جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Data Analytics
• Machine Learning
• +۳ مورد دیگر

Professor Xiaohui Tao is a distinguished academic at the University of Southern Queensland's School of Mathematics, Physics and Computing, leading the Computing Discipline Team and chairing the ICT Programs Governance Committee. He holds a PhD in Information Technology from Queensland University of Technology (2009). His research focuses on artificial intelligence, machine learning, and health informatics, with over 200 publications in top journals like IEEE TKDE and conferences such as AAAI and IJCAI. He has mentored 10 doctoral students and leads a research group developing real-world AI applications. Education: PhD in Information Technology, Queensland University of Technology, 2009 Research Interests: Dr. Tao's work spans AI-driven health informatics, machine learning algorithms, natural language processing, and privacy-preserving technologies. His projects address critical challenges like mental health monitoring, smart healthcare systems, and privacy in 5G/IoT environments. Recent advancements include federated learning for unlearning mechanisms and multimodal fusion for medical decision support. Grants & Awards: Awarded Australia Research Council grants (DP220101360), Australian Endeavour Fellowships, and multiple best paper awards at conferences like BESC’22 and WI-IAT’20. Recognized for contributions to remote patient monitoring and AI in depression treatment. Labs & Teams: Leads a research group focused on AI applications in healthcare and data science, collaborating on projects like computational social science for mental health and privacy-preserving IoT systems.

• Artificial Intelligence
• Bioinformatics
• Machine Learning
• +۷ مورد دیگر

Enrico Blanzieri is an Associate Professor at the University of Trento's Department of Information Engineering and Computer Science. His research focuses on artificial intelligence, bioinformatics, quantum computing, machine learning, and computational biology. He actively contributes to interdisciplinary projects such as Vitis OneGenE for gene network analysis and quantum machine learning pipelines. His teaching includes courses like Algorithms for Bioinformatics, Quantum Machine Learning, and Data Mining. Blanzieri's work bridges theoretical foundations and practical applications, with notable contributions in quantum annealing-based algorithms, gene network expansion using distributed computing, and socially-competent artificial agents. His research integrates computational methods with biological systems, addressing challenges in precision agriculture and systems biology. His recent publications emphasize quantum machine learning advancements, causal role attribution in gene networks, and ethical AI frameworks for language models. Collaborative projects include the GENE@HOME volunteer computing initiative for bioinformatics tasks. Blanzieri also engages in educational activities, supervising courses that blend theoretical computer science with real-world applications.

Dheryta Jaisinghaniمشاهده پروفایل

استادیار

• Wireless Networks
• Mobile Computing
• IoT Systems
• +۳ مورد دیگر

Dheryta Jaisinghani is an Assistant Professor in the Department of Computer Science at the University of Northern Iowa (UNI), affiliated with the College of Humanities, Arts, and Sciences. They hold a Ph.D. in Computer Science from Indraprastha Institute of Information Technology. Their research focuses on wireless networks, mobile computing, IoT systems, and machine learning applications in health monitoring and smart environments. Research interests include wearable sensor systems for activity recognition (e.g., tooth brushing detection, sleep posture analysis), low-cost IoT solutions for healthcare, and network optimization in dense WiFi environments. Their work integrates machine learning with sensor data to address challenges in social behavior analysis, indoor localization, and industrial robotics. Recent publications (2021–2024) explore topics like flying IoT networks, socially distanced classroom systems, and neural networks for social interaction tracking. Their work emphasizes practical, unobtrusive technologies with applications in healthcare, education, and industrial automation. No scientific awards or grants are explicitly mentioned in the provided text. No advisees are listed, though their teaching includes wireless networks and mobile computing. No lab affiliations are noted.

