جست‌وجوی استاد و پژوهشگر

Atefeh Farzindarمشاهده پروفایل

مدرس پاره‌وقت

• Natural Language Processing
• Health Informatics
• Artificial Intelligence
• +۴ مورد دیگر

Atefeh Farzindar is a Part-Time Lecturer in the Department of Computer Science at the University of Southern California (USC), affiliated with the Viterbi School of Engineering. She holds a PhD in Computer Science from the University of Montreal and a Doctorate in automatic summarization of legal documents from Paris-Sorbonne University. Her academic roles include Adjunct Professorships at the University of Montreal and Polytechnique Montréal, alongside leadership positions in organizations like the Canadian Artificial Intelligence Association and the Natural Sciences and Engineering Research Council of Canada (NSERC). Her research focuses on Natural Language Processing (NLP), Health Informatics, Machine Learning, and their applications in healthcare and social media analysis. She has authored/co-authored influential books including Natural Language Processing for Social Media and Transforming Healthcare with Big Data and AI . Her work bridges technology and art, reflected in her painting exhibitions and a published collection of science fiction short stories, Gazelle in New York . Farzindar actively engages in industry-academia collaboration, serving as CEO and co-founder of NLP Technologies Inc., and leading non-profit initiatives like "Dreams Without Borders" to promote ethical AI development. She has received awards such as the Femmessor-Montréal contest for balancing innovation in technology and art.

Ian Laneمشاهده پروفایل

دانشیار

• Natural Language Processing
• Speech Recognition
• Machine Translation
• +۵ مورد دیگر

Ian Lane is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin Engineering school, serving as Program Director for the Natural Language Processing Professional Master's Degree Program. He joined UCSC in Fall 2022 after an extensive career spanning academia and industry. His research centers on computational systems that understand spoken human language, spanning speech recognition, transcription, meaning interpretation, and contextually appropriate responses. Key research areas include: Natural Language Processing for real-world applications Conversational AI systems development Speech-to-speech translation technologies Multimodal interaction (audio-visual integration) Language technologies that learn through real-world interaction His recent publications demonstrate strong focus on hallucination detection in LLMs, tabular data understanding, explainable AI, and robust speech recognition systems. Current work emphasizes "in the wild" language technologies that adapt through user interaction. Dr. Lane has received recognition through impactful industry applications including Jibbigo (the first mobile speech translation app) and military translation systems deployed in Iraq and Afghanistan. He actively mentors students and collaborates across UCSC's Silicon Valley Campus programs including Games and Playable Media and Human-Computer Interaction. His vision includes integrating NLP with virtual environments for language learning and skill acquisition.

• Machine Learning
• Sentiment Analysis
• Hybrid Filtering Algorithms
• +۵ مورد دیگر

Hayder Murad is a researcher with expertise in machine learning, sentiment analysis, and hybrid filtering algorithms, particularly applied to intelligent tutoring systems and medical diagnostics. His work bridges computational methods with practical applications in education and healthcare. He earned his PhD from the University of Portsmouth in 2019 with a thesis titled An integrated approach to recommending online video materials through sentiment analysis and hybrid filtering algorithms . His recent publications focus on knowledge graph construction, large language model applications, and open science initiatives. Notable contributions include enhancing systematic literature reviews, improving dataset interoperability, and advancing FAIR data principles across disciplines. Research Interests Machine learning for healthcare diagnostics Hybrid recommendation systems Knowledge graph development Open science infrastructure Intelligent tutoring systems LLM-based research synthesis

• Data Mining
• Machine Learning
• Natural Language Processing
• +۵ مورد دیگر

Luca Cagliero is an Associate Professor in the Department of Control and Computer Engineering at Politecnico di Torino (Polytechnic University of Turin), Italy. His research spans multiple domains within computer science, with particular expertise in data mining, machine learning, natural language processing, and multimodal analysis. He has established a prolific research career with over 150 publications spanning from 2009 to the present, demonstrating consistent scholarly productivity. Dr. Cagliero's research interests focus on the intersection of artificial intelligence and practical applications. His work addresses fundamental challenges in data mining, information retrieval, and educational technology, with recent publications showing increasing emphasis on large language models, multimodal analysis, and explainable AI. He has made significant contributions to text summarization techniques, database systems, and applying machine learning to educational contexts. His recent publications (2023-2025) demonstrate a clear research trajectory toward multimodal AI systems, with particular attention to the integration of vision and language processing. His work spans theoretical contributions in machine learning methods as well as practical applications in educational technology, social media analysis, and document understanding. The breadth of his collaborations across different application domains indicates a versatile research profile that bridges theoretical and applied computer science. Dr. Cagliero has mentored numerous researchers who have become his frequent collaborators, including Lorenzo Vaiani, Moreno La Quatra, and Davide Napolitano. His work has appeared in top-tier venues including ACL, IEEE Transactions on Knowledge and Data Engineering, and Expert Systems with Applications, reflecting the high quality and impact of his research contributions.

