Dr. Changxing Dong is a Research Associate at the Leibniz Institute of Agricultural Development in Transition Economies (IAMO) since November 2010, specializing in agent-based modeling and agricultural policy simulation. He previously worked at Martin-Luther-University Halle-Wittenberg. Current affiliation: IAMO Department of Structural Development of Farms and Rural Areas Prior affiliation: Martin-Luther-University Halle-Wittenberg His research focuses on: Agent-based modeling of agricultural systems Ecosystem services quantification Land market dynamics and resilience Policy simulation tools (AgriPoliS) Multifunctional agricultural land use Recent publications highlight trends in: Deep reinforcement learning integration with agent-based models AgriPoliS software sustainability Spatial analysis of grazing intensity in Kazakhstan Simulation games for policy evaluation He collaborates extensively on projects like AgEnRes, AgriPoliS, MULTAGRI, and Rehwinkel Resilience, working with Alfons Balmann, Ruth Njiru, and Franziska Appel on AI-enhanced agricultural simulations.
Alessandro Lameiras Koerich is a Professor in the Department of Software Engineering and IT at École de technologie supérieure (ÉTS), part of the Université du Québec network in Montreal, Canada. He is affiliated with the LIVIA (Imaging, Vision and Artificial Intelligence Laboratory), where he conducts research in machine learning, computer vision, and pattern recognition. His work focuses on developing AI solutions for real-world applications in healthcare, security, and multimedia analysis. His research interests span multiple domains of artificial intelligence, with particular emphasis on multimodal machine learning, affective computing, and trusted machine learning systems. Dr. Koerich's work explores how AI can interpret human emotions through facial expressions and audio analysis, develop robust models resistant to adversarial attacks, and create efficient learning systems that can adapt to changing data streams. His research bridges theoretical advances with practical applications in healthcare monitoring, security systems, and multimedia content analysis. Analysis of his recent publications reveals a strong focus on multimodal emotion recognition, with increasing emphasis on transformer architectures, knowledge distillation techniques, and addressing challenges in real-world settings (in-the-wild recognition). His work consistently combines computer vision with audio processing, demonstrating expertise in both visual and auditory modalities for affective computing applications. Recent trends show growing interest in large language models for audio classification and addressing challenges of concept drift in text stream mining. Dr. Koerich actively supervises numerous graduate students across various research projects, with a particular focus on multimodal expression recognition, music genre classification, and robust machine learning systems. His collaborations span multiple institutions and research groups, reflecting the interdisciplinary nature of his work in artificial intelligence. He is a key member of the LIVIA laboratory, which has established ÉTS as a leading institution in computer vision research in Canada (ranked 6th according to CSRankings). The laboratory focuses on solving real-life problems through AI engineering, particularly in developing complex deep learning models with massive amounts of data that have incomplete annotations.
Dr. Julian Tachella is a CNRS Research Scientist at the Sisyph Laboratory of École Normale Supérieure de Lyon, with co-founder/CSO roles at Blur Labs. His career spans signal processing, machine learning, and computational imaging, focusing on inverse problems and self-supervised learning. Affiliation: CNRS (French National Centre for Scientific Research), Sisyph Laboratory, École Normale Supérieure de Lyon Co-founder & CSO: Blur Labs (AI/Imaging startup) Research Interests: At the intersection of signal processing and deep learning , his work addresses imaging inverse problems through self-supervised methodologies (e.g., UNSURE, Generalized R2R) that eliminate ground-truth requirements. Key contributions include equivariant imaging frameworks for stability, spline sketches for photon-counting lidar compression, and uncertainty quantification techniques with equivariant bootstrapping. Recent Trends: 2025 publications emphasize lightweight architectures for multi-domain reconstruction (CT, super-resolution) and noise-agnostic SURE methods. 2024 works focus on audio declipping , compressed lidar , and nonlinear algorithm unrolling with applications in autonomous vehicles and medical imaging. Scientific Awards: Best Student Paper Award at ICASSP’22 Collaborations & Leadership: He leads the DeepInverse open-source project and develops algorithms for real-time 3D lidar reconstruction. His team includes researchers from University of Edinburgh and Grenoble INP, with applications in automotive lidar and underwater imaging.
