Xuesong Zhou is a Professor of Transportation Systems at the School of Sustainable Engineering and the Built Environment , Arizona State University (ASU). He leads the ASU Transportation+AI Lab and develops open-source tools like DTALite, NEXTA, and OSM2GMNS with over 100,000 downloads. His research focuses on multimodal transportation planning , dynamic traffic assignment , and rail scheduling with methodological contributions to traffic flow theory and operations research . Dr. Zhou's research bridges transportation system operations , computer applications for ITS , and logistics optimization . His work on differentiable programming reformulations and state-space-time network modeling has advanced real-time traffic prediction and multi-echelon facility scheduling . Scientific awards include: 2022 Elsevier Multimodal Transportation Best Article Award 2018 Transportation Research Part C Best Associate Editor Award 2012 INFORMS Railway Applications Section Best Paper Award He has advised 9 PhD students and 6 postdoctoral researchers to completion, with mentees now at institutions like Georgia Institute of Technology and Michigan State University. Current projects include NSF CONNECT and DOE Argonne collaborations on multi-scale traffic simulation and smart campus cyberinfrastructure .
Shane Dawson is the Executive Dean of UniSA Education Futures and Professor of Learning Analytics at the University of South Australia. His work bridges social network analysis and learner interaction data to enhance teaching quality and educational outcomes. Affiliation : University of South Australia Research Focus : Learning Analytics, Curriculum Mapping, K-12 Decision-Making Systems, and AI in Education Recent Research Trends : Shane’s 2025 publications emphasize generative AI for curriculum analytics, ethical considerations in K-12 dashboards, and longitudinal graduate attribute monitoring. His articles often integrate psychometric models, social network tools, and open-source software like OVAL and SNAPP . Advising and Collaboration : As a co-developer of key learning analytics tools and a supervisor for research students, he collaborates globally with institutions such as Johns Hopkins University and Shahid Beheshti University of Medical Sciences. Labs and Teams : Shane leads UniSA’s Teaching Innovation Unit and is a founding member of the Society for Learning Analytics Research , driving institutional and international initiatives in educational technology.
Dr. Magdalena Schreter-Fleischhacker works at the Technical University of Munich within the Professorship of Simulation for Additive Manufacturing . Her research focuses on physics-based computational modeling of coupled liquid-powder-gas dynamics in metal additive manufacturing, including melt pool dynamics and powder-gas interactions . She specializes in multi-phase flow modeling using cut-element and diffuse interface methods with continuous/discontinuous Galerkin schemes . She also develops constitutive models for quasi-brittle materials like 3D printed concrete and rock, incorporating anisotropy , gradient-enhanced damage mechanics , and micropolar continua . Her computational work leverages matrix-free algorithms and parallel computing , with significant contributions to the deal.II finite element library . Research Interests Physics-based computational modeling of coupled liquid-powder-gas dynamics in additive manufacturing Multi-phase flow simulation using sharp/diffuse interface methods Advanced constitutive modeling for quasi-brittle materials (rock, soils, 3D printed concrete) High-performance computing and matrix-free algorithms Notable Contributions Development of consistent diffuse-interface models for melt-vapor dynamics Improvements to continuum surface flux models in additive manufacturing Formulation of gradient-enhanced damage-plasticity models for geological materials Principal contributor to the deal.II library (version 9.6) Supervised Student Projects Johannes Resch (2024): DG-based thermo-hydrodynamic melt pool simulations Julian Brotz (2024): DEM-FEM coupling for fluid-powder interaction Andreas Ritthaler (2024): Matrix-free cutDG formulation for complex flows Tinh Vo (2023): Laser modeling for melt pool simulations Scientific Awards ERC Starting Grant recipient
Prof. Dr. Stefan Eicker is a Professor and Chairholder of Business Information Systems and Software Engineering at the Faculty of Computer Science, University of Duisburg-Essen, Germany. He has held this position since April 2004, following previous academic appointments at the Technical University of Clausthal, the University of Essen, and other German institutions. His research spans multiple domains within information systems and software engineering, with a particular focus on digital transformation and emerging technologies. Prof. Eicker's research interests center around Smart Products , Service Systems , Internet of Things , and Platform Economics . His work explores how digital technologies transform traditional business models and create new value propositions. He has developed taxonomies for smart services and investigated quality factors in self-tracking solutions, demonstrating his interdisciplinary approach that bridges technical and business perspectives. His research particularly emphasizes the integration of physical and digital components in modern products and services. His recent publications (2019-2024) reveal a strong focus on digital platform economies, smart services, and IoT applications. The research shows a clear trajectory toward understanding value creation mechanisms in digital ecosystems, with increasing attention to practical applications in energy systems, self-tracking technologies, and business model innovation. His work often involves collaboration with colleagues like Gero Strobel and Tobias Brogt, indicating an active research group focused on digital transformation. Prof. Eicker has contributed significantly to the academic community through his extensive publication record spanning nearly two decades, with work appearing in journals, conference proceedings, and edited volumes. His research bridges theoretical frameworks with practical applications in business contexts. He maintains an active role in academic administration and education at the University of Duisburg-Essen, where he has contributed to curriculum development and the implementation of systems for managing academic information. His work on the bolognaT3 system demonstrates his commitment to improving academic processes through technology.
