جست‌وجوی استاد و پژوهشگر

Simonetta Montemagniمشاهده پروفایل

پژوهشگر

• Computational Linguistics
• Natural Language Processing (NLP)
• Corpus Linguistics
• +۳ مورد دیگر

Simonetta Montemagni is the Interim Director and Research Manager at the Institute of Computational Linguistics (ILC) of the National Research Council of Italy (CNR) in Pisa, leading the ItaliaNLP Lab. She specializes in computational linguistics and natural language processing, with a focus on corpus linguistics, dialectology, and NLP applications in education and legal domains. Her work includes developing machine-actionable resources such as the ParlaMint-IT corpus of parliamentary debates and advancing syntactic dependency treebank research. She has contributed to projects like the Great Dictionary of the Italian Language (GDLI) lemmarium, exploring quantitative trends in historical vocabulary networks and their educational implications. Montemagni’s research also addresses dialectometric studies of Tuscan dialects and the integration of NLP tools for improving readability assessments of health-related texts. Her research interests span computational methods for analyzing language variation, including phonetic and lexical patterns, as well as text simplification and automated knowledge extraction from school documentation. She has edited conference proceedings for events like CLiC-it and EVALITA, highlighting her role in advancing the field through collaborative initiatives. Her contributions often involve cross-lingual and typological analyses, leveraging dependency treebanks and multilingual resources to study syntactic and semantic phenomena. Montemagni is deeply involved in building domain-specific corpora and tools, such as the BioLexicon for biomedical text mining and frameworks like Text-2-Knowledge (T2K) for semantic indexing. Montemagni has collaborated on large-scale projects like the TrAVaSI initiative for historical Italian language resources and contributed to the MEG II experiment at CERN, though her primary focus remains linguistic research. She has pioneered the digitalization of dialectal resources such as the ALT-Web tool and explored the application of NLP in legal ontologies, educational settings, and sociological studies of language use.

Ross Mitchellمشاهده پروفایل

استاد

• Artificial Intelligence in Health
• Medical Imaging Informatics
• Cancer Informatics
• +۳ مورد دیگر

Dr. Ross Mitchell is Professor and Alberta Health Services Chair in AI in Health at the University of Alberta's Faculty of Medicine & Dentistry, with adjunct appointments in Computer Science. As a Canada CIFAR AI Chair and Fellow of the Alberta Machine Intelligence Institute, he leads research on AI applications in healthcare, particularly in medical imaging and cancer informatics. His work focuses on developing machine learning algorithms for improved cancer detection, treatment planning, and outcome prediction. Research Focus: Federated learning for multi-institutional medical collaborations AI algorithms for brain tumor segmentation and analysis Natural language processing of clinical reports Radiomics and radiogenomics for cancer characterization Awards and Honors: Top Achievement Award, H. Lee Moffitt Cancer Center (2021) Outstanding Researcher Designation, US Citizenship and Immigration Service (2015) Alumni Crowning Achievement Award, University of Regina (2011) Dr. Donald Paty Career Development Award (2006) Collip Medal, Western University (1996) Leadership Roles: Senior Program Director of Artificial Intelligence Adoption, Alberta Health Services Former Inaugural AI Officer, H. Lee Moffitt Cancer Center Former Director of Medical Imaging Informatics, Mayo Clinic Arizona

Wankun Dengمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Transcriptomics
• +۳ مورد دیگر

Dr. Wankun Deng is an Assistant Professor in the Department of Bioinformatics and Systems Medicine at the McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth). He joined UTHealth in February 2023 following a postdoctoral fellowship at the Children’s Hospital of Philadelphia/University of Pennsylvania, where he studied RNA dynamics. His expertise lies at the intersection of bioinformatics, machine learning, and systems biology, with a focus on deciphering biological mechanisms through multi-omics data integration. Dr. Deng earned his PhD in Biomedical Engineering from Huazhong University of Science and Technology, Wuhan, China (2017). His research spans post-translational modification (PTM) analysis, RNA regulation via m6A modifications and RNA-binding proteins (RBPs), and the application of computational methods to neurodegenerative diseases. He aims to uncover disease mechanisms by systematically analyzing complex biological datasets. His technical strengths include algorithm development for predicting biological pathways, transcriptomic/proteomic data interpretation, and leveraging machine learning for systems-level insights. While specific awards are not listed, his work reflects sustained innovation in biomedical informatics. Dr. Deng’s current role at UTHealth continues his trajectory of integrating computational tools with biomedical questions, particularly in neurodegenerative disorders. His lab focuses on multi-omics approaches to trace complex traits in diseases such as Alzheimer’s or Parkinson’s, though specific grants or student advisees are not detailed in the provided text.

