Miles Cranmer is an Assistant Professor in Data Intensive Science at the University of Cambridge, affiliated with the Department of Applied Mathematics and Theoretical Physics (DAMTP) and the Institute of Astronomy (IoA). His research focuses on AI for scientific discovery, particularly in symbolic regression, machine learning for physics, and large-scale data analysis in astrophysics. He leads the AstroAutomata research group, which develops open-source tools like PySR and SymbolicRegression.jl for symbolic regression. Cranmer's work bridges machine learning and domain sciences, with applications ranging from galaxy evolution to planetary dynamics. Key research themes include: Interpretable AI for scientific modeling Surrogate models for PDEs and physics simulations Benchmarking frameworks for symbolic regression His recent publications explore novel methods in symbolic regression, physics-informed machine learning, and large-scale astronomical data analysis. Cranmer actively contributes to open-source software ecosystems for scientific computing.
Shaahin Filizadeh is a Professor in the Department of Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering. He previously served as the Department's Associate Head (Graduate Programs) from 2012 to 2019 and maintains an active research program in power systems and power electronics. His educational background includes a B.Sc. (1996) and M.Sc. (1998) in Electrical Engineering from Sharif University of Technology, Tehran, Iran, and a Ph.D. (2004) in Electrical Engineering from the University of Manitoba. Power Systems Power Electronics Modeling and Simulation of Power-Electronic Intensive Networks HVDC Converters and Modular Multilevel Converters (MMCs) Dynamic Phasor Modeling Techniques Electrified Vehicular Systems and Energy Storage Integration His research focuses on advanced energy conversion systems, power-electronic intensive networks, and specialized modeling methods including average-value models and dynamic phasors. Current work emphasizes low-inertia systems, battery energy storage converters, and MMC topologies with DC fault blocking capability. He directs the Power Electronics and Energy Conversion Atelier for Modeling, Prototyping, and Simulation (PEEC-AMPS), a state-of-the-art facility equipped with PSCAD/EMTDC, RTDS, and modular multilevel converters. Dr. Filizadeh serves as Chair of the IEEE Task Force on Dynamic Phasor Modeling Techniques and holds editorial positions at IEEE Transactions on Energy Conversion and IEEE Power Engineering Letters. He has supervised over 40 graduate students including 9 doctoral candidates and 32 master's students. His research has been supported by grants enabling hardware-in-loop simulation capabilities and advanced modeling tools for power system applications. His laboratory, PEEC-AMPS, features EMT simulation software, real-time simulators, modular multilevel converters, and power-electronic prototyping equipment for rapid development of energy conversion systems.
Mathias Weske is a Professor at the Hasso Plattner Institute in Potsdam, Germany. His research focuses on Business Process Management (BPM), Process Mining, Blockchain-based process execution, and Robotic Process Automation (RPA). He has contributed extensively to advancing methodologies for resource allocation in processes, decision support systems, and integrating blockchain technology into collaborative processes. Key research areas include business process analysis, data-driven case management, and healthcare process optimization. His work spans theoretical frameworks (e.g., BPMN extensions, choreography models) and practical tools (e.g., OpenBPT platform). Recent efforts emphasize RPA complexity metrics, accessibility in process modeling for visually impaired users, and democratizing RPA mining techniques. Publications frequently explore interdisciplinary applications like healthcare process mining and blockchain transaction ordering. His contributions bridge theory and practice, addressing challenges in automation, collaboration, and process intelligence across industries.
Peter Kjær Willendrup serves as a Senior Research Engineer and Special Consultant at the Department of Physics at the Technical University of Denmark (DTU), where he has been employed since January 2012. He is also seconded to the European Spallation Source (ESS) Data Management and Software Center (DMSC), initially at 33% capacity from June 2014 to January 2023, and at 100% capacity since April 2023. His primary role involves leading major computational projects that support neutron and X-ray scattering research infrastructure. Willendrup's research spans multiple disciplines including neutron & X-ray scattering, computational physics, computer science, data analysis, project management, and software development. His work focuses on creating simulation tools for neutron scattering instrumentation, with particular emphasis on applications for the European Spallation Source in Lund, Sweden. He has made significant contributions to computational methods for neutron scattering experiments and instrument design. His publication record shows a strong trend toward neutron instrumentation development for the ESS facility, with increasing focus on simulation techniques, data analysis methods, and computational approaches to optimize neutron scattering experiments. Recent work demonstrates growing integration of machine learning techniques with traditional simulation methods, particularly for assisting users with model selection in neutron scattering experiments. Willendrup leads two major software projects: McStas (since 2002), which is world-leading in simulation of instrumentation for neutron scattering and virtual neutron scattering experiments, and McXtrace, its X-ray equivalent. These projects involve collaboration with multiple international institutions including DTU, ESS, NBI, ILL, and PSI. His educational background includes an M.Sc. in Physics and a B.Sc. in Mathematics from the University of Copenhagen (1992-2000). Prior to joining DTU, he worked as a research assistant at Rigshospitalet's Neurobiology Research Unit (2000-2002) and briefly as a physics teacher at Skt. Annæ Gymnasium (1998-1999). He is multilingual, speaking Danish, English, Swedish, German, and French fluently.
