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Catherine Neish is an Associate Professor in the Department of Earth Sciences at The University of Western Ontario. She serves as the Associate Director of Research for Western Space and is a Co-Investigator on NASA's Dragonfly mission to Titan. Her research focuses on planetary radar observations, impact cratering processes, and the geological evolution of planetary surfaces, particularly on the Moon, Titan, and other Solar System bodies. Ph.D. in Planetary Sciences, University of Arizona (2008) B.Sc. in Combined Honours Physics and Astronomy, University of British Columbia (2004) Her recent publications highlight studies on lunar impact crater thermophysics, Titan's impact melt dynamics, and radar-based analyses of planetary surfaces. She has contributed to missions including Lunar Reconnaissance Orbiter (Mini-RF), Cassini RADAR, and Dragonfly. Scientific Awards: College of New Scholars, Royal Society of Canada (2021) Early Researcher Award, Ontario (2017) Minor Planet 16972 Neish (2017) AGU Ron Greeley Award (2014) NASA Postdoctoral Fellowship (2012) NASA Group Achievement Award (2010) NSERC Postgraduate Scholarship (2005-2008) Julie Payette-NSERC Research Scholarship (2004-2005) Dr. Neish supervises a dynamic lab with current and former students investigating planetary geology, impact cratering, and remote sensing. Her work bridges field studies (e.g., Earth analogs), laboratory experiments, and spacecraft data analysis.

• Planetary Science
• Radar Geology
• Impact Cratering
• +3 more

Sebastian Trimpe is a Full Professor and Head of the Institute for Data Science in Mechanical Engineering at RWTH Aachen University, concurrently serving as Co-Executive Director of the RWTH Center for Artificial Intelligence since 2023. Previously, he led a Max Planck Research Group at the Max Planck Institute for Intelligent Systems from 2018 to 2022. His educational background includes: Ph.D. in Dynamic Systems and Control from ETH Zurich (2013) Dipl.-Ing. (M.Sc.) in Electrical Engineering from TU Hamburg (2007) MBA in Technology Management from TU Hamburg (2007) B.Sc. in General Engineering from TU Hamburg (2005) Professor Trimpe's research integrates machine learning with control theory to address safety and efficiency challenges in autonomous systems. His work spans theoretical frameworks for robust decision-making under uncertainty and practical implementations in robotics, with particular emphasis on event-triggered control, distributed systems, and data-efficient learning methodologies. Key contributions include novel approaches to safe reinforcement learning and model predictive control with guaranteed stability. Analysis of his recent publications reveals a pronounced focus on bridging machine learning with control engineering, especially in safety-critical robotics applications. Common themes include distribution-aware learning for medical diagnostics, diffusion-based control approximation, and hardware-in-the-loop validation of theoretical frameworks, demonstrating strong alignment between algorithmic innovation and real-world deployment. His scientific achievements have been recognized with prestigious honors: IFAC World Congress Interactive Paper Prize (2011) Klaus Tschira Award for public understanding of science (2014) Best Paper Award at International Conference on Cyber-Physical Systems (2019) Future Prize by Ewald Marquardt Stiftung (2020) As institutional leader, he directs the Institute for Data Science in Mechanical Engineering and co-leads the RWTH AI Center, overseeing strategic research initiatives and industry collaborations. His academic service includes editorial roles for IEEE Control Systems Society conferences and participation in the Cluster of Excellence 'Internet of Production'. The Institute for Data Science in Mechanical Engineering operates as a multidisciplinary hub where fundamental research in learning-based control meets industrial applications. Current projects focus on drone swarm coordination, deformable object manipulation, and medical diagnostics systems, leveraging both simulation environments and physical testbeds like the Mini Wheelbot platform.

• Machine Learning
• Control Systems
• Robotics
• +3 more

Erdinç Altuğ serves as a Professor in the Department of Mechanical Engineering at Istanbul Technical University, specializing in advanced aerial robotics and control systems. His work bridges theoretical control methodologies with practical UAV applications. Research interests focus on Unmanned Aerial Vehicle design and fault tolerance Quadcopter dynamics and vertical takeoff systems Adaptive control for parametric variations Rapid prototyping of hybrid VTOL platforms His recent publications demonstrate consistent innovation in autonomous flight systems, particularly in fault-tolerant operational modal analysis and modular multi-drone configurations. Current projects include: Hibrit İnsansız Hava Aracı Ile Otonom Teslimat Sistemi Geliştirilmesi (TUBITAK, 2020-2022) Mini Jet Motorlu Dikine Kalkıp İnebilen Otonom Taşıyıcı Robot Geliştirilmesi (TUBITAK, 2018-2021) Kampüs içi ve bina içi ortamlarda çalışacak otonom taşıma aracı algoritmaları (SRP, 2018-2021) Supervising 26 graduate works, his research impacts both academic and industrial UAV development.

