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Luigi CeruloView profile

Associate Professor

• Bioinformatics
• Computational Genomics
• Machine Learning
• +3 more

Luigi Cerulo is an Associate Professor in Computer Science at the Department of Science and Technology (DST) , University of Sannio in Benevento. He co-directs the bioinformatics research group with Francesco Napolitano, focusing on integrating computational methods into life sciences. His teaching includes advanced topics like Computational Genomics and Statistics and Bioinformatics for Master's programs in Biotechnology and Biology. Academic Rank: Associate Professor Department: Science and Technology, University of Sannio Email: lcerulo@unisannio.it Research Interests: Driven by cross-disciplinary innovation, his work bridges machine learning and bioinformatics. Key areas include reverse engineering gene regulatory networks using deep graph neural networks, predicting ligand-receptor interactions via graph autoencoders, and exploring non-coding RNA functions with self-supervised learning. His team also investigates drug synergy prediction and AI applications in biological data. Recent Publications: His 15 most recent works span 2022 to 2007, emphasizing machine learning in gene network reconstruction (Chang et al., 2020), deep learning for RNA classification (Noviello et al., 2020), and foundational contributions to software engineering tools (Canfora et al., 2007-2008). Research Infrastructure: The group utilizes high-performance systems including a Superdome Flex server (224 CPU cores, 1.5 TB RAM) and a GPU cluster at Biogem Institute. Advising and Grants: He mentors PhD students in bioinformatics and leads projects such as GENOMA E SALUTE (EU-funded) and Non-Coding RNA Explosion (Ministry of Research grant). Collaborations span institutions like the University of Naples and Biogem.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +8 more

Rohini Srihari is a Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York, where she also serves as Associate Chair. Additionally, she holds an Adjunct Professor position in the Department of Linguistics. Her academic journey includes a PhD in Computer Science from the University at Buffalo (1992) and a B. Math degree from the University of Waterloo (Canada). Dr. Srihari's research spans multiple cutting-edge areas in artificial intelligence, with a strong focus on Natural Language Processing, Information Retrieval, and Text Mining. Her work encompasses both theoretical foundations and practical applications, particularly in Conversational AI systems, disinformation detection, and social unrest prediction. She has pioneered approaches in multilingual text mining for less commonly taught languages and developed innovative methods for social media analysis. Her recent research emphasizes AI for Social Good, addressing critical societal challenges through technology. Her publication record shows a clear evolution from foundational work in multimedia information retrieval and document analysis toward increasingly socially impactful applications. The recent publications demonstrate a strong focus on using AI to address misinformation, predict civil unrest, and develop empathetic conversational agents. Her work increasingly integrates multiple data sources and leverages state-of-the-art deep learning techniques while maintaining a focus on real-world applicability and social impact. Awards & Recognition: Bronze prize in the Alexa Prize Socialbot Grand Challenge 4 (2021) Two US patents in text mining and image analysis Extensive publication record with over 100 papers and an h-index of 40 Dr. Srihari has secured significant research funding from major agencies including the National Science Foundation (NSF), DARPA, and IARPA. She has directed multiple research projects focused on AI applications for social good, including an NSF-funded project on Purposeful Conversational Agents based on Hierarchical Knowledge Graphs. She supervises students across both Computer Science and Linguistics departments, fostering interdisciplinary research. Her research group, focused on Conversational AI for Social Good, addresses critical issues like combating disinformation and assisting the disabled through trustworthy socialbots. She also directs the Center of Excellence for Document Analysis and Recognition (CEDAR) and has led interdisciplinary student teams in major competitions. Her industry experience includes founding and directing technology startups focused on big data analytics, two of which were acquired by larger media analytics companies.

