Prof. Gabriele Schrag holds the Professorship of Microsensors and Actuators at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. Her research focuses on MEMS (Micro-Electro-Mechanical Systems), including microsensors, actuators, and their applications in acoustics, microfluidics, and bioengineering. She has pioneered work in virtual prototyping for system-level modeling to enhance device robustness and performance. Education: PhD (summa cum laude) from TUM on 'Modeling coupled effects in microsystems' Habilitation in sensor systems technology (2018) Acting head of the Chair of Technical Electrophysics (2018-2023) Research emphasizes acoustic MEMS transducers , electrohydrodynamic printing , and physics-based modeling . Notable projects include developing piezoelectric MEMS microphones with corrugated membranes and integrated micropump systems. Awards include the Bavarian Prize for Good Teaching (2021) and Eurosensors Fellow Award (2019). Her work bridges virtual prototyping with real-world applications , addressing challenges in miniaturization, energy efficiency, and sensor integration for medical and industrial systems.
Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University of London , within the College of Engineering, Design and Physical Sciences . He serves as the Vice-Dean of the NCUT Transnational Education (TNE) programme, overseeing a joint school with North China University of Technology. He has been at Brunel since 2002, progressing from Lecturer to Professor in 2013. Education: PhD, Institute of Software, Chinese Academy of Sciences (1997) Postdoctoral Research, School of Computer Science and Informatics, Cardiff University (1999–2002) His primary research interests lie in high performance computing, big data analytics, and artificial intelligence, with applications in smart grids, smart manufacturing, and cybersecurity. He focuses on developing interpretable, robust, and lightweight AI models, including work in causal AI, parallel machine learning, and edge computing. His research integrates advanced techniques such as deep learning, reinforcement learning, and blockchain for real-world system optimization. An analysis of his recent publications reveals a strong and consistent research trajectory in AI-driven solutions for environmental monitoring (e.g., PM2.5 prediction), industrial defect detection, IoT security, and intelligent transportation. His work frequently combines deep learning with graph-based modeling and federated or reinforcement learning, emphasizing scalability, efficiency, and robustness in distributed and edge environments. Scientific Awards and Recognition: Fellow of the Institution of Engineering and Technology (IET) Fellow of the British Computer Society (BCS) Shortlisted for the Computing UK BIG DATA EXCELLENCE AWARDS 2018 in the category of Most Innovative Big Data Solution Maozhen Li has successfully supervised 25 PhD students and examined over 30 PhD theses externally. He has secured significant research funding from EPSRC, the European Union (Horizon 2020), Innovate UK, and the Royal Society , with projects including Z-BRE4K, IoRL, and TDX-ASSIST. He serves as an Associate Editor for journals such as the Journal of Cloud Computing and the International Journal of Grid and High Performance Computing . Research Groups and Teams: He is affiliated with the Intelligent Engineering Frameworks (IEF) research group at Brunel, contributing to collaborative efforts in AI, IoT, and smart systems. His leadership in transnational education also fosters international research collaboration between Brunel and Chinese institutions.
Elisa Perrone is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. Her research focuses on dependence modeling, copula theory, and their applications in fields such as public transport analysis, environmental risk assessment, and renewable energy forecasting. Academic Rank: Assistant Professor University: Eindhoven University of Technology (TU/e) Department: Mathematics and Computer Science Elisa’s work explores discrete copulas, zero-inflated data, optimal experimental design, and uncertainty quantification. She has contributed to modeling dependence structures in complex datasets, particularly in transportation systems and climate science. Recent research outputs highlight copula-based statistical post-processing for weather forecasts, analysis of multi-way contingency tables, and uncertainty reduction in LED health management. Her publications span top-tier journals and conferences in statistics and applied mathematics. Scientific Award : Second Best Poster Presentation Award (2015) Elisa actively organizes workshops like the Eurandom Workshop on Dependence Modeling and contributes to editorial activities. She teaches courses on linear statistical models, regression models, and dependence modeling.
