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Valentino SantucciView profile

Associate Professor

• Artificial Intelligence
• Evolutionary Computation
• Machine Learning
• +7 more

Valentino Santucci is an Associate Professor of Computer Engineering at the University for Foreigners of Perugia, Italy, affiliated with the Department of International Human and Social Sciences (SUSI). Since 2021, he has served as the Rector's Delegate for Technological Innovation and Information Flows, highlighting his leadership in digital transformation within the institution. His research spans key areas in Artificial Intelligence, particularly Evolutionary Computation, Natural Language Processing, Machine Learning applications in e-learning and sustainability, and digital technologies in education. He employs algebraic techniques to analyze combinatorial search spaces and evolutionary algorithm dynamics, contributing to both theoretical and applied advancements. The most recent publications reflect a strong trend in combinatorial optimization using algebraic frameworks, hybrid evolutionary-swarm algorithms, and applications in text complexity classification and educational technology. His work bridges computer science with humanities and sustainability, reflecting a multidisciplinary approach. PhD in Computer Science and Mathematics, University of Perugia (2012) He teaches courses in Artificial Intelligence, Computer Science for Humanities, Cybersecurity, and Sustainability across various degree programs. His editorial roles include contributing to WoS/Scopus-indexed journals, and he has taught at the University of Perugia and Hong Kong Baptist University. Dr. Santucci actively mentors through teaching and research supervision. While specific student names are not listed, his involvement in academic projects and publications suggests advisory roles. He has no explicitly mentioned grants, but his research output and leadership position indicate active project engagement. He is involved in institutional innovation through his role in technological advancement and has contributed to projects integrating soft skills and learning technologies. His work emphasizes practical applications of AI in education and sustainability, positioning him at the intersection of technical innovation and societal impact.

Stefania SpinaView profile

Professor

• Corpus Linguistics
• Linguistics
• Glottology
• +12 more

Stefania Spina is a Full Professor of Glottology and Linguistics at the University of Perugia for Foreigners, affiliated with the Department of Italian Language, Literature, and Arts in the World (LILAIM). She has held this position since August 1, 2020, and is actively engaged in research, teaching, and academic leadership. Her work focuses on corpus-based linguistic analysis, particularly in the context of Italian as a second language. Her research interests include corpus linguistics, second language acquisition, learner corpus development, phraseology, discourse analysis of media and academic texts, text complexity assessment, and computational processing of linguistic data. She has created several influential linguistic resources, such as the Perugia Corpus, Corpus of Spoken Italian (CIP), and learner corpora including LOCCLI, COLI, and CELI. She also developed MALT-IT2, a web tool for automated text difficulty classification aligned with CEFR levels. Her recent publications reflect a strong engagement with contemporary language trends, digital humanities, and media discourse. Topics include the lexical impact of AI, metaphor in political journalism, and the evolution of public language. A central theme across her work is the application of corpus methods to understand how language functions in educational, media, and technological contexts. She is the principal investigator of the PRIN 2022 project DICI-A, which aims to build a digital collocation dictionary for Italian L2 learners, in collaboration with computer scientists. Her teaching portfolio includes courses in sociolinguistics of new media, corpus linguistics, and digital humanities. She maintains an active public intellectual presence through her blog (now hosted on Substack) and social media. Email: stefania.spina@unistrapg.it Twitter: @sspina She supervises academic projects and leads research teams but no individual students are named in the provided texts. There are no mentions of scientific awards or grants beyond the PRIN project. She works in digital environments, utilizing tools like Python and AI for linguistic analysis, and contributes to the development of language technologies for education and research.

