جست‌وجوی استاد و پژوهشگر

• Health Informatics
• Data Mining and Analytics
• Machine Learning in Healthcare
• +۶ مورد دیگر

Bunyamin Ozaydin is an Associate Professor in the Department of Health Services Administration at the University of Alabama at Birmingham (UAB), concurrently serving as a Scientist in the School of Medicine Informatics Institute and holding roles in the Department of Biomedical Informatics & Data Science. He holds a PhD in Computer Engineering and a Master's in Electrical Engineering, with a B.S. in Physics. His research focuses on health information systems, data infrastructure for healthcare analytics, machine learning applications, and decision support systems. He has led projects in database design for clinical research, including contributions to the Multicenter Perioperative Outcomes Group (MPOG) and Anesthesia Quality Institute (AQI) databases. Ozaydin teaches advanced courses in health informatics, including systems analysis, databases, and healthcare business intelligence. He has authored over 30 peer-reviewed publications and serves on editorial boards for journals like Health Systems . His awards include the UAB President’s Award for Diversity (2007) and the SDPS Service Award (2000). He mentors doctoral students in health services administration and interdisciplinary engineering. His funded research includes NIH-supported projects on diabetes management and cancer disparities, along with grants focusing on healthcare technology and financial performance analysis.

Balu Bhasuranمشاهده پروفایل

استادیار مهمان

• Clinical Natural Language Processing (NLP)
• Machine Learning in Healthcare
• Generative AI Applications
• +۷ مورد دیگر

Balu Bhasuran is a Visiting Assistant Professor at the School of Information (iSchool), Florida State University (FSU), within the College of Communication & Information. He holds a PhD in Computational Biology from Bharathiar University, India, and has extensive postdoctoral experience at UCSF and FSU. His research focuses on Clinical NLP, Machine Learning, and Generative AI applied to healthcare, with expertise in EHR analysis, knowledge graphs, and causal inference. Education: PhD in Computational Biology (Bharathiar University, 2020) MCA in Computer Applications (Mahatma Gandhi University, 2013) Research Interests: Dr. Bhasuran specializes in developing machine learning models for biomedical data, including clinical NLP for disease diagnosis, EHR-based prediction systems, and LLM applications in healthcare. He has contributed to projects like NASHDetection (NLP for liver disease identification) and Lab-AI (LLM-driven lab test interpretation). Publications & Awards: He has over 40 peer-reviewed publications, including work in npj Digital Medicine , JAMIA , and Biomedicines . Notable awards include the 2024 American Transplant Congress Pediatric Poster Award and best paper/poster awards at ICICC and BioIndica conferences. He holds Indian copyrights for software tools like D-NER, DisGeReExT, and GDMiner. Grants & Collaborations: He leads NIH- and FDA-funded grants on transplant rejection prediction, adverse event detection in IBD, and lab test interpretation tools. Collaborations include UCSF’s Real-World Evidence Lab and Merck/Alnylam on NASH research. Labs & Teams: He is part of FSU’s eHealth Lab, UCSF’s Real-World Evidence Lab, and the DRDO-BU Computational Biology Division. His work bridges clinical informatics, text mining, and AI for healthcare innovation.

Sebastian Padóمشاهده پروفایل

استاد

• Natural Language Processing
• Computational Linguistics
• Computational Semantics
• +۴ مورد دیگر

Sebastian Padó is a Professor and Chair of Theoretical Computational Linguistics at the Institute for Natural Language Processing (IMS) of the University of Stuttgart. He serves as Study Dean for Natural Language Processing and Computational Linguistics within Faculty 5 (Computer Science, Electrical Engineering, and Information Technology). His research focuses on computational semantics, leveraging data-driven approaches to model meaning in language, with applications in political discourse analysis, emotion analysis, and cross-lingual NLP. He leads projects such as CEAT (Emotion Analysis via Appraisal Theory), FIBISS (Fact-Checking in Biomedical Information), and MARDY (Argument Dynamics Modeling). Collaborations include institutions like the University of Tübingen, University of Pisa, and University of Zagreb. His work bridges computational linguistics with social sciences, addressing topics like political positioning, media bias detection, and historical text analysis. Padó oversees the Department of Theoretical Computational Linguistics, managing research and teaching activities. His group's contributions span semantic modeling, neural network interpretability, and interdisciplinary applications in digital humanities. He actively publishes in top venues, with recent work on multilingual models, political text analysis, and transformer architectures. Key achievements include developing methods for emotion analysis in literature and news, creating datasets on political debates (e.g., DEbateNet-mig15), and advancing frame semantics through distributional models. His lab also explores explainable AI techniques for NLP systems, emphasizing transparency and ethical considerations.

