جست‌وجوی استاد و پژوهشگر

Sally Totmanمشاهده پروفایل

استاد

• Middle Eastern Politics
• International Relations
• Cultural Studies
• +۳ مورد دیگر

Professor Sally Totman is a political scientist and Head of School of Social Work and Arts at Charles Sturt University, Bathurst. She holds a Bachelor of Arts (Psychology), Master of Politics and Public Policy, and a Doctor of Philosophy. Previously, she held roles at Deakin University, Macquarie University, and Champlain College. Her expertise lies in Middle Eastern politics, international relations, and cultural representation in Hollywood. She co-founded the Middle East Studies Forum at Deakin University and led the Centre for Middle East and North African Studies at Macquarie University. Research Interests: Professor Totman focuses on Middle Eastern and North African politics, Hollywood's portrayal of foreign policy, and political leadership analysis. She has authored books like *How Hollywood Projects Foreign Policy* and co-edited *Global Crises and Risks*. Her current research examines MENA representation in film and popular culture. Teaching & Awards: With 25+ years of teaching experience in online and located environments, she has earned numerous teaching awards. Her pedagogical innovations include online simulations for Middle East politics education. Professional Activities: She serves on the Gulf/2000 Project at Columbia University and Australian Institute of International Affairs. Recent publications address U.S.-Middle East relations, Tunisia's democracy challenges, and Australia's strategic engagements in the region. Grants & Supervision: She has successfully supervised HDR candidates and contributed to educational equity through simulations for off-campus students.

• Machine Learning
• Computer Vision
• Medical Imaging
• +۷ مورد دیگر

Ali Gooya is a Senior Lecturer (Associate Professor) in Machine Learning at the School of Computing Science, University of Glasgow, UK. His research focuses on probabilistic deep learning applied to medical imaging, particularly in cardiology and oncology, emphasizing semi/unsupervised methods due to sparse expert annotations. He holds a PhD in medical image analysis from the University of Tokyo (2007) and has held academic positions at the University of Leeds and Sheffield before joining Glasgow in 2022. Affiliations: Senior Lecturer in Machine Learning, University of Glasgow (2022–present) Lecturer in Computing, University of Leeds (2018–2022) Lecturer in Computing, University of Sheffield (2016–2018) Postdoctoral Researcher, University of Pennsylvania (2008–2011) Research Interests: Deep learning for medical imaging, probabilistic modeling, cardiac and cancer imaging, computational anatomy, and marker discovery. Key applications include motion analysis, segmentation, and predictive modeling in healthcare. Key Achievements: Won prestigious fellowships including Allen Touring Institute (2022), JSPS Short-Term (2020), Marie-Curie IIF (2014), and JSPS-PDRA (2008). Pioneered Bayesian deep learning frameworks for cardiac motion assessment and generative models in medical imaging. Grants & Supervision: EPSRC Impact Acceleration Award (PI) EPSRC New Investigator Grant (EP/S012796/1) Actively supervising PhD students in areas like Bayesian deep atlases for cardiac motion analysis. Labs & Teams: Leads research in medical AI within the School of Computing Science, collaborating on projects integrating imaging and patient metadata for clinical decision support.

• Algorithms for Big Data Analytics
• Computational Geometry
• Machine Learning
• +۴ مورد دیگر

Jeff M Phillips is a Professor in the Kahlert School of Computing at the University of Utah, specializing in algorithms for big data analytics, computational geometry, and machine learning. He holds a BS in Computer Science and Mathematics from Rice University (2003) and a PhD in Computer Science from Duke University (2009). He serves as Director of the Utah Center for Data Science, Director of the Data Science Program in the Kahlert School of Computing, and Faculty Co-Director of the One U Data Science Hub. His research focuses on geometric data analysis, coresets, sketches, and handling uncertainty in data. Education: BS/BA (Rice University, 2003), PhD (Duke University, 2009) CI Postdoctoral Fellow at University of Utah (2009–2011) His research interests include algorithms for big data analytics, computational geometry, machine learning, spatial statistics, and AI. He has led NSF-funded projects on spatial data analysis, cosmic origins via AI, and reactive flow data modeling. Phillips has advised numerous PhD and master’s students, contributing to topics like trajectory classification and bias mitigation in word embeddings. His publications span computational geometry, data science, and machine learning. Notable work includes coresets for kernel density estimates, bias mitigation in language models, and scalable spatial scan statistics. Phillips is also active in academic service, serving as co-PC chair for SoCG 2024 and on program committees for major conferences like NeurIPS and ICML.

