جست‌وجوی استاد و پژوهشگر

• Uncertainty Quantification
• Inverse Problems
• Probabilistic Numerics
• +۳ مورد دیگر

Dr. Tim Sullivan is an Associate Professor in Predictive Modelling at the University of Warwick, affiliated with the Mathematics Institute and School of Engineering. He serves as Co-Director of the Warwick Centre for Predictive Modelling. His research focuses on uncertainty quantification, inverse problems, probabilistic numerics, and mathematical data science. These areas bridge applied mathematics, computational science, and Bayesian statistics, emphasizing rigorous methodologies for modeling and analyzing complex systems under uncertainty. His academic work includes contributions to kernel matrix compression, Bayesian probabilistic numerical methods, and critiques of Bayesian inference robustness. His recent publications (2011–2021) highlight themes in optimal uncertainty quantification, probabilistic numerical techniques for PDE-constrained problems, and foundational Bayesian analysis. Sullivan’s textbook Introduction to Uncertainty Quantification (2015) remains a key resource in the field. In teaching roles, Sullivan oversees tutorial responsibilities across Mathematics and Engineering disciplines, including courses on predictive modeling fundamentals and control theory. His work at the Warwick Centre for Predictive Modelling underscores interdisciplinary collaboration in advancing computational and mathematical approaches to predictive science.

Juan Restrepoمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Mathematics
• Data Science
• +۱۲ مورد دیگر

Juan Restrepo is a Professor of Mathematics with courtesy appointments in Statistics, EECS, and Physical Oceanography at Oregon State University, where he holds a Courtesy Faculty position in the Department of Mathematics within the College of Science. He is also affiliated with the University of Tennessee and Oak Ridge National Laboratory, where he holds appointments in the Computer Science and Mathematics Division. He serves as Co-Director of the Dynamics and Data Science Institute (D2SI), a leading interdisciplinary research center. Ph.D. in Physics, Pennsylvania State University, 1992 M.S. in Engineering (Acoustics), Pennsylvania State University, 1987 B.S. in Music, New York University, 1983 Restrepo's research lies at the intersection of data science and dynamics, with two primary tracts: (1) applying statistical physics and data science to complex non-equilibrium systems such as climate and financial markets; and (2) studying ocean dynamics and transport in climate and nearshore processes. His work emphasizes uncertainty quantification, adaptive time series analysis, extreme events, and data assimilation. His recent publications reflect a strong focus on modeling geophysical systems under uncertainty, using advanced computational and statistical techniques. Themes include climate sensitivity, oil spill dispersion, sediment transport, wave breaking, and high-performance computing for large-scale simulations. His work integrates machine learning, stochastic modeling, and dynamical systems theory. SIAM Fellow SIAM Geosciences Career Award ORISE Distinguished Post-doctoral Fellow DOE Young Investigator Award Ruth Homeyer Graduate Student Award Restrepo has secured over $30 million in research funding from NSF, DOE, NASA, and GoMRI. He has advised numerous students in applied mathematics and computational sciences, and has held leadership roles in professional societies, including President of the Nonlinear Geophysics Section at the American Geophysical Union. He is actively involved in promoting diversity in science and mentoring underrepresented groups. He leads interdisciplinary research teams at D2SI and collaborates with national labs such as Argonne and Los Alamos. His work bridges applied mathematics, climate science, oceanography, and computational engineering, making significant contributions to both theory and real-world applications.