Yasaman Amannejadمشاهده پروفایل

دانشیار

• Machine Learning
• Data Analytics
• Cloud Computing
• +۷ مورد دیگر

Yasaman Amannejad is an Associate Professor in the Department of Mathematics and Computing at Mount Royal University (MRU), Faculty of Science. She holds a PhD in Software Engineering from the University of Calgary (2017) and an MSc/BSc in Computer Information Technology from Amirkabir University of Technology (2011/2008). Her research focuses on applying machine learning to healthcare diagnostics, performance analysis of cloud and edge computing systems, and addressing social challenges like domestic violence and homelessness. Her work has been funded by NSERC, Petro-Canada, and the New Frontiers in Research Fund. Education: PhD in Software Engineering, University of Calgary (2017) MSc in Computer Information Technology, Amirkabir University of Technology (2011) BSc in Computer Information Technology, Amirkabir University of Technology (2008) Research Interests: Machine Learning for Healthcare (e.g., tropical disease diagnosis, Multiple Myeloma cancer) Performance Optimization in Cloud/Edge Systems Resource-Constrained Device Learning (wearables, IoT) Social Impact Technologies (domestic violence detection, homelessness solutions) Awards & Grants: NSERC Discovery Grant (2020) New Frontiers in Research Fund-Exploration (2020) Petro-Canada Young Innovator Award (2019) Multiple Teaching Awards (2015-2016) Teaching & Industry: Incorporates industry experience into courses, emphasizing real-world applications Outstanding Teaching Performance Award (2016) Over 5 years of industry experience in software engineering Labs/Teams: Part of interdisciplinary teams at MRU's Faculty of Science Collaborates on NSERC-funded projects

Jonathan Henningerمشاهده پروفایل

استادیار

• RNA Biology
• Gene Regulation
• Cell Fate Determination
• +۵ مورد دیگر

Jonathan Henninger is an Assistant Professor in the Department of Biological Sciences at Carnegie Mellon University (CMU), part of the Mellon College of Science. His research focuses on RNA's role in regulating gene expression and cell fate through molecular biology, soft-matter physics, and super-resolution imaging approaches. The Henninger Lab investigates RNA's interactions with proteins and condensates to uncover mechanisms underlying gene control and disease. Education: Ph.D. in Developmental and Regenerative Biology from Harvard University, followed by postdoctoral training at the Whitehead Institute for Biomedical Research, MIT. Key research interests include RNA-mediated transcriptional regulation, nuclear condensates, RNA structure-function relationships, and the development of RNA-based therapies. The lab combines experimental and computational methods to study how RNA defects contribute to human diseases like cancer and neurodevelopmental disorders. Recent work includes identifying how RNA synthesis patterns nuclear condensates and discovering roles for rRNA G-quadruplexes in nucleolar protein condensation. The lab received a Kaufman Foundation New Investigator Grant to explore RNA's role in directing transcription. Advising and grants: Jonathan mentors a diverse team of PhD, Master’s, and undergraduate students. Notable advisees include Haoran Wang, Lydia Phillips, and Arth Banka. Current projects involve synthetic transcriptional condensates and bioinformatic discovery of RNA regulatory elements. Labs and collaborations: The lab is equipped with state-of-the-art microscopy (e.g., Zeiss Lattice SIM 5) and collaborates with groups at CMU, Pitt, and NYU. Regular scientific events include the Steel City Condensate Club Symposium.

Sarah Bennettمشاهده پروفایل

دانشیار

• Evidence-Based Policing
• Procedural Justice
• Experimental Criminology
• +۷ مورد دیگر

Associate Professor Sarah Bennett, Program Director of the Bachelor of Criminology and Criminal Justice at the University of Queensland's School of Social Science, is a leading scholar in translational criminology and evidence-based policing. With internationally recognized expertise, she focuses on procedural justice, legitimacy, experimental criminology, and organizational practice to enhance policing outcomes in Australia and the UK. Her work bridges research and policy through partnerships with law enforcement and government agencies. Her research aims include: 1) advancing police training for survivor, offender, and community outcomes; 2) applying rigorous methods in real-world settings; and 3) developing organizational theories for effective practice. A Fellow of the Academy of Experimental Criminology, she has secured significant grants (e.g., ARC Discovery Projects) and contributes to policy via reports on sexual assault investigations, youth diversion, and traffic safety. Notable collaborations include the SocioHealthLab and UQ Bioarchaeology and Osteology Network (BONE) . She supervises PhD candidates in areas like domestic violence, police effectiveness, and cybersecurity, with a focus on gender dynamics and spatio-temporal policing strategies. Her current grants completed supervision of 10+ PhD projects 55+ conference presentations 44+ peer-reviewed articles underscore her impact.