Stacey Hancock is an Associate Professor of Statistics at Montana State University (MSU), where she contributes to the Department of Mathematical Sciences. She earned her Ph.D. in Statistics from Colorado State University (2008), M.S. from Montana State University (2004), and B.A. in Mathematics and Music from Concordia College (2001). Her research focuses on statistics and data science education, including student learning processes in statistical concepts, flipped classroom dynamics, and curriculum development. She leads the NSF-funded MSU Storytelling Group, which introduces computer science to rural and Indigenous middle school students in Montana through storytelling. Additional research interests include time series analysis (particularly change-point detection) and ecological statistics. Dr. Hancock teaches courses such as Stat 401 (Applied Methods in Statistics) and Stat 505 (Linear Models). She actively develops innovative teaching methods, including integrating Tableau for data visualization and hands-on simulation activities. Her NSF grant work emphasizes bridging computer science education with environmental applications. She has published widely on statistical pedagogy, computational skills in environmental science, and interdisciplinary curriculum design.
Pierre-Emmanuel LENI is an Assistant Professor at the UMR 6249 Chrono-environnement, University of Bourgogne Franche-Comté, France. His primary research focuses on medical physics, particularly in radiation dosimetry and computational modeling for radiotherapy. He combines advanced Monte Carlo methods with artificial neural networks to develop innovative dosimetric models and treatment planning systems. His work bridges theoretical mathematics (e.g., Kolmogorov superposition theorem) with practical applications in medical imaging and radiation therapy. Key research areas include: Medical physics and radiation dosimetry Neural network applications in healthcare Monte Carlo simulations for treatment planning Image processing algorithms Respiratory motion management in radiotherapy His publications emphasize interdisciplinary approaches, such as optimizing case-based reasoning for organ modeling and enhancing polymer gel dosimetry for proton beams. He collaborates actively with clinical teams to improve treatment planning accuracy and software validation. While no awards are explicitly mentioned, his contributions to computational radiation physics demonstrate significant academic impact.
Prof. Dr. Patrick Mäder is a University Professor and head of the Group of Data-intensive Systems and Visualization at TU Ilmenau's Department of Computer Science and Automation. He previously served as an Assistant Professor in Software Engineering for Safety-Critical Systems. His career includes doctoral research on software traceability (earning the Thuringian STIFT Prize), a Lise Meitner Fellowship at Johannes Kepler University Linz, and research stays at DePaul University Chicago. Bachelor/Master studies at TU Ilmenau PhD in 2005 on software traceability Postdoc at Johannes Kepler University Linz (2010-2012) Research focuses on secure/reliable software systems, scalable machine learning, explainable AI, computational biology/ecology, and interdisciplinary projects like the award-winning Flora Incognita plant identification app. He has coordinated numerous third-party funded projects in software engineering and machine learning, expanding his team to 18 researchers by 2020. Awards include the 2020 Thuringian Research Prize for applied research and the STIFT Prize for his dissertation on software traceability.
Nikolaos Papadakis is an Associate Professor at the Department of Electrical and Computer Engineering of the Hellenic Mediterranean University (HMU). He holds additional roles including Vice President of the Department of Social Work at HMU and Head of the Telecommunication and Informatic Technology team at the same department. His academic journey includes a BSc in Computer Science from the University of Cyprus (1997), an MSc from the University of Crete (1999), and a PhD in Computer Science from the University of Crete (2004). Prior to HMU, he held visiting professorships at the University of Crete, Technical University of Crete, and Technological Educational Institute of Crete, alongside research contributions at FORTH from 1997-2006. His research focuses on Databases and Knowledge Representation Artificial Intelligence Applications Semantic Web Technologies Software Engineering Methodologies Distributed Algorithms and Communication Protocols Renewable Energy Systems (wind, solar, energy efficiency) Notable projects include the SAVE initiative for net-zero sports facilities and studies on wind turbine blade optimization. His work bridges theoretical computer science with applied engineering solutions. Recent publications emphasize sustainable energy systems, composite material analysis, and quantum optics technologies. He maintains active collaborations in interdisciplinary areas such as epidemiological data modeling and space optical systems development. His career trajectory includes tenure at the Technological Educational Institute of Crete (2009-2019), reflecting a consistent commitment to academic innovation and applied research. Current research continues to explore emerging trends in renewable energy integration and advanced material science.