Maria Mushtaq is an Associate Professor at Telecom Paris , affiliated with the Information Processing and Communication Laboratory (LTCI) and the Secure and Safe Hardware (SSH) Lab . She received her PhD in Information Security from the University of South Brittany, France (2019) and completed 2 years of postdoctoral research at LIRMM, University of Montpellier under the CNRS excellence post-doc grant. Research Focus: Microarchitectural vulnerability assessment, runtime mitigation against side/covert-channel attacks, cryptanalysis, OS-based security primitives, and hardware-software interface security Technical Expertise: Cache timing attacks, transient execution attacks (Spectre/Meltdown), Hardware Performance Counter analysis, gem5 simulation Her recent work involves RISC-V security analysis using gem5 simulations and machine learning for attack detection. She serves as Guest Editor for the Journal of Applied Sciences special issue on Side Channel Attacks in Embedded Systems and has been on the Program Committee for the European Test Symposium (2020-2021). 2021 HiPEAC Collaboration Grant recipient Organized IP Paris Winter School on Microarchitectural Security (2022) Active in international conferences as panelist and keynote speaker
Tracy Hall is Professor in Software Engineering at Lancaster University's School of Computing and Communications, where she holds a Chair in Software Engineering Research and serves as Director of Post Graduate Teaching. Previously, she was Professor and Head of Computer Science at Brunel University London, and has held visiting positions at University College London and adjunct roles at the University of Oslo. With over 20 years of empirical software engineering research experience, she maintains extensive industrial collaborations. Her research focuses on: Software defect prediction and automatic repair Code analysis methodologies Software testing frameworks Human factors in software development Empirical studies of developer behavior Tool development for software engineers She leads research in automated defect repair techniques and vulnerability prediction, with recent work exploring AI-driven approaches to software quality improvement. Her publication portfolio (100+ papers) shows consistent focus on software quality enhancement, with recent emphasis on explainable AI for vulnerability prediction (2025), developer-centric testing tools (2024), and human factors in bug resolution (2022). Research frequently involves large-scale empirical studies and industry partnerships. Awards include multiple best paper awards for her contributions to software engineering research. As Principal Investigator, she secured significant funding including: EPSRC Fixie project: £400,000 for defect prediction/repair (2018-2020) EPSRC Fault Analysis grant: £128,578 (2016-2019) Current PhD supervisees include Gaz Bennett, Jesse Phillips, and Miles Walker working on software engineering challenges. She contributes to the Cyber Security Research Centre , Security Lancaster , and DSI-Foundations research groups. Teaches courses on IT Architecture and Software Studio.