• Earthquake Source Studies
• Strong Motion Seismology
• Seismic Wave Propagation
• +۴ مورد دیگر

Ralph Archuleta is a Professor of Earth Science at the University of California, Santa Barbara (UCSB), affiliated with the Earth Research Institute (ERI). His primary research focuses on Earthquake Source Studies and Strong Motion Seismology, emphasizing the dynamics of fault rupture, ground motion prediction, and seismic hazard assessment. He has advised numerous PhD students and collaborated with postdoctoral researchers, contributing to advancements in seismological modeling and field data analysis. Education: B.S., University of Wyoming, 1969 Ph.D., UC San Diego, 1976 Research Interests: Earthquake source mechanics and rupture dynamics Strong ground motion simulation and site effects Seismic wave propagation in heterogeneous media Development of numerical models for fault systems His work integrates field observations, laboratory experiments, and computational simulations to address seismic hazard challenges. Publications (Recent Trends): Archuleta’s recent work explores fault heterogeneity, nonlinear site response, and supershear earthquakes. Key themes include stochastic modeling of slip distributions, radiated seismic energy analysis, and improvements to ground motion prediction methods. Scientific Contributions: While no explicit awards are listed, his extensive publications and leadership in seismology reflect significant scholarly impact. He has mentored over 20 graduate students and supervised postdocs globally, advancing careers in academia and industry. Labs/Teams: Active in the ERI at UCSB, contributing to collaborative projects like the COSMOS Virtual Data Center for strong motion data dissemination.

Richard M. Goldenمشاهده پروفایل

استاد

• Cognitive Science
• Statistical Machine Learning
• Computational Psychometrics
• +۴ مورد دیگر

Richard M. Golden is a Professor of Cognitive Science and Participating Faculty in the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas (UTD). He directs the COgnitive INformatics and Statistics (COINS) Lab, focusing on computational psychometrics and statistical machine learning applied to educational technologies. His research integrates cognitive science, machine learning, and usability engineering to improve personalized instruction. He authored the textbook Statistical Machine Learning: A Unified Framework and hosts the Learning Machines 101 podcast. Golden leads UTD's Undergraduate Cognitive Science and Graduate Applied Cognition and Neuroscience programs. Education : Ph.D. in Experimental Psychology (Brown, 1987), M.S. in Electrical Engineering (Brown, 1986), B.S. in Electrical Engineering & Psychology (UC San Diego, 1982). Research : Prior work includes neural network models of text comprehension, model misspecification analysis, and Bayesian inference. Current projects include diagnostic assessment tools and AI-driven educational systems. Awards : Secretary-Treasurer Service Award (2017), IEEE Senior Member (1999), multiple invited speaker roles. Labs/Teams : COINS Lab develops tools like the ARCADE system for automated reading assessment. Collaborates with organizations like the Veteran's Administration and National Science Foundation.

Pietro D'Antuonoمشاهده پروفایل

پژوهشگر

• Offshore Wind Turbines
• Structural Health Monitoring
• Fatigue Analysis
• +۵ مورد دیگر

Pietro D'Antuono is a postdoctoral researcher affiliated with the Applied Mechanics Acoustics & Vibration Research Group. His research focuses on fatigue analysis, structural health monitoring, and offshore wind turbine systems. He has contributed to advancing methodologies for data-driven lifetime assessment of support structures and foundations, integrating physics-informed machine learning and probabilistic modeling. His work bridges mechanical engineering, civil engineering, and renewable energy systems. Research Interests: Offshore wind turbine structural dynamics Fatigue and damage mechanics Data-driven predictive models Structural health monitoring Materials degradation analysis Key Contributions: Developed the py-Fatigue open-source toolbox for fatigue assessment. Authored 24+ peer-reviewed publications and datasets on structural reliability and offshore energy systems. Awards: Best Paper Award (2nd place, ex-aequo) at an international conference (2022). Professional Activities: Presenter at conferences on topics like fatigue modeling, data-driven methodologies, and structural health monitoring. Contributed to international research collaborations in Europe and beyond.

