Pedro Galeano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid (UC3M) since 2009. He holds a PhD in Statistics (2004) under Prof. Daniel Peña, focusing on multiple time series. Previously, he served as Visiting Assistant Professor of Statistics and Econometrics at the University of Chicago’s Graduate School of Business and as a Postdoctoral Fellow at the Department of Statistics and Operations Research at Universidade de Santiago de Compostela. His research focuses on time series analysis, outlier detection, Bayesian inference in financial models, and functional data analysis with applications to missing data. He is an Associate Editor of the Journal of Time Series Analysis and advises the Heliyon journal. Key contributions include developing methodologies for detecting structural breaks, modeling systemic risk via copula approaches, and advancing robust statistical techniques for high-dimensional data. Active in academic leadership, Galeano co-organized the NICDA Workshop 2025 and has published extensively on topics like dynamic factor models, sequential parameter change detection, and functional data applications in energy markets. His work bridges theoretical statistics with practical applications in finance, economics, and environmental science.
Professor Craig Wheeler is a distinguished academic in the School of Engineering at the University of Newcastle, specializing in Mechanical Engineering with a focus on bulk solids handling and belt conveyor technology. As Associate Director of the Centre for Bulk Solids and Particulate Technologies and Deputy Chairman for the Australian Society for Bulk Solid Handling, he has established the university as a global leader in fundamental and applied research within this field. Wheeler's research interests primarily center on reducing the energy intensity and environmental impact of ore and mineral transportation globally. His work develops novel theoretical approaches to model and optimize belt conveyor and bulk handling systems, with significant contributions in energy-efficient transportation, dust emission control, and innovative conveying technologies like the Rail Conveyor system. His research bridges fundamental computational techniques with practical industrial applications, addressing real-world challenges in bulk material handling. His extensive publication record demonstrates trends toward increasingly sophisticated modeling techniques, combining continuum mechanics, discrete element methods, and computational fluid dynamics to solve complex problems in bulk material flow and energy consumption. Recent work shows particular emphasis on large-diameter idler rollers for energy savings, rail-running conveyor systems, and advanced dust control methodologies. 2023 Engineers Australia - Australian Society for Bulk Solids Handling 2017 Significant Contributions to Engineers Australia's Warman Design and Build Competition (Weir Minerals) 2017 Australian Council of Engineering Deans National Award for Engineering Education Excellence 2016 Innovative Technology Award (Australian Bulk Handling) 2010 Rising Star Award (Newcastle Innovation, The University of Newcastle) 2009 Pro-Vice Chancellor's Award for Research Excellence 2006 Best Research and Development Project (Australian Bulk Handling Review) 2000 A.W. Roberts Award (Australian Society for Bulk Solids Handling) Professor Wheeler has successfully led numerous Linkage Projects with major companies including Rio Tinto, Veyance Technologies, and Laing O'Rourke, securing significant cash and in-kind contributions for research projects. His industrial consulting experience, built on a 10-year engineering career with BHP, provides valuable insights that bridge fundamental research with practical applications. He actively supervises research students and contributes to professional development courses both within Australia and internationally. As a key member of the Centre for Bulk Solids and Particulate Technologies in association with TUNRA Bulk Solids, Wheeler leads research teams focused on developing eco-friendly conveying solutions. His work has resulted in new licensed technologies, internationally recognized testing methods, design guidelines, and Australian Standards that have transformed industry practices worldwide.
Jeff M Phillips is a Professor in the Kahlert School of Computing at the University of Utah, specializing in algorithms for big data analytics, computational geometry, and machine learning. He holds a BS in Computer Science and Mathematics from Rice University (2003) and a PhD in Computer Science from Duke University (2009). He serves as Director of the Utah Center for Data Science, Director of the Data Science Program in the Kahlert School of Computing, and Faculty Co-Director of the One U Data Science Hub. His research focuses on geometric data analysis, coresets, sketches, and handling uncertainty in data. Education: BS/BA (Rice University, 2003), PhD (Duke University, 2009) CI Postdoctoral Fellow at University of Utah (2009–2011) His research interests include algorithms for big data analytics, computational geometry, machine learning, spatial statistics, and AI. He has led NSF-funded projects on spatial data analysis, cosmic origins via AI, and reactive flow data modeling. Phillips has advised numerous PhD and master’s students, contributing to topics like trajectory classification and bias mitigation in word embeddings. His publications span computational geometry, data science, and machine learning. Notable work includes coresets for kernel density estimates, bias mitigation in language models, and scalable spatial scan statistics. Phillips is also active in academic service, serving as co-PC chair for SoCG 2024 and on program committees for major conferences like NeurIPS and ICML.