Md Mobashir Hasan Shandhi is a tenure-track Assistant Professor at Arizona State University , jointly appointed in the School of Electrical, Computer and Energy Engineering and the Biodesign Institute Center for Bioelectronics and Biosensors . His work focuses on developing equitable digital health technologies—wearable sensors and AI/ML algorithms—for personalized care and remote monitoring of chronic and infectious diseases. Education PhD, Electrical and Computer Engineering, Georgia Institute of Technology, 2020 Postdoc, Biomedical Engineering, Duke University, 2021–2024 MS, Electrical and Computer Engineering, University of Utah, 2016 BSc, Electrical and Electronics Engineering, Bangladesh University of Engineering and Technology, 2011 Research Interests Dr. Shandhi’s lab designs low-cost, reliable wearable sensors and machine-learning models to enable remote monitoring of cardiovascular, respiratory, and infectious disease patients. His goal is to reduce healthcare disparities by translating these technologies into resource-limited settings. Scientific Awards & Grants American Heart Association Career Development Award Mayo Clinic–ASU Alliance Faculty Summer Residency Fellowship AHA Postdoctoral Fellowship Duke Heart Center & Translating Duke Health cardio-oncology grant NIH mHealth Training Institute Scholarship Best Paper, Runner-up Best Paper, First Place Research Awards Distinguished Poster Nominee Advising & Funding Dr. Shandhi is currently recruiting PhD students with backgrounds in electrical/biomedical engineering or computer science. He also welcomes postdocs and MS/undergraduate researchers to join the SHANDHI Lab. Interested candidates should email him directly with a CV and statement of interest. Labs & Teams He directs the SHANDHI Lab at ASU, where interdisciplinary teams of engineers, clinicians, and data scientists collaborate on translating wearable health technologies from bench to bedside.
Veronica Frans is a Stanford Science Fellow at Stanford University and a sixth-year PhD Candidate in Fisheries & Wildlife and Ecology, Evolutionary Biology & Behavior at Michigan State University (MSU). She is affiliated with MSU’s Center for Systems Integration and Sustainability (CSIS), the Klausmeier-Litchman lab at Kellogg Biological Station, and holds certifications in Community Engaged Scholarship and Spatial Ecology. Her research focuses on human influence on species distributions, integrating ecological modeling, GIS, and community outreach. BS/BA in Environmental Sciences and French from Messiah College MSc in International Nature Conservation from Goettingen University Her research spans ecology, conservation biology, and sustainability, emphasizing human-nature interactions through metacoupling, telecoupling, and stakeholder collaboration. She has conducted fieldwork in Alaska, Hong Kong, and the Falkland Islands, leveraging local knowledge to address global conservation challenges. Recent publications highlight her expertise in species distribution modeling, sustainability tool development (e.g., seesus, SDGdetector), and conservation policy analysis. Articles frequently explore metacoupling, anthropogenic impacts, and marine ecosystem dynamics, with applications in deforestation, biodiversity, and SDG implementation. NSF GRFP Fellow University Enrichment Fellow Outstanding Paper in Landscape Ecology Award Veronica collaborates with the Klausmeier-Litchman lab at MSU’s Kellogg Biological Station and works under Dr. Jianguo (Jack) Liu at CSIS. Her work bridges marine environments, stakeholder engagement, and global sustainability through interdisciplinary research and community-based conservation.
Raghavendra Selvan, an Assistant Professor (Tenure Track) at the University of Copenhagen, holds joint appointments in the Machine Learning Section (Department of Computer Science), Kiehn Lab (Department of Neuroscience), and the Data Science Laboratory. His academic journey includes a PhD in Medical Image Analysis (2018), MSc in Communication Engineering (2015), and BSc in Electronics and Communication Engineering (2009). PhD - Medical Image Analysis, University of Copenhagen (2018) MSc - Communication Engineering, Chalmers University (2015) BSc - Electronics and Communication Engineering, BMS Institute of Technology, India (2009) His research focuses on Bayesian Machine Learning with emphasis on Medical Image Analysis, Graph-based Learning, Tensor Networks, Approximate Inference, and Multi-Object Tracking Theory. Recent publications highlight his contributions to environmentally sustainable AI practices, efficient deep learning in medical imaging, and novel applications of tensor networks. Key research areas: Green AI and Environmental Sustainability Medical Image Analysis Graph Neural Networks Crystal Structure Prediction Model Compression Materials Science Applications
Katharina Eggensperger is an Early Career Research Group Leader at the University of Tübingen , leading the AutoML for Science group within the Cluster of Excellence Machine Learning for Science . She previously completed her Ph.D. at the University of Freiburg under Frank Hutter and Marius Lindauer (2022), and actively contributes to the AutoML community through open-source tool development and competition leadership. Co-developer of AutoML.org tools Faculty member of IMPRS-IS Chair for multiple AutoML workshops/conferences (2019-2025) Her research focuses on automated machine learning (AutoML) with specific attention to: AutoML Systems Hyperparameter Optimization Tabular Machine Learning Scientific Applications of ML She has organized multiple AutoML schools and conferences, including serving as Program Chair for AutoML 2024 and Non-archival Track Chair for AutoML 2025. Her work emphasizes making machine learning accessible through automation while maintaining scientific rigor and interpretability, particularly for tabular data applications. Katharina actively recruits PhD students through IMPRS-IS and collaborates with institutions like the University of Freiburg and Cyber Valley .
