جست‌وجوی استاد و پژوهشگر

Mayank Chadhaمشاهده پروفایل

استادیار مدعو

• Structural Health Monitoring
• Applied Mechanics
• Machine Learning
• +۷ مورد دیگر

Mayank Chadha is an Assistant Adjunct Professor in the Department of Structural Engineering at the University of California, San Diego. He holds a Ph.D. and M.S. in Structural Engineering from UC San Diego and a B.E. in Civil Engineering from PSG College of Technology, Anna University. His research focuses on integrating applied mechanics, machine learning, and Bayesian methodologies to address challenges in structural health monitoring (SHM), risk analysis, and optimal sensor design. He also explores the application of physics-constrained machine learning in hydrology and battery degradation modeling. Education: 2019: Ph.D. in Structural Engineering, UC San Diego 2023: Postdoctoral Fellowship, UC San Diego 2014: B.E. in Civil Engineering, PSG College of Technology Dr. Chadha’s research interests include structural health monitoring, applied mechanics, uncertainty quantification, and the development of decision-making frameworks for engineering systems. He has pioneered work on Bayesian model optimization, sensor placement strategies, and value-of-information analysis. His interdisciplinary approach bridges theoretical mechanics with practical data-driven solutions for modern engineering problems. His recent work emphasizes hybrid physics-based machine learning models for streamflow forecasting and battery RUL prediction, demonstrating the fusion of computational efficiency and physical principles. He has authored numerous articles on optimal sensor design, VoI metrics, and risk-informed decision-making in SHM systems. Scientific Awards: Gold medal for CBSE 12th exams (2010) Central Government Fellowship (2014) Gold medal for Civil Engineering proficiency (2014) In addition to academia, Dr. Chadha co-founded LogSpi LP, a quantitative investment hedge fund. He teaches courses on mechanics and signal processing, emphasizing foundational engineering principles and their real-world applications. His research also extends to the development of higher-order geometrically exact beam theories, with applications in computer graphics and shape sensing. He actively collaborates with the U.S. Army Corps of Engineers on projects involving inland waterway infrastructure and risk-based sensor design.

Anaïs Couasnonمشاهده پروفایل

پژوهشگر

• Flood Risk Management
• Climate Change
• Environmental Science
• +۳ مورد دیگر

Anaïs Couasnon is a PhD researcher at the Department of Water and Climate Risk, Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, part of the Faculty of Science. Her research focuses on compound flood risk modeling, probabilistic methods, and global hazard assessment under climate change. She is supervised by Dr. Philip Ward and Dr. Hessel Winsemius as part of a VIDI project. Education: MSc in Hydraulic Engineering (TU Delft, 2017); BSc in Civil Engineering (McGill University, 2010). Research interests include probabilistic modeling, multivariate dependence analysis, flood risk management, and climate impacts on coastal-riverine interactions. She contributes to global datasets like COAST-RP and socio-hydrological benchmarking. Key activities include: Developing frameworks for compound flood risk assessment Modeling extreme sea-level events and storm surges Collaborating on global hazard frameworks and disaster risk reduction strategies Contributions to datasets include: COAST-RP dataset Panta Rhei socio-hydrological benchmark dataset

Sina Moghadas Khorasaniمشاهده پروفایل

استادیار

• Innovation Management
• Information Design
• Supply Chain Management
• +۵ مورد دیگر

Sina Moghadas Khorasani is an Assistant Professor of Operations Management and Quantitative Methods at Vanderbilt University's Owen Graduate School of Management. He holds a Ph.D. in Operations Management from the University of Utah (2019) and completed a postdoc at UC San Diego's Rady School of Management. His research focuses on innovation management, supply chain optimization, and the application of AI/business analytics to competitive environments. Education: Ph.D., Operations Management, University of Utah, 2019 M.Eng., Engineering Management, University Putra Malaysia, 2013 B.Sc., Civil Engineering, Sharif University of Technology, 2011 Research emphasizes dynamic contests, information design in innovation, and strategic competition. Notable work includes optimizing feedback mechanisms in contests and analyzing impacts of consumer cooperative mergers. Teaching evaluations consistently exceed department averages, with courses like Advanced Business Analytics (Python) and Operations Management. Awards include the 2018 Doctoral Teaching Excellence Award and recognition for his contest-screening research from INFORMS.

