جستجوی استاد راهنمای دانشگاهی

• Pharmacology Education
• Neurobiology
• Molecular Pharmacology
• +۳ مورد دیگر

Katelin Dannen, PhD, is an Assistant Professor of Neuroscience and Pharmacology and Associate Chair for Education at the University of Iowa's Department of Neuroscience and Pharmacology. Her research focuses on improving pharmacology education through active learning strategies and understanding the impact of pharmacology training on drug consumer knowledge. She also investigates the molecular mechanisms of RGS6 proteins in neurodegenerative disorders and cancer. Education: B.A. in Biology, Augustana College (Sioux Falls, SD) Ph.D. in Biology (Neurobiology emphasis), University of Iowa Postdoctoral Fellow, Department of Pharmacology, University of Iowa Research Interests: Dr. Dannen combines educational innovation with molecular biology to address gaps in pharmacology pedagogy. Her work includes developing application-based small-group exercises and diagramming activities to enhance student engagement. Concurrently, she explores RGS6’s role in Parkinson’s disease, intellectual disability, and cancer progression, focusing on protein isoforms and signaling pathways. Publications: Her research spans pharmacology education and molecular studies, with recent contributions to RGS6’s role in neurodegeneration, cardiovascular development, and tumor suppression. Notable work includes identifying novel RGS6 isoforms linked to Parkinson’s and demonstrating RGS6’s tumor suppressor function in bladder carcinogenesis. Awards: Favorable peer review from MERLOT (2023) for her Critical Thinking Pharmacology Worksheets Advising & Grants: No student advisees are listed in available records. Grant information is not specified in the provided data.

• Computer Vision
• Machine Learning
• Image Classification
• +۳ مورد دیگر

Jalila Filali is a Researcher at Université Laval, affiliated with the Computer Vision and Systems Laboratory. Her work focuses on advancing computer vision techniques through ontology integration and deep learning models, with applications in environmental monitoring and wildlife tracking. She holds a post-doctoral position and specializes in image classification, object detection, and semantic annotation systems. Her research interests include developing ontology-driven systems for image annotation, comparing machine learning models like HMAX and Bag-of-Visual-Words for large-scale classification, and applying YOLOv5 frameworks to analyze wildlife behavior via camera-collar videos. She has contributed to projects involving environmental information extraction in northern Quebec ecosystems. Key technical trends in her publications include hybrid ontology-machine learning architectures, classifier fusion techniques, and the integration of visual features with semantic knowledge bases. She has explored both theoretical advancements in computer vision algorithms and practical implementations in ecological monitoring contexts. Jalila Filali's work is supported by the Computer Vision and Systems Laboratory at Université Laval, where she contributes to interdisciplinary projects merging artificial intelligence with ecological research.

Dena Bazazianمشاهده پروفایل

مدرس

• Robotics
• Machine Vision
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Dena Bazazian is a Lecturer in Robotics and Machine Vision at the University of Plymouth's School of Engineering, Computing and Mathematics. Previously, she held positions as a Senior Research Associate at the University of Bristol's Visual Information Laboratory, Research Scientist at CTTC (Barcelona), and postdoctoral researcher at Universitat Autònoma de Barcelona, where she earned her PhD in 2018. Her international research includes visiting positions at Barcelona Supercomputing Center (2024), NAVER LABS Europe (2019), and University of Florence (2017). Her research focuses on robotic vision, underwater technologies, and AI-driven applications. Key interests include: 3D reconstruction and neural rendering techniques Anomaly detection using graph neural networks Underwater perception systems for marine environments Intergenerational co-design of immersive technologies Point cloud processing and geometric deep learning Publication analysis reveals strong emphasis on computer vision applications across robotics, industrial inspection, heritage preservation, and marine technology. Recent works increasingly integrate human-centered approaches with XR technologies and underwater systems. She currently supervises 8 PhD students across robotics, healthcare, and environmental monitoring domains: Offshore wind turbine maintenance drones (EPSRC-UDLA funded) Autonomous sea vessel swarms for climate forecasting (ARIA funded) AI for marine biodiversity monitoring (Cefas funded) Elderly fall detection systems (URS General funded) Healthcare anxiety detection through visual analysis (EPSRC DTP HMT funded) Dr. Bazazian leads modules including Artificial Vision and Deep Learning and Computer Vision . She co-organizes workshops including the Deep Learning for Geometric Computing series (CVPR/ICCV) and Women in Computer Vision workshops (CVPR/ECCV).

