جستجوی استاد راهنمای دانشگاهی

Frank Soboczenskiمشاهده پروفایل

مدرس

• Transformers
• Large Language Models
• Machine Learning
• +۱۱ مورد دیگر

Dr. Frank Soboczenski is a Lecturer in the Department of Computer Science at the University of York, with an affiliate scientist position at King's College London supported by the NVIDIA GPU Grant Program. His research spans multiple domains including healthcare, space research, and quantum machine learning applications. He serves as a STEM scientist for NASA's and NOAA's GLOBE program and is actively involved in various NASA initiatives including the Frontier Development Lab. Dr. Soboczenski's primary research interests include Transformers and Large Language Models, Machine Learning with focus on Uncertainty Quantification and Explainability, and advanced applications of Quantum Machine Learning in Healthcare/Biomedicine and Space Research domains. His work on the RobotReviewer project applies Deep Learning and Natural Language Processing to healthcare. Previously, he has worked in Human-Computer Interaction, Cyber-Security, and Real-Time Systems in cooperation with organizations including the German Police Force, GCHQ, Rapita Systems, INRIA, Barcelona Supercomputing Center, and Airbus. His recent publications demonstrate a strong focus on applying machine learning techniques to healthcare informatics and space research, with particular emphasis on systematic reviews, clinical decision support, atmospheric retrieval for exoplanets, and medical data analysis. His work bridges the gap between theoretical AI advancements and practical applications in critical domains. NASA TechLeap Prize - Quantum Machine Learning NASA/NOAA/U.S. Department of State Outstanding Efforts to Mentor and Support Students (2019-present) NASA Frontier Development Lab AI Research Award of Merit Data Samaritan Award Steely Eyed Operator Award NASA Kennedy Space Center OsirisREx launch invitation SpaceApps 3M Thesis Competition UK National Winner (2013) Deggendorf Institute of Technology Robotics Challenge Award Dr. Soboczenski actively mentors students, as evidenced by his NASA/NOAA award for mentoring. His research is supported by the NVIDIA Corporation through the GPU Grant Program. He serves on multiple program committees including NeurIPS (2019-present), AAAI (2019-present), and various specialized workshops at major AI conferences. He is also involved in organizing NASA Space Apps challenges and serves on the NASA GeneLab Analysis Working Group on AI/ML. As an active member of the academic community, Dr. Soboczenski participates in numerous professional organizations including the NASA Nancy Grace Roman Spacecraft Science Working Group, NASA Technosignatures research group, IBM Quantum Researchers Program, PolarAI Research Group of the ACM, Huggingface BigScience Team, International Astronomical Union, and several others focused on AI and space research.

Khandakar Ahmedمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Cyber Security
• Digital Health
• +۸ مورد دیگر

Khandakar Ahmed is an Associate Professor of Information Technology at Victoria University's College of Arts, Business, Law, Education & IT (CoABLEIT) and Discipline Leader for Emerging Trends in Science, IT & Engineering at the Institute for Sustainable Industries and Liveable Cities (ISILC). His career spans roles at Victoria University (2017–present), RMIT University (2015–2017), and Shahjalal University of Science and Technology (2007–2015). Education: PhD in Electrical and Computer Engineering (RMIT University, 2014), MSc in Networking and e-Business Centered Computing (University of Reading, 2009), BSc in Computer Science and Engineering (Shahjalal University of Science and Technology, 2007) His research expertise includes Artificial Intelligence, Cyber Security, Digital Health, Federated Learning, Quantum Computing, and the Internet of Things . He has published over 100 refereed papers with 4,200+ citations (h-index 32) and secured $3.4 million+ in funding since 2018 from partners like Australia's Economic Accelerator and Western Health. His recent work focuses on AI-driven mental health analysis , blockchain security frameworks , and edge computing optimizations . He serves as lead supervisor for 6 PhD and 1 Master of Research student and has mentored over 100 students through industry-linked projects. Dr Ahmed actively contributes to editorial roles for leading journals such as Scientific Reports , IEEE Transactions , and ACM Computing Surveys . His research aligns with UN Sustainable Development Goals 3 (Good Health), 9 (Industry Innovation), and 11 (Sustainable Cities).

