Sándor Ádány is a Professor at the Department of Structural Mechanics , Budapest University of Technology and Economics. His work focuses on advanced structural analysis of thin-walled members and systems-based design methodologies. Research Interests: Specializes in buckling behavior of thin-walled structures, finite element modeling, cold-formed steel stability, and modal decomposition techniques. Key areas include Lateral-torsional buckling Displacement mapping in constrained FEM Stiffener optimization in plate structures Combined loading stability of tubular members Prebuckling deformation effects Fourier-based numerical methods Article Trends (2023-2025): Recent publications emphasize elastic stability analysis of thin-walled beams and tubular structures under complex loading conditions, with particular attention to prebuckling deflections, torsional rigidity effects, and numerical validation of analytical models. Innovations include Fourier-series displacement approximations and constrained finite element methodologies.
Nate Foster is a Professor of Computer Science at Cornell University and a Visiting Researcher at Jane Street . During the 2023-24 academic year, he served as a Visiting Professor at EPFL in the Data Center Systems Laboratory. His research bridges Programming Languages and Networking , focusing on formal verification, data plane programming, and language design for networked systems. His recent work includes developing symbolic automata for network verification (e.g., Active Learning of Symbolic NetKAT Automata , StacKAT ), creating efficient verifiers like KATch , and advancing type safety in data plane programming (e.g., SafeP4 ). These efforts span formal methods, algorithm optimization, and practical networked systems. Notable scientific awards include: NSF CAREER Award Sloan Research Fellowship ACM SIGCOMM Rising Star Award ACM SIGPLAN Robin Milner Award He actively contributes to PLDI, POPL, OOPSLA, and ICFP, serving as author, editor, and committee member. His GitHub repositories include tools for academic website templates and programming language research codebases.
Hossam H. H. Mousa is a Doctoral Researcher at Aalto University's Department of Electrical Engineering and Automation, School of Electrical Engineering. He also serves as an Assistant Lecturer at South Valley University's Department of Electrical Engineering since 2020. B.Sc. in Electrical Engineering (2017), South Valley University M.Sc. in Electrical Power and Machines Engineering (2020), South Valley University His research focuses on electrical power engineering, including maximum power point tracking (MPPT) for renewable energy, power systems analysis, energy management, and machine learning applications in grid optimization. He has published extensively on topics like hosting capacity estimation, unbalanced microgrids, and hydrogen storage integration. The 15 most recent articles emphasize modern power systems optimization through machine learning (2025), smart inverter applications in renewable integration (2025), and hydrogen storage's role in cold climate energy management (2025). Earlier works include best practice studies on capacitor allocation (2024) and photovoltaic system controls (2024), earning him the 2024 Best Paper Award in the International Journal of Electrical Power & Energy Systems. Best Paper Award (2024), International Journal of Electrical Power & Energy Systems His scholarly activities span energy conversion, microgrid stability, and applied machine learning, contributing to sustainable energy transition solutions. He has collaborated on international research books addressing distribution network hosting capacity (2025) and future energy systems challenges.
Yanja Dajsuren is an Assistant Professor and Program Director of the PDEng Software Technology program at Eindhoven University of Technology's Department of Mathematics and Computer Science. Her work bridges academic research and industry applications in software engineering and mobility systems. Education: PhD in Computer Science (2015), TU/e PDEng in Software Technology (2005), TU/e MBA (2002), Maastricht School of Management Research Focus: Yanja specializes in software architecture for autonomous and cooperative driving systems, with a focus on model-driven development, functional safety, and system modularity. Her work contributes to Cooperative Intelligent Transport Systems (C-ITS) and aligns with UN Sustainable Development Goals related to smart cities and innovation. Projects: Current projects include i-CAVE (Project #6) and Horizon 2020 C-MobILE, addressing challenges in automotive software integration and cooperative driving systems. Past projects at Philips Research, NXP Semiconductors, and CWI demonstrate her long-term expertise in industrial software development. Academic Contributions: She co-founded the International Workshop on Automotive Software Architectures (WASA) and launched the Journal of Automotive Software Engineering (JASE). Her collaborations span academia and industry, including partnerships with ASML, Philips Hue, and international research teams.