James Martinمشاهده پروفایل

استاد

• Artificial Intelligence
• Cognitive Science
• Natural Language Processing
• +۵ مورد دیگر

James Martin is a Professor of Computer Science at the University of Colorado at Boulder and a Fellow in the Institute of Cognitive Science. He holds a B.S. in Computer Science from Columbia University and a Ph.D. in Computer Science from the University of California at Berkeley. His research focuses on computational semantics, particularly how languages convey meaning to humans and computers, with a specific emphasis on metaphor processing and non-literal language analysis. He co-authored the widely used textbook Speech and Language Processing (3rd edition in progress), and his work extends to applications in healthcare and education through projects like the TalkMoves dataset analyzing classroom discourse. Current research includes cross-document event coreference resolution, AMR parsing tools (e.g., X-AMR), and AI-driven educational systems via the Institute for Student-AI Teaming (iSAT). Education History: Bachelor of Science in Computer Science, Columbia University Doctor of Philosophy (Ph.D.) in Computer Science, University of California, Berkeley Research Interests: Natural Language Processing (NLP) and Computational Linguistics Metaphor Analysis in Language and AI Semantic Parsing and Knowledge Representation AI Applications in Healthcare and Education Dialogue Systems and Classroom Discourse Analysis Recent Work Trends: His 2023-2024 publications emphasize multimodal NLP, cross-document semantic analysis (e.g., event coreference resolution), and AI tools for education (e.g., TalkMoves application for teacher feedback). NSF-funded National AI Institutes collaborations highlight strategic technology development. No scientific awards explicitly listed, though his textbook and research contributions are widely recognized in the field. Currently not actively recruiting PhD students. Labs/Teams: Core contributor to NLP initiatives at University of Colorado, particularly in semantic parsing and educational AI through iSAT. Involved with projects like the TalkMoves dataset and AMR annotation tools (CAMRA, X-AMR).

Zhihua Jiangمشاهده پروفایل

استاد

• Animal Phenomics
• RNA Variants Analysis
• Genome-to-Phenome Research
• +۵ مورد دیگر

Professor Zhihua Jiang leads research in Animal Phenomics at Washington State University's Department of Animal Sciences within the College of Agricultural, Human, and Natural Resource Sciences. His work focuses on bridging genomics and phenomics to optimize livestock systems, emphasizing RNA variants' roles in phenotype-genome interactions. Key areas include transcriptome profiling, epigenomics, and alternative polyadenylation studies across species like sheep, pigs, and rats. Research highlights include the FarmGTEx project analyzing livestock transcriptomes and studies on hybridization in peafowl. He actively recruits students for the Genome-to-Phenome PhD Program. Advising and grants details are pending specific mentions in provided texts. His lab's contributions span domestication genomics, microbiome interactions, and disease-related transcriptomic mechanisms.

Adrian Rebmannمشاهده پروفایل

پژوهشگر

• Process Mining
• Event Data Preprocessing
• Event Abstraction
• +۳ مورد دیگر

Adrian Rebmann is a Researcher in the Process Analytics research group at the Data and Web Science Group, School of Business Informatics and Mathematics, University of Mannheim. He is supervised by Prof. Dr. Han van der Aa, focusing on advancing process mining methodologies through semantic event parsing and event abstraction. His work emphasizes preprocessing event data for meaningful process analysis, leveraging natural language processing and machine learning techniques. Research Interests: Process Mining, Event Data Preprocessing, Event Abstraction, Semantic Event Parsing, and Business Process Management. Teaching: Co-lecturer for Advanced Process Mining (since 2021) and Process Management and Analytics courses. Supervised Process Analysis Seminars and Team Projects. Service: PC Member of workshops like NLP4BPM and ZEUS (2021-2024), and reviewer for journals like Information Systems and conferences like ECIS and Modellierung. Labs/Teams: Active contributor to the Data and Web Science Group, focusing on Process Mining innovations and tool development (e.g., M-Viz for inductive miner visualization).