Mariam Adedoyin-Oloweمشاهده پروفایل

مدرس

• Data Mining
• Data Science
• Artificial Intelligence
• +۳ مورد دیگر

Mariam Adedoyin-Olowe is a Lecturer and Programme Leader for the MSc Artificial Intelligence at Birmingham City University . Her research focuses on Data Mining, Data Science, and Artificial Intelligence, particularly in analyzing social media data for event detection and decision-making support. Doctorate in Computing Science from Robert Gordon University First degree from University of Portsmouth Research Interests center on applying data mining techniques to Twitter data for tracking real-world events, sentiment analysis, and customer segmentation. Her work bridges computational methods with practical applications in politics, sports, and healthcare domains. Key Publications include studies on: Transformer-based transfer learning for cross-lingual news events AI-driven mental fatigue detection systems Temporal clustering methods for social media event resolution Deep learning approaches for sound classification Rule dynamics frameworks for political event detection Academic Affiliations include membership in BCU's Data Mining Group and Data Analytics & Artificial Intelligence Research Group, as well as former involvement with RGU's Data Science and Information Retrieval groups. She regularly reviews for international conferences and journals.

• Statistical Physics
• Complex Systems
• Percolation Theory
• +۷ مورد دیگر

Professor Peter Grassberger is a distinguished researcher at the Jülich Supercomputing Centre (JSC) within the Jülich Research Centre, one of Germany's leading national research institutions. His work spans multiple decades in theoretical physics and computational science, with a particular focus on complex systems and statistical mechanics. His research has significantly advanced our understanding of critical phenomena and phase transitions in various physical systems. Grassberger's research interests center around statistical physics and complex systems, with specializations in percolation theory, self-organized criticality, and random walk models. His work explores the universal properties of phase transitions, critical phenomena, and the behavior of complex networks. His theoretical contributions have provided fundamental insights into how simple local rules can lead to complex global behavior in physical systems. His recent work continues to push boundaries in understanding extreme-value statistics, entropy estimation methods, and the dynamics of interface models. Through his extensive publication record spanning over 40 years, Grassberger has established himself as a leading authority in computational statistical physics. His work shows consistent focus on understanding universal properties of critical systems while developing innovative computational methods to analyze complex phenomena. His research bridges theoretical physics with practical computational approaches, making significant contributions to both fundamental understanding and methodological development in the field. Grassberger's scientific impact is evident through his numerous influential publications and his development of important computational techniques like the Grassberger-Procaccia algorithm for estimating fractal dimensions. His work on self-organized criticality, particularly his 2022 review "Self-Organized Criticality, Three Decades Later," demonstrates his enduring contribution to this important field. His recent publications continue to explore cutting-edge questions in statistical physics, showing active engagement with contemporary research challenges.

Liana Ermakovaمشاهده پروفایل

مدرس

• Natural Language Processing
• Information Retrieval
• Artificial Intelligence

Liana Ermakova is a Lecturer in Computer Science at Université de Brest , associated with the research group Heritage and Creation in Text and Image (HCTI, EA 4249) . Her work focuses on Natural Language Processing , Information Retrieval , and Artificial Intelligence . Doctorate in Computer Science (2016) from the Toulouse Computer Science Research Institute (IRIT) with distinction Her research bridges computational methods and humanistic approaches, particularly through projects like JOKER (humour/wordplay translation) and SimpleText (scientific text simplification). Key publication themes include: Query expansion and tweet contextualization Humour recognition in multilingual contexts Scientific text simplification for non-experts Analysis of medical and pandemic-related discourse Evaluation metrics for summarization LLM reliability in scientific information access She actively contributes to the CLEF Association workshops and publishes in top IR/NLP venues. Current projects examine: Overcoming LLM hallucinations in scientific search Automated complexity spotting in abstracts Contextualization of short texts

Mohammad Zubairمشاهده پروفایل

استاد

• High performance computing
• digital libraries
• autonomic computing
• +۳ مورد دیگر

