Dr. Michael W. Schmidt is a postdoctoral researcher at the Karlsruhe Institute of Technology (KIT) , affiliated with the Institute for Technology Assessment and Systems Analysis (ITAS) since 2020. His work bridges philosophy with technology assessment, focusing on the Philosophy of Technology , Reflective Equilibrium , and Political Philosophy . He explores ethical dimensions of autonomous vehicles , AI ethics , and robotics , emphasizing public reason and human rights . Education : PhD in Philosophy (KIT, 2022) with a thesis on reflective equilibrium as a form of life. Research Themes : Methodology of reflective equilibrium, epistemology of understanding, Rawlsian political theory, and ethics of socio-technical systems. Recent Publications : Analyze AI ethics implementation, social media's impact on democracy, and governance frameworks for sustainable energy transitions.
Mizan Rahman is an Assistant Professor in the Department of Civil, Construction, and Environmental Engineering at the University of Alabama's College of Engineering. He serves as the Director of the Connected and Automated Mobility Laboratory (CAM Lab) at UA, leading research in cutting-edge transportation technologies. His work bridges civil engineering with computer science, focusing on the intersection of physical infrastructure and digital systems. Dr. Rahman's educational background includes: B.S. in Civil Engineering from Bangladesh University of Engineering and Technology (2008) M.S. in Civil Engineering from Clemson University (2013) Ph.D. in Civil Engineering from Clemson University (2018) His research interests span artificial intelligence, machine learning, cybersecurity, and digital twins applied to transportation systems. Dr. Rahman takes an interdisciplinary approach to solving evolving mobility challenges, with particular focus on connected and automated transportation systems for smart cities. His work addresses critical issues in AI-based predictive analytics, cybersecurity and privacy for connected mobility, driver behavior modeling, heterogeneous wireless communication, and transportation cyber-physical systems. Analysis of Dr. Rahman's recent publications reveals a strong emphasis on digital twin technology for traffic management, cybersecurity for autonomous vehicles (particularly GNSS spoofing detection), and connected vehicle applications. His work demonstrates a clear trajectory toward creating safer, more efficient transportation systems through the integration of advanced computing technologies with traditional civil infrastructure. The research spans theoretical development, numerical validation, and real-world experimental implementation. Dr. Rahman has received significant recognition for his work: NSF CAREER Award (2024) IEEE George N. Saridis Best Transactions Paper Award (2020) U.S. Department of Transportation Dwight D. Eisenhower Doctoral Fellowship (2013-2014) U.S. Department of Transportation Dwight D. Eisenhower Master's Fellowship (2012-2013) He has secured multiple federally funded projects from the National Science Foundation (NSF), the U.S. Department of Transportation (USDOT), and the Federal Motor Carrier Safety Administration (FMCSA). His research has practical applications in developing low-cost solutions for self-driving cars to detect GPS hacking and improve overall transportation safety. Dr. Rahman actively collaborates across disciplines, taking advantage of UA's cybersecurity program in the computer science department to advance research in connected and automated transportation systems. As director of the CAM Lab, Dr. Rahman leads a team working at the forefront of transportation innovation, with a vision to build smart transportation systems that enhance quality of life and contribute to economic prosperity. His work is particularly relevant as cities become increasingly connected and automated, moving toward the reality of smart cities.
