جستجوی استاد راهنمای دانشگاهی

Diana Inkpenمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +۶ مورد دیگر

Professor Diana Inkpen is a faculty member at the School of Electrical Engineering and Computer Science, University of Ottawa. She holds a Ph.D. from the University of Toronto's Department of Computer Science and degrees from the Technical University of Cluj-Napoca, Romania (M.Sc. and B.Eng.). Her research focuses on computational linguistics, natural language processing (NLP), and artificial intelligence, with specialties in natural language understanding/generation, lexical semantics, and semantic web agents. Education: Ph.D., Computer Science, University of Toronto M.Sc., Computer Science and Engineering, Technical University of Cluj-Napoca B.Eng., Computer Science and Engineering, Technical University of Cluj-Napoca Affiliations: Director, NLP Lab Editor-in-Chief, Computational Intelligence journal Associate Editor, Natural Language Engineering journal Her research has led to roles such as program co-chair for AI 2012 and leadership in organizing international workshops. She has received funding from NSERC, SSHRC, and OCE, and was honored with a Visiting Professor title at the University of Wolverhampton, UK. Her teaching spans courses like Information Retrieval, Natural Language Processing, and Prolog programming. Professor Inkpen's work bridges NLP innovations with societal challenges, including mental health surveillance via social media analysis and bias mitigation in AI systems. She actively contributes to interdisciplinary projects, such as detecting hate speech and legal text entailment, while maintaining a global research network through collaborations and international conference involvement.

Oisin Mac Aodhaمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• 3D Understanding
• +۳ مورد دیگر

Oisin Mac Aodha is a Reader (Associate Professor) in Machine Learning at the School of Informatics, University of Edinburgh. He is also an ELLIS Scholar and founder of the Turing interest group on biodiversity monitoring and forecasting, having previously served as a Turing Fellow from 2021-2025. Mac Aodha completed his undergraduate degree in electronic engineering from the University of Galway in Ireland, followed by his MSc and PhD at University College London (UCL). His academic journey includes postdoctoral positions at UCL (2013-2016) working with Prof. Gabriel Brostow and Prof. Kate Jones, and at Caltech (2016-2019) in Prof. Pietro Perona's Computational Vision Lab as part of the Visipedia team. His research centers on computer vision and machine learning with emphasis on 3D understanding, human-in-the-loop methods, and AI for conservation and biodiversity monitoring. He has made significant contributions to monocular depth estimation (including the influential Monodepth2 paper), fine-grained visual categorization, and biodiversity monitoring systems. His work bridges theoretical machine learning with practical ecological applications, developing tools for species identification, range estimation, and conservation efforts. Recent publications reveal a strong trend toward ecological applications while maintaining fundamental contributions to 3D vision and representation learning. His major scientific achievements include: Turing Fellow (2021-2025) ELLIS Scholar Founder of the Turing interest group on biodiversity monitoring and forecasting Co-organizer of the Fine-Grained Visual Categorization (FGVC) workshop series at major vision conferences Mac Aodha advises multiple PhD students and postdocs working on computer vision for biodiversity monitoring, 3D understanding, and human-in-the-loop learning. His team has developed practical tools like Whombat (an open-source annotation tool for bioacoustics) and contributed to field-deployed biodiversity monitoring systems. He has served as Area Chair for top conferences including NeurIPS, CVPR, ICCV, and ICML, demonstrating his standing in the computer vision community. His research group collaborates extensively with ecologists at University College London, particularly with Prof. Kate Jones' team, bridging machine learning expertise with ecological domain knowledge. The Vision at Edinburgh group he contributes to focuses on developing practical AI tools that address real-world conservation challenges while advancing fundamental computer vision research.

Onur Varolمشاهده پروفایل

استادیار

• Computational Social Science
• Network Science
• Machine Learning
• +۴ مورد دیگر

Onur Varol is an Assistant Professor at Sabanci University's Computer Science Department and leads the VIRAL Lab, which focuses on computational social science, network science, and machine learning. He has affiliations with the Center of Excellence for Data Analytics. His research spans social bot detection, misinformation analysis, and online behavior modeling.