Mohammad Zubair is a Professor in the Department of Computer Science at Old Dominion University's College of Sciences, specializing in high performance computing, digital libraries, autonomic computing, and peer-to-peer networks. His work bridges scientific computing and information systems with significant federal and industry collaborations. Education: Ph.D. in Computer Science, Indian Institute of Technology Delhi (1987) B.S. in Electrical & Electronics Engineering, University of Delhi (1981) Research Interests: Dr. Zubair's expertise centers on high performance computing for scientific applications and large scale data analytics. He develops scalable algorithms for scientific simulations (e.g., particle physics, structural health monitoring) and advances digital library systems through autonomic computing, collaborative classification, and metadata extraction. His work integrates parallel computing with domain-specific challenges in finance, physics, and digital preservation. Publication Trends: His recent publications (2017-2008) show strong focus on GPU-accelerated scientific computing (particle colliders, structural monitoring) and computational finance (binomial option pricing), while maintaining digital library research in collaborative classification and metadata extraction. The work increasingly addresses energy efficiency in cloud systems and leverages parallel architectures from multicore CPUs to GPUs. Scientific Awards: IBM Faculty Award (2006) IBM Faculty Award (2005) Grants and Funding: Dr. Zubair has secured over $800,000 in research funding since 2004, including projects like 'Design AMD Impl Scalable Opt Kernels for Lrg Sc Simu USN Fund3d On Emer' ($24,890, 2017-2018), 'Graduate Research Award Program On Public Sector Aviation Issues' ($35,519, 2016-2018), and foundational digital library work with the Library of Congress and NASA. He frequently collaborates with K. J. Maly and R. Mukkamala on autonomic systems and metadata extraction. Labs and Teams: His research integrates with ODU's high performance computing initiatives and digital library projects, particularly through collaborations with federal agencies (NASA, Library of Congress) and industry partners like IBM. Current work focuses on GPU-accelerated scientific simulations and energy-efficient cloud management systems.

Waseem Afzalمشاهده پروفایل

مدرس ارشد

• Information Behaviour
• Information and Extremism
• Information Properties and Well-being
• +۴ مورد دیگر

Dr. Waseem Afzal is a Senior Lecturer at the School of Information & Communication Studies, Charles Sturt University , with over 15 years of academic experience. His research spans Information Behaviour , Extremism Informatics , and Library Impact Assessment , while his teaching focuses on Information Organization , Research Methods , and User-Centered Design . Research Pillars : Developing theoretical frameworks for information needs and information asymmetry Analyzing extremist content and virtual reality countermeasures Quantifying library community engagement and economic value of library services Advancing affective information behavior models integrating psychology Publication Trends : Recent work examines theoretical erosion in information science , Australian pandemic information flows , and regional academic librarian perspectives . His 39 publications (2006-2025) show sustained interdisciplinary collaboration with institutions in Australia, USA, and Sweden. Scientific Recognition : Elfreda Chatman Research Award (2013) International Academy of Business & Public Administration Disciplines Award (2007) Certificate of Recognition for Information Science Contributions (2022) Academic Leadership : Chairs School Research Management Committee Peer-review roles at ASIS&T and IFLA Supervises HDR students in information science Media commentator on social media illusions and library roles

• Immunology
• T-cell Biology
• Cell Signaling
• +۴ مورد دیگر

Yuan Lui is a Postdoctoral Research Fellow at the Radcliffe Department of Medicine , University of Oxford, working within the Davis Group focusing on T-cell Biology. Their research spans immunology, structural biology, and computational biology. Current focus on T-cell signaling, receptor-ligand interactions, and structural immunology. Historical work includes contributions to computer science and information retrieval. Research Trends : Recent publications emphasize T-cell receptor dynamics, immune checkpoint mechanisms, and structural analysis of surface proteins. Earlier work in computational linguistics highlights interdisciplinary expertise. Labs and Teams : Affiliated with the Davis Group , a multidisciplinary team investigating T-cell signaling and immune regulation.

Fabrice Boissierمشاهده پروفایل

دانشیار

• Formal Concept Analysis
• Topic Modeling
• Text Processing
• +۷ مورد دیگر

Fabrice Boissier is an Associate Professor at EPITA, specializing in digital methods for humanities and social sciences. He earned his PhD and MSc from Université Paris 1 Panthéon-Sorbonne, focusing on knowledge extraction and reuse in knowledge-intensive processes, enterprise modeling for decentralized organizations, and applications of formal concept analysis, natural language processing, and data visualization. Current Research: Formal Concept Analysis, Topic Modeling, Text Processing, Data Visualization, Knowledge Extraction, and Knowledge-Intensive Processes. Collaborations: Working with Nida Meddouri in the Security and Systems team and Marie Puren in the DMHSS (MNSHS) team. Teaching: Courses in algorithmics, computer architecture, programming languages, and operating systems at EPITA's Bachelor CyberSécurité program. Supervision: Mentoring research and industry interns from EPITA, Université de Sousse, and Université Paris 1 Panthéon-Sorbonne.