Dr. Hui Lu is an Assistant Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA), where he has been serving since September 2023. Prior to joining UTA, he was an Assistant Professor at SUNY Binghamton from 2017 to 2023. His academic journey includes a Ph.D. in Computer Science from Purdue University (2017), and Master’s and Bachelor’s degrees in Electronic Engineering from Shanghai Jiao Tong University. Ph.D., Computer Science, Purdue University, 2017 M.S., Electronic Engineering, Shanghai Jiao Tong University, 2009 B.S., Electronic Engineering, Shanghai Jiao Tong University, 2006 Dr. Lu's research centers on systems software with a focus on operating systems, virtualization, cloud computing, file and storage systems, and computer networks. His work emphasizes performance optimization and security in cloud-native environments. He has collaborated with leading industrial research labs including HPE Labs, IBM Research, Microsoft Research, AT&T Labs, and NEC Labs. His recent publications span top-tier venues such as OSDI, SOSP, USENIX ATC, and VLDB. The article trends reflect a strong emphasis on secure container technologies, memory tiering, packet processing optimization in virtualized networks, and efficient cloud storage systems. His work increasingly integrates hardware-aware optimizations and lightweight security mechanisms. NSF CAREER Award (2023) UT System Rising STARs Award (2023) Summer Faculty Fellowship, Air Force Research Lab (2019) Dr. Lu has successfully advised multiple Ph.D. students, including Jiaxin Lei, who is now an Assistant Professor at Kean University. His research is supported by major grants from the National Science Foundation (NSF) and the Air Force Research Lab (AFRL), focusing on secure containers, non-volatile memory management, and cloud-native virtualization. He has served as Principal Investigator (PI) on multiple funded projects, demonstrating strong leadership in research and innovation. He is actively involved in teaching core courses such as Operating Systems and advanced topics in systems and architecture. He mentors a growing group of Ph.D. students and welcomes motivated individuals to join his research group.
Yongle Zhang is an Assistant Professor in the Department of Computer Science at Purdue University, joining in Spring 2021. His research focuses on systems software, particularly improving reliability and availability in complex distributed systems through failure detection and diagnosis. He holds a Ph.D. from the University of Toronto and has prior degrees from Shandong University and the Chinese Academy of Sciences. **Education:** Ph.D., University of Toronto, Computer Engineering (2020) Master, Institute of Computing Technology, Chinese Academy of Sciences (2013) Bachelor, Shandong University, Computer Science (2010) **Research Interests:** His work addresses challenges in distributed systems, including root cause diagnosis in cloud environments, diagnosable software design, and concurrency bugs in persistent memory applications. Recent projects include analyzing live debugging activities in production systems and detecting cross-system interaction failures. **Awards & Grants:** SIGOPS Dennis M. Ritchie Thesis Award (2021) Meta 2022 Systems Research Award NSF Core Grant (2021) **Advising & Labs:** Advises PhD and Master’s students in distributed systems research (e.g., Shangshu Qian, Panchapakesan Chitra Sruthi). Leads a lab focused on production system reliability, with collaborations on cloud infrastructure and failure analysis tools.
Dr. Ying He is a Senior Lecturer at the School of Electrical and Data Engineering, University of Technology Sydney (UTS). Her research focuses on wireless communication networks, particularly integrating machine learning with satellite and terrestrial systems. She holds a BEng from Beijing University of Posts and Telecommunications (2009) and a PhD from UTS (2017). Prior to her academic role, she worked on TD-LTE chip design at the Chinese Academy of Sciences. Affiliations : Faculty of Engineering and Information Technology Global Big Data Technologies Centre (GBDTC) Education : BEng in Telecommunications Engineering, Beijing University of Posts and Telecommunications (2009) PhD in Engineering (Telecommunications), UTS (2017) Her research interests include satellite communication (GEO-LEO integration), spectrum sharing, vehicular communication, and applying machine learning to physical layer algorithms. Notable contributions include optimizing beam design in LEO networks and developing secure IoT systems. She supervises PhD/Master’s students and teaches courses like CCNA and capstone projects. Funded projects span satellite networks, IoT security, and supply chain tracking. Recent grants include SmartSat CRC initiatives and collaborations with industry partners like Intel and Ericsson. Her work addresses challenges in 6G, UAV-enabled computing, and resilient quantum algorithms.
Dr. Sie Teng Soh is an Associate Professor at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences. With qualifications including a PhD from Louisiana State University, he specializes in computer networks, wireless systems, and algorithm design. Research focuses on: Network topology optimization for UAV systems Energy-efficient IoT task scheduling Reliable wireless communication protocols Game-theoretic network management Green computing in software-defined networks Publication trends show advancing work in UAV network optimization, with recent articles addressing max-min rate optimization, energy harvesting in IIoT, and machine learning approaches for coverage prediction. His research consistently addresses practical challenges in wireless network deployment under real-world constraints. Teaching areas include advanced courses in network reliability and traffic engineering. Professional service includes editorial roles for IEEE Transactions on Parallel and Distributed Systems and program committee memberships for major conferences including FAST and EuroSys.
