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Jihyeon HaView profile

Assistant Professor

• Social Media
• Digital Content
• Machine Learning
• +3 more

Jihyeon Ha is an Assistant Professor in the Department of Marketing at the Tippie College of Business, University of Iowa. She holds a PhD in Marketing from Emory University, an MS in Business Administration, and a BBA from Seoul National University. Education: PhD in Marketing, Emory University MS in Business Administration, Seoul National University BBA, Seoul National University Her research focuses on leveraging Social Media , Digital Content , and Unstructured Data Analysis using Machine Learning techniques to explore Causal Inference in marketing contexts. A key publication analyzed paywall suspensions' impact on digital news subscriptions in Marketing Science (2023). Recent presentations at institutions like University of South Florida, Texas A&M, and National University of Singapore highlight her expertise in Multimodal Representation Learning for branded content and Natural Language Processing in brand personality assessment.

Vijay NarayananView profile

Professor

• Power Aware Computing
• Computer Architecture
• Embedded Systems
• +4 more

Vijay Narayanan is the Robert Noll Chair Professor in Computer Science & Engineering and Electrical Engineering at Pennsylvania State University. He co-directs the Microsystems Design Lab and leads research in embedded visual analytics, self-powered processors, and system design using emerging devices. Education: B.E in Computer Science and Engineering (1993) from University of Madras, India Ph.D. in Computer Science and Engineering (1998) from University of South Florida, USA His research spans Power Aware Computing , Computer Architecture , and Embedded Systems , with emphasis on Visual Cortex on Silicon and Self-Powered Processors . Current work includes Non-Volatile Processors and Design Automation under unreliable power conditions via NSF ERC ASSIST. Recent publications focus on GPU architecture (Tensor Cores, ACE), Memory Consistency Verification (QED), and Neural Radiance Fields (Disorf, Distwar) for robotics and rendering. Key collaborations include Tsinghua University and DARPA/SRC LEAST Center . Scientific Awards: IEEE Fellow ACM Fellow He leads the Architecture, Benchmarking and Circuits Thrust in the DARPA/SRC LEAST Center and contributes to NSF ERC ASSIST for self-powered systems. Grants and projects emphasize cross-layer optimizations and hardware-software co-design.

Vigor YangView profile

Professor

• Aerospace Propulsion
• Combustion Science
• Fluid Mechanics
• +5 more

Vigor Yang is a Regents’ Professor and Ralph N. Read Endowed Chair in the Department of Aerospace Engineering at the Georgia Institute of Technology . His work bridges fundamental and applied research in propulsion systems , combustion science , and machine learning . Ph.D., Mechanical Engineering, California Institute of Technology M.S., Mechanical Engineering, The Pennsylvania State University B.S., Power Mechanical Engineering, National Tsing Hua University, Taiwan Dr. Yang’s research spans supersonic combustion , neural networks for fluid dynamics, supercritical fluid mixing , and energetic materials . His work integrates experimental diagnostics , high-fidelity simulations , and data-driven modeling to advance aerospace propulsion and energy systems. Recent publications highlight interdisciplinary trends in machine learning for CFD , detonation dynamics , and multi-scale combustion modeling . Articles emphasize turbulent flow prediction, surrogate modeling, and thermodynamic property evaluation under extreme conditions. AIAA Air-Breathing Propulsion Award (2005) Pendray Aerospace Literature Award (2008) Propellants and Combustion Award (2009) Worcester Reed Warner Medal (2014) by ASME Lifetime Achievement Award (2014) by JANNAF Reed Aeronautics Award (2025) Honorary doctorate (2019) by Cheng Kung University Dr. Yang leads the Ben T. Zinn Combustion Laboratory and contributes to the Strategic Energy Institute . He has served as editor-in-chief for journals in propulsion and machine learning, and his Yang Aero Maker Space fosters hands-on aerospace education.

Dario ColazzoView profile

Professor

• Databases
• Programming Languages
• Cloud Computing
• +7 more

Dario Colazzo is a Professor at Université Paris-Dauphine, affiliated with the LAMSADE laboratory. His research focuses on database systems and programming languages, particularly in cloud databases, type systems for semi-structured data, and query processing optimizations. He has contributed extensively to XML and XQuery research, including parallel query execution and static analysis techniques. His teaching includes courses on programming (Java), UML, database systems, and semantic web technologies at both undergraduate and graduate levels (in French). Key research interests span XML/XQuery optimization, JSON processing, distributed systems, and type systems for safe and efficient data handling. His work emphasizes scalable cloud-based solutions and efficient query execution models. Publications highlight advancements in parallel query processing (e.g., PAXQuery), type-based optimizations for XML updates, and semantic graph analytics. He has collaborated with institutions like EDBT, WWW, and VLDB conferences.