Enayat Rajabiمشاهده پروفایل

دانشیار

• Machine Learning
• Knowledge Engineering
• Data Analytics
• +۴ مورد دیگر

Dr. Enayat Rajabi is an Associate Professor of Data Analytics at the Shannon School of Business , Cape Breton University , Canada. He also serves as an Adjunct Professor at Dalhousie University and is affiliated with Nova Scotia Health as a scientist. His academic journey included a Ph.D. in Information and Knowledge Engineering from the University of Alcalá, Spain, and he has contributed extensively to machine learning and semantic web domains. Education: Ph.D. in Information and Knowledge Engineering, University of Alcalá, Spain (2015) Master of Software Engineering, Ferdowsi University of Mashhad, Iran (2004) Bachelor of Software Engineering, Razi University, Iran (2001) Dr. Rajabi's research focuses on machine learning , knowledge engineering , and semantic web applications in healthcare and smart cities. His work explores explainable AI frameworks, knowledge graph construction, and data-driven solutions for sustainable transportation and clinical decision support systems. His recent publications highlight trends in knowledge graph integration with large language models for healthcare, graph neural networks , and predictive analytics in urban environments. He has secured significant grants, including the NSERC Discovery Grant and Mitacs Research Training Award , to advance these domains. Scientific Contributions: NSERC Discovery Grant (2020-2025) - Semantic Web Analysis over Nova Scotia Open Data ($156,000) New Health Investigator Grant (2022-2024) - Machine Learning for ALC Patients ($97,418) Mitacs Globalink ($4,250) - Graph Neural Networks CBU RISE grants for Explainable Clinical Decision Support Systems and Multi-Label Text Classification Dr. Rajabi has mentored numerous research assistants across projects and maintains active collaborations with institutions in Canada, Spain, and Iran. His technical expertise spans Python, Tableau, Databricks, and PySpark, with teaching responsibilities in Predictive Analytics , Data Visualization , and Quantitative Methods .

• Digital Humanities
• Computational Humanities
• Text Mining
• +۱۰ مورد دیگر

Manuel Burghardt is a full-time Professor of Computational Humanities at the Institute of Computer Science , Leipzig University . He coordinates the Bachelor's and Master's programs in Digital Humanities and serves as a spokesperson for the GI's Computer Science and Digital Humanities Group and the Forum for Digital Humanities Leipzig . PhD in Information Science (summa cum laude) from University of Regensburg Magisterstudium in Information Science, English Linguistics, General Linguistics, and Corpus Linguistics at University of Regensburg Burghardt's research spans multiple Computational Humanities domains: Digital Environmental Humanities : Integrating Computational Literary Studies with Biodiversity Research via NLP and Information Retrieval Immersive Humanities : Applying AR/VR/XR, Eye Tracking, and Tangible Interfaces for Digital Humanities Text Mining & NLP : Focused on text reuse detection, similarity analysis, and sentiment modeling Video Analytics : Analyzing news videos and cinematic media through Distant Viewing techniques Theory of Digital Humanities : Investigating methodological foundations and scientometric trends Computational Game Studies : Multimodal empirical analysis of games Computational Spatial Humanities : Spatial data analysis in humanities contexts His recent publications demonstrate strong focus on: Improving OCR for historical documents (2015-2020) Computational drama analysis using sentiment and text mining techniques (2016-2019) Music information retrieval applications in folk song and manuscript analysis (2015-2019) Development of specialized tools for humanities data processing

Joachim Baumeisterمشاهده پروفایل

استاد

• Semantic Information Systems
• Knowledge Graphs
• Deep Learning
• +۵ مورد دیگر

Joachim Baumeister is a Professor at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems within the Institute of Computer Science at the University of Würzburg's Faculty of Mathematics and Computer Science. While his primary employment since September 2010 has been at denkbares GmbH, a company specializing in knowledge-based systems, he continues to regularly give lectures at the university. His research focuses on Semantic Information Systems, Knowledge Graphs, Deep Learning applications, Natural Language Processing, and Knowledge-based Configuration for Industry 4.0. Professor Baumeister's work bridges theoretical AI research with practical industry applications, particularly in knowledge-based configuration systems and semantic technologies. His recent publications (2020-2024) reveal a strong emphasis on product configuration systems, semantic knowledge representation, regulatory document processing, and knowledge-based systems. His research has evolved from foundational work on semantic wikis and knowledge engineering to more recent applications involving deep learning and large language models, demonstrating adaptability to emerging technologies while maintaining focus on practical knowledge representation problems. Professor Baumeister's work demonstrates significant contributions to case-based reasoning, knowledge configuration, and semantic technologies, with applications spanning regulatory compliance, industrial configuration systems, and document processing. His current research areas include: Semantic Information Systems and Knowledge Graphs Deep Learning for Image Recognition and Language Understanding Knowledge-based Configuration for Industry 4.0 Natural Language Processing Intelligent Personal Assistants and Chat Bots Though specific students aren't listed in the provided information, Professor Baumeister actively invites students to contact him regarding projects, bachelor theses, and master theses in his areas of expertise. His work at denkbares GmbH focuses on the design, implementation, and evolution of knowledge-based systems and semantic information systems.