Jamal Bentaharمشاهده پروفایل

استاد

• Artificial Intelligence
• Multi-Agent Systems
• Federated Learning
• +۷ مورد دیگر

Prof. Jamal Bentahar is a Professor at the Concordia Institute for Information Systems Engineering (CIISE), part of Concordia University's Faculty of Engineering and Computer Science. His research focuses on intelligent agents, multi-agent systems, federated learning, reinforcement learning, cybersecurity, and their applications in healthcare, IoT, and autonomous systems. He leads research initiatives in AI-driven medical imaging, edge-cloud computing, and blockchain-empowered distributed systems. Key research areas include: Multi-Agent Systems: Formal verification of trust and commitments, group trust modeling, and distributed decision-making Federated Learning: On-demand client/model deployment, trust-aware optimization, and privacy-preserving frameworks Medical AI: Cardiac ultrasound analysis, CPR signal processing, and robotic telemedicine systems Edge/IoT Intelligence: UAV-enabled vehicular networks, energy-efficient LoRa gateways, and fog computing optimization His work bridges theoretical foundations (e.g., multi-valued model checking) with practical applications in healthcare robotics, smart city infrastructure, and autonomous vehicle safety. Recent publications highlight innovations in transformer-based reinforcement learning, spherical topic modeling, and explainable AI for cybersecurity. Prof. Bentahar's contributions include tools like MV-Checker for multi-valued verification and frameworks like CACTUS for cardiac ultrasound analysis. He actively publishes in top venues and serves as editor for special sections on federated learning and AI applications.

• Artificial Intelligence
• Natural Language Processing
• Computational Linguistics
• +۳ مورد دیگر

Sabine Bergler is a Professor in the Department of Computer Science and Software Engineering at Concordia University. Her research focuses on artificial intelligence, natural language processing, and computational linguistics with particular emphasis on biomedical text processing and social media analysis. She leads projects applying NLP techniques to health-related social media content and biomedical literature. Her work spans over two decades, with notable contributions in named entity recognition (NER), sentiment analysis, and knowledge integration in biomedical domains. She has pioneered methodologies for analyzing speculative language in scientific texts and developing systems for health issue detection in social media. Her team (CLaC Lab) participates actively in shared tasks like BioCreative and SemEval, addressing challenges in clinical text mining and public health surveillance. Recent research highlights include multi-lingual NER for disease mentions in Spanish tweets, integration of heterogeneous knowledge sources for biomedical analysis, and exploring annotator demographics in sexism detection tasks. Her work emphasizes practical applications of NLP in healthcare informatics and social media monitoring. Dr. Bergler has published extensively in top venues such as BioNLP, SemEval, and IEEE/ACM conferences. She currently focuses on advancing computational linguistics methods for real-world challenges in health communication and biomedical data analysis.

Leila Kosseimمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics
• +۳ مورد دیگر

Leila Kosseim is a Professor in the Department of Computer Science and Software Engineering at Concordia University, co-directing the CLaC (Computational Linguistics @Concordia) lab. She holds a PhD from the University of Montreal (1995) and has conducted postdoctoral research at Druide informatique inc. and the RALI group. Her academic leadership roles include Vice-President (2017-2019), President (2019-2021), and Past-President (2021-2023) of the Canadian AI Association (CAIAC), and current Graduate Program Director for the PhD Programs in CSSE. Her research focuses on Natural Language Processing (NLP) and Artificial Intelligence , with particular emphasis on discourse analysis, computational linguistics, and text generation. Notable projects include automated analysis of argumentative essays, clinical trial inference, and meme persuasion detection. Her work integrates deep learning, neural networks, and transformer models to address challenges in text coherence, discourse parsing, and multilingual NLP. Leila has supervised 8 PhD and over 20 Master’s students, contributing to the next generation of computational linguists. Her CLaC lab has participated in multiple international challenges (SemEval, TREC, DEFT) and produced influential tools like TIMBERT and the Concordia NLG Surface Realizer.