• Optimization methods
• Metaheuristics
• Heuristics
• +12 more

Mina Mirhosseini is a Research Fellow at the Faculty of Logistics, Molde University College, Norway. She holds a PhD in Computer Science from Shahid Beheshti University of Tehran, Iran, and has prior academic experience as a faculty member and lecturer in Iran and as a remote teaching assistant at the University of Hertfordshire, UK. Her primary research interests include Optimization Methods, Metaheuristics, Heuristics, Linear Integer Programming, Parallel Processing, Machine Learning, Artificial Intelligence, and Logistics. She has made significant contributions to solving complex computational problems such as the n-similarity problem and Mixed Integer Linear Programming (MILP) models using hybrid and parallel algorithms, particularly in the context of high-level synthesis and wireless sensor networks. The analysis of her recent publications reveals a strong focus on developing and applying advanced optimization techniques, especially quantum-inspired gravitational search algorithms and parallel genetic algorithms, to real-world engineering and computational challenges. Her work consistently emphasizes performance improvement, scalability, and load balancing in distributed and heterogeneous computing environments. Mina Mirhosseini has an extensive publication record in high-impact journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Supercomputing, and Computers and Electrical Engineering. Her research has been published across a range of venues, reflecting interdisciplinary work at the intersection of computer science, electrical engineering, and applied optimization. She has actively contributed to the academic community through roles such as program committee member and executive committee member for conferences on fuzzy systems, swarm intelligence, and evolutionary computation. Her academic journey includes teaching and research roles in Iran, demonstrating a sustained commitment to higher education and scientific inquiry. Mina Mirhosseini is part of the research group focused on Planning, Optimization and Decision Support at Molde University College. Her current work continues to advance the state-of-the-art in parallel and metaheuristic optimization methods, with applications in logistics, synthesis, and sensor network design.

• Entrepreneurship
• Innovation Management
• Sustainable Tourism
• +3 more

Roberta Kontošić Pamić is an Assistant Professor at the Faculty of Economics and Tourism of the University of Primorska , based in Pula, Croatia. She holds the position of Lecturer in charge for undergraduate courses such as Basics of Organisation , The Basics of Entrepreneurship , and graduate courses like Innovation management and lean startup . Her academic activities focus on entrepreneurship, innovation, sustainable tourism, and SME development. Her research interests include the analysis of business performance in Croatian regions, innovation-driven wage dynamics, and sustainable tourism models such as indoor camping in heritage sites. She has explored topics like environmental reporting gaps in Croatian enterprises, contributions of SMEs to EU employment, and the economic impacts of entrepreneurial zones. Her publications reflect a strong emphasis on practical applications, including the valorization of cultural heritage sites and innovative accommodation models. She has contributed to studies on Croatian construction sectors and cross-country labor market comparisons between Croatia and Slovenia. While no scientific awards are explicitly mentioned in the text, her extensive publication record indicates significant contributions to her field. She currently advises on courses related to entrepreneurship and innovation but no specific student names are listed.

Ilker HacihalilogluView profile

Associate Professor

• Artificial Intelligence in Healthcare
• Medical Image Processing
• Machine Learning Applications
• +8 more

Dr. Ilker Hacihaliloglu is an Associate Professor in the Department of Radiology and Medicine at the University of British Columbia's Faculty of Medicine. He holds a PhD from UBC (2010) and has held faculty positions at Rutgers University (2014-2021). His research focuses on applying machine learning to medical imaging, aiming to bridge clinical and engineering research for practical healthcare solutions. Key areas include AI-driven diagnostics, ultrasound imaging, and personalized medicine. Education: B.Sc. and M.Sc., Istanbul Technical University (2001, 2004) Ph.D., University of British Columbia (2010) Postdoctoral Fellow, Vancouver General Hospital (2010-2013) Research Interests: Artificial Intelligence in Medical Imaging Ultrasound-Based Diagnostics Image-Guided Surgery Computer-Aided Diagnostics for Orthopedics and Neurosurgery His work emphasizes explainable AI, standardization in machine learning metrics, and clinical translation of technologies. Scientific Contributions: Over 35 peer-reviewed articles (2019-2025) highlight innovations in medical image segmentation, AI-driven radiomics, and cross-modality imaging. Recent trends focus on synthetic data augmentation, diffusion models for medical imaging, and curriculum development in AI for healthcare. Grants and Advising: No explicit grants mentioned, but active mentorship in interdisciplinary research teams. Collaborations span clinicians, engineers, and computer scientists to address real-world healthcare challenges. Labs/Teams: Engaged in computational ultrasound and AI-driven diagnostics initiatives, though specific lab names are not provided in the text.

