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Giovanni TrappoliniView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Multimodal Deep Learning
• +5 more

Giovanni Trappolini serves as Assistant Professor at Sapienza University of Rome within the Department of Computer, Control, and Management Engineering, conducting research at the RSTLess Lab under Prof. Fabrizio Silvestri. Previously, he completed his Ph.D. in Machine Learning at Sapienza under Prof. Emanuele Rodolà, following an MSc in Data Science where he graduated cum laude as a Sapienza honor graduate. His educational background includes: MSc in Data Science, Sapienza University of Rome (cum laude, Sapienza honor graduate) Ph.D. in Machine Learning, Sapienza University of Rome (2022) BSc from Luiss Guido Carli Trappolini's research bridges Machine Learning and Deep Learning with emphases on multimodal systems and information retrieval . He pioneers applications in Graph Neural Networks security, Federated Learning architectures, and Italian-language Large Language Models —notably creating Fauno , Italy's leading LLM. His work extends to operating system innovation through generative AI, 3D shape analysis using transformers, and creative applications like AI-driven music generation. Analysis of his 2023 publications reveals dominant trends toward integrating retrieval systems with generative models (RAG), developing robust neural databases, and enhancing cross-modal understanding. Key themes include adversarial defense for graph networks, privacy-preserving federated retrieval, and transformer-based geometric learning—demonstrating consistent focus on foundational AI infrastructure. Scientific recognition includes: Sapienza honor graduate Cum laude graduate distinction Trappolini actively contributes to academic instruction through courses including Advanced Data Mining and Language Technologies (Sapienza, 2023) and multiple iterations of Python Programming for Data Science (2019-2023). He maintains significant research collaborations with Stanford, Technion, Meta, Amazon, TII, and UniPi while preparing new PhD-level coursework in Geometric Deep Learning for 2024. His research operates within the RSTLess Lab ecosystem, focusing on scalable AI systems and multimodal integration.

Pablo Gamallo OteroView profile

Professor

• Natural Language Processing
• Information Extraction
• Machine Learning
• +7 more

Pablo Gamallo Otero is a Full Professor at the University of Santiago de Compostela (USC) with extensive experience in Natural Language Processing and Information Extraction. His academic career began with a Linguistics thesis defended in 1998 at the Université Blaise Pascal, followed by post-doctoral work at the Artificial Intelligence Center of the Universidade Nova de Lisboa. Since 2004, he has been affiliated with USC, progressing from Isidro Parga Pondal research fellow to Ramón y Cajal research fellow, and ultimately to his current position as Full Professor. His research focuses on Natural Language Processing and Information Extraction, with current projects centered on semantic relation extraction and the development of hybrid language models (neuro-symbolic) applied to sentiment analysis and disinformation detection. His work spans machine learning, language technologies, and trustworthy AI, with a particular emphasis on Galician language processing. Gamallo has led numerous research projects including iRead4Skills, HYBRIDS, LingUMT, DeepR3.gal, and Big-eRisk, demonstrating his commitment to advancing language technologies for both academic and practical applications. His recent publications reveal a strong focus on Galician language models, neural machine translation, and the development of resources for low-resource languages. The research trends show an evolution from dependency parsing and lexical normalization toward contemporary challenges in large language models, sentiment analysis, and cross-lingual transfer learning, particularly for Romance languages and Galician specifically. As an entrepreneur in the field, Gamallo is the promoter and founding partner of Cilenis, a USC spin-off specializing in language technologies, and serves as the main developer of linguistic software tools including Linguakit and Avalingua. His contributions to the field extend beyond academia into practical applications that support language preservation and technological accessibility.