• Comparative Politics
• Electoral Systems
• Political Parties
• +۲ مورد دیگر

Liam Weeks is a full-time Lecturer in the Department of Government at University College Cork (UCC), Ireland. He holds a PhD in Political Science from Trinity College Dublin (TCD) and has an extensive academic background including a Postgraduate Diploma in Statistics (TCD, 2004) and a Postgraduate Certificate in Teaching and Learning in Higher Education (UCC, 2009). His research focuses on comparative and Irish politics, particularly electoral systems, voting behavior, political parties, and non-party politics. BA in History & Political Science (TCD, 2001) Postgraduate Diploma in Statistics (TCD, 2004) PhD in Political Science (TCD, 2008) Postgraduate Certificate in Teaching and Learning in Higher Education (UCC, 2009) Liam Weeks’ research has been published in journals such as Australian Journal of Political Science , Government and Opposition , Party Politics , Parliamentary Affairs , and Irish Political Studies . His work explores themes like the role of independent politicians, electoral reform, party system evolution, and voter behavior under PR-STV. Articles often focus on Ireland’s unique political landscape and comparative studies with Australia, New Zealand, and other Westminster democracies. He has received prestigious awards including the Best Conference Paper (PSAI, 2017), Erasmus+ Staff Mobility Grants (2017), and Royal Irish Academy Mobility Grant (2010). His grants also include funding from the Irish Research Council, Waseda Institute of Advanced Study, and Enterprise Ireland. Liam Weeks actively supervises PhD students in comparative politics (elections, electoral systems) and Irish politics. He has directed the BA(Politics) program at UCC and served as Associate Editor of ECPR Press (2018–present) and Editor of Irish Political Studies (2021–present).

• Large-scale nonparametric and kernel methods
• Robust and trustworthy machine learning
• Multiresolution data and data fusion
• +۳ مورد دیگر

Professor Dino Sejdinovic is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. Previously, he held positions as Lecturer and Associate Professor at the University of Oxford's Department of Statistics (2014–2022). His academic qualifications include a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research focuses on the intersection of statistical methodology and machine learning, encompassing large-scale nonparametric methods, robust machine learning, multiresolution data fusion, and measures of dependence. He has contributed to kernel methods, Bayesian inference, causal discovery, and applications in climate science, quantum computing, and social science data analysis. Education: PhD in Electrical and Electronic Engineering, University of Bristol (2009) Diplom in Mathematics and Theoretical Computer Science, University of Sarajevo (2006) Sejdinovic's work emphasizes bridging theoretical foundations with practical applications, such as cloud type classification using vision transformers and machine learning-driven quantum device optimization. His recent publications explore topics like kernel-based causal inference, Bayesian neural networks, and uncertainty quantification in statistical models. Advising and grants: Eligible to supervise Masters and PhD students in machine learning and statistics, though specific grants or student advisees are not explicitly listed in the provided texts.

• Data Science
• Concept-Drift Management
• Database Systems
• +۱۲ مورد دیگر

Tania Cerquitelli is a Full Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where she leads research in data science, concept-drift management, and inclusive AI technologies. She is a member of SmartData@PoliTO, the GEDI Observatory for Gender Equality, and serves in leadership roles related to social affairs and community policies at the university level. She also acts as a scientific advisor for the partnership with Accenture. Her research interests span Data Science , Concept-Drift Management , Database Systems , Conversational Data Science , and Industry 4.0 . She applies AI and machine learning to industrial, societal, and ethical challenges, particularly in promoting inclusive communication and gender equality in research. The most recent publications highlight her work in explainable AI, concept drift detection, multimodal diagnostics, and AI for social good. Her research integrates machine learning, natural language processing, and computer vision to address real-world problems in manufacturing, healthcare, agriculture, and education. She is an Associate Editor for several prestigious journals including Expert Systems with Applications , Computer Networks , Future Generation Computer Systems , and Knowledge and Information Systems . She has served on the program committees of major conferences such as ECML PKDD, EDBT/ICDT, and ACM KDD, and has been a reviewer and selection committee member for ETH Zurich and EMPA. She actively supervises PhD students and teaches a wide range of courses including Data Science and Database Technologies, Business Intelligence for Big Data, and Gender and Diversity in Research. She is involved in multiple national and international research projects such as E-MIMIC, WEBFARE, and EnABLES, focusing on inclusive AI, smart data, and industrial applications. Her lab affiliations include the DBDM - Database and Data Mining Group (DAUIN) and the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory , where she contributes to advancing data science methodologies and their societal impact.