J. Stephen DownieView profile

Professor

• Information Retrieval
• Digital Libraries
• Digital Humanities
• +4 more

J. Stephen Downie is a Professor and Interim Executive Associate Dean at the School of Information Sciences, University of Illinois. He also serves as the Associate Dean for Research and Co-Director of the HathiTrust Research Center. His academic roles include leadership in digital libraries, digital humanities, and music information retrieval (MIREX). He holds a PhD in Library and Information Science from the University of Western Ontario. Research interests focus on information retrieval systems, multimedia MIR, political economy of communication networks, and database/web technologies. He co-founded ISMIR and directs the annual MIREX evaluation initiative. His work bridges computational analysis, cultural heritage preservation, and digital tool development. Recent scholarly output includes studies on plant systematics (Apiaceae family) and cultural heritage technologies. His projects involve collaborative research on digital libraries, data capsules, and federated repository systems. Notable organizational roles include leadership in HathiTrust and academic service in the National Center for Supercomputing Applications. Teaching responsibilities include doctoral seminars and independent studies. Active projects span computational text mining, structural analysis of music information, and accessibility initiatives for academic content.

• Computer Science
• Software Security
• Object-Oriented Programming
• +3 more

Arran Stewart is a Lecturer in the Department of Computer Science and Software Engineering at the School of Physics, Maths and Computing, University of Western Australia. His academic work focuses on secure software development, particularly in object-oriented programming and Java code analysis. Research Interests : Software Security and Vulnerability Detection Object-Oriented Programming Analysis Side-Effect Free Methods in Java Secure State Management Frameworks His publications between 1995 and 2017 demonstrate expertise in software engineering and legal systems, with recent work emphasizing secure coding practices and method classification in Java. While no scientific awards or student advisement details are explicitly mentioned in the provided texts, his research output spans interdisciplinary domains including computer science and legislative jurisprudence.

Joseph GlazView profile

Professor

• Applied Probability
• Geometrical Probability
• Probability Approximations
• +7 more

Joseph Glaz is a Professor in the Department of Statistics at the University of Connecticut. His work focuses on applied probability and statistical methodology, particularly in scan statistics and related inference techniques. Contact: joseph.glaz@uconn.edu Phone: (860) 486-4193 (Storrs Campus) Research highlights: Develops scan statistics for discrete , continuous , and conditional data frameworks Specializes in change-point detection for normal data mean/variance Innovates robust methods for outlier-prone datasets Extends scan statistics to network and graph structures Contributed to foundational texts like the Handbook of Scan Statistics Publication trends include: Scan statistics for genomic data (Hi-C translocation detection) Nonparametric and Bayesian extensions of scan methods Approximations and inequalities for sequential testing Applications in quality control , medical imaging , and sensor networks

Irena VodenskaView profile

Professor

• Network Theory in Finance
• Systemic Risk Modeling
• Complexity Science in Macroeconomics
• +12 more