Yakov Sinai is a distinguished Professor in the Department of Mathematics at Princeton University since 1993, with a parallel role as Senior Researcher at the Landau Institute of Theoretical Physics since 1971. His academic journey began at Moscow State University, where he earned a B.S. (1957), Ph.D. (1960), and Doctor Degree (1963). Sinai's work bridges dynamical systems, mathematical physics, statistical physics, probability theory, and mathematical fluid dynamics. Education: B.S. (1957), Ph.D. (1960), Doctor Degree (1963) from Moscow State University. Research Interests: Sinai's scholarship focuses on foundational and applied aspects of dynamical systems, including ergodic theory and chaos, alongside interdisciplinary contributions to mathematical physics, statistical mechanics, and fluid dynamics. His work has profound implications for understanding complex systems and stochastic processes. Scientific Awards & Honors: Sinai is a recipient of the 2014 Abel Prize, 2006 Galileo Chair, 2005 Moore Distinguished Scholar, 1997 Wolf Prize, and 1986 Boltzmann Gold Medal. He holds honorary degrees from Warwick, Hebrew, and other universities and is a member of prestigious academies including the Royal Society of London and the American Academy of Arts & Science.
George Bosilca is a Research Professor at the University of Tennessee, Knoxville, affiliated with the Department of Electrical Engineering and Computer Science and the Innovative Computing Laboratory. He holds a PhD in Computer Science (University of Paris XI, 2004) and an MS in Math and Computer Science (University of Paris XI, 1999). His research focuses on distributed algorithms, parallel programming paradigms, performance modeling/optimization, and resilience in programming models. He contributes to exascale computing initiatives through projects like PaRSEC and Open MPI. Key research areas include task-based runtimes, MPI standardization for exascale systems, and fault-tolerant distributed computing. His work emphasizes scalable and portable constructs for high-performance applications. Bosilca is involved with the Innovative Computing Laboratory (ICL) and collaborates on projects like the EPEXA ecosystem and Argobots threading framework. Recent publications highlight advancements in asynchronous many-task systems, GPU-accelerated collective operations, and resilience strategies for HPC platforms. His contributions span theoretical frameworks and practical implementations, bridging algorithmic innovation with real-world HPC challenges.
Stephen Harrington is a researcher at Queensland University of Technology , affiliated with the Faculty of Creative Industries, Education and Social Justice and the School of Communication . His work bridges media studies , journalism , political communication , and digital culture , focusing on how entertainment , satire , and social media shape public knowledge and information dissemination. Research Interests : Media convergence and television in the digital age Dynamics of disinformation , conspiracy theories , and political communication Role of satire and unorthodox news forms in public engagement Notable Contributions : Analyzed social media usage during the COVID-19 pandemic (2025) Developed computational methods for studying problematic news-sharing on Facebook (2023) Co-authored QUT DMRC submissions to Senate inquiries on digital misinformation (2024) His recent 15 publications (2004–2025) span topics like media policy , platform accountability , and audience behavior across social media , TV news , and digital journalism . While specific scientific awards and students are not documented in the provided materials, his work consistently addresses the intersection of entertainment and information in contemporary media landscapes.
Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.
Andrew Ng is an Adjunct Professor at Stanford University's Computer Science Department and a globally recognized leader in AI. He is the Founder of DeepLearning.AI, Executive Chairman of LandingAI, General Partner at AI Fund, and Co-Founder of Coursera. His work has revolutionized machine learning and online education, with over 200 research papers in AI, robotics, and related fields. He was named to the 2023 Time100 AI list of most influential figures in AI. Ng's research focuses on machine learning, deep learning, reinforcement learning, and their applications in robotics and education. He pioneered the development of massive open online courses (MOOCs), notably through Stanford's early experiments in 2011 that attracted hundreds of thousands of learners. His contributions include foundational work in algorithms like Latent Dirichlet Allocation (LDA) for text analysis and advancements in spectral clustering and inverse reinforcement learning. His publications span topics from robotic hand design to scalable deep learning systems, emphasizing practical and scalable solutions. Ng's educational initiatives, such as the Machine Learning and Deep Learning Specializations, have educated millions worldwide. He advocates for accessible AI education and ethical AI development, emphasizing collaboration between academia and industry.
