جست‌وجوی استاد و پژوهشگر

Babak Jamhiriمشاهده پروفایل

پژوهشگر

• Structural Health Monitoring
• Geotechnical Engineering
• Machine Learning Applications in Civil Engineering
• +۳ مورد دیگر

Babak Jamhiri holds a Doctoral Researcher position with expertise in structural health monitoring and reliability analysis. He is affiliated with the 3DCP and Construction Automation Research Group, focusing on AI-driven simulations, infrastructural risk assessment, and probabilistic modeling. His research spans geoenvironmental reliability, machine learning algorithms for signal processing, and risk assessment in construction materials. Education: BSc, MSc, and PhD in relevant disciplines (institutions unspecified). Current projects include structural health monitoring of 3DCP components, machine learning for signal processing, and probabilistic crack propagation in problematic soils. Recent work involves performance assessment of post-tensioned concrete beams and developing compressed earth bricks with marl sediment. Research interests emphasize geotechnical challenges such as expansive soils, desiccation cracking, and soil stabilization using industrial byproducts. He explores Bayesian inference, Markov decision chains, and fractal approaches in clay-based materials for nuclear waste disposal applications. Contributions include YouTube tutorials on information value theory, signal processing for anomaly detection, and probabilistic modeling with R. His work bridges theoretical advancements with practical engineering solutions, emphasizing uncertainty quantification and decision-making under uncertainty.

Frederick W. Faltinمشاهده پروفایل

عضو هیئت علمی

• Process Monitoring and Control
• Manufacturing and Service Process Improvement
• Risk Management
• +۳ مورد دیگر

Frederick W. Faltin is a Professor of Practice in the Department of Statistics at Virginia Tech's College of Science , serving as Director of Corporate Partnerships for the Academy of Data Science . His roles include bridging academic research with industry applications through collaborative projects and educational initiatives. Education: S.B. in Mathematics, Massachusetts Institute of Technology M.S. in Operations Research and Mathematical Statistics, Cornell University Research Interests: Focuses on process monitoring and control , manufacturing/service process improvement , and risk management . Specializes in statistical methodologies for quality assurance, including control charts, algorithmic process optimization, and failure mode analysis. Extends his expertise into healthcare analytics and software testing frameworks. Publications & Contributions: Authored/co-edited influential works like Statistical Methods in Healthcare (2012) and Encyclopedia of Statistics in Quality & Productivity (2007). Recent research explores statistical engineering paradigms and defect rate paradoxes in supplier-customer dynamics. Awards & Recognition: Fellow, American Statistical Association (ASA) Shewell Prize, American Society for Quality (ASQ) Teaching & Outreach: Instructs CMDA 4864 (Senior Capstone in Data Analytics) and STAT 4214 (Advanced Regression Methods) . Leads corporate partnerships to integrate industry needs into academic curricula. Office located at 312 Data and Decision Sciences, Blacksburg, VA. Labs & Teams: Engaged in Virginia Tech's Academy of Data Science, fostering cross-disciplinary collaborations between academia and enterprise sectors.

Meimei Liuمشاهده پروفایل

استادیار

• Deep learning
• Bayesian data analysis
• Learning theory
• +۳ مورد دیگر

Dr. Meimei Liu is an Assistant Professor in the Department of Statistics at Virginia Tech. She holds a Ph.D. in Statistics from Purdue University (2018), an M.S. from the University of Science and Technology of China (2013), and a B.S. in Mathematics from Anhui University (2010). Prior to Virginia Tech, she conducted postdoctoral research at Duke University under Prof. David Dunson, focusing on Bayesian methods and neural imaging analysis. Her research interests span deep learning (graph embedding, variational autoencoders), Bayesian data analysis, learning theory (stochastic gradient descent, variational inference), big data methodologies (domain adaptation, random projection), and semi/non-parametric inference. She has been recognized with awards including the Virginia Tech ICTAS Diversity Inclusion Seed Fund (2023-2024) and COLT Travel Award (2019). Dr. Liu teaches courses such as CMDA 2005: Integrated Quantitative Science and STAT 3615: Biological Statistics. Her work emphasizes interdisciplinary applications, particularly in neuroscience and medical imaging. She actively contributes to the academic community through memberships in the American Statistical Association, International Chinese Statistical Association, and Women in Machine Learning.

