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Jeremy LaliberteView profile

Professor

• Composite Materials
• Fibre Metal Laminates (FMLs)
• Uninhabited Aerial Vehicles (UAVs)
• +7 more

Professor Jeremy Laliberte is a faculty member in the Department of Mechanical and Aerospace Engineering at Carleton University. He focuses on novel aircraft structures, composite materials, and next-generation UAV/MAV design. Previously a Research Officer at the National Research Council Institute for Aerospace Research (2001-2008), he now leads the Advanced Aircraft Design Lab and co-leads the NSERC CREATE UTILI initiative with multiple Canadian universities. Research areas include: Composite and hybrid material manufacturing Biomimetic aerospace structures Hydrogen fuel cell aircraft power systems Finite element analysis of impact damage Urban air mobility safety protocols Recent publications emphasize: High-fidelity composite modeling Low-velocity impact analysis Hybrid delivery logistics Battery lifecycle optimization Scientific recognition includes: Best Poster at ICAF 2023 Best Student Paper/Presentation awards (2022 AEAC) He supervises a research group working on projects with industry partners like Bell Helicopter, Bombardier, and NRC.

• Theoretical Physics
• Astrophysics
• General Relativity
• +6 more

Daniela Doneva, Ph.D., is a Lecturer at the Institute for Astronomy and Astrophysics (IAAT) , University of Tübingen. Her work spans theoretical and computational astrophysics, focusing on nonlinear gravitational phenomena in modified theories of gravity. Key affiliations: Institute for Astronomy and Astrophysics (IAAT), University of Tübingen Degree: Ph.D. in Physics Research interests include: Black Hole Scalarization : Spin-induced and curvature-induced scalarization mechanisms in black holes and neutron stars. Modified Gravity Theories : Einstein-Gauss-Bonnet, scalar-tensor theories, and teleparallel gravity extensions. Gravitational Wave Physics : Modeling waveforms, stability analysis, and LISA mission applications. Numerical Relativity : Simulations of compact object mergers and nonlinear evolutions. Her publications emphasize computational methods (e.g., neural network surrogates), stability analysis, and observational constraints from gravitational wave detectors like LISA. No scientific awards or student advisement details were found in the provided texts.

Shu-Wei HuangView profile

Associate Professor

• Photonics
• Quantum Engineering
• Nonlinear Optics
• +5 more

Shu-Wei Huang is an Associate Professor in the Department of Electrical, Computer, and Energy Engineering at the University of Colorado Boulder, affiliated with the College of Engineering and Applied Science. His research focuses on advanced photonics and quantum engineering, with specialties in nonlinear optics, frequency combs, and ultrafast laser systems. He holds affiliations with both the ECEE department and the Photonics and Quantum Engineering group. His work emphasizes novel laser designs, microresonator-based systems, and applications in optical sensing and quantum technologies. Dr. Huang's research interests include the development of high-performance laser systems, such as counterpropagating all-normal dispersion (CANDi) fiber lasers, and the creation of advanced frequency comb technologies. He explores topics like dissipative soliton generation, parametric oscillation, and the integration of machine learning for predictive modeling in nonlinear optics. His experimental work involves cutting-edge platforms such as lithium niobate microresonators and graphene-enhanced devices. His recent contributions span innovations in photonic flywheel systems for stable frequency combs, broadband magnetometry using magnetic nanoparticles, and lidar measurement techniques. He has pioneered methods for deterministic microcomb generation and explored applications in high-resolution imaging and biochemical sensing. His research bridges fundamental physics with engineering applications, addressing challenges in precision metrology and quantum-enabled technologies. Dr. Huang's lab is located in ECEE 1B79, and his work is supported by grants focusing on nonlinear optics, ultrafast lasers, and integrated photonics. His team actively collaborates on projects involving coherent dual-comb spectroscopy, electrically tunable frequency combs, and nanophotonic devices for next-generation optical systems.

