Ana Sanchez Rodriguez is a researcher affiliated with the Natural Resources and Environmental Engineering department at ETS Mining Engineering, University of Vigo. Her work focuses on advanced structural inspection methodologies for terrestrial infrastructure networks. Doctorate (2020) from University of Vigo Thesis: Automated structural inspection of facilities using laser scanning data Her research intersects disciplines such as civil engineering, geomatics, and infrastructure monitoring. Utilizing technologies like laser scanning systems, she contributes to structural health assessment through innovative geometric and radiometric data analysis.
Serap ÇAKAR KAMAN is an Assistant Professor at Sakarya University's Faculty of Computer and Information Sciences, Department of Computer Engineering. She has maintained a continuous academic career at the university since 2000, progressing from Research Assistant to Lecturer and finally to Assistant Professor. Her educational background includes: Doctorate in Electrical Engineering (2003-2009) from Sakarya University Institute of Science with thesis on "DIGITAL IMAGE WATERMARKING METHODS BASED ON MOMENT-BASED NORMALIZATION" Master's in Computer and Information Engineering (2000-2003) from Sakarya University Institute of Science with thesis on "DETECTING, TRACKING AND TARGET IDENTIFICATION OF OBJECT MOVEMENTS ON VIDEO IMAGES" Bachelor's in Electrical-Electronics Engineering (1996-2000) from Sakarya University Faculty of Engineering Dr. ÇAKAR KAMAN's research spans multiple domains within computer vision and artificial intelligence, with particular expertise in object detection, deep learning applications, and image processing. Her work bridges theoretical computer vision with practical applications in industrial automation, medical imaging, and electric vehicle technology. She has made significant contributions to metal surface defect detection, autonomous charging systems, and facial expression recognition for healthcare applications. Her research demonstrates a consistent pattern of applying advanced computer vision techniques to solve real-world problems across diverse domains. Analysis of her publication trends reveals a clear evolution from foundational work in digital image watermarking to cutting-edge applications of deep learning in industrial settings. Her recent publications (2023-2025) focus on practical implementations in agricultural technology (hazelnut kernel analysis), business intelligence (employee feedback analysis), and marine infrastructure (shore-to-ship charging). The consistent thread through her research is the development of robust computer vision algorithms that enhance automation and precision in various technical contexts. With an H-index of 4 in Web of Science and 114 citations, Dr. ÇAKAR KAMAN maintains an active research profile. Her academic contributions include teaching in the Department of Computer Engineering and likely supervision of graduate students, though specific student information isn't documented in the available materials. Her work contributes significantly to Sakarya University's research profile in computer engineering and supports Turkey's growing expertise in artificial intelligence applications. Her laboratory work appears to focus on computer vision systems with emphasis on real-time processing applications. The research groups she participates in likely include teams working on industrial automation, electric vehicle infrastructure, and medical image analysis, though specific team structures aren't detailed in the available information.
Prof. Dario Floreano serves as Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), directing the Laboratory of Intelligent Systems within the School of Engineering's Institute of Microengineering. He maintains additional teaching appointments in Microengineering, Mechanical Engineering, and the Doctoral School, while serving on EPFL's Committee of Academic Evaluation. His academic credentials include: M.A. in Vision M.S. in Neural Computation PhD in Robotics Research at the convergence of biology and engineering defines Prof. Floreano's work, with pioneering contributions across multiple robotics domains. His laboratory specializes in bio-inspired approaches that transform theoretical concepts into functional systems: Aerial Robotics : Avian-inspired morphing wings/tails for agile drone flight Evolutionary Robotics : Algorithmic co-design of morphology and control Soft Robotics : Transient edible systems and variable-stiffness mechanisms Swarm Intelligence : Emergent control through Hebbian learning Medical Robotics : Fiber-jamming catheters for cardiac procedures Recent publications (2024-2025) reveal three dominant trajectories: (1) biomimetic aerial systems with avian-inspired morphing capabilities for energy-efficient flight, (2) transient edible robotics using biodegradable materials for environmental/medical applications, and (3) advanced variable-stiffness mechanisms enabling new medical interventions. These themes reflect his consistent focus on bio-inspired solutions to engineering challenges. His scientific impact is recognized through prestigious honors: 2000: SNSF Assistant Professorship (Swiss National Science Foundation) 2021: Fellow of the ELLIS Society 2022: IEEE Fellow (Robotics and Automation Society) 2024: Julian Francis Miller Award (The Species International Society) As mentor to 44+ PhD students and founding director of Switzerland's National Center of Competence in Robotics (2010-2022), Prof. Floreano has shaped robotics education through EPFL's Master's program and Swiss Robotics Day. His research leadership generated 15+ spinoffs (including senseFly and Flyability) and secured major grants from Swiss National Science Foundation and European Commission programs. The Laboratory of Intelligent Systems maintains strong industry partnerships while advancing fundamental research in embodied intelligence, with current projects focusing on edible robotics, swarm autonomy, and bio-hybrid systems for medical applications.