Sotiris Christodoulou is an Associate Professor at the Department of Electrical and Computer Engineering within the College of Engineering at the University of Peloponnese. He also serves as a research associate at the 'Diofantos' Institute of Computer Technology and Publishing. His academic career spans multiple institutions where he has taught graduate and undergraduate courses across seven different universities since 2004. Dr. Christodoulou earned his B.A. in Computer Engineering and Informatics from the University of Patras in 1994 and completed his PhD in Web Engineering from the same institution in 2004. His educational background established the foundation for his extensive research career focused on web technologies and applications. His primary research interests include Web Engineering, Web Application Performance Optimization, Web Code Quality, Semantic Web technologies, Hypermedia Systems, and emerging Web 2.0 and Web 3.0 technologies. His work extends to Virtual Interactive Environments, 3D and Augmented Reality applications, and Spatial Hypertext systems. Christodoulou's research bridges theoretical web engineering principles with practical applications in cultural heritage, education, and urban infrastructure systems. His research output comprises over 45 publications in international journals, book chapters, and conferences, accumulating more than 450 citations. He has participated in over 23 European and National Research and Development Projects focused on web software technology, hypermedia applications, and 3D educational and cultural applications. Professional member of ACM Professional member of IEEE Member of organizing committees for over 15 international scientific conferences Reviewer for recognized international journals (ACM, IEEE, etc.) Christodoulou has extensive teaching experience across seven universities, specializing in programming languages, web software engineering, software quality, and data management. His research projects typically combine applied research with cutting-edge technology implementation for real-world problems in large organizational information systems. He maintains regular office hours at Building K, Office K2.02 at the University of Peloponnese, with appointments available on Mondays and Thursdays.
Morteza Zihayat is an Associate Professor and Canada Research Chair (Tier 2) in Human-Centered Artificial Intelligence at Toronto Metropolitan University. He holds dual appointments in the Faculty of Engineering and Architectural Science (Department of Electrical, Computer, and Biomedical Engineering) and the Ted Rogers School of Management. Additionally, he serves as an Adjunct Professor at the University of Waterloo in Management Sciences and is a Faculty Fellow at IBM's Centre for Advanced Studies. Dr. Zihayat's educational background includes: PhD in Computer Science from York University (2016) MSc in Computer Engineering from University of Tehran (2011) Postdoctoral Research Fellowship at University of Toronto's Faculty of Information (2017) His research lies at the intersection of AI, security, and society with a focus on building fair and transparent AI systems. Dr. Zihayat's expertise spans human-centered AI, fair information retrieval systems, and blockchain-enabled AI infrastructures. His work emphasizes creating AI systems that are accountable and designed to serve the public good, with applications in healthcare, digital media, and social networks. Dr. Zihayat has received numerous accolades including the Canada Research Chair (Tier 2) in Human-Centered AI (2024), Dean's Outstanding Scholarly, Research, and Creative Activity Award (2023), Best Short Paper Award at ECIR (2023), and IBM CAS Faculty Fellowship (2021). His research has attracted over $1.7 million in external funding from agencies such as NSERC, Mitacs, and multiple industry partners including Toronto Transit Commission, The Globe and Mail, AT&T, and IBM. Dr. Zihayat serves as Associate Editor of the Computational Intelligence Journal and is an active reviewer for top-tier venues. He is also Co-director and Co-founder of the Digital Enterprise Analytics and Leadership (DEAL) Research Center.
Dr. Shaojun Feng is an Honorary Principal (Professorial) Research Fellow at the Centre for Transport Studies within the Department of Civil and Environmental Engineering, Faculty of Engineering, Imperial College London. He is a leading expert in Global Navigation Satellite Systems (GNSS), with over thirty years of research experience focusing on GNSS integrity, high-accuracy positioning, augmentation systems, and functional safety for autonomous and safety-critical applications. His research interests span a wide range of topics including GNSS integrity monitoring, precise point positioning (PPP), real-time kinematic (RTK), ionospheric modeling, spoofing detection, software-defined receivers, and integrated navigation systems. He has made pioneering contributions to the development of GNSS correction services with integrity, enabling lane-level navigation in smartphones and certified use in autonomous vehicles. His work bridges theoretical advances with real-world deployment, notably through a commercial service benefiting over 1.5 billion users. The recent publications highlight a strong focus on ionospheric modeling and correction using machine learning (e.g., LSTM, ConvLSTM), sparse reconstruction techniques in tomography, and integrity-aware positioning for autonomous systems. There is a clear trend toward integrating AI with GNSS, improving real-time accuracy, and ensuring safety through robust integrity monitoring—especially in multi-constellation environments (GPS, Galileo, BeiDou). Dr. Feng has been recognized with several prestigious honors: Michael Richey Medal, Royal Institute of Navigation ESA certification for leading Galileo receiver development Recognition by UK Space Agency for UK leadership in Galileo adoption SGS certification for safety-critical GNSS correction service He is actively involved in the academic and standards communities, serving as Associate Editor for the Journal of Navigation and GPS Solutions , and as Chairman of WG3 in RTCM Special Committee 134, which develops international standards for GNSS integrity monitoring. He is a Chartered Engineer (CEng) and Fellow of both the Institution of Engineering and Technology (FIET) and the Royal Institute of Navigation (FRIN), the latter presented by HRH Prince Philip. His leadership in research, editorial roles, and standardization underscores his significant impact on the field of navigation and positioning. Dr. Feng leads research initiatives at Imperial College London with strong industry and agency collaborations, including with the European Space Agency and SGS. His work on integrity-certified GNSS services represents a major advancement in positioning for autonomous systems, with implications for future smart mobility, transportation safety, and resilient navigation infrastructure.