• Knowledge Graphs
• Natural Language Processing
• Machine Learning
• +3 more

Sareh Aghaei serves as a Research Fellow at the Institute of Management Sciences within the Faculty of Mechanical Engineering and Industrial Management at Vienna University of Technology (TU Wien), focusing on knowledge-driven solutions for industrial maintenance and healthcare systems. Her academic credentials include: Ph.D. in Computer Science from the University of Innsbruck (2023) M.Sc. in Computer Science from the University of Isfahan Dr. Aghaei's research integrates knowledge graphs with natural language processing and machine learning to develop explainable AI systems. Her work spans industrial maintenance optimization, clinical decision support, and tourism information systems, emphasizing ontology engineering and question-answering frameworks that transform unstructured data into actionable knowledge. Analysis of her 2021-2025 publications reveals a strategic shift toward domain-specific knowledge graph applications, particularly in maintenance management (2022-2025) and health informatics (2023-2024). This evolution demonstrates increasing specialization in medical knowledge representation while maintaining foundational contributions to semantic web technologies established in earlier works like her 2011 Web services architecture research. Her scholarly recognition includes: netidee Grant Call 17: Austria's award for most innovative doctoral theses Dr. Aghaei's doctoral research was funded through the netidee scholarship. Current documentation indicates no active student supervision or major grant leadership beyond her postdoctoral position at TU Wien. Within TU Wien's Institute of Management Sciences, she contributes to research bridging production engineering and artificial intelligence, developing knowledge-based systems for predictive maintenance and industrial process optimization through interdisciplinary collaboration.

• Artificial Intelligence
• Data Science
• Machine Learning
• +3 more

Professor Kevin Swingler is a Professor and Head of the Division of Computing Science and Mathematics at the University of Stirling. His research focuses on Artificial Intelligence (AI), particularly in data science, machine learning, and computer vision, with a strong emphasis on healthcare applications. He leads the AISLA project , developing assistive technologies for visually impaired individuals using AI tools like computer vision and natural language understanding. His work addresses challenges in AI innovation while aiming to enhance independence for people with sight loss. Before academia, he ran a software company specializing in neural network-based solutions for banking, insurance, and marketing. He champions innovative teaching methods, including graduate apprenticeships, online degrees, and international programs. His research extends to applying AI in livestock disease surveillance, social media analysis for farming communities, and digital health tools for elder care. He also runs a university spin-out company providing data collection and analytics services. His recent publications span AI-driven health monitoring systems, neuro-symbolic models for small datasets, and haptic interfaces for accessibility. Collaborations with charities and health organizations highlight his commitment to translational research addressing societal challenges.

Suzan ArslanturkView profile

Associate Professor

• Medical Informatics
• Predictive Analytics
• Machine Learning
• +4 more

Dr. Suzan Arslanturk is Associate Professor in Computer Science and Industrial & Systems Engineering at Wayne State University's College of Engineering. She directs the Machine Learning and Health Informatics Laboratory, focusing on predictive analytics for healthcare applications. Research domains include: Cancer subtyping through multi-omics data integration Biomarker discovery for prostate cancer using cross-cancer learning Drug repurposing for cancers with DNA-repair deficiencies Neonatal brain anomaly detection via MRI analysis Operational healthcare optimization during medical surges Leads the development of deep learning frameworks for medical imaging segmentation and clinical text analysis. Publications demonstrate innovations in multimodal data fusion, domain adaptation, and unsupervised abnormality detection. Advises PhD candidates in computational healthcare research and directs academic programs in data mining and intelligent systems.

• Internet of Things
• Machine Learning
• Data Analytics
• +3 more

Li-minn Ang (Kenneth) is Professor of Electrical and Computer Engineering at the University of the Sunshine Coast's School of Science and Engineering. His research integrates Internet of Things (IoT), machine learning, and embedded systems for applications in smart cities, agriculture, and health. He has secured over $1.8 million in research grants and published three books with 180+ papers. Current projects develop application-specific IoT architectures and multimodal analytics for big data systems. Professor Ang teaches Internet of Things (ENG103), Applied Mathematics (MTH103), and Calculus (MTH104), serving as program coordinator for Electrical Engineering. Professional memberships include Senior IEEE status and HEA Fellowship. Laboratory resources support FPGA development, sensor networks, and IoT prototyping.