• Unmanned Aerial Vehicles (UAVs)
• Quadcopter Design
• Flight Control Systems
• +5 more

Professor Jean-Luc Dugelay is a faculty member at EURECOM's Digital Security Department, specializing in facial image processing, image forensics, and biometrics. He holds a PhD in Image Processing from the University of Rennes (1992) and is a Fellow of IEEE (2012) and IAPR (2018). His research focuses on security applications like deepfake detection, privacy protection, and multi-spectral imaging. He leads projects on UAV surveillance, cross-spectrum face recognition, and deepfake countermeasures. Education: PhD in Image Processing (1992, University of Rennes), HDR (2013, University of Nice). Notable projects include HEIMDALL (deepfake detection), CONVERGE (transport security), and ImVerif (image forensics). His work on biometric systems and digital watermarking has been recognized with awards like the SEE Blondel Medal (2010). Research interests span facial analysis, video surveillance, and thermal imaging. He has advised students on topics like age estimation via GANs and cross-spectrum face recognition. Key publications include work on deepfake detection (IPAS 2025), thermal-to-visible face conversion, and event-based vision systems.

• Biometrics
• Image Forensics
• Video Surveillance
• +7 more

Anirban Baishya is an Assistant Professor in the Department of Communication Arts at the University of Wisconsin–Madison. He joined the university in January 2022 after serving as an Assistant Professor in Communication and Media Studies at Fordham University, New York. His educational background includes: PhD in Cinema and Media Studies from the University of Southern California (2018) M. Phil. in Cinema Studies from Jawaharlal Nehru University, New Delhi (2013) M.A. in Arts & Aesthetics from Jawaharlal Nehru University, New Delhi (2011) B.A. in Political Science from Cotton College, Gauhati University (2006) Baishya's research examines digital media's role in reshaping public spheres globally, focusing on how platforms reconfigure private/public boundaries through media forms like selfies, memes, and GIFs. His work critically engages with India's digital landscape, analyzing political mobilization, vernacular pornographies, and right-wing populist technoculture within sociohistorical contexts. His recent publications demonstrate a concentrated focus on South Asian digital cultures, particularly political meme ecosystems in Indian elections, weaponization of hate speech during pandemics, and decolonial approaches to porn studies. Key trajectories include anti-caste digital activism, pandemic witnessing via drone media, and emergent Indian erotic performance cultures on platforms like OnlyFans. Scientific recognition includes: Social Science Research Council’s Rapid-Response Grant on Covid-19 (2020) RESTS Mellon Interdisciplinary Seminar Fellowship at Fordham University (2020-21) UCI Global Fellows Early Career Fellowship (2020) Signal Culture Researcher in Residence (2019) Baishya's grant portfolio features the SSRC Covid-19 project analyzing online hate speech in India, alongside editorial leadership for special journal issues on South Asian Pornographies and small media cultures. His collaborative research with scholars like Darshana S. Mini spans multiple publications, though specific student advising details remain unlisted. Current book projects examining Indian selfie cultures and South Asian pornocultures represent significant contributions to digital media scholarship.

• Digital Media
• Public Sphere
• Social Media
• +5 more

Fouad KHENFRI is a Professor and researcher at ESTACA (Ecole Supérieure des Techniques Aéronautiques et de Construction Automobile) , specializing in embedded systems and control engineering. He holds a PhD in Computer Science and Applications from the University of Nantes (2016), a Master's in Automatic Control from EMP Algiers (2011), and an Engineering Degree in Automation from the University of Biskra (2008). Key research areas: Systems Control, Embedded Software Design, and Artificial Intelligence Recent publications focus on UAV control systems, Neural Architecture Search, and energy optimization Supervising doctoral students on topics including drone development and deep learning applications

• Systems Control
• Embedded Software Design
• Embedded Systems Performance Analysis
• +5 more

