جست‌وجوی استاد و پژوهشگر

Robert L. Givanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Reinforcement Learning
• Knowledge Representation
• +۴ مورد دیگر

Robert L. Givan is an Associate Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. His academic affiliations include Purdue’s Department of Electrical and Computer Engineering and the Max W and Maileen Brown Family Hall. He holds a B.S. in Mathematics and Biology from Stanford University (1987), an M.S. in Computer Science from Stanford (1987), and a Ph.D. in Computer Science from MIT (1996). His research focuses on artificial intelligence, reinforcement learning, automated reasoning, knowledge representation, and stochastic modeling. Notable projects include applying automated reasoning to AI planning domains and exploring reinforcement learning techniques for stochastic control problems. He has contributed to areas such as probabilistic planning, heuristic search, and formal verification. His recent publications span topics like stochastic enforced hill-climbing, automated feature induction, and formal verification methods. His work often bridges theoretical foundations with applied challenges in AI and robotics. Givan’s research has been supported by NSF grants in AI and computer engineering. Labs and collaborations include involvement with Purdue’s AI and robotics initiatives, though no specific lab names are mentioned. He emphasizes student collaboration through directed research and thesis opportunities, particularly in AI and related fields.

Conor Mayo-Wilsonمشاهده پروفایل

استادیار

• Analytic Philosophy
• Decision and Game Theory
• Epistemology
• +۸ مورد دیگر

Conor Mayo-Wilson is an Assistant Professor in the Department of Philosophy at the University of Washington, part of the College of Arts & Sciences. He holds a Ph.D. (2012) and two M.S. degrees (2009) from Carnegie Mellon University, along with B.S./B.A. degrees (2006) in Mathematics and Philosophy from Stanford University. His research focuses on formal and social epistemology, philosophy of science/mathematics, decision/game theory, and logic. Key areas include causal inference, testimonial norms in science, and the epistemology of probability. Education: Ph.D. Philosophy, Carnegie Mellon University (2012) M.S. Logic, Computation, and Methodology (2009) M.S. Mathematics (2009) B.A. Philosophy (Honors), Stanford University (2006) B.S. Mathematics, Stanford University (2006) Research interests span formal epistemology (scoring rules, imprecise credences), social epistemology (scientific collaboration, testimony reliability), and foundational issues in science/mathematics (causality, historical developments). Notable publications include work on structural chaos, wisdom of crowds dynamics, and the independence thesis in group epistemology. Awards include the Josephine de Karman Fellowship (2010-2011) and Stanford’s Chick D’Arpino Prize (2003). Teaching includes courses on philosophy of mathematics, epistemology, probability, and the scientific revolution. He coordinates the MA program at the Munich Center for Mathematical Philosophy and has organized conferences like FEW 2017. His work integrates computational models (e.g., agent-based simulations) to study epistemic processes and scientific communities.

• Decision Neuroscience
• Neuroeconomics
• Cognitive Neuroscience
• +۳ مورد دیگر

Scott Huettel is a Professor in the Department of Psychology and Neuroscience at Duke University, holding concurrent roles as Senior Associate Dean for Research in Trinity College of Arts & Sciences and Bass Fellow. He also serves as Professor of Neurobiology and Psychiatry and Behavioral Sciences, and holds affiliations with multiple interdisciplinary centers including the Center for Cognitive Neuroscience and Duke Institute for Brain Sciences. His research focuses on decision neuroscience, investigating brain mechanisms underlying economic and social decisions using fMRI, behavioral assays, and computational methods. He has authored influential textbooks like Functional Magnetic Resonance Imaging and pioneered applications of fMRI analysis techniques such as functional connectivity and pattern classification. Education: Ph.D. in Psychology from Duke University (1999). Research Interests : Decision neuroscience, neuroeconomics, social decision making, and the neural basis of individual differences in behavior. His work bridges cognitive neuroscience with computational models to explore how brain systems mediate complex choices, particularly in contexts involving risk, ambiguity, and social dynamics. Grants & Leadership : Leads major grants including the NIH-funded Neurobiology Training Program and Duke’s Psychiatry Physician-Scientist Residency Program. Past roles include Chair of Psychology and Neuroscience and Interim Co-Director of the Duke Institute for Brain Sciences. Active in educational innovation through Bass Connections and courses like Decision Neuroscience. Labs & Teams : Core faculty in the Center for Brain Imaging and Analysis, contributing to advanced neuroimaging methodologies. Collaborates across disciplines to address translational challenges in health behavior, consumer decision-making, and aging.

