جستجوی استاد راهنمای دانشگاهی

• Educational Data Mining
• Artificial Intelligence in Education
• Learning Analytics
• +۷ مورد دیگر

Steven Moore is a prolific researcher in educational data mining, AI in education, and learning analytics. He has co-authored 43 indexed works from 2011 to 2025, collaborating extensively with scholars such as John C. Stamper and Huy Anh Nguyen. His research addresses crowdsourcing in education, automated question generation, knowledge component extraction, and the application of generative AI to instructional design. Research Interests: Educational Data Mining & Learning Analytics AI-driven Assessment & Question Generation Crowdsourcing & Learnersourcing at Scale Generative AI for Curriculum and Content Design Human-Computer Interaction in Learning Systems Across his recent publications, Moore explores how large language models and automated toolkits can enhance the quality and scalability of educational content. His work increasingly integrates advanced NLP techniques—such as GPT-4-based evaluation—with rule-based systems to support both formative and summative assessment processes.

• Complex Geometry
• Kähler Geometry
• Stability of Algebraic Varieties
• +۵ مورد دیگر

Chi Li is an Associate Professor in the Department of Mathematics at Rutgers, The State University of New Jersey. Their research focuses on Complex Geometry, including canonical metrics in Kähler geometry, stability of algebraic varieties, and pluripotential theory. They hold an office in HLL-511 and can be contacted at cl1412@rutgers.edu. Research interests emphasize foundational aspects of geometric analysis with applications to algebraic structures. Recent work explores interdisciplinary applications in AI, such as anomaly detection, cross-lingual NLP, and large language model optimization. Key contributions include frameworks for domain-oriented model compression and dynamic routing in mixed LLMs. Publications span 2020–2025, addressing topics from turbulence forecasting to spatio-temporal recommendation systems. Notable trends include integration of geometric principles with machine learning methodologies. Extensive contributions to log anomaly detection (GLAD, Cat), time series meta-contrastive learning, and graph-based cross-lingual transfer. Active in developing interpretable imitation learning and uncertainty quantification techniques for in-context learning.

Thomas Steffekمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Healthcare Informatics
• Clinical Decision Making
• +۳ مورد دیگر

Thomas Steffek is a researcher at Beuth University for Applied Sciences, affiliated with the Data Science and Text-based Information Systems (DATEXIS) group. He holds a Bachelor of Science (2019) and Master's degree (2023) from the same institution. His research focuses on applying Natural Language Processing (NLP) to healthcare challenges, including clinical outcome prediction, assertion detection in medical notes, and contextualized document retrieval for clinical decision-making. He has contributed to projects like Smart-MD and explored data drift in clinical data analytics. His work spans healthcare informatics, machine learning applications in medicine, and neural network-based retrieval systems. Collaborations include projects with Alexander Löser and Paul Grundmann, addressing topics like media bias detection and in-database text mining (e.g., INDREX). While no formal teaching roles are explicitly stated, he has tutored peers during his studies.

Akshat Guptaمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Machine Learning
• Data Science
• +۳ مورد دیگر

Akshat Gupta is a Master's student in Data Science at Beuth University of Applied Sciences and serves as a research assistant at the Data Science and Text-based Information Systems (DATEXIS) group. He previously spent four years at Latenview Analytics in India as a Business Analyst, focusing on Supply Chain and Web Analytics. Currently, he collaborates with PhD student Benjamin Winter on applying transformer models in NLP. His research interests span NLP, machine learning, and text-based systems, with projects including INDREX (in-database text mining) and TASTY (interactive entity linking). He also contributes to healthcare NLP applications like clinical outcome prediction and Smart-MD systems. Beyond academia, Akshat enjoys strategy games like DotA and cooking spicy Indian cuisine. Education: Master's in Data Science, Beuth University of Applied Sciences Research Focus: Akshat’s work integrates transformer models for NLP tasks, with applications in healthcare and database systems. His projects emphasize scalable text mining (e.g., IDEL for neural entity linkage) and contextualized document representations for clinical decision-making. Current trends in his research include evaluating embedding combinations and explaining transformer architectures. Labs/Teams: Active member of the DATEXIS research group, contributing to initiatives like the COVID19 Literature Search Engine and NOHATE bias detection systems.