• Database Systems
• Data-Driven Applications
• Declarative Programming
• +۷ مورد دیگر

Nitin Gupta is a researcher with affiliations spanning institutions like Google, Cornell University, and Carnegie Mellon University. His work bridges theoretical and applied computer science, focusing on database systems, data-driven applications, and knowledge graphs. Research interests include: Structured data on the web and semantic publishing Scalability in virtual environments and declarative programming Integration of large language models (LLMs) into scholarly workflows FAIR data principles and research data management Recent publications explore hybrid question answering datasets, LLM applications for literature reviews, and knowledge graph construction for software engineering. His contributions emphasize machine-actionable metadata, open science, and enhancing reproducibility through standardized research artifacts. Collaborations span institutions and disciplines, with coauthorships on topics like: Game development and data coordination Non-invasive brain stimulation analysis Neuro-symbolic approaches for digital libraries Ontology matching and FAIR digital objects

• Uncertain Data Management
• MapReduce Algorithms
• Social Choice Theory
• +۵ مورد دیگر

Anish Das Sarma is a researcher affiliated with Google, USA , specializing in uncertain data management, MapReduce algorithms, and knowledge graph systems. He earned a PhD from Stanford University in 2010 under the supervision of Jennifer Widom and Alon Halevy, with a dissertation on "Managing Uncertain Data." His career spans collaborations with leading institutions, focusing on scalable data integration, social choice theory, and machine learning applications in scholarly knowledge organization. PhD in Computer Science, Stanford University (2010) Key collaborations: Stanford, Google Research, NFDI4DataScience His research interests intersect uncertain data modeling , MapReduce optimization , and large language model applications for scientific synthesis. Recent work includes FAIR data frameworks, ontology learning, and clinical entity linking. Article trends highlight his evolution from foundational database systems (2004-2015) to modern applications of LLMs in scholarly communication (2023-2024). Key areas: scalable algorithms, research data management, and ethical AI.

Philipp Cimianoمشاهده پروفایل

استاد

• Knowledge Representation
• Ontologies
• Knowledge-Based Natural Language Processing
• +۶ مورد دیگر

Philipp Cimiano is a Professor at the Faculty of Engineering, Bielefeld University, and leads the Semantic Databases Group. He holds additional roles as Coordinator of the Cognitive Interaction Technology Center (CITEC) and Director of the Joint Artificial Intelligence Institute (JAII). His research focuses on the intersection of language, semantics, and knowledge representation, with applications in Explainable AI , Knowledge Graphs , and AI in Medicine . Education: University of Stuttgart, University of South Australia, Karlsruhe Institute of Technology (KIT) Key Research Areas: Knowledge Representation, Ontologies, Explainable AI, Clinical Decision Support His recent work emphasizes dialogue-based XAI , federated learning , and semantic data integration . He has secured funding from the German Research Foundation (DFG) and the European Union for projects like TRR 318 "Constructing Explainability" and Pret-a-LLOD. Scientific Awards Carl Adam Petrie Prize, KIT Faculty of Business and Economics Editorial Roles Co-editor, Journal of Applied Ontology Area Editor, Semantic Web Journal Editorial Board, Journal of Web Semantics Notable Projects TRR 318 (Subprojects B01, C05, INF) 3B: Bots Building Bridges for online deliberation LLM4KMU: Open Source LLMs for SMEs

• Natural Language Processing
• Text Mining
• Machine Learning
• +۳ مورد دیگر

Yuping Wu is a researcher specializing in Natural Language Processing , Text Mining , and Machine Learning . Her work focuses on enhancing large language models , data augmentation , and attention mechanisms in computational linguistics and clinical domains. She actively contributes to peer-reviewed research outputs in conferences like EACL , ProbSum , and journals such as Entropy . Research areas: Natural Language Processing , Machine Learning , Data Augmentation , Clinical Informatics , Large Language Models , and Text Mining . Key publications: Extractive summarization, clinical named entity recognition, data augmentation with black-box LLMs, attention networks in computer vision, and cost index prediction models. Collaborations: Active with teams like PULSAR and researchers including X. Zeng , G. Nenadic , and V. Schlegel . Her research spans artificial intelligence , computer vision , and healthcare text mining , often leveraging transformer architectures and neural network innovations . She explores applications in clinical data summarization and construction cost forecasting, demonstrating interdisciplinary impact through UN Sustainable Development Goals (SDGs) related to technological advancement and healthcare.