Diego Patiñoمشاهده پروفایل

استادیار

• Machine Learning
• Geometric Computer Vision
• 3D Reconstruction
• +۷ مورد دیگر

Diego Patiño is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), a position he began in September 2024. He earned his Ph.D. in Computer Engineering from the National University of Colombia in 2020, following M.S. and B.S. degrees from the same institution. Prior to joining UTA, he served as a Postdoctoral Fellow at Drexel University and a Postdoctoral Researcher at the GRASP Laboratory, University of Pennsylvania. B.S. in Computer Engineering, National University of Colombia, 2010 M.S. in Computer Engineering, National University of Colombia, 2012 Ph.D. in Computer Engineering, National University of Colombia, 2020 Dr. Patiño's research centers on geometric computer vision and machine learning, with applications in robotics and 3D vision. His primary interests include 3D reconstruction, graph neural networks, symmetry detection, physics-informed machine learning, and reinforcement learning. He develops algorithms that integrate geometric priors and physical constraints into deep learning models to improve robustness and generalization in real-world robotic systems. His recent publications demonstrate a strong trend in leveraging implicit neural representations for 3D shape reconstruction, applying graph neural networks to swarm robotics, and enhancing computer vision tasks with self-supervised and physics-informed learning. Work spans high-impact venues such as IEEE RA-L, ICRA, ICPR, and MICCAI, showing a consistent focus on geometric reasoning, robotic perception, and medical imaging applications. His scientific contributions have been recognized with awards from the UTA Division of Student Affairs for exceptional dedication and positive impact (2024 and 2025). He is actively involved in securing research funding, with multiple grants under review from NSF, Air Force SBIR, and industry partners like Sony. Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (December 9, 2024) Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (April 30, 2025) Dr. Patiño advises and serves on committees for multiple graduate students in computer science and engineering, including doctoral and master’s candidates. He is also leading or co-leading several research grants under review, covering topics such as aerial swarm navigation, neuromorphic sensing, and industrial computer vision. He teaches graduate courses in computer vision and is involved in service roles including PhD admissions and faculty appointments committees. He is affiliated with research initiatives at UTA, including the UTARI Research Institute, where he has presented on geometric modeling and physics-informed learning. His lab focuses on developing next-generation computer vision algorithms for robotics, industrial inspection, and safety-critical systems.

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.

Christian Kniggeمشاهده پروفایل

استاد

• accretion phenomena
• associated outflows
• cataclysmic variables
• +۳ مورد دیگر

Professor Christian Knigge serves as Professor of Astrophysics at the University of Southampton and is a core member of the Southampton Theory Astrophysics and Gravity (STAG) Research Centre within the Institute for Life Sciences. His research program investigates accretion phenomena across diverse cosmic scales, from stellar-mass compact objects to supermassive black holes. Knigge's primary research focuses on accretion phenomena and associated outflows, cataclysmic variables, close binaries, globular clusters, and active galactic nuclei. He examines the physical mechanisms driving accretion disk instabilities, outflow generation, and explosive events in binary systems, with particular emphasis on white dwarf and black hole accretors. His work integrates observational data with theoretical modeling to unravel the complex physics of these high-energy environments. Recent publications (2024-2025) reveal a strong emphasis on multi-wavelength observational campaigns, especially leveraging JWST capabilities, alongside theoretical code development. Key research threads include characterizing disk winds in active galactic nuclei, identifying quasi-periodic oscillations in white dwarf systems, classifying optical outbursts in cataclysmic variables, and developing computational tools like the SIROCCO radiative transfer code. These studies demonstrate his leadership in connecting observational signatures with fundamental accretion physics across different astrophysical regimes. Supervision: Currently guides six PhD students in Physics (Austen George William Wallis, Cordelia Brown, Brian Luff, Zackery Alexander Irving, Arianna Clarissa Albayati, Pornisara Nuchvanichakul) Grants: Leads STFC-funded projects including 'Line-Driven Disk Winds in Active Galactic Nuclei', 'C Knigge - Astrophysics at Southampton', and previously held a Leverhulme Trust Research Fellowship for 'The Universal Nature of Accreting Compact Objects' As an integral member of the STAG Research Centre, Knigge collaborates within a multidisciplinary team investigating gravity-dominated systems, contributing to Southampton's prominence in theoretical and observational astrophysics through both individual research initiatives and institutional consolidation grants.