Dr. Ajit Ahlawat is an Assistant Professor at the Department of Geoscience and Remote Sensing , Faculty of Civil Engineering and Geosciences , Delft University of Technology (TU Delft) . His expertise lies in urban air pollution dynamics, aerosol instrumentation, and advanced environmental monitoring techniques. Education: Ph.D. in Aerosol Instrumentation (2019) - CSIR-National Physical Laboratory, Delhi, India Research Focus: Dr. Ahlawat's research centers on understanding air pollution in urban environments, particularly the formation of haze and fog events. He employs cutting-edge techniques including drone-based monitoring , low-cost sensor networks , AI/ML applications , and miniature instruments to measure and analyze urban air quality. His work addresses critical environmental challenges by developing innovative methodologies for real-time air quality assessment, with applications ranging from policy-making to public health interventions. Current Projects: DAAD-FCT PPP Project (2024-2026): Principal Investigator for "Air pollution profiling through UAV-assisted monitoring" - aims to characterize pollutant dispersion using drone-mounted sensors Teaching: He teaches "Air pollution meteorology & dispersion" course (2024/25 Q3) at TU Delft. Contact & Availability: Building 23, Room 2.29 Stevinweg 1, 2628 CN Delft Available: Monday, Tuesday, Thursday at faculty; Wednesday, Friday working remotely

• Urban air pollution dynamics
• Aerosol instrumentation
• Haze and fog formation
• +3 more

Jaskirat Sodhi is a Senior Lecturer in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT). He holds a Ph.D. in Mechanical Engineering from NJIT and a B.S. in Aeronautical Engineering from Punjab University. His work focuses on innovative teaching methodologies, inclusive design in engineering education, and improving student engagement through active learning strategies. He has developed courses such as Introduction to Computer Aided Design (ME 430) and Analysis and Design of Machine Elements (MET 301) , integrating hands-on projects and technology tools like MATLAB and 3D printing. Dr. Sodhi’s research interests include curriculum development for first-year engineering students, embedding ethics into introductory courses, and addressing challenges in online education during the pandemic. He has pioneered initiatives like the GIFTS program to enhance self-development through engineering design projects, and Milestones-Based Learning frameworks to boost student performance. His work often involves collaborative projects with librarians and interdisciplinary teams to improve research and writing skills among undergraduates. He has presented extensively at workshops on topics such as competition-based drone education, cheating prevention strategies, and inclusive design modules. His earlier research in materials science includes studies on light-activated shape memory polymers, reflecting his background in mechanical engineering.

• Engineering Education
• Educational Technology
• Active Learning Strategies
• +3 more

Dr. Vuk Marojevic is an Associate Professor in the Department of Electrical and Computer Engineering at Mississippi State University (MSU), holding the Paul B. Jacob Chair. He leads research in wireless communications, focusing on open radio access networks (O-RAN), AI-driven security, and UAV (drone) communications. His work emphasizes software-defined radio (SDR), spectrum sharing, and testbed development for 5G/6G networks. Education: Ph.D. and M.S. in Electrical Engineering from Barcelona Tech–UPC (Spain) and University of Hannover (Germany), respectively. Key projects include leading NSF-funded initiatives like AERPAW and Open Artificial Intelligence Cellular (OAIC), which prototype AI-enabled control systems for cellular networks. He also co-designed the AERPAW testbed for UAV experimentation. Research interests span O-RAN security, AI at the physical layer, wireless security protocols, and vehicular communications. His contributions include open-source implementations of 4G/5G protocols and frameworks for secure slicing and intrusion detection in O-RAN deployments. He actively collaborates with industry and academic partners to advance standards for unmanned aircraft systems (UAS) in cellular networks. Publications highlight innovations in reinforcement learning for resource allocation, RIS (reconfigurable intelligent surfaces) applications, and secure UAV communications. He serves as an associate editor for IEEE Transactions on Vehicular Technology and IEEE Vehicular Technology Magazine. Current research includes prototyping zero-trust security architectures (ZTRAN) and evaluating interference mitigation in drone corridors. He mentors students in MS and Ph.D. programs focusing on wireless systems, security, and AI integration in next-gen networks.

