Prof. Dr.-Ing. Elisabeth Clausen is a Professor and Director of the Chair and Institute for Advanced Mining Technologies at RWTH Aachen University. She holds key roles in the Specialist Group for Raw Materials and Disposal Technology, serves as a rectorate representative, and leads the Commission for EU Research Funding. Her research spans Underground mining automation Acoustic emission diagnostics Sustainable mining systems Space resource extraction Advanced sensor technologies Her recent publications focus on autonomous mining machinery, underground communication systems, and acoustic emission analysis across 15+ studies from 2013–2025, with particular emphasis on Ultra-wideband positioning Thermographic detection Crack monitoring in planetary gearboxes Explosive atmosphere safety Mineral processing diagnostics Digitalization trends Prof. Clausen contributes to mining education reform through initiatives like CDIO™ and has developed innovative learning spaces in underground mines. She coordinates international educational labs and integrates sustainability into mining engineering curricula, with publications on Adaptive ventilation systems Mining education frameworks Future-proof mineral extraction Entrepreneurial mindset in engineering
Maximilian Hilger is a doctoral researcher at the Chair of Perception for Intelligent Systems, part of the Munich Institute of Robotics and Machine Intelligence at Technische Universität München (TUM). He joined the chair in 2024 and specializes in radar perception for autonomous systems. M.Sc. in Automation Engineering (RWTH Aachen, 2023) Doctoral studies previously at AASS, Örebro University, Sweden His research focuses on radar-based localization, mapping, and introspection in challenging environments, with publications addressing 4D imaging radar SLAM, loop closure techniques, and sensor fusion methodologies. Recent work includes evaluating radar odometry algorithms and developing robust mapping systems using intensity-augmented normal distributions transform. Key research themes: Radar perception for autonomous systems SLAM robustness and introspection Occlusion-resistant localization Sensor fusion in dynamic environments He collaborates with team members including Prof. Achim Lilienthal, Valeria Salazar, and Thomas Wiedemann at TUM's Siemens Technology Center campus in Garching, Germany.
Prof. Dr. Helma Wennemers serves as a Full Professor at ETH Zurich's Department of Chemistry and Applied Biosciences, leading the Laboratory for Organic Chemistry. Her research group operates from HCI H 313 at Vladimir Prelog Way 1-5/10 in Zurich, Switzerland, with active teaching responsibilities including Organic Chemistry I and Chemical Biology - Peptides for the Fall 2025 semester. Her research program centers on the intersection of organic chemistry and chemical biology , with particular emphasis on collagen triple helix engineering , peptide-catalyzed asymmetric synthesis , and development of chemical tools for tissue remodeling diagnostics . Key focus areas include designing hyperstable collagen heterotrimers for fibrosis monitoring, creating fluorophore-based probes for collagen cross-linking visualization, and pioneering organocatalytic methodologies for complex heterocycle synthesis. Her group actively explores how hydrophobic modifications and proline derivatives influence collagen stability and cellular uptake mechanisms. Analysis of her 15 most recent publications (2024-2025) reveals three dominant research trajectories: (1) collagen structural engineering for biomedical applications, (2) innovative peptide/organocatalysis enabling stereoselective transformations, and (3) chemical probe development targeting tissue remodeling processes. These works consistently integrate synthetic chemistry with biological validation, demonstrating translational potential in fibrosis diagnostics and regenerative medicine. While specific grant details aren't provided in available sources, her research program clearly supports advanced laboratory infrastructure including peptide synthesis facilities and photochemical reaction systems like the ETHos photoreactor. Her group maintains strong industry and clinical collaborations evident in applications targeting liver cancer cells and prostate cancer diagnostics. The Laboratory for Organic Chemistry functions as an interdisciplinary hub where synthetic organic chemists collaborate with biologists to develop collagen-based diagnostic platforms and catalytic systems. Current projects focus on lysyl oxidase-responsive probes for real-time tissue monitoring and engineered peptide catalysts for sustainable chemical synthesis under environmentally relevant conditions.