Dr. Boming Zhang is a Lecturer and Postdoctoral Research Officer at the School of Mechanical and Manufacturing Engineering, UNSW. He holds a PhD in Mechanical and Manufacturing Engineering (2024) and a Bachelor of Mechanical Engineering (Honours Class 1, 2019). His expertise spans composite materials, finite element modelling (FEA), and material characterization using tools like ANSYS, Abaqus, and Digital Image Correlation. He also focuses on micromechanics of composites, stochastic analysis for material reliability, and experimental techniques involving tomography and serial sectioning. Research Interests: Composite materials, material modelling, FEA software development, Boeing Onset Theory, and optical material analysis. Awards: Recipient of the University International Postgraduate Award (2020–2023), Dean's Award (2019), and multiple Honours List recognitions at UNSW. Teaching: Primary Convenor for ENGG1300, MMAN4410, and ENGG2400 courses. Specializes in integrating research and industrial applications into teaching. His long-term goals include expanding interdisciplinary research and fostering student supervision capabilities to enhance engineering education.
Anjali Sandip is a Teaching Assistant Professor in the Mechanical Engineering Department at the University of North Dakota's College of Engineering and Mines. She holds a Ph.D. in Mechanical Engineering from the University of Kansas and maintains an active research program in computational mechanics, high-performance computing, and machine learning. Her educational background includes a Doctor of Philosophy and Master of Science in Mechanical Engineering from the University of Kansas, and a Bachelor of Engineering in Mechanical Engineering from Osmania University in Hyderabad, India. She previously served as a post-doctoral researcher at the University of Nebraska, where she developed patient-specific computational models for peripheral artery disease treatment. Dr. Sandip's research spans computational mechanics, high-performance computing, uncertainty quantification, physics-informed machine learning, and multi-physics modeling. Her work has significant applications in ice sheet dynamics, medical device modeling, and multi-phase flow simulations. She has developed open-source software frameworks that integrate finite element and finite volume methods with uncertainty quantification tools. Her recent publications demonstrate a strong focus on developing computational frameworks for multi-physics problems, with particular emphasis on GPU acceleration, uncertainty quantification, and machine learning integration. The research shows consistent application of these methods to challenging problems in earth sciences, biomedical engineering, and traditional mechanical engineering domains. Scientific Awards: NSF EPSCoR Research Fellow (2024-25) Dr. Sandip actively mentors both undergraduate and graduate researchers, and she is currently seeking Master's and Ph.D. students interested in computational mechanics, applied mathematics, scientific machine learning, and earth sciences. She serves as an active member of the Association of Computational Mechanics (USACM & IACM) and has delivered numerous presentations at professional conferences. Her research has received support from prestigious organizations including the National Science Foundation (NSF), Department of Energy (DOE), and NVIDIA. She teaches courses including Introduction to Mechanical Engineering, Thermodynamics, Machine Component Design Laboratory, Advanced Finite Element Methods, Modeling Glaciers and Ice Sheets, Statics, and Engineering Ethics, demonstrating her broad expertise across mechanical engineering disciplines.
Xiaofei Xie is an Assistant Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). He received his PhD from Tianjin University in 2018 and was a postdoctoral researcher at Nanyang Technological University (2018-2021) before joining SMU in 2022. His research focuses on software engineering, AI systems, and cybersecurity. Dr. Xie's primary research areas include program analysis, software testing, vulnerability detection, and quality assurance of AI systems. His work spans: Testing methodologies for autonomous systems and games AI security including backdoor detection and model robustness Automated program repair and code generation Formal methods and semantic code analysis His recent publications demonstrate strong emphasis on AI/ML system testing, cybersecurity applications, and program analysis techniques. Research trends show increasing focus on LLM-based program repair, autonomous system validation, and federated learning security. Major Awards: ACM SIGSOFT Distinguished Paper Awards (ASE'23, ISSTA'22, ASE'19, FSE'16) CCF Outstanding Doctoral Dissertation Award (2019) 3rd place in AI Singapore's Trusted Media Challenge (2022) Wallenberg-NTU Presidential Postdoctoral Fellowship (2019) APSEC Best Paper Award (2020) He currently advises 7 PhD/Master's students including CHENG Mingfei, KONG Jiaolong, and YU Jiongchi. Dr. Xie leads research in software reliability and AI security at SMU's SCIS.
