Data Annotation Techniques Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۲۱-۷۳۵ از ۱٬۴۶۶ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Meysam Alizadehمشاهده پروفایل

پژوهشگر ارشد

University of ZurichSwitzerland

Meysam Alizadeh is a Research Fellow at the Department of Political Science, University of Zurich. His work focuses on digital media governance, social network analysis, and the application of artificial intelligence in political and social contexts. He investigates issues such as platform governance, content moderation, fake news propagation, and the impact of social media on political discourse. Alizadeh has collaborated extensively on projects involving large language models (LLMs) for text annotation, comparing their performance to human workers, and developing methods to detect information operations and hate speech. His research bridges computer science, political science, and data science, addressing both theoretical and applied challenges in digital society. Key areas of investigation include analyzing cryptocurrency market dynamics through social media data, exploring the relationship between Russian information campaigns and hate crimes, and examining conspiracy theory proliferation during the pandemic. He has contributed to methodological advancements in creating national random samples of Twitter users and optimizing LLM-based tools for academic and practical applications. Alizadeh's publications span journals like Political Communication , Scientific Reports , and Proceedings of the National Academy of Sciences , reflecting interdisciplinary collaboration with institutions worldwide. His research often emphasizes the ethical and policy implications of emerging technologies in public communication and governance.

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Johan Boyeمشاهده پروفایل

دانشیار

KTH Royal Institute of TechnologySweden

Johan Boye is an Associate Professor at the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, part of the Speech, Music, and Hearing department. He leads the SWE-QUEST project, developing automated question generation for Swedish language education, funded by Vinnova and Digital Futures. His research focuses on dialogue systems, natural language processing, and spatial language understanding, with applications in education, robotics, and human-computer interaction. Education background includes a docent title (Swedish associate professor) and extensive academic experience. He has supervised over 20 master’s and PhD theses in NLP and AI, with recent topics including large language models, coreference resolution, and multimodal retrieval systems. Teaching responsibilities include courses on language technology, search engines, and speech processing. His work spans cross-disciplinary collaborations with Stockholm University and RISE Research Institutes in the Digital Futures initiative, addressing societal challenges through digital innovation. Key projects include the SpaceBook EU project (2010–2014) exploring pedestrian navigation via dialogue systems and the NICE game (2005) for child-computer interaction. Research outputs include over 50 peer-reviewed publications in journals like the Journal of Natural Language Engineering and conferences such as ACL and SIGDIAL. He maintains active roles in academic service, including editorial work and program committees for NLP conferences. His lab develops tools like Textinator for NLP evaluation and UDon2 for dependency tree manipulation, emphasizing open-source contributions to the research community.

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Seth J Davisمشاهده پروفایل

استاد

University of YorkUnited Kingdom

Professor Seth J. Davis holds the Chair of Plant Biology at the University of York's Department of Biology. His research focuses on plant circadian clocks, environmental responses in extremophilic microalgae (Galdieria), and their applications in crop improvement and biotechnology. He integrates systems biology approaches, including metabolite profiling, genomics, and quantitative genetics to unravel circadian mechanisms. His work bridges fundamental science with agricultural innovation, particularly in stress-resilient crop development. Teaching emphasizes skills-based learning, including student-led research projects, grant-writing exercises, and critical analysis of scientific literature. Courses cover genetic modification, plant model systems, and data analysis. He offers final-year projects on circadian rhythms, Galdieria biochemistry, and genomics. His research highlights include genomic studies of Galdieria's extremophile adaptations, circadian clock regulation in Arabidopsis and barley, and metabolic pathways influencing crop yield under adverse conditions. Collaborations focus on applying circadian insights to improve crop resilience through genetic variation analysis. Publications emphasize circadian clock dynamics, extremophile biology, and translational biotechnology. His lab explores telomere-to-telomere genome assembly techniques to address evolutionary questions in extremophiles. Grant activities and advising are central to his academic leadership, fostering interdisciplinary approaches to plant science challenges.

