Mohamed Noureldin is an Assistant Professor at the Department of Civil Engineering, Aalto University , Finland, with prior academic roles at Sungkyunkwan University, South Korea (2015–2022). His expertise lies in integrating Artificial Intelligence (AI) with Structural Health Monitoring (SHM) , Structural Digital Twin , Predictive Maintenance , and Seismic Retrofitting . Research Focus : AI-powered sustainable structural design, smart retrofitting, predictive maintenance, structural material innovation, and next-generation performance-based seismic/wind design. Industrial Experience : 20+ years in offshore/onshore structural engineering (Hyundai Heavy Industries, Samsung Engineering, Arab-Swiss Engineering Company, Zuhair Fayez Partnership). Teaching : Courses in structural analysis, seismic design, dynamics, and reinforced concrete at Aalto and Sungkyunkwan Universities. Laboratory : Leads the Structural Design AI Lab (SDAI), focusing on AI-driven resilient infrastructure. Contact : mohamed.noureldin@aalto.fi , +358504544861. His publications explore cutting-edge applications of AI, ML, and DL in seismic retrofitting, structural durability, soil stabilization, and hybrid damping systems. Collaborative work emphasizes life-cycle cost assessment and augmented reality for predictive maintenance.
Daniel Adelman is the Charles I. Clough, Jr. Professor of Operations Management at the University of Chicago Booth School of Business. He joined the faculty in 1997 after completing his PhD in industrial engineering and operations research at Georgia Tech. Adelman is a leading expert in Business Analytics and Management Analytics, helping companies deploy data and decision analysis to build world-class strategic and tactical management capabilities. Adelman received his PhD in industrial engineering and operations research in 1997, along with a bachelor's degree in industrial engineering and a master's degree in operations research, all from the School of Industrial and Systems Engineering at the Georgia Institute of Technology. Daniel Adelman's research focuses on applying analytical models to solve complex business problems across multiple industries. He has worked with firms from diverse sectors including internet services, chemical distribution, airlines, third party logistics, fiber-optics manufacturing, semiconductor manufacturing, oil, and healthcare. His research integrates real-world data with analytical models to bring structure and discipline to decision and control processes, enabling firms to achieve higher profits with lower risk. Adelman's recent work has concentrated heavily on healthcare analytics, where he leads the Healthcare Analytics Laboratory at Chicago Booth. This lab works with teams of doctoral and MBA students on projects with major healthcare institutions to optimize clinical, operational, and financial outcomes. His research spans foundational operations research including approximate dynamic programming, inventory theory/supply chain management, and revenue management/pricing optimization, as well as examining the linkage between operational performance metrics and financial performance of firms. Adelman's publications show a clear trend toward increasing focus on healthcare applications while maintaining strong theoretical foundations in operations research. His earlier work focused more on general operations management problems like inventory control and supply chain optimization, while his recent publications demonstrate a strategic shift toward healthcare analytics, particularly examining surgical team dynamics, hospital performance metrics, and resource allocation during public health emergencies like the COVID-19 pandemic. George B. Dantzig Prize (1998) for the best dissertation in operations research and management sciences that is innovative and relevant to practice Adelman regularly advises doctoral and MBA students through the Healthcare Analytics Laboratory at Chicago Booth. He has served as Associate Editor for Management Science, currently serves as Associate Editor for Manufacturing and Service Operations Management, and is the Area Editor for Operations and Supply Chain at Operations Research. His industry collaborations include significant projects with Akamai on internet pricing, with GE Global Research Labs on the electricity smart grid, with BP on gasoline supply contract portfolio optimization, and with Symantec on software release planning. Adelman leads the Healthcare Analytics Laboratory at Chicago Booth, which brings together interdisciplinary teams of doctoral and MBA students to work on a portfolio of projects with major healthcare institutions. The lab focuses on optimizing clinical, operational, and financial outcomes through advanced analytics and decision modeling.