Sunyang Fuمشاهده پروفایل

استادیار

• Aging informatics
• Clinical NLP
• Real-world data
• +۳ مورد دیگر

Sunyang Fu is an Assistant Professor in the Department of Clinical and Health Informatics at UTHealth Houston, and Associate Director of Team Science at the Center for Translational AI Excellence and Applications in Medicine (TEAM-AI). He holds affiliations with the UTHealth Institute on Aging, the Network for Investigation of Delirium, and the Mayo Clinic's Division of Epidemiology. Education : PhD in Biomedical Informatics (2021, University of Minnesota), MHI in Health Informatics (2017, University of Michigan), BBA in Management Information Systems (2014, University of Iowa). Research : Focuses on accelerating EHR secondary use for equitable clinical research, aging informatics, and clinical NLP. Notable projects include developing delirium detection algorithms using the Confusion Assessment Method (CAM) framework, and leading national initiatives in aging informatics through ENACT, OHDSI, and AMIA working groups. Funding : Supported by NIH (NIA, AITC for Aging Research), CPRIT, and NIDUS. Awards : NIH AIM-AHEAD Leadership Fellow (2023-24).

Amber Stubbsمشاهده پروفایل

دانشیار

• Natural Language Processing
• Medical Informatics
• Computational Linguistics
• +۳ مورد دیگر

Amber Stubbs is an Associate Professor, Chair of the Department of Computer, Data, and Mathematical Sciences, and Undergraduate Computer Science & Informatics Program Director at Simmons University. She holds a PhD in Computer Science from Brandeis University (2013), focusing on natural language processing and corpus annotation methodologies. Her research emphasizes annotation practices for high-level information extraction, particularly in medical narratives. She co-authored the influential textbook *Natural Language Annotation for Machine Learning* (O'Reilly, 2012) and developed the MAE/MAI annotation tools used globally. Stubbs' academic career includes postdoctoral work at SUNY Albany (2012–2014) and adjunct roles at MIT and Brandeis. She teaches courses in data science, programming languages, and interdisciplinary computing. Her grants include NSF-funded research on syntactic corpus development and security in brain-computer interfaces. She has advised numerous student projects, including database creation for historical incarceration records and cybersecurity analysis of medical APIs. Education: PhD Computer Science (Brandeis, 2013), BS Computer Science & English (Simmons, 2005) Research Focus: NLP in healthcare, annotation methodologies, longitudinal clinical data analysis Awards: Cultural Competence in Computing Fellow (2022–2024)

Santaneel Ghoshمشاهده پروفایل

استاد

• Nanoscale Engineering
• Drug Delivery Systems
• Tumor Therapeutics
• +۲ مورد دیگر

Dr. Santaneel Ghosh is Professor of Engineering at Southeast Missouri State University, directing Nano-Bio Laboratory research since 2007. His PhD in Mechanical Engineering (University of Arizona) informs interdisciplinary work spanning nanodevice design, CNS drug delivery, and cancer therapeutics. Research collaborations include partnerships with NIST, Colorado School of Mines, and University of North Texas. Patents cover medical device innovations and therapeutic delivery systems. Grant support spans NSF, NASA, and AFOSR funding. No recent publications listed in provided text.

Martha Garlickمشاهده پروفایل

استاد

• Ecology
• Mathematical Biology
• Biostatistics
• +۳ مورد دیگر

Martha Garlick is a Professor at the South Dakota School of Mines & Technology. Her research focuses on mathematical and statistical modeling applied to ecological and biological systems, including animal movement, disease spread, and quantitative PCR optimization. She holds a B.S., M.S., and Ph.D. from Utah State University. Education B.S., Utah State University M.S., Utah State University Ph.D., Utah State University Her work bridges computational methods with ecological and biomedical challenges, such as modeling chronic wasting disease in deer and optimizing PCR techniques. Notable contributions include applying homogenization techniques to large-scale ecological models and analyzing power-law behaviors in insect outbreaks. Dr. Garlick’s articles emphasize interdisciplinary approaches, combining differential equations, statistical modeling, and computational efficiency to address real-world problems in biology and ecology. No scientific awards are listed in the provided materials. Advising & Grants No advisees or grant information is specified in the text. Labs/Teams: No specific labs or collaborative teams are mentioned.