Manfred ReichertView profile

Professor

• Business Process Management (BPM)
• Service-Oriented Architectures
• E-health
• +7 more

Prof. Manfred Reichert is a full professor at the University of Ulm, serving as Director of the Institute of Databases and Information Systems and Dean of Studies at the Faculty of Engineering and Computer Science. He holds dual expertise in Computer Science and Mathematics, with academic leadership roles including chairing examination boards and strategic research initiatives. His interdisciplinary affiliations include co-opted membership in the Faculty of Mathematics and Economic Sciences. Reichert's research focuses on Business Process Management (BPM), IoT-driven processes, service-oriented architectures, and e-health applications. Notable contributions include co-developing the ADEPT process management system and pioneering object-centric process modeling. He has led over 20 international research projects (EU FP7, DFG, Industry) and authored >200 peer-reviewed papers, earning an h-index of 60+ and prestigious awards like the Merckle Forschungspreis. Education: PhD in Computer Science & Mathematics Diploma Leadership: Former associate professor at University of Twente, former director of CTIT research center Service: Conference chair for BPM, CoopIS, EDOC; steering committee member for GI SIG Databases Recent work explores IoT integration in BPM, generative AI for process models, and neuroscience-informed usability studies. His research bridges technical innovation with human-centric design, addressing challenges in process lifecycle management, federated learning, and mobile health interventions. Awards: IFIP TC2 Manfred Paul Award, doIT Software Award Key Projects: BPMN extensions for IoT, data-driven healthcare solutions

Zachary K. CollierView profile

Assistant Professor

• Causal Machine Learning
• Structural Equation Modeling
• Educational Measurement
• +3 more

Zachary K. Collier is an Assistant Professor in the Department of Educational Psychology at the University of Connecticut. He holds a Ph.D. in Measurement and Statistics from the University of Florida (2018) and a B.S. in Special Education from Winthrop University (2013). His research integrates Causal Machine Learning and Structural Equation Modeling with critical perspectives to advance equitable data analysis methods. He developed the CritSEM framework to examine how social and structural inequalities shape measurement constructs. Founded the MUDD Lab (Methods for Unstructured and Difficult to use Data) in 2020 Secured over $9 million in external funding His work emphasizes fairness, data ethics, and methodological transparency across education, public health, and social sciences. Selected publications demonstrate innovative applications of machine learning to educational measurement challenges. Scientific recognition includes: Emerging Scholar Featured Mathematician

• Managing Emerging Technologies
• Technology Diffusion
• Business Models
• +7 more

Bharat Rao is an Associate Professor in the Department of Technology Management and Innovation at New York University’s Tandon School of Engineering. His research focuses on managing emerging technologies, innovation strategy, and the societal impacts of disruptive technologies such as AI, drones, and 3D printing. He explores topics including defense innovation cycles, technology diffusion in underdeveloped societies, and the role of universities in fostering entrepreneurship. Affiliations: Tandon School of Engineering, NYU Office: LC 401 Dibner Email: bharat.rao@nyu.edu His work bridges technology adoption, business models, and strategic implications across sectors like defense, entertainment, and environmental sustainability. Rao’s recent publications address AGI prospects, AI’s impact on defense industries, and social entrepreneurship approaches for river revitalization. His research also examines innovation ecosystems in government agencies (e.g., Defense Innovation Unit) and multinational corporate expansion strategies (e.g., TCS in Europe). Rao has published extensively on topics ranging from cloud computing in developing regions to the cultural economics of magic performances. His work integrates technical analysis with organizational and policy perspectives, often using case studies to illustrate innovation dynamics.