Marcel Turcotteمشاهده پروفایل

دانشیار

• Bioinformatics
• Computational Biology
• Algorithm Design
• +۳ مورد دیگر

Dr. Marcel Turcotte is an Associate Professor at the School of Electrical Engineering and Computer Science (EECS) at the University of Ottawa. He holds a Ph.D. from Université de Montréal and has conducted postdoctoral research at the University of Florida and the Imperial Cancer Research Fund in the UK. His research focuses on bioinformatics, computational biology, algorithm design, and machine learning applications in biological systems. Education: Ph.D., Université de Montréal (advisor: Guy Lapalme and Robert Cedergren) Postdoctoral Work: University of Florida (Steve Benner lab), Imperial Cancer Research Fund (Biomolecular Modelling Laboratory) His research interests include RNA structure prediction, protein secondary structure analysis, evolutionary-based bioinformatics methods, and computational intelligence applied to genomic data. He has developed algorithms like WACS for ChIP-seq analysis and RiboFSM for RNA structural motif discovery. His work bridges computer science and molecular biology, addressing challenges in genomic data interpretation and predictive modeling. Dr. Turcotte’s recent publications emphasize machine learning applications in RNA and protein structure analysis, as well as computational tools for genomic sequence mining. He has contributed to understanding gene fragmentation in Diplonema mitochondria and host susceptibility prediction for RNA viruses. His interdisciplinary approach integrates algorithm design with biological problem-solving. No scientific awards or grants are explicitly mentioned in the provided text. His academic profile includes collaborations in bioinformatics, computational biology, and educational methodologies for engineering students.

Mehmet Orgunمشاهده پروفایل

استاد

• Artificial Intelligence
• Biomedical Image Processing
• Knowledge Discovery
• +۳ مورد دیگر

Mehmet Orgun is a Professor at the School of Computing, Macquarie University. He holds a BSc and MSc from Hacettepe University (Turkey) and a PhD from the University of Victoria (Canada). His research spans artificial intelligence, biomedical image processing, multi-agent systems, and secure systems. He has supervised over 30 PhD and master's students and led multiple ARC-funded projects totaling over $4M. His professional service includes roles at PRICAI 2010 and SIN 2019. Research Interests: Artificial Intelligence Quantum Cryptography Trusted Systems Medical Imaging Recommender Systems Awards: Best Student Runner Up Award (ADMA'23) Best Paper Award (MoMM2020) Best Student Paper Award (ICWS2016) Advising & Grants: 30+ PhD/Master's students ARC grants exceeding $4M Labs/Teams: Collaborations include Australian Taxation Office and DSTO. Projects focus on trust-oriented data analytics and secure systems.

Eelko Hakمشاهده پروفایل

استاد

• Pharmacoepidemiology
• Vaccinology
• Drug Safety
• +۵ مورد دیگر

Prof. Eelko Hak is a Full Professor of Pharmacoepidemiology and Chair of the PharmacoTherapy, Epidemiology, and Economics department at the University of Groningen's Faculty of Science and Engineering. He leads the PharmLines Initiative, a public-private collaboration leveraging big healthcare and biobank data for precision medicine research. His expertise spans statistical methods for drug safety assessment, including case-control, cohort, and instrumental variable analyses. As Educational Director of Pharmacy and Program Director of the Research Master's in Medical Pharmaceutical Sciences, he has driven curriculum innovations and achieved top rankings in Dutch education evaluations. He holds leadership roles in medical ethics committees and European vaccine trials, contributing to vaccination policy and influenza research. Awards include the Mitchel B. Balter Award and top abstract recognitions. His work bridges academic research, public health, and clinical practice through real-world data applications. Research Interests: Pharmacoepidemiology of preventive medications and vaccines Real-world data methodologies (e.g., sibling designs, meta-analyses) Implementation science for sustainable drug use Infection risk profiling and vaccination strategies Advising & Grants: Supervised >30 PhD theses, including Yen Hao Tran. Led EU-FP7 projects on universal influenza vaccines and Dutch Health Council advisory roles. Active in global health initiatives like Nigeria's vaccination campaigns. Labs/Teams: PharmLines Initiative (80,000+ participants), GUIDE graduate school (PEGET research group), and collaborations with UMCG and Isala hospitals.