• Semantic Computing
• Text Mining
• Artificial Intelligence
• +۴ مورد دیگر

Dr. René Witte is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on semantic computing, text mining, artificial intelligence, and data science, with applications in knowledge graphs, semantic publishing, and intelligent information systems. He leads the Semantic Software Lab, developing tools like ScholarLens and OpenTrace to enhance semantic analysis and knowledge integration. Key research areas include semantic user profiles, automated knowledge base construction, and natural language processing (NLP) for scientific literature. His work bridges NLP, semantic web technologies, and software engineering, addressing challenges in data interoperability and user-centric systems. Publications highlight advancements in semantic search, Linked Open Data frameworks, and tools for text mining in biomedical and software engineering contexts. His contributions span academic conferences and journals, emphasizing practical applications of semantic technologies.

Chao Liuمشاهده پروفایل

مدرس

• Smart and Sustainable Manufacturing
• Digital Twin
• Cyber-Physical Production Systems
• +۵ مورد دیگر

Dr. Chao Liu is a Lecturer in Digital Manufacturing at Aston University, College of Engineering and Physical Sciences. He holds a Ph.D. in Mechanical Engineering from the University of Auckland and has held roles at The Hong Kong Polytechnic University and Cardiff University. His research focuses on Smart and Sustainable Manufacturing, including Digital Twin, Cyber-Physical Production Systems, and Machine Learning applications. He has secured significant funding, including £70k from the Royal Society and €481k from the Horizon EU Project. Dr. Liu has authored over 60 publications and serves as a reviewer/Editor for top journals like Robotics and Computer-Integrated Manufacturing and International Journal of Production Research. Education: Ph.D. (2018, University of Auckland), MSc (2014, Northeastern University), BSc (2012, Northeastern University). Research Interests: Smart Manufacturing technologies, Industrial IoT, Machine Tools, Big Data Analytics, and Augmented Reality. Awards include the Vice-Chancellor’s Research Excellence Award (2024) and multiple top reviewer awards. Grants and Projects: Royal Society grants, Horizon EU-funded H2GLASS project, and UK KTP collaborations with industries like Colemans and JDD Furniture Ltd. He leads interdisciplinary projects in AR-assisted human-machine collaboration and energy-efficient manufacturing systems. Labs/Teams: Involved in the Smart and Sustainable Manufacturing research group at Aston University, focusing on Cyber-Physical Systems and Digital Twin applications.

• Data Science
• Machine Learning
• Mobility Data Analysis
• +۶ مورد دیگر

Gunopulos Dimitrios is a Professor at the National and Kapodistrian University of Athens, Department of Informatics and Telecommunications. His research focuses on data science, machine learning, mobility data analysis, and interdisciplinary applications in urban systems, finance, and high-energy physics. He has contributed to frameworks like INSIGHT for urban traffic management and REMI for heterogeneous data mining. His work spans cloud computing, explainable AI, and spatiotemporal analysis, addressing challenges in real-world systems. Education: Education details not explicitly provided in the text. Research Interests: Dimitrios explores cutting-edge topics such as mobility data science, serverless computing, deep learning for financial forecasting, and causal reasoning. His work bridges theoretical computer science with practical applications in urban infrastructure, healthcare, and sensor networks. Recent efforts include developing algorithms for sparse data handling, fault detection in traffic systems, and counterfactual explainability in AI. Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: No listed advisees or grant details available. His contributions are primarily through collaborative frameworks and conference engagements. Labs/Teams: Involved in projects like INSIGHT for urban data integration and Dione for big data application profiling. Collaborates on heterogeneous data analysis and cloud resource optimization initiatives.