Yoeri R.J. PoelsView profile

Researcher

• Fusion Energy
• Tokamak Engineering
• Deep Learning Applications
• +4 more

Yoeri R.J. Poels is a researcher at Eindhoven University of Technology specializing in fusion energy and artificial intelligence applications. His work focuses on enhancing tokamak operations through data-driven modeling and AI techniques, with significant contributions to the Eurofusion Tokamak Exploitation Team and MAST Upgrade collaboration. His research interests bridge fusion energy engineering and artificial intelligence , particularly in developing surrogate models and deep learning algorithms for tokamak control systems. Key areas include power exhaust management, divertor technology, and real-time plasma control solutions for next-generation fusion reactors. His work integrates physics-based modeling with advanced machine learning techniques to address critical challenges in fusion energy development. Analysis of his publication record reveals a strong trajectory in applying AI methods to fusion challenges , with increasing focus on practical implementation in experimental tokamak devices. His recent work demonstrates how data-driven approaches can enhance control systems for managing transient heat loads and improving reactor stability. As part of major international collaborations including the MAST Upgrade team and TCV tokamak research, Poels contributes to cutting-edge fusion research across multiple institutions. His work appears in high-impact journals including Nature Energy , Communications Physics , and Nuclear Fusion , reflecting the interdisciplinary nature of his research at the intersection of physics, engineering, and computer science.

Marianna SafronovaView profile

Professor

• Quantum Metrology
• Dark Matter Searches
• Atomic Clocks
• +7 more

Marianna Safronova is a Professor in the Department of Physics & Astronomy at the University of Delaware, affiliated with the College of Arts & Sciences. She leads the Safronova Group, which focuses on quantum metrology, dark matter searches, and precision atomic calculations. Her work spans quantum sensors for space applications, atomic clock development, and fundamental physics tests. Education details are not explicitly provided, but her extensive contributions suggest advanced training in theoretical atomic physics. Her research emphasizes high-precision atomic data via the Atomic Data Portal, collaborations on the Thorium Nuclear Clock project (ERC Synergy Grant), and NSF-funded quantum sensing algorithms. Key research areas include: quantum sensors for dark matter detection, Coulomb-crystal clocks, space-based quantum technologies, and applications of neural networks in atomic physics. She co-organizes the 2025 Bad Honnef Physics School on quantum metrology for new physics searches. Labs/Teams: Safronova Group, Q-SEnSE Institute, Thorium Nuclear Clock collaboration. Current projects involve space mission proposals for dark matter detection and development of next-generation atomic clocks.

Dirk ThißenView profile

Researcher

• Distributed Systems
• Web Services
• Quality of Service (QoS)
• +3 more

Dirk Thißen is a Postdoctoral Researcher at RWTH Aachen University, affiliated with the COMSYS research group. His work focuses on distributed systems, web services, and quality of service (QoS) management. He has contributed to interdisciplinary projects like SensorCloud, aiming to develop trustworthy platforms for interconnected sensors and actuators. His research spans service-oriented architectures, network protocols (e.g., SIP/IMS), and middleware solutions for collaborative engineering and distributed systems. Key areas: Sensor networks, cloud computing, QoS optimization, and service composition Notable projects: SensorCloud, electronic service markets, and IMS service validation Thißen's publications address challenges in distributed systems, including service replication for reliability, multimedia integration in design processes, and protocol validation for next-generation networks. His work often bridges theoretical concepts with practical implementations in telecommunications and collaborative environments. His research consistently emphasizes performance evaluation and scalability, with contributions to both academic conferences and industry-relevant applications. While no specific awards are noted in the provided text, his extensive publication record reflects sustained engagement in high-impact fields.

Torsten BraunView profile

Professor

• Machine Learning
• Networking and Telecommunications
• Edge Computing
• +3 more

Prof. Torsten Braun is a Professor and Head of the Communication and Distributed Systems (CDS) research group at the Institute of Computer Science, University of Bern. His research focuses on advanced networking technologies, edge computing, and machine learning applications in telecommunications. He leads projects addressing challenges in 5G/6G networks, federated learning optimization, and intelligent systems for smart cities. His work integrates theoretical frameworks with practical implementations, emphasizing distributed systems, service-oriented architectures, and IoT security. Key research areas include edge caching strategies for VR/AR applications, trajectory prediction using reinforcement learning, and resilient network design against jamming attacks. Braun has contributed to innovations in vehicular networks (V2X), RAN intelligence, and decentralized machine learning frameworks. He holds leadership roles in developing adaptive resource management systems for edge-cloud environments and has pioneered solutions for energy-efficient federated learning in heterogeneous IoT ecosystems. Publications span topics like spatial-temporal point cloud sensing, mobility-aware service orchestration, and secure positioning systems. His team explores cross-disciplinary applications such as LoRaWAN-based urban heat monitoring and blockchain-inspired public key infrastructures for IoT (Veritaa-IoT). Braun actively engages in standardization and industry collaborations to advance next-generation network architectures.