Salvador Garcia Munozمشاهده پروفایل

استاد مهمان

• Chemical Engineering
• Pharmaceutical Engineering
• Process Analytics
• +۵ مورد دیگر

Salvador Garcia Munoz serves as a Visiting Professor in the Department of Chemical Engineering at Imperial College London's Faculty of Engineering. His expertise spans pharmaceutical engineering, process analytics, and chemometrics, with a strong focus on applying multivariate data analysis methods to chemical and pharmaceutical processes. Education: PhD in Chemical Engineering from McMaster University, Canada MS in Chemical Engineering from Monterrey Tech, Mexico BS in Chemical and Computer Systems Engineering from Monterrey Tech, Mexico Professor Garcia Munoz specializes in multivariate data analytics for process understanding and control, particularly in pharmaceutical manufacturing. His work bridges the gap between theoretical chemical engineering principles and practical industrial applications. He has developed expertise in Principal Components Analysis (PCA), Partial Least Squares (PLS), process monitoring, and optimization techniques. His research focuses on creating data-driven models for complex systems where data is abundant, correlated, and noisy, with direct applications in continuous manufacturing, quality by design, and real-time release testing in the pharmaceutical industry. His publication record demonstrates a strong trend toward integrating advanced statistical methods with chemical engineering principles to solve pharmaceutical manufacturing challenges. The majority of his recent work focuses on process analytics, model-based design, and optimization for pharmaceutical processes, with particular emphasis on continuous manufacturing, design space definition, and quality control. His articles reveal expertise in applying chemometrics to pharmaceutical unit operations including granulation, dissolution, and powder flow systems. Professor Garcia Munoz actively contributes to the academic community through teaching specialized courses on process analytics and multivariate methods. He is also a key contributor to open-source scientific software, notably as one of the authors of KIPET (Kinetic Parameter Estimation Toolkit), which is used for parameter estimation in chemical reaction systems. His work with PYOMO (Python-based Optimization Modeling Objects) demonstrates his commitment to developing accessible computational tools for the engineering community. His research group collaborates extensively with industry partners, particularly in the pharmaceutical sector, to develop practical solutions for manufacturing challenges. The focus on real-world applications is evident in his publications, which often include case studies using data from actual industrial processes. His work bridges the gap between academic research and industrial implementation, making significant contributions to the advancement of pharmaceutical manufacturing technologies.

• Applied Econometrics
• Time Series Analysis
• Forecasting
• +۳ مورد دیگر

Filip Van den Bossche serves as Professor and Vice Dean of Education and Students at the Faculty of Economics and Business (FEB) of KU Leuven, where he is affiliated with the Department of Decision Sciences and Information Management and the Operations Research and Statistics Research Group (ORSTAT). He also holds membership in the KU Leuven Institute for Mobility and has been a visiting professor at IÉSEG School of Management in Lille, France since 2016. His administrative leadership extends to the FEB Council, Board, International Mobility and Admissions Committee, and multiple university education committees. His research expertise spans applied econometrics, time series analysis, and composite indicator methodology, with primary applications in road safety and transportation policy. He has developed macroscopic safety models, uncertainty assessment frameworks for safety indices, and evaluation tools for road safety campaigns. His work also addresses market efficiency in finance through adaptive markets hypothesis testing and robust CSR investment screening, alongside healthcare logistics optimization for organ transplant systems. Teaching responsibilities include Statistics for Data Science, Predictive Business Analytics, and time series forecasting courses. Analysis of his 15 most recent publications reveals consistent methodological focus on econometric modeling and time series analysis applied across transportation safety, financial markets, and healthcare logistics. The research demonstrates evolution from foundational road safety modeling toward interdisciplinary applications in sustainable development goals and pandemic response evaluation, with growing emphasis on composite indicators and uncertainty quantification. Cross-cutting themes include policy evaluation, optimization under constraints, and translation of quantitative methods to real-world decision support. As an educational leader, Van den Bossche has directed multiple academic programs since 2012 and currently oversees FEB's educational strategy as Vice Dean. His current sabbatical project (2025-2026) focuses on time series analysis for Sustainable Development Goals. He actively contributes to ORSTAT's research on operations research applications and the KU Leuven Institute for Mobility's interdisciplinary transportation studies, fostering collaborations between business, economics, and public policy domains.