Dr. Timothé Krauth is a researcher at Zurich University of Applied Sciences (ZHAW) School of Engineering within the Aviation Infrastructure department. Holding a Ph.D. in Applied Mathematics from ISAE-Supaero (2021-2024), his work focuses on applying machine learning techniques to air traffic management challenges. He actively contributes to research projects like "Achieving Human-Machine Collaboration with Artificial Situational Awareness" and "Making I-CNS A Reality" for integrated aviation systems. Ph.D. in Applied Mathematics, ISAE-Supaero (2021-2024) His research interests center on aerospace engineering and artificial intelligence, specializing in trajectory modeling, collision risk estimation, and uncertainty quantification in air traffic systems. He has developed deep learning frameworks for flight path analysis and safety prediction. Krauth's publications demonstrate expertise in: Collision risk modeling using variational autoencoders Deep generative modeling for aviation safety Large-scale trajectory dataset creation Adaptive importance sampling techniques Mid-air collision probability assessment Terminal area traffic management As a team member in ongoing projects, he applies these methods to create safer and more efficient aviation systems through integrated communication, navigation, and surveillance technologies.
Dr. Manideep Tummalapudi is an Assistant Professor in the Department of Construction Management at California State University, Fresno, affiliated with the Lyles College of Engineering. He holds a Ph.D. in Construction Management and Education Sciences from Colorado State University, an M.S. in Civil Engineering from Bradley University, and a B.Tech. in Civil Engineering from GITAM University, India. With prior industry experience as a project manager across international projects, he provides consulting services in sustainability and workforce development. His research focuses on: Construction finance and economics Transportation infrastructure and workforce development Integration of emerging technologies (AI, drones, data analytics) Innovative construction education methodologies His publications emphasize practical solutions for industry challenges including workforce retention, project closeout delays, and technology adoption. Awards & Honors: Best Research Poster Award, TRB Conference (2022) Outstanding Graduate Student Award, Colorado State University (2022) Outstanding Young Professional, ASCE (2021) People's Choice Award, CSU Research Demo Day (2021) UN Youth Assembly Delegate (2019) Grants & Projects: Secured funding as Principal Investigator for strategic initiatives including: Bridge inspection technologies STEM career outreach for construction professions Open-resource educational models Construction cash flow benchmarking Collaborates with agencies like Fresno State Transportation Institute and AGC Education Foundation.
Hovannes Kulhandjian is an Associate Professor in the Department of Electrical and Computer Engineering at California State University, Fresno (Fresno State), within the Lyles College of Engineering. He teaches undergraduate and graduate courses in electrical and computer engineering and conducts research in wireless communications, applied machine learning, and their applications in transportation and agriculture. His educational background includes: Ph.D. in Electrical Engineering from the State University of New York at Buffalo (2014) M.S. in Electrical Engineering from the State University of New York at Buffalo (2010) B.S. in Electronics Engineering with high honors (magna cum laude) from the American University in Cairo (2008) Dr. Kulhandjian's research spans wireless communications, applied machine learning, and their applications. His work in intelligent transportation systems includes AI-based road inspection and pedestrian detection, while in precision agriculture, he develops drone-based systems for weed detection and tree health monitoring. He also explores underwater acoustic communications, visible light communications, and physical layer security. His recent publications demonstrate a strong trend in applying artificial intelligence to solve real-world problems in transportation and agriculture, often using drones and multi-sensor fusion. In communications, he advances techniques for next-generation wireless systems, including OTFS and NOMA for 6G, and optical wireless for IoT. Scientific awards and honors: IEEE Senior Member Outstanding Reviewer Award from ELSEVIER Ad Hoc Networks Outstanding Reviewer Award from ELSEVIER Computer Networks Claude C. Laval Award for Innovative Technology and Research Dr. Kulhandjian advises Master's students through thesis (ECE 299) and project (ECE 298) courses. His research is supported by multiple grants, including the Department of Defense Research and Education Program, NSF-ADVANCE Research Alliance Seed Grant, CSU-WATER Faculty Research Incentive, and the Fresno State Transportation Institute SB1 Research Grant for six consecutive years. During his doctoral studies, he worked in the Wireless Networks and Embedded Systems (WiNES) Laboratory at SUNY Buffalo. At Fresno State, he leads a research group focused on the development of innovative solutions in wireless communications and AI applications, collaborating with various institutions and industry partners.
