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Hailong ChenView profile

Associate Professor

• Computational Mechanics
• Computational Materials
• Meshfree Methods
• +6 more

Hailong Chen is an Associate Professor in the Department of Mechanical and Aerospace Engineering within the Stanley and Karen Pigman College of Engineering at the University of Kentucky. His academic journey includes a Ph.D. in Mechanical Engineering from Arizona State University (2015) and an M.S. in Mechanical Engineering from the University of Florida (2012). Dr. Chen's research focuses on Computational Mechanics & Materials, with expertise spanning meshfree methods, multi-scale multi-physics modeling, mechanics of stochastic heterogeneous microstructures, and pervasive fracture and impact modeling. His work bridges theoretical developments with practical engineering applications through the CM 3 (Computational Mechanics and Methods) research group, which develops advanced computational techniques for real-world mechanics problems. The CM 3 group specializes in multi-scale multi-physics modeling of solid materials, damage and failure analysis under extreme conditions, mechanics of stochastic heterogeneous microstructures (composites, polycrystals), and computational materials engineering. Recent publications demonstrate strong activity in peridynamics, lattice particle methods, and computational homogenization techniques for fibrous and porous materials. Dr. Chen's research has resulted in numerous publications in top journals including Computer Methods in Applied Mechanics and Engineering and Mechanics Research Communications, with recent work focusing on generalized peridynamic formulations, micro-CT-based property computation, and fluid-structure interaction frameworks for hypersonic applications. His academic progression shows steady advancement from Postdoctoral Computational Scientist at Idaho National Laboratory (2015-2018) to Assistant Professor (2018-2024) and currently Associate Professor (2024-present) at the University of Kentucky.

Jonny HolmströmView profile

Professor

• digital innovation
• digital transformation
• digital entrepreneurship
• +3 more

Jonny Holmström serves as Professor at Umeå University's Department of Informatics and directs the Swedish Center for Digital Innovation (SCDI), which he co-founded. He holds an additional affiliation as Professor at the Centre for Transdisciplinary AI, focusing on bridging theoretical research with practical AI applications across sectors including forestry, banking, and public services. His work appears in premier journals such as MIS Quarterly, Information Systems Journal, and Journal of Information Technology. His research centers on digital innovation, transformation, and entrepreneurship, examining how organizations navigate digital change through platform governance, AI integration, and entrepreneurial storytelling. Recent work investigates generative AI's impact on business model design, data work practices, and organizational transformation, emphasizing practical frameworks for managing digital transitions while addressing resistance and ethical considerations. Analysis of his 15 most recent publications (2024-2026) reveals a dominant focus on generative AI's organizational implications, particularly its role in reshaping platform governance, facilitating innovation through prompting, and transforming business models. Concurrent themes include digital platform evolution, data flow management in innovation networks, and citizen-centric digital government design, reflecting a consistent emphasis on practical implementation challenges in real-world contexts. Holmström leads significant research initiatives including a 28 MSEK program at Umeå University and the Kempe Foundation-funded SCDI AI Business Lab. His current project 'Using No-Code AI to Teach Machine Learning in Higher Education' (2024) aims to democratize AI education. He serves on editorial boards for CAIS, EJIS, Information and Organization, and JAIS, and heads the Swedish Center for Digital Innovation research group while participating in 'AI and society' collaborations. He founded and directs the Swedish Center for Digital Innovation (SCDI), which operates the SCDI AI Business Lab exploring practical AI applications for businesses. His work integrates with the Centre for Transdisciplinary AI to advance cross-sector AI implementation, particularly in public services and sustainable business models within the circular economy framework.

Julio Lloret-FillolView profile

Professor

• Catalysis
• Inorganic chemistry
• Artificial photosynthesis
• +7 more

Prof. Julio Lloret-Fillol is a Group Leader at the Institute of Chemical Research of Catalonia (ICIQ) and an ICREA Research Professor since 2015. His work bridges homogeneous catalysis , material science , and automation to develop sustainable chemical processes and solar fuels. He earned his PhD in 2006 from Universidad de Valencia under Prof. Lahuerta and J. Pérez-Prieto, followed by postdoctoral research at University of Heidelberg (MEyC and Marie Curie Fellowships). Awards: 2024 RSEQ-GEQO Award on Excellence 2023 Fellow of the Royal Society of Chemistry 2022 Ramón Areces Grant 2019 Thieme Chemistry Journals Award 2017 Young Academy of Europe 2015 Young Researcher RSEQ Award Research Interests: Water oxidation catalysis CO₂ reduction to value-added chemicals Artificial photosynthesis Electrocatalytic hydrogen generation Spin-off technologies for green hydrogen and photoreactors Notable Publications: 2024: Angew. Chem. Int. Ed. (electrocatalytic ketones from CO₂) 2024: ACS Catal. (Fe-doped NiO for OER) 2023: ACS Catal. (COF-based cobalt catalysts) 2022: JACS (OER mechanism with cobalt complexes) 2022: Angew. Chem. Int. Ed. (chloroalkane activation) Spin-offs: Treellum Technologies (photoreactors) JOLT Solutions (electrodes for hydrogen production)

