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Tuyen (Robert) LeView profile

Associate Professor

• Digital Project Delivery
• Big Data in Construction Management
• Civil Infrastructure Management
• +11 more

Dr. Tuyen (Robert) Le is an Associate Professor in the Glenn Department of Civil Engineering at Clemson University, South Carolina. He leads the Human-centered Construction Informatics Group, investigating digital and automation technologies across the architecture, engineering, construction, and facility-management (AEC/FM) sectors. Education B.S., Civil Engineering – Ho Chi Minh City University of Technology (2011) M.S., Construction Engineering and Management – Ho Chi Minh City University of Technology (2014) Ph.D., Construction Engineering and Management – Iowa State University (2017) Research Interests Dr. Le’s scholarship converges on digital project delivery and big-data analytics in construction management. His work leverages Building Information Modeling (BIM) , Civil Integrated Management (CIM) , and AI-driven methodologies such as machine learning and natural-language processing to optimize life-cycle performance of transportation and civil infrastructure systems. Additional foci include schedule/cost analytics, human–AI collaboration, and socio-economic implications of technology adoption in the future-of-work landscape for engineers and construction workers. Publication Trends Between 2016 and 2020 Dr. Le published extensively on computational techniques that transform raw construction and transportation data into actionable intelligence, emphasizing data modeling, visualization, semantic classification, and life-cycle integration for asset management. Professional Memberships Construction Research Council (CRC) American Society of Civil Engineers (ASCE) Advising & Graduate Opportunities Dr. Le’s research group is actively recruiting motivated Ph.D. students. Successful applicants receive full graduate assistantships covering tuition plus monthly stipends for up to four years. Selection prioritizes academic excellence (GPA, GRE, English proficiency) and demonstrated programming or research experience. Laboratory & Team He directs the Human-centered Construction Informatics Group , a multidisciplinary lab housed in 202C Lowry Hall that collaborates with industry and government to advance digital transformation in the built environment.

Chenjuan GuoView profile

Associate Professor

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +4 more

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

• Stochastic Simulation
• Optimization
• Machine Learning
• +5 more

Giulia Pedrielli is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence, with affiliations as a Senior Global Futures Scientist. She holds a Ph.D., M.Sc., and B.S. in Mechanical and Industrial Engineering from Politecnico di Milano, Italy. Her research focuses on stochastic simulation, optimization, and machine learning applications in biomanufacturing, digital twins, and cyber-physical systems. She has contributed to frameworks for pandemic modeling (PySIRTEM), RNA design, and high-dimensional optimization. Pedrielli has collaborated with institutions like NUS and UC Berkeley, and her work spans biomedical engineering, supply chain resilience, and security of critical infrastructure. Recent articles emphasize co-simulation tools, AI-driven logic translation, and falsification methods for CPS security. Her teaching includes advanced stochastic simulation courses and thesis supervision across multiple disciplines. Education: Ph.D. Mechanical Engineering, Politecnico di Milano, Italy M.Sc. Industrial Engineering, Politecnico di Milano, Italy B.S. Industrial Engineering, Politecnico di Milano, Italy Research interests integrate simulation-based optimization with machine learning, particularly in safety-critical systems like healthcare and smart infrastructure. She develops digital twin methodologies for real-time control under uncertainty, leveraging Gaussian processes and Bayesian approaches. Pedrielli's work bridges theoretical advancements (e.g., BASSO optimization algorithm) with practical applications in biomanufacturing and pandemic response.