Michele Bombardieriمشاهده پروفایل

استاد

• Autoimmunity
• Rheumatology
• Immunology
• +۳ مورد دیگر

Professor Michele Bombardieri is a Professor of Immuno-Rheumatology at Queen Mary University of London’s Faculty of Medicine and Dentistry, leading the Immuno-Rheumatology Research Group and the Sjögren’s Clinical Research Team. His work focuses on cellular and molecular mechanisms of autoimmunity, particularly in Sjögren’s syndrome and rheumatoid arthritis, with an emphasis on B cell activation, tertiary lymphoid structures, and translational research for novel therapies. He holds a PhD from King’s College London and has been supported by prestigious fellowships, including the ARUK Clinician Scientist Fellowship and NIHR grants. His research has attracted over £2.4 million in funding. Education: Graduated from the Catholic University of Rome, specialist training in Rheumatology under Prof. Valesini at Sapienza University, and a PhD in Experimental Rheumatology at King’s College London. Research Interests: Autoimmune mechanisms, B cell tolerance, ectopic lymphoid neogenesis, viral interactions in autoimmunity, and biomarker development. Awards/Grants: Over £2.4M in research income, ARUK and NIHR fellowships, and leadership roles in international societies. Collaborations: Collaborates with Prof. Mauro Perretti (QMUL), Prof. Guido Valesini (University of Rome), and others globally. His lab investigates how viruses like EBV drive autoimmunity and B cell dysfunction in salivary glands. Key projects include developing AI-driven diagnostics and stratification tools for Sjögren’s syndrome. He actively contributes to clinical guidelines and precision medicine strategies for autoimmune diseases.

Aitor Aguirreمشاهده پروفایل

دانشیار

• Stem Cell Biology
• Tissue Engineering
• Cardiovascular Health
• +۳ مورد دیگر

Aitor Aguirre is an Associate Professor in the Department of Biomedical Engineering at Michigan State University (MSU), affiliated with the Institute for Quantitative Health Science & Engineering (IQ) and the Division of Developmental and Stem Cell Biology. His research focuses on stem cell and tissue engineering approaches to improve cardiovascular health, including heart organoid development and cardiac regeneration. Education: B.S. in Biology (University of the Basque Country, 2002), M.Sc. in Biochemistry (2005) and Tissue Engineering (Technical University of Catalonia, 2008), Ph.D. in Tissue Engineering (2010). Postdoctoral training at the Salk Institute (2011). Research interests include: Engineering human heart models for disease study and therapy Pluripotent stem cell-derived organoids In vivo cellular reprogramming Cardiac regeneration mechanisms Lab collaborations span MSU and global institutions. Current work emphasizes fetal/adult heart models and translational applications for cardiovascular conditions. The lab uses advanced microscopy, optogenetic systems, and in vivo imaging for organoid analysis. Key technical advancements include: Self-organizing heart assembloids Bioengineered cardiac neural crest tissues Sex-specific nanomedicine efficacy studies

Dmitriy Babichenkoمشاهده پروفایل

استاد بالینی

• Serious Games
• Immersive Media
• Medical Education Technology
• +۳ مورد دیگر

Dmitriy Babichenko is a Clinical Professor in the Department of Informatics and Networked Systems (DINS) at the University of Pittsburgh's School of Computing and Information (SCI). With 16 years of industry experience in IT and educational software, he specializes in game design, user experience engineering, and medical education technology. He co-founded SCI’s Learning Technologies Laboratory in 2015, focusing on serious games and immersive media for healthcare and education. His research interests include serious games for healthcare interventions, immersive media applications in cultural preservation (e.g., HeritageRoots project), and AR/VR in surgical training. He teaches courses in programming, data mining, and database management. His work bridges educational technology and clinical medicine, with notable projects like the Dr. Zoo game for pediatric anxiety reduction and SterileAR for sterile compounding training. Publications span medical informatics, IBD biomarker research, and educational game design. Key contributions include studies on dietary impacts on IBD severity, AR surgical tools, and community-engaged serious game development. His lab explores both technological and pedagogical innovations, emphasizing practical applications in healthcare and STEM education.