Gene E Robinsonمشاهده پروفایل

استاد

• Entomology
• Genomics
• Social Behavior
• +۶ مورد دیگر

Gene E. Robinson is the Maybelle Leland Swanlund Endowed Chair and Professor of Entomology at the University of Illinois at Urbana-Champaign. He leads the Carl R. Woese Institute for Genomic Biology (IGB) and holds affiliate appointments in multiple departments, including the Center for Advanced Study, Neuroscience Program, and Beckman Institute. His work integrates genomics, neuroscience, and behavioral ecology to study social insect behavior, primarily using honey bees as a model. Robinson earned his Ph.D. in Entomology from Cornell University (1986) and joined UIUC in 1989. His research focuses on the genetic and neuroendocrine mechanisms underlying social behavior, division of labor, aggression, and communication in honey bees. Key projects include studying nutritional regulation of brain gene expression, gene regulatory networks in social evolution, and automated behavioral monitoring using RFID and AI. He has authored over 350 publications, secured $70M+ in research funding, and trained ~60 early-career scientists. His honors include the Wolf Prize in Agriculture, Guggenheim Fellowship, and memberships in the National Academy of Sciences and National Academy of Medicine. Robinson’s interdisciplinary approach bridges molecular biology, ecology, and computational methods to address societal challenges in agriculture and health. Affiliations: Carl R. Woese Institute for Genomic Biology, Department of Entomology, Center for Advanced Study Leadership: Director of IGB (2001–present), former Neuroscience Program co-director Research Themes: Social evolution, genomics, neurogenomics, and bee health

Michael A Normanمشاهده پروفایل

دانشیار

• Discovery Systems
• Transaction Log Analysis
• Web-scale Discovery Services
• +۳ مورد دیگر

Michael A Norman is an Associate Professor in the University Library at the University of Illinois Urbana-Champaign, serving as Discovery Services Librarian and Integrated Library System (ILS) Coordinator. His work focuses on advancing discovery systems, transaction log analysis, and web-scale library technologies. He has contributed to innovations like the Bento discovery framework and conducted extensive research on library resource management, user behavior, and digital preservation. Norman has collaborated on projects involving Primo, SFX, and schema.org integration, emphasizing usability and interoperability in library systems. Education: Details not explicitly stated in provided texts. Research Interests: Discovery systems architecture, user-centered design in library technology, web-scale resource management, metadata standards implementation, and historical collection analysis. His work bridges technical innovation with practical library services, addressing challenges in digital resource access and preservation. Publications: Over 13 peer-reviewed articles and datasets, focusing on discovery systems evaluation, copyright renewal studies, and collection management strategies. Recent work emphasizes Bento architecture and user interaction analysis within digital libraries. Awards: No specific awards listed in provided materials. Advising/Grants: No formal advisee list provided; contributions include collaborative research grants related to library technology projects.

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

Wenjie Yin is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. She specializes in Data Mining and Machine Learning, focusing on Natural Language Processing (NLP), hate speech detection, and social media analysis. Her research addresses challenges like cross-domain generalization in hate speech detection and the impact of AI tools like ChatGPT on information platforms. She teaches postgraduate modules on Data Mining and Machine Learning, emphasizing practical algorithm exploration and real-world applications. Recent work includes SemEval tasks on explainable NLP and studies on implicit abusive language detection. No specific awards or grants are listed, and her academic contributions are centered on advancing NLP techniques for societal challenges.

Natalia Sidorovaمشاهده پروفایل

استادیار

• Process Mining
• Petri Nets
• Workflow Analysis
• +۳ مورد دیگر

Natalia Sidorova is an Assistant Professor at the Eindhoven University of Technology , affiliated with the Mathematics and Computer Science department and the Process Analytics research group. She also contributes to the EAISI Health initiative. Her work focuses on process mining, Petri nets, workflow analysis, and healthcare informatics. Research Interests: Dr. Sidorova specializes in formal process modeling, conformance checking, resource-constrained systems, and applications in healthcare and smart environments. Her research bridges theoretical foundations (e.g., soundness verification of workflow nets) with practical tools like log alignment techniques and adaptive workflow systems. She explores data-driven methods for refining process models and analyzing complex event logs. Key Contributions: Her publications cover topics such as log alignment algorithms, resource-aware Petri nets, and stress management systems using sensor data. She co-authored the 2018 Best Paper Award-winning work on intelligent environments and contributed to foundational studies on workflow net soundness undecidability (2008). Teaching: She teaches Foundations of Data Analytics (2023–present). Labs/Teams: Active in Process Analytics and EAISI Health, collaborating on artifact-centric processes and health informatics.