Arash Joorabchiمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Educational Technology
• +۴ مورد دیگر

Arash Joorabchi is an Assistant Professor at the Department of Electronic and Computer Engineering, Faculty of Science and Engineering, University of Limerick, Ireland. His research focuses on the intersection of machine learning, educational technology, and digital library systems, with particular emphasis on automated assessment, text mining, and knowledge organization techniques. Research Trends: Analysis of his publications reveals sustained contributions to automated short-answer grading, Arabic text classification, and semantic integration of Wikipedia with academic resources. Key methodologies include sentence transformers, hybrid text representation models, and citation-based indexing techniques. Technical Domains: His work spans natural language processing, educational data mining, metadata management, and semantic web technologies. Specific applications include Q&A platform analysis, library resource discovery, and curriculum development systems.

Raymond J. Mooneyمشاهده پروفایل

استاد

• Artificial Intelligence
• Bioinformatics and Computational Biology
• Machine Learning
• +۱۴ مورد دیگر

Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin, where he has been a faculty member since 1987. He is the Director of the UT Artificial Intelligence Laboratory and affiliated with multiple research groups including the Machine Learning Research Group, UT Computational Linguistics Lab, and the UT Center for Computational Biology and Bioinformatics. He holds a B.S., M.S., and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, where his thesis was supervised by Gerald DeJong. His research spans diverse areas in artificial intelligence, machine learning, and natural language processing: Natural Language Learning Connecting Language and Perception Statistical Relational Learning Information Extraction Transfer and Active Learning Abductive Reasoning Text Mining and Clustering Recommender Systems Knowledge-Base Refinement Recent publications highlight trends in grounded language processing, human-robot interaction, and multimodal reasoning. He has been recognized with prestigious fellowships including ACL (2014), ACM (2010), and AAAI (2005). Scientific awards: Fellow of the Association for Computational Linguistics (2014) Fellow of the Association for Computing Machinery (2010) Fellow of the American Association for Artificial Intelligence (2005) Classic Paper Award (2019) Best Paper Awards (2007, 2004, 1996) He teaches graduate courses like CS 371R: Information Retrieval and Web Search (Fall 2025) and CS 395T: Grounded Natural Language Processing (Spring 2025). His research labs include: UT Artificial Intelligence Laboratory Machine Learning Research Group UT Computational Linguistics Lab UT Center for Computational Biology and Bioinformatics

• Egyptology
• Coptic Studies
• Computational Linguistics
• +۵ مورد دیگر

Eliese-Sophia Lincke is a Junior Professor at the Department of History and Cultural Studies, Freie Universität Berlin, since May 2022. Her work bridges computational methods with Egyptology, focusing on digital tools for studying ancient texts. Bachelor's and Master's in Egyptology, Humboldt-Universität zu Berlin (2007) PhD in "The Conception of Spaces in Language" (TOPOI Cluster, 2012) Research interests include: Digital Humanities : Developing machine learning models for Hieroglyphic, Demotic, and Coptic text processing Linguistic Typology : Analyzing classifier systems in Ancient Egyptian and Sign Languages Spatial Linguistics : Investigating prepositions and spatial adverbs in Egyptian-Coptic Recent publications focus on Neural Lemmatization , OCR for Coptic , and Classifier Semantics , demonstrating her commitment to computational Egyptology. Scientific awards include the Humboldt-Preis 2008 for best Master's thesis and the Prize for Good Teaching 2014 . She has co-organized workshops like "Wege zum Ägyptischen" and served as Co-Editor for Lingua Aegyptia . Her teaching contributes to the Digital Studies of Ancient Texts Master's program.

Claude DELPHAمشاهده پروفایل

استاد

• Signal Processing
• Fault Diagnosis and Prognosis
• Electrical Engineering Systems
• +۴ مورد دیگر