• Turkish Language Education
• Educational Technology
• Augmented Reality in Education
• +۳ مورد دیگر

Assoc. Prof. Bilal Şimşek is an active faculty member at Akdeniz University's Faculty of Education, Department of Turkish Language Education, where he has served as a Research Assistant since 2015. His academic career focuses on the intersection of Turkish language education and emerging technologies, particularly augmented and virtual reality applications in educational settings. His educational background includes: Doctorate (2018-2022): Akdeniz University, Institute of Educational Sciences, Turkish and Social Sciences Education Postgraduate (2015-2018): Akdeniz University, Institute of Educational Sciences, Turkish Language Education Undergraduate (2012-2015): Anadolu University, Faculty of Open Education, Turkish Language and Literature Undergraduate (2010-2014): Necmettin Erbakan University, Faculty of Education, Turkish Teaching Şimşek's research primarily investigates how technology-enhanced learning environments impact language acquisition, reading comprehension, and writing skills. His work demonstrates particular expertise in augmented reality applications for storybooks and vocabulary development, with significant contributions to understanding how digital and traditional reading mediums affect cognitive processing. His research methodology frequently employs comparative studies examining both quantitative metrics (citation counts, H-indices across multiple databases) and qualitative classroom observations. His scholarly output shows consistent growth with 64 publications indexed in Web of Science, demonstrating strong international recognition. The publication trend reveals increasing focus on immersive technologies in language education, with recent works exploring virtual reality's impact on story retelling performance and the temporal effects of augmented reality experiences on cognitive load. Among his notable recognitions is the TUBITAK Domestic Doctoral Scholarship (2211) awarded from 2018-2022. His research has been supported through multiple funded projects including EU-supported initiatives like 'Mapping Teacher Training in Europe' and institutional projects documenting regional dialects in Antalya province. As an academic advisor, he currently supervises postgraduate research, most recently guiding B.ACAR's 2025 thesis on metacognitive writing strategies and writing anxiety. His teaching portfolio spans both undergraduate and postgraduate levels, covering courses in academic writing for Turkish education, augmented/virtual reality applications, reading education, Turkish language, and cultural geography.

Fahrettin Horasanمشاهده پروفایل

دانشیار

• Machine Learning
• Data Security
• Recommender Systems
• +۳ مورد دیگر

Dr. Fahrettin Horasan is an Associate Professor in the Department of Computer Engineering at the Faculty of Engineering and Natural Sciences . His research focuses on applied computational methods in data security, machine learning, and information retrieval systems. Specialized in medical image watermarking and encryption using matrix decomposition techniques (SVD, ULV) Developed novel collaborative filtering recommender systems with hybrid approaches and matrix approximation Contributions in sentiment analysis for healthcare and e-commerce domains His recent publications highlight interdisciplinary work combining cryptology, biomedical imaging, and large-scale data processing. Notable methods include chaotic system-based encryption, latent semantic indexing, and gradient boosting for darknet traffic analysis. While specific awards, educational background, and student advisement details are not publicly detailed in the provided texts, his technical output reflects active engagement in computational science advancements.

Mitsunori Ogiharaمشاهده پروفایل

استاد

• Bioinformatics
• Computer Science
• Computer Theory
• +۴ مورد دیگر

Mitsunori Ogihara is a Professor of Computer Science at the University of Miami's College of Arts and Sciences, with secondary appointments in Electrical and Computer Engineering (ECE), Molecular and Microbiology (MMI), College of Arts (CoA), and Human Genetics and Genomics (HGG). He serves as Director of Workforce Development and Education at IDSC and Director of Graduate Studies for the Computer Science department. Education Ph.D. in Computer Science, Tokyo Institute of Technology (1993) Research Interests Professor Ogihara maintains a diverse research portfolio spanning multiple domains of computer science. His work demonstrates significant contributions to: Theoretical Computer Science - with publications on computational complexity and dynamical systems Bioinformatics and Medical Data Science - pioneering work on multi-omics data integration for Type 1 Diabetes biomarker discovery Data Mining and Machine Learning - developing novel algorithms for knowledge extraction from complex datasets Music Information Retrieval - editing the seminal book "Music Data Mining" and applying computational methods to music analysis Digital Humanities - advancing NLP techniques for historical Japanese text processing Publication Trends Professor Ogihara's recent publications reveal a strong interdisciplinary trajectory , increasingly connecting computer science with biomedical applications and cultural analytics. His work on computational data augmentation for small biomedical datasets represents a significant methodological innovation with potential clinical impact. Simultaneously, he continues to advance foundational work in computational complexity while expanding applications in entertainment analytics and historical text processing, demonstrating remarkable intellectual breadth. Teaching and Mentorship Professor Ogihara has supervised numerous PhD students who have secured positions at leading technology companies (Google, Amazon, IBM) and academic institutions worldwide. His commitment to education extends to textbook authorship, including "Exploring Data Science with R and the Tidyverse" and his upcoming "An Introduction to Theory of Computation," which aim to make complex computational concepts accessible to undergraduate students.