Koen Hindriksمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +۴ مورد دیگر

Prof. Koen Hindriks is a Full Professor at Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Department of Artificial Intelligence. He holds additional roles at the Network Institute and Social AI research groups. His research focuses on social robotics, human-robot interaction, and multi-agent systems, with applications in healthcare, education, and retail. He leads projects like SOROCOVA (social robotics for educational support post-COVID) and Droomrobot (a robot companion for pediatric medical stress reduction). Key research interests include dialogue management, empathy elicitation via robots, and long-term human-robot relationships. He teaches courses such as Multi-Agent Systems and Socially Intelligent Robotics. Recent work emphasizes ethical AI integration and practical deployment of social robots in real-world scenarios. His contributions span over 220 publications, addressing topics like robot learning from human demonstrations and automated speech processing in HRI contexts. Projects: Principal Investigator for SOROCOVA (2021-2023) Robot Companion for Childhood Cancer Patients (2019-2021) Current Droomrobot project (2024-2026) Awards: None explicitly listed, but recognized through extensive peer-reviewed contributions. Advising: Supervised 4 PhD theses, focusing on child-robot interaction and healthcare robotics. Prof. Hindriks also engages in ancillary roles, including Director at Ex Machina (Utrecht) and speaker at the Amsterdam Speakers Academy. His work aligns with UN SDGs, particularly in improving health and education through technology.

Stanisław Dunin-Horkawiczمشاهده پروفایل

پژوهشگر

• Computational Biology
• Protein Structure and Function
• Molecular Evolution
• +۳ مورد دیگر

Dr. Stanisław Dunin-Horkawicz is affiliated with the University of Warsaw's Laboratory of Structural Bioinformatics. His research focuses on computational methods to study protein sequences, structures, and functions, with emphasis on machine learning applications, evolutionary origins of protein families, and fibrous proteins like coiled-coil domains. He leads a systems biology project on molecular pathways related to multicellularity (NCN OPUS grant 2020/37/B/NZ2/03268). Key contributions include developing tools like DeepCoil for coiled-coil prediction, pLM-BLAST for homology detection, and localpdb for protein structure management. His work spans structural bioinformatics, protein engineering, and evolutionary biology, addressing topics such as HAMP domain signaling, RNA structure prediction, and viral protein evolution. Collaborative projects involve international teams and cutting-edge methods like AlphaFold2 for protein modeling. His research has implications for understanding neurodegenerative diseases, bacterial pathogenesis, and enzyme specificity engineering.

Michael A. Longمشاهده پروفایل

استاد

• Molecular Neuroscience
• Cellular Neuroscience
• Translational Neuroscience
• +۵ مورد دیگر

Michael A. Long is the Thomas and Suzanne Murphy Professor of Neuroscience and Vice Chair for Education in the Department of Neuroscience and Physiology at NYU Grossman School of Medicine. He also holds a Professor position in the Department of Otolaryngology-Head and Neck Surgery. PhD from Brown University Postdoctoral Training at MIT's McGovern Institute for Brain Research His research focuses on neural circuits governing skilled movements, particularly vocal interactions, using a comparative approach across songbirds, parrots, singing rodents, and humans. Key areas include molecular/cellular neuroscience, systems/cognitive neuroscience, and computational modeling. The Long Lab investigates vocal communication mechanisms, neural dynamics during speech, cross-species brain network convergence, and develops advanced neurotechnologies like DREDge motion correction algorithms. Their work bridges basic neurobiology and translational applications for speech disorders. Grants from NYSCF, Rita Allen Foundation, Klingenstein Foundation, and Herschel-Weill Foundation support his research program.