Claude DELPHA is a Full Professor at Université Paris Saclay, affiliated with CentraleSupélec’s Laboratoire des Signaux et Systèmes (L2S). He holds an IEEE Senior Member status and has been with L2S since 2001. His expertise spans signal processing, fault diagnosis, electrical engineering systems, and machine learning. He leads the Modelling and Estimation team (GME) at L2S and oversees engineering admissions at Polytech Paris Saclay. Education: PhD in Instrumentation & Measurements and Signal Processing from Université de Metz, with a focus on intelligent sensor systems. Graduate degree in Electrical and Signal Processing Engineering. Research Interests: Multidimensional/statistical signal processing, fault diagnosis/prognosis (modeling, detection, estimation), electrical systems (drives, converters, PV), data hiding (watermarking), and pattern recognition (machine/deep learning). Active in energy systems, industry 4.0, and health/biology applications. Professional Roles: Director of GME research team, Polytech admissions lead, member of Polytech’s executive and academic boards, and IUT department council member. Engaged in labs like SYCOMORE and ILOCOS. Publications: Over 200 works since 2015, focusing on fault diagnosis in electrical systems, photovoltaic modules, bearings, and tidal turbines. Key methods include Kullback-Leibler divergence, Jensen-Shannon divergence, Mahalanobis distance, and PCA-based approaches. Awards: Not explicitly listed in provided texts.

Tim Oatesمشاهده پروفایل

استاد

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

Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building

Rebecca Nugentمشاهده پروفایل

استاد

• Clustering Methodology
• Record Linkage
• Educational Data Mining
• +۵ مورد دیگر

Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science and Department Head at Carnegie Mellon University. She holds a PhD in Statistics from the University of Washington (2006), an MS in Statistics from Stanford (2006), and a BA in Mathematics, Statistics, and Spanish from Rice University (2002). Her research spans clustering methodology , record linkage , educational data mining , public health , and semantic organization , with a focus on high-dimensional data and adaptive learning environments. She leads the Integrated Statistics Learning Environment (ISLE) and Corporate Capstone programs, emphasizing low-barrier data platforms for education and industry collaboration. Academic Roles : Department Head, Carnegie Mellon; Affiliated Faculty, Block Center for Technology and Society Research Grants : NSF (2017-2019), NIH (2018), Carnegie Mellon ProSEED/Simon Initiative (2020, 2018), Berkman Fund (2014) Her 15 most recent publications focus on data science pedagogy, clustering algorithms, record linkage applications in historical and medical data, educational data mining, and semantic organization studies. Awards include the ASA Waller Education Award (2015) and the William H. and Frances S. Ryan Award (2015) . She mentors a diverse group of PhD, Master's, and undergraduate students, with alumni pursuing careers in academia, industry, and sports analytics.

Pascal Vincentمشاهده پروفایل

دانشیار

• Machine Learning
• Representation Learning
• Deep Learning
• +۶ مورد دیگر

Pascal Vincent is an Associate Professor at the Department of Computer Science and Operational Research , University of Montreal, and a key member of the Montreal Institute for Learning Algorithms (MILA) . He holds a PhD in Computer Science from the University of Montreal and has been pivotal in advancing machine learning and artificial perception. Education: PhD in Computer Science (University of Montreal, 2003) His research spans machine learning , deep learning , representation learning , and neural networks , focusing on unsupervised methods and geometrically inspired algorithms. He explores how intelligent systems can autonomously build meaningful representations from raw data, driven by principles like the manifold hypothesis . Key projects include generative stochastic networks , contractive autoencoders , and high-dimensional sequence transduction . His work has resulted in 15+ recent publications in top venues like NIPS, ICML, and CVPR. Scientific Awards : Best student-paper award at ICML 2012 Honorable mention at NIPS 2011 Funded by FCI, FRQNT, CRSNG, CIFAR, and IBM Pascal has supervised 15+ doctoral and Master’s students , including Florian Bordes, Tom Bosc, and Nicolas Boulanger-Lewandowski, across topics like representation learning and generative models . He is also a co-founder of the UNIQUE (Union Neurosciences & Intelligence Artificielle Québec) research consortium.

