جست‌وجوی استاد و پژوهشگر

Michael Robertsمشاهده پروفایل

دانشیار

• Image Processing
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Dr. Michael Roberts is a Principal Research Associate (equivalent to Reader/Associate Professor) at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics (DAMTP) and Department of Medicine. He leads the BloodCounts! consortium and the algorithm development team for the global AIX-COVNET collaboration. His interdisciplinary research focuses on variational methods for image processing, machine learning for medical imaging, and addressing reproducibility challenges in AI-driven science. Education: PhD in Mathematics, University of Liverpool (2019) MSc in Mathematics with Honors, Durham University (2015) Research Interests: Image segmentation and registration Machine learning applications in medical imaging (e.g., lung diseases, cardiovascular imaging) Scientific integrity and reproducibility in AI Collaborations with clinicians, computer scientists, and applied mathematicians Key Projects: BloodCounts!: Developing AI tools for blood cell analysis AIX-COVNET: Global collaboration for AI-driven COVID-19 imaging analysis Publications Highlight Trends: His work emphasizes improving AI reliability in medical contexts, addressing challenges like image quality assessment, artifact correction, and mitigating biases in machine learning models. Recent studies include OCT plaque analysis, chest radiograph quality control, and consensus-based guidelines for machine learning research (ReFORMS). Grants and Labs: Leads major interdisciplinary initiatives and collaborates with institutions like AstraZeneca. Active in Cambridge Image Analysis (CIA) group.

Johann Gagnon-Bartschمشاهده پروفایل

دانشیار

• Causal Inference
• Machine Learning
• Nonparametric Methods
• +۷ مورد دیگر

Johann Gagnon-Bartsch is an Associate Professor in the Department of Statistics at the University of Michigan, Ann Arbor, and serves as Director of Undergraduate Programs and Associate Director for Undergraduate Programs. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Center for Computational Medicine and Bioinformatics. His research focuses on causal inference, machine learning, and nonparametric methods with applications in biological and social sciences. He holds a PhD in Statistics from UC Berkeley (2012), an MS in Statistics from UC Berkeley (2007), and a BS/BA in Math, Physics, and International Relations from Stanford University (2003). Education: PhD in Statistics, UC Berkeley, 2012 MS in Statistics, UC Berkeley, 2007 BS/BA in Math, Physics, and International Relations, Stanford University, 2003 Research Interests: His work spans high-throughput biological data analysis, particularly in addressing systematic errors via negative controls, and integrating experimental/observational data. Recent focuses include causal inference in education experiments, social media analytics for public opinion tracking, and reproducible research workflows. He develops methods to correct for unobserved confounders and laboratory variability in genomic datasets. Grants & Awards: MIDAS Reproducibility Challenge Winner (2020) Thomas R. Ten Have Award (2016) Evelyn Fix Prize (2013) Outstanding Graduate Student Instructor (2010) Teaching & Mentorship: Teaches courses in applied statistics and statistical learning. Mentors numerous PhD, master's, and undergraduate students in areas like genomic data analysis and causal inference. Active in curriculum development and student advising within the Statistics Department. Labs & Collaborations: Leads projects at MIDAS and collaborates with researchers in computational medicine, bioinformatics, and social data science. His lab develops open-source tools like the RUV package for batch effect correction and Docker-based reproducibility workflows.

Margo Seltzerمشاهده پروفایل

استاد

• Computer Systems
• Operating Systems
• Database Systems
• +۷ مورد دیگر

Margo Seltzer is a Professor and Co-Head of the Department of Computer Science at the University of British Columbia (UBC). She holds the Canada 150 Research Chair in Computer Systems and the Cheriton Family Chair in Computer Science. Her research focuses on computer systems, including operating systems, databases, data provenance, graph analytics, and interpretable machine learning. She is also involved in educational initiatives and has held leadership roles in academia, including serving as Dean of Computer Science and Engineering at Harvard University. Education: PhD in Computer Science from UC Berkeley (1992), AB in Applied Mathematics from Harvard (1983). Professional roles include Chief Technology Officer at Sleepycat Software and architect at Oracle Corporation. She has received prestigious awards such as the ACM Athena Lecturer Award, Killam Teaching Prize, and membership in the National Academy of Engineering. Research Interests: Systems, databases, transaction processing, provenance capture, healthcare informatics, and cybersecurity. Her work emphasizes practical applications of systems research, including optimizing storage, improving operating system performance, and enhancing data security. She has contributed to open-source projects like Berkeley DB and CamFlow provenance system. Key Contributions: Developed systems for provenance-aware storage, sparse decision trees (e.g., GOSDT), and intrusion detection (FRAPpucino). She advocates for reproducible research through tools like Encapsulator and Rclean. Her teaching and mentorship have influenced countless students and junior faculty in computer science. Awards: ACM Athena Lecturer (2023), SIGMOD Systems Award (2020), USENIX Lifetime Achievement Award (2019), and election to the American Academy of Arts and Sciences (2021). Her work bridges theoretical computer science with real-world applications, emphasizing both technical innovation and societal impact.