Khaled Sayedمشاهده پروفایل

استادیار

• Electrical and Computer Engineering
• Computational Biology
• Data Science
• +۳ مورد دیگر

Khaled Sayed is an Assistant Professor in the Department of Electrical & Computer Engineering and Computer Science at the University of New Haven's Tagliatela College of Engineering. He holds a Ph.D. in Electrical and Computer Engineering from the University of Pittsburgh (2019) and a postdoctoral background in computational biology at the University of Pittsburgh's Department of Computational and Systems Biology. His research focuses on computational modeling of complex biological systems, machine learning for multi-omics data analysis, and neural engineering applications. He teaches courses in Deep Learning and Natural Language Processing within the university's data science program. Education: B.S. in Biomedical Engineering, Cairo University, Egypt M.Sc. in Biomedical Engineering, Cairo University, Egypt Ph.D. in Electrical and Computer Engineering, University of Pittsburgh, USA Research Interests: Dr. Sayed's work bridges electrical engineering, computational biology, and data science. Key areas include automated systems biology modeling, analysis of microbiome and multi-omics datasets, arrhythmia classification via signal processing, and development of computational tools for biomedical research. His methodologies integrate machine learning, systems biology, and hardware-accelerated simulation frameworks. Teaching & Engagement: He actively engages undergraduate and graduate students in data science education, emphasizing hands-on learning in deep learning and NLP. His courses prepare students for interdisciplinary challenges at the intersection of computing and life sciences. Labs & Collaborations: While specific lab affiliations are not detailed, his research involves collaborations in computational systems biology, microbiome analysis, and translational biomedical engineering projects.

Wallapak Tavanapongمشاهده پروفایل

استاد

• Data Sciences
• Applied Machine Learning
• Multimedia Systems
• +۳ مورد دیگر

Wallapak Tavanapong is a Professor at Iowa State University, specializing in Data Sciences, Applied Machine Learning, and Multimedia Systems. His work integrates computational techniques with medical imaging and political science applications. He leads research in automated quality assessment for colonoscopy procedures and interpretable AI models for healthcare diagnostics. Research focuses on improving medical imaging analysis through machine learning, including real-time feedback systems during colonoscopies and developing datasets like IDCIA for cellular image analysis. He has pioneered methods for handling class imbalance in medical image classification and leveraging social media data for policy agenda analysis. His recent articles emphasize interpretable AI (e.g., CountXplain), confusion-based training strategies, and visual concept-based active learning. His work bridges technical innovation with clinical and policy applications, addressing challenges in healthcare quality and data-driven decision-making. No scientific awards are explicitly listed in the provided materials. His research has been applied in multi-center clinical trials for colonoscopy improvement and has contributed to advancements in endoscopic procedure monitoring systems.

Rabia Bashirمشاهده پروفایل

پژوهشگر ارشد

• Software Engineering
• Clinical Health Informatics
• Machine Learning
• +۳ مورد دیگر