Robert Tibshiraniمشاهده پروفایل

استاد

• Applied Statistics
• Biostatistics
• Statistical Learning
• +۷ مورد دیگر

Robert Tibshirani is a Professor at Stanford University , holding joint appointments in the Department of Statistics and Department of Biomedical Data Science . He is also affiliated with Bio-X , a multidisciplinary research group. Co-author of foundational texts: Generalized Additive Models , An Introduction to the Bootstrap , and Elements of Statistical Learning Developer of influential statistical software packages like glmnet , lasso , and selectiveInference in R and MATLAB Recipient of prestigious awards: WNAR Outstanding Impact Award (2024) and COPSS Distinguished Achievement Award Research Focus : Tibshirani specializes in applied statistics , biostatistics , and data mining . His work spans methodologies like the Lasso and its extensions, with applications in genomics , proteomics , and medical research . He has pioneered techniques for post-selection inference , survival analysis , and high-dimensional data problems. Awards : • 2024 : WNAR Outstanding Impact Award • 2024 : COPSS Distinguished Achievement Award Students : • Erin Craig • Daisy Ding • Hui Xu Key Collaborations : Partnered with Trevor Hastie , Bradley Efron , and Balasubramanian Narasimhan on software development and theoretical advancements. His work has been supported by grants from the National Science Foundation and National Institutes of Health .

• Security Studies
• Urban Sociology
• Science and Technology Studies (STS)
• +۵ مورد دیگر

Dr. Elisa Pieri is a Senior Lecturer in Sociology at the University of Manchester. Her research focuses on security, urban sociology, and the ethical implications of emerging technologies. She leads the 'Securing Cities Against Global Pandemics' project funded by the Simon Fellowship, exploring pandemic risk framing, urban preparedness, and technology roles in city security. Pieri teaches courses such as Urban Sociology, Pandemic Security, and Global Social Challenges, and has supervised numerous postgraduate and undergraduate dissertations. Education: Laurea (University of Bologna), MSc (University of Edinburgh), PhD (University of Manchester) Expertise: Pandemic preparedness, biopolitics, science communication, and media analysis Her work bridges sociology and policy, addressing how cities securitize against pandemics and the societal impacts of biometric technologies. Awards include the Higher Education Academy Fellowship and Simon Research Fellowship. Pieri contributes to global health policy debates, media commentary on pandemic ethics, and interdisciplinary research networks.

Vololona Rabeharisoaمشاهده پروفایل

استاد

• Medical Sociology
• Science and Technology Studies
• Health Governance
• +۳ مورد دیگر

Vololona Rabeharisoa is a Professor affiliated with MINES ParisTech, specializing in the sociological study of civil society's engagement with scientific and technical activities. Her research focuses on transformative effects of patient organizations in biomedical research, governance of health systems, and drug development, particularly in rare diseases. She also explores genomics' societal implications and the role of lay expertise in criminal justice. Her work bridges Science and Technology Studies with medical sociology, emphasizing interdisciplinary approaches. Research Interests: She examines how patient advocacy shapes therapeutic innovation, the ethics of drug pricing, and the intersection of genetics with public health. Recent projects include genetic diagnosis of CADASIL syndrome and DNA fingerprinting in forensic contexts. Her studies highlight sociological dimensions of clinical research, including patient experiences with rare diseases and the evolving role of victims in legal systems. Advising & Grants: While no specific advisees are listed, her research projects suggest involvement in collaborative initiatives on health policy and genomics. Funding sources and grant details are not detailed in the provided text. Labs/Teams: Affiliation details with specific research groups or laboratories are not explicitly mentioned, though her work aligns with interdisciplinary teams studying science-society interactions.

Sen Wangمشاهده پروفایل

دانشیار

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

Sen Wang is an Associate Professor in the School of Electrical Engineering and Computer Science at The University of Queensland (UQ). He holds an ARC DECRA Senior Research Fellowship and has expertise in machine learning, deep learning, data mining, and health informatics. His research focuses on applications such as ICU data analysis, clinical collaborations with hospitals, and developing algorithms for feature selection, semi-supervised learning, and healthcare analytics. Dr. Wang has published over 80 papers in top-tier conferences and journals, including CVPR, AAAI, and IEEE Transactions series. He has secured several grants, including industry collaborations and ARC funding. Education: PhD in Computer Science (UQ, 2014), MSc and BSc in Computer Science (Jilin University and Liaoning Shihua University). He has taught courses in data science, cloud computing, and databases at UQ and Griffith University. Research interests include AI-driven healthcare solutions, multimodal data analysis, and efficient machine learning models. His work addresses challenges in medical data interpretation, patient outcome prediction, and robust model adaptation in dynamic environments. Key achievements include developing the BiteNet model for medical outcome prediction and contributions to EEG-based intention recognition systems. He actively supervises PhD students in areas like zero-shot learning and healthcare informatics.