Jin Zhu is a Researcher in the Department of Statistics at the London School of Economics and Political Science (LSE), working with Prof. Chengchun Shi on reinforcement learning and machine learning. His research focuses on developing algorithms with statistical and computational guarantees, alongside statistical software design to enhance algorithmic applications. Prior to LSE, he earned his PhD in Statistics at Sun Yat-Sen University under Dr. Xueqin Wang and Dr. Na You. Key expertise includes reinforcement learning, machine learning, and computational statistics. His work addresses challenges in off-policy evaluation, robustness in RL, and sparsity-constrained optimization. He has contributed to open-source tools like skscope and abess for efficient statistical computation. Research interests also span causal inference, high-dimensional data analysis, and algorithmic design for complex systems. Notable contributions include methodologies for genetic factor identification, spatial experimental design, and nonparametric statistical inference. Jin’s research bridges theoretical advancements with practical software implementations to address real-world computational and statistical challenges.
Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).
Mohamed Allali is an Associate Professor at Chapman University, affiliated with both the Fowler School of Engineering (Department of Electrical Engineering and Computer Science) and Schmid College of Science and Technology (Department of Mathematics). His research spans data engineering, machine learning, climate informatics, and mathematical education. He has contributed to constraint-based intelligent tutoring systems, data drift detection using KL divergence, and geospatial analysis for environmental monitoring. Education: University of Oklahoma (BS, MA, PhD). His recent work focuses on data distribution divergence, climate modeling for sea turtle habitats, and neural network applications in medical imaging. He has collaborated extensively on drought indices, satellite data validation, and educational technologies. His publications highlight expertise in statistical learning, computational methods, and interdisciplinary environmental applications.
Patrik Voštinár is an Assistant Professor at the Department of Computer Science , Faculty of Natural Sciences , Matej Bel University . Holding a PhD in Applied Computer Science from the same university (2014-2017), he teaches courses in Programming , Discrete Mathematics , Web Technologies , and Android Programming . As department head and study advisor, he actively shapes academic programs and student experiences. Matej Bel University (2017-present) Department of Computer Science Faculty of Natural Sciences His research focuses on computer science education and educational technology , with particular emphasis on: VR/AR applications in teaching Game-based learning environments Microcontroller programming pedagogy Mobile application development education Physical computing tools for K-12 Adaptive learning interfaces His work with MakeCode , micro:bit , and EEG-controlled games demonstrates innovative approaches to programming education. He has received multiple eLearning competition awards for educational courseware development. Heart on the palm (Project of the year 2019) 2nd price in eLearning competition (2023) for Discrete Mathematics course Price of České společnosti pro systémovou integraci (2023) for Web Technologies 1st price in eLearning competition (2023) for Geometry Didactics Voštinár actively contributes to academic communities through: Membership in DIDINFO conference program (since 2017) Editorial Board member of Elementary Mathematics Education Journal Organizing workshops for primary/secondary students Popularizing informatics through extracurricular programs
Andrea Vinci is an accomplished researcher with 66 publications and 1,261 citations, specializing in the intersection of quantum computing, edge-cloud architectures, and Internet of Things (IoT) systems. His work demonstrates significant contributions to solving complex computational problems through innovative approaches that bridge theoretical quantum algorithms with practical distributed computing applications. His research interests span quantum computing applications for resource management, multi-density clustering techniques for urban analytics, and platform-independent IoT application development. Vinci has pioneered work in variational quantum algorithms for cloud/edge resource allocation, quantum kernels for IoT data classification, and distributed AI for cognitive building systems. His research demonstrates a consistent focus on addressing NP-hard problems through quantum-classical hybrid approaches. Analysis of Vinci's publication trends reveals a strategic research trajectory moving from foundational work in smart city analytics and crime prediction toward cutting-edge quantum computing applications for IoT and edge-cloud systems. His recent publications (2023-2025) show increasing focus on quantum machine learning techniques specifically tailored for IoT data processing, with significant attention to practical implementation challenges. Vinci maintains an extensive collaborative network, frequently publishing with researchers including Fabrizio Marozzo, C. Mastroianni, J. Settino, and Antonio Guerrieri across multiple high-impact venues including IEEE Transactions, ACM conferences, and specialized journals in quantum computing and distributed systems. His technical contributions include the development of the COGITO platform for cognitive buildings, novel approaches to multi-density crime prediction, and significant advancements in quantum kernel methods for IoT data analysis. Vinci's tutorial publications indicate his role in educating the broader research community about emerging quantum computing applications for distributed systems.
