Cornelia Schneider is a Professor and Head of the Institute of Computer Science at the University of Applied Sciences Wiener Neustadt. She leads research in digital health and care technologies, focusing on applications like Augmented Reality, sensor data analysis, and social robotics for vulnerable populations. Her work emphasizes user-centered design and has been supported by projects funded by FFG and the Active Assisted Living Programme. Her research spans telemedicine infrastructure, elderly care robotics, and health monitoring systems. Notable projects include 24/7-Digital (2023-2026), Care about Care (2021-2023), and AgeWell (2019-2022). She has pioneered solutions like CARU cares and DigiCare training programs. Dr. Schneider has been recognized with the AAL Award (2014) and the tecnet | accent Innovation Award (2021). She coordinates interdisciplinary teams and actively participates in conferences like Health Informatics Meets Digital Health. Her lab collaborations include FOTEC and Salzburg Research, where she previously led the e-Health Competence Center until 2019.
Hamid Mansoor is an Assistant Professor in the Department of Computer Science at the University of Manitoba. He holds a PhD in Computer Science from Worcester Polytechnic Institute under Prof. Emmanuel Agu, and was part of the DARPA-funded WASH project. His research focuses on data visualization, digital health, and smartphone-based behavioral analysis. He previously served as a Postdoctoral Fellow at the VIXI Lab, University of Victoria, Canada, under Prof. Miguel Nacenta. Education: PhD in Computer Science, Worcester Polytechnic Institute Research Interests: Interactive data visualization frameworks for health monitoring Mobile and ubiquitous computing for behavioral analysis Smartphone-sensed human behavior and health informatics Visual representation of text-based and sensor data Publications highlight trends in visual analytics for healthcare, including tools like ARGUS and INPHOVIS for detecting bio-behavioral disruptions and smartphone-based phenotyping. His work integrates machine learning with visualization to address challenges in health data interpretation. Awards: Best short paper honorable mention (EuroVis 2020) His contributions span academic collaborations in health informatics and mobile computing, with a focus on bridging theory and practical applications in healthcare technology.
Charl FJ Faul is a Professor of Materials Chemistry and Associate Pro Vice Chancellor (Global Engagement) at the University of Bristol’s Faculty of Science. He leads the Faul Research Group, focusing on functional materials for sustainable energy and soft robotics, including conjugated microporous polymers (CMPs) for CO2 conversion, electroactive materials, and 3D-printed soft actuators. His roles include academic leadership, global engagement initiatives, and research collaboration across institutions like Kyoto University and Tsinghua University. Education: B.Sc., M.Sc., Ph.D.(Stellenbosch). Research emphasizes scalable CMP synthesis, bio-adhesives, and materials for mobility assistance. Collaborates on projects like the £14M EPSRC-funded VIVO Hub for Enhanced Independent Living. Advises over 15 students, including recent PhD graduates Dr Helal Alharbi and Dr Yubing Wang. Research outputs span 2025-2023, highlighting advancements in hydrogen storage, CO2 capture, and soft robotic actuators. The group actively publishes in journals like Small and Journal of Materials Chemistry A . Grants include EPSRC funding for VIVO Hub and Innovate UK support for conductive composites.