• Cost Management
• Corporate Governance
• Accounting Education
• +۳ مورد دیگر

Anura De Zoysa is an Associate Professor in the School of Business at the University of Wollongong. He holds professional qualifications including CPA, CMA, and FCA memberships, and has over 30 years of teaching experience across Sri Lanka, Japan, and Australia. His research focuses on cost management, corporate governance, accounting education, CSR, and sustainability. He has authored over 90 publications, with 82,000+ downloads on Research Online, and has an H-index of 18 (Google Scholar). De Zoysa earned his PhD in Accounting from the University of Wollongong (2001). Prior to academia, he spent 8 years in Japan researching Japanese cost management systems (e.g., Genka Kikaku, JIT). He teaches management accounting and financial management, and has supervised 17 HDR students, including 12 PhD candidates. His awards include the 2019 Vice Chancellors Award for Outstanding Teaching and Research Supervision (Highly Commended). He has secured grants such as the New Colombo Plan (2022-2023) and served on institutional committees, including the University Thesis Examination Committee and Faculty Education Committee. His work bridges academic research with practical applications in accounting education and corporate sustainability.

Kalil Erazoمشاهده پروفایل

استاد آموزشی

• Structural Health Monitoring
• Civil Infrastructure Resilience
• Stochastic Methods
• +۴ مورد دیگر

Kalil Erazo is an Assistant Teaching Professor in the Department of Civil and Environmental Engineering at Rice University. His research focuses on structural health monitoring (SHM) for resilient civil infrastructure, particularly in regions prone to natural hazards. Erazo emphasizes integrating stochastic methods, Bayesian estimation, and advanced sensor technologies to assess structural integrity and predict performance under extreme events. Education: Postdoctoral Scholar, Rice University (2015-2016) Ph.D. in Civil and Environmental Engineering, University of Vermont (2015) M.S. in Civil and Environmental Engineering (Fulbright Fellow), Georgia Tech (2012) B.S. in Civil Engineering, Instituto Tecnológico de Santo Domingo (2009) Research Interests: Erazo’s work bridges theory and practice in resilient infrastructure design. Key areas include SHM for historic structures (e.g., UNESCO’s Colonial City of Santo Domingo), stochastic modeling for uncertainty quantification, Bayesian methods for nonlinear systems, and post-disaster decision-making frameworks. He advocates for integrating computational tools with physical infrastructure to enhance safety and sustainability. Research Group: The Structural Monitoring for Resilient and Sustainable Infrastructure group addresses National Academy of Engineering Grand Challenges by developing cyber-physical systems that monitor infrastructure health and predict performance under hazards like hurricanes and earthquakes. Outputs include sensor-based frameworks, digital twin technologies, and risk-assessment protocols.

• Resilience of Infrastructure Systems
• Disaster Management
• Transportation and Supply Network Analysis
• +۶ مورد دیگر

Dr. Jinzhu Yu is an Assistant Professor at the University of Texas at Arlington, holding primary affiliation in Civil Engineering and a secondary appointment in Industrial, Manufacturing, and Systems Engineering. He earned his Ph.D. in Interdisciplinary Systems Engineering from Vanderbilt University and conducted postdoctoral research at Rensselaer Polytechnic Institute. His work focuses on resilient urban systems through network science, operations research, and AI/ML. Research interests include infrastructure resilience, disaster management, transportation networks, and decision-making under uncertainty. Education: PhD, Interdisciplinary Systems Engineering, Vanderbilt University (2020) MS, Civil Engineering, Tongji University (2016) BS, Civil Engineering, Tongji University (2013) Research Highlights: Dr. Yu develops models for infrastructure resilience, climate adaptation, and equity. Recent projects include TxDOT-funded work on transportation asset management and crowd-sourced bicycle/pedestrian safety data. His lab integrates data science and network analytics to enhance urban systems. Awards: Urban Resilience Fellow (2024) STARs Award (2022) Student Merit Award (2017) Grants & Advising: Active projects include $319K TxDOT grant (2024) and leadership in NSF reviews. Supervises interdisciplinary students in civil engineering, systems engineering, and computer science.