• Software Engineering
• Model Checking
• Formal Verification
• +4 more

Professor Tomoji Kishi is a distinguished faculty member at Waseda University's School of Creative Science and Engineering, where he has been serving since 2009. Previously, he held academic positions at Japan Advanced Institute of Science and Technology (2003-2009) following a 21-year career at NEC Corporation (1982-2003). He earned his Ph.D. in Information Science from Japan Advanced Institute of Science and Technology in 2002, building upon his earlier engineering graduate studies at Kyoto University. Professor Kishi's research focuses on software engineering, particularly in software product line development, model checking, formal verification, and aspect-oriented modeling. His work bridges theoretical formal methods with practical applications in embedded systems, automotive software, and IoT technologies. He has made significant contributions to scalability challenges in model checking for configurable systems and has pioneered approaches to variability management and approximate modeling techniques. His publication record demonstrates remarkable consistency and evolution, with 42 papers and 153 citations according to Scopus data (h-index: 7), spanning from foundational work in software architecture in the 1990s to cutting-edge research on AI-enhanced verification methods in 2025. His recent work shows increasing application of machine learning techniques to traditional formal methods problems, particularly in the context of highly configurable systems and IoT applications. ITS Standardization Activity Merit Prize (2022) from Society of Automotive Engineers of Japan IPSJ/ITSCJ Standardization Contribution Award (2017) IPSJ/ITSCJ Project Editor Award (2016 and 2013) Information Processing Society of Japan Society Activity Contribution Award (2010) IPA/SEC Journal Best Paper Award (2007) Information Processing Society of Japan Yamashita Memorial Research Award (1998) Professor Kishi has led multiple JSPS-funded research projects, including recent work on 'variability management methods prioritizing usability through variability mining' (2020-2023) and 'utility-first modeling method' (2017-2020). His industry collaborations, particularly with automotive systems developers, demonstrate the practical impact of his research. He maintains active membership in major professional societies including IEEE Computer Society, ACM, and the Information Processing Society of Japan.

Rebecca HedreenView profile

Researcher

• Information Science
• Library Science
• Health Sciences
• +5 more

Rebecca Hedreen serves as the Life and Clinical Sciences Librarian and Distance Learning Coordinator at Southern Connecticut State University's Hilton C. Buley Library. With expertise spanning biology, health sciences, nursing, psychology, and information literacy, she provides specialized research support to students and faculty across multiple disciplines. Her role encompasses developing subject-specific research guides, offering citation management instruction, and coordinating distance learning library services. Dr. Hedreen's research interests focus on information literacy in health sciences, citation management tools, nursing literature search methodologies, and virtual librarianship. Her work bridges traditional library services with digital innovations, particularly in supporting distance learners through technologies like screencasts and virtual environments. She has made significant contributions to understanding how librarians can effectively support evidence-based practice in nursing and healthcare education. Her scholarly output demonstrates consistent engagement with evolving library practices over nearly two decades, from early explorations of virtual reference services in Second Life to current work on citation collection methods and professional development for library staff. Recent publications highlight her focus on precise search strategies for nursing literature and innovative approaches to supporting online learners across time zones. As a dedicated educator, Dr. Hedreen provides extensive support through research consultations, classroom instruction, and online resources. She has developed numerous subject-specific research guides covering biology, nursing, psychology, and health sciences, helping students navigate complex information landscapes. Her commitment to open access and educational resources has informed her work on OER initiatives and citation management tools. Dr. Hedreen actively contributes to the library profession through her research on distance learning, virtual reference services, and information literacy assessment. Her work helps shape best practices for academic librarians supporting online and hybrid education models, particularly in health science disciplines where precise information retrieval is critical for evidence-based practice.

• Human-Computer Interaction
• Educational Technology
• Computer Science Education
• +3 more

Francisco Javier Oliver Bernal is a Lecturer at the University of Deusto in Bilbao, Spain, within the Faculty of Education and Sport and the Department of Physical Activity and Sports Sciences. He teaches across Computer Engineering, Physical Activity and Sports Sciences, and Primary Education bachelor's programs, as well as the Master's in Secondary Education. He earned his Doctor of Medicine and Surgery from the University of the Basque Country. His research spans Human-Computer Interaction, Educational Technology, and Science Education, with a strong emphasis on accessibility for visually impaired users. He has developed tools for e-learning, digital resource centers, and innovative teaching methodologies in computer science and natural sciences, aiming to enhance educational experiences through technology. His scholarly output, spanning from the 1990s to 2023, demonstrates a consistent focus on technology-enhanced learning, evolving from early work in 3D interfaces and computer graphics to recent applications in health, music, and interdisciplinary educational contexts. Key trends include the integration of accessibility features, the development of domain-specific educational tools (e.g., for biology and astronomy), and responses to contemporary challenges like the COVID-19 pandemic. He has supervised multiple theses on digital accessibility and cooperative systems, though student names were not listed. Details on research grants were not provided in the available text. As a member of the eVida research group (officially recognized by the Basque Government), he contributes to projects advancing accessible educational technologies, including the ACCE project for audiovisual accessibility and READIS digital resource centers for visually impaired users.