Alice E. Smith is the Joe W. Forehand, Jr. Distinguished Professor in the Department of Industrial and Systems Engineering at Auburn University’s Samuel Ginn College of Engineering. She holds a joint appointment with the Department of Computer Science and Software Engineering and previously served as Department Chair from 1999 to 2011, during which the department experienced exponential growth in enrollment, research funding, and national ranking. She is a member of the National Academy of Engineering (elected 2025) and holds fellowships in IEEE, INFORMS, and IISE. Her educational background includes a BSCE from Rice University, an MBA from Saint Louis University, a PhD from Missouri University of Science and Technology, and a BA in Spanish from Auburn University (2022). She is a Registered Professional Engineer and has received numerous honors, including the INFORMS WORMS Award, IIE Holzman Educator Award, and multiple best paper awards. Dr. Smith’s research centers on computational intelligence, focusing on optimization, modeling of complex systems, and bio-inspired algorithms. Her work integrates artificial intelligence with traditional operations research and has applications in defense, logistics, and industrial systems. She has published extensively, with over 18,000 citations and two influential books on women in engineering. Her recent publications reflect a trend toward interdisciplinary, globally collaborative research with strong societal impact, particularly in sustainable systems and intelligent optimization. Her scientific awards include: Election to the National Academy of Engineering (2025) Joe W. Forehand, Jr. Distinguished Professor (2023) INFORMS WORMS Award (2009) Four-time Fulbright Scholar (2013, 2016, 2017, 2020) IEEE Distinguished Lecturer and INFORMS Official Speaker Multiple best paper awards from INFORMS and IIE She has been principal investigator on over $12 million in sponsored research from NSF, NASA, DoD, DHS, and industry partners like Lockheed Martin and Toyota. She mentors doctoral students and has led international research exchanges, particularly with Norway, Turkey, and Colombia. She currently serves as Editor-in-Chief of the INFORMS Journal on Computing and Area Editor for Computers & Operations Research . Dr. Smith leads research groups and collaborates with visiting scholars, focusing on intelligent optimization and sustainable logistics. Her lab has produced highly cited work in journals like Reliability Engineering & System Safety and IEEE Transactions on Reliability . She continues to expand Auburn’s global research footprint through partnerships with institutions like NTNU in Norway.
Mitra Baratchi is an Associate Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University. She leads the Spatio-temporal data Analysis and Reasoning (STAR) research group, co-leads the Automated Design of Algorithms (ADA) group, and founded the Special Interest Group on Spatio-Temporal Data Mining (SIG-SDTM) . PhD from University of Twente (Mobility Data) Master’s/Bachelor’s in Computer Engineering, Iran Research Interests focus on automated pattern extraction from spatio-temporal data across urban, environmental, and industrial domains. Key applications include: Automated Machine Learning (AutoML) for Earth Observations Time-Series Forecasting for public health (e.g., pandemic modeling) Urban Mobility Optimization with ESA, Honda, and municipalities Reliable Vehicular Communication Systems Smart Garments for Health Risk Detection Geocast Protocols for Internet-wide Communication Grant Highlights include €120K NWO-Aspasia, €2.9M Marie Skłodowska-Curie, €350K NWO-KLEIN, and €135K Center for BOLD Cities funding. She has supervised 12 PhD students and 4 current Master’s students since 2011, with notable best paper award at WWIC'16. Teaching includes Machine Learning (2020-present) and Urban Computing (2018-present) at Leiden, plus past courses in Data Visualization, Software Engineering, and Research Methods.