Jean-Paul M.G. LinnartzView profile

Professor

• Optical Wireless Communication
• LiFi Technology
• Information Security
• +2 more

Jean-Paul M.G. Linnartz is Full Professor in the Signal Processing Systems group at Eindhoven University of Technology and Research Fellow at Signify (Philips Lighting). His research spans optical wireless communications, security systems, and signal processing algorithms. Key contributions include: Pioneering work on LiFi technology and visible light communication systems Innovations in biometric security and electronic watermarking OFDM and MC-CDMA techniques for wireless networks Publications show consistent focus on bridging fundamental algorithms with practical applications in lighting and communication systems. Recent work advances LED channel modeling and photonic communication frameworks. He holds over 75 patents and has co-founded three technology ventures. As Senior Director at Philips Research (1995-2008), he led groups in security, connectivity, and IC design. Honors include IEEE Fellowship (2011) for contributions to communications technology. Current projects include OptiLiFi (optimizing LiFi communication) and ELIoT (Enhance Lighting for Internet of Things), developing next-generation optical wireless networks.

Sara Betancur GiraldoView profile

Researcher

• Computer Science
• Operations Research
• Network Flow Modeling
• +3 more

Sara Betancur Giraldo is an Education/Research Assistant in the Department of Mechanical Engineering at Eindhoven University of Technology. Her work focuses on the intersection of autonomous mobility systems and fairness optimization, particularly in intermodal transportation networks. Research Interests: Using rigorous mathematical modeling, she investigates accessibility fairness in autonomous mobility-on-demand systems, operational cost optimization, and network flow dynamics. Her research aligns with UN Sustainable Development Goals related to sustainable cities and transportation. Research Output: In 2024, she published a peer-reviewed conference article titled On Accessibility Fairness in Intermodal Autonomous Mobility-on-Demand Systems in IFAC-PapersOnLine . This work introduces fairness-aware optimization frameworks for next-generation mobility systems. Her thesis Optimization Models for Accessibility-fairness-aware Planning of Intermodal Mobility Systems was supervised by Dr. M. R. U. Salazar, Dr. F. Paparella, and Dr. L. Pedroso.

• Neuroscience
• Brain Connectivity
• Neuroimaging
• +7 more

Aurina Arnatkeviciute is a Research Fellow at Monash University's Turner Institute for Brain & Mental Health within the Faculty of Medicine, Nursing and Health Sciences. Her work focuses on the intersection of neuroscience, genetics, and psychiatry, with particular emphasis on brain connectivity, neuroimaging, and the genetic underpinnings of psychiatric disorders. She actively contributes to large-scale international collaborations including the ENIGMA consortium. Dr. Arnatkeviciute's research interests span multiple domains of cognitive neuroscience and psychiatric research. She investigates how genetic factors influence brain connectivity and network organization, with applications to understanding conditions like schizophrenia, ADHD, and autism spectrum disorders. Her work bridges molecular neuroscience with systems-level brain organization, utilizing advanced neuroimaging techniques combined with genetic and transcriptomic data. She has made significant contributions to imaging transcriptomics - the integration of brain-wide gene expression data with neuroimaging findings. Her publication record demonstrates expertise across multiple methodologies including genome-wide association studies, diffusion MRI connectomics, and large-scale international collaborations. Her recent work examines how socioeconomic factors like gender inequality impact brain structure, how genetic variations affect inhibitory control, and the neuroanatomical signatures of schizotypy across diverse populations. Australasian Cognitive Neuroscience Society (ACNS) Emerging Researcher Award (2020) Australasian Neuroscience Society Paxinos-Watson Award for the most significant neuroscience paper published by a member of the Society (2022) Discovery Early Career Researcher Award (DECRA) (2022) Monash University Dean's Award for Doctoral Thesis Excellence for "Genetics of brain network hubs" (2020) Monash University Early Career Researcher Publication Prize for "A practical guide to linking brain-wide gene expression and neuroimaging data" (2020) Dr. Arnatkeviciute serves as a supervisor for Honours students at Monash University, teaching units including PSY4215: Advanced Data Science and PSY4130: Developmental Psychology and Clinical Neuroscience. She is a Chief Investigator on the NHMRC-funded project "Neuropharmacology of decision-making: causal brain network modelling across species" (2022-2026), demonstrating her leadership in securing competitive research funding. Her supervisory activities extend through 2025, indicating ongoing commitment to mentoring the next generation of neuroscience researchers. As a member of the Turner Institute for Brain & Mental Health, Dr. Arnatkeviciute collaborates with a multidisciplinary team of neuroscientists, clinicians, and data scientists. Her work within the ENIGMA consortium connects her with researchers across more than 30 countries, facilitating large-scale analyses of brain structure and function across diverse populations. This collaborative environment supports her research at the intersection of genetics, neuroimaging, and psychiatric disorders.