• Blockchain Technology
• Social Network Analysis
• Reputation Systems
• +۶ مورد دیگر

Associate Professor Aphrodite Tsalgatidou holds a faculty position at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. Her research focuses on blockchain technology, social network analysis, and reputation systems with applications in e-commerce, distributed systems, and business process management. Her work spans over three decades, addressing challenges in interoperability of heterogeneous services, P2P systems, and mobile commerce. Notable contributions include frameworks for secure blockchain scaling (Adrestus), real-time social tracking systems (BLACE), and reputation mechanisms for e-commerce platforms. She has also explored abstracting BPMN models for complex process comprehension. Her research integrates computer science with practical applications in distributed systems, cybersecurity, and service-oriented architectures. Recent efforts emphasize scalable blockchain solutions and AI-driven big data pipelines. Despite extensive contributions, no scientific awards are explicitly mentioned in available materials. Her advising and grant activities are not detailed in the provided data, though her prolific publication history suggests active involvement in academic collaborations. No lab affiliations or teams are specified in the text.

Jeffrey A. Thompsonمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Healthcare Informatics
• +۴ مورد دیگر

Jeffrey A. Thompson is an Associate Professor in the Department of Biostatistics & Data Science at the University of Kansas Medical Center (KUMC) and concurrently serves as the joint Chief Research Informatics Officer for KUMC and the University of Kansas Health System. He holds a PhD in Quantitative Biomedical Science from Dartmouth College and a BS in Computer Science from the University of Southern Maine. His research focuses on leveraging artificial intelligence, machine learning, and informatics to improve healthcare outcomes through innovative tools like the Curated Cancer Clinical Outcomes Database (C3OD), which accelerates clinical trial patient recruitment. Thompson has pioneered digital research infrastructure modernization at KUMC, including HIPAA-compliant cloud-based platforms and centralized data systems. He is an award-winning educator, having received the KU Edwards Campus Faculty Teaching Excellence Award (2021) and the Department of Urology Teacher Appreciation Award (2022). Thompson leads key roles such as co-lead of the Frontiers Clinical and Translational Science Institute’s Informatics Core and Associate Director of the Quantitative Omics Core at the Kansas Institute of Precision Medicine. His work spans genomic data analysis, clinical trial optimization, and advancing data science education through summer workshops. Publications highlight his contributions to predictive modeling in oncology, EHR analytics, and bioinformatics tools. Thompson’s work emphasizes interdisciplinary collaboration, evidenced by his leadership in integrating research systems across electronic health records, biospecimen management, and clinical imaging. His efforts in rural cancer outcome disparities and personalized screening frameworks further demonstrate his commitment to equitable healthcare solutions.

Hongshik Ahnمشاهده پروفایل

استاد

• Biostatistics
• Survival Analysis
• Bioinformatics
• +۳ مورد دیگر

Hongshik Ahn is a Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. His research focuses on biostatistics and survival analysis, particularly in tree-structured regression modeling for censored survival data. He has extensive experience collaborating with biomedical researchers, including work at the National Center for Toxicological Research (NCTR) on carcinogenicity and drug stability analysis. His research has been supported by NIH funding. Education: Ph.D., University of Wisconsin-Madison (1992). Research Interests: Ahn's work spans biostatistical methods for clinical and genomic data, subgroup analysis in cancer treatment, and statistical methodologies for high-dimensional biomedical datasets. He has developed novel approaches for analyzing survival data, batch testing strategies for infectious diseases, and machine learning applications in psychiatric diagnostics. His publications highlight trends in applying statistical models to real-world health challenges, including optimizing diagnostic protocols, evaluating treatment efficacy, and leveraging genomic data for personalized medicine. Notable contributions include advancements in subgroup analysis for cancer therapies and multimodal biomarker development for psychiatric disorders. Grants & Funding: NIH-funded research on statistical methods in biomedical decision-making. No specific awards mentioned in the provided text. Labs/Teams: Collaborations with biomedical researchers at Stony Brook and external institutions like NCTR. Focus on translating statistical methodologies into practical clinical tools.