Ravi Ravichandranمشاهده پروفایل

استاد

• Geotechnical Engineering
• Finite Element Modeling
• Soil-Structure Interaction
• +۴ مورد دیگر

Ravi Ravichandran is a Professor of Civil Engineering at Clemson University, specializing in geotechnical engineering with a focus on resilience and sustainability under extreme events like earthquakes and climate change. His work integrates advanced computational modeling and experimental investigations. Education: BSCE (University of Peradeniya), M.Eng. (University of Tokyo), Ph.D. (University of Oklahoma) Research: Finite element modeling of geotechnical systems, bio-inspired foundation designs, seismic site response analysis, climate-adaptive infrastructure, and robust optimization. Recent publications highlight his work on coupled hydro-mechanical modeling, seismic amplification in Charleston, SC, and bio-inspired foundation systems leveraging tree root mechanisms. He actively collaborates with the South Carolina Department of Transportation and employs numerical simulations for hurricane hazards, retaining wall optimization, and wind turbine foundation analysis. As a member of ASCE, Geo-Institute, and NEES, he contributes to advancing geotechnical practices. He teaches courses in finite element analysis, geotechnical design, and soil-structure interaction.

• Coastal Engineering
• Coastal Hazards
• Numerical Modeling
• +۳ مورد دیگر

Matthew Janssen is a Research Assistant Professor at the Department of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology. His research focuses on coastal hazards, littoral processes, and developing computationally efficient models to assess risks to coastal infrastructure using field observations, numerical modeling, and data-driven techniques. He holds a PhD (2022), MS (2016), and BS (2011) in Ocean Engineering from Stevens Institute of Technology and the University of Rhode Island, respectively. His work emphasizes understanding storm erosion potential, dune performance under climate change scenarios, and the impact of coastal structures. Notable contributions include methodologies for quantifying storm erosion considering sea level rise and probabilistic forecasting of coastal storm impacts. He currently serves as Assistant Director of the NJ Coastal Protection Technical Assistance Service and has prior industry experience with firms like Rising Tide Waterfront Solutions and McLaren Engineering Group. Key Research Areas: Coastal resilience, numerical modeling, climate adaptation, dune dynamics, sediment transport. Recent Focus: Long-term dune performance under extreme and nuisance erosion events; integration of machine learning (CART models) for erosion prediction. Publications highlight his work on hurricane impacts, breakwater effectiveness, and navigation channel management. He received the John P. Breslin Award (2022) and is active in professional societies like ASBPA and COPRI. His technical reports include analyses of New Jersey beach sediment characteristics and shoreline impacts at North Wildwood. He collaborates on projects balancing engineering solutions with ecological and economic considerations.

Lei Wangمشاهده پروفایل

استادیار

• Geotechnical computational modeling
• Geological hazards and landslides
• Uncertainty quantification
• +۶ مورد دیگر

Lei Wang is an Assistant Professor in the Department of Civil and Architectural Engineering and Construction Management at the University of Cincinnati, directing the Sustainable and Resilient Geotechnical Infrastructure Laboratory. His research focuses on geotechnical computational modeling, geological hazards, uncertainty quantification, and space exploration geotechnics. He holds a Ph.D. from Clemson University and has authored over 70 peer-reviewed publications. Education: Ph.D. (Civil Engineering, Clemson University, 2013), M.S. (Geotechnical Engineering, Tongji University, 2010), B.S. (Civil Engineering, China University of Geosciences, 2008). Research interests include lunar regolith characterization, landslide risk assessment, and resilient infrastructure design. Key projects include NASA-funded studies on energy-efficient lunar drilling and USGS-supported probabilistic levee risk analysis. Awards include the Taiwan Geotechnical Society Best Paper Award. Grants: Over $3.5M in funding from NSF, NASA, USGS, and state agencies. Notable grants include the NASA-infused curriculum development ($270K) and Ohio's levee risk assessment ($24K). Labs: Leads the Sustainable and Resilient Geotechnical Infrastructure Lab, focusing on multiphysics modeling and space geotechnics. Collaborates on projects involving additive manufacturing for geothermal systems and mixed reality education tools.

