound approaches to AI that can be applied to real-world problems. The group emphasizes human-centered AI approaches that consider ethical implications and practical usability alongside technical excellence.

• Algorithmic Databases
• Data Mining
• Computational Geometry
• +۵ مورد دیگر

Stavros Sintos is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). He joined UIC after a postdoctoral fellowship at the University of Chicago, where he was part of the ChiData group under Sanjay Krishnan. He earned his Ph.D. in Computer Science from Duke University in 2020, advised by Pankaj K. Agarwal. His research focuses on designing efficient algorithms for databases, data mining, and computational geometry . Key themes include: Theoretical guarantees for practical problems Compact indexing structures for query efficiency Geometric optimization combined with database systems Fairness in algorithmic design (e.g., Fair Set Cover, FairHash) Temporal data analysis and dynamic query processing Notable scientific contributions include a Best Paper Award at ICDT 2024 for work on range entropy queries. His publications span top venues like SIGMOD, PODS, VLDB, and SODA, covering areas such as clustering algorithms, synthetic query witnesses, and temporal join optimizations.

• Distributed control
• Autonomy and Control
• Aerospace Systems
• +۶ مورد دیگر

Dengfeng Sun is a Professor and Associate Head of the Gambaro Graduate Program in the School of Aeronautics and Astronautics at Purdue University. His research focuses on distributed control systems, autonomy, resilient networks, and air traffic management. Sun holds a B.Eng. from Tsinghua University, an M.S. from The Ohio State University, and a Ph.D. from UC Berkeley. His work spans advanced air mobility, UAV trajectory planning, and stochastic optimization for large-scale systems. Key contributions include resilient UAV traffic control, distributed state estimation algorithms, and fault detection methods for navigation systems. Sun's research has been published in top journals like IEEE Transactions on Intelligent Transportation Systems and Transportation Research Part E. Education: B.Eng., Tsinghua University (2000) M.S., Ohio State University (2002) Ph.D., UC Berkeley (2008) He advises on cutting-edge projects integrating robotics, autonomous systems, and cloud-based traffic modeling. His lab develops solutions for urban air mobility, emergency medical UAV networks, and next-generation air traffic control systems. Notable collaborations include work with NASA and industry partners on continuous descent approach procedures and metroplex routing paradigms. Sun's work bridges theoretical control systems with practical applications in aviation and infrastructure optimization.

• Structural Engineering
• Resilient Infrastructure Design
• Fire Safety Engineering
• +۶ مورد دیگر

Maria Garlock is the Daniel Tsui Professor in Engineering at Princeton University, serving as Co-Director of the Program in Architecture and Engineering and Head of Forbes College. Her roles also include membership in the Executive Committee of the Council on Science and Technology, Associated Faculty in the School of Architecture, and Associated Faculty in the Program in Latin American Studies. Garlock holds a PhD in Structural Engineering (Lehigh University, 2002), an MS in Civil Engineering (Cornell University, 1993), and a BS in Civil and Environmental Engineering (Lehigh University, 1991). Her research focuses on resilient structural design for extreme hazards like fires, earthquakes, and storm surges. She explores both isolated and cascading multi-hazard scenarios while also analyzing historical structural designs (e.g., Félix Candela’s thin-shell concrete umbrellas) and improving STEM education for non-technical majors through innovative teaching methods, including MOOCs and scale model exhibitions. Recent work emphasizes coastal defense systems using hyperbolic-paraboloid forms and steel-concrete girder performance under shear stress. Garlock has received notable honors including the ASCE SEI Fellowship (2016 T.R. Higgins Lectureship), President’s Award for Distinguished Teaching (2012), and the Emerson Electric Co. Faculty Advancement Award (2006). In education and grants, she teaches courses like Structures and the Urban Environment and Advanced Design of Steel/Concrete Structures , and has secured government funding for STEM literacy initiatives. Her research collaborations include the BRITE Pivot project and studies on Cuba’s historic National School of Ballet domes. She also leads efforts in deploying kinetic umbrellas as flood barriers and advancing probabilistic models for fire fragility in multi-hazard contexts. Garlock’s work bridges engineering and art, exemplified by her preservation studies of Candela’s architectural masterpieces and pedagogical innovations that emphasize creativity in structural design.

Rebecca Nugentمشاهده پروفایل

استاد

• Clustering Methodology
• Record Linkage
• Educational Data Mining
• +۵ مورد دیگر

Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science and Department Head at Carnegie Mellon University. She holds a PhD in Statistics from the University of Washington (2006), an MS in Statistics from Stanford (2006), and a BA in Mathematics, Statistics, and Spanish from Rice University (2002). Her research spans clustering methodology , record linkage , educational data mining , public health , and semantic organization , with a focus on high-dimensional data and adaptive learning environments. She leads the Integrated Statistics Learning Environment (ISLE) and Corporate Capstone programs, emphasizing low-barrier data platforms for education and industry collaboration. Academic Roles : Department Head, Carnegie Mellon; Affiliated Faculty, Block Center for Technology and Society Research Grants : NSF (2017-2019), NIH (2018), Carnegie Mellon ProSEED/Simon Initiative (2020, 2018), Berkman Fund (2014) Her 15 most recent publications focus on data science pedagogy, clustering algorithms, record linkage applications in historical and medical data, educational data mining, and semantic organization studies. Awards include the ASA Waller Education Award (2015) and the William H. and Frances S. Ryan Award (2015) . She mentors a diverse group of PhD, Master's, and undergraduate students, with alumni pursuing careers in academia, industry, and sports analytics.