Eamonn Bellمشاهده پروفایل

استادیار

• Digital Humanities
• Music Technology History
• Computational Musicology
• +۳ مورد دیگر

Eamonn Bell is an Assistant Professor in the Department of Computer Science at Durham University, specializing in the digital humanities and the history of technology in music production. His research examines the intersection of computational methods with musicology, including early uses of digital computers in musical analysis and the cultural impact of formats like the CD. Previously, he was a postdoctoral Research Fellow at Trinity College Dublin (TCD), funded by the Irish Research Council (2019–2021). Bell holds a PhD in Music Theory from Columbia University (2019), where he explored computational approaches to musical scores under Joseph Dubiel, and a B.A. (Mod.) in Music and Mathematics from TCD (2013). His work spans topics such as the socio-technical history of CD audio formats, algorithmic creativity in early AI research, and the preservation of obsolete digital media. He teaches across computer science curricula, emphasizing interdisciplinary methods in digital humanities. Bell’s research has been supported by UK Research and Innovation (UKRI) and smaller institutional grants, and he contributes to projects like DRI initiatives for UK-based arts researchers. Key awards include the Government of Ireland Postdoctoral Fellowship. His teaching experience includes courses on digital music critique and music theory fundamentals at Columbia, alongside workshops in data visualization and music technology. Bell’s lab and team collaborations focus on emerging technologies in musicology, such as AI’s role in cultural analysis and the challenges of preserving born-digital artifacts.

Keriann Marie Backusمشاهده پروفایل

دانشیار

• Chemoproteomics
• Cysteine Profiling
• Protein Interactome
• +۵ مورد دیگر

Keriann Marie Backus is an Associate Professor in the Department of Biological Chemistry at the University of California Los Angeles (UCLA) School of Medicine. Her research program focuses on developing and applying chemoproteomic approaches to understand protein function, particularly through cysteine profiling and covalent ligand discovery. Dr. Backus leads an active research laboratory that bridges chemical biology, proteomics, and systems biology to address fundamental questions in protein biochemistry and disease mechanisms. Dr. Backus's research interests center on chemoproteomics, with particular emphasis on cysteine reactivity profiling, protein interactome mapping, and covalent ligand discovery. Her laboratory develops innovative chemical and proteomic methodologies to characterize the cysteinome—the complete set of reactive cysteines in the proteome—and to understand how cysteine modifications impact protein function in health and disease. Her work spans fundamental protein biochemistry, chemical probe development, and translational applications in areas including inflammation, cancer, and infectious disease. The lab employs cutting-edge mass spectrometry, chemical biology, and bioinformatics approaches to map protein modifications, interactions, and functions at systems-level resolution. Her publication record demonstrates significant contributions to chemoproteomics methodology development and biological applications. Recent work has focused on creating scalable sample preparation techniques (CySP3-96), organelle-specific cysteine capture methods, cholesterol interactome mapping, and cysteine databases (CysDB). Her research bridges basic chemical biology with translational applications, including tuberculosis imaging and understanding vascular inflammation mechanisms. Dr. Backus has received significant research funding, including an NIH DP2 New Innovator Award (GM146246) for 'A systems-level approach to decipher the protein interactome' and an NIH F32 postdoctoral fellowship (GM108208) for 'Fragment-Based Ligand Discovery in Proteomes.' These awards reflect recognition of her innovative approaches to chemical proteomics and protein interaction mapping. Dr. Backus actively mentors graduate students and postdoctoral researchers in her laboratory, with numerous trainees appearing as first or co-first authors on high-impact publications. Her research program maintains strong collaborations across UCLA and with other institutions, particularly with researchers in the Cravatt laboratory (where she completed her postdoctoral training) and with clinical researchers applying chemoproteomic approaches to disease mechanisms. The Backus laboratory functions as an interdisciplinary research hub at the intersection of chemical biology and proteomics. It combines expertise in organic synthesis, mass spectrometry, computational biology, and cell biology to develop and apply chemoproteomic technologies. The lab maintains strong connections with the broader UCLA research community, particularly through the Graduate Program in Bioscience and collaborations with clinical departments studying inflammation, cancer, and infectious diseases.