Ling Changمشاهده پروفایل

دانشیار

• Remote Sensing
• Synthetic Aperture Radar (SAR)
• InSAR Techniques
• +۵ مورد دیگر

Ling Chang is an Associate Professor at the University of Twente, affiliated with the Faculty of Geo-Information Science and Earth Observation (ITC) and the Department of Earth Observation Science. She holds a Ph.D. from Delft University of Technology (2015) and an M.S.E. from Tongji University (2010). Her research focuses on statistical hypothesis testing, time series modeling, and change detection using satellite remote sensing, particularly InSAR techniques. Key projects include AlignSAR (ESA Open SAR library) and RailRadar (TUDelft-ProRail collaboration). Education: M.S.E. in Geodesy and Survey Engineering, Tongji University, China (2010) Ph.D. in Geodesy, Delft University of Technology, Netherlands (2015) Research interests emphasize InSAR applications in infrastructure monitoring, environmental geology, and disaster risk assessment. Notable achievements include the Prof. J.M. Tienstra Research Prize (2022) and leadership in the NCG talent program (2020). Teaching responsibilities include coordinating the Radar Remote Sensing course. Her work contributes to UN SDGs related to sustainable infrastructure and climate action.

Yaser Gamilمشاهده پروفایل

مدرس

• Construction Management and Informatics
• BIM (Building Information Modeling)
• Circular Economy
• +۴ مورد دیگر

Dr. Yaser Gamil is a Lecturer and researcher at the Malaysia School of Engineering, Monash University. His work focuses on cutting-edge technologies in project management and construction informatics, emphasizing sustainable practices and digital transformation. He holds a PhD in Construction Management and Informatics (2020) from Monash, alongside a Master of Engineering (2015) and Bachelor of Civil Engineering (2013). Key research areas include BIM implementation, circular economy principles, smart infrastructure automation, and digital twins. He leads projects like the 'Leveraging Digital Twins for Sustainable WEFE Nexus Management' (2024-2025) as a Partner Investigator. His recent studies address plastic waste utilization in construction materials and AI-driven progress monitoring. Dr. Gamil actively mentors PhD students in topics such as BIM socioeconomic challenges and AI applications in construction. He has contributed 18 publications since 2023, with a focus on sustainable construction technologies and material science innovations.

Majid Sartajمشاهده پروفایل

استاد

• Sustainable Solid Waste Management
• Bioreactor Landfilling
• Biogas Production
• +۵ مورد دیگر

Dr. Majid Sartaj is a Professor in the Department of Civil Engineering at the University of Ottawa. He earned his B.Sc. in Iran, followed by M.A.Sc. and Ph.D. in Environmental Engineering from the University of Ottawa (2001). Prior to joining Ottawa, he served as faculty at Isfahan University of Technology and worked as a Project Engineer for a Caribbean landfill project. University of Ottawa (Professor, 2011–present) Isfahan University of Technology (previous faculty) His research focuses on sustainable solid waste management , particularly bioreactor landfilling and biogas/NRG production , alongside bioremediation of contaminated soils and adsorption/ion-exchange processes for industrial contaminants. Additional work includes water quality modeling and groundwater protection. The 15 most recent publications demonstrate expertise in: Green technologies for contaminant removal Advanced biogas production methods Nanomaterials in environmental remediation Machine learning for process optimization Resource recovery from waste streams Current research extends into statistical modeling of environmental systems and eco-friendly approaches to waste valorization.

Xin Liuمشاهده پروفایل

استاد مدعو

• Machine Learning
• Cybersecurity
• Network Security
• +۳ مورد دیگر

Xin Liu is an Adjunct Professor in the Department of Civil Engineering at the Faculty of Engineering. His research focuses on cybersecurity, machine learning applications, and IoT security, with a particular emphasis on network intrusion detection, data compression, and risk-aware access control systems. He holds a Ph.D. from the University of Ottawa, Canada, and M.Sc. and B.Sc. degrees from Hebei University of Technology, China. Education: Ph.D., University of Ottawa, Canada M.Sc., Hebei University of Technology, China B.Sc., Hebei University of Technology, China Research Interests: Dr. Liu's work bridges machine learning and cybersecurity, addressing challenges in IoT security, adversarial attacks, and network traffic analysis. His contributions include developing AI-driven intrusion detection systems, optimizing data compression techniques for IoT devices, and formulating risk-aware access control frameworks. Recent efforts focus on advanced persistent threats (APTs) and privacy leakage mitigation in language models. Articles Trends: His publications emphasize practical applications of machine learning in cybersecurity, including network attack detection, privacy-preserving methods, and resilient IoT infrastructure. Recent work highlights innovations in transformer-based models for intrusion detection and realistic benchmarking for APT simulations. Awards: No scientific awards explicitly mentioned in the provided materials. Advising & Grants: No advising records or grant information available in the current data. Labs/Teams: No specific lab affiliations or collaborative teams noted in the profile.