Arbana KadriuView profile

Professor

• Natural Language Processing
• Computer Science
• VR/AR in Education
• +9 more

Arbana Kadriu is a Full Professor at the Faculty of Contemporary Sciences and Technologies of South East European University , with a full-time academic engagement since 2019. She previously held academic positions as Associate Professor (2013-2019), Assistant Professor (2008-2013), and various educational roles dating back to 1998. PhD in Computer Sciences (2008) from Faculty of Natural Sciences and Mathematics, Institute of Informatics, Ss. Cyril and Methodius University MSc in Electrical Technical Sciences (2002) from Electrotechnical Faculty, Ss. Cyril and Methodius University Engineer of Informatics (1997) from Faculty of Natural Sciences and Mathematics, Institute of Informatics, Ss. Cyril and Methodius University Her research focuses on Natural Language Processing , VR/AR in education , E-Learning systems , and gender gap analysis in computer science . Her recent publications analyze authorship attribution in Albanian texts, ethno-fusion music datasets, and educational technology trends, while historic work includes text classification and bibliometric studies. Arbana's academic contributions include 40+ publications across diverse topics: Albanian language processing Virtual/Augmented Reality applications Gender dynamics in computing Learning analytics Information retrieval systems Software engineering pedagogy

Christoph WehnerView profile

Researcher

• Interactive Machine Learning
• Explainable AI
• Knowledge Graphs
• +5 more

Christoph Wehner is a doctoral candidate and Research Assistant at the Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-Universität Bamberg. He works in the Cognitive Systems Group on interactive and interpretable machine learning methods applied to knowledge graphs. PhD research focuses on integrating human knowledge and preferences into AI systems Active in industrial AI applications for manufacturing and automotive sectors Specializes in reinforcement learning with rule-based expert feedback His work combines knowledge graphs with explainable AI techniques to solve real-world problems in root cause analysis and failure mode detection. Current research trends include: Developing hybrid human-AI systems for link prediction Creating interpretable structures from knowledge graph embeddings Applying causal discovery algorithms to industrial processes Thesis advising focuses on practical implementations of knowledge graph-based AI systems in production environments.

• Retrieval-Augmented Generation
• Large Language Models (LLM)
• Autonomous Driving
• +3 more

Vahid Zolfaghari is a Ph.D. candidate and researcher at the Chair of Robotics, Artificial Intelligence, and Real-Time Systems at the Technical University of Munich (TUM) , Germany. His work focuses on advancing technologies in robotics, AI, and autonomous systems. Education: Master of Science in Information Technology Engineering - Communication Networks, Amirkabir University of Technology, Tehran, Iran (09/2011 - 02/2014) Bachelor of Science in Information Technology Engineering, Birjand University, Iran (09/2005 - 02/2010) Research Interests: Retrieval-Augmented Generation Large Language Models (LLM) Autonomous Driving Safety Analysis for Autonomous Vehicles Career History: Ph.D. candidate and researcher at TUM (02/2022 - NOW) Devops engineer/researcher at the Human Brain Project, Institute of BioRobotics, Scuola Superiore Sant'Anna, Pisa, Italy (12/2019 - 01/2022) Researcher at the REPLICATE Project, Institute for Informatics and Telematics, CNR, Pisa, Italy (12/2017 - 10/2019) Senior Test Engineer at Sharif University of Technology, Tehran, Iran (08/2014 - 12/2017)

Trond LinjordetView profile

Research Fellow

• Machine Learning
• Knowledge Graphs
• Computational Chemistry
• +3 more

Trond Linjordet is a Postdoctoral Fellow at the Hylleraas Center for Quantum Molecular Sciences, University of Oslo, affiliated with the Department of Chemistry. His academic background includes a Bachelor's in Nanotechnology, Master's in Quantum Information and Optics, and a PhD focused on machine learning applications in linguistic domains using knowledge graphs. He possesses commercial experience as a data scientist and scientific advisor, along with research experience in smart cities projects. Linjordet's research centers on machine learning theory and applications across multiple domains. Primary interests include: Machine learning for catalyst discovery in chemistry Knowledge graph construction and applications Natural language processing and question answering systems Synthetic data generation and optimization Quantum molecular sciences applications His publications demonstrate consistent focus on knowledge graph technologies, question answering systems, and urban informatics. He maintains active research collaborations as evidenced by co-authorship networks. No awards, student advisements, or grant information are documented in the provided materials.

