Find Academic Supervisors

• Additive Manufacturing in Construction
• Building Information Modeling (BIM)
• Digital Twinning
• +3 more

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

Debarshi SenView profile

Assistant Professor

• Structural Dynamic Systems
• Infrastructure Monitoring and Resilience
• Statistical and Machine Learning in Monitoring
• +2 more

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).

• Robotics
• Autonomous Systems
• Spatial Cognition
• +6 more

Patric Jensfelt is a full-time Professor at the Robotics, Perception and Learning (RPL) division within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology . He contributes to robotics and autonomous systems through research, teaching, and industry collaboration. Academic Rank: Professor Department: Robotics, Perception and Learning (RPL) School: School of Electrical Engineering and Computer Science (EECS) University: KTH Royal Institute of Technology His research focuses on robotics and autonomous systems , particularly spatial cognition for enabling robots to understand and model their environments. He integrates sensor data with high-level symbolic reasoning to advance navigation, mapping, and system integration. Recent work includes applications in service robotics, 3D foot scanning (via the company Volumental ), and drone-based perception. The 15 most recent articles highlight advancements in scene flow estimation, autonomous driving, LiDAR processing, and domain adaptation. They emphasize self-supervised learning , dynamic environment modeling , and robust spatial representations for autonomous systems. Patric supervises Master’s and PhD students, particularly in project courses like DD2410 Introduction to Robotics , DD2419 Project Course in Robotics and Autonomous Systems , and the doctoral course FDD3356 System Integration for Robotics . He is actively involved in the WASP Graduate School , teaching Autonomous Systems annually. He leads the Robotics, Perception and Learning (RPL) division, which fosters innovation in robotics through experimental projects and industry partnerships like Intelligent Machines . The division supports internships (unpaid) and encourages applicants to demonstrate prior engagement with his research.

• Traffic Flow Analysis and Modeling
• Traffic Control
• Effects of Traffic Flow on Environment
• +1 more

Dr. Sue Ahn serves as a Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison, where she leads research in traffic flow theory, Intelligent Transportation Systems (ITS) applications, and traffic operational impacts on environment and safety. Her work bridges theoretical traffic dynamics with practical transportation solutions through quantitative analysis and ITS implementation. Her academic foundation includes: PhD in Civil and Environmental Engineering, University of California, Berkeley (2005) MS in Civil and Environmental Engineering, University of California, Berkeley (2001) BS in Civil Engineering, Ohio State University (2000) Dr. Ahn's research program focuses on three interconnected pillars: fundamental traffic flow understanding through observation and statistical methods; environmental and safety impact analysis of traffic phenomena; and development of traffic control strategies using ITS. Her expertise spans traffic flow modeling , adaptive traffic control systems , emissions quantification , and connected/automated vehicle integration , with recent work emphasizing machine learning applications in mixed-traffic environments. She translates theoretical insights into practical transportation management tools while maintaining rigorous scientific methodology. Analysis of her 2023-2024 publications reveals dominant themes in connected/automated vehicle control (70% of output), traffic flow theory advancements (20%), and environmental impact assessment (10%). Key methodological trends include deep learning integration (LSTM networks, graph neural networks), stochastic behavioral modeling, and multi-dimensional trajectory optimization – reflecting the field's shift toward data-driven solutions for heterogeneous traffic systems. Dr. Ahn's scientific recognition includes: 2023 Transportation Research Board Greenshields Prize (Best Paper Award) 2020 University of Wisconsin-Madison Vilas Associate award 2019 Transportation Research Board Best Paper in Traffic Flow Theory 2016 Transportation Research Board Cunard Award 2012 NSF CAREER Award 2008 Annual ITS Arizona Conference Best ITS Planning Project She actively mentors graduate researchers through CIV ENGR 890 (Pre-Dissertator's Research) and CIV ENGR 990 (Thesis) courses, with her NSF CAREER grant providing foundational support for traffic flow theory investigations. Current advising focuses on automated vehicle behavior modeling and environmental impact quantification, while future work appears directed toward heterogeneous traffic management frameworks and real-world ITS deployment validation.