Auroop Ratan Ganguly is a Professor of Civil and Environmental Engineering at Northeastern University's College of Engineering , with affiliate appointments in the Khoury College of Computer Science and School of Public Policy and Urban Affairs. He serves as Director of the Sustainability and Data Sciences (SDS) Laboratory and Co-Director of the Global Resilience Institute at Northeastern, while holding a joint appointment as Chief Scientist at Pacific Northwest National Laboratory (PNNL). PhD in Civil and Environmental Engineering, MIT (2002) MS in Civil Engineering, University of Toledo (1997) B.Tech (Hons.) in Civil Engineering, IIT Kharagpur (1993) His research bridges Climate Extremes & Water Sustainability with Infrastructure Resilience and Hybrid Physics-AI Systems . Key contributions include novel methods for climate extremes under global warming, network science applications to infrastructure resilience, and physics-integrated machine learning for weather and hydrology. His work has been cited in UN and US National Climate Assessments, with media coverage in New York Times , Nature , and AGU publications. Recent articles focus on AI for Climate Risk (2025), Nonlinear Dynamics (2025), and Carbon Cycle Extremes (2025). His lab develops Explainable AI tools for climate adaptation and environmental justice. Fellow, American Society of Civil Engineers Distinguished Member, ACM (2023) Outstanding Mentoring Awards, Oak Ridge National Laboratory Best Paper Awards, SIAM Data Mining (2011) and IEEE (2017) As an advisor, he has mentored PhD students like Evan Kodra and Kate Duffy , who co-founded climate startups including risQ (acquired by ICE/NYSE) and Zeus AI . His leadership roles include co-chairing NCAR's Societal Dimensions Working Group and serving on the editorial boards of Scientific Reports and Frontiers in Water .
Thomas C Henderson is a tenured Professor at the School of Computing within the College of Engineering at the University of Utah. His research focuses on autonomous systems , computational models , and intelligent machine systems , with specific interests in biosystem simulation, distributed systems, and adaptive algorithms. He has contributed to Bayesian sensor networks , UAS traffic management , and probabilistic logic frameworks for intelligent agents. Current academic appointments since 1989 Adjunct roles in Biomedical Engineering Active in IEEE committees (2024-2025) Research trends in recent publications emphasize autonomous aircraft coordination , probabilistic reasoning , and multi-sensor integration . Key subfields include lane-based airspace modeling, reinforcement learning for UAS, and pseudogradient navigation techniques. His work has received recognition through a Best Paper Award (IEEE 2008) and the US Air Force Summer Faculty Fellowship . Strategic deconfliction protocols Dynamic data-driven applications Structural health monitoring systems He actively mentors undergraduate research through courses like Deep Learning Capstone and Senior Capstone Design . Current grants include "Deep Learning in AI and Robotics" (2024-2029) and "Robust Reasoning using Geometric SAT/PSAT" (2022-2023).
Dennis Prangle is an Associate Professor in Statistics at the University of Bristol, conducting research at the intersection of Bayesian statistics and machine learning. His academic profile demonstrates expertise in developing novel computational inference methods with applications across multiple scientific domains. Dr. Prangle's primary research interests include: Approximate inference methods such as simulation-based inference and variational techniques Likelihood-free inference through Approximate Bayesian Computation (ABC) Experimental design for high-dimensional problems Applications in population genetics, physics, ecology, and epidemiology Stochastic differential equations and composite likelihood approaches His publication record shows consistent methodological contributions with increasing integration of machine learning techniques. Recent work focuses on normalizing flows with flexible tails for improved density estimation, Bayesian emulation of complex systems, and optimal combination of composite likelihoods. His research bridges theoretical statistics with practical applications in financial modeling, infrastructure engineering, and fair classification algorithms. Dr. Prangle maintains an active academic blog where he discusses technical aspects of Bayesian statistics, experimental design, and computational methods, demonstrating his commitment to scholarly communication. His detailed posts on topics like Fisher information gain versus Shannon information gain in experimental design highlight his theoretical contributions to the field.