Giorgio Gilestroمشاهده پروفایل

استاد

• Neurobiology
• Sleep Biology
• Animal Neurobiology
• +۵ مورد دیگر

Giorgio Gilestro is a Reader in Systems Neurobiology at Imperial College London's Department of Life Sciences, part of the Faculty of Natural Sciences. He holds a PhD in Developmental Biology from the University of Vienna (2006) and completed postdoctoral work in Sleep Biology at the University of Wisconsin-Madison (2009). His research focuses on sleep biology and neurobiology using Drosophila as a model organism, exploring topics such as synaptic plasticity, sleep function evolution, and subconscious experiences during sleep. He has pioneered innovative computational tools like pySolo and Ethoscopes, advancing sleep analysis methodologies. His lab's work intersects genetics, engineering, and bioinformatics to address fundamental questions in systems neurobiology. Research interests include: Evolutionary mechanisms of sleep behavior Neurobiological basis of sleep-wake regulation Development of open-source analytical platforms for behavioral research Subconscious processing during sleep in invertebrate models Gilestro has secured over £2.5M in grants from the BBSRC and mentored seven PhD students, three of whom have established independent research groups. His affiliations include the Centre for Neurotechnology, Grantham Institute, and the Physics of Life initiative. He actively administers the Drosophila-focused Mastodon server (@drosophila.social) to foster scientific communication.

Arash Shaghaghiمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Internet of Things (IoT) Security
• Zero Trust Architecture
• +۴ مورد دیگر

Arash Shaghaghi is a Senior Lecturer in Cyber Security and Acting Director of Online Education at the School of Computer Science and Engineering (CSE) at UNSW Sydney. Previously affiliated with RMIT University, Deakin University, Data61 CSIRO, The University of Melbourne, and The University of Texas at Dallas, he holds a PhD in Computer Science and Engineering from UNSW Sydney, an MSc in Information Security from University College London, and a BSc with Distinction from Heriot-Watt University. His research spans cybersecurity with emphasis on IoT security, zero trust architecture, blockchain applications, and posthumous data management. With over 1.7 million AUD in research funding as Lead CI/CI, his work addresses critical challenges in intelligent transport systems, quantum-safe security, and digital legacy solutions. He serves as Associate Editor for the Journal of Ad Hoc Networks and reviews for top conferences including NDSS and IEEE S&P. His 15 most recent publications demonstrate expertise in IoT security frameworks, digital will solutions, encrypted traffic analysis, and vulnerability prediction models. These works appear in premier venues like IEEE TDSC, IEEE S&P, and ACM WWW, reflecting his leadership in solving practical security challenges for emerging technologies. Early Career Academic Excellence Award (Research), UNSW Engineering, 2024 Educational Excellence & Innovation Award, UNSW Engineering, 2023 Heidelberg Laureate Forum Selection (Top 200 young scientists), 2023 Best Demo Award, IEEE LCN Conference, 2020 As an award-winning educator, Shaghaghi leads UNSW's Master of Cyber Security online programs and previously directed Deakin University's Graduate Certificate in Information Technology. His research team includes postdocs, software developers, and 3 current PhD students. He actively engages with media outlets including ABC and The Guardian on cybersecurity topics, particularly digital legacy management and IoT security.