Dr. Zhe Cheng is an Associate Professor in the Department of Mechanical Engineering at Colorado State University, part of the Walter Scott, Jr. College of Engineering. Prior to this, he held tenured positions at Florida International University (2013–2024) and was a research investigator at DuPont (2008–2013). His research focuses on advanced ceramic materials for energy applications, including solid oxide fuel cells (SOFCs), photovoltaics, and high-temperature ceramics. He holds a Ph.D. (2008), M.S. (2004), and B.S. (2001) in Materials Science & Engineering from Georgia Tech and Tsinghua University. Education: Ph.D., Materials Science & Engineering, Georgia Institute of Technology (2008) M.S., Materials Science & Engineering, Georgia Institute of Technology (2004) B.S., Materials Science & Engineering, Tsinghua University (2001) Research Interests: Dr. Cheng specializes in novel synthesis and processing of high-temperature ceramics, including high-entropy nitrides, and their applications in energy conversion systems. His work emphasizes in situ characterization techniques to understand material behavior under operational conditions. Key areas include SOFC cathodes, proton-conducting electrolytes, and wearable sensor technologies. Publications & Awards: With over 5,284 citations and an h-index of 27, his work spans 44 peer-reviewed articles. Notable awards include the NSF CAREER Award (2019) and the American Ceramic Society Ross Coffin Purdy Award (2010). His research has been funded by NSF, DOE, and NASA. Advising & Grants: Dr. Cheng has advised numerous graduate students and secured $2.3 million in research funding. Key grants include DOE projects on additive manufacturing for plasma-facing materials and NSF support for SOFC hydrogen electrode fundamentals. Labs & Teams: He leads research in advanced ceramics and electrochemical systems at CSU, fostering interdisciplinary collaborations in materials science and energy engineering.
Dr. Heesung Woo is an Assistant Professor of Advanced Forestry at the College of Forestry, Oregon State University , specializing in robotics, sensor integration, and precision forestry. His work focuses on autonomous forestry machinery, AI-driven forest management, and sustainable practices. He advises two graduate students and collaborates internationally through research projects. Research Interests: Autonomous Forest Machinery Development Sensor Integration & ICT Solutions Precision Forestry via Remote Sensing/LiDAR/GIS Machine Learning for Forest Inventory Advanced Forestry Practices for Sustainability Publications emphasize innovative applications of technology in forestry, including LIDAR integration, harvester data analytics, and carbon offset project modeling. His work bridges engineering, environmental science, and policy. Dr. Woo leads the Advanced Forestry Lab at Oregon State, focusing on real-world deployment of cutting-edge technologies to address challenges in forest operations, sustainability, and resource optimization.
Alyssa B. Apsel is a Professor of Electrical and Computer Engineering at Cornell University since 2002 and a Visiting Professor at Imperial College London. She became the IBM Professor of Engineering in 2023 and Director of Electrical and Computer Engineering at Cornell in 2018. Education: B.S., Electrical Engineering, Swarthmore College (1995) M.S., Electrical Engineering, California Institute of Technology (1996) Ph.D., Electrical Engineering, Johns Hopkins University (2002) Research Focus: She specializes in power-aware mixed-signal circuits for scaled CMOS and modern systems. Her group explores cost-effective designs addressing device scaling challenges (variation, noise, reduced analog performance) through analog/mixed-signal innovation, particularly in IoT radios and reconfigurable multi-standard wireless systems. Key areas: RF circuits, VLSI integration, biomedical telemetry, and low-power design. Publication Trends: Her 2016 work spans RF transceiver design, thermometer DAC calibration, biomedical ICs with UWB telemetry, and jitter-measurement circuits. These reflect her expertise in low-power wireless systems, analog/mixed-signal design, and biomedical electronics. Awards: IEEE Fellow (2020) IEEE CAS Distinguished Lecturer (2018-2019) ISLPED Design Contest Second Place (2010) Multiple Student Paper Awards (2000, 1995) Fellowships: Abel Wolman (1997), Caltech Institute (1995) Grants & Leadership: She founded EchoICs, which received a $275,000 NSF STTR Phase I Award (2024) for flexible spectrum radios. Her lab focuses on RF interfaces for implantable electronics and photonic integration.