Jenny Liu is a Professor in the Department of Materials and Pavement Engineering at Missouri University of Science and Technology, specializing in pavement engineering for cold and Arctic regions with emphasis on sustainable materials and infrastructure resilience. Her research spans asphalt mixture design, binder rheology, moisture susceptibility, thermal cracking mechanisms, and machine learning applications for pavement condition assessment. Key contributions include phase change material integration for thermal regulation, waste product utilization in asphalt rejuvenation, and innovative solutions for permafrost-protected embankments. Analysis of her recent publications reveals a strong trend toward computational modeling and sustainability, addressing critical challenges like autonomous vehicle impacts on pavement roughness, greenhouse gas emissions from truck platooning, and AI-driven failure analysis in transportation infrastructure. No information on scientific awards is available in the provided text. Dr. Liu's work demonstrates active collaboration with transportation agencies, particularly the Missouri Department of Transportation (MoDOT) and Alaskan infrastructure projects, focusing on practical applications for cold-region pavement durability. While specific grant details are unlisted, her research directly informs construction specifications and maintenance strategies for extreme climates.
Ruwen Qin is an Associate Professor in the Department of Civil Engineering at Stony Brook University. Her research focuses on integrating data analytics, machine learning, and systems engineering into civil infrastructure systems to develop cyber-physical systems and intelligent automation. She applies these technologies to enhance human-AI collaboration, improve transportation safety, and advance smart infrastructure monitoring. Developing AI models for structural health monitoring Applications in worker safety and transportation systems Specializes in computer vision and sensor fusion Her recent work includes deep learning frameworks for drone-assisted inspections, structural component segmentation using weak annotations, and attention-based networks for traffic risk prediction. She also explores explainable AI for crash anticipation and interactive systems for bridge inspectors. Ruwen Qin's research spans interdisciplinary domains, combining civil engineering with AI-driven analytics to address challenges in infrastructure resilience, transportation safety, and human-centric automation systems.
Hyun-Jong Lee is a Professor in the Department of Civil and Environmental Engineering at Sejong University. He has been serving at Sejong University since 2001 and has established himself as a leading researcher in pavement engineering and infrastructure maintenance with applications of artificial intelligence techniques. Dr. Lee's educational background includes: Ph.D. from North Carolina State University (1996) M.S. from KAIST (1988) B.S. from Yonsei University (1986) His research interests focus on asphalt theory and mixtures, pavement performance and life cycle, design of long term performing pavements, and pavement maintenance and management using machine learning. Dr. Lee has pioneered the application of deep learning techniques to infrastructure inspection problems, bridging traditional civil engineering with modern artificial intelligence approaches. His fingerprint research areas include Pavement Engineering (100%), Asphalt Concrete Engineering (90%), Asphalt Mixture Engineering (86%), and related subfields. Dr. Lee's recent publications demonstrate a strong trend toward applying advanced computer vision and deep learning techniques to solve critical infrastructure maintenance challenges. His work spans road manhole detection, pavement patching detection, bridge deck crack analysis, and road marking deterioration assessment, showcasing a comprehensive approach to transportation infrastructure monitoring and maintenance. Among his notable achievements are: Development of Seoul City asphalt overlay design guide Establishment of pay adjustment factors for Seoul City Sinkhole/Cavity detection using 3D GPR scanner Dr. Lee has successfully led research projects that translate theoretical advancements into practical applications for city infrastructure management. His work with Seoul City demonstrates the real-world impact of his research on urban transportation systems. With an h-index of 23 and over 2010 citations, he maintains an active research profile with growing publication output in recent years. His laboratory focuses on developing cutting-edge technologies for infrastructure inspection, particularly utilizing deep learning algorithms for automated detection and assessment of road and bridge conditions. The research team has established collaborations with transportation authorities to implement their research findings in real-world infrastructure management systems.