Dessislava Georgieva Petrova-Antonova is a Professor at Sofia University's Faculty of Mathematics and Informatics , specializing in the Department of Software Technologies . Her work focuses on service-oriented architectures, web services, and big data systems for smart cities. She holds a PhD in Software Technologies from Technical University of Sofia (2007) and an MSc in Computer Systems and Control (2000). PhD: Technical University of Sofia, Faculty of Computer Systems and Control (2007) MSc: Technical University of Sofia, Faculty of Computer Systems and Control (2000) Research Interests: She pioneers methodologies for testing web service compositions (e.g., TASSA ), develops big data platforms for smart governance, and explores digital twin modeling for urban environments. Her projects integrate IoT, quality of service (QoS) frameworks, and data-driven policy tools. Projects: Leads initiatives like National CogniTwin (National Science Fund, 2019–2022) and Big4Smart (NSF, 2017–2020). Collaborates in EU programs such as Digital Twin Cities Centre (2020–2025) and People Network+ (FP7, 2012–2013). Labs & Collaborations: Affiliated with the GATE Center of Excellence and contributes to international teams in big data and smart city research. Her work bridges academia and industry through tools like TESSI (Web Service Testing Tool) and faultInjector (BPEL Fault Injection Tool).
Michael Hilton is an Associate Teaching Professor in the Software and Societal Systems Department of the School of Computer Science at Carnegie Mellon University. He also serves as the Associate Department Head for Education and directs both the Software Engineering Minor and Software Engineering Concentration programs. His work bridges academic research with practical software engineering education. Ph.D. in Computer Science, Oregon State University (2017) M.S. in Computer Science, Cal Poly San Luis Obispo (2013) B.S. in Computer Science, San Diego State University (2002) Professor Hilton's research primarily focuses on understanding and improving the developer experience, with particular emphasis on flaky tests, continuous integration practices, and software engineering education. His work combines empirical studies of real-world development practices with educational innovations to enhance how software engineers are trained. He has conducted extensive research on test flakiness, identifying patterns, causes, and potential solutions to this pervasive problem in modern software development. His scholarly contributions reveal a consistent focus on practical software engineering challenges, particularly those affecting developer productivity and software quality. The research trajectory shows increasing attention to educational aspects of software engineering, including team-based learning, structured feedback mechanisms, and the impact of emerging technologies like AI on programming education. Professor Hilton has over 20 years of professional experience in software development, including 9 years at SPAWAR Pacific where he worked on projects for the US Navy, Coast Guard, and White House. This industry background informs his teaching approach, which emphasizes preparing students for real-world challenges they'll face after graduation. He teaches software engineering-focused courses and has developed educational approaches that integrate practical development experience with theoretical foundations. His teaching philosophy centers on providing students with both immediate practical skills and enduring principles that will serve them throughout their careers, with special attention to software engineering in startup environments.
Sangyoung Park is an Assistant Professor of Smart Mobility Systems at the Faculty of Mechanical Engineering and Transport Systems, Technical University of Berlin, and is co-affiliated with the Einstein Center for Digital Future. His research focuses on two main areas: enhancing vehicle safety through digitalization and connectivity, and advancing the electrification of the transport sector with emphasis on electric vehicle battery systems design and management. He leads the Chair of Smart Mobility Systems at TU Berlin, where his team investigates how vehicle connectivity can improve energy efficiency, traffic flow, and safety in autonomous vehicle systems. Dr. Park completed his PhD in Electrical Engineering and Computer Science at Seoul National University in Korea, where he focused on energy management techniques for hybrid energy storage systems in electric vehicles. Before joining TU Berlin in 2018, he conducted postdoctoral research at the Technical University of Munich, working on energy management for smartphones in collaboration with Google and studying battery aging processes. His research interests span smart mobility systems, electric vehicle battery management, energy consumption optimization, vehicle connectivity, and autonomous driving systems. Park's work bridges the gap between design engineers and software engineers, investigating how different energy storage components (fuel cells, supercapacitors, lithium-ion batteries) should be interconnected and managed together for maximum efficiency. His research also addresses the design of charging infrastructure for electric vehicles. Analysis of Dr. Park's recent publications reveals a strong focus on digital twin technology for teleoperated driving, battery management systems for electric vehicles, and vehicle connectivity for improved safety and efficiency. His research increasingly integrates cybersecurity aspects of connected vehicles and explores novel approaches to extend battery lifespan through advanced cell balancing techniques. The interdisciplinary nature of his work connects electrical engineering, computer science, transportation systems, and urban infrastructure planning. Dr. Park supervises multiple doctoral students, including Philipp Kremer, Ongun Türkçüoglu, Kil Young Lee, Maria Claudia Miguel de Priego, Muzaffer Citir, Andrea Reindl, Subhendu Bhadra, and Hueseyin Türkyilmaz. His research is supported by various funding sources including the ECDF grant, DAAD projects (ide3a), and government scholarships. He collaborates with institutions including OTH Regensburg and Siemens Mobility. His laboratory, the Smart Mobility Systems group, focuses on developing system-level approaches for measuring, analyzing, and balancing energy consumption in battery-powered mobile systems. The team investigates how direct communication among autonomous vehicles can enable control scenarios that improve energy efficiency, traffic flow, and safety beyond what human drivers or isolated autonomous vehicles can achieve.