Riadh Munjy serves as a Professor in Geomatics Engineering at California State University, Fresno's Lyles College of Engineering, where he teaches core courses including CE 205 (Computing in Engineering Analysis), GME 123 (Stereo-Photogrammetry), and GME 125 (Analytical Photogrammetry). His academic foundation includes a Ph.D. in Civil Engineering (1982), M.S. in Applied Mathematics (1981), and M.S.C.E. (1979) from the University of Washington, complemented by a B.S. in Civil Engineering from the University of Baghdad (1976). Ph.D., Civil Engineering, University of Washington (1979-1982) M.S., Applied Mathematics, University of Washington (1980-1981) M.S.C.E., Civil Engineering, University of Washington (1978-1979) B.S., Civil Engineering, University of Baghdad (1972-1976) Professor Munjy's research centers on advancing photogrammetric methodologies with particular emphasis on Unmanned Aircraft Systems (UAS) mapping accuracy, sensor calibration, and novel processing techniques for LIDAR and IFSAR data. His work bridges theoretical mathematics with practical applications in transportation infrastructure, disaster response, and terrain modeling, consistently addressing industry pain points like rolling shutter effects in UAV imagery and LIDAR strip adjustment challenges. He has pioneered standards for GPS-controlled photogrammetry adopted by Caltrans and contributed foundational chapters to the ASPRS Manual of Photogrammetry. Analysis of his 15 most recent publications (2010-2020) reveals a strategic evolution from traditional photogrammetric techniques toward UAS-centric workflows, with increasing focus on automated processing, accuracy validation, and integration of multi-sensor data. His research consistently targets real-world implementation barriers in transportation mapping and corridor projects, demonstrating strong industry relevance through Caltrans collaborations and ASPRS leadership. American Society of Photogrammetry and Remote Sensing Fellow Award (2020) ASPRS Fairchild Award (2014) Caltrans Research Innovation Award (2004) Five School of Engineering Research Excellence Awards (1992, 1996, 1998, 2002, 2003) ASPRS Meritorious Service Awards (1992, 1997) Halliburton Research Award (1992) Professor Munjy's research program has been consistently supported through Caltrans-funded projects including GPS Photogrammetry (2005), UAS Research (2018), and corridor mapping standards development. His advisory impact extends through co-authoring the ASPRS Manual of Photogrammetry and mentoring numerous conference presentations, though formal student lists aren't documented. Current initiatives focus on sUAS data accuracy validation and rolling shutter effect mitigation, positioning his work at the forefront of evolving UAV mapping regulations.
Jose Capa Salinas serves as an Assistant Professor in the Civil Engineering Department at the University of St. Thomas' School of Engineering. His academic journey includes a BS from Universidad Tecnica Particular de Loja (Ecuador), followed by two years in industry before returning for MS and PhD degrees at Purdue University. Research Focus: Dr. Capa Salinas leads innovative work at the intersection of traditional civil engineering and emerging technologies. His primary interests include: Infrastructure inspection through drone applications Structural health monitoring of bridges Steel and concrete behavior under natural hazards Machine learning integration for predictive infrastructure analysis His recent publications demonstrate strong focus on unmanned aerial systems (UAS) for infrastructure assessment, with significant contributions to pavement evaluation and bridge inspection methodologies. The research portfolio shows increasing emphasis on integrating AI with structural engineering while maintaining practical applications for transportation agencies. Professional Recognition: 2025 Fred Burggraf Award from Transportation Research Board ASCE Civil Engineering Magazine feature Multiple fellowship recognitions including Purdue Trailblazer in Engineering Dr. Capa Salinas actively serves on technical committees for NCHRP, TRB, and ASCE/SEI 7-28 standards. His teaching portfolio includes advanced steel design, civil engineering materials, and water resources courses. The research group partners with state DOTs, FHWA, and private industry to translate findings into practice, with undergraduate students directly involved in lab work and field applications.