Jörg-W. Linkمشاهده پروفایل

مدرس

• Educational History
• School Reform
• Pedagogy
• +۳ مورد دیگر

PD Dr. Jörg-W. Link is a Lecturer at the Department of Education, University of Potsdam, specializing in educational history, school reform, and reform pedagogy . He habilitated at the Faculty of Humanities and Social Sciences, University of Augsburg (2022) with a thesis on Conditions for the Success of School Reform , focusing on educational history as a framework for understanding school development. His career spans roles as research assistant (since 2000), acting director of the Rochow Museum Reckahn (2001), and guest lecturer at the University of Kassel (2005). Education: Teacher training in history and German (Mainz, Bonn, Marburg 1985-1992); Doctorate (University of Potsdam, 1998) Research: Analyzes educational reform through historical lenses, particularly in the Weimar Republic and National Socialist contexts. Key publications include works on school camps, media pedagogy, and comparative educational systems. Recent articles explore radio as an educational medium (2022), Weimar school camps (2025), and transatlantic educational exchanges (2024), emphasizing interdisciplinary methodologies and long-term pedagogical impacts. Scientific Recognition: Awarded Potsdam University Society Prize (1998/99) and nominated for Heinz Maier-Leibnitz Prize (2000).

Ryan Marcusمشاهده پروفایل

استادیار

• Machine Learning
• Query Optimization
• Index Structures
• +۴ مورد دیگر

Ryan Marcus is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research focuses on integrating machine learning into data management systems to create adaptive tools that optimize hardware utilization, invent novel processing strategies, and interpret user intentions. Currently based in Office 407, Amy Gutmann Hall, he actively explores query optimization, index structures, intelligent clouds, programming language runtimes, program synthesis for data processing, and reinforcement learning applications to systems challenges. Key research themes include machine learning for databases , learned query optimization , intelligent cloud systems , and blockchain adaptability . Scientific achievements include the Best Paper Award at SIGMOD '21 for the Bao system and the development of AutoSteer, a cross-database learned query optimizer. Notable PhD advisees co-advised include Peizhi Wu (with Zack Ives), Jeffrey Tao (with Andrew Head), and Zixuan Yi (with Zack Ives). His recent work, presented at venues like VLDB and SIGMOD, emphasizes scalable LLM-augmented data systems (ScaleLLM), robust cardinality estimation, and adaptive Byzantine fault-tolerant consensus (BFTBrain). For full system evaluations, he created testing environments such as BFTGym. Contact: rcmarcus@seas.upenn.edu

• Information Retrieval
• Recommender Systems
• Fairness and Ethics in AI
• +۴ مورد دیگر

Fernando Diaz is an Associate Professor at Carnegie Mellon University's Language Technologies Institute within the School of Computer Science . His research spans Information Retrieval , Recommender Systems , and the Societal Impacts of Artificial Intelligence , with a focus on fairness, ethics, and evaluation metrics. Education : PhD in Computer Science from University of Massachusetts Amherst Research Interests include: Information Retrieval (web search, crisis informatics, search latency) Recommender Systems (multi-interest personalization, cultural content recommendation) AI Fairness (exposure fairness, data minimization, bias mitigation) Evaluation Methodology (metric robustness, contextual meta-evaluation) Human-AI Collaboration (mouse behavior analysis, preference-based evaluation) Retrieval-Augmented Generation (fair ranking, model synthesis) His recent work analyzes scaling laws , tip-of-the-tongue retrieval , and multisided fairness in AI systems. Diaz also explores the cultural implications of AI in music recommendation and content curation. Teaching : Leads courses on Search Engines and LTI Colloquium Advisees : Shaily Jagat Bhatt, Athiya Deviyani, Alfredo Gomez, Jessica Huynh, To Eun Kim