Ulrik Dam NielsenView profile

Associate Professor

• Naval Architecture
• Ship Motion Dynamics
• Sea State Estimation
• +7 more

Ulrik Dam Nielsen is an Associate Professor in the Section for Fluid Mechanics, Coastal and Maritime Engineering at the Department of Civil and Mechanical Engineering, Technical University of Denmark (DTU). He also held an external position as Associate Professor II at the Norwegian University of Science and Technology (NTNU) from 2014 to 2023, reflecting strong international collaboration. His work contributes to UN Sustainable Development Goals related to sustainable maritime operations and clean energy. His research focuses on naval architecture and ship motion dynamics , particularly in the context of sea state estimation , added resistance in waves , and real-time prediction of vessel responses . He integrates data analytics , estimation theory , and machine learning to develop methods for monitoring hydrodynamic performance and enhancing maritime safety and energy efficiency. A central theme of his work is using ships as mobile wave sensors—transforming operational vessels into 'sailing wave buoys' for environmental monitoring. His recent publications show a clear shift toward data-driven methodologies, especially machine learning applications in sea state estimation, added resistance modeling, and performance monitoring. These works span journals like Ship Technology Research and Journal of Offshore Mechanics and Arctic Engineering , and conferences such as IEEE MetroSea, highlighting interdisciplinary innovation at the intersection of classical marine engineering and modern AI. Best Paper Presented by a Young Researcher Award (First Classified), 2024 (jointly awarded) He actively supervises PhD students—such as R. E. G. Mounet, M. Mittendorf, J. P. Tomy, and A. Oikonomakis—on projects funded by DTU and collaborative initiatives. His leadership in projects like WEFOSWAB (Wave Estimation and Forecasting Using Ships as Buoys) and data-driven added resistance modeling underscores his role in advancing smart maritime technologies. He also contributes to open science through the public release of datasets such as NetSSE . He teaches core courses including Introduction to Ships and Floating Structures , Marine and Ocean Engineering , and Ship Operations , shaping the next generation of maritime engineers.

• Wireless Networks
• Health Informatics
• Sensor Networks
• +3 more

Ibrahim Orhan serves as a Lecturer at KTH Royal Institute of Technology within the Division of Health Informatics and Logistics. His primary responsibilities include teaching core courses such as Communication Networks (HE1033), Mobile Communications and Wireless Networks (HI1035), and Routing in IP Networks (HI2002), while also supervising first-cycle degree projects in Computer and Electrical Engineering. His research centers on wireless sensor networks with specialized applications in healthcare and education. Key focus areas include time synchronization protocols for multi-sensor systems, performance monitoring in contention-based networks, and data fusion techniques for health monitoring applications like fall detection. Recent work demonstrates a strategic expansion into educational technology through serious games for engineering education. Analysis of his 15 most recent publications (2006-2023) reveals an evolving research trajectory: initial work concentrated on fundamental wireless network performance (2008-2011), shifting toward healthcare applications (2012-2016), and culminating in educational technology innovations (2023). Persistent themes include Bluetooth synchronization, mobile sensor integration, and quality-of-service management in resource-constrained environments. Dr. Orhan actively supervises undergraduate degree projects in computer and electrical engineering, though specific student names aren't publicly listed. His research has been supported through KTH institutional channels, with publications spanning IEEE conferences, specialized journals like the International Journal of Serious Games, and collaborative projects focused on ambient assisted living solutions.