Alejandra Magana serves as the W.C. Furnas Professor in Enterprise Excellence of Applied and Creative Computing and Professor of Engineering Education at Purdue Polytechnic, Purdue University. She directs the ROCkETEd Lab and holds significant editorial roles including Deputy Editor for the Journal of Engineering Education and Co-Editor of the Education Department for IEEE Computer Graphics & Applications. Her leadership extends to multiple editorial boards in engineering and science education journals. Ph.D. in Engineering Education, Purdue University (2009) Post-doc in Engineering Education, Purdue University (2010) M.S. in Educational Technology, Purdue University (2007) M.S. in Electronic Commerce, Instituto Tecnologico y de Estudios Superiores de Monterrey (2003) B.S. in Information Systems Engineering, Instituto Tecnologico y de Estudios Superiores de Monterrey (2000) Magana's research focuses on how students develop computer and data science self-regulated learning skills through computational cognitive apprenticeship and authentic modeling practices. She investigates how learning analytics and AI can support student learning in computing-intensive domains and engineering thinking in K-12 education. Her work bridges educational theory with practical applications in STEM fields, emphasizing the integration of computational thinking across disciplines. She employs design-based research methodologies to create and evaluate innovative learning environments that leverage simulation, visualization, and emerging technologies. Analysis of Magana's recent publications reveals a strong emphasis on collaborative learning in digital environments, with growing attention to AI applications in education. Her work spans multiple domains including virtual reality for STEM education, teamwork dynamics in software development, AI ethics education, and computational thinking assessment for teachers. A consistent theme is the development of representational competence through modeling and simulation practices, with increasing integration of learning analytics to understand student cognition and team processes. Fellow Member, American Society for Engineering Education (2024) Fulbright Specialists Appointment (2024) Purdue Polytechnic Charles B. Murphy Outstanding Undergraduate Teaching Award (2021) W.C. Furnas Professorship (2020) Purdue University Faculty Scholar (2016) ASEE-ERM Apprentice Faculty Grant (2012) Magana leads multiple significant research grants totaling over $15 million, primarily from NSF, focusing on computational education, AI in learning environments, and engineering education transformation. Her ROCkETEd Lab serves as the hub for these research activities, which often involve interdisciplinary collaborations across engineering, computer science, and education. She has mentored numerous graduate students who appear as co-authors on her publications, though specific advising relationships aren't explicitly documented. Her research program demonstrates strong continuity from foundational work on modeling and simulation to current projects integrating generative AI and immersive technologies. The ROCkETEd Lab (Research in Organizing Computational Knowledge for Engineering Thinking and Education) functions as Magana's primary research unit, focusing on computational cognitive apprenticeship approaches. The lab investigates how students develop representational fluency through modeling and simulation practices, with recent expansion into AI-enabled learning environments. Current projects examine hidden curricula in computer science education, high-performance computing virtual environments for AI education, and culturally relevant programming learning experiences. The lab maintains strong industry and international partnerships, particularly with institutions involved in advanced manufacturing and educational technology development.