Tony Jebaraمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Graph Theory
• +۳ مورد دیگر

Professor Tony Jebara is a faculty member in the Department of Computer Science at Columbia University, where he chairs the Center on Foundations of Data Science and directs the Columbia Machine Learning Laboratory. His research focuses on machine learning with applications in vision, graphs, and spatio-temporal data. He holds a PhD from MIT (2002) and has advised startups including Sense Networks, Evidation Health, and Agolo. Notable awards include the NSF Career Award (2004), Best Paper at ICML 2009, and recognition as one of Esquire's Best and Brightest (2008). His work has been featured in major media outlets. Jebara's academic contributions span over 100 peer-reviewed papers and a textbook on machine learning. He served as General Chair for ICML 2017 and Program Chair for ICML 2014. His research explores generative and discriminative models, Bayesian inference, and optimization techniques. Current projects include neural ensemble analysis in neuroscience and robust learning algorithms for environmental modeling. Recent publications emphasize scalable methods for collaborative filtering, survival analysis in online experiments, and graphical model applications in neuroscience. His work bridges theoretical advancements with practical systems, including contributions to privacy-preserving algorithms and recommendation systems.

Luis Ortizمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computational Game Theory
• +۱۲ مورد دیگر

Luis Ortiz is an Associate Professor at the University of Michigan - Dearborn in the Department of Computer and Information Science , College of Engineering and Computer Science. He previously held faculty positions at Stony Brook University (2008-2015), University of Puerto Rico, Mayaguez (2007-2008), and postdoctoral roles at MIT (2004-2006) and University of Pennsylvania (2001-2004). His research spans Artificial Intelligence , Machine Learning , Game Theory , and Graphical Models , with applications to networks, security, finance, and health. Education: Ph.D., Computer Science, Brown University (2002) B.S., Computer Science, University of Minnesota, Twin Cities His recent publications focus on graphical games, causal inference, and probabilistic modeling, with contributions to Nash equilibrium computation, strategic network analysis, and multi-agent systems. He has received multiple NSF awards , including a CAREER grant. Scientific Awards: 2019 NSF Research Award 2011 NSF CAREER Award 2008 NSF Faculty Diversity Award Ortiz has advised projects in computational game theory , machine learning , and network security . His research grants include NSF funding and industry support from Huawei/FutureWei Technologies. He has led projects on interdependent defense games, wireless localization, and causal strategic inference in networked systems. Labs & Teams: Collaborates with the Stony Brook Game Theory Summer Festival , University of Pennsylvania's Computer and Information Science Department , and industry partners in cellular networking and security.

• Real-Time Systems
• Parallel Computing
• Cyber-Physical Systems
• +۳ مورد دیگر

Jing Li is an Assistant Professor at the Department of Computer Science in the Ying Wu College of Computing at New Jersey Institute of Technology. She holds a Ph.D. in Computer Science from Washington University in St. Louis (2017), advised by Chenyang Lu and Kunal Agrawal. Ph.D. in Computer Science, Washington University in St. Louis (2017) M.S. in Computer Science, Washington University in St. Louis (2014) B.S. in Computer Science, Harbin Institute of Technology, China (2011) Her research spans Real-Time Systems , Parallel Computing , Reinforcement Learning for System Design , and Scheduling Theory . Current projects include NSF-funded work on real-time systems with parallel resources and ARPA-E/IBM collaborations on reinforcement learning for converter design. Recent publications focus on AI-driven scheduling , parallel task optimization , and reinforcement learning applications in traffic control and circuit design. Key venues include AAAI, RTSS, PPoPP, and RTAS. Outstanding Achievement in Research (2022) Outstanding Paper Awards at RTSS (2018), RTAS (2016), ECRTS (2013) Turner Dissertation Award (2017) She advises graduate students in real-time systems and parallel computing, mentors NSF/ARPA-E projects, and leads professional services as TPC member and workshop organizer.