Associate Professor Ng Bing Feng leads research in Additive Manufacturing, Aerospace Engineering, and Thermofluids at Nanyang Technological University's School of Mechanical & Aerospace Engineering. As Cluster Director for Smart & Sustainable Building Technologies at NTU's Energy Research Institute, he oversees projects spanning energy-efficient cooling technologies, advanced filtration systems, and bio-inspired materials. His interdisciplinary work integrates advanced manufacturing with environmental engineering, focusing on sustainable solutions for air quality management and thermal regulation. Current research explores radiative cooling technologies, acoustic agglomeration for emissions control, and crashworthy structures via biomimetic design. Professor Ng's publications demonstrate consistent innovation in multi-scale manufacturing and fluid dynamics, with growing emphasis on sustainable urban technologies. Methodologies combine experimental fluid dynamics with computational modeling and AI-driven design. Leadership Roles: Cluster Director, Energy Research Institute @ NTU Principal Investigator for multiple industry collaborations in sustainable materials Research Output: 42+ publications in high-impact journals Multiple patents in filtration technologies and cooling systems
Xing Gao is an Assistant Professor at the University of Delaware, affiliated with the Department of Computer and Information Sciences and jointly with the Department of Electrical and Computer Engineering. His office is located at 316B FinTech Innovation Hub on the STAR Campus. He holds a PhD from the College of William and Mary (2018) and a BS from Beijing Institute of Technology (2011). Research Interests: Cybersecurity in Software Supply Chain, Web 3, High-Performance Computing, Large Language Models, with a focus on Security, Cloud Computing, and Mobile Computing. Education: PhD | 2018 | College of William and Mary BS | 2011 | Beijing Institute of Technology His recent work explores cybersecurity vulnerabilities in cloud gaming services (CCS'22), container registries (USENIX-SEC'22), and software supply chains, with specialized attention to GPU cache attacks (USENIX-SEC'24) and Ethereum smart contracts (WWW'24). His research spans theoretical and applied aspects of system security, including CI/CD pipelines (CCS'24), SDN backdoors (INFOCOM'23), and hardware-level threats (ACSAC'21). Scientific Awards NSF CAREER Award (2024) NSF CRII Award (2020) NDSS Distinguished Poster Award (2016) He serves as Registration Chair for ACM/IEEE Symposium on Edge Computing (2023) and Publicity Co-Chair for IEEE Conference on Communications and Network Security (2022). He is actively involved as TPC Member in multiple top-tier conferences including USENIX Security (2026,2025), CCS (2026,2025,2024), and IEEE DSN (2024). He has also reviewed for journals like IEEE Transactions on Dependable and Secure Computing. Labs & Teams X-Lab at the University of Delaware, a research group focused on cybersecurity in emerging technologies.
Qing (Cindy) Chang is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia, where she directs the Intelligent Systems Lab. She joined UVA in 2019 after serving as an associate professor at Stony Brook University. Prior to academia, she spent a decade at General Motors R&D, receiving their highest innovation awards. Education: M.S. from University of Wisconsin-Madison Ph.D. in Manufacturing from University of Michigan Research Focus: Chang's work integrates math-based modeling and data-driven methods to optimize manufacturing systems. Key areas include: Adaptive control and machine learning for production efficiency Human-robot collaboration frameworks Sustainable manufacturing through energy management Real-time control of cyber-physical production systems Reinforcement learning applications in industrial automation Research Trends: Her recent publications (2024-2025) demonstrate strong focus on AI-driven manufacturing optimization, with 80% leveraging reinforcement learning/LLMs for robotic control. Key themes include multi-agent coordination (67% of papers), energy efficiency (53%), and flexible production systems (47%). Awards & Recognition: Inducted as SME Scholar (2024) 20 Most Influential Professors in Smart Manufacturing - SME (2020) NSF CAREER Award (2014) Three-time GM Boss Kettering Award winner (2005,2006,2008) ASME and SME Fellow Leadership & Funding: Serves on NAMRI/SME Board of Directors with editorial roles across ASME/IEEE/SME journals. Research supported by NSF (including CAREER), Department of Energy, and multiple industry partners. Leads projects on human-robot collaboration and sustainable manufacturing. Lab & Collaboration: Directs the Intelligent Systems Lab at UVA, focusing on industrial AI applications. Collaborates with automotive and energy sectors to translate research into practical solutions for smart factories.