Zhenyu ChenView profile

Professor

• AI for Testing
• Testing for AI
• Software Testing
• +3 more

Zhenyu Chen is a Full Professor and Director of the iSE Laboratory at Nanjing University, specializing in AI-driven software testing methodologies. His research bridges artificial intelligence and software engineering with dual focus areas: leveraging AI to enhance testing processes ( AI for Testing ) and validating AI/ML systems ( Testing for AI ). His research interests center on deep learning framework testing , crowdsourced testing optimization , and Large Language Model applications in verification . Recent work demonstrates innovative approaches to metamorphic testing of neural networks, LLM-based test report analysis, and security hardening of code models against backdoors. Key contributions include the development of mooctest.com and frameworks like DevMuT for mutation testing of deep learning APIs. His publication trajectory reveals evolving focus from crowdsourced testing (2018-2020) to deep learning system validation (2021-2023) and current emphasis on LLM-powered testing solutions. Major venues include ASE, ICSE, and ISSTA where he serves regularly on program committees.

Gerardo Aragon CamarasaView profile

Senior Lecturer

• Robotics
• Artificial Intelligence
• Computer Vision
• +5 more

Gerardo Aragon Camarasa is a Senior Lecturer at the School of Computing Science, University of Glasgow, where he leads research in the Computer Vision and Autonomous Systems group. His work focuses on solving real-world challenges in robotic perception, manipulation, and grasping using advanced AI techniques. Research Interests: Robotics and AI for advanced manufacturing systems Perception and manipulation of deformable objects Autonomous robotic systems for chemical and domestic applications Robot behavior modeling and self-awareness His publications demonstrate strong emphasis on robotic vision (garment perception, hand-eye calibration), AI integration (multimodal LLMs, reinforcement learning), and industrial applications (chemical robotics, Industry 5.0 ethics). Recent work shows growing focus on foundation models for robotics and simulation frameworks. Research Leadership: He leads multiple grants including an EPSRC programme grant for chemical robotics and a Royal Society project on robotic teleoperation. Actively supervises 7+ PhD students and has graduated 8+ doctoral researchers in robotics and computer vision. Infrastructure: Leads research using dual-arm robots and maintains active GitHub repositories and YouTube channels demonstrating robotic manipulation systems. Regular contributor to top robotics conferences (ICRA, IROS) and journals.

Elena Simona LohanView profile

Professor

• Wireless Positioning
• GNSS Algorithms
• Wearable Computing
• +7 more

Elena Simona Lohan is a Professor at Tampere University's Faculty of Information Technology and Communication Sciences , leading the Signal Processing for Wireless Positioning research group. She also holds a Visiting Professor position at Universitat Autonoma de Barcelona's SPCOMNAV group. Education : MSc in Electrical Engineering (Politehnica University of Bucharest, 1997), DEA in Econometrics (École Polytechnique, Paris, 1998), PhD in Telecommunications (Tampere University of Technology, 2003) Research Focus : GNSS algorithms, interference detection, wearable computing, and 5G positioning convergence. Her work spans signal processing for GNSS (Galileo, GPS, GLONASS, BeiDou), indoor/outdoor localization, and IoT communication. She coordinates the H2020 MSCA European Joint Doctorate A-WEAR and serves as Associate Editor for the Journal of Navigation and IET Radar, Sonar & Navigation . Notable projects include GRAMMAR (Galileo receivers), UJI IndoorLoc Platform , and SP4TE collaborations. She has authored over 250 publications and 6 patents.

Naghmeh KarimiView profile

Associate Professor

• Hardware Security
• Design-for-Trust
• Fault Tolerance
• +9 more

Naghmeh Karimi is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland Baltimore County (UMBC), where she has held this position since 2023, after serving as an Assistant Professor from 2017 to 2023. She is a recipient of the NSF CAREER Award (2020) and the Best Paper Award (2019). Her research focuses on hardware security, trustworthiness, and reliability of integrated circuits, with a particular emphasis on cryptographic devices, PUF-based authentication, and aging-related vulnerabilities. She directs the SECure, REliable and Trusted Systems (SECRETS) Lab at UMBC, which explores topics including hardware security countermeasures, fault tolerance, and AI-driven security solutions. Prior to UMBC, she was affiliated with Rutgers University, New York University, Duke University, and Yale University. Her research interests span hardware security, design-for-trust, fault tolerance, AI for security, and VLSI design. Recent work emphasizes aging effects on cryptographic circuits, PUF resilience, and digital sensor-based failure detection. Her publications address challenges in side-channel attacks, fault injection, and secure IoT frameworks. Dr. Karimi’s work is supported by sponsors, and she actively mentors Ph.D. students in hardware security and reliability. Her lab offers openings for self-motivated researchers in these areas.