Dr. Ehsan Pashajavid is a Senior Lecturer at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences, within the Faculty of Science and Engineering. His research focuses on stochastic optimization, renewable energy integration, microgrid control, and electric vehicle systems. Research interests include: Microgrid and smart grid control algorithms Renewable energy resource management Power system stability and operation Energy storage optimization Electric vehicle-grid integration His publications demonstrate significant contributions to power system resilience, with recent work emphasizing battery storage economics, fault-tolerant converters, and model predictive control for grid stability. Article trends show strong focus on renewable integration challenges and optimization techniques for modern energy systems. Awards include Senior Member status in IEEE and its Power & Energy, Industrial Applications, and Power Electronics societies. Teaching responsibilities encompass graduate courses in Renewable Power Generation Systems, Smart Grid Control, and Renewable Energy Principles.
Rui Teixeira is an Assistant Professor in the School of Civil Engineering at University College Dublin (UCD). He leads UCD's Centre for Critical Infrastructure Research (CCIR) and focuses on Uncertainty Quantification, Safety, and Risk in civil engineering systems, with applications to infrastructure resilience. His research emphasizes reliability analysis, multi-fidelity modeling, and AI-driven risk assessment. Education: MSc in Civil Engineering, University of Porto, Portugal PhD in Civil Engineering, Trinity College Dublin Professional Certificate in University Teaching and Learning, UCD Research Interests: Development of novel reliability analysis techniques Resilience of infrastructure systems Artificial intelligence applications for risk assessment Probabilistic system evaluation and safety standards Grants & Projects: Smart Enforcement of Transport Operations (SETO), Horizon Europe (2023–2026) Optimality-Tracking Civil Engineering Systems, Enterprise Ireland (2023–2025) Floating Offshore Wind Dynamic Cables (FlOWDyn), Sustainable Energy Authority of Ireland (2024–2027) Teaching: Coordinates courses such as 'Civil Engineering Systems' and 'Design of Structures 1'. Labs/Teams: Director of the Centre for Critical Infrastructure Research (CCIR), focusing on interdisciplinary approaches to infrastructure resilience.
Farokh B. Bastani is a Professor of Computer Science at the University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a Ph.D. from the University of California, Berkeley. His research focuses on AI-driven software synthesis, embedded real-time systems, formal methods, high-assurance autonomous systems, and fault-tolerant distributed systems. He leads research in the NSF Industrial/University Cooperative Research Center (IUCRC). Education: Ph.D., Computer Science, UC Berkeley His work emphasizes software reliability, safety assurance, and modular parallel programming. Research outputs include journal and conference publications, though specific titles are not listed here. The awards section appears incomplete (404 error noted). Labs/Teams: Active involvement with the NSF IUCRC program. No advising records or grant details provided in the text.
Reza Curtmola is a Professor in the Department of Computer Science at NJIT. His research focuses on cybersecurity, distributed systems, and network security with an emphasis on secure routing, cloud computing, and privacy-preserving technologies. He holds a Ph.D. in Computer Science from Johns Hopkins University (2007), an M.S. from the same institution (2003), and a B.S. from the Politehnica University of Bucharest (2001). Dr. Curtmola’s work addresses challenges in wireless mesh networks, vehicular communication systems, and mobile-cloud integration. His contributions include innovative solutions for secure network coding, distributed resource management (e.g., parking assignment systems), and auditable data storage mechanisms. He has developed middleware frameworks like Moitree for mobile-cloud applications and has explored defenses against side-channel attacks, cache leaks, and entropy-based network vulnerabilities. His research also extends to privacy in vehicular DSRC protocols, dynamic traffic optimization, and verifiable code review systems. He has published extensively on topics ranging from cryptographic defenses in distributed systems to practical implementations of remote data checking in untrusted clouds. Current research activities include advancing secure cloud infrastructure, improving mobile crowdsensing reliability, and mitigating threats in IoT-enabled urban environments. His work often bridges theoretical foundations with practical system implementations, emphasizing real-world applicability in smart cities and critical infrastructure systems.