Eugen Hellmannمشاهده پروفایل

استاد

• Arithmetic Geometry
• Representation Theory
• Langlands Program
• +۷ مورد دیگر

Prof. Dr. Eugen Hellmann is a full Professor at the Mathematical Institute of the University of Münster , within the Department of Mathematics and Computer Science . He is a leading researcher in arithmetic geometry and representation theory, actively contributing to the CRC 1442 Geometry: Deformations and Rigidity and Mathematics Münster excellence cluster. His work focuses on the p-adic aspects of the Langlands program, moduli spaces of Galois representations, and p-adic Hodge theory. Research Interests: His primary research areas include Arithmetic Algebraic Geometry , the Langlands Program (especially its p-adic and categorical formulations), p-adic Hodge Theory , p-adic Galois Representations , and p-adic Automorphic Forms . His work often involves the study of (phi,Gamma)-modules, eigenvarieties, and deformation spaces, aiming to understand the deep connections between automorphic forms and Galois representations in the p-adic setting. Publication Trends: His most recent publications (2022–2023) show a strong focus on the derived and categorical aspects of the p-adic Langlands program, including the derived category of Hecke algebras and a categorical framework for the entire program. Earlier works established foundational results on the smoothness of eigenvarieties, the geometry of trianguline varieties, and the structure of moduli spaces for Galois representations. His research consistently bridges abstract algebra, number theory, and algebraic geometry. Scientific Awards: No specific awards or fellowships are mentioned in the provided texts. Advising and Grants: While a list of former research group members (e.g., Dr. Claudius Heyer, Dr. Damien Junger) is provided, their exact status as PhD advisees is not explicitly confirmed. He leads significant research projects funded by the DFG, including CRC 1442 - A01: Automorphic forms and the p-adic Langlands programme and CRC 1442 - A02: Moduli spaces of p-adic Galois representations , as well as a project within the EXC 2044 - A1: Arithmetic, geometry and representations cluster. He is also a co-author on a preprint titled "Patching and multiplicities of p-adic eigenforms," indicating active collaboration on grant-funded research. Labs and Teams: He is a central figure in the arithmetic geometry group at Münster. He organizes and leads the Research Seminar "p-adic arithmetic" and the Mittagsseminar "Arithmetic" , which serve as key forums for his research group and collaborators to present and discuss current work. His research team has included several postdoctoral researchers and doctoral students, contributing to a vibrant research environment focused on cutting-edge problems in number theory.

Meng Heمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Computational Geometry
• +۷ مورد دیگر

Meng He is a Professor in the Faculty of Computer Science at Dalhousie University. He obtained his PhD from the Cheriton School of Computer Science at the University of Waterloo in 2008 and held postdoctoral and research positions at Carleton University and the University of Waterloo before joining Dalhousie. He is affiliated with the Algorithms & Bioinformatics research cluster and is actively recruiting graduate students for master's and PhD studies, as well as supervising honors theses and USRA internships. His research focuses on the design and analysis of efficient algorithms and data structures, particularly in the areas of computational geometry, databases, text retrieval, and bioinformatics. His work often involves developing succinct and dynamic data structures for fundamental problems in graph theory, trees, and geometric data. His recent publications show a strong focus on path and distance queries in various graph types (especially interval graphs and trees), range counting, mode queries, and succinct representations. The research trend emphasizes theoretical foundations combined with practical efficiency, often addressing dynamic and space-constrained scenarios. Alberto Apostolico Best Paper Award of CPM 2017 Dr. He has supervised numerous PhD and master's students and collaborators, frequently co-authoring with researchers such as J. Ian Munro, Travis Gagie, Gonzalo Navarro, and Norbert Zeh. His research has been supported by grants from NSERC and other funding agencies, though specific grant details are not listed in the provided text. He has also contributed significantly to the academic community through editorial work for journals like Computational Geometry - Theory and Applications and Algorithmica , and by organizing major conferences such as CCCG and WADS. He leads a research group focused on algorithms and data structures, fostering collaborations both within Dalhousie and internationally. Future work is likely to continue exploring the theoretical and practical aspects of dynamic and succinct data structures, with applications in large-scale data processing and information retrieval systems.

Raghu Garudمشاهده پروفایل

استاد

• Entrepreneurship
• Organizational Innovation
• Technological Design
• +۷ مورد دیگر

