Find Academic Supervisors

• Database Systems
• Data-Driven Applications
• Declarative Programming
• +7 more

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
• +5 more

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 CimianoView profile

Professor

• Knowledge Representation
• Ontologies
• Knowledge-Based Natural Language Processing
• +6 more

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
• +3 more

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
• +3 more

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 HorasanView profile

Associate Professor

• Machine Learning
• Data Security
• Recommender Systems
• +3 more

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 OgiharaView profile

Professor

• Bioinformatics
• Computer Science
• Computer Theory
• +4 more

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 LeppänenView profile

Researcher

• Natural Language Generation
• Educational Data Mining
• Learning Analytics
• +3 more

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 CarrilView profile

Professor

• Information Retrieval (IR)
• Machine Learning
• Natural Language Processing
• +3 more

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
• +3 more

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. DavidsonView profile

Assistant Professor

• Political Sociology
• Social Movements
• Computational Sociology
• +7 more

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

Višnja Kabalin BorenićView profile

Lecturer

• Language in International Business
• Second Language Motivation
• English as a Global Language
• +5 more

Višnja Kabalin Borenić, Ph.D., is a Lecturer at the Department of Business Foreign Languages within the Faculty of Economics and Business, University of Zagreb. She holds a doctoral degree in European Studies (Languages and Cultures in Contact) from the University of Zadar (2013) and has participated in advanced training programs, including the Erasmus+ International Staff Week (2024) and Belt & Road Advanced Training (2019). Her research focuses on language in international business communication , L2 motivation , and globalization of English , with additional interests in academic skills and multilingualism. She teaches courses like Business English 1/2 , Advanced English , and English for International Communication . Ph.D., European Studies (Languages and Cultures in Contact), University of Zadar (2004-2013) M.A., English Language and Literature, University of Zagreb (earlier education) Her research explores language dynamics in business contexts , including student motivation and attitudes toward English, multilingualism, and the impact of EU policies on language education. She has published extensively on topics such as online learning tools , game-based language teaching , and language myths in tourism , with recent work on lexical error taxonomy adaptation (2025) and digital engagement (2024). Key trends in her publications include technology integration in language education , student-centered methodologies , and cross-cultural language challenges . Scientific awards include a 2005 University of Zagreb Certificate for advancing internationalization. She is a member of the European Association for Languages for Specific Purposes (AELFE) , Croatian LSP Teachers' Association , and the International Research Group on Management & Language (GEM&L) . Her teaching emphasizes business communication , academic writing , and language policy , with grants and projects tied to EU initiatives and the Bologna Process.

Tamara Sladoljev-AgejevView profile

Senior Lecturer

• Cognitive models of text comprehension
• Computational linguistics
• Academic writing
• +3 more

Tamara Sladoljev-Agejev, PhD, is a Senior Lecturer at the University of Zagreb, Faculty of Economics & Business, where she has taught English for Business and Economics since 1998 and served as a full-time faculty member since 2005. She is affiliated with the Business Foreign Languages Faculty and teaches courses ranging from Business English and Academic Writing to English for Accounting and Finance. Education 2018 – PhD in Cognitive Science, University of Zagreb 2002 – Postgraduate Diploma in European Studies (Diplôme universitaire, Etudes européennes), Université Panthéon-Assas Paris II & University of Zagreb 1993 – MA in English and French Language and Literature, University of Zagreb Research Focus Dr Sladoljev-Agejev’s scholarship lies at the intersection of cognitive science, computational linguistics, and applied linguistics. She investigates how readers—especially second-language learners—comprehend and summarize complex academic and professional texts, and how technology can support the development of these skills. Her work has contributed novel insights into rhetorical-structure-aware pedagogy, automated summary evaluation, and evidence-based instructional design for English for Academic/Specific Purposes (EAP/ESP). Publication Trends Across more than twenty peer-reviewed articles, book chapters, and conference proceedings (2004–2025), her research exhibits a consistent trajectory: from early classroom-centred studies on syllabus design and motivation, through empirical investigations of reading strategies and note-taking, to cutting-edge computational analyses of summary coherence and the integration of AI writing tools in multilingual university contexts. The disciplinary keywords that emerge are English for Academic/Specific Purposes, second-language reading and writing, automated text analysis, and higher-education pedagogy. Professional Service & Memberships Board member, Association of LSP Teachers at Higher Education Institutions (since 2020) Scientific Committee member, European Literacy Network II Summit (2024) Member, European Association for the Teaching of Academic Writing Former board member, Croatian Applied Linguistics Society (2010–2012) Member, Association for Computational Linguistics (2019–2020) Teaching & Student Support Dr Sladoljev-Agejev coordinates and teaches on multiple compulsory and elective English courses within the Bachelor and Master programmes at her faculty. Her office hours are held on Tuesdays or Wednesdays 12:30–14:00 (by email appointment), and she actively mentors students in academic writing and research methods. No specific doctoral or master’s advisees are listed in the provided material.