Juan Carlos Preciado Rodríguez is an Associate Professor of Computer Languages and Systems at the University of Extremadura (Spain), affiliated with the Quercus Software Engineering Group. He has held significant academic roles, including Deputy Rector for over 8 years. His research focuses on Web Engineering, Big Data, Machine Learning, and Smart Cities, with over 60 scientific publications. He founded Homeria Open Solutions SL and MetrikaMedia SL, two spin-offs from UEx. He has led multiple R&D projects, including 1 European, 1 national, and 1 regional initiative, and participated in 18 R&D contracts as Principal Investigator in 5 cases. He holds 2 national patents and actively contributes to academic and entrepreneurial ventures. His educational background includes a PhD, though specific details are not provided. Research interests span Model-Driven Engineering (MDE), Business Intelligence, and Data Visualization. Recent work includes applications in demand forecasting, smart cities, and educational data analysis. Notable projects involve leveraging data-driven approaches for university dropout prediction and optimizing vocational training programs using TOPSIS methodology. He has engaged in collaborative ventures such as SCPL (Social Cooperative Programming Language) and AutoCRUD for workflow automation. His work bridges software engineering with societal challenges, including unmanned aerial systems for smart city services and advanced visualization tools like LiveSankey. His contributions span both technical innovation and archaeological studies, reflecting interdisciplinary engagement.
Chris Cramer is a Research Professor at the Center for Earthquake Research and Information (CERI) at the University of Memphis. He holds a B.S. from the University of Puget Sound (1969) and M.S. and Ph.D. degrees from Stanford University (1973, 1976). His career spans 30 years with the California Division of Mines and Geology (now California Geological Survey) and the U.S. Geological Survey before joining CERI. Dr. Cramer’s research focuses on probabilistic seismic hazard analysis, strong ground motion effects, fault geometry visualization, and innovative educational tools using 3D printing and virtual reality (VR). He pioneered the creation of scaled 3D-printed models of California’s fault system to address public misconceptions and developed VR platforms to explore fault dynamics and rupture propagation. Recent projects include a New Zealand fault system model and immersive VR experiences for earthquake education. His articles emphasize the integration of advanced visualization technologies, such as VR and 3D printing, with geophysical research and educational outreach. Key themes include fault dip angle visualization, rupture front propagation analysis, and the application of these tools to subduction zones, geothermal areas, and historical earthquakes like the 2010 El Mayor-Cucapah event. Dr. Cramer has no listed scientific awards but has advised Vis Lab interns (e.g., Michael Methvin, Dianne Pham) who presented projects at AGU and SCEC meetings. His work extends to creating educational tools for K-12 audiences, such as the 2020 collaboration with Manila Junior-High robotics students. Labs/Teams: He leads the CERI Visualization Lab (founded 2019), focusing on geophysical VR/3D printing applications. The lab collaborates with institutions like UC Riverside and leverages facilities like the Creat’R Lab for printing. Ongoing projects aim to expand 3D printing to global fault systems and develop a 3-credit VR course for university education.
Dr. Federico Raimondo is a post-doctoral researcher at the Institute of Neuroscience and Medicine (INM-7) , Research Center Jülich, Germany. His work focuses on developing trustworthy AI algorithms to address clinical challenges in neurosciences, particularly in understanding consciousness and cognition. He holds a PhD in Computer Science (University of Buenos Aires) and Cognitive Neurosciences (Sorbonne University), followed by a postdoc at the University of Liège, Belgium. Education: Bachelor’s/Master’s in Computer Science, University of Buenos Aires PhD in Computer Science (joint program with Sorbonne University’s Cognitive Neurosciences) Research Interests: Machine Learning, Clinical AI Transparency, EEG/MEG Signal Processing, Disorders of Consciousness, and Neuroimaging Analytics. He develops tools like NICE-EEG Cleaner and contributes to libraries such as Julearn and JuHarmonize to ensure reproducible and bias-free AI models. Labs/Teams: Applied Machine Learning Group at INM-7, collaborating on projects like the CONNECT-ME Study for ICU patient prognosis.
René Widera is a researcher at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), specifically within the Laser Particle Acceleration department of the Institute of Radiation Physics. His work focuses on advancing high-performance computing (HPC) techniques for plasma simulations, particularly leveraging GPU architectures and exascale computing frameworks. He contributes to the development and optimization of the PIConGPU code, a leading particle-in-cell (PIC) simulation tool. His research integrates machine learning for real-time data analysis, parallel algorithms for HPC scalability, and cross-platform visualization strategies. Areas of expertise include laser plasma acceleration, high-energy-density physics, and the design of efficient numerical methods for large-scale simulations. He explores hardware-agnostic solutions for computational challenges, including memory access optimizations and DAG-based parallelism. Collaborations involve international HPC initiatives and open-source software projects like openPMD and alpaka . Key projects include the TWEAC initiative to overcome limitations in laser-wakefield acceleration and the development of in-situ visualization pipelines for real-time simulation insights. He also evaluates modern GPU architectures (e.g., AMD, ARM-based systems) for scientific workloads. His contributions bridge theoretical plasma physics with practical computational advancements, aiming to enable next-generation high-intensity laser experiments.