Dr. Irina T. Garces is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Carleton University. She holds a Ph.D. from the University of Alberta and specializes in composite-smart materials, additive manufacturing, and polymer processing. Her research focuses on developing 'materials as machines'—adaptive material systems for applications in soft robotics, biomedical devices, and aerospace. Key areas include shape memory polymers, electro-active composites, and novel 3D printing technologies for biodegradable materials. Her publications demonstrate consistent focus on smart material innovation, with recent work exploring moisture effects in additive manufacturing and cellulose-based medical implants.
Jasmin Grosinger is an Associate Professor at Graz University of Technology's Institute of Microwave and Photonic Engineering, specializing in wireless systems and RF engineering. Her research advances sustainable wireless technologies through innovations in energy harvesting and communication systems. Primary research areas include: Radio Frequency Identification (RFID), antenna design for IoT applications, wireless power transfer efficiency, and development of batteryless sensor systems. Recent work emphasizes miniaturization challenges in metal environments and radiation-hardened space applications. Publication analysis reveals strong focus on: impedance measurement methodologies, NFC/WPT interoperability solutions, ultra-low power circuit design, and robust wireless systems. Research consistently addresses energy efficiency in passive electronics. Awards recognize contributions to microwave theory and practical implementations: Administrative Committee Member of IEEE MTT-S, Distinguished Microwave Lecturer award, and multiple best paper contest awards. Current projects investigate electromagnetic compatibility in challenging environments and next-generation wireless standards.
Dr. Min Sun is an Associate Professor and Director of the Undergraduate Program in the Department of Civil Engineering at the University of Victoria (UVic). He holds a PhD from the University of Toronto. His research focuses on structural engineering and steel structures, particularly in the areas of steel connections, seismic resilience, and numerical modeling. Dr. Sun has extensive academic and professional experience, including roles as Assistant Professor at UVic (2016–2022), Lecturer at the University of Toronto, and structural design roles in industry. His research interests emphasize the performance of steel structures under extreme loads, including earthquake engineering and material behavior. Recent work includes studies on stress concentration factors in steel connections, thermal integrity of piles, and wood-frame building reliability under lateral loads. He actively contributes to professional organizations, such as serving as Vice President (Western Region) for the Canadian Society for Civil Engineering (2018–2020). Dr. Sun teaches courses including Advanced Structural Analysis (CIVE 421) and Solid Mechanics (CIVE 220) at UVic. He currently supervises graduate students in structural steel design and construction. His publications span experimental and numerical analyses, with a focus on improving design standards for steel and wood structures. Labs/Teams: Affiliated with UVic's Engineering and Computer Science faculty and the IESVIC (Institute for Energy Systems and Sustainability at UVic), though specific lab names are not explicitly stated in the text.
Monica Maly is a Part-Time Associate Professor in Rehabilitation Science within the Faculty of Health Sciences at McMaster University. Her academic profile demonstrates extensive expertise in biomechanics and rehabilitation, with particular focus on knee osteoarthritis research. She maintains an active research program with numerous recent publications spanning rheumatology, biomechanics, and rehabilitation science. Dr. Maly's research interests center on understanding the biomechanical and physiological factors contributing to knee osteoarthritis progression and developing effective interventions. Her work examines knee joint mechanics, muscle strength and capacity, gait analysis, pain management strategies, and the impact of exercise interventions on OA symptoms. She has conducted significant research on sex differences in OA, racial disparities in pain experiences, and the relationship between obesity, inflammation, and joint function. Her methodological approaches include biomechanical analysis, clinical trials, systematic reviews, and innovative technologies like soft robotics for knee bracing. Analysis of her recent publications (2023-2025) reveals a strong focus on understanding knee osteoarthritis mechanisms through biomechanical and physiological lenses, with increasing attention to social determinants of health and health disparities. Her work spans multiple disciplines including rheumatology, biomechanics, rehabilitation science, and public health, demonstrating interdisciplinary collaboration. Key trends include examining racial disparities in pain experiences, developing novel interventions like soft robotic knee braces, and investigating the complex relationships between joint loading, biomarkers, and cartilage changes. Dr. Maly has collaborated extensively with researchers across multiple institutions, as evidenced by her numerous publications in high-impact journals such as Osteoarthritis and Cartilage, Arthritis & Rheumatology, and Clinical Biomechanics. Her work often utilizes data from large longitudinal studies including the Osteoarthritis Initiative and the Canadian Longitudinal Study on Aging. While specific grant information isn't detailed in the provided text, her extensive publication record suggests successful funding of multiple research projects.