Gregory Carman is a Distinguished Professor in the Department of Mechanical and Aerospace Engineering and Department of Materials Science and Engineering at the University of California, Los Angeles (UCLA) . He serves as the Director of the Center for Translational Applications of Nanoscale Multiferroic Systems (TANMS) and holds the Ben Rich-Lockheed Martin Chair in Advanced Aerospace Technologies (46-147N, Eng IV). Education: Virginia Tech, 1991 Research Interests focus on advanced materials and aerospace technologies, including: Nanoscale Multiferroic Materials Piezoelectric Materials Magnetostrictive Materials Thin Film Shape Memory Alloys Fiber Optic Sensors Scientific Awards & Recognition : 2016 IEEE Magnetics Society Distinguished Lecturer Society of Experimental Mechanics Keynote Lecture (May 2015) SPIE Smart Structures and Materials Lifetime Achievement Award (November 2014) UCLA Dean’s Recognition Award (February 2012) ASME Smart Materials, Adaptive Structures, and Intelligent Systems Keynote (2009) American Helicopter Society Howard Hughes Award (2009) ASME Best Paper Awards (2007, 2001, 1996) ASME Adaptive Structures and Material Systems Prize (2004) ASME Fellow (March 2003) Honorary Professor, Baotou University of Iron and Steel Technology (2002) Northrop-Grumman Young Faculty Award (1995) Labs & Centers : Leads the Active Materials Lab (AML) and directs TANMS, a translational research center focused on nanoscale multiferroic systems.
Parv Venkitasubramaniam is a Professor in the Department of Electrical & Computer Engineering at Lehigh University, affiliated with the P.C. Rossin College of Engineering. Previously, he served as a postdoctoral researcher at UC Berkeley under Prof. Venkat Anantharam. His research focuses on theoretical foundations of privacy and security in networks, leveraging statistical signal processing, information theory, and game theory. Key application areas include smart grids, transportation systems, and peer production networks. Education includes a Ph.D. and M.S. in Electrical Engineering from Cornell University, and a B.Tech from the Indian Institute of Technology. His doctoral work concentrated on wireless sensor networks, particularly distributed communication and statistical inference. Research interests span privacy-utility tradeoffs, cybersecurity in control systems, and resilient network design. He explores topics like stealthy attacks on dynamical systems, privacy-aware stochastic games, and resilient energy storage systems. Recent work emphasizes transportation system resilience and cyber-physical system security. His publications address cutting-edge challenges in anonymizing networks, detecting cyber attacks, and optimizing privacy-preserving mechanisms. Notable projects include NSF-funded research on anonymous networking and information-theoretic security frameworks.
Dr. Scott Chen is an Assistant Professor in the Department of Electrical & Computer Engineering at McMaster University, where he focuses on teaching and research in embedded systems, RF technologies, and biomedical sensors. He previously held roles as a lecturer at the University of Waterloo and program coordinator at Conestoga College, alongside industry experience in embedded systems engineering and sensor development. Education: B.A.Sc. (Simon Fraser University, 2007) and Ph.D. (University of Waterloo, 2015), followed by a MITAC postdoctoral fellowship. His industry experience includes roles at Thalmic Labs/North, Sober Steering Sensors, and Equustek Solutions. Research interests span embedded systems for IoT, RF biomedical sensors, cleanroom micro/nano-fabrication, and game design in Unity. Notable achievements include a 2018 US patent for ethanol sensing technologies and a 2021 teaching award nomination. Current courses taught include Principles of Programming (COMPENG 2SH4), Data Structures and Algorithms (COMPENG 2SI3), and Introduction to Electrical Engineering (ELECENG 2CI4). Awards: US Patent 9,958,444B2 (2018), nominated for Aubrey Hagar Distinguished Teaching Award (2021). His work bridges academia and industry, emphasizing practical applications in wearable sensors, quantum computing components, and interdisciplinary engineering solutions.