Yada ZhuView profile

Researcher

• Machine Learning
• Time Series Analysis
• Graph Learning
• +5 more

Dr. Yada Zhu is a Researcher at IBM T. J. Watson Research Center , affiliated with the Future of Computing – Finance Research team. She leads initiatives in foundational AI/ML capabilities for financial decision-making and risk management. Research Focus: High-dimensional time series analysis, heterogeneous data modeling, graph learning, and statistical applications in finance, e-commerce, and smart energy. Grants: Principal Investigator for MIT-IBM AI Lab projects funded by Refinitiv and a major financial institution. Leadership: Technical lead in analytics projects contributing to commercialized IBM products. Scientific Achievements: IBM Research Division Outstanding Technology Achievement Award High Valuable Innovation Award Her publications span AI/ML, statistical modeling, and financial applications, with recent work on graph transformers, time-series unification, and zero-shot tool optimization for LLM agents. She serves on editorial boards for statistics journals and as Senior Program Committee member for AAAI.

• Natural Language Processing
• Corpus Linguistics
• Augmentative and Alternative Communication
• +5 more

Anaïs LEFEUVRE-HALFTERMEYER is a Senior Lecturer at the University of Orleans, where she is affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. She serves as a member of the DIAMS steering committee and is an associate member of both LIFAT and LLL research groups. Her academic profile reflects strong engagement with both teaching responsibilities and research activities in computer science with a human-centered focus. Dr. LEFEUVRE-HALFTERMEYER's research spans multiple interconnected domains within computational linguistics and human-computer interaction. Her primary interests include Natural Language Processing , Corpus Linguistics , and Augmentative and Alternative Communication systems. She has developed significant expertise in Explainable Artificial Intelligence approaches, particularly for image captioning systems, and has conducted substantial work on social media analysis, temporal annotation, and accessibility technologies. Her research consistently demonstrates a commitment to practical applications that address real-world communication challenges, especially in rehabilitation contexts. Analysis of her publication record reveals a clear trajectory from foundational corpus linguistics work toward increasingly applied research with strong human impact. Early publications focused on French spoken language treebanks and syntactic annotation tools, while more recent work addresses accessibility technologies, ethical considerations in AI, and explainability in vision-language models. There's a notable emphasis on bridging theoretical linguistic concepts with practical implementation, particularly for assistive technologies that serve people with communication disabilities. Dr. LEFEUVRE-HALFTERMEYER actively contributes to the academic community through mentorship and organizational roles. She has served as a mentor for student projects, including work on French language model comparison, and previously organized the JOLICO (JOurnées jeunes chercheurs de la LInguistique de COrpus) conference in 2015 as part of the scientific committee. Her institutional involvement includes membership on the DIAMS steering committee, demonstrating leadership within her research community.

Yufei DingView profile

Associate Professor

• Quantum Computing
• Machine Learning
• Compiler Optimization
• +3 more

Yufei Ding is an Associate Professor in the Computer Science & Engineering Department at the University of California, San Diego (UCSD), and founder of the PICASSO Lab. She earned a Ph.D. in Computer Science from North Carolina State University and a B.S. in Physics from the University of Science and Technology of China. Research Interests: Quantum Computing Machine Learning Domain-Specific Languages Compiler Optimization Hardware Acceleration High-Performance Computing Her recent publications focus on quantum compilation, error correction, and machine learning systems, with a particular emphasis on hardware-aware optimizations. She has received multiple prestigious awards, including the NSF CAREER Award (2020) and the IEEE TCHPC Early Career Researchers Award (2019). Scientific Awards: NSF CAREER Award (2020) IEEE Computer Society TCHPC Early Career Researchers Award (2019) Yufei actively advises Ph.D. students and postdoctoral researchers in quantum computing and machine learning systems. Her lab offers openings for both quantum computing and machine learning research.