Irena Vodenska is Professor of Finance and Director of Finance Programs at Boston University’s Metropolitan College, Department of Administrative Sciences. She holds a PhD in statistical finance and an MA in economics from Boston University, an MBA from Vanderbilt University, and a BS in computer information systems from the University of Belgrade. She is also a Chartered Financial Analyst (CFA) charter holder. Her research is at the intersection of finance, complexity science, and artificial intelligence, focusing on systemic risk modeling, ESG investments, and financial network dynamics. She has led major interdisciplinary research projects funded by the National Science Foundation, the European Commission, and the U.S. Army Research Office. PhD, Statistical Finance – Boston University MA, Economics – Boston University MBA – Owen Graduate School of Management, Vanderbilt University BS, Computer Information Systems – University of Belgrade Dr. Vodenska’s research interests include network theory in finance, systemic risk propagation, AI-powered ESG analysis, cryptocurrency price forecasting, and financial regulation. She employs big data, machine learning, and natural language processing to analyze financial news, market dynamics, and corporate sustainability. Her work investigates how climate disinformation spreads via social networks and influences public policy and governance. The recent articles highlight a consistent focus on modeling financial and economic systems using network science and AI. Trends include systemic stress testing, sentiment analysis in financial markets, cascading failures, and the interplay between macroeconomic indicators and financial networks. Her work spans econophysics, behavioral finance, public health economics, and ethical AI in fintech. National Science Foundation (NSF) research grant (2023) NSF EAGER Award (2014–2015) European Commission FET Open Grant (2012–2014) U.S. Army Research Office (ARO) Grant (2020–2021) MEXT Post-K Computer Grant, Japan (2016–2019) Alexander Hamilton Fulbright Fellowship (1994) Owen Graduate School Fellowship (1995–1996) Dr. Vodenska teaches core finance courses such as Investment Analysis and Portfolio Management, Derivatives Securities, and Financial Regulation and Ethics. She co-developed the MET AD 678 course with Professor Tamar Frankel from BU Law, emphasizing real-world case studies and ethical decision-making. Her research grants have supported innovative work in systemic risk modeling, AI for ESG, and financial network stability. She is actively involved in mentoring, conference organization, and editorial roles in leading journals. She is a key organizer of the International School and Conference on Network Science (NetSci) and the Big Data in Economics, Science, and Technology (BEST) Conference. Her lab and research team focus on complexity in financial systems, bringing together economists, physicists, computer scientists, and data analysts to study global financial stability and sustainability.

Elizabeth YaleView profile

Associate Professor

• History of Science
• Early Modern European History
• Book History
• +7 more

Elizabeth Yale is an Associate Professor in the Department of History at the University of Iowa, College of Liberal Arts and Sciences, where she also serves as Director of Undergraduate Studies. Her research lies at the intersection of the history of science, book history, and early modern European intellectual culture, with a focus on how knowledge was created and preserved in domestic and archival contexts. Her educational background includes a PhD in the History of Science from Harvard University (2008). She previously taught at Western Carolina University and Harvard before joining the University of Iowa. Dr. Yale's research explores the material and social practices of knowledge-making, particularly in early modern Britain. She emphasizes the role of families, manuscript culture, and archival infrastructures in the development of scientific thought. Her work spans themes such as gender, materiality, and the transmission of medical and natural knowledge. She teaches courses on early modern Europe, the history of science and medicine, book history, and gender, often incorporating hands-on analysis of texts through the University of Iowa Center for the Book. Her recent publications reveal a consistent interest in the material forms of knowledge—letters, lists, marginalia, and archives—and their role in shaping scientific epistemology. She frequently examines how women’s intellectual labor and domestic spaces contributed to scientific advancement. Her collaborative projects, including an NEH-funded initiative with Matthew Brown, aim to build innovative educational spaces for studying global print and manuscript cultures. Visiting Scholar, Max Planck Institute for the History of Science, Berlin (2016) President, Andrew W. Mellon Society of Fellows in Critical Bibliography (2021–2022) Senior Fellow, Andrew W. Mellon Society of Fellows in Critical Bibliography Dr. Yale has been active in mentoring and curriculum development, particularly through experiential learning programs like the Hawkeye History Corps. She has secured external funding, including an NEH Humanities Initiatives Grant, to support interdisciplinary teaching and research. Her advising likely extends to undergraduate and graduate students interested in history of science, gender studies, and material text analysis, though specific students are not listed. She is also engaged with public history, contributing accessible commentary to outlets like The Atlantic , Quartz , and Iowa Public Radio , bridging academic research with broader societal conversations on science, gender, and race. She is involved in collaborative scholarly communities, especially through the Rare Book School and the University of Iowa Center for the Book, where she contributes to projects that integrate book history with scientific and archival studies. These affiliations support a vibrant research and teaching environment centered on material knowledge practices.