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Chinmay Hegdeمشاهده پروفایل

دانشیار

New York UniversityUnited States

Machine Learning
Algorithms
Big Data
+۶ مورد دیگر

Chinmay Hegde is an Associate Professor at the Tandon School of Engineering, NYU, with a joint appointment in Computer Science and Electrical Engineering. His research focuses on machine learning, signal processing, and their applications in cybersecurity, imaging, and materials science. He leads the DICE Lab, exploring data-driven solutions for complex engineering problems. Previously, he held positions at Iowa State University and MIT. Education: Ph.D. in Electrical and Computer Engineering, Rice University (2012) M.S. in Electrical and Computer Engineering, Rice University (2010) B.Tech. in Electrical Engineering, IIT Madras (2006) Research Interests: Machine Learning, Algorithms, Big Data, Signal Processing, Generative Models, Scientific Computing. Awards: NSF CAREER Award (2018-2023) NeurIPS Top Reviewer Award (2019) MRS Open Data Challenge Award (2019) Ralph Budd Award for Best Thesis (2013) Labs & Teams: Director of the DICE Lab, collaborating with industry partners on projects like real-time deepfake detection (GOTCHA), generative models for microstructure design, and AI-driven materials science.

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Kristian Flikkaمشاهده پروفایل

دانشیار

University of BergenNorway

Kristian Flikka is an Associate Professor at the Department of Informatics, University of Bergen. His research focuses on computational methods in proteomics, bioinformatics, and mass spectrometry. He has expertise in developing tools for proteomic data analysis, including clustering algorithms, spectral quality filtering, and biomarker discovery. His work bridges computational techniques with life sciences, addressing challenges in data preprocessing, error reduction, and interpretability in AI models. Education: PhD in Computational Methods in High-Throughput Proteomics Research (2007) MSc in Methods for Analyzing 2D Electrophoretic Gel Images (2002) Research Interests: His research spans proteomics, computational biology, and AI applications in time series classification. Key areas include: Development of open-source tools for mass spectrometry data (e.g., ms_lims) Prediction of membrane protein structures (BOMP program) Pre-analytical proteomic data treatment Explainable AI for end-user model interpretation Publications: Over 15 publications since 2002, with notable contributions in BMC Bioinformatics , Proteomics , and Nucleic Acids Research . Recent work explores XAI in time series classification, reflecting expanded interests in AI interpretability. Awards & Grants: No specific awards listed, but his research has been supported by grants like the Norwegian Research Council (329745, 2022-2026). Labs/Teams: Collaborates with interdisciplinary teams at the University of Bergen, NORCE Norwegian Research Center, and international partners (e.g., Ghent University). Active in computational proteomics and AI-driven bioinformatics projects.

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Steven Zimmerlyمشاهده پروفایل

استاد

University of CalgaryCanada

Steven Zimmerly is a Professor in the Department of Biological Sciences at the University of Calgary, Faculty of Science. His research focuses on group II introns' catalytic RNA properties, retroelement behavior, and evolutionary implications. He holds a B.A. in Chemistry from Goshen College (1986) and a Ph.D. in Biophysics from Yale University (1992). Research interests include the self-splicing mechanisms of group II introns, their mobility strategies, structural diversity, and evolutionary relationships. Notably, his work revealed how some introns insert after transcriptional terminators to avoid disrupting host genes. Zimmerly has contributed to databases tracking mobile group II introns and elucidated their roles in horizontal gene transfer and parasitic genomic dynamics. He has been recognized with the GREAT Supervisor Award (2018), AHFMR Senior Scholar (2003), and AHFMR Scholar (1998). Courses taught include BCEM 413 (Laboratory Techniques II) and CMMB 451 (Molecular Analysis of Biological Systems). Over 15 key publications span RNA structure-function studies, retroelement evolution, and bacterial genomic analyses. Grants & Funding: Not explicitly listed in the text. Labs/Teams: Research group focuses on RNA biology and molecular evolution; collaborates widely (e.g., with Lambowitz, Doulatov, and others).