• Laser Scanning
• Point Cloud Processing
• Geospatial Information Systems
• +3 more

Tamás Lovas serves as Associate Professor at the Department of Photogrammetry and Geoinformatics, Faculty of Civil Engineering, Budapest University of Technology and Economics. He teaches advanced courses in Building Information Modeling, Laser Scanning, Remote Sensing, and Intelligent Transportation Systems while supervising diploma theses in Surveying and Geoinformatics Engineering. Education: 1994: High school graduation, Városmajor High School, Budapest 1999: Certified Surveyor and Geoinformatics Engineer, Budapest University of Technology, Faculty of Civil Engineering 2005: PhD (Earth Sciences), Budapest University of Technology and Economics, Faculty of Civil Engineering Research Interests: Dr. Lovas specializes in laser scanning technologies and geospatial data processing with emphasis on airborne and terrestrial point cloud analysis. His work bridges civil engineering applications and computational methods, particularly in infrastructure monitoring and digital representation. Processing, classification, and modeling of airborne laser scanned data Accuracy testing of terrestrial laser scanning Processing and modeling of terrestrial laser scanned data Comparative study of spatial data acquisition technologies His 2022-2025 publications demonstrate accelerating integration of artificial intelligence in point cloud processing, with significant contributions to road surface extraction, urban land cover classification, and BIM automation. Current research trends show strong focus on digital twin development for autonomous vehicles and infrastructure management. Scientific Awards: Republic Scholarship (1998-1999) Karlsruhe Chancellor's Scholarship (1999) ERASMUS scholarship (2000) Korányi Fellowship (2001-2002) János Bolyai Research Scholarship (2008-2011) OHV 1st place (2008) Dean's commendation for ERASMUS committee work (2014) For Students Award - Teaching Department (2017) Advising and Grants: Dr. Lovas mentors students through diploma theses and TDK research projects on topics including object survey with amateur sensors and hull modeling. His research funding includes the prestigious János Bolyai Research Scholarship and international fellowships supporting collaborations with institutions like The Ohio State University. Labs and Teams: As founding member and supervisory board member of the Hungarian BIM Association, he drives industry-academia collaboration. His leadership roles include Deputy Dean of Education at the Faculty of Civil Engineering and responsibility for English language training programs, facilitating international academic exchange.

Tomonari FurukawaView profile

Professor

• Robotics
• Computational Mechanics
• Autonomous Systems
• +3 more

Tomonari Furukawa is a Professor and Zinn Faculty Scholar at the University of Virginia, leading the VICTOR Lab. He holds a B.Eng. in Mechanical Engineering from Waseda University (1990), an M.Eng. in Mechatronic Engineering from the University of Sydney (1993), and a Ph.D. in Quantum Engineering and Systems Science from the University of Tokyo (1996). His research focuses on robotics, computational mechanics, autonomous systems, and advanced sensor technologies. He has published over 300 papers, contributed to editorial boards, and secured grants such as the U.S. DOD DURIP grants for advanced research infrastructure. His work spans topics like autonomous robotic mapping, sensor fusion, and real-time deformation measurement for automotive safety. He has developed systems for tire tread profiling, crash deformation analysis, and 3D road surface reconstruction. Furukawa’s methodologies often integrate Bayesian approaches, neural networks, and multi-sensor data fusion to solve complex engineering challenges. His contributions to the NSF I/UCRC Centre for Tire Research highlight his impact on applied mechanics. Recipient of multiple career and paper awards, Furukawa emphasizes translational research. His VICTOR Lab explores cutting-edge robotics, including compliant bipedal designs for disaster response (e.g., DARPA Robotics Challenge) and autonomous navigation systems. Current projects leverage AI and advanced vision systems for infrastructure monitoring and human-robot collaboration.