Jayantha Kodikaraمشاهده پروفایل

استاد

• Geotechnical Engineering
• Geoenvironmental Engineering
• Pipeline Engineering
• +۶ مورد دیگر

Professor Jayantha Kodikara is a leading academic in Geomechanics and Geo-infrastructure Engineering at Monash University's Department of Civil and Environmental Engineering. He currently heads the Monash Geomechanics Group, a large research team with 5 faculty members, 26 PhD students, and 7 Research Fellows. His expertise spans geotechnical engineering, pavement engineering, and infrastructure resilience, with over 200 publications and $20M in research grants. Education: BSc (Civil Engineering), University of Peradeniya, Sri Lanka (1983, 1st Class Honours) PhD in Geotechnical Engineering, Monash University (1989) Research Interests: His work focuses on unsaturated soil behavior, infrastructure resilience, pipeline integrity, and smart monitoring technologies. Key areas include desiccation cracking modeling, pavement performance under climate change, and fiber-reinforced geomaterials. He leads global initiatives like the Critical Water Pipe Project and the SPARC Hub for smart pavements. Grants & Awards: Over $20M in external/internal grants since 2001 Nominated for multiple awards, including VC’s Postgraduate Supervision Award and Dean’s Research Excellence Award Advising & Labs: Supervised 15+ PhD students, many now in academic or industry leadership roles. Leads the Monash Geomechanics Group and collaborates internationally on projects like Advanced Condition Assessment of Water Pipes. His lab work includes fiber-optic sensing and machine learning for infrastructure monitoring.

Zhang Pinمشاهده پروفایل

استادیار

• Explainable AI for Science
• Intelligent Computational Mechanics
• Uncertainty Quantification
• +۵ مورد دیگر

Dr. Zhang Pin is a Presidential Young Professorship (PYP) Assistant Professor in the Department of Civil and Environmental Engineering at the National University of Singapore (NUS). He previously held roles at the University of Cambridge as a Royal Society Newton International Fellow, Department Teacher, and Wolfson College Research Associate. His research group focuses on explainable AI for science, intelligent computational mechanics, uncertainty quantification, granular mechanics, coastal infrastructure resilience, and digital twins. Dr. Zhang earned his PhD from The Hong Kong Polytechnic University (2022), with a visiting scholar year at the University of Oxford. He holds a Master of Engineering (2019) and a Bachelor of Engineering (2016) from Hunan University and Hefei University of Technology, respectively. His research interests include: Explainable AI for scientific modeling Physics-informed machine learning Uncertainty quantification in geotechnical systems Micro/macro-scale granular mechanics Resilient coastal infrastructure design Digital twin technologies Notable achievements include recognition as a World’s Top 2% Scientist (2023-2024), PolyU PhD Thesis Merit Award, and the prestigious Royal Society Newton International Fellowship. His work bridges computational methods with geotechnical engineering challenges, emphasizing practical AI applications. Dr. Zhang has advised students like He GF and Wu HN, and his research has been published in top-tier journals such as Computer Methods in Applied Mechanics and Engineering and Géotechnique . He actively contributes to professional bodies including the British Geotechnical Association and International Society for Soil Mechanics (ISSMGE).