Afshin BeheshtiView profile

Professor

• Space Biology
• Space Biomedicine
• Regenerative Medicine
• +7 more

Afshin Beheshti, PhD , is Professor of Surgery and Computational & Systems Biology at the University of Pittsburgh School of Medicine , Director of the Space Biomedicine Program , and Associate Director of the McGowan Institute for Regenerative Medicine . He additionally serves as Visiting Researcher at the Broad Institute of MIT and Harvard and remains an active investigator at NASA Ames Research Center through the Blue Marble Space Institute of Science. Education & Training: PhD in Physics, Florida State University (2002) Postdoctoral training in cancer research, systems biology, space biology, and radiation biology Research Focus: Dr. Beheshti’s work sits at the intersection of space biomedicine, regenerative medicine, and systems biology . He investigates how the space environment—microgravity, radiation, and altered circadian cycles—affects human physiology at molecular, cellular, and systemic levels. Core themes include microRNA-mediated gene regulation, mitochondrial dysfunction, radiation countermeasures, and precision diagnostics using label-free optical imaging combined with multi-omics. Recent Scientific Impact: In 2024, Dr. Beheshti co-led the Space Omics and Medical Atlas (SOMA) , the largest international compendium of space biology data to date, published across Nature and companion journals. His portfolio also spans COVID-19 and Long COVID investigations through the non-profit COVID-19 International Research Team (COV-IRT) and democratized AI development via the Kwaai Personal AI initiative. Honors & Awards: NASA Exceptional Scientific Achievement Medal ISS Research & Development Award – Compelling Results in Biology KBR Award for Excellence NASA Outstanding Service Award – Ames Safety Award Program II Funding & Collaborations: Dr. Beheshti has secured continuous funding from NASA, Department of Defense, and other federal agencies to advance countermeasure development against space radiation and microgravity-induced pathologies. His teams integrate wet-lab experiments, animal models, clinical data from astronauts, and advanced computational pipelines to deliver translational insights for both space exploration and terrestrial medicine. Laboratory & Outreach: At Pittsburgh he is establishing a next-generation Space Biomedicine Laboratory outfitted with multi-modal imaging, single-cell multi-omics, and AI-driven analytics. Parallel outreach programs target K-12, undergraduate, and graduate trainees to cultivate the next generation of space life scientists.

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +17 more

Majjed Al-Qatf is a Researcher at the University of Galway , collaborating with Prof. Edward Curry. His work focuses on data spaces, knowledge graphs for data sharing, large language models, and retrieval augmented generation for question answering. Education: B.S. in Network Technology and Computer Security (Sana’a University, 2013), M.S. in Computer Science and Technology (Central South University, 2019), Ph.D. in Computer Science and Technology (University of Science and Technology of China, 2023) His research spans artificial intelligence, particularly deep learning , machine learning , NLP , and computer vision , with applications in network intrusion detection , image captioning , activity recognition , and explainability . Prior work includes image enhancement , knowledge graphs , and federated learning . Publications appear in journals such as IEEE Transactions on Multimedia , ACM Transactions on Multimedia Computing, Communications and Applications , and conferences like the IEEE International Conference on Big Data and International Conference on Software and Computer Applications .

• Natural Language Processing
• Knowledge Extraction
• Semantic-based Information Access
• +12 more

Paul Buitelaar is a Professor in Data Analytics and Deputy Director of the Data Science Institute at the University of Galway. He leads the Multimodal Data Analysis research program through the Insight SFI Research Centre for Data Analytics, focusing on Natural Language Processing (NLP) for knowledge extraction and semantic information access. Co-Director of SFI Centre for Research Training in AI Co-PI of Insight SFI Research Centre for Data Analytics Collaborates with industry partners like Fidelity Investments, Huawei, and Irish Times His research spans ontology learning, linguistic linked data, and social media analysis, particularly in legal and healthcare domains. He developed the Saffron framework for knowledge extraction and contributed to EU-funded projects such as Monnet and MixedEmotions. His publications emphasize NLP applications in legal text analysis, taxonomy generation, and health content categorization. Projects like Pret-a-LLod and MixedEmotions highlight his interdisciplinary approach combining NLP with computer vision and sensor analysis. Paul's work integrates knowledge graphs and back-translation techniques to enhance machine translation, alongside unsupervised methods for analyzing social media health data.