Dr. Wade Smith is a Senior Lecturer within the School of Mechanical and Manufacturing Engineering at the University of New South Wales. He is an active member of the WAVES research group (Wear, Aeroacoustics and Vibration in Engineering Systems) and conducts his research in the Tribology and Machine Condition Monitoring laboratory. His primary research interests include vibration-based diagnostics of rotating machinery, prognostics of rotating machinery, gear wear monitoring and prediction, simulation and modeling of rotating machines for diagnostic applications, and signal processing of machine vibration signatures using cyclostationarity. His work has significant applications in industrial machinery health monitoring and predictive maintenance systems. Dr. Smith's recent publications demonstrate a consistent focus on advanced diagnostic techniques for rotating machinery, with particular emphasis on gear systems and bearings. His research integrates traditional mechanical engineering principles with modern signal processing and machine learning approaches to develop more effective condition monitoring solutions. He actively supervises PhD and Masters students on projects related to gear diagnostics, wear monitoring, and vibration analysis. His current research projects include gear diagnostics in planetary gearboxes using internal sensors, gear wear monitoring and prediction, sliding contact-induced vibration studies, and transmission-error-based gear diagnostics. Dr. Smith's laboratory is equipped with specialized facilities including gearbox test rigs (both planetary and parallel configurations), a rolling element bearing test rig, an engine test rig, friction rig, tribometer, high-quality microscope, and extensive instrumentation for vibration analysis. His research has attracted collaborations with institutions including Queensland University of Technology, SpectraQuest (USA), Weir Minerals, University of Technology Sydney, RWTH Aachen University (Germany), and Safran.
Paul Erhart is a Professor in Condensed Matter and Materials Theory at the Department of Physics, Chalmers University. He received his PhD from Technische Universität Darmstadt in 2006, followed by postdoctoral and staff positions at Lawrence Livermore National Laboratory from 2007, before joining Chalmers in 2011. His research bridges computational physics, materials science, and machine learning to tackle fundamental problems in materials design and characterization. Dr. Erhart's research focuses on computational materials science with particular emphasis on condensed matter physics, nanomaterials, and quantum materials. His work spans from developing computational methods like machine-learned potentials (GPUMD, neuroevolution potentials) to studying fundamental phenomena in perovskites, 2D materials, thermal transport, and plasmonics. He has pioneered approaches connecting simulation with experimental techniques through correlation functions and has made significant contributions to understanding phase transitions, defect physics, and electronic structure in complex materials systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional computational physics methods. His work increasingly focuses on developing and applying neuroevolution potentials to study thermal properties, phase transitions, and optical phenomena in materials. There's also a clear emphasis on connecting computational results with experimental observations, particularly in neutron scattering, Raman spectroscopy, and plasmonic sensing applications. His research spans fundamental materials physics to applied areas like hydrogen sensing and sustainable materials development. Dr. Erhart has contributed to numerous software packages essential to the computational materials science community, including WulffPack for Wulff constructions, Dynasor for extracting dynamical structure factors, calorine for neuroevolution potential models, and ICET for alloy cluster expansions. His collaborative work spans multiple institutions and disciplines, reflecting the interdisciplinary nature of modern materials research. His contributions to understanding perovskite materials, thermal transport phenomena, and plasmonic systems have established him as a leading researcher in computational materials science.
Atakan Aral serves as an Associate Professor at the Faculty of Computer Science, University of Vienna, where he leads research in edge computing, distributed systems, and environmental monitoring applications. His work focuses on developing efficient and resilient computing systems for environmental applications, with particular emphasis on neuromorphic edge AI and the cloud-edge continuum. He maintains an active teaching schedule offering courses in Distributed Systems Engineering, Cloud Computing, and Practical Software Courses with Bachelor's Thesis work across multiple semesters through 2025. Dr. Aral's research interests span several critical areas in modern computing including edge computing architectures, federated learning approaches, neuromorphic computing for environmental monitoring, and resilient systems design. His work addresses fundamental challenges in resource-constrained environments, particularly focusing on latency-sensitive applications and energy-efficient computation. The interdisciplinary nature of his research bridges theoretical computer science with practical environmental applications, developing systems that can operate effectively in remote or resource-limited settings. Analysis of his recent publication trajectory reveals a clear evolution from foundational cloud computing research toward increasingly specialized edge intelligence systems. Early work focused on resource allocation and scheduling in cloud environments, while his current research emphasizes neuromorphic approaches for sustainable environmental monitoring. His publications demonstrate growing interdisciplinary collaboration, particularly with environmental scientists, and increasing focus on practical implementations of theoretical concepts in real-world monitoring systems. Dr. Aral leads significant research projects including TROCI (Towards Resilient Operation of Critical Infrastructure), an ongoing initiative, and SWAIN (Sustainable Watershed Management Through IoT-Driven AI), which ran from February 2021 to February 2024. His work spans multiple dimensions of computing systems, from hardware-aware algorithms to application-level implementations, with consistent contributions to major conferences and journals in distributed systems and edge computing. He is an active member of the Scientific Computing research group at the University of Vienna, working from Room 6.49 at Währinger Straße 29. His research environment includes collaboration with the Environment and Climate Research Hub, reflecting the interdisciplinary nature of his work that bridges computer science with environmental applications. His publications indicate strong international collaboration across European institutions and research groups.