• Wireless Communications
• Software-Defined Radio
• Open Radio Access Networks (O-RAN)
• +7 more

Steve Martin is a Researcher at the QUT Centre for Robotics, Queensland University of Technology. He holds a Bachelor of Mechatronics (QUT, 2009) and was a former PhD student under Professors Gordon Wyeth and Peter Corke at the QUT Cyphy Lab. Since rejoining QUT in 2016, he has contributed to the Australian Centre for Robotic Vision’s engineering projects, focusing on robotics maintenance, software development, and electrical design. His work spans diverse applications including autonomous agricultural systems for weed control, semi-automated power infrastructure inspection, and robotic coral reef monitoring. Key projects include the 'Mini Autonomous Vehicles' initiative (AOS: Kelpie) and coral reef surveillance systems. Research interests emphasize real-time perception, long-term robot navigation, and energy-efficient robotic systems. He has developed solutions for traversability mapping, proprioceptive sensing, and distributed object tracking. Notable contributions include pandemic-related ventilator design and light field camera adapters. Steve collaborates across disciplines, bridging robotics engineering with environmental and medical applications. Advising and grants: While no current advisees are listed, his PhD supervision experience at QUT and contributions to large-scale robotic projects highlight his mentorship role. He actively engages with industry partnerships, particularly in agricultural and infrastructure robotics. Labs/Teams: Core member of the QUT Centre for Robotics, contributing to cross-disciplinary research in autonomous systems and robotic vision.

• Robotics
• Autonomous Systems
• Computer Vision
• +4 more

Louis-Martin Rousseau is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds the Tier 1 Canada Research Chair in Healthcare Analytics and Logistics (HANALOG) and manages the Combinatorial Optimization and Reasoning in Artificial Intelligence Laboratory (CORAIL). He is also a member of the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) and the Institute for Data Valorization (IVADO). B.Sc., Ph.D. from Université de Montréal His research focuses on hybridizing classical operations research techniques with constraint programming (AI methods) for applications in transport logistics, schedule optimization, and healthcare resource management. He has pioneered work in healthcare analytics, particularly in liver transplantation survival prediction (CASE method), radiation therapy treatment planning, and pandemic triage protocols. Recent publications highlight his work in deep reinforcement learning for inventory control, dual bound generation in constraint programming, and dynamic homecare scheduling. Keywords include Healthcare Analytics , Machine Learning , Combinatorial Optimization , and Logistics . Scientific awards include the 2019 Outstanding Achievement Award from the IEOM Society International. He supervises 5 doctoral, 2 master's, and 2 postdoctoral researchers, with a total of 23 PhD and 30 master's theses completed under his guidance since 2003. His lab (CORAIL) collaborates with CIRRELT and IVADO on problems involving synchronized vehicle routing, stochastic programming, and machine learning integration. Media appearances discuss healthcare system reforms, including private mini-hospitals and the Santé Québec agency's impact on patient access.

• Operational Research
• Logistics
• Algorithms
• +4 more

Dr. Abhishek Phadke is an Assistant Professor at Christopher Newport University (CNU) within the School of Engineering and Computing. His research focuses on resilient cyber-physical systems, UAV swarms, robotics, blockchain, and smart grids. He leads the ADAPT Lab, exploring heterogeneous robotics, sensor security, and UAV swarm optimization. PhD in Geospatial and Computer Science, Texas A&M University-Corpus Christi MS in Electrical Engineering, Texas A&M University-Kingsville BE in Electronics Engineering, University of Mumbai His research spans Cyber-Physical Systems , where he investigates robustness and adaptability in dynamic environments; UAV Swarms , emphasizing navigation, communication, and resiliency; and Blockchain , particularly for security and data integrity in decentralized systems. Additional interests include Smart Grids , renewable energy, and vehicular robotics. The ADAPT Lab, under Dr. Phadke, has produced numerous publications on UAV swarm resilience, blockchain integration, and simulation frameworks. His work has secured $116,527 in external funding. Current graduate students in the ADAPT Lab include George Hill and Mason Beckmeyer, focusing on applied physics and blockchain-driven cybersecurity. Undergraduate Cole Thomsen explores drone hardware and 3D printing. Dr. Phadke's prior advisor was Dr. F. Antonio Medrano. Notable collaborations and lab activities emphasize cross-disciplinary innovation, bridging robotics, cybersecurity, and sustainable technologies.

• Cyber Physical Systems
• Unoccupied Aerial Vehicles (UAV)
• Swarm Robotics
• +8 more