Yong Wangمشاهده پروفایل

دانشیار

• Autonomous systems
• Energy systems
• Manufacturing systems
• +۳ مورد دیگر

Yong Wang is an Associate Professor and Associate School Director at the School of Systems Science and Industrial Engineering, Binghamton University. He holds dual PhDs from the University of Illinois and Huazhong University of Science and Technology. His research focuses on autonomous systems, energy systems, manufacturing systems, healthcare systems, operations research, and data science . Notable areas include smart energy infrastructure optimization, autonomous vehicle navigation, and healthcare analytics through machine learning and deep learning techniques. Recent work demonstrates a strong emphasis on real-time control systems, sustainable energy integration, and data-driven decision making . His publications span topics such as battery lifecycle modeling, pandemic prediction systems, and optimization of manufacturing logistics. Labs: Director of the Smart Energy Operations Research Laboratory (SEORL) Education: PhD in Engineering (University of Illinois), PhD in Systems Engineering (Huazhong University of Science and Technology) Research outputs highlight interdisciplinary approaches combining robotics, energy systems, and healthcare, with applications in smart grids, autonomous driving, and medical imaging.

• Software Testing
• Model-Based Testing
• Software Product Lines
• +۷ مورد دیگر

Assoc. Prof. Tugkan Tuglular is affiliated with the Department of Computer Engineering at Izmir Institute of Technology, leading the Safety, Security and Reliability of Software Systems (S2RS2) Laboratory. His research focuses on software testing methodologies, model-based systems, and security applications across software product lines. His work spans formal methods for testing (e.g., event sequence graphs, feature modeling), blockchain-enhanced data sharing, and empirical software engineering. He has extensively studied automated test case generation, change impact analysis, and robustness measurement using Bayesian networks. Notable contributions include domain-specific languages for business applications and frameworks for incremental testing in evolving systems. Recent efforts explore federated platforms for source code quality analysis and AI-driven methods for completing graphical software models. His research integrates theoretical foundations with practical tools, emphasizing security and reliability across diverse software domains. Awards: No specific awards listed. Advising/Grants: No student advising records or grant details provided in the text. His lab (S2RS2) focuses on advancing software safety and security through rigorous testing and modeling approaches. Labs/Teams: Leads the S2RS2 Lab, which investigates software systems' reliability through advanced testing techniques and formal modeling.

Halvard Arntzenمشاهده پروفایل

دانشیار

• Operations Research
• Statistical Modeling
• Econometrics
• +۵ مورد دیگر

Halvard Arntzen is an Associate Professor at Molde University College, affiliated with the Faculty of Logistics and the Department of Logistics. He currently serves as the acting Dean for the Department of Logistics. His academic background includes a Cand. Scient. in Mathematics from the University of Oslo (1994) and extensive experience in logistics and operations research, including roles as a scientific assistant at the University of Oslo (1994–2000) and faculty member at Molde University College since 2000. Research Interests: Applied mathematics, statistical modeling, econometrics, operations research, planning, optimization, decision support systems, and sport analytics. His work spans topics such as stochastic vehicle routing, team sports outcome prediction, and heuristic algorithms for complex optimization problems. Teaching: Courses include Log 708 - Applied Statistics and Mat 210 - Statistics II , focusing on practical applications of statistical methods and regression analysis. Research Groups: Member of the Planning, Optimization and Decision Support research group. Publications: Over 15 peer-reviewed articles, including influential works on sport analytics, stochastic vehicle routing, and optimization algorithms. His research addresses real-world challenges in logistics, healthcare, and sports through data-driven methodologies.

Gregory Youngمشاهده پروفایل

استاد

• Energetic Materials
• Combustion
• Rocket Propulsion
• +۴ مورد دیگر

Gregory Young is a Professor in the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech. He leads the Propulsion and Energetics Laboratory (PropEL), focusing on energetic materials, combustion, and propulsion systems including solid rocket motors, hybrid rockets, and hypersonic propulsion. His research emphasizes high-speed propulsion, additive manufacturing of energetic materials, and advanced diagnostics. Education: Ph.D. in Mechanical Engineering from the University of Maryland (2007), M.S. and B.S. in Mechanical Engineering from Penn State (2001, 1999). Research interests span energetic materials synthesis, combustion dynamics in high-speed systems, and propulsion optimization. Key projects include hypergolic solid fuels, solid-fuel ramjets, and scramjet combustion. His work integrates experimental testing with computational modeling to advance propulsion technologies. Publications reflect a focus on solid fuels, hybrid rocket performance, and combustion diagnostics. Recent studies address hypergolic ignition mechanisms and additive-manufactured fuels. Awards: 2023 Virginia Tech Research Excellence Award, 2024 Mary Virginia Jones Research Professorship, and multiple industry accolades. Professional Service: Executive Committee member for the International Symposium on Special Topics in Chemical Propulsion and leadership roles at JANNAF. Labs/Teams: Propulsion and Energetics Laboratory (PropEL) at Virginia Tech, with collaborations at Sandia National Labs and aerospace industry partners.