Jure Leskovecمشاهده پروفایل

استاد

• Machine Learning
• Social Networks
• Data Mining
• +۴ مورد دیگر

Jure Leskovec is a Professor of Computer Science at Stanford University, affiliated with the Stanford AI Lab, Machine Learning Group, and the Center for Research on Foundation Models. He holds academic appointments in the Department of Computer Science and is a member of Bio-X, the Institute for Human-Centered Artificial Intelligence (HAI), and the Wu Tsai Neurosciences Institute. Leskovec earned his BSc from the University of Ljubljana (2004), PhD from Carnegie Mellon University (2008), and postdoctoral training at Cornell University. His research focuses on social networks, data mining, machine learning, and computational biomedicine, with contributions to graph neural networks, drug discovery, and AI applications in healthcare. His work has been applied to combat the COVID-19 pandemic and integrated into products at major tech companies. Leskovec’s publications reflect his expertise in network analysis, medical AI, and biological systems. His recent work includes foundational contributions to graph neural networks (e.g., PyG) and medical AI frameworks. His research has garnered numerous awards, including the Microsoft Research Faculty Fellowship and ICDM Research Contributions Award. Leskovec advises numerous doctoral and postdoctoral researchers, contributing to over 200 publications. His interdisciplinary collaborations span computational biology, healthcare analytics, and social systems, with a focus on leveraging AI to address real-world challenges.

• Operator Algebra
• C*-algebras
• K-theory
• +۳ مورد دیگر

Søren Eilers is a Professor at the Department of Mathematical Sciences , University of Copenhagen. His research focuses on Operator Algebras , particularly the classification of C*-algebras related to discrete and low-dimensional structures. He is a member of the FNU network 'Automorphisms and Invariants for Operator Algebras' and advocates for experimental mathematics using computational methods in pure mathematics. Education: MS in Mathematics and Computer Science, University of Copenhagen (1993) PhD in Mathematics, University of Copenhagen (1995) Research Interests: Operator Algebras K-theory Symbolic Dynamics Discrete Mathematics Experimental Mathematics Recent Publications (2016-2024) demonstrate expertise in graph C*-algebras , symbolic dynamics , and computational approaches to pure mathematics, with key collaborations in Denmark, Japan, Canada, and the U.S. Scientific Leadership: President, Danish Mathematical Society (2006-2008) Principal Investigator, Villum Fonden (2012-2016) Main Organizer, Mittag-Leffler Institute Program (2016) Advisory Roles: Supervised 28 master's theses and mentored 9 PhD students/postdocs (2003-2022) across institutions in Denmark, Canada, Japan, and the U.S.

Johannes Bjervaمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Linguistic Typology
• +۵ مورد دیگر

Johannes Bjerva is a Full Professor at Aalborg University's Department of Computer Science (Campus Copenhagen), leading the Copenhagen branch and conducting interdisciplinary NLP research integrating linguistic typology. His work focuses on low-resource languages, language model security, and societal AI impact. PhD (University of Groningen, 2017): Thesis on multitask/multilingual lexical modeling M.A. & B.A. in Computational Linguistics (Stockholm University) Research interests span linguistically-informed NLP , language model security , and low-resource language technology . Current projects include the DFF Sapere Aude grant (2025) for language model detection security and the LM2-SEC project (2025–2030). His 2024 ACL paper on embedding inversion security and 2024 EMNLP paper on typological diversity exemplify recent work. Scientific awards include: 2021: Teacher of the Year (AAU Computer Science) 2019: Google Cloud research credits 2022: Carlsberg Semper Ardens (5M DKK) 2024: Novo Nordisk Data Science grant (~10M DKK) Supervision includes 8 PhD students across projects like CreoleVal and HiFi-KPI . He serves on the Industrial Researcher Committee at Innovation Fund Denmark and is a member of Det Unge Akademi (2023–2028).