Celine Taylor Parkins-Ozephius serves as Junior Assistant Professor at Utrecht University's School of Law, specifically within the Willem Pompe Institute for Criminal Law and Criminology. Her academic appointment falls under the Faculty of Law, Economics and Governance, where she focuses on the intersection of criminal procedure and digital technology. Her research centers on digital evidence reliability in criminal courts, with particular expertise in smartphone forensics, biometric authentication, and cross-border digital investigations. Current projects examine judicial frameworks for assessing technical evidence, EU standards for digital searches, and privacy implications of law enforcement's access to encrypted devices. Her work frequently addresses tensions between investigative needs and fundamental rights in digital contexts. Teaching responsibilities include coordination of the master's course "In-Depth Criminal Procedure Law," where she delivers lectures and tutorials. She actively supervises master's theses in criminal procedure and digital evidence topics. Her publications demonstrate consistent engagement with emerging challenges in digital criminal justice, including EncroChat investigations, two-factor authentication evidence, and ECHR compliance in digital evidence collection. Collaborative work appears across multiple Dutch legal journals and the EHRC Updates platform, often with Dave van Toor and other Willem Pompe Institute colleagues. Her research methodology combines doctrinal legal analysis with practical examination of investigative techniques, emphasizing the need for updated judicial frameworks in digital evidence assessment.
Siobhán Clarke is a Professor at the School of Computer Science and Statistics, Trinity College Dublin, specializing in software systems for smart urban environments . Her work addresses dynamic software adaptation in large-scale, mobile IoT ecosystems , with a focus on QoS optimization and collaborative agent models . Director, Enable : National SFI IoT Research Programme Director, Future Cities Centre for Smart & Sustainable Cities Co-Lead, ADVANCE : SFI Centre for Advanced Networks Co-PI, CONNECT (Future Networks) and Lero (Software Research) Her research spans smart city infrastructure , edge computing , and multi-agent coordination , informed by 15+ years of publications on service-oriented architectures , QoS prediction , and self-adaptive systems . Key project contributions include DIVERSIFY (2016) and TRANSFoRm (2015). Scientific awards include election to the Royal Irish Academy (2023) and a Best Student Paper at IEEE ICWS 2011. She has supervised 20+ PhD/MSc students, including Fan Li (2020: SLA Negotiation Systems), Gary White (2020: IoT QoS Forecasting), and Andrei Palade (2019: Stigmergic Optimization).
Anna Meyer is an Assistant Professor in the Computer Science department at Carleton College, where she has been since 2025. She earned her Ph.D. in Computer Science from the University of Wisconsin-Madison, advised by Aws Albarghouthi and Loris D’Antoni, and a B.A. in Mathematics from Carleton College. Before her academic career, she worked as a software developer at Epic in Madison. Her research focuses on improving the trustworthiness of machine learning models by addressing multiplicity—the phenomenon where models with similar performance produce divergent outputs, undermining reliability and fairness. She employs formal methods, machine learning techniques, and human-computer interaction frameworks to analyze and control multiplicity across ML pipelines. Her work has been recognized through publications at top venues like CHI, UAI, FAccT, and NeurIPS, with a workshop paper at ICLR 2024. Anna teaches courses such as CS 251: Programming Languages and CS 320: Machine Learning . Her technical contributions include the AntIDoTe-P repository, which implements abstract interpretation techniques to certify robustness against data bias in decision trees. The codebase supports datasets like COMPAS, Adult Income, and Drug Consumption, with preprocessing documented in Jupyter notebooks.