Ming Huangمشاهده پروفایل

دانشیار

• AI and NLP in Healthcare
• Clinical and Translational Research
• Data Mining
• +۷ مورد دیگر

Ming Huang, PhD is an Associate Professor at the Center for Translational AI Excellence and Applications in Medicine (TEAM-AI) within the McWilliams School of Biomedical Informatics at UTHealth Houston. His research focuses on advancing AI and NLP methods to address challenges in clinical and translational healthcare, particularly in mental health, consumer health, and telehealth. He leads NIH-funded projects on computational phenotyping of mental health disorders and vaping-related social media analysis. Previously, he served as an Assistant Professor at Mayo Clinic and held editorial roles in journals like Frontiers in AI and conferences like IEEE ICHI. Dr. Huang earned his PhD in Scientific Computation from the University of Minnesota (2014) and dual BSc in Computer Science and Applied Chemistry from Beijing Normal University (2004). His work emphasizes leveraging AI for actionable healthcare insights, including patient portal messaging analysis and drug repurposing through EHR data. His research portfolio includes developing frameworks for suicide risk phenotyping using LLMs, improving digital equity in smoking cessation programs, and analyzing vaping-related public sentiment during health crises. He actively contributes to NSF review panels and NIH grant initiatives, reflecting his leadership in AI-driven healthcare innovation. Dr. Huang seeks motivated students/postdocs interested in AI/health informatics to tackle real-world healthcare challenges through collaborative, interdisciplinary research. His lab focuses on translating AI advancements into practical tools for clinical and translational medicine.

Xiaoyang Ruanمشاهده پروفایل

استادیار

• Predictive Modeling
• Deep Phenotyping
• Explainable AI
• +۳ مورد دیگر

Xiaoyang Ruan, Ph.D., is an Assistant Professor at UTHealth Houston's McWilliams School of Biomedical Informatics and a member of the Center for Translational AI Excellence and Applications in Medicine (TEAM-AI). He holds a multidisciplinary background in biopharmacy, genetic epidemiology, and bioinformatics, with experience spanning academic healthcare and the pharmaceutical industry. His research focuses on real-world data-driven deep learning architectures for real-time risk modeling, precision medicine through deep phenotyping, and explainable AI. Notable contributions include developing the first individualized dynamic risk model for chronic kidney disease and real-time post-surgical complication prediction systems. Dr. Ruan emphasizes collaboration across disciplines to ensure AI applications are clinically implementable and ethically sound. Future research directions include advancing peri-operative excellence via real-world evidence, obesity deep phenotyping for precision interventions, Alzheimer's risk modeling, and self-explainable longitudinal AI architectures. He collaborates with UTHealth's Obesity Clinic and Memorial Hermann Health System, and seeks trainees with strong programming skills in data science and biomedical domains.

Tanvi Banerjeeمشاهده پروفایل

دانشیار

• Artificial Intelligence in Healthcare
• Multimodal Data Fusion
• Wearable Sensing
• +۶ مورد دیگر

Dr. Tanvi Banerjee is an Associate Professor in the Department of Computer Science and Engineering at Wright State University with a secondary appointment in the Department of Geriatrics at Boonshoft School of Medicine. She leads the Data Science for Healthcare (DASH) lab, focusing on AI-driven solutions for healthcare challenges. Her research integrates multimodal data fusion, wearable sensing, and machine learning to develop tools for dementia management, sickle cell disease monitoring, and chronic pain assessment. Education includes a Ph.D. and Master's degree from the University of Missouri (2014), where she researched elder care technologies at the Center for Eldercare and Rehabilitation Technology (CERT). Research Interests: Her work bridges clinical knowledge with data science through non-invasive sensing, including: AI for early dementia detection using smartphone/wearable data Multimodal fusion of physiological/behavioral data for chronic disease management NLP analysis of clinical texts and social media for public health insights Fairness-aware machine learning in healthcare applications Publication Trends: Recent articles (2023-2025) demonstrate strong focus on: Transformer-based phenotyping from clinical/EHR data Wearable sensor development for pain/cognition monitoring Algorithmic fairness in healthcare AI Social media analysis for public health surveillance Awards & Grants: NIH K01 Career Development Award for dementia management technology (2016) Co-investigator on NIH R01 grants for sickle cell disease and asthma management Advising & Labs: Mentors 6 PhD students and 15+ past advisees in the DASH lab. Current research includes DOE-funded manufacturing analytics and USAF-supported supply chain NLP projects. The lab specializes in developing clinical decision support systems using multimodal data.