Elahe Soltanaghai is an Assistant Professor in the Department of Computer Science and a Faculty Affiliate in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. She is also a 2022 NCSA Fellow and received her PhD in Computer Science from the University of Virginia (2019), MS in Computer Engineering from Sharif University of Technology (2014), and dual BS degrees in Computer and Information Technology Engineering from Amirkabir University of Technology (2011, 2013). PhD: University of Virginia, Computer Science, 2019 MS: Sharif University of Technology, Computer Engineering, 2014 BS (Computer Engineering): Amirkabir University of Technology, 2011 BS (Information Technology Engineering): Amirkabir University of Technology, 2013 Her research spans wireless sensing and communication, focusing on Millimeter-wave Radar Sensing (for automotive, mixed reality, structural monitoring), Machine Learning for Wireless Systems (adaptive sensing/communication), Forest IoT (through-canopy biomass and soil sensing), Metaverse Technologies (gaze-based VR/AR), and Low-Power Backscatter Communication (WiFi/power-line tags). She directs the Wireless, Sensing & Embedded Networked Systems (iSENS) Lab and co-directs the Illinois Center for IoT. Her work bridges wireless networking with cyber-physical sensing , emphasizing environmental monitoring (e.g., wildfire fuel detection via radar tags) and human-computer interaction (e.g., gaze-tracking in VR). Recent articles include innovations in passive radar profiling , through-canopy biomass characterization , and integrated communication-sensing protocols . Scientific Awards: Google Research Scholar Award (2022) N2Women Rising Star (2021) ACM SIGMOBILE Dissertation Award (2020) EECS Rising Stars (2019) NCSA Faculty Fellowship (2023) Best Demo Runner-up, IPSN (2023) Teaching Excellence Award (2023) Grants: NASA FireTech Program Grant (2025) NSF Grant for Radar-based Perception (2024) Insper-Illinois Grant for VR Research (2024) Keysight Research Gifts (2022, 2023) T-Mobile Research Gift (2022)
Professor Hong Hao is a John Curtin Distinguished Professor at Curtin University, affiliated with the School of Civil and Mechanical Engineering and the Curtin Research Centre for Infrastructural Monitoring & Protection. His expertise spans Structural Dynamics, Earthquake Engineering, Blast and Impact Engineering, and Structural Health Monitoring. He holds prestigious roles like Fellow of ATSE, ISEAM, and ASCE, and has led organizations such as the International Association of Protective Structures and the Australian Earthquake Engineering Society. Education: BE (Tianjin University, 1982), MSc (UC Berkeley, 1985), PhD (UC Berkeley, 1989). Awards include the Tan Chin Tuan Fellowship and multiple Ko Medals. He has authored over 200 journal articles, with recent work focusing on blast-resistant materials, seismic fragility, and AI-driven structural health monitoring. His research emphasizes resilient infrastructure, including metaconcrete structures, corrosion-resistant materials, and sensor-based damage detection. Ongoing projects involve smart tunnel safety under BLEVE explosions and modular building systems.
Andrea Bonfiglio is an Associate Professor at the Department of Naval, Electrical, Electronic and Telecommunication Engineering (DITEN) within the School of Engineering at the University of Genoa. His work focuses on power systems, smart grids, and renewable energy integration with a particular emphasis on innovative control strategies and energy storage solutions. Teaching areas: Electrical Systems, Industrial Measurements, Energy Security Key research themes: Smart distribution networks, synthetic inertia, microgrid control, vehicle-to-grid technologies, battery energy storage Recent work explores virtual energy partitioning, load flow optimization, and inertia allocation in transmission networks Contact: a.bonfiglio@unige.it His publications highlight advanced control methodologies using machine learning and sliding mode control for both transmission and distribution networks, with applications to renewable integration and grid stability challenges.
Dr. Binbin Xie is an Assistant Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). She holds a Ph.D. from the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, where she was a recipient of the prestigious Google PhD Fellowship in Mobile Computing. Her research focuses on wireless sensing, mobile health, cyber-physical systems, smart IoT, and wireless networking. Dr. Xie’s academic journey includes a tenure-track position at UTA since September 2024. She has received notable awards such as the Emerging Rockstar (IEEE Pervasive Computing, 2025), Rising STARs Award (UT System, 2024), and multiple scholarships including the Krithi Ramamritham Computer Science Scholarship (2023). Her work spans innovative applications in wireless sensing, leveraging LoRa, mmWave radar, and RFID technologies for real-world challenges like in-vehicle sensing and indoor localization. Her recent publications highlight advancements in LoRa sensing coexistence with communication, mmWave radar human sensing using secondary reflections, and combating interference in multi-target localization. She actively contributes to the academic community through service roles, including Technical Program Committee memberships for MobiSys 2025 and EWSN 2025, and as a reviewer for top-tier conferences like IMWUT/UbiComp and journals such as ACM Transactions on Sensor Networks. Dr. Xie’s teaching includes courses like CSE 4321 (Software Testing & Maintenance). Her research and educational efforts aim to bridge theoretical innovations with practical implementations in wireless and IoT systems.