Julien Warnan is a researcher at the Catalysis Research Center (CRC) of the Technical University of Munich (TUM). He leads a multidisciplinary research group focusing on renewable energy, particularly artificial photosynthesis and photocatalytic systems for fuel production. His work emphasizes molecular dyes, catalysts, polymers, and hybrid materials to transform CO2 and water into value-added chemicals. He holds the title of Researcher and is actively involved in academic leadership, including co-editing special issues and organizing international conferences like the ECAT conference. His research has been recognized with awards such as the TUM Chemistry Supervisory Award 2021. Recent activities include visiting scientist roles at Imperial College London and collaborations with groups at TUM and other institutions. Research interests encompass MOF-based photocatalysis, biohybrid systems, and sustainable energy conversion. Key achievements include pioneering studies on metal-organic frameworks for CO2 reduction and solar fuel production. His team has published extensively in high-impact journals like Angewandte Chemie and Advanced Materials , with a focus on functional hybrid materials and electrochemical systems. PhD supervision: Nadine Schmaus, Philip Stanley, Johanna Eichhorn, and others Notable collaborations: Shustova Lab, Rieger Group, Fischer Group Labs: Catalysis Research Center (CRC)
Prof. Dr.-Ing. Hans-Georg Herzog is a Professor of Energy Conversion Technology at the Technical University of Munich (TUM), School of Engineering and Design. He has headed the Energy Conversion Technology group at TUM since 2002 and is a Senior Member of IEEE and member of VDE and VDI professional organizations. His research focuses on energy-efficient electromechanical drives and related technologies critical for modern electric and hybrid vehicles. Prof. Herzog's research interests encompass energy-efficient electromechanical drives, with key expertise in design and optimization of hybrid-electric and battery-electric powertrains, automated design methods for electromechanical actuators, energy and power management systems, and analysis of loss mechanisms in soft magnetic materials. His work bridges fundamental electromagnetic theory with practical automotive applications, particularly in fault-tolerant systems and reliability engineering for electric propulsion. His recent publication trends show a strong focus on vehicular power systems, with particular emphasis on electronic fuses, fault diagnosis in multiphase machines, wireless power transfer, and reliability analysis of electric aircraft propulsion systems. The research spans from fundamental electromagnetic modeling to practical automotive applications, with increasing attention to autonomous driving power requirements and next-generation vehicle electrical architectures. Prize for Good Teaching of the Free State of Bavaria (2010) Prof. Herzog leads a substantial research team including doctoral candidates and postdoctoral researchers who contribute to his extensive publication record. His research group collaborates with automotive industry partners on various grants focused on electric vehicle technology, power system reliability, and advanced electromagnetic systems. The team regularly develops novel methodologies for machine design, fault tolerance analysis, and power system optimization. The research is conducted within TUM's Energy Technology Workshop with specialized facilities for electrical machine testing, power electronics development, and automotive power system simulation. The team maintains strong connections with industry partners in the automotive and aerospace sectors, facilitating technology transfer from academic research to practical applications.
Renaud BACHELOT is a full Professor of Physics at the University of Technology of Troyes (UTT) since 1996. He leads the Light, Nanomaterials, and Nanotechnologies (L2n) laboratory and directs the Graduate School 'Nano-optics & Nanophotonics'. He holds adjunct professorships at the University of Paris-Saclay (LuMIn Lab) and Shanghai University (1000-talents Grant). His research focuses on nano-optics, plasmonics, and hybrid nanoplasmonics, with expertise in photopolymerization and plasmon-driven chemical processes. Education: PhD and graduate studies at Université Paris-Cité and ESPCI Paris Research Interests: BACHELOT’s work spans nanoscale light-matter interactions, including plasmonic nanostructures, photopolymerization-based fabrication, and applications in optical sensing and quantum photonics. His lab employs advanced techniques like near-field scanning optical microscopy (NSOM) and two-photon polymerization. Grants & Projects: ANR-PIA3 STRONG-NANO (2023-2026) ANR ADVANSPEC (2022-2025) International collaborations with NTU Singapore and Argonne National Lab Labs & Teams: Directs L2n (CNRS-UMR 7076) and collaborates across interdisciplinary platforms like InSyTE and LIST3N. His team develops novel hybrid materials and nanophotonic devices.