Timothy Rogersمشاهده پروفایل

مدرس ارشد

• Structural Dynamics
• Structural Health Monitoring (SHM)
• Machine Learning
• +۳ مورد دیگر

Dr. Timothy Rogers is a Senior Lecturer in Mechanical Engineering at the University of Sheffield, affiliated with the Dynamics Research Group (DRG) within the Department of Mechanical Engineering. He holds an MEng and PhD in Mechanical Engineering from the same institution. His research focuses on applying machine learning and Bayesian statistical methods to structural dynamics problems, particularly in nonlinear system identification and Structural Health Monitoring (SHM). His work bridges theoretical advancements in probabilistic modeling and practical applications in aerospace, civil, and mechanical engineering systems. Education MEng in Mechanical Engineering, University of Sheffield PhD in Mechanical Engineering, University of Sheffield (thesis: 'Towards Bayesian System Identification: With Application to SHM of Offshore Structures') Research Interests Rogers' interests include: Bayesian statistical modeling for structural systems, machine learning for SHM, probabilistic nonlinear system identification, and joint input/state/parameter estimation. His work emphasizes integrating physics-based knowledge with data-driven techniques to improve reliability and efficiency in engineering diagnostics and predictive maintenance. Advising & Grants Rogers collaborates on projects funded by industrial and academic partnerships, including work on aircraft modal testing and offshore structure dynamics. He advises on advanced methodologies for vibration-based damage detection and has contributed to frameworks for population-based SHM and risk-informed decision-making. Labs & Teams He is part of the Dynamics Research Group (DRG), leading efforts in Bayesian system identification and machine learning applications. His team engages in interdisciplinary projects involving aerospace structures, composite materials, and advanced sensor technologies.

Elizabeth Crossمشاهده پروفایل

استاد

• Structural Health Monitoring
• Machine Learning
• Nonlinear System Identification
• +۲ مورد دیگر

Professor Elizabeth Cross is Faculty Director of Research & Innovation at the University of Sheffield's School of Mechanical, Aerospace and Civil Engineering. She holds an EPSRC Innovation Fellowship and co-directs the Laboratory for Verification and Validation, specializing in grey-box models for structural health monitoring. Research Interests: Focuses on developing hybrid approaches combining physics-based models with machine learning for structural assessment. Special expertise in handling environmental variations in monitoring data and nonlinear system identification. Publication Trends: Recent work emphasizes Bayesian methods and probabilistic modeling for structural dynamics, with applications ranging from bridge monitoring to manufacturing systems. Articles show strong integration of statistical learning with physical constraints.

Kristian Sandahlمشاهده پروفایل

استاد

• Software Engineering
• Test Automation
• Machine Learning Applications in Testing
• +۳ مورد دیگر

Kristian Sandahl is a Professor at Linköping University's Department of Computer and Information Science (IDA), affiliated with the Software and Systems (SAS) division. His research focuses on software testing methodologies, machine learning applications in software engineering, and continuous integration/delivery (CI/CD) processes. He leads projects addressing flaky test detection, test prioritization, and requirements engineering challenges in large-scale systems. Recent work includes a Vinnova-funded project (SEK 5.6M) advancing AI-generated software quality assurance. He collaborates extensively with industry partners, emphasizing practical solutions for modern software development challenges. Notable contributions include the 'Testing Hopscotch Model' redefining tester roles and machine learning-driven approaches to test case selection. His publications span IEEE journals and conferences, addressing topics like flakiness classification in C++ projects, industrial CI/CD challenges, and security risk integration in requirements engineering.