Thomas Weberمشاهده پروفایل

استاد

• Credit collections
• Decision making under risk
• Behavioral economics
• +۸ مورد دیگر

Thomas Weber is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , holding the Chair of Operations, Economics, and Strategy (OES) within the College of Management (CDM) . He serves as Director of the Doctoral Program in Management of Technology and contributes to academic governance through roles in committees such as the CDM Academic Evaluation Committee. PhD Students: Zhang Ru, Han Jun, Mark Michael, Razeghian Jahromi Maryam Email: thomas.weber@epfl.ch His research spans behavioral economics, risk analysis, and optimization in dynamic systems, with a focus on sharing economy applications, inventory management, and cryptocurrency market dynamics. Recent publications address robust decision-making frameworks, self-exciting point processes, and economic implications of information endogeneity. Key teaching activities include: Information: Strategy & Economics Innovation & Entrepreneurship in Engineering Microeconomics

• Artificial Intelligence
• Data Science
• Computer Vision
• +۶ مورد دیگر

Niklas Beuter is a Professor of Artificial Intelligence and Data Science at TH Lübeck, Germany, within the Department of Electrical Engineering and Computer Science. He has been in this position since 2023, contributing to both research and education in AI and computer vision. His educational background includes a Diplom in Computer Science with a focus on robotics from Universität Bielefeld, completed with distinction, followed by a Ph.D. from the same institution in 2011. His doctoral research centered on 3D human detection and tracking for mobile robotic platforms, supporting situation awareness in dynamic environments. Beuter's research is deeply rooted in artificial intelligence, computer vision, and robotics , with a focus on dynamic 3D scene analysis, human-robot interaction, and autonomous systems . His work bridges theoretical models with real-world applications, particularly in autonomous driving and intelligent robotic perception. He has led research teams in industry, demonstrating strong leadership in applied AI. The trend in his publications reflects a consistent focus on perception systems for robots and vehicles , evolving from foundational work in 3D reconstruction and gesture-based interaction to advanced topics like pedestrian intent forecasting using deep learning. His contributions span top conferences such as IEEE ICRA, CVPR, and Intelligent Vehicles, indicating sustained engagement with the core AI and robotics communities. While no formal scientific awards are listed in the provided text, his leadership roles and publication record reflect significant professional recognition. He actively supervises student theses and invites students to register for thesis topics via email, indicating an ongoing commitment to mentoring. Although no specific grants are mentioned, his industrial and academic research leadership suggests involvement in funded projects. He is a member of research groups including CoSA and serves as Deputy Head of ISy, showing active participation in institutional research organization. His research has been conducted in collaboration with teams at Universität Bielefeld, Daimler Research Center, and Robert Bosch GmbH, reflecting a strong interdisciplinary and industry-academia network. His work continues to influence both academic research and industrial applications in AI and autonomous systems.

Mathias Pelkaمشاهده پروفایل

استاد

• Intelligent Automation Technology
• Smart Factory
• Industry 4.0
• +۶ مورد دیگر

Mathias Pelka is a Professor at the Department of Electrical Engineering and Computer Science at Technische Hochschule Lübeck. His work focuses on intelligent automation technology, smart factory concepts, and Industry 4.0 applications. Education in Informatik (University of Lübeck), Applied Information Technology (Lübeck University of Applied Sciences), and Electrical Engineering (Milwaukee School of Engineering) His research spans localization algorithms, LiDAR-based object detection, neural networks for robotics, and underwater optical distance estimation. Key technologies include sensor fusion, recursive Bayesian filters, and time-based ranging methods. Recent publications highlight trends in autonomous vehicle sensing, indoor positioning systems, and unified location architecture frameworks. His work integrates machine learning, signal processing, and wireless communication for smart infrastructure solutions. Prior roles include software development and Scrum Master positions at Drägerwerk AG & Co. KGaA (2020-2021), software engineering at Ibeo Automotive Systems GmbH (2018-2020), and research associate positions at TH Lübeck (2013-2018).