Rabia Bashir is an Honorary Postdoctoral Fellow at the Centre for Health Informatics (Australian Institute of Health Innovation) at Macquarie University. She holds a PhD in Health Innovation from Macquarie University (2019), an MRes in Computer Software Engineering from National University of Sciences and Technology, and a BS in Computer Science from Federal Urdu University. Her research focuses on improving systematic review methodologies using software engineering principles, natural language processing, and machine learning to enhance transparency and efficiency in clinical research. Education PhD in Health Innovation, Macquarie University (2019) MRes in Computer Software Engineering, National University of Sciences and Technology BS in Computer Science, Federal Urdu University of Arts, Science and Technology Research Interests Rabia’s work addresses inefficiencies in systematic reviews and clinical trials, leveraging software engineering and machine learning. Key areas include: Automated data extraction from systematic reviews Prediction of conclusion changes in review updates Integration of crowdsourcing and machine learning for collaborative research Awards and Honors Certificate of Recognition (Higher Degree Research Mentors Program, 2019) Gold Medal for First Position in BS (Computer Science, 2009) Merit Certificate for First Position in BS (Computer Science, 2009) PhD Student of the Year (2018) Postgraduate Research Fund (2018) Grants and Advising Rabia has received the Postgraduate Research Fund and mentored students through her mentorship programs. Her work emphasizes reducing waste in clinical research through improved systematic review practices. Labs and Teams She is affiliated with the Centre for Health Informatics, collaborating on projects that integrate technology with healthcare research methodologies.

• Health Informatics
• Artificial Intelligence in Healthcare
• Medical Guidelines
• +۳ مورد دیگر

David Fraile Navarro is a Postdoctoral Research Fellow in Generative AI at Macquarie University's Australian Institute of Health Innovation (AIHI) Centre for Health Informatics. His work focuses on advancing AI applications in healthcare, particularly in clinical decision support, natural language processing (NLP), and living guidelines. He contributes to projects like 'AI advisories for health professionals' and 'Harnessing Artificial Intelligence in the Australian health and aged care sector.' His research emphasizes ethical AI integration, guideline development methodologies, and improving patient care through technology. Research interests include AI ethics, clinical NLP applications, evidence-based medicine, and healthcare innovation. Notable projects involve designing generative AI systems for primary care and ensuring transparency in healthcare guidelines via frameworks like the RIGHT-COI&F checklist. He collaborates internationally on topics such as living guidelines for consumers and pandemic response strategies. Recent publications highlight his contributions to evaluating LLMs for clinical dialogue summarization and exploring patient engagement with living guidelines. His work has been cited in journals like Scientific Reports and JAMA Internal Medicine.

Douglas Parryمشاهده پروفایل

استادیار

• Digital Media Use
• Cognitive Psychology
• Mental Health
• +۷ مورد دیگر

Douglas Parry is an Assistant Professor in the Department of Communication Science at Vrije Universiteit Amsterdam, affiliated with the Media Psychology Group. He holds a PhD in Socio-Informatics from Stellenbosch University and previously served as a Senior Lecturer there. His research focuses on adolescents' digital media use, its cognitive and mental health impacts, and strategies for thriving in digital societies. He employs quantitative methods like longitudinal surveys, experiments, and computational techniques (NLP, machine learning), emphasizing open science through pre-registration and data sharing. Parry teaches courses including Problematic Media Effects and Media Psychology for Impact. His ancillary roles include External Associate Researcher at the Weizenbaum Institute (Berlin) and External Research Associate at Stellenbosch University. His work spans 30+ articles in top journals like Nature Human Behaviour and JAMA Pediatrics , addressing topics such as media multitasking, API accessibility challenges, and systematic reviews on social media effects. Key research themes include digital well-being, open science practices, and methodological rigor. His recent work critiques platform-controlled social media APIs and explores discrepancies between self-reported and logged media use. Collaborations span global institutions, reflecting his interdisciplinary approach to media psychology and computational communication science.

• Machine Learning
• Graph Mining
• Data Science
• +۵ مورد دیگر

Sourav Medya is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC), part of the College of Engineering. His research focuses on machine learning on graphs, explainable AI, and data science with applications in healthcare, infrastructure, and computational social science. He holds a Ph.D. in Computer Science from the University of California, Santa Barbara (UCSB), and a Master of Engineering degree from the Indian Institute of Science (IISc), Bangalore. Before UIC, he was a Research Assistant Professor at Northwestern University’s Kellogg School of Management and Northwestern Institute on Complex Systems (NICO). He has contributed to over 50 peer-reviewed publications in top conferences such as NeurIPS, ICML, ICLR, and ACL. His work has been recognized with a Best Paper Award at the Machine Learning on Graphs (MLoG) workshop in 2023. He actively serves on program committees for leading conferences including KDD, AAAI, and ICLR. His teaching includes courses on machine learning on graphs and data science. He advises a team of PhD and MS students focusing on multimodal learning, graph neural networks, and efficient AI systems.