Mike Mooneyمشاهده پروفایل

استاد

• Tunneling Engineering
• Underground Construction
• Geotechnical Engineering
• +۵ مورد دیگر

Mike Mooney is a Professor of Mechanical Engineering at the Colorado School of Mines, holding the Grewcock Chair in Underground Construction & Tunneling. He directs the Center for Underground and leads the Heavy Construction Studio, focusing on advancing smart, rapid, and cost-effective construction technologies for urban tunneling and challenging ground conditions through instrumentation integration and field experimentation. His educational background includes: PhD in Civil Engineering, Northwestern University, 1996 MS in Civil Engineering, University of California, Irvine, 1993 BS in Civil Engineering, Washington University, St. Louis, 1991 BA in Physics, Hastings College, 1991 Mooney's research integrates instrumentation into tunnel boring machines and horizontal directional drilling systems, studying robotic excavation, soil transformation using polymers/foams, and ground-mechanical interactions via physics models and machine learning. His group conducts extensive field campaigns embedded in real construction projects worldwide, emphasizing data-driven approaches to complex geotechnical challenges. Recent publications (2018-2023) demonstrate expertise in TBM performance optimization, boulder detection systems, soil conditioning, and machine learning applications for ground prediction. Key contributions include real-time vibration monitoring for Venice lagoon restoration, annular pressure management, foam stability analysis, and autonomous tunneling frameworks across major projects in Seattle, Toronto, Los Angeles, and New York City. No scientific awards or fellowships were mentioned in the provided text. Professor Mooney actively advises graduate students as evidenced by extensive student co-authorship in publications. His research is directly applied to international construction projects including the Venice lagoon restoration and urban tunneling initiatives. He is a registered Professional Engineer in Colorado (License #39682) and provides technical consultation for construction projects globally. He directs the Center for Underground at Colorado School of Mines, where his Heavy Construction Studio develops instrumentation systems and conducts field experiments integrated into active construction sites. The lab specializes in real-time monitoring technologies, ground characterization methods, and machine learning applications for tunneling operations.

• Atmospheric Physics
• Atmospheric Chemistry
• Extreme Weather Events Prediction
• +۵ مورد دیگر

Marina Astitha is an Associate Professor at the University of Connecticut's College of Engineering , specifically within the School of Civil and Environmental Engineering . Her research bridges atmospheric science with practical applications in energy systems and environmental management. Ph.D. in Physics from the University of Athens (2007) Specializes in high-resolution weather modeling and machine learning integration Research Interests include: Atmospheric Physics, Dynamics, and Chemistry Extreme Weather Event Prediction Multi-Media Modeling Systems Uncertainty Quantification in Atmospheric Models Climate Change Impacts on Wind Energy Resources Real-Time Weather and Air Quality Forecasting Scientific Awards : No awards explicitly mentioned in the provided data, but her publications and research activities indicate significant contributions to meteorology and environmental engineering fields. Advising and Grants : Specific details about grants and students are not provided in the available information, though her research focus suggests involvement in funded projects related to climate change, renewable energy, and environmental modeling. Labs and Teams : Leads the Atmospheric Modeling Group at UConn, integrating numerical weather prediction with machine learning techniques for environmental and energy applications.

Natalya Pya Arnqvistمشاهده پروفایل

دانشیار

• Statistical Regression Modelling
• Functional Data Analysis
• Shape Preserving Smoothing
• +۷ مورد دیگر

Natalya Pya Arnqvist is an Associate Professor in Mathematical Statistics at Umeå University, Sweden. She is affiliated with the Department of Mathematics and Mathematical Statistics and has active roles in research groups focused on Functional Data Analysis, Semiparametric Regression, and Statistical Learning for Spatio-Temporal Data. Her career includes prior positions at Nazarbayev University (2015-2020), University of Bath (2011-2015), and KIMEP University (2000-2005). Education : PhD in Statistics (University of Bath, UK, 2007-2010), CSc in Physical and Mathematical Sciences (Institute for Mathematics, Kazakhstan, 2000-2005). Her research centers on statistical regression modelling and functional data analysis , with significant contributions to shape constrained additive models (SCAMs) and model-based functional clustering . She has developed R packages such as scam , fdaMocca , and nilde for these methodologies. Recent publications highlight applications in applied demography , defect detection , and nonlinear state space modeling , reflecting her focus on bridging statistical theory with industrial and ecological challenges. She teaches undergraduate and graduate courses in probability, regression, and statistical learning.