Lance Manuelمشاهده پروفایل

استاد

• Uncertainty Quantification
• Wind Energy
• Offshore Structures
• +۵ مورد دیگر

Lance Manuel is a Professor of Engineering at The University of Texas at Austin. His research focuses on uncertainty quantification in engineered systems, particularly wind energy and offshore structures. He has led projects related to wind turbine fatigue analysis, hurricane risk assessment, and climate change adaptation. Ph.D., Civil Engineering (Stanford University) M.S., Civil Engineering and Applied Mechanics (University of Virginia) B.Tech., Civil Engineering (Indian Institute of Technology, Bombay) His work bridges civil infrastructure with climate resilience, emphasizing probabilistic methods for structural reliability. Key areas include extreme climate modeling , floating offshore wind turbines , and fatigue damage prediction under non-stationary conditions. Recent publications highlight interdisciplinary trends in renewable energy systems and climate hazard quantification . He advises graduate students like Taemin Heo and Ding Peng Liu, whose work spans stochastic processes and offshore structural reuse.

• Geotechnical Engineering
• Ground Improvement
• Seepage Analysis
• +۶ مورد دیگر

Yutao Pan is an Associate Professor in Geotechnical Engineering at the Department of Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU). His academic journey includes a BSc and MSc from Huazhong University of Science and Technology (2010, 2012) and a PhD from the National University of Singapore (NUS) in 2016, followed by a postdoctoral research fellowship. Research Focus: Ground improvement, seepage mechanics, risk assessment, and offshore geotechnical engineering with emphasis on spatial variability and numerical modeling. Key Contributions: Development of algorithms for water-tightness estimation in cut-off walls, stability analysis of tunnels in cement-admixed soils, and transient-state leakage evaluation methods. His publications reflect expertise in cement-treated soils, tunneling mechanics, and probabilistic risk assessment. Collaborations with researchers like Yong Liu, Fook-Hou Lee, and Michael A. Hicks appear frequently. Recent work integrates physical mechanisms with AI for geotechnical problem-solving.

Conrad Sandersonمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• AI Ethics
• +۷ مورد دیگر

Dr. Conrad Sanderson is a Researcher and Team Leader at the Data61 division of CSIRO , focusing on artificial intelligence, machine learning, AI ethics, and high-performance numerical computing. He is also an Adjunct Professor at Griffith University . With over 150 publications and 11,000+ citations, he is renowned for developing influential open-source libraries like Armadillo and RcppArmadillo . Research Interests Artificial Intelligence & Deep Learning Responsible AI, Safe AI, and Ethical Trade-offs Numerical Linear Algebra and High-Performance Computing Recent Publications highlight advancements in: Dynamic graph anomaly detection via extreme value theory Fire propagation uncertainty estimation using neural emulators Resolving ethical tensions in AI implementation GPU-accelerated machine learning Scientific Awards Most cited paper award for thesis-based article Armadillo framework: 30+ million downloads Collaborations include researchers from Facebook, NASA, Boeing, and institutions like MIT and Stanford. His work bridges academia and industry through open-source contributions and interdisciplinary applications.

Kolbjørn Engelandمشاهده پروفایل

استاد

• Hydrology
• Flood Risk Assessment
• Climate Change Impact on Water Resources
• +۵ مورد دیگر

Kolbjørn Engeland is a Professor at the Section for Geography and Hydrology, University of Oslo. His work focuses on hydrology, flood risk assessment, and climate change impacts on water resources, utilizing Bayesian and stochastic modeling techniques. He collaborates on international projects like 'Advancing frequency analysis of nonstationary hydrological extremes' and 'SnowSub'. Affiliation: University of Oslo, Department of Geosciences Research Groups: Hydrology and Water Resources, LATICE (Land-Atmosphere Interactions in Cold Environments) His research integrates climate science, statistical hydrology, and renewable energy planning. Key projects address flood risk reduction, snow sublimation in hydropower, and long-term hydrological variability. He has published extensively on Bayesian modeling, flood frequency, and climate-hydrology interactions. Recent publications include advancements in geostatistical runoff modeling, climate change adaptation in flood mapping, and stochastic methods in Nordic hydrology. His work spans both theoretical and applied hydrology, with applications to Norwegian and European catchments. Engeland’s advising and project leadership include collaborations with researchers in geosciences and water management, though specific student names are not listed. He contributes to sustainable hydropower analysis and climate change scenarios.