Steve Boker is a Professor of Psychology at the University of Virginia, directing the Human Dynamics Laboratory and the LIFE Academy. His research focuses on quantitative psychology, structural equation modeling (SEM), and dynamical systems analysis for longitudinal and time series data. Dr. Boker has pioneered methods like Differential Structural Equation Modeling (dSEM) , Latent Differential Equations (LDE) , and the Windowed Cross-Correlation (WCC) method. He co-developed the widely used OpenMx SEM software framework and invented the RAMpath method for path diagram analysis. Key Research Areas: Dyadic conversation dynamics, adaptive systems in addiction, motion symmetry in social interactions, maternal-infant coupling, and resilience modeling through longitudinal data. Awards & Honors: 2024 Distinguished Researcher Award (UVA) 2020 Saul Sells Award for lifetime achievement in multivariate psychology Fellow, American Psychological Association Fellow, Association for Psychological Science His methodological contributions span over 150 publications, with recent work emphasizing nonlinear dynamics, surrogate data validation, and complexity metrics like the Tangle index for short time series analysis.
Professor Kelly Lyons is a Professor at the Faculty of Information at the University of Toronto, cross-appointed to the Department of Computer Science. Her research focuses on service science, knowledge mobilization, social media, and data-driven innovation. Prior to academia, she held roles at IBM Toronto Lab's Centre for Advanced Studies. She has secured extensive funding from NSERC, IBM, and industry partnerships, and has advised numerous graduate students. Her work bridges interdisciplinary collaboration, emphasizing AI governance, digital economy impacts, and fostering Women in Technology initiatives. Research interests include the application of social platforms in service systems, data science for knowledge translation, and the societal implications of AI. Key projects involve analyzing gender dynamics in user reviews, open-source software structures, and pandemic-driven innovation trends. Her grants span data science, healthcare analytics, and smart city technologies. Publications span empirical studies on GitHub collaboration, AI governance frameworks, and biomedical knowledge systems. She received the Best Paper Award for 'The Effect of Collaborative Games on Group Work' (2015). Her teaching emphasizes service systems design and project management, with a focus on practical, interdisciplinary learning. Active in scholarly service, she chairs the Consortium for Software Engineering Research and serves on ACM-W's Executive Council. Collaborations include cross-institutional projects with University College London and UCL on AI governance frameworks. Her work extends to promoting STEM education and diversity in tech.