• Uncertainty Quantification
• Civil Engineering Systems
• Resilience Engineering
• +۵ مورد دیگر

Rui Teixeira is an Assistant Professor in the School of Civil Engineering at University College Dublin (UCD). He leads UCD's Centre for Critical Infrastructure Research (CCIR) and focuses on Uncertainty Quantification, Safety, and Risk in civil engineering systems, with applications to infrastructure resilience. His research emphasizes reliability analysis, multi-fidelity modeling, and AI-driven risk assessment. Education: MSc in Civil Engineering, University of Porto, Portugal PhD in Civil Engineering, Trinity College Dublin Professional Certificate in University Teaching and Learning, UCD Research Interests: Development of novel reliability analysis techniques Resilience of infrastructure systems Artificial intelligence applications for risk assessment Probabilistic system evaluation and safety standards Grants & Projects: Smart Enforcement of Transport Operations (SETO), Horizon Europe (2023–2026) Optimality-Tracking Civil Engineering Systems, Enterprise Ireland (2023–2025) Floating Offshore Wind Dynamic Cables (FlOWDyn), Sustainable Energy Authority of Ireland (2024–2027) Teaching: Coordinates courses such as 'Civil Engineering Systems' and 'Design of Structures 1'. Labs/Teams: Director of the Centre for Critical Infrastructure Research (CCIR), focusing on interdisciplinary approaches to infrastructure resilience.

• Decision Making
• Risk Literacy
• Cognitive Psychology
• +۵ مورد دیگر

Dr. Jinan Allan is an Assistant Professor at Clemson University's College of Behavioral, Social and Health Sciences, Department of Psychology. She holds a PhD in Cognitive Psychology from the University of Oklahoma (2021), an MS from the same university (2018), and a BS in Mathematics-Statistics from Michigan Technological University (2015). She completed a postdoctoral fellowship at the Max Planck Institute for Human Development. Her research focuses on cognitive abilities, statistical numeracy, and their impact on decision-making in contexts like medical scenarios, cybersecurity, and extreme weather risks. She is affiliated with the Media Forensics Hub and teaches courses on judgment and decision-making. Education: PhD Cognitive Psychology, University of Oklahoma, 2021 MS Cognitive Psychology, University of Oklahoma, 2018 BS Mathematics-Statistics, Michigan Technological University, 2015 Research emphasizes behavioral science and psychometric methods to develop decision-making metrics. Key themes include risk communication for tornado/hurricane warnings, climate change judgments, and cybersecurity decision-making. Her work bridges cognitive psychology with real-world applications in disaster preparedness and public health. Recent publications analyze extreme weather response patterns, numeracy's role in science comprehension, and probabilistic risk communication strategies. She is currently reviewing graduate student applications for 2025-2026. Affiliations include the Media Forensics Hub at Clemson University, indicating interdisciplinary collaboration in digital security and information integrity.

Paul Hornمشاهده پروفایل

استاد

• Combinatorics
• Graph Theory
• Probability
• +۳ مورد دیگر

Paul Horn is a Professor and Associate Chair of Graduate Studies in the Department of Mathematics at the University of Denver, within the College of Natural Sciences and Mathematics. He earned his Ph.D. in Mathematics from the University of California, San Diego (2009), under the supervision of Fan Chung. Prior to joining DU in 2013, he held postdoctoral positions at Emory University and Harvard University. His research focuses on combinatorics, graph theory, and probability, with a particular emphasis on applying probabilistic, algebraic, and geometric methods to analyze networks and graphs. Dr. Horn co-organizes the Rocky Mountains-Great Plains Graduate Research Workshop in Combinatorics (GRWC) and contributes to the graph theory section of the Masamu Advanced Studies Institute in southern Africa. He also serves as the graduate coordinator in the Mathematics Department, overseeing graduate student advising and program administration. His work spans theoretical contributions to graph structure, stochastic processes on networks, and applications in multi-agent systems and sensor networks. Publications highlight his expertise in graph curvature, network robustness, and combinatorial optimization, reflecting his interdisciplinary approach to discrete mathematics and its real-world applications. His research bridges pure and applied mathematics, addressing challenges in algorithm design, network dynamics, and geometric graph theory. Horn’s advising and mentorship activities include guiding graduate and undergraduate students in mathematics, emphasizing hands-on research experiences through workshops and collaborative projects. His contributions to academic leadership and research dissemination are evident through editorial roles and conference organization in combinatorics and graph theory.