Bradley SchmerlView profile

Researcher

• Software Engineering
• Software Architecture
• Self-Adaptive Systems
• +5 more

Bradley Schmerl serves as a Principal Systems Scientist in the Software and Societal Systems Department (S3D) within Carnegie Mellon University's School of Computer Science. His research advances software engineering practices for modern challenges in distributed heterogeneous systems, self-adaptation, and cyber-physical integration. He leads the ABLE research group and actively mentors students in the Masters in Software Engineering program while teaching core courses like Software Architecture and Software Engineering Practicum. Dr. Schmerl's work addresses critical challenges in composing continuously evolving software systems, including components from untrusted third parties and on-the-fly recomposition for environmental changes. His research develops reusable, analyzable tools for software composition with emphasis on model-based adaptation, uncertainty management, and cross-language integration. Key projects include Rainbow for runtime architecture reflection, Acme for formal architectural foundations, and Cyber-physical Systems research linking software models with physical dynamics. Analysis of his 2023-2025 publications reveals intensifying focus on robotics software architecture (particularly ROS-based systems), explainable AI for architectural tradeoff analysis, and configuration management in adaptive systems. Trends show growing integration of machine learning for auto-tuning, empirical studies of misconfigurations, and dimensionality reduction techniques for visualizing design spaces—consistently bridging theoretical rigor with practical tool development for real-world applications. Scientific Awards: No specific awards were documented in the source materials. Dr. Schmerl serves as Practice Area Lead and mentor in CMU's Masters in Software Engineering program, guiding client projects including Rainbow UI for self-adaptive framework interfaces, CoBot UI for telepresence robots, and Educational Telepresence Tasking Language development. His research receives support through ABLE group projects funded by grants targeting software architecture foundations, adaptation mechanisms, and cyber-physical system validation. As a core member of the ABLE research group, he directs investigations into architecture-based self-adaptation with active projects spanning Rainbow (runtime architecture models for dynamic adaptation), Acme (formal architectural styles and tools), and Cyber-physical Systems (software-physical model integration). The group also maintains legacy work in End-User Architecting, Architecture Evolution, and service-oriented platforms for intelligence analysis through SORASCS.

• Business Administration
• Operations Research
• Supply Chain Management
• +6 more

Prof. Dr. Julia Rieck is a Full Professor of Business Administration at the University of Hildesheim , leading the Department of Business Administration and Operations Research within the Faculty of Mathematics, Natural Sciences, Economics and Computer Science. As Dean of the Faculty , she oversees academic programs, quality management, and research initiatives. Her roles include academic advising for the Business Information Systems (B.Sc./M.Sc.) programs and active participation in examination boards and quality committees. Education: PhD in Political Science (Dr. rer. pol.) with summa cum laude (2008), Habilitation at Clausthal University of Technology (2014), and studies in Business Mathematics (Diploma, University of Hamburg, 2003) and Mathematics (Georg-August-University Göttingen, 2000). Research: Focuses on Operations Research , Supply Chain Management , Project Planning , and Logistics . Her work integrates mathematical modeling , machine learning , and real-world applications , particularly in disaster response , dynamic transportation , and sustainable e-commerce . Projects: Leads third-party funded initiatives like "IT für die sorgende Gesellschaft" (AI in healthcare/social sectors) and contributes to the HULLS real-lab (AI in aging societies). Collaborates with regional companies (e.g., Youco, ADITUS) and institutions (HAWK, University of Hannover). Teaching: Emphasizes practical application through case studies, industry partnerships, and the IT-Speed Dating event for student-company connections. Her courses cover project resource planning , logistics , and digital transformation . Labs & Teams: Active in the Institute of Business Administration & Business Information Systems , contributing to the KET Kompetenzwerkstatt (entrepreneurship support) and interdisciplinary teams in AI and sustainability research.