Dr. Gabriel Rosselló Calafell is an Assistant Professor at the University of the Balearic Islands (UIB), specializing in Carthaginian diplomacy and Roman Republican expansion . He earned his Ph.D. in History (2020) from UIB with a thesis on Punic diplomatic practices in Roman sources and holds a DEA in Roman Republican taxation (2005). His research spans collaborations with the Grupo Civitas (UIB) , external membership in Grupo Occidens (UAM) , and participation in networks like Libera Res Publica and Toletum . His academic focus includes: Diplomatic Mechanisms : Analysis of Punic-Roman treaties, Numidian diplomacy, and symbolic gift exchange. Geopolitical Studies : Roman provincial administration in North Africa, post-Punic War alliances, and Mediterranean power shifts. Critical Source Analysis : Deconstruction of Greco-Roman etnocentrism in historical narratives. Recent publications examine diplomatic spaces (2024), Numidian monarch roles (2025), and aristocratic networks (2024). Current projects include directing the Master's in Mediterranean Studies and researching at the Institut Ausonius as part of PID2022-137408NB-100. He also leads an educational innovation project on audiovisual historical narratives vs. misinformation (PID232511).
Helen Oleynikova is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, where she is part of the Autonomous Systems Lab. She works on the intersection of perception and planning, particularly for micro-aerial vehicles. Her research focuses on real-time onboard mapping, planning, and localization using visual-inertial systems and signed distance fields. Research Interests: Helen's work spans robotics, autonomous systems, and computer vision, with a focus on enabling safe and efficient navigation in complex environments. She specializes in visual-inertial odometry, SLAM, 3D mapping using signed distance fields, and real-time path planning for MAVs. Her projects often involve embedded systems and FPGA-based high-speed vision for obstacle avoidance. Publication Trends: Her recent publications (2023–2019) show a consistent focus on real-time, onboard algorithms for autonomous navigation. Key themes include signed distance function maps, collision-free motion generation, global localization, and efficient exploration. She frequently publishes in top-tier robotics conferences such as ICRA and IROS, and journals like IEEE RA-L and Journal of Field Robotics. Professional Experience: Senior Researcher, Autonomous Systems Lab, ETH Zürich Senior Software Engineer, Isaac 3D Perception, Nvidia Senior Scientist, Microsoft Mixed Reality and AI Lab, Zürich Software Engineer, Google (StreetView) Contributor, Willow Garage (ROS, TurtleBot Arm) Education: PhD in Robotics, ETH Zürich (2019) MSc in Robotics, ETH Zürich BSc in Robotics, Olin College of Engineering (2011) Advising and Grants: While no formal students are listed, she has collaborated extensively with researchers at ETH Zürich and industry labs. Her work has been supported through institutional affiliations and industry research roles. She has contributed to open-source robotics software, particularly in ROS-based systems for manipulation and navigation. Labs and Teams: Helen is a key member of the Mobile Manipulation team at the Autonomous Systems Lab at ETH Zürich. She has also been involved in projects at Nvidia, Microsoft, Google, and Willow Garage, focusing on real-world deployment of autonomous systems.
Merve Acer Kalafat is an Associate Professor in the Department of Mechanical Engineering at Istanbul Technical University. She specializes in robotics, control systems, compliant mechanisms, and additive manufacturing, with a focus on sensor integration and advanced material applications. Her research involves origami-inspired mechanisms, tactile sensors, and flexible electronics. Her work spans interdisciplinary areas such as piezoelectric actuators, parallel manipulators, and neural network-based performance analysis. Collaborations include studies on textile-based strain sensors and inkjet-printed flexible electronics. She has supervised 3 ongoing theses and contributed to over 20 publications since 2011. Key research interests include improving manufacturing processes (e.g., FDM parameters), developing foldable robotics systems, and advancing tactile sensing technologies for soft robotics applications.