• Social Psychology
• Group Processes
• Intergroup Relations
• +5 more

Jared B. Kenworthy is a Professor of Psychology at The University of Texas at Arlington, where he has held academic positions since 2005, including roles as Lewis Hollweg Endowed Professor of Industrial-Organizational Psychology (2023–present). His primary affiliations include the Department of Psychology and contributions to the College of Science. Kenworthy’s research focuses on social psychology, particularly intergroup relations, group processes, collaborative creativity, and meta-analysis. He has extensive expertise in studying how intergroup contact reduces prejudice, the dynamics of creative group processes, and the role of technology in enhancing collaborative innovation. Kenworthy’s education includes a PhD in Social Psychology from the University of Southern California (2002), preceded by an MA (1998) and BS in Psychology (1996) from the same institution. His work has been supported by grants from the National Science Foundation (NSF), Army Research Office, and other federal agencies, totaling over $10 million in funding. Key projects include studies on collaborative creativity in dynamic networks, AI integration in team processes, and reconciliation in post-conflict societies. His research has led to over 100 peer-reviewed articles, book chapters, and edited volumes, including seminal works on group brainstorming strategies and intergroup trust. Notable publications include Group and Team Processes: Science and Practice (2021) and Creativity and Innovation: Cognitive, Social, and Computational Approaches (2022). Kenworthy has advised over 30 doctoral and master’s students, guiding them in topics ranging from social identity theory to computational creativity assessment. He has served on numerous university committees, including the Graduate Affairs Committee and the College of Science Research Space Committee, and has been a frequent presenter at conferences such as the Interdisciplinary Network for Group Research (INGRoup). His teaching spans advanced social psychology, group processes, and multivariate data analysis, reflecting his commitment to both research and education.

Jeremy BlackburnView profile

Associate Professor

• Data Science
• Social Media Analysis
• Hate Speech Detection
• +3 more

Jeremy Blackburn is an Associate Professor at Binghamton University's School of Computing, where he joined in fall 2019. His research focuses on data science, large-scale measurements, and modeling of toxic online behavior, hate speech, and extremist web communities, with significant media coverage from The Washington Post, New York Times, and BBC. Education: PhD, University of South Florida MSc, University of South Florida BSc, University of South Florida Dr. Blackburn's work centers on computational analysis of social dynamics , specializing in fringe online communities and cyber social threats . His methodology combines large-scale data collection with advanced modeling to dissect hate speech evolution, platform migration effects, and disinformation campaigns. Key contributions include developing detection frameworks for coordinated aggression and analyzing ideological spaces across platforms like Reddit, Telegram, and Rumble. Recent publications (2023-2025) reveal expanding research into adaptive hate speech detection systems, LLM capabilities for counterfactual reasoning, and cross-platform extremism analysis. His work increasingly integrates vision-language models while maintaining focus on real-world applications for content moderation, with growing attention to emerging platforms like Lemmy and Rumble's podcast ecosystem. Scientific Awards: No major scientific awards were explicitly documented in source materials Dr. Blackburn secured two significant NSF SaTC grants: (1) a 2023 Medium grant for iDRAMA.cloud (measuring information manipulation), and (2) a 2021 Small grant for detecting influence campaign accounts. While specific advisees aren't listed, his active research profile suggests graduate student mentorship in data science and cybersecurity projects. His research infrastructure includes the Social Media Analysis Toolkit (SMAT) and datasets like pushshift reddit/telegram collections, supporting collaborative work on cyber social threats through his lab environment focused on computational social science and security.

Giancarlo CrocettiView profile

Assistant Professor

• Data Science
• Distributed Analytics
• Web Data Mining
• +2 more

Giancarlo Crocetti is an Assistant Professor in the Department of Computer Science, Mathematics and Science at the Collins College of Professional Studies, St. John's University. His teaching focuses on data science, distributed analytics, and web data mining. He holds a Doctor of Professional Studies (D.P.S.) in Applied Computing from Pace University, along with Master's degrees in Data Mining (Central Connecticut State University) and Computer Science (La Sapienza University, Rome). Education: D.P.S., Applied Computing, Pace University M.S., Data Mining, Central Connecticut State University M.S., Computer Science, La Sapienza University - Rome His research interests span cognitive science and semantic representation of knowledge. Crocetti actively contributes to cutting-edge fields like telemedicine analytics, biomarker development for Parkinson's disease, and ensemble models in histological examination. His work bridges computer science with healthcare applications through innovative data mining techniques. Notable research includes developing algorithms for social conversation analysis in NSCLC patients and creating a think tank simulator for virtual expert consultations. His publications demonstrate expertise in both foundational computer science and applied biomedical informatics.