Jelena ZdravkovicView profile

Professor

• Requirements Engineering
• Enterprise Modeling
• Business Process Management
• +6 more

Jelena Zdravkovic is a Professor and Head of the Department of Computer and Systems Sciences (DSV) at Stockholm University. She leads the PRECIS research group which focuses on Process, Requirements, Enterprise, Capability, and Information Systems modelling. Her work spans theoretical and practical aspects of enterprise and IT solutions with a particular emphasis on digital transformation. Professor Zdravkovic's research interests center around Digital Business Ecosystems , Digital Twins , and Data-driven Requirements Engineering . Her work in Enterprise Modeling explores capability-oriented and consumer-oriented approaches to requirements engineering. She investigates how digital transformation and big data can be leveraged to improve requirements elicitation processes, and how organizations can model and manage complex digital business ecosystems. Her research has significant implications for how businesses can adapt to rapidly changing technological environments while maintaining resilience and competitiveness. Her recent publications reveal a clear trajectory toward integrating artificial intelligence with digital modeling techniques, particularly in the context of smart buildings and business ecosystems. There's a consistent focus on how data-driven approaches can transform traditional requirements engineering practices, making them more responsive to the velocity and variety of digital data sources. Her work bridges theoretical modeling with practical applications across various industries including healthcare, energy, and transportation. Professor Zdravkovic has been actively involved in mentoring PhD students, including supervising research on the Management Framework of Resilient Digital Business Ecosystems. She has participated in numerous national and international projects focused on interoperability and model-driven engineering, securing research funding for innovative work at the intersection of business and technology. She leads the PRECIS research group which deals with theories, methods and tools for analysis and design of organizational and IT solutions in congruence. The group's research covers three key topics – Enterprise Modelling, Business Process Management, and Conceptual Modelling. Their work brings together academic rigor with practical applications to solve real-world business challenges through innovative information systems approaches.

• Neuroscience
• Neurobiology
• Computational Neuroscience
• +6 more

Prof. Dr. Fred Wolf is a leading scientist affiliated with the Campus Institute for Dynamics of Biological Networks (CIDBN) at Georg-August-Universität Göttingen. His research focuses on the intersection of neuroscience, computational biology, and epithelial morphogenesis, utilizing advanced imaging techniques and theoretical models to study neural circuits and tissue dynamics.

Hwanhee HongView profile

Associate Professor

• Bayesian Statistical Methods
• Comparative Effectiveness Research
• Network Meta-Analysis
• +5 more

Hwanhee Hong is an Associate Professor in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine and a member of the Duke Clinical Research Institute. Affiliated with the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core and the B&B Faculty, Dr. Hong maintains an active research program focused on statistical methodology development for clinical applications. Dr. Hong's educational background includes a Ph.D. in Biostatistics from the University of Minnesota (2013), an M.S. in Biostatistics from Harvard University (2010), and a B.S. in Statistics from Chung-Ang University, Korea (2008). Prior to joining Duke, they completed a postdoctoral fellowship at Johns Hopkins Bloomberg School of Public Health under Dr. Elizabeth A. Stuart. Research interests center on Bayesian statistical methods for comparative effectiveness research, network meta-analysis, causal inference, measurement error correction, and data integration across multiple sources. Their work emphasizes adaptive information borrowing to address clinical and public health questions, particularly in developing flexible Bayesian modeling frameworks that synthesize diverse data streams for evidence generation. Analysis of recent publications reveals consistent methodological focus on network meta-analysis techniques, causal inference with error-prone covariates, generalizability assessment, and time-to-event analysis. The research spans applications in pediatrics, cardiology, obesity treatment, and vaccine effectiveness, demonstrating strong translational impact across medical domains. Dr. Hong actively collaborates with biostatisticians, epidemiologists, clinicians, and health policymakers through multiple funded projects, though no formal awards or fellowships are documented in the provided materials. Participant, Faculty Success Program, National Center for Faculty Development & Diversity (2023) Participant, Faculty Curriculum on Anti-Racism, Duke Office of Faculty Advancement (2021) Teaching responsibilities include BIOSTAT 719: Generalized Linear Models. Current grant funding spans eight major projects totaling over $20 million, primarily from NIH and PCORI, addressing pediatric care coordination, mental health interventions, obesity treatment, diabetes management, and advanced biostatistical methodology development. Dr. Hong's laboratory work focuses on computational approaches for data integration, maintaining active collaborations with the Duke Clinical Research Institute and multiple external institutions including Johns Hopkins University and North Carolina State University.