Ehsan Kamelمشاهده پروفایل

دانشیار

• Building Energy Modeling
• Sustainable Architecture
• Renewable Energy Infrastructure
• +۴ مورد دیگر

Ehsan Kamel is an Associate Professor in the Department of Energy Management at the New York Institute of Technology's College of Engineering and Computing Sciences. He joined the university in 2017 after earning his Ph.D. in Civil Engineering from Pennsylvania State University. His research focuses on building energy modeling, sustainable construction materials, and extraterrestrial habitat design for Mars. He directs the Energy and Green Technologies Laboratory (EnTech Lab), fostering student hands-on experience in energy and green technologies. Kamel has secured over $575,000 in grants from organizations like the National Science Foundation and NYSERDA, advancing energy efficiency and sustainability initiatives. His work bridges academia with industry, addressing real-world challenges in energy systems and climate resilience. Education: Ph.D. in Civil Engineering (Pennsylvania State University, 2017); M.S. in Civil Engineering; Certified Energy Manager (CEM). Research Interests: Building energy modeling integrates physics-based simulations and machine learning to optimize energy use in buildings and urban systems. He explores climate change impacts on energy systems, low-income housing energy burdens, 3D-printed construction, and sustainable materials. His extraterrestrial research models habitats for Mars using Martian climate data and locally sourced materials. Recent Projects: Urban-scale energy modeling with HPC systems, energy burden analysis in low-income households, and Mars habitat simulations. His work has been recognized with awards including the inaugural Trailblazers in Clean Energy (2024) and AEE Energy Innovator of the Year (2023). Grants and Collaborations: Principal investigator for NYSERDA grants ($500,000) and ASHRAE-funded projects. Collaborates with universities globally and energy firms on building efficiency solutions. Manages a research portfolio totaling $575,000 since 2017. Labs/Teams: Directs EnTech Lab, advises ASHRAE student branch, and leads interdisciplinary teams on energy-smart buildings and digital twin technologies.

• Environmental Engineering
• Water Resources Management
• Climate Change Adaptation
• +۷ مورد دیگر

Dr. Aavudai Anandhi is an Associate Professor of Biological Systems Engineering at Florida A&M University. Her research focuses on computational intelligence for modeling ecosystem processes at the food-energy-water (FEW) nexus, addressing environmental changes through systems engineering approaches. Key areas include artificial intelligence applications, spatial statistics, and machine learning for sustainable resource management. Education: Ph.D. in Water Resources and Environmental Engineering, Civil Engineering, Indian Institute of Science (2008) M.Sc. in Irrigation and Water Management, Indian Institute of Science (1995) B.Sc. in Agriculture, Anna University (1992) Research interests emphasize interdisciplinary sustainability solutions, climate change adaptation, and nexus frameworks for resilient ecosystems. Recent work explores land degradation, water footprint assessments, and habitat suitability modeling. Awards and recognitions include the 2018 Teacher of the Year (ASABE), 2017 Florida A&M Emerging Researcher Award, and multiple fellowships for educational innovation and research excellence. Her work bridges climate science with practical decision-making tools for agriculture and water resources. Key contributions include the Ogallala Aquifer Project (USDA Honor Award, 2013) and development of the DIEM framework for habitat threat analysis. She actively engages in educational outreach, creating tools like the 'Leaf Model for Watershed Teaching.'

Faegheh Moazeniمشاهده پروفایل

استادیار

• Mathematical Optimization
• Design Optimization of Critical Infrastructure
• Cybersecurity of Smart Systems
• +۵ مورد دیگر

Faegheh Moazeni is an Assistant Professor in the Department of Civil & Environmental Engineering at Lehigh University. Her research focuses on mathematical optimization, cybersecurity of critical infrastructure systems, and smart city technologies. She leads projects in water-energy nexus systems, resilient microgrids, and data-driven predictive control. Her work integrates machine learning, nonlinear model predictive control (MPC), and cyber-physical security frameworks to enhance infrastructure resilience. Recent efforts include hardware-in-the-loop validation of control systems and stochastic modeling for offshore renewable energy. Key research areas include securing smart water systems against cyberattacks, optimizing energy dispatch in islanded microgrids, and developing adaptive algorithms for real-time operational challenges. Her interdisciplinary approach addresses challenges at the intersection of environmental engineering, control theory, and cybersecurity. Notable contributions include frameworks for detecting cyberattacks in water networks, economic dispatch models for water-energy systems, and stability-guaranteed control architectures for naval and civilian infrastructure.