• Computer Science
• Artificial Intelligence
• Reinforcement Learning
• +6 more

Eric Eaton is a prominent researcher in Computer Science, specializing in Artificial Intelligence, Reinforcement Learning, and Federated Learning. His work bridges theoretical advancements with practical applications in healthcare, robotics, and educational technology, as evidenced by his collaborations with institutions like the Vector Institute and co-authors such as Marcel Hussing and Amir-massoud Farahmand. Research Focus: Lifelong Learning, Object-Centric Representation, and Algorithmic Fairness Key Contributions: ELLA algorithm, Distributed Continual Learning frameworks, and AI integration in surgical video analysis His recent publications address critical challenges in high update ratio reinforcement learning, federated learning for surgical data, and ethical considerations in algorithmic fairness. These works highlight his interdisciplinary approach, combining AI with healthcare and education. Eaton's leadership in projects like FORLA and Slot-BERT demonstrates innovation in unsupervised learning and temporal coherence. His involvement in the CS2023 curriculum design underscores his commitment to advancing computer science education.

• Machine Learning
• Artificial Intelligence
• Catalysis
• +6 more

Gabriel Gomes is an Assistant Professor in the Departments of Chemistry and Chemical Engineering at Carnegie Mellon University, where he leads the Gomes Group research program focused on the intersection of machine learning and chemistry. His work centers on developing new chemical reactions, catalysts, and materials using state-of-the-art machine learning and automated synthesis techniques. Dr. Gomes received his Ph.D. in Chemistry from Florida State University in 2018 under Professor Igor V. Alabugin, where he researched stereoelectronic effects. Prior to joining CMU in 2022, he was a Banting Postdoctoral Fellow at the University of Toronto in the Matter Lab led by Professor Alán Aspuru-Guzik. He earned his B.S. in Chemistry from Federal University of Rio de Janeiro, Brazil in 2013. His research program focuses on developing autonomous platforms for reaction discovery, with emphasis on catalysis. Gomes aims to create inverse design frameworks where algorithms can propose new functional catalysts from scratch based on desired properties. His work integrates quantum mechanics with machine learning to teach computers chemical intuition, enabling novel approaches to catalyst design and chemical synthesis. He strongly advocates for the integration of AI with human creativity to transform chemical research. Gomes' recent publications demonstrate a clear trend toward integrating large language models with chemical discovery, developing transferable machine learning potentials for catalysis, and advancing molecular representations with quantum chemical insights. His work spans from fundamental quantum chemistry to practical applications in biocatalysis and materials design. Scialog Fellow (2023) C&EN Talented Twelve (2022) NSERC Banting Postdoctoral Fellowship (2020-2021) CAS SciFinder Future Leaders Program (2018) ACS COMP Chemical Computing Group Excellence Award (2018) FSU's Graduate Student Research and Creativity Award (2018) IBM Ph.D. Scholarship (2017) Dr. Gomes mentors several graduate students including Robert MacKnight and Daniil Boiko, who have contributed to projects like the Catnip web application for biocatalyst prediction. His research has received funding through prestigious programs like the Scialog Automating Chemical Laboratories awards. Gomes actively promotes open science initiatives and has contributed to the Open Reaction Database to improve data accessibility for machine learning in chemistry. The Gomes Group operates at the cutting edge of AI-driven chemistry research, developing tools like Coscientist that bridge the gap between natural language prompts and actual chemical reactions. Their work on autonomous reaction discovery systems represents a significant step toward fully automated chemical laboratories where machines can 'dream of molecules' with specific desired properties.