Hossain ShahriarView profile

Professor

• mobile security
• web security
• mhealth security
• +7 more

Dr. Hossain Shahriar serves as Professor and Associate Director for the Center for Cybersecurity at the University of West Florida. His academic career bridges cybersecurity research with practical healthcare applications, focusing on critical protection of sensitive medical information systems. His research spans several vital cybersecurity domains: Mobile and web security mHealth applications security EHR systems and healthcare security HIPAA compliance checking mechanisms Malware analysis and vulnerability detection Quantum computing applications in security Blockchain security frameworks Dr. Shahriar's work demonstrates strong interdisciplinary focus, particularly at the intersection of healthcare and cybersecurity. His research portfolio shows increasing emphasis on quantum security applications, AI-powered defense mechanisms, and healthcare information protection systems. Recent publications reveal expertise in large language models for security applications, blockchain vulnerabilities analysis, and HIPAA compliance systems for mHealth applications. His scholarly output indicates strategic focus on both theoretical advancements and practical implementations addressing evolving cybersecurity threats across multiple domains. The research shows particular strength in developing automated systems for regulatory compliance checking and vulnerability detection. Dr. Shahriar has secured significant research funding from major federal agencies including National Science Foundation, National Security Agency, Department of Defense, and National Institutes of Health, demonstrating the practical relevance and impact of his work. His educational contributions include developing hands-on learning modules for cybersecurity education with emphasis on authentic learning experiences for students. He has held leadership roles as Program Chair (IEEE ICDH), Publication Chair (ACM SAC), and Proceedings Chair (IEEE COMPSAC), indicating his standing within the broader cybersecurity research community. His teaching portfolio includes Ethical Hacking, AI in Cybersecurity, Health Information Security and Privacy, and other critical cybersecurity courses developed with open-source materials.

• Linguistics
• Computational Linguistics
• Cognitive Science
• +7 more

Aniello De Santo is an Assistant Professor in the Department of Linguistics at the University of Utah, where he has been serving since July 2020. He earned his PhD in Linguistics from Stony Brook University in 2020 and is fluent in Italian, which informs his research on Italian syntax and processing. His research spans multiple disciplines with key interests in: Computational Linguistics and Syntax Cognitive Science and Theoretical Frameworks Artificial Intelligence and Machine Learning Applications Formal Language Theory and Complexity Psycholinguistic Processing Models De Santo's scholarly work demonstrates remarkable interdisciplinary integration, with publications in top journals across linguistics, cognitive science, and computer science. His recent work focuses on theoretical foundations in cognitive science (2024 publications on plausibility criteria and theoretical virtues), empirical investigations of Italian relative clause processing (2024), and innovative applications of machine learning techniques to linguistic problems. His research often bridges formal theoretical analysis with computational modeling and empirical validation. He has developed innovative educational approaches including an asynchronous general-education course "Language in the United States" (2023) and teaches a diverse range of courses in computational linguistics, syntax, and language studies. His extensive teaching portfolio includes Computers & Language, Introduction to Syntax, Computational Linguistics, and various research/thesis courses at both undergraduate and graduate levels. De Santo is actively engaged in community outreach as an organizer of the North American Computational Linguistics Open Competition (NACLO) for high school students, conducting practice sessions and hosting competition rounds since 2022. This work connects academic linguistics with pre-college education and demonstrates his commitment to promoting computational thinking in language studies.

• Medical Informatics
• E-learning
• Semantic Web
• +5 more

Biagio Lenzitti is a Researcher at the Department of Mathematics and Computer Science , University of Palermo, School of Basic and Applied Sciences. He has taught courses like Programming and Laboratory , Computer Networks , and Educational Planning of Information Systems since at least 2015. Current Office Hours: Monday 9:00-11:00, Studio 201 Contact: biagio.lenzitti@unipa.it | +3909123891101 His research focuses on medical text simplification , patient empowerment , and interactive educational tools , including the development of systems like: MED-VTD: Multilingual Medical Dictionary SimpleHealth Platform: Medical Text Simplification Tools U-MedSearch: Meta Search Engine for Medical Content ETN-FETCH Project: Future Education in Computing Recent publications address AI-driven health information systems , IoT data sharing , and conversational agents for medical education. Key collaborations include the Anghelos Communication Studies Center and European networks like ETN-FETCH . He has supervised theses on topics spanning semantic web applications , open data , and network security since at least 2005.