Raghu Garud is the Alvin H. Clemens Professor of Entrepreneurial Studies in the Department of Management and Organization at the Smeal College of Business, Pennsylvania State University. He is an active researcher in the fields of entrepreneurship, innovation, and organizational theory. His research focuses on the emergence of novelty , exploring how new ideas are generated, valued, and commercialized. Key areas include entrepreneurial framing , digital innovation , organizational innovation , and technological design . He employs process-based and narrative approaches to understand innovation dynamics in contexts such as platform-based ventures, regional regeneration, and identity-challenging technologies. Recent publications reveal a strong trend toward understanding legitimacy , institutional categorization , and performative processes in innovation. His work spans comparative studies (e.g., e-cigarettes in US vs UK), in-depth case analyses (e.g., Uber, Gmail), and conceptual advancements in entrepreneurial framing and performativity. Collaborators include Y. Snihur, L. D. W. Thomas, N. Phillips, J. Gehman, and T. Tharchen. His research has appeared in top journals such as Academy of Management Review , Strategic Organization , Journal of Business Venturing , and Strategic Management Journal . While no formal awards are listed in the provided text, his sustained publication record in premier outlets and named professorship indicate significant scholarly recognition. He advises graduate students and collaborates on projects related to innovation and entrepreneurship, though specific advisees are not listed. Garud's work is associated with research on digital platforms, regional innovation ecosystems, and the cultural dimensions of entrepreneurship. He contributes to ongoing scholarly conversations on the processual nature of innovation and the role of narrative and design in entrepreneurial success.

Jing Yangمشاهده پروفایل

استاد

• Data Visualization
• Visual Analytics
• High Dimensional Data Analysis
• +۷ مورد دیگر

Jing Yang is a full Professor and Director of MS CS Program in the Computer Science Department at the University of North Carolina at Charlotte (UNCC). She has been actively involved in data visualization and visual analytics research since joining UNCC in 2005 after completing her PhD at Worcester Polytechnic Institute. Dr. Yang earned her Bachelor's degrees in Engineering Mechanics and Computer Science from TsingHua University in 1997 and completed her Ph.D. in Computer Science from Worcester Polytechnic Institute in May 2005 under advisors Matthew O. Ward and Elke A. Rundensteiner. Her research focuses on developing visual analytics techniques for abstract data including multidimensional data, time-oriented data, networks, hierarchies, text documents, and trajectory data. She conducts design studies for application domains such as sports, bioinformatics, finance, network security, and health, while exploring fundamental visualization topics like interactions, insight management, clustering-based approaches, and animations. Her recent work emphasizes sports analytics, urban data, and multivariate time series visual analytics. Analysis of Dr. Yang's publication record reveals a strong focus on practical applications of visualization techniques across diverse domains. Her work consistently bridges theoretical visualization frameworks with real-world data challenges, particularly in transportation (taxi trajectories), sports (tennis match analysis), financial transactions, and bioinformatics. The publications demonstrate an evolution from foundational visualization techniques to increasingly sophisticated domain-specific applications. Dr. Yang has secured substantial research funding from NSF, EPA, DHS, and industry partners including Google and Bank of America. Her grants portfolio includes significant projects like TrajAnalytics (NSF, $200,950), Visualizing Event Dynamics (NSF EAGER, $75,317), and Visualizing High Dimensional Categorical Datasets (Google Faculty Research Award, $60,000), among others totaling over $4 million in research support. As an educator, Dr. Yang has taught numerous courses including Visual Analytics, Information Visualization, and Database Design. She directs the Charlotte Visualization Center and has mentored numerous students through research projects. Her collaborative approach is evident in her extensive co-authorship network spanning multiple institutions and disciplines. Current research directions include narrative animation for streaming text visualization and advanced techniques for exploring high-dimensional categorical datasets.

Andrew Ngمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Robotics
• +۴ مورد دیگر

Andrew Ng is an Adjunct Professor at Stanford University's Computer Science Department and a globally recognized leader in AI. He is the Founder of DeepLearning.AI, Executive Chairman of LandingAI, General Partner at AI Fund, and Co-Founder of Coursera. His work has revolutionized machine learning and online education, with over 200 research papers in AI, robotics, and related fields. He was named to the 2023 Time100 AI list of most influential figures in AI. Ng's research focuses on machine learning, deep learning, reinforcement learning, and their applications in robotics and education. He pioneered the development of massive open online courses (MOOCs), notably through Stanford's early experiments in 2011 that attracted hundreds of thousands of learners. His contributions include foundational work in algorithms like Latent Dirichlet Allocation (LDA) for text analysis and advancements in spectral clustering and inverse reinforcement learning. His publications span topics from robotic hand design to scalable deep learning systems, emphasizing practical and scalable solutions. Ng's educational initiatives, such as the Machine Learning and Deep Learning Specializations, have educated millions worldwide. He advocates for accessible AI education and ethical AI development, emphasizing collaboration between academia and industry.