Ing. Eugen Šlapak, PhD., is a researcher at the Intelligent Information Systems Laboratory (IISLAB) of the Technical University of Košice, Slovakia, where he has worked since 2017. His research focuses on machine learning applications for cellular wireless networks modeled as multi-agent systems, with emphasis on 5G/6G optimization and autonomous vehicle communications. Education: BSc in Informatics, Technical University of Košice (2016) MSc in Informatics, Technical University of Košice (2018) Dr. Šlapak's research integrates Deep Learning , Physics-based modeling , and Large Language Models to solve wireless network challenges. His work on HetNets, UAV-assisted communications, and blockchain-enabled resource allocation demonstrates expertise in applying unsupervised learning and evolutionary algorithms to coverage optimization, radio resource management, and network topology design for next-generation mobile systems. His 13 publications (2017-2023) reveal consistent focus on machine learning-driven wireless innovations, with increasing emphasis on deep learning for 5G/6G management and blockchain applications in vehicular networks. Key trends include UAV swarm coordination, HetNet spatial optimization via clustering algorithms, and multi-agent resource allocation frameworks addressing computational and radio resource constraints. Scientific Awards: No awards documented in source material Dr. Šlapak collaborates extensively with Assoc. Prof. Juraj Gazda and international researchers on EU-funded telecommunications projects. His laboratory work centers on developing self-organizing network frameworks through the Intelligent Information Systems Laboratory (IISLAB). As a core IISLAB researcher, he contributes to experimental validation of AI-driven network solutions using agent-based modeling and real-world wireless testbeds, with recent work focusing on autonomous vehicle communication stacks and UAV-based mobile network deployment.

• Deep learning
• Physics
• Large language models
• +2 more

Derek Long is a Senior Lecturer at the School of Agriculture and Environmental Science and a researcher with the Centre for Agricultural Engineering (CAE) at the University of Southern Queensland, specializing in image analysis applications for cotton, grains, and livestock industries. His educational background: Bachelor of Engineering (Honours), University of Southern Queensland PhD, University of Southern Queensland Research interests focus on applied computer vision in agriculture using drones, tractors, and smartphones for automated monitoring. Current work emphasizes smartphone sensing for on-farm crop scouting, augmented reality systems, and neural network applications in irrigation management and animal welfare. Long actively integrates generative AI (GenAI) as an assistive tool for both agricultural data processing and pedagogical innovation. Scientific recognition: UniSQ Excellence Award for University Learning and Teaching (2024) - highest internal L&T award for GenAI integration impact As a research supervisor, Long mentors students in Agricultural Engineering, Computer Vision, and Machine Learning fields. His funded projects include drone irrigation monitoring, autonomous tractor systems, cotton whitefly parasitism tracking, and chicken welfare assessment tools, often with industry partners like John Deere. He also leads outreach initiatives through school workshops and public demonstrations. Long is a core member of the Centre for Agricultural Engineering (CAE), where he develops real-world agricultural monitoring systems. His 2024 Ekka demonstration featured Boston Dynamics' Spot Mini robot for farm applications alongside media engagements with ABC and 4BC radio.

• Agricultural Engineering
• Computer Vision
• Machine Learning
• +5 more

Dr. Marc C. Green serves as a Postdoctoral Research Fellow within the School of Science, Engineering & Environment at the University of Salford, affiliated with the Acoustics Innovation Institute. His research bridges acoustic engineering, machine learning, and environmental psychology with focus on unmanned aircraft systems noise impact assessment. PhD in Music Technology, University of York (2020) Dr. Green's research investigates how drone noise interacts with urban soundscapes through innovative methodologies including soundwalk assessments in controlled environments, psychoacoustic laboratory testing , and deep learning model development . His work specifically examines time-variant noise characteristics, flight operation impacts, and environmental interactions (such as pine tree barriers in urban parks) to understand human annoyance responses. The integration of spatial audio techniques with machine learning enables novel approaches to predicting UAV sound affect beyond traditional sound pressure level metrics. Analysis of his 11 recent publications (2024-2025) reveals a strong focus on practical solutions for urban drone integration, with 55% dedicated to psychoacoustic modeling, 27% to field assessment methodologies, and 18% to technical noise characterization. His work consistently addresses the tension between societal benefits of drone technology and community noise impacts. EPSRC connected nation pioneers prize for 'Creative Computing for the Digital Economy' (for EigenScape database development) Currently leading the RefMap project to reduce UAS environmental impact, Dr. Green's research receives direct institutional support through the Acoustics Innovation Institute. His EigenScape database continues to serve as a foundational resource for global soundscape researchers. The systematic review framework he developed for UAS noise impact testing provides regulators with evidence-based assessment protocols. Dr. Green operates within the Acoustics Innovation Institute's collaborative ecosystem, leveraging specialized recording equipment and computational resources. His research group maintains comprehensive drone noise datasets across multiple platforms and develops open methodologies for community noise assessment in evolving urban air mobility scenarios.

• Spatial Audio
• Machine Learning for Audio
• Environmental Soundscapes
• +3 more