Luigi Acerbi is an Associate Professor in the Department of Computer Science at the University of Helsinki, where he leads the Machine and Human Intelligence research group. He is also an active member of the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning and computational neuroscience, particularly on developing efficient methods for statistical inference, Bayesian models of perception, and resource-constrained rationality. His work bridges machine learning and cognitive science, with applications in Bayesian optimization, simulation-based inference, and image completion. The recent publications highlight a strong trend toward unifying probabilistic conditioning across diverse tasks using transformer-based meta-learning frameworks like the Amortized Conditioning Engine (ACE). These works emphasize amortized inference, flexible latent variable modeling, and the integration of prior knowledge at runtime, enabling efficient and scalable Bayesian methods for complex problems. Scientific Affiliations: University of Helsinki, Department of Computer Science Finnish Center for Artificial Intelligence (FCAI) ELLIS (European Laboratory for Learning and Intelligent Systems) Education: PhD in Computational Neuroscience, Doctoral Training Centre, Edinburgh, UK Advisor: Sethu Vijayakumar and Daniel Wolpert Visiting work at Computational and Biological Learning Lab, Cambridge Postdoctoral Experience: Alex Pouget’s lab, University of Geneva, Switzerland Wei Ji Ma, New York University, USA Collaboration with the International Brain Laboratory Luigi Acerbi mentors PhD students including Daolang Huang and Nasrulloh Loka, and collaborates widely with researchers such as Samuel Kaski. He has contributed to open-source tools like PyVBMC and is involved in community initiatives such as the EurIPS conference. His work is supported by grants from the Research Council of Finland, Business Finland, and the UKRI Turing AI World-Leading Researcher Fellowship. He leads a research lab focused on amortized probabilistic inference, with ongoing projects including PriorGuide and Stacked VBMC, aiming to make Bayesian methods more practical and accessible for real-world scientific and engineering applications.
Professor Ingrid Bouwer Utne is a faculty member at the Department of Marine Engineering, Norwegian University of Science and Technology (NTNU). Her primary research focuses on risk analyses of ships, marine systems, and autonomy, with emphasis on operational safety, maintenance management, and risk control in autonomous maritime technologies. Key Projects: Leader of the Risk Group at NTNU, involved in the SFI Autoship initiative and ERC AdG BREACH project addressing risk-based rationality in autonomous systems. Research Interests: Autonomous systems design, probabilistic risk assessment, safety engineering, and risk-informed decision-making for marine operations. Grants & Funding: Secured funding from the Research Council of Norway, MAROFF, and industry partners for projects like ORCAS (Online Risk Management for Autonomous Ships) and UNLOCK (Supervisory Risk Control). She supervises numerous PhD students and postdocs, focusing on topics such as autonomous vessel navigation, risk modeling for underwater robotics, and decarbonization of maritime systems. Her work bridges theoretical risk analysis with practical applications in marine autonomy and safety systems.
Dr. Abdallah Chehade is an Associate Professor in the Department of Industrial and Manufacturing Systems Engineering at the University of Michigan-Dearborn , where he leads the Informatics, Reliability, and Data Analytics (IRDA) lab . He holds a Ph.D. in Industrial Engineering from the University of Wisconsin-Madison (2017), with minors in Computer Sciences and Statistics, alongside an M.S. in Mechanical Engineering and a B.E. in Mechanical Engineering from the American University of Beirut. Research Interests span safe and robust deep learning solutions , explainable AI , data fusion for degradation modeling , and Bayesian statistical modeling . His work integrates AI/ML with prognostics and Internet of Things (IoT) to address challenges in reliability analytics and industrial data science . Publications highlight advancements in deep autoencoders , LSTM networks , and hybrid models for warranty forecasting , with applications in battery cells , sheet metal stamping , and rail transportation . His grants from Ford, Honda, and the U.S. Army focus on smart manufacturing , AI for sensor modeling , and digital twins . Lab Members include Ph.D. students working on topics like physics-based AI , computer vision , and deep learning for prognosis . He serves on the INFORMS Quality, Statistics, and Reliability (QSR) Council and maintains affiliations with IEEE , INFORMS , and IISE .