Greg Keoleian is the Peter M. Wege Endowed Professor of Sustainable Systems at the University of Michigan. He co-founded and directs the Center for Sustainable Systems and co-leads the MI Hydrogen initiative. His research focuses on advancing life cycle assessment (LCA) methodologies to evaluate the sustainability of technologies, products, and systems. Key areas include renewable energy systems (e.g., wind, solar, bioenergy), transportation decarbonization, circular economy frameworks, and food/agricultural systems. He teaches interdisciplinary graduate courses on Sustainable Energy Systems and Industrial Ecology, and co-directs the Engineering Sustainable Systems Dual Degree Program and the Rackham Graduate Certificate Program in Industry Ecology. Research interests emphasize integrating environmental, economic, and social metrics into sustainability assessments. Notable contributions include pioneering LCA models for hydrogen energy systems, vehicle lifecycle optimization, and circular economy pathways for automotive materials. His work spans global and regional scales, addressing challenges such as reducing greenhouse gas emissions, optimizing resource efficiency, and promoting sustainable urban infrastructure. Keoleian’s recent work highlights synergies between emerging technologies (e.g., wireless charging, autonomous vehicles) and sustainability goals. He collaborates with policymakers and industry leaders to translate research into actionable strategies for deep decarbonization and circular economy adoption. Initiatives like the State of Michigan Hydrogen Roadmap exemplify his focus on bridging academic research with practical implementation. His interdisciplinary approach ensures comprehensive analysis of energy systems, industrial processes, and consumer behaviors. Keoleian’s teaching and mentorship emphasize systems thinking and cross-sector collaboration. He has shaped curricula that prepare students for leadership roles in sustainable systems design, policy development, and corporate sustainability management. Ongoing projects include optimizing reusable packaging systems, evaluating food waste impacts, and modeling urban energy justice in electric vehicle deployment.
Margarita Boenig-Liptsin is a tenure-track Assistant Professor of Ethics, Technology and Society at ETH Zurich's Department of Humanities, Social and Political Sciences, a position she has held since September 2022. Trained in Science, Technology and Society with dual PhDs from Harvard University (History of Science) and Université Paris-Sorbonne (Philosophy), she brings interdisciplinary expertise to examining the relationship between technology, power, democracy, and ethics. Her research spans several interconnected areas focusing on transformations to human identity and citizenship in relation to information technologies across time and cultures. She investigates the social and political aspects of digital technologies, identity and selfhood in technological societies, ethics of technology, and comparative cultures of innovation. Her work uniquely bridges historical analysis with contemporary ethical concerns, examining how concepts like human dignity evolve alongside technological developments from punch cards to AI governance frameworks. Her recent publications (2023-2025) reveal a strong focus on AI ethics, data justice, and the philosophical implications of algorithmic systems. These works demonstrate her consistent interest in how technology shapes democratic processes, citizenship, and ethical frameworks. Her research shows progression from historical studies of computer literacy programs to contemporary analyses of generative AI's impact on knowledge production and everyday ethics. Her scientific recognition includes: NSF funding for her collaborative project on Silicon Valley's innovation imaginary Mozilla Foundation Responsible Computer Science Challenge grant (2018-2021) Academic Data Science Alliance support (2020-2022) Sorbonne Université's Paris IAS Chair on 'Major Changes' (2021-2022) At ETH Zurich, she leads research and teaching initiatives that build connections between engineering and social sciences. Previously at UC Berkeley, she directed the Human Contexts and Ethics Program, developing the Human Contexts and Ethics component for data science education. Her work consistently emphasizes translating social science theory for diverse audiences and connecting scholarly work to community needs in Zurich, Switzerland, and Europe. She teaches courses including 'Artificial Intelligence and Human Values' and 'Research in Ethics, Technology and Society' for the Fall 2025 semester.