Benedikt Schmitz is a PostDoc researcher at the Technical University of Darmstadt, working at the Institute of Nuclear Physics (IKP) and the Theory of Electromagnetic Fields (TEMF). His research spans multiple domains of physics including superconductivity, laser-plasma interactions, and AI-supported modeling of complex physical phenomena. PhD in Physics from Technical University of Darmstadt (2023) Master's research at Helmholtz-Zentrum Berlin (2016-2018) Dr. Schmitz's research focuses on superconductivity, particularly magnetic field interactions with superconductors, and laser-plasma physics for particle acceleration. His work on radiochromic film dosimetry led to pyRES, an open-source evaluation tool. He pioneered AI applications in physics research, developing surrogate models using deep learning for neutron yield prediction and liquid target experiments. His research bridges traditional physics with modern computational approaches, demonstrating how machine learning can transition from research subject to research tool. His publication record shows a clear evolution from superconductivity research toward laser-plasma physics and AI modeling. Early works focused on SRF cavity diagnostics, while recent publications center on laser-driven neutron sources and deep learning applications. This progression reflects his doctoral work and growing expertise in computational physics. His articles demonstrate interdisciplinary approaches combining plasma physics, nuclear engineering, and machine learning to solve complex problems in particle acceleration and detection. First prize at Medtech:Hack with BIOSCAN at CERN (April 2018) Dr. Schmitz has led multiple research projects including SRF Magnetometry during his Master's work, Neutron Prediction and TNSA Liquid Leaf for his PhD, and ongoing development of pyRES. His BIOSCAN detector project resulted in a patent and demonstrates his ability to translate physics concepts into medical applications. He has developed software tools like LabTab for electronic lab journals and maintains active GitHub repositories for his research code. His projects consistently combine experimental work with computational modeling and increasingly incorporate machine learning approaches. His research is conducted within collaborative teams including the TEMF group at TU Darmstadt under Prof. Boine-Frankenheim for his doctoral work, and previously with Prof. Jens Knobloch's group at Helmholtz-Zentrum Berlin. His work spans multiple laboratories and computational environments, utilizing particle-in-cell simulations, Monte Carlo methods, and deep learning frameworks to advance understanding in his fields of interest.
Brendan Mumey is a Professor of Computer Science at Montana State University, affiliated with the Gianforte School of Computing under the College of Engineering. He holds a Ph.D. in Computer Science from the University of Washington (1997), an MS from the University of British Columbia, and a BS in Mathematics from the University of Alberta. His research focuses on applied algorithms, computational biology, and optimization, particularly in pangenomics, flow decomposition, and genomics. Key research areas include DNA/RNA sequence multiassembly, pangenomics, and algorithms for flow decomposition in networks. He is part of the Applied Algorithms Group and develops software through the MSU Algorithms Lab. Notable projects involve NSF-funded initiatives to scale flow decomposition and explore plant genetic diversity using pangenomic tools. Selected awards include multiple Excellence in Research Awards (2007, 2011, 2012, 2013) and an Excellence in Service Award (2011). His teaching spans algorithms, discrete structures, and computational biology at both undergraduate and graduate levels. Grants include NSF support for pangenomic tools, functional genomics, and interdisciplinary mentoring programs. Collaborative work emphasizes bridging theoretical algorithms with practical applications in biology and sustainability.
Dr. Robb Lindgren is a Professor of Educational Psychology and Curriculum & Instruction at the University of Illinois, Urbana-Champaign , with affiliate appointments at the Beckman Institute, National Center for Supercomputing Applications, iSchool Informatics, Center for Social & Behavioral Science, and Grainger College of Engineering. His research focuses on interactive and immersive digital technologies for learning, particularly in STEM education . PhD in Learning Sciences and Technology Design from Stanford University MA in Psychology from Stanford University BS in Computer Science from Northwestern University Dr. Lindgren explores how body-based interactions with technologies like virtual reality and augmented reality enhance understanding of abstract concepts. His work combines embodied cognition with digital learning environments , examining how physical engagement shapes multimodal learning and collaborative problem-solving . Recent projects include haptic feedback systems and gesture-augmented simulations for molecular visualization and climate change education. His publications analyze trends in XR (Extended Reality) applications, emphasizing gestural agency , collaborative discourse , and multisensory learning . Key findings show how embodied interactions improve conceptual retention in physics and explanatory modeling in thermal conduction, alongside innovative assessment methods using attention tracking and behavioral analytics. 2017 - Jan Hawkins Award for Early Career Contributions Multiple NSF-funded projects on technology-enhanced learning As Director of the Technology Innovations in Educational Research and Design (TIER-ED) initiative, Dr. Lindgren fosters cross-campus collaboration to address educational challenges through emerging technologies. He teaches courses on digital learning environments , educational game design , and embodied learning theories , while developing prototypes for STEM education in partnership with schools and museums.