Dr. Praveen Tammana is an Assistant Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Hyderabad , where he leads the NetX lab focusing on systems, networks, and security research. He completed his PhD from University of Edinburgh (2013-2018) and holds a Masters from IIT Madras (2009-2011). Education: Ph.D in Computer Science, University of Edinburgh, UK (2013-2018) Masters in Computer Science, IIT-Madras, India (2009-2011) B.E. in Computer Science, Vasavi College of Engineering, Osmania University (2005-2009) Research Focus: His research spans systems and networking , network security , software-defined networking (SDN) , machine learning for networks , wireless communication , high performance computing , and cybersecurity . His lab specializes in programmable data planes using technologies like P4, eBPF, DPUs, and PISA switches. Research Applications: His recent work demonstrates strong focus on AI infrastructure systems , including distributed AI learning and serving, networked robotics, GPU-centric packet processing, and edge cloud applications for autonomous systems. His research bridges the gap between networking infrastructure and AI workloads. Recent Publications Trend: His 2021-2025 publications show significant contributions to network security , programmable switches , edge computing , and AI systems integration . Key themes include securing traffic control systems, AI workload optimization, malicious traffic detection, and real-time applications for autonomous vehicles. Current Funding: Actively funded by industry partners including Mondee (systems for AI workloads), Marvell Technology (data acceleration offload), and ZF India (efficient load-balancer for edge applications). Lab and Team: Leads NetX lab at IIT Hyderabad, actively recruiting full-time research candidates for exciting projects in AI infrastructure, networked systems, and programmable networks. The lab focuses on building next-generation systems for AI workloads and secure network architectures.
Daniele De Martini is a Departmental Lecturer in Mobile Robotics at the Oxford Robotics Institute (ORI) and a College Lecturer in Engineering Science at Pembroke College, University of Oxford. He co-leads the Mobile Robotics Group (MRG) with Professor Paul Newman. His academic journey began in Pavia, Italy, where he earned his BSc in Mechanical Engineering, followed by an MSc in Mechatronic Engineering from Politecnico di Torino, and completed his PhD in Robotics at Università degli Studi di Pavia under the supervision of Prof. Tullio Facchinetti. De Martini's research focuses on robust navigation and scene understanding for mobile robots, with particular emphasis on enabling robot operation in challenging weather conditions and diverse scenarios. His work spans from odometry and localization to detection and segmentation, utilizing various sensing technologies including vision, laser, and radar systems. A significant portion of his research explores FMCW scanning radar technology as a more robust alternative to traditional visual perception systems, especially in adverse weather conditions. He also investigates techniques to enhance the training of perception modules using machine learning approaches, believing that learning and adaptation are paramount for achieving true robot autonomy. His publication record demonstrates a clear progression from early work on energy management and scheduling algorithms to his current focus on mobile robotics and perception. The analysis of his recent publications reveals strong emphasis on radar-based perception, robust localization methods, scene generation for autonomous driving simulation, and long-term autonomy systems. His work bridges the gap between theoretical robotics and practical field deployment, as evidenced by projects like The Hulk vehicle platform designed for weather-proof outdoor operations. As an educator, De Martini teaches Structures and Mechanics at Pembroke College and contributes to the academic mission of the Department of Engineering Science. His research group actively develops solutions for real-world robotic challenges, with applications ranging from urban mobility safety assessment to long-term autonomous inspection systems.
Glenn Washer is a Professor in the Department of Civil and Environmental Engineering at the University of Missouri-Columbia, where he serves as a Dean’s Fellow in the College of Engineering. Previously, he was the Director of the FHWA Nondestructive Evaluation (NDE) program at the Turner Fairbank Highway Research Center (TFHRC) in McLean, Virginia. PhD in Materials Science and Engineering, Johns Hopkins University MS in Materials Science, University of Maryland BS in Materials Engineering, Worcester Polytechnic Institute His research focuses on advanced condition assessment technologies for civil infrastructure, particularly bridges. Key areas include nondestructive evaluation (NDE) methods such as ultrasonic stress measurement, thermal imaging, and acoustic emission monitoring. He investigates the reliability of inspection technologies and develops risk-based inspection frameworks to optimize maintenance strategies. Glenn Washer has authored over 120 publications on NDE applications in bridge engineering, with recent work emphasizing machine learning integration, hybrid testing methodologies, and wireless sensor networks. His studies span ultrasonic testing of welds, microwave-based concrete diagnostics, and seismic resilience assessments. Fellow of the American Society for Nondestructive Testing (ASNT) He contributes to industry standards through collaborations with the Missouri Department of Transportation (MoDOT) and the Missouri Center for Transportation Innovation. His laboratory focuses on field-deployable NDT systems and infrastructure preservation technologies.