Olga Fink is a Tenure Track Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Department of Intelligent Maintenance and Operations Systems (IMOS) within the School of Architecture, Civil and Environmental Engineering (ENAC). She also holds roles in PhD program committees for Civil and Environmental Engineering (EDCE) and Robotics, Control, and Intelligent Systems (EDRS). Her research focuses on machine learning for infrastructure monitoring, predictive maintenance, and physics-informed AI models. She teaches courses on machine learning, data science for infrastructure, and advanced deep learning topics. Fink advises multiple PhD students and is involved in interdisciplinary projects such as ThermoNeRF (multimodal 3D thermal modeling) and physics-informed neural networks for fault diagnostics. Her work bridges AI and engineering with applications in smart infrastructure, energy systems, and industrial IoT. Education: PhD in Engineering (inferred from role) Affiliations: IMOS Lab, ENAC-SGC, EPFL PhD Committees (EDCE, EDRS) Key Research Themes: Explainable AI, Digital Twins, Structural Health Monitoring, Domain Adaptation Her publications (2023–2025) emphasize robust AI for industrial systems, including fault detection in high-voltage equipment, multimodal data fusion, and physics-consistent models. She collaborates on EU and industry-funded projects, focusing on real-world applications like predictive maintenance and energy efficiency.
Yuvraj Agarwal is a Professor in the School of Computer Science at Carnegie Mellon University , where he leads the SYNERGY Labs . His research focuses on Systems and Networking with emphasis on Embedded Systems , Security , and Energy Efficiency in computing environments. He has been recognized with the NSF Expeditions in Computing Award for Computational Decarbonization research and multiple best paper awards. Education : PhD in Computer Science from University of California, San Diego Research Leadership : Founder/Director of SYNERGY Labs; Executive Director of NSF Expeditions in Variability (2010-2013) Research Trajectory : Recent publications highlight his work on Privacy-preserving smart classroom systems (EduSense, ClassID) IoT security labeling frameworks Audio privacy protection mechanisms Computational decarbonization of infrastructure His research combines hardware-software co-design with societal impact considerations. Scientific Recognition : NSF Expeditions in Computing Award for CoDec (2024) Ubicomp/IMWUT Distinguished Paper Award (2024) CHI Best Paper Honorable Mention (2022) Advising & Collaboration : Mentors students in IoT systems , privacy research , and green computing . Collaborates with institutions including University of Massachusetts Amherst and industry partners like Johnson Controls. Grants include NSF funding for multi-university research initiatives.
Prof. Dr. Stefanie Hellweg serves as a Full Professor for Ecological Systems Design at the ETH Zürich , Department of Civil, Environmental and Geomatic Engineering. She is Deputy Head of the Institute of Environmental Engineering and leads groundbreaking research at the intersection of industrial ecology, life cycle assessment, and circular economy systems. PhD in Environmental Engineering (ETH Zürich, 2000) Industrial Engineering Degree (Karlsruhe University, 1996) Her research develops advanced methodologies for environmental impact assessment of products and technologies, focusing on: Life Cycle Assessment (LCA) with correlated uncertainty modeling Circular economy implementation in chemical and energy sectors Industrial symbiosis optimization for heat and nutrient flows Urban wastewater systems redesign for resource recovery Chemical pollution analysis in plastic materials Key publication trends reveal expertise in: Hybrid wastewater treatment systems Energy-efficient greenhouse cultivation Exergy analysis for sustainability metrics Machine learning applications in environmental modeling Policy-relevant industrial ecology studies She actively participates in global sustainability initiatives as: President of the International Society of Industrial Ecology (ISIE) Member of UN Environment's International Resource Panel Editorial board member for leading environmental journals
Professor Yasamin Mostofi is a faculty member in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. She is also affiliated with the Department of Computer Science and the Center for Control, Dynamical Systems and Computation. Her work bridges wireless systems, robotics, and machine learning. PhD, Stanford University MS, Stanford University MS, Sharif University of Technology Research Interests : Her lab focuses on wireless systems and autonomous agents , developing novel mathematical models for RF sensing and communication-aware robotics. Current directions include WiFi/millimeter wave/6G-based imaging and analytics, networked robotics for cellular systems, human-robot collaboration, and machine learning integration. Recent Publications : Her work spans through-wall crowd analytics , robot-assisted connectivity , and vision-aided wireless sensing , with applications in smart health, security, and retail optimization. Key trends include cross-modal integration (WiFi and vision), synthetic data generation, and real-world clinical validation. Scientific Recognition : Presidential Early Career Award for Scientists and Engineers (PECASE) Antonio Ruberti Prize, IEEE Control Systems Society NSF CAREER Award IEEE Region 6 Outstanding Engineer Award IEEE Fellow (2020) Advising and Leadership : She mentors PhD students in systems research, with graduates joining leading companies like Qualcomm and Google. She co-founded NPJ Wireless Technology (Nature Portfolio) and serves on editorial boards including IEEE Transactions on Control of Network Systems .