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Daniel Patelمشاهده پروفایل

پژوهشگر

University of BergenNorway

Daniel Patel is an Adjunct Researcher at the University of Bergen's Department of Informatics. His work focuses on advancing visualization techniques for geological and geophysical data, with a particular emphasis on interactive modeling, real-time rendering, and domain-specific tools for scientific communication. He has contributed to projects such as 'Geological Storytelling' and 'Rapid Sketch-Based Modeling,' which aim to bridge the gap between geologists' conceptual ideas and computational modeling. Patel’s research integrates computer graphics innovations with domain-specific challenges in geology and marine science. His research interests include volume rendering, sketch-based modeling, seismic data interpretation, and educational approaches to PhD training. He collaborates with interdisciplinary teams, including the VisGroup at UiB, to develop tools for visualizing complex geological structures and sonar data. Patel holds a PhD in expressive visualization from TU Wien (2009) and has published extensively on topics ranging from chromatic shadows in medical visualization to statistical transfer functions for noisy data. Key contributions include the development of occlusion shading models, instant convolution shadows, and knowledge-assisted visualization frameworks for seismic exploration. His work emphasizes rapid prototyping and user-centric design, enabling domain experts to rapidly communicate and explore geological hypotheses.

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Gary Tamمشاهده پروفایل

مدرس ارشد

Swansea UniversityUnited Kingdom

Dr. Gary Tam is Senior Lecturer in Computer Science at Swansea University and member of the Visual Computing Group. Education: B.Eng. from Hong Kong University of Science and Technology M.Phil. from City University of Hong Kong Ph.D. from Durham University PGCtHE from Swansea University Research: Focuses on 3D computer vision, visual analytics, and geometric processing. Recent work includes point cloud compression, saliency detection, and 3D reconstruction techniques. Publications: Research demonstrates strong focus on 3D data processing (point clouds, reconstruction) and visual attention modeling, with increasing application of deep learning across domains including healthcare.

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Paolo Ferraginaمشاهده پروفایل

استاد

University of PisaItaly

Prof. Paolo Ferragina is a Full Professor at the Department of Computer Science, University of Pisa. He leads the Advanced Algorithms and Application Lab (Acube), focusing on algorithms for Big Data compression, indexing, and analysis. His work includes collaborations with MIT, Harvard, Google, and others. He has received prestigious awards like the ACM Paris Kanellakis Award (2022) and the ESA Test of Time Award (2023). Education: Laurea (summa cum laude, 1992) and PhD (1996) from the University of Pisa, Postdoc at Max-Planck Institute for Informatics (1997–1998). He has held roles such as Vice-Rector for ICT (2019–2022) and President of the PhD in Computer Science program (2017–2020). Research interests span compressed data structures, search engines, sports analytics, and DNA-based data storage. His work on the FM-index and TAGME system has had significant impact. He co-founded PlayeRank, a startup for soccer performance analysis. Grants and collaborations include Google Cloud Research Innovator (2022), Yahoo! Research Award (2007–2010), and projects with European Broadcasting Union and ST Microelectronics. He has authored over 180 papers and books like Computational Thinking and Pearls of Algorithm Engineering . Labs/Teams: Acube Lab at University of Pisa, collaborations with MIT’s Senseable City Lab, Harvard’s Pinello Lab, and industry partners like Bloomberg and ENEL.

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Anders Skaarup Johansenمشاهده پروفایل