Dongil Han is a Professor in the Department of Computer Engineering at Sejong University, where he has been since 2003. He has also held administrative roles, including Dean of the Office of Library and Informations (2021–present) and Dean of the College of Software Convergence (2020–2021). Education : B.S. (1988) from Korea University M.S. (1990) and Ph.D. (1995) from KAIST Han’s research focuses on Computer Vision , Deep Learning , and Visual Recognition , with notable work on neural network architectures, hardware design for face detection, and AI applications in plant pathology. His recent publications highlight advancements in out-of-distribution detection, vision transformers, and drone-based infrastructure inspection. His research output (92+ publications) spans Deep Learning Methods , Face Detection , Hardware Architecture , and Field Programmable Gate Arrays . He has contributed to color correction techniques for visual impairments and CNN models inspired by the human visual cortex.
Barış Salman is a faculty member in the Department of Civil Engineering at Middle East Technical University Northern Cyprus Campus . His research focuses on Infrastructure Asset Management , Deterioration Modeling , and Risk Assessment . Specializes in culvert and wastewater infrastructure Develops predictive models for infrastructure decay Research Interests : Barış Salman's work addresses the challenges of managing aging drainage and wastewater systems. He employs statistical methods like logistic regression to model deterioration and assess failure risks, aiming to optimize maintenance strategies under budget constraints. Publications (2010-2012) : His research includes studies on metal culvert deterioration models, wastewater line failure prediction using regression analysis, and risk assessment frameworks integrating probability and criticality. He also explores trenchless technologies for culvert renewal. Scientific Awards : No awards mentioned in the provided texts.
Sapountzakis Evangelos serves as a Professor in the Department of Structural Engineering within the School of Civil Engineering at the National Technical University of Athens (NTUA). His academic work is centered at the Statics and Aseismic Research Laboratory, where he conducts research on advanced structural dynamics and earthquake engineering solutions. His institutional affiliation places him within Greece's premier technical university, known for its strong engineering programs and research contributions to civil infrastructure. Professor Sapountzakis' research interests focus on innovative approaches to structural protection against seismic events and vibrations. His work particularly emphasizes the development and application of negative stiffness mechanisms, vibration absorbers, and the KDamper technology for various structural systems. His research spans multiple structural types including multi-story buildings, bridges, and wind turbine towers, addressing both theoretical modeling and practical implementation challenges in earthquake engineering. Analysis of Professor Sapountzakis' recent publications reveals a consistent research trajectory focused on seismic protection systems, with particular emphasis on the Extended KDamper technology. His work combines numerical modeling, optimization techniques, and experimental validation to develop practical solutions for structural vibration control. The publications demonstrate increasing sophistication in handling complex structural dynamics problems, including soil-structure interaction, nonlinear responses, and multi-objective optimization approaches for enhanced structural performance during seismic events. Professor Sapountzakis maintains active research collaborations focused on structural dynamics and earthquake engineering. His work appears to involve both theoretical development and practical implementation of vibration control systems, with evidence of experimental testing alongside numerical simulations. While specific grant information isn't detailed in the available text, his extensive publication record suggests sustained research funding and active participation in the structural engineering research community. Professor Sapountzakis is affiliated with the Statics and Aseismic Research Laboratory at NTUA, which serves as the primary facility for his experimental work on vibration control systems and seismic protection devices. This laboratory appears to support both theoretical research through computational modeling and practical validation through physical testing of structural components and systems, particularly focusing on the development and refinement of the KDamper technology for various structural applications.
Samanta Robuschi is a Postdoctoral Researcher at Chalmers University of Technology, Sweden, affiliated with the Department of Architecture and Civil Engineering, Structural Engineering. Her work focuses on corrosion mechanisms in reinforced concrete structures, particularly chloride-induced degradation in marine environments. Email: samanta.robuschi@chalmers.se ORCID: 0000-0002-8613-4127 Research Interests: She investigates steel reinforcement corrosion in concrete, bond-slip relationships in corroded structures, and 3D imaging techniques (neutron/X-ray tomography) to analyze degradation mechanisms. Her studies address cracks' impact on corrosion propagation and structural longevity in marine exposures. Key Publications (2017-2023): Her 15 recent articles in high-impact journals like Cement and Concrete Research and Construction and Building Materials analyze topics such as: Chloride-induced corrosion in cracked concrete 3D tomography of corrosion morphology Bond strength of aged reinforcement bars Anchorage capacity in corroded structures Projects: She leads and contributes to projects funded by the Swedish Transport Administration, including Cracks and Reinforcement Corrosion (2023–2026) and Cracor (2019–2022), focusing on assessing load-carrying capacity and corrosion dynamics in infrastructure.