• Automation and Control Systems
• Cyber-Physical Systems
• Networked Control Systems
• +3 more

Professor Peng Shi is a faculty member in the School of Electrical and Mechanical Engineering at the University of Adelaide, specifically within the Department of Electrical and Electronic Engineering. He holds a Professor rank and is actively involved in research and academic leadership. Academic Rank: Professor Affiliations: University of Adelaide, School of Electrical and Mechanical Engineering Research Interests: Automation and control systems, cyber-physical systems, networked control systems, autonomous robotics, artificial intelligence, and control theory. Professor Shi has an extensive track record in research, with over 30 years of contributions to advanced control theory and interdisciplinary applications. He has authored 25 monographs, 800+ journal articles, and numerous conference papers, generating over 90,000 citations with an h-index of 164. His work emphasizes cyber-physical systems, human-machine collaboration, and resilient control under cyber attacks. His recent publications (2023–2025) focus on advanced control methodologies for robotics, power systems, and cybersecurity, addressing challenges such as fault diagnosis, consensus control, and secure estimation in networked environments. Scientific Awards: Fellowships from IEEE, IET, IMA, IEAust, and IETI Highly Cited Researcher (2014–present) Professor Shi advises students in advanced control systems and collaborates on grants related to smart grids, robotics, and cybersecurity. He leads research groups focused on resilient control systems and has developed methodologies for distributed control and fault-tolerant designs.

• Robotics
• Control Systems
• Nuclear Fusion Engineering
• +5 more

Alberto Vale is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Lisbon's Instituto Superior Técnico. His research focuses on robotics, control systems, nuclear fusion engineering, and autonomous systems. He is affiliated with the Institute of Plasmas and Nuclear Fusion and teaches courses such as Control of Cyber-Physical Systems and Autonomous Systems . His work emphasizes remote maintenance systems for nuclear facilities, radiation detection technologies, and mobile robotics applications. Research interests include: Advanced control strategies for cyber-physical systems Development of autonomous systems for nuclear fusion facilities Radiation detection and localization using drones and mobile robots Path planning and navigation in constrained environments Recent publications highlight innovations in fusion diagnostics (e.g., reflectometry systems for DEMO), LIDAR-based SLAM techniques, and optimization of autonomous fleets for solar farm inspection. He has received the EDA Research, Technology, and Innovation Papers Award 2023 and the Best Presentation Award .

• Robotics
• Control Systems
• Autonomous Systems
• +6 more

Zehui Lu is a Visiting Assistant Professor at the Department of Aeronautics and Astronautics, Purdue University. Their research focuses on robotics, control systems, autonomous systems, and optimization, with applications in multi-agent systems, aerospace engineering, and battery management. Dr. Lu’s work integrates machine learning techniques with advanced control methodologies to address challenges in trajectory planning, distributed algorithms, and real-time mission execution in complex environments. Key research trends in their publications emphasize distributed optimization for multi-robot systems, adaptive control strategies for autonomous navigation, and deep learning-based approaches for dynamic system modeling. Notable contributions include innovations in spacecraft swarm coordination, battery network management under extreme conditions, and human-in-the-loop robotic formation control. Dr. Lu’s interdisciplinary work bridges theoretical control frameworks with practical engineering implementations, particularly in aerospace and mechatronics domains. No scientific awards or grants are explicitly listed in the provided information. Advising details are not available, but their research group actively explores topics such as adaptive policy learning, distributed motor-motion co-design, and real-time cluttered environment navigation. The full scope of their lab activities and collaborations remains unspecified in the current data.