• Fluid Power Systems
• Additive Manufacturing
• Digital Twins
• +۴ مورد دیگر

Jose Garcia Bravo is an Associate Professor at Purdue University's College of Engineering within the Department of Agricultural & Biological Engineering . His work focuses on fluid power systems , additive manufacturing , and smart manufacturing using Industrial Internet of Things technologies. He has gained national recognition for his applied fluid power research, particularly in 3D printed components for soft actuators, valves, and sensors . Education PhD in Engineering (Fluid Power Specialization) - Purdue University (2011) MSc in Engineering - Purdue University (2006) MSc in Teaching of Spanish - Purdue University (2004) BSc in Mechanical Engineering - Universidad de los Andes, Colombia (2002) Research Portfolio Dr. Garcia-Bravo's research bridges mechanical engineering with agricultural applications , including: Digital twins for sustainable manufacturing Fluid power & motion control systems Reverse osmosis water purification Mixed reality in engineering education 3D printed sensors and actuators Academic Contributions His 15 most recent publications (2017-2022) demonstrate expertise in: Hydraulic system efficiency Flexible sensor development Biopolymer-based packaging Wave-powered desalination Digital twin integration Fluid power education tools Honors & Patents 2019 Polytechnic Outstanding Faculty in Learning 2022 Polytechnic Outstanding Faculty in Engagement 2017-2022 Certified Fluid Power Hydraulic Specialist US Patent for 3D printed lens gratings Three provisional patents for additively manufactured sensors Academic Leadership As Associate Head for Faculty Affairs , he developed the fluid power student club , designed miniature electro-hydraulic excavators for education, and facilitated international student mobility programs with institutions in Mexico, Colombia, and Peru.

Bora Cetinمشاهده پروفایل

استاد

• Sustainable Geotechnical Engineering
• Recycled Materials in Infrastructure
• Pavement Design and Performance
• +۵ مورد دیگر

Dr. Bora Cetin is a Professor in the Department of Civil and Environmental Engineering at Michigan State University (MSU) and Associate Director of the National Center for Infrastructure Transformation. His research focuses on sustainable geotechnical engineering, recycled materials in infrastructure, pavement design, frost heave mitigation, and environmental remediation. He has been funded by agencies including USDOT, NSF, and multiple state DOTs. Education: Ph.D., Civil Engineering, University of Maryland, 2012 M.S., Civil Engineering, University of Maryland, 2009 B.S., Civil Engineering, Istanbul Technical University, 2007 Research interests span eight key areas: (1) Sustainability of geotechnical structures using waste materials, (2) Soil remediation with industrial byproducts, (3) Nuclear waste disposal in shale media, (4) Pavement design improvements, (5) Frost heave and thaw weakening prediction, (6) Heated pavement systems, and (7) Engineering education innovations. Recent work emphasizes engineered water repellency for cold-climate resilience and recycled aggregate utilization. Publications reflect a focus on pavement mechanics, material characterization, and environmental sustainability. Awards include the 2022 Stephen J. Ressler Best Paper Award and 2022 ASCE Journal of Civil Engineering Education Award. Grants and partnerships include projects on recycled aggregates, freeze-thaw resistant materials, and pavement ME design optimization. He collaborates with state agencies and federal organizations to translate research into practical infrastructure solutions.

• Thyroid Diseases
• Endocrinology
• Thyroid Cancer
• +۴ مورد دیگر

Trevor Angell, MD is an Associate Professor of Clinical Medicine at the Keck School of Medicine of USC . He serves as the Associate Medical Director of the Thyroid Center and is board-certified in Internal Medicine and Diabetes, Endocrinology, and Metabolism. Clinical Expertise: Thyroid nodules, thyroid cancer, and thyroid disorders during pregnancy Research Focus: Thyroid nodule evaluation, molecular testing, immune checkpoint inhibitor-related thyroid dysfunction Dr. Angell's research explores the intersection of thyroid pathology, molecular diagnostics, and immune therapy complications. His 15 most recent publications (2025–2022) address topics including: Thyroid Cancer: Survival analysis in anaplastic thyroid cancer, surgical extent for medullary thyroid cancer Immune Therapy Toxicity: Mechanisms of checkpoint inhibitor diabetes and thyroiditis, JAK inhibitor interventions Diagnostic Innovation: Afirma genomic classifiers for medullary and indeterminate nodules Population Health: Environmental and social determinants of thyroid function in pregnancy Dr. Angell has received funding from the American Thyroid Association Research Grant and contributes to editorial boards of Thyroid and the Journal of Clinical Endocrinology and Metabolism . He actively participates in the American Thyroid Association Thyroid Nodule Guidelines Taskforce and the International Thyroid Oncology Group .