• Biomedical Engineering
• Signal Processing
• Machine Learning
• +3 more

Andrea Ronco is a Researcher affiliated with the Department of Rehabilitation Engineering at ETH Zürich. His work focuses on advancing sensor technologies and their applications in biomedical engineering, autonomous systems, and IoT. He leads research in mmWave radar systems for non-invasive vital sign monitoring, in-ear gesture recognition, and low-power embedded sensor solutions. Professional Affiliation: Staff of Professorship for Rehabilitation Engineering Research interests span radar technology optimization, real-time signal processing for healthcare, and robust sensor fusion for autonomous vehicles. His recent work emphasizes miniaturization and energy efficiency in wearable and embedded systems. Key contributions include TinyssimoRadar and In-ear-voice platforms that integrate advanced sensing with minimal power consumption. Ronco's publications address challenges in sensor robustness against environmental conditions and real-world deployment scenarios. His research outputs demonstrate expertise in interdisciplinary domains combining electrical engineering, machine learning, and biomedical applications. Current projects likely explore next-generation sensing modalities for wearable health monitoring and smart infrastructure systems.

Yvonne Yu-Hsuan ChenView profile

Professor

• Synthetic Biology
• Tumor Immunology
• CAR-T Cell Therapy
• +4 more

Dr. Yvonne Yu-Hsuan Chen is a Professor at the University of California, Los Angeles (UCLA) School of Medicine, Department of Microbiology, Immunology, and Molecular Genetics. She earned her B.S. in Chemical Engineering from Stanford and a Ph.D. from Caltech, followed by postdoctoral training at Harvard Medical School and Seattle Children’s Research Institute. At UCLA since 2013, she co-directs the Tumor Immunology and Immunotherapy Program at the Jonsson Comprehensive Cancer Center and is a Parker Institute member. Education: Stanford (BS), Caltech (PhD), Harvard Medical School (Postdoc) Key research areas: Synthetic Biology, CAR-T cell engineering, tumor immunology, gene therapy Dr. Chen pioneers next-generation CAR-T therapies, focusing on overcoming tumor microenvironment challenges like hypoxia, antigen escape, and immune suppression. Her work integrates synthetic biology to reprogram T cells for enhanced specificity and metabolic compatibility with tumors. Recent studies highlight bispecific CARs, transgene silencing solutions, and metabolic reprogramming without antigen stimulation. Her publications span top journals like Nature Metabolism , Cell , and Cancer Discovery , emphasizing clinical translation. Collaborations include institutions like UCSF, Caltech, and Yale. Her lab trains future scientists, with alumni now at companies like Kite Pharma and institutions like Yale. Scientific awards: NIH Early Independence, ACGT Young Investigator, NSF CAREER Dr. Chen’s team includes PhD students and postdocs working on CAR-T trafficking, cytokine regulation, and tumor targeting. She leads clinical trials and translational research, aiming to improve CAR-T safety and efficacy for both hematologic and solid tumors.