Liji Shen is Professor of Operations Management and Chairholder at WHU – Otto Beisheim School of Management, Campus Vallendar, Germany. She is affiliated with the Supply Chain Management Group and leads research in scheduling, optimization, and sustainable manufacturing. Her academic journey includes a Ph.D. and Habilitation from Technische Universität Dresden, and she has held visiting scholar positions at institutions including École des Mines de Saint-Étienne and Huazhong University of Science and Technology. Ph.D. (Dr.rer.pol.), summa cum laude, Technische Universität Dresden (2009) Habilitation, Technische Universität Dresden (2015) Master of Business Administration (Dipl.-Kffr.), Technische Universität Dresden (2006) Liji Shen's research focuses on Operations Management , particularly scheduling optimization in manufacturing systems. Her work spans flexible job shops , parallel machine scheduling , energy-efficient production , and sequence-dependent setup times . She applies advanced techniques such as evolutionary algorithms , hybrid metaheuristics , and mathematical programming to solve complex industrial problems. Her recent publications emphasize sustainability through energy-aware scheduling and time-of-use pricing models. The 15 most recent publications highlight a consistent research trajectory in production scheduling , with increasing emphasis on energy efficiency , distributed manufacturing , and real-world constraints like eligibility and delivery times. Her work frequently appears in top journals such as European Journal of Operational Research , IEEE Transactions on Evolutionary Computation , and Computers & Operations Research , often in collaboration with leading researchers like Dauzère-Pérès, Mönch, and Buscher. Scientific Awards: European Journal of Operational Research, Best Paper Award (2021) DFG and TU Dresden, 'Support the Best' Prize for Outstanding Researchers (2013) Dr. Feldbausch-Prize for Best Dissertation, TU Dresden (2010) Scholarship for Young Researchers in Saxony (2006–2009) DAAD Prize for Best Foreign Students (2007) Best Master’s Thesis, German Operations Research Society (2007) Liji Shen has been an active advisor and researcher, leading projects in operations research and industrial optimization. Her editorial role on Operations Research Perspectives underscores her standing in the academic community. She has directed research labs and collaborated internationally, contributing to both theoretical advancements and practical applications in manufacturing and logistics. No specific grants are mentioned, but her sustained publication record and leadership roles indicate strong research support. She leads the Operations Management research group at WHU, focusing on algorithmic solutions for complex scheduling problems. Her team investigates energy-aware production, hybrid flow shops, and distributed systems, aiming to bridge the gap between theoretical models and industrial implementation. The lab collaborates with researchers across Europe and China, fostering a global research network in operations research and supply chain management.
Daniel Hernández is a Postdoctoral Researcher at the Institute for Artificial Intelligence (KI) under the Cluster of Excellence IntCDC at the University of Stuttgart. He is part of the Analytic Computing group within the Institute for Parallel and Distributed Systems (IPVS). His work focuses on Semantic Web technologies , particularly SPARQL , RDF , and knowledge graph applications in interdisciplinary design workflows . Research Trends : His publications (2015–2025) emphasize semantic query processing , provenance computation , and interoperability between architectural data and knowledge graphs . Key innovations include the eSPARQL language for epistemic queries, NPCS for native provenance in SPARQL, and BHoM to bhOWL for integrating building data with ontologies. Teaching & Collaborations : He has held teaching roles at the University of Stuttgart ( Human-Computer Interaction with Knowledge Graphs ), University of Aalborg ( Group Supervisor ), and University of Chile ( Lecturer for The Web of Data ). Collaborations span institutions like Buro Happold , TU Wien , and INRIA , with publications in journals like Proceedings of the VLDB Endowment and conferences such as WWW and ISWC .
Volker Markl is a Professor at Technische Universität Berlin in the Institute of Software Engineering and Theoretical Computer Science, with additional affiliations at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the German Research Center for Artificial Intelligence (DFKI). His research spans database systems, stream processing, and distributed data management with significant contributions to both theoretical foundations and practical implementations. Markl's research interests focus on next-generation data management systems, particularly for streaming and IoT environments. His work addresses critical challenges in distributed query processing, system integration, and performance optimization. He has pioneered approaches for stream processing in volatile infrastructures and developed innovative techniques for GPU-accelerated database operations. His NebulaStream project represents a major contribution to distributed stream processing systems. His publication record demonstrates consistent impact across top database venues including VLDB, SIGMOD, and ICDE. Recent work shows increasing focus on machine learning integration with database systems, privacy-preserving query processing, and educational approaches for teaching large-scale data management. Markl has mentored numerous researchers who have become prominent in the database community, with frequent collaborators including Steffen Zeuch, Tilmann Rabl, and Philipp Grulich. His leadership extends to major research initiatives and collaborations across European institutions.
Nuno Pereira Lopes is an Associate Professor at Instituto Superior Técnico , part of Universidade de Lisboa , and a researcher at INESC-ID . He also serves as an advisor at FuriosaAI , focusing on tensor contraction processors for AI workloads. Research Interests : Compilers, formal verification of LLVM optimizations, machine learning frameworks, undefined behavior exploitation, probabilistic model checking, blockchain security, and many-core code generation. Teaching : Compilers and Computer/Informatics Engineering projects. Funding : Supported by Google, Matter Labs, NLnet, Oracle, PRACE, RNCA, and Woven by Toyota. Recent Publications focus on LLVM backend validation , PyTorch pipeline parallelism , C++ dynamic cast optimization , undefined behavior in C/C++ , and AI tensor processors . His work bridges compiler design, formal methods, and AI hardware. Academic Service includes representing Portugal in ISO/IEC JTC 1/SC 22 (C++), organizing FLoC'26 , and serving on program committees for PLDI, EuroLLVM, and CGO.