Marco Tamburelliمشاهده پروفایل

استاد

• Linguistics
• Bilingualism
• Language Policy
• +۷ مورد دیگر

Professor Marco Tamburelli is a faculty member at Bangor University's School of Language, Culture and the Arts , specializing in Linguistics with a focus on bilingualism, language policy, and linguistic diversity. He holds a PhD in Linguistics (Bilingualism) and a BA in Linguistics from University College London. Education PhD in Linguistics (Bilingualism), University College London BA in Linguistics, University College London Research Interests His work explores contested languages, bilingual acquisition in diglossic contexts, intelligibility measurements, and the sociopolitical dimensions of language attitudes. He investigates how language policies impact linguistic diversity and the role of implicit/explicit attitudes in language vitality. Recent publications examine Welsh-English bilingualism, Lombard-Italian communities, and Moselle Franconian-German dynamics, emphasizing methodological innovations like the L'ART Research Assistant app for standardized data collection. His studies highlight the interplay between early exposure, language policy, and spontaneous linguistic behavior. Scientific Awards Santander Scholarship donation (2012-2015) Supervision PhD students: Conor Glackin, Judit Vari, Mara Leonardi, Abigail Ruth Price, Lissander Brasca (part-time), Athanasia Papastergiou Outreach & Collaborations Active in public engagement, he presented at international conferences on language contestation, participated in Italian regional policy discussions for Lombard, and served on advisory boards for projects like CIRCE (Counteracting Accent Discrimination Practices in Education). He collaborates with institutions across Italy, Belgium, Germany, and the UK.

• Geography
• Environmental Change
• Land Cover Change
• +۷ مورد دیگر

Tina Delahunty is an Assistant Professor in the Department of Physical and Environmental Sciences at Bloomsburg University, where she contributes to the academic and research mission in geography and environmental sciences. Her work bridges geospatial technologies with environmental change analysis. Education: Ph.D. in Geography — University of Florida M.A. in Geography — Florida Atlantic University Her research focuses on the spatial and temporal dynamics of Holocene land cover and environmental change. She employs advanced tools such as GIS, remote sensing, and palynology to investigate long-term ecological transformations. Her interdisciplinary approach integrates geospatial data with paleoenvironmental proxies to reconstruct past landscapes and assess modern environmental impacts. The recent publications highlight consistent engagement in remote sensing applications, including agricultural monitoring, urban extent mapping using nighttime lights, and ecological assessment via environmental DNA. These works reflect a strong methodological foundation in multiscale geospatial analysis and environmental monitoring across diverse ecosystems. Scientific Contributions: Co-authored research in high-impact journals such as International Journal of Remote Sensing , Freshwater Biology , and International Journal of Applied Earth Observation and Geoinformation . Active contributor to interdisciplinary environmental research involving geography, ecology, and geospatial science. She is involved in mentoring and research supervision, though specific advisees are not listed. Her work is supported by institutional affiliation and access to geospatial laboratories and field resources. She contributes to advancing methodologies in land cover classification and environmental change detection. Dr. Delahunty leads research in GIS and remote sensing laboratories at Bloomsburg, where she likely supervises student projects and collaborates on regional and global environmental studies.

Benjamin Evansمشاهده پروفایل

استادیار

• Computational Neuroscience
• Spiking Neural Networks
• Convolutional Neural Networks
• +۷ مورد دیگر

Dr. Benjamin Evans is an Assistant Professor in Computer Science & AI (Informatics) at the University of Sussex , affiliated with the School of Engineering and Informatics . His research integrates computational neuroscience and artificial intelligence, focusing on biologically inspired neural networks. Current Position: Assistant Professor, Department of Informatics, University of Sussex Previous Roles: Research Associate at University of Bristol, University of Exeter, Imperial College London, and University of Oxford Education: DPhil in Computational Neuroscience (University of Oxford), MSc in Intelligent Systems (UCL), BA in Experimental Psychology (Oxford) His research centers on how neural systems self-organize to produce intelligent behavior, studied through both biological and computational modeling. He investigates spiking neural networks , convolutional neural networks , and the role of biological constraints in enhancing AI robustness and human-like perception. He is particularly interested in how spike-based information processing contributes to adaptive cognition in noisy environments. His recent publications reveal a strong trend in evaluating deep neural networks as models of human vision, questioning their biological plausibility while proposing bio-inspired improvements. He also works on optogenetics simulation (e.g., PyRhO platform), developmental biology modeling , and reproducible data science through containerization tools like Docker. His scientific contributions have been recognized through publications in high-impact journals such as Nature Communications , PLoS Computational Biology , and Behavioral and Brain Sciences . EPSRC Grant: "Exploring the multiple loci of learning and computation in simple artificial neural networks" (2023–2024) EPSRC Grant: "Using ant biology and natural environments to enhance models of vision and robot navigation" (2022–2026) Dr. Evans actively contributes to open science through GitHub repositories (e.g., PyRhO, DPE, BioNet) and promotes reproducible research. He has no listed advisees in the provided data, but leads funded research projects involving junior researchers. He is a core member of the Informatics research group at Sussex, contributing to both AI and neuroscience domains.