Sunnie Sun ChungView profile

Professor

• Big Data Analytics
• Artificial Intelligence
• Natural Language Processing
• +3 more

Dr. Sunnie Sun Chung is a Professor in the Department of Electrical Engineering and Computer Science at Cleveland State University's College of Engineering. She leads the Big Data Analytics & AI Lab and has received multiple Faculty Merit Recognition Awards (2020-2024) and Golden Apple Awards (2021-2022). Research Interests : Big Data Analytics, Artificial Intelligence, Natural Language Processing, Cyber Security, Machine Learning, and Optimization in Cloud/IoT environments. Teaching : Advanced courses in Data Mining, Big Data Systems, and Deep Learning (DSA460/CIS492/593, CIS611/711, etc.) with dedicated teaching labs. Publications reflect expertise in Knowledge Graphs for Medical QA , Network Intrusion Detection with Graph Models , Privacy-Preserving Cloud Learning , and Basketball Analytics . Her work spans journals like IEEE Transactions on AI and IEEE Access. Scientific Awards : Faculty Merit Recognition Awards (4x) Golden Apple Teaching Award (2x) Students Supervised : Over 10 PhD/Master’s students including Asanka Mananayaka (NLP-based Intrusion Detection), Yixi Luo (Homomorphic Encryption), Dennis Risch (Medical QA Systems), and collaborators with Case Western Reserve University. Labs & Collaborations : Director of the Big Data Analytics & AI Lab since 2018. Hosted annual IBM Health collaborative workshops since 2018. Maintains strong industry ties with Microsoft, Amazon, Oracle, and Cleveland Clinic.

• Data Stream Management
• Real-time Data Analytics
• Data Quality
• +7 more

Sandra Geisler is a Junior Professor for Data Stream Management and Analysis at the Department of Computer Science, RWTH Aachen University, a position she has held since September 2021. She is also the leader of the Digital Health Spaces group at the Fraunhofer Institute for Applied Information Technology (FIT) in St. Augustin, reflecting her dual expertise in academic research and applied digital health solutions. Bachelor/Master: Diploma in Computer Science, RWTH Aachen University (2008) PhD: Doctoral degree in Computer Science, RWTH Aachen University (2016) Her research focuses on data stream systems, real-time analytics, data quality, and their applications in digital health and industrial processes. She has made significant contributions to ontology-based data quality management, edge computing for stream processing, and FAIR data principles. Recent work explores the integration of large language models into data management workflows and the development of privacy-preserving platforms for industrial data exchange. Her recent publications demonstrate a strong trend in distributed and edge-based stream processing, interdisciplinary applications in healthcare and supply chains, and the use of AI for data discoverability and quality. Topics include in-network computing, simulation of edge queries, self-tonometry for glaucoma, and cross-company data sharing with privacy awareness. She has served as Associate Editor for the Data & Knowledge Engineering Journal (Elsevier), Public Relation Chair for QDB Workshop (VLDB 2016), and Workshop Chair for IMMoA and HIMoA workshops. She has also edited a special issue on Information Management in Mobile Applications in the Pervasive and Mobile Computing Journal. Geisler has supervised multiple theses on topics including LLM-based ontology integration, edge anomaly detection, and data ecosystem modeling. She has been actively involved in research grants and projects related to industrial data processing, digital health, and sustainable production. She teaches courses such as Data Stream Management and Analysis and Data Ecosystems Lab. She leads the Digital Health Spaces research group at Fraunhofer FIT, focusing on innovative solutions for health data management and patient-centric digital tools. Her work bridges computer science, healthcare, and industrial applications, promoting secure, efficient, and intelligent data ecosystems.