T M Indra Mahlia , a Distinguished Professor at the School of Civil and Environmental Engineering , University of Technology Sydney (UTS), leads cutting-edge research in sustainable energy systems and environmental engineering. As a core member of the Centre for Technology in Water and Wastewater and the Centre for Advanced Modelling and Geospatial Information Systems , he bridges engineering innovation with practical climate solutions. PhD from University of Malaya (Kuala Lumpur, Malaysia) Fluency in English, Indonesian, Malay, and Achinese for peer review His research spans Techno-Economic Analysis , Circular Economy , and Water-Energy Nexus challenges, supported by over $5 million in grants. His work focuses on: Hydrogen energy systems optimization Advanced materials for energy storage Low-cost water purification technologies Sustainable biodiesel production Thermal management innovations As a Highly Cited Researcher (Clarivate Analytics, 2017-2022) and The Australian 's 2019/2025 Sustainable Energy Leader , he mentors future researchers - notably guiding two Highly Cited PhD students ( H.C. Ong and A.S. Silitonga ). His publications across 2024-2026 demonstrate technical advancements in: Hydrogen carrier systems Microalgae-derived lubricants High-entropy alloy corrosion resistance Artificial neural network optimization Phase change material thermal sinks Biohydrogen production pathways
Angus Wilkinson serves as Professor and Associate Chair for Operations and Academic Programs at Georgia Institute of Technology, holding joint appointments in the School of Chemistry and Biochemistry (College of Sciences) and School of Materials Science and Engineering (College of Engineering). His research bridges inorganic chemistry and materials science with focus on functional materials. Education includes: B.A. in Chemistry from Oxford University (1988) D.Phil. in Chemistry from Oxford University (1992) Junior Research Fellowship at Christ Church, Oxford (1991-1993) Postdoctoral work at University of California Santa Barbara Wilkinson's research centers on low and negative thermal expansion materials , particularly fluorides and oxides with ReO 3 -type structures. His group develops synchrotron X-ray methods for in-situ studies under extreme conditions (high pressure/temperature), with applications in oil well cement hydration and gas-containing perovskites . Current work explores helium incorporation into crystal frameworks and thermal expansion control through structural modifications. The research combines materials synthesis at Georgia Tech with neutron scattering at HFIR/SNS facilities and X-ray studies at APS. Analysis of recent publications (2019-2025) reveals strong focus on gas-containing perovskites (particularly helium clathrates), thermal expansion engineering through anion interstitials, and high-pressure behavior of fluorides. Methodologically, the work integrates advanced scattering techniques with computational modeling and specialized sample environments. Awards include: NSF CAREER award (1996) Sigma Xi award for outstanding junior faculty research (1996) Linus Pauling Prize from American Crystallographic Association (1991) Wilkinson leads research utilizing major national facilities including Advanced Photon Source (APS), High Flux Isotope Reactor (HFIR), and Spallation Neutron Source (SNS). His group maintains active collaborations across materials characterization fields, with current projects examining cement hydration under oil well conditions and novel CO 2 adsorbents. As Associate Chair, he oversees academic operations while maintaining an active research program evidenced by consistent high-impact publications.
Kun Gao is an Assistant Professor at the Department of Architecture and Civil Engineering at Chalmers University, leading the Urban Mobility Systems research group. His work bridges transportation engineering and data science to develop sustainable mobility solutions through electrification, shared systems, and connected infrastructure. Research Focus: Electric vehicle integration, charging infrastructure optimization, multimodal mobility systems Funding: Supported by JPI Urban Europe, FORMAS, Swedish Innovation Agency, Swedish Energy Agency, and Chalmers AoA Transport/Energy Methods: Machine learning, big data analytics, system optimization His recent publications emphasize autonomous vehicle safety , renewable energy integration , and equity in mobility systems . Current work explores AI-driven infrastructure planning and coupled transportation-energy systems.
Roland Reitberger is a Research Fellow at the Chair of Energy Efficient and Sustainable Design and Building at the Technical University of Munich . His work focuses on Life Cycle Assessment , thermal-dynamic building simulation , and parametric methods for analyzing the built environment. He investigates interactions in urban systems and synergy effects in sustainable construction. Research interests include climate-resilient urban planning, integration of green infrastructure with building systems, and data-driven optimization of building energy performance. His recent publications demonstrate expertise in multi-objective decision support for neighborhoods, GIS-based urban tree analysis , and holistic climate assessments of construction projects. Reitberger contributes to urban climate resilience research through parametric modeling of building density-vegetation relationships and develops frameworks for circular economy strategies in building densification and refurbishment. His methodological innovations combine machine learning with building simulation for explainable AI applications in office energy management.