• Health Psychology
• Behavioral Interventions
• Cancer Survivorship
• +4 more

Professor Vera Araujo-Soares is a leading academic at Newcastle University, specializing in health psychology and behavioral intervention research. Her work spans cancer survivorship, chronic disease self-management, and public health initiatives, with significant contributions to understanding caregiver experiences in neuro-oncology and developing evidence-based interventions for physical activity promotion and obesity prevention. Her research examines: Psychosocial adaptation to chronic illnesses like glioma Mechanisms of behavior change in physical activity interventions Parental recognition of childhood obesity Implementation science frameworks for healthcare delivery She employs diverse methodologies including qualitative studies, randomized controlled trials, and systematic reviews. Recent publication trends (2023-2025) highlight three dominant themes: Neuro-oncology support : 50% of recent papers address glioma self-management and caregiver burden Behavioral interventions : 30% focus on physical activity/adolescent health and obesity prevention trials Mental health integration : 20% examine suicide prevention and substance use impacts

• Information Retrieval
• XML Search Engines
• Text Search Engines
• +8 more

Shlomo Geva is an Adjunct Professor in the School of Computer Science at Queensland University of Technology's Faculty of Science. His research focuses on information retrieval systems, particularly in specialized areas including XML search engines, text search engines, link discovery, and document computing. His academic work spans multiple disciplines within computer science, with particular emphasis on information retrieval technologies and their applications. Professor Geva's research interests include clustering algorithms, cross language information retrieval, focused information retrieval, information retrieval systems, link discovery mechanisms, search engine technologies, text indexing and retrieval methods, and XML indexing and retrieval techniques. His work demonstrates a consistent focus on improving the efficiency and effectiveness of information access systems across various data formats and domains. His recent publications reveal a trend toward applications of information retrieval techniques in diverse fields including remote sensing, bioinformatics, and data stream processing. The research shows an evolution from traditional information retrieval problems toward more specialized applications requiring advanced clustering algorithms and efficient data processing techniques for large-scale datasets. Professor Geva has successfully supervised numerous doctoral students whose research topics include indoor environment mapping by robots, cross-language information retrieval, natural language query interfaces for XML, evolvable hardware, and autonomous robot behavior systems.

• Biomedical Engineering
• Complex Medical Engineering
• Networked Systems
• +14 more

Dr. Shahab Abdulla serves as an Associate Professor at the University of Southern Queensland within the College of English Language, specializing in pathway programs for international students transitioning to tertiary education. His academic role bridges mathematics, engineering, and language preparation through foundational courses including Linear Systems and Control, Mathematical Methods, and General Science. His academic qualifications include: BSc from University of Technology, Baghdad MSc from University of Technology, Baghdad PhD from University of Southern Queensland Dr. Abdulla's research spans Biomedical Engineering , Complex Medical Systems , and Networked Control , with pioneering work in Deep Learning for environmental forecasting and medical diagnostics. His AI research encompasses Neural Networks, Reinforcement Learning, and graph-based models applied to climate impact assessment and resource management, reflecting a commitment to solving interdisciplinary global challenges through computational innovation. Analysis of his 15 most recent publications reveals consistent application of decomposition techniques (wavelet, Fourier, curvelet) integrated with deep learning architectures across domains including ocean energy forecasting, medical imaging, and social network analysis. This demonstrates a methodological signature of combining signal processing with AI to extract patterns from complex temporal and spatial data. Though specific awards aren't detailed, his leadership is evidenced by chairing IEEE conferences and international committee service. He has supervised over 14 higher-degree research students while leading the Advanced Data Analytics Modelling Research Simulation Group, generating 55+ Q1 journal publications with 1000+ recent citations and 30+ global seminars. The Advanced Data Analytics Modelling Research Simulation Group under his direction develops AI-driven solutions for environmental monitoring, medical diagnostics, and engineering systems, maintaining active collaborations across the Middle East, US, Japan, Europe, China, and Canada.

Nikos NtounisView profile

Senior Lecturer

• Place Management
• Place Branding
• Urban Studies
• +4 more

Dr. Nikos Ntounis is a Senior Lecturer in Marketing at Manchester Metropolitan University , affiliated with the Institute of Place Management . His work focuses on place management theory and practice, place branding, town centre management, and business improvement districts, with a strong emphasis on urban sustainability. Editor-in-Chief of the Journal of Place Management and Development Senior Fellow of the Institute of Place Management Member of the Regional Studies Association He leads research on urban regeneration, place identity, and high street viability, utilizing methodologies like chronotopic analysis and machine learning. Recent projects include ESRC-funded work on Circular Society Innovations for equitable urban development. His teaching covers research methods, place leadership, and alternative marketing forms. Scientific recognition includes: Senior Fellow of Institute of Place Management Current research explores the intersection of place marketing, legal geography, and stakeholder engagement in urban spaces.