ZHANG Li is a Professor in the Department of Mechanical and Automation Engineering (MAE) and a Professor by Courtesy in the Department of Surgery at The Chinese University of Hong Kong (CUHK). He serves as Director of the SIAT-CAS-CUHK Joint Laboratory of Robotics and Intelligent Systems. Research Focus: Small-scale robotics, magnetic slime robots, microrobotic swarms, functional materials for biomedical applications. Editorial Roles: Associate Editor/Editorial Board Member of 10 journals including IEEE T-RO, IEEE/ASME T-MECH, and Advanced Intelligent Systems. His research spans translational biomedicine and minimally invasive interventions, with publications in high-impact journals like Science Robotics , Nature series, and Science Advances . His work on artificial bacterial flagella and microrobotic swarms has been recognized in the Guinness Book of World Records (2012) and Hong Kong’s Top 10 Innovation and Technology News (2022-2024). Major Awards: Fellow of IEEE, ASME, RSC, FAAIA, and FHKIE Gold medals at Geneva (2023) and IIFME2024 Multiple IEEE conference best paper awards RGC Early Career Award (2013), RGC Research Fellow Award (2021/22) He has secured over 30 competitive grants from Hong Kong RGC, ITC, Croucher Foundation, NSFC, and Shenzhen government. His projects include META System for stroke treatment and 3D Printing of Miniature Robots for ophthalmology.
Dana Z. Anderson is a Professor and Fellow at JILA at the University of Colorado Boulder, holding the Glen Murphy Endowed Chair in the Department of Physics within the College of Engineering and Applied Science (CEAS) . His research focuses on nonlinear optics , atom optics , and optical precision measurements . Key projects include advancing atomtronics (quantum analogs of electronic systems), neutral atom quantum computing , and ultracold atom gyroscopes . He leads the Anderson Optical Physics (AOPy) group , pioneering applications like shaken lattice interferometry for space navigation and quantum sensor development . Anderson's work bridges fundamental physics and applied technologies. His group develops window atom chip technology for ultracold atom manipulation and in-situ imaging systems . Collaborations include NASA's Cold Atom Laboratory (CAL) mission for microgravity experiments on the International Space Station (ISS). Notable contributions include demonstrating matterwave transistor oscillators and optical lattice-based quantum devices . His research has been recognized in high-impact journals like Physical Review Letters and Review of Modern Physics . He actively engages in public outreach and industry partnerships , serving as Chief Strategy Officer at ColdQuanta, a quantum tech startup spun from his lab's innovations.
Professor Byung S. Lee is a distinguished faculty member in the Department of Computer Science at the University of Vermont's College of Engineering and Mathematical Sciences. He joined UVM in 1999 and continues to be actively engaged in teaching, research, and service. His office is located in Innovation Hall at the Burlington campus, where he maintains regular office hours and oversees his research lab. Professor Lee holds a Ph.D. from Stanford University, an MS from Korea Advanced Institute of Science and Technology, and a BS from Seoul National University. His educational background provided the foundation for his extensive career in computer science research and education. Professor Lee's research spans multiple domains within computer science, with a particular focus on database systems, data mining, and data science. His work increasingly integrates machine learning techniques with traditional database approaches, especially in the analysis of time series data. He has made significant contributions to graph theory applications, anomaly detection methods, and environmental data analysis. His research often bridges computer science with practical applications in healthcare, environmental science, transportation, and astrophysics through interdisciplinary collaborations. An analysis of his recent publications reveals a strong trend toward time series analysis and anomaly detection, particularly applied to environmental monitoring and healthcare data. His work demonstrates a consistent evolution from foundational database research to more applied machine learning approaches, with increasing emphasis on real-world problem solving across multiple scientific domains. Professor Lee has served as primary advisor for numerous graduate students across multiple cohorts, including PhD candidates, Master's students, and postdoctoral researchers. His advising portfolio reflects the breadth of his research interests, with students working on topics ranging from graph neural networks to medical informatics applications. He has also been actively involved in professional service, serving on program committees for major conferences including SAC, PAKDD, DASFAA, and CIKM. Professor Lee leads a vibrant research laboratory that focuses on cutting-edge data science methodologies and their applications. His team collaborates extensively with researchers in environmental science, hydrology, and healthcare, demonstrating the interdisciplinary nature of modern data science research. The lab maintains active projects in time series analysis, graph analytics, and environmental monitoring systems, often working with large-scale datasets from real-world applications.