Martin Slepicka is a researcher at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich. His work focuses on bridging Building Information Modeling (BIM) with additive manufacturing and digital twinning technologies. Department: Civil and Building Engineering Research Group: Digital Twinning, Construction Robotics Research Highlights: Slepicka specializes in: Integrating BIM with digital fabrication workflows Developing closed-loop systems for additive manufacturing Real-time data exchange in construction robotics Automated parameter calibration using machine learning Semantic enrichment of BIM through multi-sensor platforms Academic Contributions: Recent publications demonstrate expertise in: Extrusion-based additive manufacturing control Non-planar path planning for 3D-printed components Autonomous robot grasping solutions From fabrication models to simulation frameworks Laboratories: Active in: BIM-Lab Robotic Fabrication Lab Mobile Machinery development
Ajay Kalra serves as an Associate Professor of Water Resources Engineering in the Civil Engineering Department at Southern Illinois University Carbondale's College of Engineering. His office is located in Engineering Building, Room 114, where he teaches undergraduate and graduate courses including Fluid Mechanics, Open Channel Hydraulics, Water Resources Engineering, and Advanced Hydraulic Design. Dr. Kalra's research spans hydro-climatology, urban sustainability, artificial intelligence applications in water resources, drought frequency analysis, and probabilistic forecasting. His interdisciplinary work connects climate science with practical water management solutions, focusing on how large-scale climate patterns influence regional hydrology. He has developed innovative approaches using machine learning to improve streamflow forecasting and drought prediction, particularly in western U.S. river basins. His scholarly output shows consistent productivity with research trends indicating increasing focus on machine learning applications for hydrological prediction, climate change impacts on water resources, and urban flood management. Recent publications demonstrate strong integration of remote sensing data with traditional hydrological models to address water challenges in both gauged and ungauged basins. Outstanding dissertation award 2011 (UNLV) Outstanding dissertation award 2011 (UNLV-College of Engineering) Best poster award (2nd Place, UNLV-College of Engineering) GPSA merit award 2010-2011 Member Tau Beta Pi (Engineering Honor Society) Dr. Kalra actively mentors graduate students and collaborates with researchers across institutions. His professional service includes membership in the American Geophysical Union and American Society of Civil Engineers. His current research program continues to explore the connections between oceanic-atmospheric oscillations and regional hydrology while expanding into urban water sustainability challenges under changing climate conditions.
Dr. Debarshi Sen is an Assistant Professor in the Department of Civil Engineering at Southern Illinois University Carbondale (SIU), specializing in structural engineering. His research focuses on structural dynamic systems, infrastructure monitoring and resilience, and the application of statistical and machine learning techniques in monitoring and seismic response control. Education: Ph.D. in Civil Engineering (2018), Rice University, Houston, TX M.S. in Civil Engineering (2013), Indian Institute of Technology Kharagpur, India B.S. in Civil Engineering (2011), Indian Institute of Technology Kharagpur, India Research Interests: Dr. Sen's research integrates advanced computational techniques with structural engineering challenges. His work spans: Development of dynamic systems for infrastructure monitoring Application of machine learning algorithms for structural health assessment Seismic response control and regional fragility assessment Innovative approaches using crowdsourced data and mobile sensing for bridge monitoring Publications Overview: Dr. Sen has an extensive publication record, including over 20 journal articles and numerous conference presentations. His recent work focuses on leveraging AI and machine learning for bridge condition assessment, with significant contributions to crowdsourced mobile sensing technologies. His research also explores experimental and analytical studies on seismic protection systems, particularly using negative stiffness devices. Labs and Teams: Dr. Sen is affiliated with the Structural Engineering research group at SIU and maintains collaborations with institutions such as MIT (as a Research Affiliate since 2020) and Lehigh University (as a Postdoctoral Research Associate from 2020-2022).