Abigail A. BickleyView profile

Assistant Professor

• Nuclear Forensics
• Nuclear Proliferation Detection
• Computational Nuclear Reactor Modeling
• +3 more

Dr. Abigail A. Bickley is an Assistant Professor of Nuclear Engineering at the Air Force Institute of Technology (AFIT), Wright-Patterson Air Force Base. She specializes in nuclear forensics, focusing on nuclear proliferation signature identification through materials analysis, computational reactor modeling, and AI-aided facility activity detection. With over 60 peer-reviewed publications, Dr. Bickley also serves as AFIT's Radiation Safety Officer and manages the high-performance computing cluster for the nuclear engineering graduate program. University of Maryland, Chemistry, Ph.D. (2004) Dartmouth College, Chemistry, B.A. (2000) Her research integrates machine learning with nuclear detection technologies, including magnetic field sensor analysis and neutron spectrometry. Key projects involve convolutional neural networks for fuel cycle characterization, particle morphology classification, and spatially-variant burnup modeling for treaty monitoring. Dr. Bickley received multiple awards including the AFTAC Endowed Term Chair (2021), AFIT GSEM Distinguished Teaching Award (2021), and Battelle Memorial Institute's RHIC & AGS Thesis Award (2005). She teaches courses on nuclear weapons residual effects and nonproliferation technologies.

• Data-driven Optimization
• Real Option Valuation
• Fuzzy Logic
• +9 more

Markku Heikkilä is a University Lecturer at the School of Business and Economics, Faculty of Social Sciences, Business and Economics, and Law, at Åbo Akademi University in Finland. He holds a Doctor of Science (D.Sc.) degree and is actively engaged in research at the intersection of data science, business analytics, and financial decision-making. His work emphasizes computational intelligence, fuzzy logic, and data-driven optimization applied to real-world business challenges. Research Interests: His primary research areas include data-driven optimization, real option valuation using fuzzy methods, sentiment analysis in e-commerce, market segmentation via data mining, and association rule mining. He applies machine learning and artificial intelligence techniques to financial modeling, peer-to-peer lending, and customer satisfaction analysis. His interdisciplinary approach bridges computer science with economics and finance. Publication Trends: His recent publications (2020–2025) reflect a strong focus on intelligent systems in accounting, finance, and management. Key themes include granular fuzzy models for valuation, sentiment classification using machine learning, and data mining for market segmentation. He frequently publishes in journals like Expert Systems with Applications and Electronic Commerce Research , demonstrating sustained scholarly output. Supervision and Collaboration: He has contributed to at least three supervised works, indicating involvement in mentoring students or junior researchers. He collaborates closely with scholars such as József Mezei, Carlsson, and others in the domain of computational intelligence. His research network includes international collaborations, particularly in Europe. Laboratories and Research Groups: While no specific lab or research group is named in the text, his work appears to be part of broader initiatives in intelligent systems and data-driven business analytics at Åbo Akademi University. His research fingerprint highlights strong activity in value frameworks, data markets, and granular fuzzy systems.