Lingfei Wuمشاهده پروفایل

استادیار

• Computational Social Science
• Science of Science
• Team Science
• +۵ مورد دیگر

Lingfei Wu is an Assistant Professor of Information Science at the University of Pittsburgh's School of Computing and Information, specializing in computational social science. His work focuses on understanding how science and technology advance through collaborative dynamics, particularly the roles of team size and composition. He holds a Ph.D. from City University of Hong Kong. Research interests include the Science of Science, team science, and innovation studies, leveraging big data, complexity sciences, and AI. His groundbreaking studies on team dynamics in Nature and PNAS reveal that small teams drive disruptive ideas while large teams consolidate existing knowledge. He advises organizations like Novo Nordisk Fonden and the John Templeton Foundation on data-driven teamwork evaluation. Education: Ph.D., City University of Hong Kong Awards: NSF Career Award, Richard King Mellon Award, Oxford Martin School Fellowship His work bridges research, education, and industry through analyses of skill gaps in the data economy, emphasizing soft skills' growing importance. Active in Knowledge Lab at the University of Chicago and affiliated with Swarma Club and Northwestern Institute on Complex Systems (NICO).

Rui Henriquesمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Rui Henriques is an Assistant Professor at the Department of Electrical and Computer Engineering (DEI), Instituto Superior Técnico (IST), University of Lisbon. He is also an Associate Researcher at INESC-ID. His research focuses on data mining, machine learning, bioinformatics, and urban analytics, with applications in healthcare, transportation systems, and pattern recognition. He has advised numerous PhD and MSc students, contributing to impactful projects in areas like healthcare decision support, mobility optimization, and environmental data analysis. Education: PhD in Learning from High-Dimensional Data (2016) from IST, University of Lisbon. Research interests include developing algorithms for high-dimensional data analysis, integrating spatiotemporal data for urban planning, and applying machine learning to biomedical challenges. Notable contributions include tools like BicPAMS for biological data analysis and software frameworks for transportation analytics. His work spans over 100 publications in top journals and conferences, addressing topics from triclustering algorithms to fraud detection in online tests. He leads or contributes to projects such as ILU (city mobility optimization) and Data2Help (emergency medical services optimization). His research emphasizes interdisciplinary approaches, combining data science with real-world applications in health, transport, and sustainability.

Charles F. Vardeman IIمشاهده پروفایل

استاد پژوهشی

• Computational Science
• Linked Data
• Semantic Web
• +۴ مورد دیگر

Charles F. Vardeman II is a Research Professor in the Department of Computer Science and Engineering at the University of Notre Dame, where he also holds an appointment at the Center for Research Computing (CRC). He earned his B.S. and Ph.D. in Chemistry from Notre Dame, focusing on theoretical chemistry and molecular dynamics simulations of nano-metallic and glassy systems. His research integrates computational models with linked data and semantic web principles, emphasizing ontology design patterns (ODPs) to enhance data integration and discovery. He explores AI-driven approaches for knowledge representation in machine learning and symbolic neural networks. Education: B.S. in Chemistry (Notre Dame), Ph.D. in Theoretical Chemistry (Notre Dame). Research interests include computational modeling, semantic web technologies, ontology engineering, and the application of AI to material science and environmental sustainability. His work bridges domains like molecular dynamics simulations, geospatial data systems, and cyberinfrastructure design. Recent projects address hurricane risk assessment, resilient building design, and open-source molecular dynamics tools like OpenMD. Publications focus on advancing open-source computational tools, ontology-driven data integration, and interdisciplinary cyberinfrastructure. Notable contributions include developing the OpenMD molecular dynamics engine and frameworks for geospatial data interoperability. He has contributed to initiatives like the Cyberinfrastructure Center of Excellence and CyberEye for disaster response systems. His efforts emphasize linking computational experiments with real-world applications through semantic technologies.