Kathryn Ecclesمشاهده پروفایل

دانشیار

• Cultural Heritage
• Digital Humanities
• Museum Studies
• +۷ مورد دیگر

Kathryn Eccles is an Associate Professor and Senior Research Fellow at the Oxford Internet Institute (OII), University of Oxford. Her research focuses on digital culture and heritage, examining the impact of immersive technologies, AI, and social media on cultural engagement. She holds a PhD in modern British history (2008) from the Faculty of History at Oxford and has pioneered projects like Cabinet (an object-based learning platform) and Open Cabinet (exploring 3D/AR technologies in museums). Her work also includes the Hashtag Heritage project (using social media to study heritage site engagement) and AI Futures (interrogating AI’s role in cultural production). Key affiliations include the Oxford Research Centre for the Humanities (TORCH), where she led the Digital Humanities Programme (2014–2017), and the Faculty of History. She has been awarded grants from AHRC, Google, JISC, and the Dieter Schwarz Foundation. Her research interests span AI ethics, gender studies, and decolonial practices in digital culture. Notable awards include the AHRC Early Career Fellowship (2012–2013) and the Dieter Schwarz Foundation Project Award (2024). Her teaching includes courses on Cultural Analytics and mentoring students in the MSc in Digital Scholarship. She supervises a vibrant group of doctoral students exploring AI, creativity, and heritage. Current projects include AI is not Photography (examining AI’s impact on cultural production) and collaborations with institutions like the Pitt Rivers Museum and the National Trust. She also leads the GLAM Labs Playful Spaces initiative, using social media data to map visitor engagement.

Melinda Hodkiewiczمشاهده پروفایل

استاد

• Maintenance Engineering
• Asset Management
• Data Science
• +۶ مورد دیگر

Professor Melinda Hodkiewicz is a leading academic at the University of Western Australia (UWA), affiliated with the School of Engineering's Mechanical Engineering department. Her work focuses on advancing maintenance, asset management, and safety practices through data-driven approaches, particularly in Technical Language Processing (TLP) and industrial ontologies. She has held significant roles such as the BHP Fellow for Engineering for Remote Operations (2015–2022) and leadership in the $8.8M Australian Government-funded Training Centre for Transforming Maintenance through Data Science. Research interests include applying TLP to maintenance and safety texts, ontology development for industrial interoperability, and advancing standards like ISO 23726. Awards include the MESA Medal (Lifetime Achievement in Asset Management) and Fellow of the Australian Academy of Technology and Engineering (ATSE). She has contributed to international standards (ISO 55000 series) and served on boards of organizations like NOPSEMA and MRIWA. Her work aligns with UN SDG 4 (Quality Education) and SDG 9 (Industry, Innovation & Infrastructure). Key contributions include developing the Industrial Ontology Foundry's Maintenance Working Group, creating open-source tools like OpenBoltRF for wind turbine analysis, and advancing semantic quality assurance frameworks for maintenance procedures. Her research bridges technical language processing with industrial applications, enhancing safety and operational efficiency in sectors like mining, energy, and offshore engineering. Education: BA(Hons) in Metallurgy (University of Oxford, 1985), PhD in Engineering (UWA, 2014) Grants: ARC Training Centre for Transforming Maintenance (2019–2025), ARC Hub for Offshore Floating Facilities (2014–2021) Advisory Roles: Standards Australia EL-069 (Automation Systems), ISO TC184/SC4 (Industrial Data)

• Statistics Education
• Educational Psychology
• Voice Perception
• +۳ مورد دیگر

Dr. Phil McAleer is a Senior Lecturer at the University of Glasgow's School of Psychology & Neuroscience. His research focuses on improving statistics education in Higher Education, addressing negative attitudes toward statistics, and exploring how personality and emotion are conveyed through voices. He holds a B.Sc. (2002) and Ph.D. (2006) from the University of Glasgow, with postdoctoral work on intent perception and voice neurocognition. He has been a Lecturer since 2012, emphasizing reproducible research practices and teaching via the PsyTeachR initiative. His work spans over 40 publications, including contributions to journals like PLoS ONE , Perception , and Nature Reviews Psychology . He supervises multiple postgraduate students and actively participates in educational conferences and workshops. His research also examines student engagement and feedback strategies, leveraging technologies like MarkR software for enhanced academic feedback. Education: B.Sc. Psychology, University of Glasgow, 2002 Ph.D., University of Glasgow, 2006 (PACo Lab) Research Interests: Statistics education and reproducible practices Voice perception and social impressions Technology-enhanced learning and feedback systems Teaching Contributions: Developed open-source tutorials via PsyTeachR Promotes data skills and reproducibility in curricula Professional Activities: Member of the Voice Neurocognition and PACo labs Presenter at DARTP, TIER, and learning conferences

Gustavo Carneiroمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Medical Image Analysis
• +۴ مورد دیگر