Valerie Welborn is an Assistant Professor in the Department of Chemistry within the College of Science at Virginia Tech. Her research program focuses on multiscale simulation of condensed phase systems, particularly examining the role of electric fields in biological interfaces and biological-like systems. She leads an active research group that bridges computational chemistry with experimental validation through multiple collaborations. Dr. Welborn's research interests span protein dynamics and function, characterization of structural and functional water, polysaccharides in solution, and polymer design for metal chelation. Her work combines morphological, structural, dynamic and electronic factors to develop new models of biological interfaces, with particular emphasis on how water interacts at a fundamental molecular level with biological entities such as proteins and bone tissues. She specializes in electric field calculations to understand protein flexibility in catalysis and ion transport, seeking to reconcile protein dynamics with electrostatic preorganization theory. Her recent publications demonstrate strong activity across multiple domains, with particular emphasis on electric field analysis in protein function, water dynamics at biological interfaces, and polymer design for metal chelation. Her work shows a consistent trajectory toward increasingly complex biological systems and more sophisticated computational approaches, including polarizable force field methods and multiscale modeling techniques. Centre for Doctoral Training (CDT) on Theory and Simulation of Materials (TSM) Ph.D. Prize for Research Excellence, 2014 Outstanding Contribution to Outreach and Public Engagement, CDT TSM, 2014 Engineering and Physical Sciences Research Council (EPSRC) fully-funded Ph.D. Fellowship, CDT TSM, 2011 Editor-selected as '2021 Hot PCCP article' Front cover article in Phys. Chem. Chem. Phys. Dr. Welborn actively mentors a diverse group of researchers, including multiple postdoctoral associates, graduate students across chemistry and related disciplines, and undergraduate researchers. Her lab participates in the NSF Materials Innovation Platform GlycoMIP (DMR-1933525), focusing on polysaccharide research. She collaborates extensively with experimental groups, particularly with Professor Michael Schulz on polymer design for metal chelation projects. Her lab develops computational tools like the ELECTRIC software package for electric field calculations in biomolecular systems. The Welborn group maintains active research programs in four main areas: protein dynamics and function, characterization of structural and functional water, polymer design for metal chelation, and polysaccharides in solution. Each program employs specialized computational approaches to address fundamental questions in biological chemistry, with particular emphasis on how electric fields govern molecular behavior at biological interfaces.
Dr. Ashley Willis is a Senior Lecturer in Fluid Dynamics at the University of Sheffield's School of Mathematical and Physical Sciences. He holds roles including Admissions Head and Programme Leader for Study Abroad. His research focuses on fluid dynamics, turbulence, astrophysical flows, and magnetohydrodynamics, with applications in clean energy and planetary systems. Willis completed his Ph.D. in Applied Mathematics at Newcastle University (2002) and has held postdoctoral positions at the University of Bristol, Leeds, and a Marie Curie Fellowship at Ecole Polytechnique, Paris. He leads the Fluid Dynamics Group and is part of the cross-faculty Sheffield Fluid Mechanics Group. His research interests include transition to turbulence, nonlinear dynamics, and magnetic field generation in planetary interiors. He supervises PhD students like Shijun Chu and collaborates with industry on projects like turbulence suppression in pipe flows. Key contributions include the open-source Openpipeflow simulation code and studies on dynamo action in geophysical flows. Publications highlight work on turbulent transition mechanisms, optimal flow configurations, and dynamo theory. His teaching includes courses on mechanics, fluid dynamics, and mathematical modeling of natural systems.
Balazs Vedres is a **Professor** at the **Central European University (CEU)**, with a joint appointment in the **Department of Network and Data Science**. His research integrates network science, data science, and social theory to explore creativity, innovation, and historical processes in collaborative networks. He holds a **PhD in Sociology from Columbia University** and an **MA in Economics from Corvinus University**. **Research Interests**: Focuses on structural dynamics in creative fields (e.g., jazz, video games, open-source software), gender inequality in collaborative platforms, historical network evolution, and the impact of social bots on human collaboration. He analyzes how network configurations like *structural folds* and *forbidden triads* drive innovation and creativity. **Awards**: Elected **Member of the European Academy of Sociology (2017)**, recognizing his contributions to sociological research. His work bridges computational methods with sociological theory, as seen in his publications in *American Journal of Sociology* and interdisciplinary journals. **Projects**: Leads initiatives like *Gendered Creative Teams: From Marginality to Success* and *Ceunet/Indra Mapping European Network Science*. His research often involves empirical studies of transnational activism, entrepreneurial networks, and digital platforms. **Labs/Teams**: Active in CEU’s **Data and Network Dynamics Studies (DNDS)** group, fostering interdisciplinary collaboration in computational social science. His work emphasizes the interplay between global economic integration and local developmental agency.