• Theoretical Computer Science
• Artificial Intelligence
• Open Science
• +۳ مورد دیگر

Dr. Tomas Hruz is a Researcher at the Institute of Theoretical Computer Science at ETH Zurich, Switzerland. His work focuses on advancing open science practices, knowledge graph technologies, and the ethical application of large language models (LLMs). He contributes to initiatives like the Open Research Knowledge Graph (ORKG) and Project Alexandria, aiming to enhance scientific accessibility and reproducibility. His research spans theoretical computer science, AI ethics, and collaborative knowledge systems. Key areas of interest include scholarly communication reform, FAIR data principles, and leveraging AI for systematic literature reviews. He has published extensively on topics such as knowledge graph applications, metadata interoperability, and synthetic data in healthcare research. His contributions address challenges in reproducibility, ethical AI development, and interdisciplinary collaboration. Dr. Hruz is actively involved in the German National Research Data Infrastructure (NFDI), particularly in ontology development and semantic publishing. His recent work explores the potential of LLMs in scientific workflows, including abstract summarization and leaderboard tracking. Collaborations with international teams highlight his commitment to advancing open science through technological innovation.

• Artificial Intelligence in Healthcare
• Medical Image Analysis
• ADHD Diagnosis Systems
• +۵ مورد دیگر

Abdul Rehman is a computer science researcher currently affiliated with IMT Atlantique Bretagne Pays de la Loire in Brest, France, where he recently completed his PhD dissertation on 'Practical watermarking for multimedia traitor tracing' in 2024. His research spans multiple domains at the intersection of artificial intelligence and healthcare applications, with significant contributions in medical image analysis, neurodevelopmental disorder diagnosis, and wireless communications. His primary research interests focus on applying AI techniques to healthcare challenges, particularly in ADHD diagnosis systems using visual attention tasks, eye tracking, and serious games. He has also made substantial contributions to medical image analysis for breast cancer and leukemia detection, as well as work in multimedia security, serverless computing, and wireless sensor networks. His interdisciplinary approach combines computer vision, deep learning, and human-computer interaction to develop practical healthcare solutions. Analysis of his recent publications (2023-2025) reveals a strong trend toward healthcare AI applications, particularly in neurodevelopmental disorders and medical imaging. His work demonstrates expertise in developing explainable AI frameworks that bridge technical innovation with clinical needs, often collaborating with psychologists and medical professionals. The publications show increasing sophistication in multimodal approaches combining eye tracking, visual attention analysis, and deep learning. Abdul Rehman has established productive collaborations with researchers including Ilona Heldal, Jerry Chun-Wei Lin, and Waqas Sultani, with whom he has co-authored multiple papers on ADHD diagnosis systems. His work appears in reputable venues including IEEE Access, IEEE Big Data, and MICCAI. As a recent PhD graduate transitioning into research positions, he is actively contributing to multiple research directions while establishing himself as a specialist in AI applications for healthcare diagnostics, particularly in the areas of neurodevelopmental disorders and medical image analysis.