Ziwei Huang is an Assistant Professor at Southeast University's School of Information Science and Engineering, Department of Communication Engineering. With a prolific publication record from 2019-2025, Huang has established expertise in wireless communications, particularly in 5G/6G channel modeling, UAV communications, and vehicular networks. Recent work demonstrates a strategic expansion into AI/ML applications, computer vision, and large language models, showing interdisciplinary research growth. Huang's research interests focus on wireless communications channel modeling with particular emphasis on non-stationary characteristics, spatial consistency, and trajectory modeling for next-generation communication systems. Key areas include UAV communications , vehicular networks , intelligent sensing-communication integration , and multi-modal data fusion . Recent work has expanded into fundus image processing , text-to-image synthesis , and hallucination mitigation in vision-language models , demonstrating a strategic expansion into AI applications while maintaining core expertise in communications. The publication trends reveal a clear evolution from traditional wireless channel modeling (2019-2021) toward more integrated sensing-communication systems (2022-2023), with a significant pivot toward AI/ML applications in 2024-2025. Huang's work spans both theoretical channel modeling and practical implementation, with increasing emphasis on cross-disciplinary applications. The research shows strong collaboration patterns with Xiang Cheng and Lu Bai, suggesting membership in a well-established research group at Southeast University. Huang has contributed to numerous high-impact publications in IEEE journals including IEEE Transactions on Wireless Communications, IEEE Transactions on Vehicular Technology, and IEEE Communications Surveys & Tutorials, as well as top conferences like AAAI and ACL. The research demonstrates consistent funding support through collaborative projects focused on next-generation wireless communication systems. The work spans multiple laboratories and research teams, including wireless communications research groups at Southeast University, collaborations with medical imaging researchers for fundus analysis, and partnerships with AI research teams working on vision-language models. Recent publications suggest active participation in interdisciplinary research initiatives bridging communications engineering with artificial intelligence.
Ernad Bešlagić is an Assistant Professor at the Department of Automation and Metrology, Faculty of Mechanical Engineering, University of Zenica (UNZE BA). He holds a Dr. Sc. degree in Polytechnics from the Faculty of Mechanical Engineering in Mostar, awarded in 2021. His academic journey includes a Master's in Metrology (2013) and a Bachelor's in Mechanical Engineering (2002), both from UNZE BA. He has been a faculty member since 2008, transitioning from roles like Senior Assistant to his current position since 2022. Bešlagić’s research focuses on additive manufacturing precision, wind energy systems, and metrology. He has contributed to projects involving 3D printing accuracy, wind tunnel testing for Darrieus turbines, and fatigue analysis of mechanical components. As a co-author of university textbooks and peer-reviewed papers, his work bridges theoretical concepts with practical engineering solutions. He also leads the Citizens' Association 'Education for the new age - STEAM education,' promoting interdisciplinary learning. His professional activities include mentoring students across mechanical engineering disciplines and contributing to national/international conferences. He has collaborated on hardware/software development for wind turbine prototyping and metrology systems, emphasizing real-world applications of engineering principles.
Diego Perez-Palacin is a Senior Lecturer in the Department of Computer Science and Media Technology at Linnaeus University, Sweden. He holds a PhD in Computer Science from the University of Zaragoza, Spain. Previously, he served as a postdoctoral researcher at Politecnico di Milano (2013-2016) and a Senior Researcher at the University of Zaragoza (2017), contributing to European (FP7/H2020) and Spanish national research projects. Research Interests: His work focuses on software quality properties, self-adaptive systems, model-based analysis using formal methods, and resilience engineering. Key areas include cyber-physical systems, digital twins, and uncertainty management in adaptive systems. His research groups include AdaptWise (self-adaptive software), Cyber-Physical Systems (CPS), and Engineering Resilient Systems (EReS). Projects: Current projects include developing Digital Twins of Organizations (DTO) and aligning architectures for DTO (Aladino). Completed projects include Smart-Troubleshooting in the Connected Society. His work often involves collaboration with interdisciplinary teams in data-intensive applications and smart industry. Publications: Over 30 peer-reviewed articles in journals like ACM Transactions on Autonomous and Adaptive Systems, IEEE Access, and Journal of Systems and Software. Recent work emphasizes digital twin frameworks, antifragile systems, and uncertainty propagation in adaptive systems.