Professor Gustavo Carneiro is a faculty member at the University of Surrey's School of Computer Science and Electronic Engineering, leading the Centre for Vision, Speech and Signal Processing (CVSSP). His primary role is as Professor of AI and Machine Learning, with a focus on medical image analysis, computer vision, and theoretical machine learning. He holds a PhD from the University of Toronto and has extensive international experience, including roles at Siemens Corporate Research and academic positions in Brazil and Australia. Research interests span applied medical imaging (e.g., mammogram analysis, colonoscopy, and histology) and theoretical advances in deep learning, semi-supervised learning, and robust models under label noise. Notable projects include the People-centred Mammogram Analysis (PecMan) initiative, aiming to improve AI trustworthiness and fairness in clinical settings. Prof. Carneiro has supervised over 30 students and postdocs, including PhD candidates in Adelaide and Surrey. His work bridges academia and industry, with publications in top venues like CVPR, MICCAI, and IEEE Transactions. Awards include Dean’s Commendation for Doctoral Thesis Excellence for multiple students. Key collaborations involve medical institutions and tech partners, addressing challenges in endometriosis diagnosis, Barrett’s esophagus detection, and AI-driven surgical tools. His lab develops open-source datasets and frameworks, such as the tumor hypoxia analysis dataset.

Bahareh Heraviمشاهده پروفایل

دانشیار

• AI and Media
• Data and Computational Journalism
• Responsible and Inclusive AI
• +۳ مورد دیگر

Dr Bahareh Heravi is an Associate Professor (Reader) in AI and Media at the University of Surrey's Institute for People-Centred AI, with a focus on responsible AI in journalism. She serves as a BRAID Fellow at BBC R&D and holds affiliations with the Surrey Business School's Department of Entrepreneurship and Innovation, as well as the Microsoft–UCD Centre for Digital Policy. Founding co-chair of European Data & Computational Journalism Conference Steering committee member of Computation + Journalism Conference Member of Irish Government's Open Data Governance Board EDI advisory board member for Royal Statistical Society Her research spans AI integration in media, data storytelling, and ethical implications of computational journalism. She has led projects at the intersection of technology and journalism, including the Insight News Lab at National University of Ireland Galway and the UCD Data Journalism Programme . Her work addresses newsroom automation, deepfake detection, and AI literacy frameworks. Key article trends include: Systematic approaches to data preservation Ethical considerations in AI-driven media Technical frameworks for local news databases Innovative storytelling structures Algorithmic accountability in journalism Global data journalism education initiatives Scientific contributions: Selected for Silicon Republic's Sci-Tech 100 Recognized as high-flying scientist by Silicon Republic (2019) Expert evaluator for European Commission and Research Data Alliance Mentored PhD students in topics including: Responsible AI Literacy in Journalism AI-Driven News Avoidance strategies Digital Local News Provision inequalities Inclusive AI design for media Digital games as journalistic modality Organizational science communication

• Computer Vision
• Machine Learning
• Cognitive Robotics
• +۳ مورد دیگر

Dr. Kasim Terzic is a Lecturer at the School of Computer Science, University of St Andrews. He is affiliated with the Centre for Research into Ecological & Environmental Modelling and the Coastal Resources Management Group. His research focuses on computer vision, machine learning, cognitive robotics, and their applications in ecology and conservation. He also promotes open-source software solutions. Terzic holds a PhD in Computer Science from the University of Hamburg and has taught modules such as Computer Graphics and Machine Learning. He currently supervises four PhD students and serves as an External Examiner at Robert Gordon University. Education Background: PhD in Computer Science, University of Hamburg MSc in Information and Communication Systems, Hamburg University of Technology MSc in Global Technology Management, Hamburg University of Technology BEng (Hons) in Electronic Engineering, University of East Anglia Research Interests: Terzic’s work bridges computer vision with ecological conservation, developing algorithms for image analysis, robotic manipulation of deformable objects, and active vision systems. He emphasizes interdisciplinary approaches, combining AI with ecological monitoring and open-source technologies to enhance accessibility. His projects include fish scale circuli detection, robotic towel manipulation, and individual re-identification in wildlife populations. Advising & Grants: Supervises four PhD students and actively contributes to academic activities, including organizing public events like Doors Open @ Computer Science. His research has been published in venues like Sensors, Biology Methods and Protocols, and IEEE conferences. Labs/Teams: Collaborates with the Centre for Research into Ecological & Environmental Modelling and Coastal Resources Management Group, focusing on applying AI to environmental challenges.