Emaad ManzoorView profile

Assistant Professor

• Reinforcement Learning
• Human-Algorithm Collaboration
• Causal Inference
• +3 more

Emaad Manzoor is an Assistant Professor of Marketing and a graduate field member of Computer Science at Cornell University's SC Johnson College of Business. He specializes in designing algorithms to enhance human decision-making, focusing on reinforcement learning, human-algorithm collaboration, and causal inference with unstructured data. His work bridges marketing science, machine learning, and computational social science. Education: PhD in Machine Learning (Carnegie Mellon University). Professional experience includes roles at Yahoo! and Pinterest, and he currently leads projects like stopping-agents and clipalyze. Teaching highlights include creating the AI course at Johnson School and teaching courses on data technologies and human-AI collaboration. Research is funded by the National Science Foundation ($562,000), Atkinson Center for Sustainability ($25,000), and others. Key contributions include causal inference methods for text analysis, status biases in online deliberation, and ethics in machine learning applications. Organized sessions at Marketing Science 2025 on 'Behavioral Insights for Algorithm Design' and co-organized the first EMNLP workshop on causal inference with text (2021). His work emphasizes practical applications in marketing, social media, and algorithmic fairness.

David A. HanauerView profile

Associate Clinical Professor

• Healthcare Informatics
• Clinical Data Analysis
• Artificial Intelligence in Healthcare
• +4 more

David A. Hanauer is a Clinical Associate Professor at the University of Michigan, holding joint appointments in Pediatrics, Learning Health Science, and the School of Information. He focuses on healthcare informatics, clinical data analysis, and AI applications in medicine. His work addresses challenges in reproducibility of large language models, healthcare data standards, and privacy-preserving techniques. Education: BA, Cornell University (1995) MD, University of Michigan Medical School (1999) MS, Harvard–MIT Division of Health Sciences and Technology (2004) Pediatric Residency, New York University (2002) Research Interests: Dr. Hanauer explores clinical natural language processing, healthcare AI validation, interoperability standards like LOINC, and privacy-preserving methods for training clinical models. He develops tools like EMERSE to enhance unstructured data utilization. Affiliations: Institute for Healthcare Policy and Innovation, Rogel Cancer Center, Center for Computational Medicine and Bioinformatics. Grants & Mentoring: Available to mentor researchers. His grants likely support clinical informatics projects given his publication focus.

Brad MillingtonView profile

Associate Professor

• Sport and Environmental Sustainability
• Sport Media
• Wearable Technology
• +3 more

Brad Millington, PhD, is an Associate Professor in Sport Management at Brock University. His research focuses on the intersection of sport with environmental sustainability, wearable technology, and media. He has authored books including *Fitness, Technology, and Society* and *The Greening of Golf*, and secured SSHRC research funding. His work critiques sport’s environmental impact while exploring innovative applications of technology in fitness and development. Professional affiliations include the North American Society for Sport Management and the Higher Education Academy (UK). Research Interests include sports analytics, sport media, wearable tech, and climate-conscious sport practices. He co-edited *Sport and the Environment: Politics and Preferred Futures* (2020), analyzing sustainable sport frameworks. Recent publications address AI in sport, urban golf course equity, and bicycles for development initiatives Recipient of SSHRC grants for environmental sport studies Supervises graduate/undergraduate research in sport sustainability and technology Awards: Research Fellow (NASSS), Higher Education Academy Fellowship. Labs/Teams: Active in interdisciplinary environmental sport research networks.

Janna OmeliyanenkoView profile

Researcher

• Knowledge Graph Integration
• Natural Language Processing
• Machine Learning
• +3 more

Janna Omeliyanenko is a researcher at the Chair of Data Science (Informatics X) , University of Würzburg, Faculty of Mathematics and Computer Science. She has been actively contributing to the DMIR research group since 2019, focusing on integrating structured knowledge representations (knowledge graphs) with unstructured language models to enhance AI performance. Her work bridges theoretical advancements in NLP with practical applications in knowledge graph completion and media bias analysis. Her teaching responsibilities include courses such as NLP and Text Mining , Selected Chapters from Machine Learning , and XtAI Labs , reflecting her interdisciplinary expertise in explainable AI and deep learning. Based in Room 50.03.018 at the Institute Building Artificial Intelligence, Campus Hubland Nord, Würzburg, Germany, she can be reached at janna.omeliyanenko@uni-wuerzburg.de. Research interests center on knowledge graph integration with LLMs, continual learning for graph completion, and bias detection in multilingual contexts. Her publications at ISWC and AAAI conferences demonstrate technical rigor in combining semantic web technologies with modern machine learning approaches. Recent work explores adaptive pre-training strategies (CapsKG/PreAdapter) and cross-linguistic social media analysis.