Imad L. Al-Qadi is the Grainger Distinguished Chair in Engineering and Director of the Illinois Center for Transportation (ICT) at the University of Illinois at Urbana-Champaign (UIUC). He holds a Ph.D. in Civil Engineering from Penn State and has held faculty positions at Virginia Tech and Penn State. His research focuses on sustainable transportation infrastructure, pavement mechanics, and advanced materials. Al-Qadi leads initiatives like the Illinois Autonomous and Connected Track (I-ACT), a high-speed test facility for autonomous vehicles and energy harvesting systems. Key roles include: Director of ICT, advancing multimodal transportation infrastructure Pioneer of the Smart Road and full-scale pavement testing Founder of the Academy of Pavement Science and Engineering Research interests span: Highway/airfield sustainability Tire-pavement interaction Energy harvesting Electric vehicles' infrastructure impact Leadership roles include past presidency of ASCE's Transportation and Development Institute and editorship of the International Journal of Pavement Engineering. He has authored over 1,000 publications, including 390 refereed papers, and secured over 180 research grants from federal agencies and industry. Awards include NSF Young Investigator Award (1994), TRB Crum Award (2023), and 2024 Executive Leadership Fellow. His work is internationally recognized, with honorary professorships at institutions in China, Sweden, and the UK.
Dr. Umberto Montanaro is a Senior Lecturer in Autonomous Systems and Control Engineering at the University of Surrey's School of Mechanical Engineering Sciences, within the Centre for Automotive Engineering. He holds PhDs in Control Engineering (2009) and Mechanical Engineering (2016) from the University of Naples Federico II, Italy. His research focuses on adaptive control algorithms for automotive and mechatronic systems, including vehicle platooning, autonomous driving, and nonlinear control strategies. He has authored over 60 peer-reviewed publications and led projects like the Innovate UK-funded GPR for Localisation (2018–2019) and the EPSRC/JLR-funded CARMA initiative (2016–2021). His work spans control of multiagent systems, optimal control, and enhanced model reference adaptive control (MRAC) applications. Dr. Montanaro has supervised multiple PhD and MEng students, including co-supervision of Shilp Dixit's research on autonomous overtaking. Research Interests: Adaptive Control, Autonomous Vehicles, Vehicle Platooning, Nonlinear Systems, Model Reference Adaptive Control Grants: CARMA (EPSRC/JLR), GPR Localisation (Innovate UK) Teaching: Control and Dynamics (ENG3611), Engine Speed Control labs Labs/Teams: Active in automotive control systems and connected autonomous vehicle research
Alberto G. Curto is an Assistant Professor at Eindhoven University of Technology, specializing in nanophotonics and semiconductor optoelectronics. He leads research on light-matter interactions at the nanoscale, focusing on atomically thin semiconductors and chiral nanophotonics applications. His work spans sensing, imaging, and spectroscopy, with contributions to directional light emission and chiral detection technologies. Education BSc in Physics, Universidad de Salamanca (2002–2007) MSc and PhD in Photonics, ICFO – The Institute of Photonic Sciences (2008–2013) Postdoctoral Fellow at Stanford University (2013–2016) Research Interests Curto’s research explores semiconductor nanophotonics for sensing and imaging, leveraging atomically thin materials and chiral optics. Key areas include: Enhanced light-matter interactions using nano-optical structures Chiral detection via dielectric resonators and metasurfaces Exciton dynamics in 2D semiconductors Publications His work emphasizes practical applications of nanophotonics, with recent studies on chiral sensing enhancement, silicon metasurfaces, and exciton manipulation. Over 47 peer-reviewed articles highlight his contributions to light confinement and directional emission. Awards ERC Starting Grant (2020) NWO START-UP Grant (2018) OSA Senior Member (2021) ICFO PhD Thesis Award (2014) Grants & Projects He leads the Zwaartekracht PSN Research Centre for Integrated Nanophotonics (2014–2025), focusing on nanowires, photonic crystals, and photonics applications. Labs & Teams His research group at TU Eindhoven develops novel nanophotonic devices and chiral sensing platforms, collaborating on projects like the ERC CHANSON grant.