• Information Theory
• Optimization
• Wireless Communications
• +1 more

Dr. Teng-Hui Huang is a Postdoctoral Research Associate at the School of Electrical and Information Engineering , University of Sydney. His academic journey includes a Ph.D. in Electrical and Computer Engineering from Purdue University (2022), and prior degrees from National Taiwan University (B.S. 2015, M.S. 2017). Research Interests: Information Theory, Optimization, Wireless Communications, and Machine Learning. Current Projects: Information-Theoretic Multi-Modal Machine Learning, Machine Learning for Next-Generation Wireless Communications, Non-convex Optimization Algorithms for Information Theoretic Privacy. Publications highlight his work on information bottleneck methods, optimization algorithms, and wireless communication systems. He advises research student Abd Al-rahman ODEH on unsupervised multimodal learning.

• Wireless Communications
• Internet of Things (IoT)
• Edge Computing
• +7 more

Zheng Chang is a Professor at the Institute of Computing Technology, School of Computer Science and Technology, University of Chinese Academy of Sciences in Beijing, China. With a PhD from the University of Jyväskylä (2013), Chang has established a prolific research career with over 240 publications spanning from 2011 to 2025. Chang maintains strong collaborative ties with researchers at the Chinese Academy of Sciences' Shenyang Institute of Automation and has developed significant international collaborations, particularly with Finnish researchers including Timo Hämäläinen. Chang's research focuses on cutting-edge areas at the intersection of wireless communications, artificial intelligence, and edge computing. Their work prominently features federated learning, UAV networks, resource allocation, and privacy-preserving techniques for IoT applications. Recent publications demonstrate a strong emphasis on vehicular edge intelligence, split learning architectures, and RIS-assisted communications. The research output shows consistent growth with 42 publications in 2024 alone, indicating an active and expanding research program. Chang's 15 most recent publications reveal a clear research trajectory toward addressing the challenges of resource-constrained edge environments through innovative learning architectures. The work spans theoretical frameworks for privacy preservation in federated learning to practical implementations for UAV networks and vehicular systems. A notable trend is the integration of AI-generated content techniques with traditional federated learning approaches to overcome data scarcity issues in edge environments. While specific awards aren't documented in the provided text, Chang's extensive publication record in top-tier IEEE journals including IEEE Transactions on Wireless Communications, IEEE Internet of Things Journal, and IEEE Transactions on Vehicular Technology demonstrates significant scholarly impact. The research has been widely cited, with multiple highly cited co-authors including Timo Hämäläinen (66 co-authored papers), Zhu Han (38 papers), and Geyong Min (31 papers). Chang's work shows strong practical applications across multiple domains including intelligent transportation systems, smart agriculture, healthcare IoT, and next-generation 6G networks. The research program appears well-funded through collaborations with major institutions and demonstrates clear translational potential for real-world deployment in edge computing environments.

Kelly VaughanView profile

Associate Professor

• Statistical Genetics
• Bioinformatics
• Genomics
• +3 more

Kelly Vaughan is the Chair and Associate Professor of Biology at King University. Her academic journey includes a BS in Biochemistry and Animal Science from Berry College, a PhD in Biochemistry from Texas A&M University, and postdoctoral work at the University of Alabama at Birmingham (UAB), where she later served as a Research Assistant Professor in Biostatistics. Her expertise spans statistical genetics, bioinformatics, and interdisciplinary genomics research. She transitioned to academia to focus on undergraduate research mentorship, emphasizing hands-on laboratory experience and Women in STEM advocacy. Her research integrates functional genomics and pathway analysis, addressing complex diseases such as obesity, rheumatoid arthritis, and cancer. A pivotal moment in her career was her 2015 cancer diagnosis, which underscored her commitment to holistic education and resilience. She actively involves students in regional and national conferences, fostering their integration into scientific communities. Her work highlights methodological rigor in genetic analysis and the ethical use of genomic data. Key contributions include studies on genetic loci associated with metabolic traits in Indigenous populations, gene expression in rheumatoid arthritis, and the impact of dietary interventions on aging biomarkers. Her teaching philosophy merges clinical relevance with cutting-edge research, aiming to inspire future scientists through both classroom and lab experiences.