پژوهشگر ارشد

Aalborg UniversityDenmark

Anders Skaarup Johansen is a Postdoctoral Researcher at Aalborg University's Department of Architecture, Design and Media Technology within The Technical Faculty of IT and Design. He works in the Section for Media Technology - Campus Aalborg, focusing on Visual Analysis and Perception as part of the AI for the People research group. His institutional email is asjo@create.aau.dk and his ORCID ID is https://orcid.org/0000-0002-9330-522X. His research interests center around Computer Vision, Artificial Intelligence, and Machine Learning with specific expertise in Image Segmentation, Object Detection, Thermal Imaging, and Benthic Habitat Mapping. Johansen's work demonstrates strong interdisciplinary applications across environmental science, marine biology, and safety systems. His research approach combines theoretical AI development with practical real-world implementation, particularly evident in his work on underwater imaging and thermal video analysis. Analysis of his recent publications reveals a clear trend toward practical AI applications with societal impact. His research spans video transformer architectures, machine unlearning verification techniques, and specialized imaging datasets for environmental monitoring. The work shows increasing sophistication in handling real-world challenges like concept drift in thermal imaging and multi-annotator systems for underwater habitat classification. Johansen actively contributes to two major research projects: REPAI (Responsible AI for Value Creation, 2023-2027) where he's a Project Participant, and JAMBO (2023-2024) where he serves as Principal Investigator studying seabed conditions and bottom-trawling gear impacts in Jammerbugt. His work has generated public policy impact related to marine research that contributed to a ban on beam trawling. His media engagement includes coverage of facial recognition technology limitations (2024) and video technology for societal safety (2022), demonstrating his commitment to translating technical research into public discourse. Johansen has also contributed to dataset development, notably the JAMBO dataset released on Hugging Face in October 2024.

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Lars Bodumمشاهده پروفایل

دانشیار

Aalborg UniversityDenmark

Lars Bodum is an Associate Professor at the Department of Sustainability and Planning, part of The Technical Faculty of IT and Design at Aalborg University. His research focuses on geoinformation, urban development, and digital technologies, particularly 3D modeling, AI, and geovisualization for sustainable cities. He co-authored Denmark’s foundational GIS textbook and leads projects like the award-winning The Digital Underground , exploring subsurface infrastructure data. Education: PhD in Urban Planning (2000) and MSc in Chartered Surveyor (1990), both from Aalborg University’s Department of Sustainability and Planning. Research interests include data integration, Citizen Science, and Digital Twins. His work bridges academia with municipal and private sector applications, emphasizing practical solutions for infrastructure management and climate adaptation. Key projects include Effektiv anvendelse af 3D-scanninger (2024–2026) and NBRACER (2023–2027), focusing on climate resilience and nature-based solutions. He has received the 2021 Outstanding Review Award for peer review contributions. Labs/Teams: Danish Centre for Spatial Planning and the Centre for 3D GeoInformation. Collaborations span Denmark and international partners like the University of California, Davis.

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Tina Zhuمشاهده پروفایل

استادیار مدعو

Queen's University at KingstonCanada

Tina Zhu, MD is an Adjunct Assistant Professor at Queen’s University and a Full-Time Cardiologist at APEX Heart Centre. She holds dual degrees from the University of Toronto: a Bachelor of Science in Immunology and a Medical Degree. Her clinical training includes Internal Medicine at Western University and Adult Cardiology at Queen’s University, alongside specialized certifications in Level III Echocardiography and Nuclear Cardiology. Her research interests focus on heart failure , women’s heart health , and digital health innovation . She actively explores applications of AI and technology in cardiology, particularly in improving diagnostic workflows and patient care. Dr. Zhu’s academic contributions span AI-driven healthcare solutions, with recent work emphasizing automated planning models, text anonymization, and robust dialogue systems. Her publications reflect interdisciplinary expertise at the intersection of cardiology and computational science. Professional affiliations include Queen’s University and APEX Heart Centre, where she balances clinical practice with academic engagement. No formal awards or grants are explicitly listed in the provided materials.

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R. Stuart Geigerمشاهده پروفایل