Sheryl Grace is an Associate Professor of Mechanical Engineering at Boston University, leading the Unsteady Fluid Mechanics & Acoustics Laboratory (UFMAL). Her primary appointment is in the Department of Mechanical Engineering within the College of Engineering. She holds a PhD from the University of Notre Dame. Her research focuses on unsteady aerodynamics, aeroacoustics, and fluid-structure interactions, with applications in aerospace systems, propulsion technologies, and biological acoustics. Notable projects include NASA-funded work on quieter vertical lift vehicles and computational modeling of gerbil hearing mechanics. Professor Grace’s research interests span aerodynamics, fluid dynamics, and acoustics. She develops analytical and computational models to predict sound and vibration generated by unsteady flows interacting with solid structures. Recent studies include noise reduction in aircraft wings, turbine blade fatigue analysis, and acoustic scattering in gerbil ears. Her work bridges theoretical models with practical engineering solutions, emphasizing cost-effective predictive tools for next-generation systems. Her publications highlight advancements in shock-droplet interactions, cavitation modeling, and machine learning applications in aeroacoustics. Collaborative projects include multi-institutional efforts to address urban air vehicle noise challenges. While no explicit awards are listed, her contributions to computational acoustics and fluid dynamics are recognized through extensive peer-reviewed output. Advising and grants: Professor Grace leads the UFMAL lab and has secured funding from agencies like NASA. Her research integrates fluid mechanics, acoustics, and computational methods to address industrial and environmental noise issues. She collaborates across disciplines, including mechanical engineering, aerospace, and biomedical acoustics.
Tara Boroushaki is an incoming Assistant Professor in Electrical & Computer Engineering at Yale University. She completed her Ph.D. at MIT (expected May 2025), advised by Prof. Fadel Adib, with a focus on sensing and mobile technologies. Her research spans wireless networking, robotics, and human-computer interaction, emphasizing multi-modal sensing for environmental perception. Key achievements include the Microsoft Research PhD Fellowship (2022–2024) and the IEEE RFID '23 Best Paper Award. Her work on RF-based 'X-ray vision' has been featured in TEDxMIT and media outlets like the BBC and World Economic Forum. She co-founded Cartesian Systems, deploying sensing technologies in retail and supply chain. Research interests include non-line-of-sight perception, RFID localization, and robotic grasping. She has developed systems like FuseBot and RFusion, highlighted as transformative in MIT's '103 Ways to Make the World Better' initiative.
Luca Demetrio is an Assistant Professor at the University of Genoa, Italy, specializing in adversarial machine learning and cybersecurity. Previously, he was a Post-doctoral Researcher at the PRA Lab within the Department of Electrical and Electronic Engineering at the University of Cagliari. He holds bachelor's (2015), master's (2017), and Ph.D. (2021) degrees from the University of Genova, with his doctoral thesis focusing on formalizing evasion attacks against security detectors. His research emphasizes enhancing the robustness of machine learning models against adversarial attacks, particularly targeting malware detectors, SQL injection defenses, and Windows security systems. He leads the development of SecML Malware, a Python library for generating adversarial Windows malware, and contributes to the SecML framework. His work has been published in top-tier journals like ACM TOPS and IEEE TIFS. Key research interests include adversarial example generation, malware analysis, and cybersecurity defense mechanisms. He has explored query-efficient attacks on phishing detectors, certified adversarial robustness via randomized smoothing, and robust synthetic data-driven threat detection. His recent studies (2023–2025) address challenges in hardening machine learning models against evasion attacks, adversarial SQL injection countermeasures, and securing autonomous driving systems from adversarial reinforcement learning attacks.