Masoud Yariمشاهده پروفایل

استاد آموزشی

• Machine Learning
• Data Science
• Dynamical Systems
• +۲ مورد دیگر

Masoud Yari is a Teaching Professor in the Department of Computer Science and Engineering at Lehigh University, part of the P.C. Rossin College of Engineering and Applied Science. He joined Lehigh in 2022 and contributes to the university's new M.S. program in Data Science. Previously, he worked as a research professor at the Bina Lab and held roles as a professional associate professor at Texas A&M University-Corpus Christi. He earned his Ph.D. in Applied Mathematics from Indiana University in 2008. Research Focus: Yari specializes in Machine Learning, Data Science, Dynamical Systems, Biomathematics, and Remote Sensing. His work bridges computational methods with environmental and geophysical applications, particularly in analyzing ice layers, snow dynamics, and seismic systems. He employs advanced techniques like deep learning, physics-informed neural networks, and multi-scale architectures to address challenges in glaciology, disaster management, and structural engineering. Key Contributions: His research emphasizes environmental monitoring through radar data analysis, including tracing snow and firn layers in polar regions. He has developed algorithms for seismic response evaluation and flood scene understanding using high-resolution aerial imagery. His work integrates machine learning with domain-specific physics to enhance predictive accuracy and data interpretation. Labs and Collaborations: His affiliation with the Bina Lab highlights collaborative efforts in computational science. His articles reflect interdisciplinary engagement across computer science, geophysics, and environmental science, addressing both theoretical and applied challenges in data-driven environmental analysis.

Felix Schneiderمشاهده پروفایل

پژوهشگر

• Structural Dynamics
• Uncertainty Quantification
• Bayesian Methods
• +۳ مورد دیگر

Felix Schneider is a Researcher at the Chair of Structural Mechanics at Technical University of Munich since 2018. He holds an M.Sc. in Civil Engineering from TU Munich (2018) and completed a semester abroad at the Norwegian University of Science and Technology. His research focuses on uncertainty analysis in structural and acoustic models, Bayesian updating techniques, and stochastic reduction methods in the frequency domain. He has contributed to advancements in rational polynomial chaos expansions and sparse Bayesian learning for structural dynamics applications. Education: 2011-2018: Study of Civil Engineering at Technical University of Munich 2018: Master of Science (M.Sc.) in Civil Engineering 2016: Semester abroad at Norwegian University of Science and Technology 2009: Abitur at Humboldtgymnasium Solingen Research emphasizes computational methods for structural dynamics, including: Bayesian parameter estimation for linear systems Rational surrogate models for uncertainty quantification Frequency-domain stochastic reduction techniques Applications in seismic risk assessment and vibroacoustic simulations Key contributions include a Maurer Söhne Prize for his Master's thesis and a published MATLAB toolbox for rational polynomial chaos expansions on the LRZ Git repository. Teaching responsibilities include courses on random vibrations, structural dynamics, and continuum mechanics. He has participated in research stays at ETH Zurich (2023) and collaborates with international institutions on uncertainty quantification projects.

Tamilselvan Gajapriyaمشاهده پروفایل

استادیار

• Aviation Safety
• Pilots' Selection and Training
• Human Factors in Aviation
• +۷ مورد دیگر

Tamilselvan Gajapriya is an Assistant Professor in the Department of Aviation Science at Saint Louis University, part of the School of Science and Engineering. He holds a Ph.D. in Aviation Science from Florida Institute of Technology, and master’s and bachelor’s degrees in Avionics and Electronics Engineering from Anna University, India. His research focuses on aviation safety, pilot selection/training, human factors, and physiological monitoring in flight environments. He directs the Aviation Safety, Aviation Physiology, and Ergonomics (ASAPE) Lab, which uses advanced equipment like EEG sensors and eye-tracking devices to study pilot performance under stress. Key research areas include cognitive workload assessment during emergencies, UAV privacy concerns, and consumer attitudes toward autonomous aircraft. He has published extensively in journals like Safety Science and Journal of Unmanned Vehicle Systems , with peer-reviewed work on topics ranging from pilot retention strategies to electronic chart usability. Professional affiliations include the Human Factors & Ergonomics Society and Women in Aviation International. His lab investigates physiological markers (e.g., galvanic skin response, EEG) to evaluate pilot decision-making and stress levels during simulated emergencies. Current projects explore factors influencing pilot career retention in commercial aviation and public acceptance of unmanned systems. Dr. Gajapriya collaborates with industry partners to translate research into practical aviation safety solutions.