Milosh Jovanovikjمشاهده پروفایل

دانشیار

• Knowledge Graphs
• Data Engineering
• Semantic Web
• +۸ مورد دیگر

Dr. Milosh Jovanovikj holds the role of Associate Professor at the Faculty of Computer Science and Engineering in Skopje, North Macedonia, and concurrently serves as a PostDoc Researcher at TU Wien in Vienna, Austria. He is also a Senior R&D Knowledge Graphs Engineer at OpenLink Software in London. His primary research focuses on Knowledge Graphs (KGs), including their lifecycle processes such as modeling, enrichment, and analytics-driven AI applications. He has contributed to over 10 international and 25 national research projects, authored 60+ publications, and co-authored 3 books. His expertise spans Data Engineering, Semantic Web technologies, Linked Data, and Applied AI. Significant contributions include developing frameworks like RDFGraphGen for RDF graph generation and PharmKE for pharmaceutical text analysis. He has led initiatives in geospatial semantic web standards (GeoSPARQL) and healthcare informatics, including predictive analytics for nephrology data. Education: PhD in Computer Science and Engineering His work bridges academia and industry, with projects like the 3DFed federation engine and Semantic Sky (a Gmail plugin). He has extensive experience in ontology development (e.g., SEOntology, VEO for CO2 emissions) and open data initiatives, including LinkedDrugs for global drug data consolidation. Active in dissemination, his articles span knowledge graph applications in music recommendation, finance sentiment analysis, and drug-disease relation discovery. He has collaborated on benchmarks like the GeoSPARQL Compliance Benchmark and Mighty Storage Challenge (MOCHA). Grants/Projects: HOBBIT, SAGE, 3DFed, LinkedDrugs He leads teams in advancing semantic web integration, cloud service platforms (Semantic Sky), and educational materials like Web Programming Basics . His labs and teams focus on AI-driven solutions for healthcare, geospatial analytics, and enterprise systems.

Tom Searleمشاهده پروفایل

پژوهشگر ارشد

• Clinical Natural Language Processing
• Clinical Informatics
• Applied Machine Learning
• +۳ مورد دیگر

Dr. Tom Searle is a Post-Doctoral Research Fellow at King’s College London, affiliated with the Department of Biostatistics & Health Informatics within the School of Mental Health & Psychological Sciences. He leads NHS AI Lab-funded initiatives focused on advancing CogStack technologies for healthcare data analysis. His work emphasizes natural language processing (NLP) applications in clinical contexts, particularly leveraging electronic health records (EHRs) to address disparities and improve diagnostic accuracy. Education: BSc in Computer Science (King’s College London), MSc in Computing with a focus on Performance Modelling (Imperial College London). He completed his PhD at King’s College London, investigating novel methods for EHR analysis and summarization. Research Interests: Clinical NLP, clinical informatics, and applied machine learning in healthcare systems. His studies explore racial/ethnic disparities in heart conditions, automated clinical text analysis, and AI-driven predictive models for hospitalization outcomes. Recent work includes developing MedCAT, a biomedical text annotation toolkit, and improving the National Early Warning Score (NEWS2) for pandemic response. Key Contributions: CogStack platform development, NHS Trust collaborations, and NHS AI Lab-funded programs to deploy scalable healthcare NLP solutions. His research spans cardiovascular disease disparities, seizure care pathway automation, and biomarker insights during the COVID-19 pandemic. Grants & Funding: Lead investigator on NHS AI Lab grants. Collaborations include multi-hospital studies on prediction models and biomarker analysis. Labs/Teams: Core contributor to the CogStack team and the Institute of Psychiatry, Psychology & Neuroscience at King’s College London.

• Mental Health
• Psychosis Research
• Public Health
• +۳ مورد دیگر

Vishal Bhavsar is an NIHR Advanced Fellow affiliated with the Health Service & Population Research department. His research focuses on mental health, particularly the intersections between violence, public health, and global health inequities. He investigates topics like domestic abuse, trauma, and the epidemiology of mental illness. Bhavsar's work often employs advanced methodologies such as natural language processing to analyze healthcare data. His research interests include violence prevention, mental health service utilization, and the societal impacts of globalization on psychiatric care. He has contributed to policy briefings and clinical guidelines addressing domestic abuse and human trafficking survivor care. Bhavsar's recent studies highlight the mortality risks linked to physical/sexual assault and the role of neighborhood factors in psychosis onset. Key awards include the NIHR Advanced Fellowship. Collaborations span global health initiatives and interdisciplinary projects analyzing violence in mental healthcare contexts. His work bridges clinical practice, public health policy, and social determinants of mental health.