• Cyber Security
• Information Systems
• Artificial Intelligence
• +۵ مورد دیگر

Mahdi Fahmideh is an Associate Professor in Cyber Security at the School of Business, University of Southern Queensland. He holds a PhD in Information Systems from UNSW Australia and has extensive industry experience as an analyst programmer. His research focuses on cybersecurity, information systems development, and the application of emerging technologies including AI, blockchain, cloud computing, and big data in business contexts. Fahmideh leads research in two primary areas: 1) Development methods for digital transformation in IT-based organizations, particularly systems using ABCD technologies (AI, Blockchain, Cloud, Big Data), and 2) Design science research methodologies. His current projects investigate Generative AI applications in business organizations and software development. His significant awards include research excellence awards from UniSQ, publication awards from UOW, best paper awards, and visiting research fellowships in Finland and Sweden. He serves as associate editor for ACM Computing Surveys and has secured research funding from ARC and international agencies.

Liudong Xingمشاهده پروفایل

استاد

• System Reliability Engineering
• Probabilistic Risk Assessment
• Fault-Tolerant Computing
• +۳ مورد دیگر

Liudong Xing is a Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Dartmouth, and Director of Research for the College of Engineering. Her research focuses on reliability modeling, analysis, and optimization of complex systems and networks, including IoT systems, cybersecurity, and fault-tolerant computing. She holds a PhD in Electrical Engineering from the University of Virginia (2002) and a BE in Computer Science from Zhengzhou University (1996). Dr. Xing teaches courses on Fault-Tolerant Computing, Master's Thesis Guidance, and Doctoral Dissertation Research. Her work has been recognized through awards such as the IEEE Region 1 Outstanding Teaching Award (2015), the ChangJiang Scholar Award (2015), and the 2018 Premium Award for Best Paper in IET Wireless Sensor Systems. She has authored influential books like Reliability and Resilience in the Internet of Things (Elsevier, 2024) and holds leadership roles in IEEE and academic editorial boards. Her research interests span system reliability engineering, probabilistic risk assessment, and decision diagrams, with recent projects funded by NSF grants (e.g., $420K for IoT cascading failure research). She advises graduate students in reliability theory and cybersecurity, and her work emphasizes practical applications in smart manufacturing, healthcare, and Industry 4.0 systems.

Sanda Koboevicمشاهده پروفایل

دانشیار

• Structural Engineering
• Earthquake Engineering
• Steel Structures
• +۳ مورد دیگر

Associate Professor Sanda Koboevic is affiliated with Polytechnique Montréal in the Department of Civil, Geological and Mining Engineering . With degrees including B.Sc. (Université de Sarajevo) , M.Eng. (McGill) , and Ph.D. (McGill) , she specializes in structural engineering, earthquake engineering, and soil-structure interaction. Education: B.Sc. (Université de Sarajevo), M.Eng. (McGill), Ph.D. (McGill) Her research focuses on seismic design of steel and concrete structures , foundation engineering , and structural resilience . She investigates how footing size , soil-structure interaction , and variability in design parameters affect seismic performance. Recent publications include studies on functional recovery time of braced frame buildings (2025), predictive equations for footing behavior using machine learning (2025), and Canadian seismic design procedures for steel braced frames (2024). These works align with her expertise in steel and concrete structures , foundation analysis , and seismic rehabilitation . Scientific Awards: 2022: H.A. Krentz Award from Canadian Institute of Steel Construction She supervises graduate students and has advised numerous Ph.D. and Master's theses on topics like seismic deformations of concrete shear walls and retrofit of steel frames. Her work impacts building codes , engineering practices , and disaster preparedness in structural engineering.