Leo Leppänenمشاهده پروفایل

پژوهشگر

• Natural Language Generation
• Educational Data Mining
• Learning Analytics
• +۳ مورد دیگر

Leo Leppänen is a Postdoctoral Researcher at the University of Helsinki's Department of Computer Science, currently working at the MOOC Center. His research spans automated natural language generation, educational data mining, learning analytics, and computer science education. PhD in Computer Science (2023) MSc in Computer Science (2017) BA in Language Technology (2015) His work investigates AI-generated content applications, particularly LLMs' impact on education and journalism. Recent projects include analyzing student confidence gaps, studying AI ethics in MOOCs, and developing cross-lingual embeddings for news media. Research trends show increasing focus on Large Language Models (LLMs), with studies comparing pre- and post-LLM student responses, analyzing LLM integration in classrooms, and exploring AI's role in journalism. His publications span conferences like AIED, ITiCSE, and Koli Calling. Pro Gradu Award for Exceptional Master's Thesis (2017) Researcher of the Year Award (2018) Active in academic service, Leppänen serves as Programme Committee Member for ACM conferences and peer-reviewer for Journalism Practice journal. His projects include RAPHAEL (Planetary Health Education) and Generative AI in Journalism (2025-2028).

David Enrique Losada Carrilمشاهده پروفایل

استاد

• Information Retrieval (IR)
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

David Enrique Losada Carril is a Full Professor in Computer Science and Artificial Intelligence at the CiTIUS Research Center of the University of Santiago de Compostela (Spain). He holds a PhD in Computer Science (2001, University of A Coruña) and has been active in Information Retrieval (IR) research since joining the university in 2003 as a senior research fellow under the Ramón y Cajal program. BS and PhD (with honors) from University of A Coruña ACM Senior Member awardee (2011) Co-founder of the eRisk CLEF Lab for early risk detection on the internet Active in IR community with roles in ACM SIGIR, ECIR, and CLEF Research Focus spans probabilistic IR models, novelty detection, health search technologies, and mental health analysis via social media monitoring. His recent work explores large language models for depression symptom assessment and misinformation detection in health contexts. Scientific Leadership includes: Principal Investigator for projects like Big-eRisk and LudoTrack Co-author of over 20 top-tier publications (SIGIR, ECIR, ACL, ECIR, Springer series) Technical Contributions feature real-time social media analysis platforms (e.g., Catenae , eXtream ) and benchmark datasets like DepreSym for depression detection.

• Machine Learning
• Explainable AI
• Fairness in AI
• +۳ مورد دیگر

Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University, Director of the Stanford AI Lab (SAIL), and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI). He also serves as Chief Scientist at Visual Layer and Virtue AI, and is a Member of the National Academy of Engineering. His research centers on Machine Learning Methods, Explainability, Fairness & Ethics of AI, and Machine Learning Systems. He develops interpretable and reliable models, addresses algorithmic fairness, and builds efficient large-scale ML systems through frameworks like XGBoost. His work bridges theoretical rigor with real-world applications in healthcare and human-centered AI. His recent publications (2023–2025) demonstrate leadership in generative AI evaluation, model reliability, and ethical frameworks. Key trends include developing live benchmarks for research synthesis, on-device calibration techniques, multi-objective optimization with constraints, and societal impact assessment tools—showcasing a trajectory from foundational ML systems to responsible AI deployment. Honors include: Member of the National Academy of Engineering Details about his advising and grant activities were not provided in source materials, though his leadership roles indicate extensive mentorship and funding oversight. As Director of SAIL, he shapes one of the world’s premier AI research centers, while his HAI fellowship drives interdisciplinary initiatives ensuring AI advances human welfare. His industry roles at Visual Layer and Virtue AI translate academic research into practical AI solutions.

Thomas R. Davidsonمشاهده پروفایل

استادیار

• Political Sociology
• Social Movements
• Computational Sociology
• +۷ مورد دیگر

Thomas R. Davidson is an Assistant Professor in the Department of Sociology at Rutgers University. His research merges computational methods with social science to investigate far-right activism, populism, hate speech, and generative artificial intelligence applications. PhD in Sociology from Cornell University (2020) Focuses on algorithmic impacts, social media dynamics, and bias in AI systems Research Interests include: Far-Right Mobilization : Analyzing how platform algorithms affect activist engagement Hate Speech Detection : Addressing racial bias in AI classifiers Generative AI : Developing methodological frameworks for sociological applications Recent Publications span topics such as: Algorithmic feedback loops in social movements European populist party digital strategies Context-aware content moderation tools AI-driven historical knowledge synthesis Scientific Awards: Foundational Integrity Research Award (Meta, 2023) Teaching Roles: Graduate and undergraduate courses in Computational Sociology, Statistics Methods, and Political Sociology. Contact: thomas.davidson@rutgers.edu | Personal Website