Marcellin AtemkengView profile

Associate Professor

• Big Data Analytics
• Statistical Signal Processing
• Artificial Intelligence
• +10 more

Marcellin Atemkeng is an Associate Professor in the Department of Mathematics at Rhodes University , with concurrent research roles at the Rhodes Centre for Radio Astronomy Techniques & Technologies and the National Institute for Theoretical and Computational Sciences (NITheCS) . He leads the Rhodes AI Research Group (RAIRG) , focusing on AI/ML applications to radio astronomy, marine ecology, and medical imaging. Education: MSc (University of Dschang), PhD (Rhodes University), PGDHE (Rhodes University) His research spans big data analytics , statistical signal processing , and deep learning , with applications in radio interferometry , medical imaging , and agricultural monitoring . His recent work emphasizes explainable AI , quantum NLP , and anomaly detection in ecological and industrial datasets. Key trends in his publications include data compression for radio astronomy, clustering algorithms for environmental monitoring, and hybrid AI architectures in precision agriculture and cybersecurity. His interdisciplinary approach bridges computational methods with real-world challenges in marine ecology and power generation . Awards: NRF Y rating (2022) Mathematical Sciences Early Career Fellowship (2022) Kambule Doctoral Award (2019, Deep Learning Indaba) SKA SA Best Poster Award (2013) He actively mentors graduate students across Africa through the African Institute for Mathematical Sciences (AIMS) network and contributes to NRF grant reviews , journal refereeing, and international conference keynotes.

Cedric du MouzaView profile

Professor

• Database Systems
• Data Mining
• Digital Humanities
• +6 more

Cédric du Mouza is a full Professor within the CEDRIC Laboratory at the Conservatoire National des Arts et Métiers (CNAM) in Paris, France. His research activities lie at the intersection of database systems, data mining, and digital humanities, with a strong emphasis on social network analysis, knowledge base population, and spatial/temporal data management. Education details are not explicitly provided in the source material. His research interests can be summarized as follows: Database Technologies: scalable indexing, distributed repositories, spatial and temporal data structures. Data Mining & Machine Learning: community detection, influence maximization, recommender systems. Digital Humanities: prosopographic databases, historical social networks, credibility assessment of historical sources. Social Media Analytics: Twitter user profiling, early detection of popular accounts, reducing filter bubbles. Across more than two decades, Prof. du Mouza has produced a rich scholarly record that blends theoretical advances with practical applications. Recent publications (2020-2024) highlight a methodological shift toward end-to-end evaluation frameworks for knowledge base population, uncertainty modeling in historical corpora, and real-time influence maximization in advertising ecosystems. These works are published in premier venues such as SIGIR , VLDB Journal , WISE , CIKM , and leading French conferences (EGC, BDA). No specific scientific awards, funded projects, or doctoral student names are mentioned in the supplied text. Prof. du Mouza collaborates extensively within interdisciplinary teams involving historians, computer scientists, and statisticians, reflecting CEDRIC’s integrative research culture. While no dedicated laboratory or team name is provided, his continuous affiliation with CEDRIC/CNAM confirms an active and ongoing role in both research and graduate-level education.