Howard E. EpsteinView profile

Professor

• Ecosystem Ecology
• Arctic Ecology
• Climate Change Impacts
• +7 more

Howard E. Epstein is the Sidman P. Poole Professor in the Department of Environmental Sciences at the University of Virginia, College of Arts and Sciences. He leads an active research group focused on ecosystem ecology, with field sites in the Arctic tundra of North America and Russia, as well as temperate forests in the U.S. Mid-Atlantic region. His work integrates field observations, remote sensing, and biogeochemical analysis to understand ecosystem responses to climate change. Ph.D., Colorado State University, 1997 Dr. Epstein's research centers on arctic vegetation dynamics , carbon cycling , and permafrost-vegetation interactions . He investigates how warming climates drive shrub expansion, ice-wedge degradation, and landscape transformation in tundra ecosystems. His work also explores carbon and water fluxes in temperate forest successional gradients. He employs advanced techniques including drone-based spectroscopy, satellite remote sensing, and flux tower measurements. The recent publications highlight a strong focus on Arctic greening , permafrost degradation , and the use of remote sensing and machine learning to detect ecological change. His work spans spatial scales from fine-resolution drone imaging to continental-scale satellite analysis, with applications in climate modeling and ecosystem monitoring. Dr. Epstein teaches core courses in ecology, including Terrestrial Ecology (EVSC 5220) and Fundamentals of Ecology (EVSC 3200) . He advises graduate students and leads the Epstein Ecosystem Ecology Lab , which conducts fieldwork in diverse ecosystems from Alaska to Virginia. His research is supported by external funding, though specific grants are not listed in the provided text. He has made significant contributions to understanding climate change impacts on high-latitude ecosystems, with publications in top journals such as Global Change Biology , Environmental Research Letters , and Nature Reviews Earth & Environment .

Lim Jit YanView profile

Lecturer

• Few-Shot Learning
• Computer Vision
• Deep Learning
• +3 more

Lim Jit Yan is a Lecturer at the School of Information Technology, Monash University Malaysia. He holds a PhD in Information Technology (2023) from Multimedia University, specializing in self-supervised metric-based meta-learning for few-shot image classification, and a B.IT (Hons) in Artificial Intelligence (2019) from the same institution. His research focuses on few-shot learning, computer vision, and deep learning, with contributions to medical image analysis (e.g., Covid-19 detection), transfer learning applications, and generative models. He has published extensively since 2021, with notable work on self-supervised feature fusion and prototypical networks for few-shot learning. Research interests include few-shot learning techniques, neural architecture design for image classification, and real-world applications like healthcare diagnostics and autonomous systems. Recent work emphasizes self-supervised learning and transformer-based models to address data scarcity challenges in AI. No formal scientific awards are listed. Advising details are not provided in the text, though his publications suggest collaborative research with colleagues like Lim K.M. and Lee C.P. His research spans theoretical advancements and applied projects, including work on pill image recognition, traffic sign detection, and brain tumor classification.

Shamik RoyView profile

Researcher

• Microbial Ecology
• Soil Biogeochemistry
• Antimicrobial Resistance
• +7 more

Shamik Roy is a Visiting Researcher at the School of Biological Sciences, University of East Anglia (UEA), actively contributing to research in microbial ecology and soil biogeochemistry. He holds a Doctor of Science from the Indian Institute of Science, awarded in 2021. Doctor of Science, Indian Institute of Science (awarded April 2021) His research focuses on microbial processes in diverse soil environments, including Arctic, volcanic, and agricultural soils. Key interests include antimicrobial resistance dynamics, trace gas metabolism, carbon cycling, and the application of metagenomics and stable isotope probing to study active microbial populations. His work bridges environmental microbiology with climate change and public health implications. The recent publications highlight a trend toward integrating genomic tools with ecological questions, particularly examining microbial functional roles in extreme environments and human-impacted ecosystems. Studies frequently involve collaborations and utilize advanced sequencing and isotopic techniques. Although no scientific awards are listed in the provided text, his work has gained significant attention, being picked up by 29 news outlets, blogged about, and widely shared on social media platforms including X (formerly Twitter), Bluesky, and Facebook. There is no available information about student advising or research grants. However, his active publication record suggests ongoing research engagement and collaboration within the environmental microbiology community. Shamik Roy is involved in a research network focusing on microbial ecology, with external collaborations across countries. His work contributes to understanding microbial roles in global biogeochemical cycles and environmental resilience.