Alessandro CasaView profile

Associate Professor

• Mixture Models
• Modal Clustering
• Graphical Models
• +5 more

Alessandro Casa is an Associate Professor of Statistics at the Faculty of Economics and Management, Free University of Bozen-Bolzano (Italy). His research focuses on developing statistical methodologies for high-dimensional and complex data, including latent variable models, density-based clustering, and applications in spectroscopy. He holds a PhD in Statistics from the University of Padova (2016-2020), preceded by master's and bachelor's degrees in Statistical Sciences and Statistics, Economics, and Finance, both from the University of Padova. Education: PhD in Statistics, University of Padova (2016-2020) Master Degree in Statistical Sciences, University of Padova (2014-2016) Bachelor Degree in Statistics, Economics, and Finance, University of Padova (2011-2014) Research Interests: Mixture models, modal clustering, graphical models, variable selection, latent variable modeling, high-dimensional data inference, computational statistics, and chemometrics. His work often addresses challenges in spectroscopic data analysis and applications in agriculture and physics. Teaching: Statistical Methods for Business Analysis (MSc programs) Research Methods and Experimental Design (Tourism Management) Statistics for Tourism, Sport, and Event Management (BSc) Professional Experience: Associate Professor (2024–Present), Free University of Bozen-Bolzano Senior Assistant Professor (2024), University of Bergamo Postdoctoral Research Fellow (2019–2021), University College Dublin Labs/Teams: Collaborations with Vistamilk SFI Research Centre and Insight SFI Research Center during his postdoctoral tenure. Active in model-based clustering research communities, contributing to conferences and workshops globally.

Nicola StrisciuglioView profile

Associate Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +5 more

Nicola Strisciuglio is an Associate Professor at the University of Twente, affiliated with the Data Management and Biometrics group. His research focuses on robustness and generalization in computer vision and machine learning, with applications in biomedical imaging, privacy in face recognition, and efficient model architectures. Key research areas include Bias mitigation in training data Physics-informed models Energy-efficient AI Robustness against image corruptions His recent work emphasizes sparse training, vision transformers, and data-efficient models, as seen in publications at ICLR, CVPR, and IEEE conferences. He supervises ongoing PhD candidates in the Netherlands and abroad. Scientific contributions span theoretical advancements and practical applications in robotics and biometrics. He was awarded the NeurIPS 2023 Outstanding Reviewer Award.

Roland Dunbrack, Jr.View profile

Professor

• Computational Structural Biology
• Bioinformatics
• Protein Structure Prediction
• +14 more

Roland Dunbrack, Jr. is a Professor and Co-Leader of the Cancer Signaling and Microenvironment Program at Fox Chase Cancer Center. He also serves as Director of both the Molecular Modeling Facility and the Organic Synthesis Facility. He holds adjunct professorships at the University of Pennsylvania School of Medicine and Drexel University College of Medicine, reflecting his broad academic engagement. His research lies at the core of computational structural biology and bioinformatics, focusing on protein structure prediction, statistical analysis of protein conformations, and the development of software tools such as SCWRL, MolIDE, and BioAssemblyModeler. His work integrates Bayesian statistics, machine learning, and computational geometry to improve the accuracy of protein modeling and to apply these methods to cancer biology, DNA repair, and antibody design. He maintains several widely used public databases, including the backbone-dependent rotamer library and ProtCID. The analysis of his recent publications reveals a sustained focus on protein structure and function, with significant contributions to understanding kinase conformations, biological assemblies, antibody CDR loops, and the structural basis of disease mutations. His work increasingly bridges computational methods with clinical applications in precision medicine, particularly in cancer. Health Equity & Social Justice Award (2024) Dr. Dunbrack leads a research group that includes graduate students and postdoctoral researchers, and he actively collaborates with experimentalists across Fox Chase and beyond. His lab has been involved in major community efforts such as CASP and CAGI, assessing the state of the art in protein and genome structure prediction. He has directed the NCI-supported Molecular Modeling Facility since 2003, providing critical resources to cancer researchers. His lab, the Dunbrack Lab, is dedicated to advancing structural bioinformatics through method development, software engineering, and collaborative science. The team works on large-scale analyses of the Protein Data Bank, develops user-friendly graphical interfaces for modeling, and applies computational insights to problems in cancer signaling and microenvironment.