Rosa Filgueiraمشاهده پروفایل

مدرس

• Distributed Systems
• Data-Streaming Optimizations
• Serverless Computing
• +۱۱ مورد دیگر

Rosa Filgueira is a Lecturer at the School of Computer Science, University of St Andrews, and Group Leader of the Systems Research Group (SRG). She holds an Honorary Research Fellow title and is a member of the UK Young Academy. Her career includes roles as Assistant Professor at Heriot-Watt University, Vice President Applied Researcher at JPMorgan Chase, and Senior Data Scientist at the British Geological Survey. She earned her BSc and MSc from the University of Deusto (Spain) and a PhD from University Carlos III Madrid (Spain). Her research focuses on advanced information processing technologies to accelerate scientific discovery, emphasizing distributed systems, data-streaming optimizations, and reproducible frameworks. Key areas include serverless computing, adaptive communication techniques, and AI-driven software analysis. She teaches CS3031 Databases (undergraduate) and CS5020 Principles of Computer Communication Systems (postgraduate), supervising multiple student projects. Rosa’s work addresses challenges in health, earth sciences, and digital humanities. Notable projects include cultural analytics frameworks for the creative industries and AI tools for historical text mining. She leads initiatives like the Laminar serverless framework and the frances NLP tool for heritage texts. Her awards include the UK Young Academy membership and National Librarian's Research Fellowship. Current grants include the British Academy-funded Decoding Democracy project. She actively collaborates internationally, contributing to IEEE eScience conferences and open-source projects like dispel4py and Inspect4py. Labs/Teams: Systems Research Group (SRG), leading interdisciplinary collaborations in HPC and data science. Her work bridges computer science with domain-specific applications, emphasizing real-world impact through reproducible software solutions.

• Labour Economics
• Health Economics
• Education Economics
• +۵ مورد دیگر

Dr. Zhang Wei is a Grant-Funded Researcher (C) at the University of Adelaide's School of Economics and Public Policy, affiliated with the Future of Employment and Skills Research Centre. He holds a PhD in economics and has held roles including Associate Lecturer at the University of Adelaide and Senior Research Fellow at Flinders University's National Institute of Labour Studies. His research focuses on labour economics, health economics, education economics, international trade, and applied game theory. Current projects include evaluations of the National Disability Insurance Scheme, impacts of ageing populations on productivity, job mismatches, and education's role in labour markets. Education: PhD in economics (thesis on multilateral vs. bilateral trade liberalisation) Research interests span policy-relevant areas such as workforce dynamics, healthcare economics, and trade agreements. He has contributed to projects funded by ARC/NHMRC, NCVER, and government departments, addressing topics like retirement policy, VET completers' job mobility, and economic security. Supervision eligibility includes co-supervision for Masters/PhD candidates.

Meysam Alizadehمشاهده پروفایل

پژوهشگر ارشد

• Political Science
• Digital Media Governance
• Social Network Analysis
• +۳ مورد دیگر

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.

S.M. Labibمشاهده پروفایل

استادیار

• Public Health
• Urban Planning
• Environmental Epidemiology
• +۶ مورد دیگر

Dr. S.M. Labib is an Assistant Professor at Utrecht University's Faculty of Geosciences, within the Department of Human Geography and Spatial Planning. He also holds a visiting role as a Research Associate at the University of Cambridge. His work focuses on urban health, environmental exposures, and sustainable cities, combining geospatial methods with data science and machine learning. Labib actively promotes open science and FAIR data principles through collaborative interdisciplinary research. Education: PhD in Geography, University of Manchester (2020) MSc in Geographical Information Science, University of Manchester (2017) Summer School on Data Science and Big Data Analytics, London School of Economics (2017) Bachelor of Urban and Regional Planning, Bangladesh University of Engineering and Technology (2014) Research Interests: Labib’s research addresses public health challenges in urban environments, emphasizing spatial and societal disparities. He explores three core tracks: (1) Urban health dynamics in global North/South contexts, (2) Geographical methods/tool development for health impact modeling, (3) AI-driven analysis of environmental and social health factors using novel datasets. His work bridges health geography, urban planning, and environmental epidemiology. Teaching & Advising: Coordinates the undergraduate Health Geography course and co-coordinates Spatial Statistics and Machine Learning in the Applied Data Science Master program. He employs active and problem-based learning strategies. Currently supervises 2 PhD students and 2 postdocs, with over 10+ MSc students supervised previously. Expresses openness to mentoring future PhD candidates aligned with his research themes. Grants & Projects: Leads the ECO-MIND project (2023–2027) on nature exposure and youth mental health funded by Utrecht University. Collaborates on the UPSCALE project (2024–2027) investigating health technology transitions, supported by an NWO grant. Previously developed the An image tells more than a thousand words project (2023–2024), analyzing streetscapes via AI and crowdsourced data. Labs & Teams: Director of the Spatial Data Science and Geo-Intelligence (SDGI) Lab , which produces open-source toolkits like R-packages and Python libraries. Collaborates globally with researchers through the Utrecht Exposome Hub and Pathways to Sustainability initiative.