Ramana V DavuluriView profile

Professor

• Machine Learning
• Cancer Data Science
• Gene Regulation
• +4 more

Ramana V Davuluri serves as Professor in the Department of Biomedical Informatics at Stony Brook University's Renaissance School of Medicine. With over 20 years of experience in bioinformatics and computational genomics, he leads research at the intersection of machine learning and cancer genomics, focusing on translating high-dimensional -omic data into clinically actionable insights through statistically rigorous methodologies. Dr. Davuluri's research spans Machine Learning applications in Cancer Data Science , isoform-level gene regulation , and precision-medicine development. His lab pioneers bioinformatics solutions for genomic data interpretation, with emphasis on developing machine learning algorithms that convert NextGen sequencing outputs into experimentally testable discovery models. A core focus involves creating rapid biomarker identification systems from human tissue and blood samples through integrated computational-experimental approaches in systems biology. Analysis of his 2023-2025 publications reveals a dominant trend toward genomic foundation models (e.g., DNABERT variants), multi-omic cancer subtyping , and time-dependent therapeutic strategies for pediatric brain tumors and ovarian cancer. His work consistently bridges computational innovation with biological validation across diverse cancer types including glioma, lung adenocarcinoma, and high-grade serous carcinoma. As Principal Investigator for multiple multi-investigator and multi-site projects, Dr. Davuluri directs research integrating high-throughput experimental procedures with advanced data-mining techniques. His laboratory maintains strong collaborations across oncology, neuroscience, and immunology domains while developing genomics-based decision support systems for clinical translation. The Davuluri Lab employs a systems biology framework to develop novel informatics tools for precision oncology, with particular emphasis on translating genomic discoveries into clinical applications through biomarker discovery and therapeutic strategy optimization.

John EberthView profile

Associate Professor

• Cardiovascular Engineering
• Biomechanics
• Mechanobiology
• +9 more

Dr. John F. Eberth is an Associate Professor at Drexel University's School of Biomedical Engineering, Science and Health Systems. As a cardiovascular engineer with expertise in mechanical controls, continuum biomechanics, and hydrogel-based extracellular matrix mimetics, he leads the Applied Biomechanics and Mechanobiology Lab (ABML) to investigate vascular behavior under mechanical stimuli. PhD in Biomedical Engineering from Texas A&M University (2008) MS in Mechanical Engineering from Clemson University (2004) BS in Mechanical Engineering from Clarkson University (2001) His research focuses on vascular pathology and mechanobiology, including: Aortopathy and aneurysm mechanics Endothelial dysfunction and arterial stiffening Hydrogel-based vascular grafts Calcification chelation therapy Coronary artery disease Perfusion tissue culture Recent publications demonstrate expertise in vascular imaging techniques, mechanical modeling, and therapeutic interventions. Key themes include drug-coated balloon development, collagen fiber mechanics, and bioreactor systems for vascular conditioning.

Hüsnü DalView profile

Professor

• Computational micromechanics
• Multiscale and multifield problems
• Materials theory
• +3 more

Hüsnü Dal is a Professor at Middle East Technical University (METU) in Ankara, Turkey, specializing in computational mechanics of materials. His research bridges engineering and biomedical applications through advanced computational modeling techniques. Education: Bachelor's Degree, Middle East Technical University, 2001 Master's Degree, University of Stuttgart, 2005 PhD, Dresden University of Technology, 2011 Research Focus: Prof. Dal's work centers on computational micromechanics, multiscale and multifield problems, and materials theory. He investigates fracture in multiphysics media with applications in lithium-ion batteries and tissue mechanics, developing novel constitutive models for complex material behaviors under extreme conditions. His research integrates thermomechanical coupling, viscoplasticity, and data-driven approaches to solve engineering challenges in both synthetic polymers and biological systems. Publication Trends: Recent publications (2023-2025) reveal a dominant focus on data-driven constitutive modeling and phase-field fracture methods. His work spans rubber mechanics, polymeric foams, biological tissues, and battery materials, characterized by strong interdisciplinary connections between materials science, biomechanics, and computational engineering. Key themes include anisotropic hyperelasticity, thermo-viscoplastic fracture, and spatial property variations in additively manufactured materials.