• Biomedical Informatics
• Artificial Intelligence in Healthcare
• Machine Learning
• +12 more

Dr. Shahzad Mumtaz is a Lecturer at the School of Natural and Computing Sciences, University of Aberdeen, where he contributes to research and education in computational health and data science. His work bridges artificial intelligence and healthcare, focusing on real-world applications in medical data modeling and digital health tools. Research Interests: His research spans biomedical informatics, machine learning, natural language processing, electronic health records (EHR), phenotype libraries, and medical image analysis. He is particularly active in developing tools for clinical data standardization, such as the Carrot tool for OMOP CDM, and in AI-driven solutions for fraud detection, deepfake identification, and clinical decision support. His interdisciplinary work integrates computer science with public health and clinical medicine. Publication Trends: His recent publications (2023–2025) emphasize AI applications in healthcare, including deep learning for fake medical image detection, NLP for financial text analysis, and vision transformers for weather and skin cancer classification. He frequently collaborates on large-scale health data initiatives like CO-CONNECT and the UK Phenotype Library, reflecting a strong focus on scalable, interoperable digital health infrastructure. Scientific Contributions: Core contributor to the CO-CONNECT project for national health data access during the pandemic. Developer of the Carrot tool for improving OMOP data curation. Active in advancing phenotype library standards and EHR-based research. Advising and Grants: While no formal students or grants are listed in the provided text, his extensive collaborative work suggests active involvement in research teams and potential supervision of graduate researchers. He is likely engaged in funded projects related to health data science and AI, given the scale and scope of his publications. Labs and Teams: Dr. Mumtaz is affiliated with research initiatives at the University of Aberdeen focused on biomedical informatics and trusted research environments. He collaborates with multidisciplinary teams across the UK, particularly in projects involving NHS data, digital phenotyping, and AI safety in healthcare.

Carlo DragoView profile

Associate Professor

• Machine Learning
• Energy Policy
• Health Economics
• +3 more

Carlo Drago is an Associate Professor of Economic Statistics at the University of Naples 'Niccolò Cusano', where he teaches Probability, Statistics, and Computer Science in the Faculty of Engineering and Economics. He previously served as an Expert at the European Commission's Joint Research Center (JRC) and is currently a member of the 'Market Competition Innovation' Working Group under Italy's Ministry of Economic Development (unpaid role). He holds a PhD in Statistics from the University of Naples 'Federico II'. His research focuses on Machine Learning applications (both supervised and unsupervised), with interdisciplinary emphasis on energy policy, healthcare economics, and socioeconomic issues like poverty measurement. Notable contributions include developing interval-based composite indicators for poverty analysis and frameworks for measuring energy resilience across 230 countries. Publications span journals such as Energy Policy , Ecological Economics , and Scientific Reports , with recent work addressing pandemic-related healthcare costs during the COVID-19 crisis and migraine treatment economic evaluations. His methodological innovations include interval K-means clustering and bootstrap approaches for governance network analysis. Dr. Drago has collaborated with EU-funded projects like MUNROS, investigating healthcare professional roles' impact on patient satisfaction. His work frequently bridges statistical methodology with practical policy applications in energy, health, and socioeconomic equity.

• Computer Vision
• 3D Reconstruction
• Sports Data Science
• +3 more

Dr. Maarten Slembrouck is a postdoctoral researcher affiliated with Ghent University, working across the Faculty of Medicine and Health Sciences (Department of Movement and Sports Sciences) and the Faculty of Engineering and Architecture (Department of Electronics and Information Systems). His research focuses on computer vision, 3D reconstruction, and sensor fusion for sports analytics and human motion analysis.

Uwe LämmelView profile

Professor

• Artificial Intelligence
• Computer Science
• Data Mining
• +3 more

Uwe Lämmel is a Professor at Wismar University of Applied Sciences since 1996, with a focus on Artificial Intelligence and Computer Science . He holds a diploma in mathematics from the University of Rostock (1981) and a Dr.-Ing. from Rostock (1985). His work bridges theoretical foundations with practical applications in fields like knowledge management, education, and business informatics. Education : Mathematics (Diploma) at University of Rostock (1976–1981); Promotion to Dr. Ing. in 1985 Lämmel's research spans Artificial Intelligence , Data Mining , and Knowledge Management , with a particular emphasis on educational frameworks like 'Object First' programming instruction and semantic wiki systems. He has co-authored multiple editions of foundational texts in AI and Data Mining. His recent work (2025) explores the enduring relevance of Gottlob Frege's logic in modern computing. Publications highlight AI deployment in healthcare insurance workflows, semantic web technologies, and pedagogical innovations in programming education.