استادیار

University of California, San DiegoUnited States

R. Stuart Geiger is an Assistant Professor at the University of California, San Diego with joint appointments in the Department of Communication and the Halıcıoğlu Data Science Institute. He holds affiliate faculty roles in Computer Science & Engineering, Computational Social Science, and the Institute for Practical Ethics, where he leads the Working Group on Data Governance & Accountability. His interdisciplinary work spans Science and Technology Studies, Data Science, and AI Ethics. Geiger earned his Ph.D. in Information Management & Systems from UC Berkeley, an M.A. in Communication, Culture, and Technology from Georgetown University, and a B.A. in Humanities from the University of Texas at Austin. His postdoctoral research at Berkeley Institute for Data Science (BIDS) focused on ethnographic studies of data science practices and institutions. Research Interests : His research examines the social, cultural, and political dimensions of data science, AI, and automation. Key areas include algorithmic fairness, open-source software governance, Wikipedia’s socio-technical dynamics, and the ethics of AI in social media moderation. He employs mixed methods, blending ethnography, trace analysis, and computational techniques. Publications Trends : Recent work critiques algorithmic bias in AI systems (e.g., ChatGPT’s salary negotiation advice), advocates for participatory auditing practices, and explores labor dynamics in open-source projects. His studies often bridge technical and social dimensions, emphasizing contextual inquiry and institutional critiques. Awards & Grants : - Best presentation award at ECSCW 2018 - 1st runner-up David B. Martin Best Paper Award (2018) - Sloan Foundation grants for open-source software studies - UC Humanities Research Institute funding (2022) - Top paper award at 2009 Science & Technology in Society Conference Advising & Grants : Geiger has led grants totaling over $200,000, including projects on open-source maintainers’ labor and data science ethics. He advises students in data science, communication, and STS, fostering interdisciplinary collaboration. His teaching includes courses on data ethics, algorithmic auditing, and communication theory. Labs & Teams : He co-founded the Best Practices in Data Science series at BIDS and collaborates with teams like the Moore-Sloan Data Science Environments. His work intersects with initiatives on diversity in data science and reproducible research practices.

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Md Abul Basharمشاهده پروفایل

پژوهشگر ارشد

Queensland University of TechnologyAustralia

Md Abul Bashar is a Research Fellow at the Queensland University of Technology's Centre for Data Science and School of Computer Science. His research focuses on developing deep learning and machine learning solutions for natural language processing tasks, including abusive content detection, misinformation analysis, and social media mining. Industrial applications include unconscious bias detection systems deployed at Fortune 500 companies and automated marketing strategy generation commercialized by Robotic Marketer. Research areas span multimodal fusion (uncertainty-guided meta-learning in 2025, pre-gating attention mechanisms in 2024), longitudinal data analysis (GAN-based imputation in 2024), and cybersecurity applications (Log4Shell threat detection in 2023). Articles demonstrate strong emphasis on NLP techniques adapted for low-resource scenarios through transfer learning and generative models. Significant Applications Unconscious bias detection for enterprise content management Automated marketing report generation (commercialized) Misogyny detection featured in Forbes, Daily Mail Energy payment propensity prediction Indigenous heritage repatriation support systems

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Julien Fumeyمشاهده پروفایل

پژوهشگر

Pasteur InstituteFrance

Julien Fumey is a computational biologist at Institut Pasteur in Paris, France, with a research focus on evolutionary biology and genomics. His work primarily investigates the genetic and evolutionary mechanisms underlying adaptation in cave-dwelling organisms, particularly the Mexican tetra ( Astyanax mexicanus ). Dr. Fumey's research spans multiple disciplines within evolutionary biology, with particular emphasis on population genetics, cave adaptation mechanisms, and computational approaches to genomic analysis. His work combines field studies with advanced bioinformatics to understand how species adapt to extreme environments over evolutionary time scales. He has made significant contributions to understanding the recent evolutionary history of cavefish populations and the genetic basis of regressive evolution in subterranean environments. Analysis of his publication record reveals a strong trend toward integrating computational methods with evolutionary questions, particularly in the analysis of ancient DNA and metagenomic data. His most recent work focuses on developing standardized approaches for processing ancient metagenomic datasets, reflecting a growing interest in methodological rigor for paleogenomic research. Dr. Fumey actively participates in the scientific community through conference organization and community-building initiatives, as evidenced by his contributions to the ISCB Student Council symposium series and his work on sustainable scientific practices. His laboratory work involves close collaboration with evolutionary biologists, geneticists, and field researchers studying cave ecosystems. He contributes computational expertise to multiple interdisciplinary projects investigating the genetic basis of adaptation in extreme environments.

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