Jed ElisonView profile

Professor

• Developmental social neuroscience
• Structural brain development and social cognition
• Visual attention
• +6 more

Jed Elison is the Irving B. Harris Professor of Child Development and Distinguished McKnight University Professor at the University of Minnesota’s Institute of Child Development. His research focuses on developmental social neuroscience, structural brain development, and early autism detection. BA in Psychology and English (2005), University of Utah PhD in Psychology (2011), University of North Carolina-Chapel Hill Postdoc in Social Neuroscience (2013), California Institute of Technology Elison’s work examines how attentional orienting drives early cognitive and social development using eye tracking and neuroimaging (MRI, DWI). Key areas include autism , emerging psychopathology , and white matter microstructure . Recent studies model longitudinal trajectories in ASD and explore social-emotional competence. His 2025 articles address infant brain imaging datasets, gesture-vocabulary relationships in autism, and adaptive functioning in corpus callosum agenesis. Collaborative work spans Developmental Science , Pediatrics , and Autism Research . Irving B. Harris Professor of Child Development Distinguished McKnight University Professor Elison advises PhD students in the Cognition and Neurodevelopmental Studies (CNS) Lab, collaborating with Dr. Megan Swanson. The CNS Lab investigates infant brain-behavior associations, particularly in high-risk populations like those with corpus callosum agenesis or congenital CMV . Techniques include MRI , EEG , and behavioral assessments.

• Computer Architecture
• Reconfigurable Computing
• Network-on-Chip (NoC)
• +7 more

Ioannis Sourdis is a Full Professor at the Department of Computer Engineering, Chalmers University of Technology, Sweden. His research focuses on computer architecture, reconfigurable computing, network-on-chip (NoC) design, memory systems, and fault-tolerant embedded systems, with applications in biomedical informatics and hardware security. Current projects include EUMMSS (Efficient Uncore Mechanisms for Multicore Space Systems, funded by the Swedish National Space Board) and eProcessor (European Processor Ecosystem, funded by the European Commission). Past initiatives include the DeSyRe project (on-demand system reliability), ECOSCALE (exascale reconfigurable computing), and SHARCS (secure hardware-software architectures). His work spans NoC router design (e.g., FastTrackNoC, DDRNoC), memory compression (MemSZ, L2C), and biomedical security applications (heartbeat-based protocols). He has published extensively in venues like DATE, ICS, PACT, and IEEE Transactions on Networking. Key research areas: Chiplet-based systems , hybrid memory architectures , FPGA acceleration , and real-time stream aggregation .

Andreas WeberView profile

Associate Professor

• Science and Technology Studies
• History of Science
• Digital Humanities
• +4 more

Andreas Weber is an Associate Professor at the University of Twente's Digital Society Institute , specializing in the Knowledge, Transformation & Society (KiTeS) research group . His work examines the long-term historical and global relationship between Science, Technology, and Society , with particular focus on colonial histories of natural history, chemistry, and sustainability , as well as computational technologies for contextualizing digitized archives . He leads the HAICu project (2023–2029) on digital cultural heritage and coordinates STS PhD training for the Netherlands Graduate Research School (2019–2024). MA & PhD in History (2005 & 2012), Leiden University Assistant Professor (2017–2023), University of Twente Andreas' research integrates digital humanities with colonial science history , emphasizing global histories of minerals , digital humanism , and AI's societal context . His 15 most recent publications (2016–2025) explore topics like colonial bias in natural history collections , FAIR data implementation , and semantic annotation of handwritten archives , spanning disciplines from history of science to computer science and museum studies . He has received 7 scientific awards , including multiple Best Teacher Awards (2020–2023) and the IEEE eScience Best Poster Award (2018). Andreas supervises PhD and postdoctoral projects while engaging in media commentary on colonial heritage issues and co-organizing international conferences like Hydrogen Pasts and Futures (2024).

R. JayakrishnanView profile

Professor

• Transportation Systems Engineering
• Dynamic Traffic Assignment
• Urban Traffic Simulation
• +3 more

R. Jayakrishnan , a Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering , University of California, Irvine, is a leading researcher in transportation systems engineering. Ph.D., University of Texas, Austin, Civil Engineering, 1992 M.S., University of Texas, Austin, Civil Engineering, 1987 B.S., Indian Institute of Technology, Madras, India, 1985 His research focuses on dynamic traffic assignment , urban traffic simulation , and real-time information systems to improve congested traffic corridors. He is developing advanced dynamic simulation-assignment models for urban traffic networks. Recent publications highlight his contributions to: Crowdsourced delivery optimization using decomposition heuristics Eco-driving algorithms with V2I communication Multi-furniture placement applications via augmented reality Subscription mobility services cost-benefit analysis Agent-based lane-changing coordination systems These works demonstrate his interdisciplinary approach combining transportation engineering, optimization algorithms, and emerging technologies like AR and connected vehicles.