• Human-Computer Interaction
• Emerging Technologies
• Digital Health
• +۳ مورد دیگر

Dr. Kiran Ijaz is a Lecturer in Human-Computer Interaction at the University of Sydney's School of Architecture, Design and Planning. She holds a PhD in Engineering from The University of Sydney and an MSc from The University of Technology, Sydney. Her research focuses on emerging technologies, digital health, and data privacy within the HCI domain, particularly exploring how these technologies can enhance human flourishing. She is also an Honorary Research Fellow at the Australian Institute of Health Innovation. Her work bridges interdisciplinary fields including affective computing, virtual reality applications, and speculative design for future urban living. Notable collaborations include projects at Design Lab and Affective Interactions Lab. Her research has been recognized through multiple teaching and research awards, though specific award names are not detailed in the provided text. Dr. Ijaz’s academic contributions span both journal articles and conference proceedings, with recent emphases on digital health interventions, privacy in smart technologies, and cognitive assessment tools. She actively contributes to the development of immersive VR platforms for health and wellbeing, as well as systematic reviews evaluating digital tools for chronic disease prevention.

• Data Science
• Precision Medicine
• Biomedicine
• +۵ مورد دیگر

Prof. Holger Fröhlich is affiliated with the University of Bonn and Fraunhofer Institute for Algorithms and Scientific Computing (SCAI). His research focuses on Data Science, precision medicine, and algorithm development for biomedical applications. He leads a team developing AI and machine learning methods for drug discovery, systems medicine, and clinical study simulation. His work emphasizes multi-modal data integration and Hybrid AI approaches to address complex biomedical questions. Research interests include statistical data mining, time series modeling, and applications in early drug discovery. He has published on topics such as AI-based drug response prediction, clustering of clinical patient trajectories, and simulation of heterogeneous clinical study data. No scientific awards or externally funded professorships are explicitly mentioned. His team's work spans drug target identification, biological system modeling, and AI-driven clinical study simulations. No student advisees are listed in the provided texts.

Daniel Gildeaمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Translation
• Semantic Parsing
• +۳ مورد دیگر

Professor Daniel Gildea is a faculty member in the Department of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester . He holds a PhD from the University of California at Berkeley. His research focuses on statistical approaches to natural language processing, including machine translation, semantic parsing, and algorithms for NLP. He explores the intersection of formal linguistics and computational methods, with notable contributions to AMR (Abstract Meaning Representation) parsing and semantic role labeling. Education : PhD in Computer Science from UC Berkeley. Research Interests : Machine Translation Semantic Parsing Statistical NLP Parsing Algorithms (e.g., AMR Parsing) Formal Semantics Graph-Based NLP Models Research Trends : Gildea’s work emphasizes structured prediction, graph decomposition techniques for NLP, and the application of formal methods to linguistic problems. Recent projects include optimizing AMR-to-text generation, exploring semantic underspecification, and developing efficient parsing strategies for context-free grammars. Awards : None explicitly mentioned in the provided texts. Advising & Grants : No formal advisees or grant details listed. His research collaborations likely involve NLP-focused projects at the University of Rochester. Labs/Teams : Engaged with computational linguistics research groups, though specific lab affiliations are not detailed here.

Dario Zancaمشاهده پروفایل

پژوهشگر

• Visual Perception
• Machine Learning
• Neural Networks
• +۴ مورد دیگر

Dario Zanca is a Postdoctoral Researcher in the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He is affiliated with the Machine Learning and Data Analytics (MaD) Lab and focuses on visual perception, eye movement analysis, and machine learning applications. His work bridges computational models of human attention with neural network behavior, contributing datasets like FixaTons and CapMIT1003. Research areas include: Biologically-inspired visual attention models Eye tracking and gaze analysis in clinical and computational contexts Deep learning interpretability and explainable AI Time series analysis and predictive process monitoring Key contributions include tools like the SaccadeAnalysisMatlabApp for gaze data processing and participation in initiatives like the PolarBearVidID dataset for wildlife behavior analysis. His interdisciplinary projects span healthcare, robotics, and environmental science, emphasizing real-world applications of AI.