Frank SoboczenskiView profile

Lecturer

• Transformers
• Large Language Models
• Machine Learning
• +11 more

Dr. Frank Soboczenski is a Lecturer in the Department of Computer Science at the University of York, with an affiliate scientist position at King's College London supported by the NVIDIA GPU Grant Program. His research spans multiple domains including healthcare, space research, and quantum machine learning applications. He serves as a STEM scientist for NASA's and NOAA's GLOBE program and is actively involved in various NASA initiatives including the Frontier Development Lab. Dr. Soboczenski's primary research interests include Transformers and Large Language Models, Machine Learning with focus on Uncertainty Quantification and Explainability, and advanced applications of Quantum Machine Learning in Healthcare/Biomedicine and Space Research domains. His work on the RobotReviewer project applies Deep Learning and Natural Language Processing to healthcare. Previously, he has worked in Human-Computer Interaction, Cyber-Security, and Real-Time Systems in cooperation with organizations including the German Police Force, GCHQ, Rapita Systems, INRIA, Barcelona Supercomputing Center, and Airbus. His recent publications demonstrate a strong focus on applying machine learning techniques to healthcare informatics and space research, with particular emphasis on systematic reviews, clinical decision support, atmospheric retrieval for exoplanets, and medical data analysis. His work bridges the gap between theoretical AI advancements and practical applications in critical domains. NASA TechLeap Prize - Quantum Machine Learning NASA/NOAA/U.S. Department of State Outstanding Efforts to Mentor and Support Students (2019-present) NASA Frontier Development Lab AI Research Award of Merit Data Samaritan Award Steely Eyed Operator Award NASA Kennedy Space Center OsirisREx launch invitation SpaceApps 3M Thesis Competition UK National Winner (2013) Deggendorf Institute of Technology Robotics Challenge Award Dr. Soboczenski actively mentors students, as evidenced by his NASA/NOAA award for mentoring. His research is supported by the NVIDIA Corporation through the GPU Grant Program. He serves on multiple program committees including NeurIPS (2019-present), AAAI (2019-present), and various specialized workshops at major AI conferences. He is also involved in organizing NASA Space Apps challenges and serves on the NASA GeneLab Analysis Working Group on AI/ML. As an active member of the academic community, Dr. Soboczenski participates in numerous professional organizations including the NASA Nancy Grace Roman Spacecraft Science Working Group, NASA Technosignatures research group, IBM Quantum Researchers Program, PolarAI Research Group of the ACM, Huggingface BigScience Team, International Astronomical Union, and several others focused on AI and space research.

Teague HenryView profile

Assistant Professor

• Network Science
• Quantitative Psychology
• Neuroscience
• +7 more

Teague Henry is an Assistant Professor at the University of Virginia with joint appointments in the Department of Psychology (Quantitative Psychology program) and the School of Data Science . His work bridges network science, dynamical systems modeling, and Bayesian estimation to analyze complex data in neuroscience, clinical science, and natural language processing. Methodological expertise: Graph theory, exponential random graph models, functional neuroimaging Substantive applications: Autism, ADHD, semantic networks, social media topic analysis His research spans functional connectivity, network psychometrics, and language networks, with a focus on cross-disciplinary similarities in network data. Recent publications emphasize time-series modeling, imputation methods, and neurodevelopmental disorders. Software development: R packages like gimme and netjack Future student advising: Plans to recruit graduate students starting 2022-2023

Niki MartinelView profile

Associate Professor

• Computer Vision
• Deep Learning
• Pattern Recognition
• +2 more

Dr. Niki Martinel is Associate Professor of Computer Vision and Machine Learning at the University of Udine's Department of Mathematics, Computer Science and Physics. His research advances machine learning methodologies with applications spanning medical imaging, underwater enhancement, and anomaly detection. Research specialties include deep learning architectures (Capsule Networks, Mamba models), self-supervised approaches, and feature representation techniques. Recent work focuses on improving robustness in challenging imaging conditions through physics-informed models and domain adaptation techniques. Publications demonstrate consistent innovation in imaging applications, particularly medical artifact reduction and underwater enhancement. Recent work shows strong emphasis on efficient architectures for mobile deployment and physics-aware models for scientific applications. Research collaborations extend to healthcare diagnostics, sports analytics, and marine robotics, showcasing cross-disciplinary applications of computer vision technologies.

Xin WeiView profile

Visiting Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Xin Wei is a Visiting Professor in the Physics Department at Stanford University, affiliated with the School of Humanities and Sciences. Their research focuses on advancing artificial intelligence, machine learning, and natural language processing, with a particular emphasis on multimodal models, data valuation, and ethical AI practices. Xin Wei’s work bridges theoretical contributions with applied studies in areas such as large language models, peer review systems, and clinical AI adoption. They have published extensively on topics including model card analysis, distribution shifts, and dataset documentation. Research interests include the societal impact of AI, improving model transparency, and addressing disparities in AI performance across diverse datasets. Their recent work explores methods to enhance multimodal state-space models, adaptive agentic systems, and the ethical implications of AI in scientific writing and healthcare. Key contributions include frameworks like LlamaFusion for multimodal generation, Metashift for evaluating distribution shifts, and Opendataval for data valuation benchmarking. Xin Wei’s interdisciplinary approach integrates computer science, physics, and ethics to drive innovation while ensuring responsible AI development.