جست‌وجوی استاد و پژوهشگر

Peter Doddsمشاهده پروفایل

استاد

• Complex systems
• Networks
• Contagion
• +۷ مورد دیگر

Peter Dodds is a Professor in the Department of Mathematics and Statistics at the University of Vermont, part of the College of Engineering and Mathematical Sciences. His research focuses on complex systems, networks, and sociotechnical phenomena, with a particular emphasis on analyzing large-scale data from social media, language, and human behavior. Education: Ph.D. in Mathematics from the Massachusetts Institute of Technology (MIT). Research Interests: Complex systems, contagion dynamics, storytelling, happiness studies, and applications of computational methods to social and biological systems. Key Projects: Co-director of the Computational Story Lab, analyzing narratives and collective behavior via Twitter and other platforms. His work bridges disciplines, combining mathematical modeling, data science, and social science to understand phenomena such as viral spread, cultural trends, and public health. Notable contributions include studying emotional arcs in stories, the universality of positivity in language, and the dynamics of social media discourse around issues like climate change and political movements. Recent studies include analyzing sleep patterns in college students using wearable devices and examining the impact of social media on collective attention during crises like the pandemic. His methodologies, such as allotaxonometry and word shift graphs, provide novel tools for comparing complex datasets across domains. Dr. Dodds collaborates extensively with colleagues in computer science, sociology, and environmental science, fostering interdisciplinary approaches to understanding human systems. His lab’s work is often applied to real-world challenges, from improving well-being through wearable tech interventions to mitigating misinformation in online spaces.

• Multimodal Deep Learning
• Clinical Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Dr. Ruchit Agrawal is an Assistant Professor of Computer Science and Head of Computer Science Outreach at the University of Birmingham Dubai. Previously, he served as a Postdoctoral Researcher in AI for Healthcare at the University of Oxford’s Computational Health Informatics Lab, and as a Marie Curie AI Researcher in the transnational MIP-Frontiers project at Queen Mary University of London. His work focuses on optimizing healthcare systems using Machine Learning, alongside contributions to Natural Language Processing, Audio Signal Processing, and Multimodal Deep Learning. He holds a PhD in Computer Science from Queen Mary University of London and an MS by Research from IIIT Hyderabad. Education: PhD in Computer Science (Queen Mary University of London, 2021) MS by Research in Machine Translation (IIIT Hyderabad, 2017) Research Interests: Clinical Machine Learning for healthcare system optimization Natural Language Processing with a focus on Indian languages and context-aware models Audio Signal Processing for music performance analysis and stuttering detection Development of multimodal deep learning frameworks for diverse applications Adaptive AI systems leveraging contextual and positional encoding techniques Publications highlight trends in healthcare AI, multilingual NLP, and audio-visual alignment. Recent work includes Arabic sentiment analysis, stuttering detection via MMSD-Net, and stock price prediction using FB-GAN. Earlier contributions address structure-aware synchronization in music performance data and transformer-based post-editing for low-resource languages. His research bridges theoretical advancements with practical implementations in clinical, financial, and cross-modal domains. Scientific awards include the prestigious Marie Skłodowska-Curie scholarship (2017–2020) supporting his deep learning research in audio signal processing. Advising and grants: While no formal advisees are listed, his roles involve leading outreach initiatives and guiding collaborative projects at the Computational Health Informatics Lab during his postdoctoral tenure. Labs/Teams: Active member of the Computational Health Informatics Lab (Oxford) and Machine Translation group at FBK (Italy). His work also intersects with the MIP-Frontiers transnational research project.

Giorgia Chinazzoمشاهده پروفایل

استادیار

• Sustainable Building Design
• Indoor Environmental Quality
• Data Science Applications in Architecture
• +۵ مورد دیگر

Giorgia Chinazzo is an Assistant Professor of Civil and Environmental Engineering at Northwestern University, leading the Architectural Engineering and Design Program. She holds a PhD in Building Science from EPFL and dual master's degrees from Politecnico di Milano and Politecnico di Torino. Her research integrates engineering, architecture, data science, and health sciences to create sustainable, human-centric buildings that enhance occupant well-being while reducing environmental impact. Key focuses include thermal comfort, indoor environmental quality, and smart technologies. Education: PhD in Building Science, Swiss Federal Institute of Technology in Lausanne (EPFL) MSc in Architectural Engineering, Politecnico di Milano (Double Degree) MSc & BSc in Architectural Engineering, Politecnico di Torino Research Interests: Multi-domain human-building interactions Climate-resilient building design Data-driven occupant feedback analysis Thermal comfort innovations Wearable sensor technologies Her work emphasizes bridging lab studies and real-world applications through living laboratories, advancing methodologies for sustainable building practices. Courses taught include Building Science fundamentals and professional development seminars.

Patrick J.F. Groenenمشاهده پروفایل

استاد

• Data Science
• Multidimensional Scaling
• Optimization Algorithms
• +۵ مورد دیگر

Patrick J.F. Groenen is a full Professor of Statistics at the Erasmus School of Economics (ESE), Erasmus University Rotterdam , and currently serves as its Dean. His academic career spans leadership roles, including Director of the Econometric Institute (2014-2020) and President of the International Association for Statistical Computing (2015-2017). He has held visiting positions at Stanford University and contributed extensively to multidimensional scaling (MDS), data science , and statistical genetics . His research focuses on numerical algorithms for MDS, support vector machines , and optimization techniques applied to diverse fields. He is a founding editor of the open-access journal Journal of Data Science, Statistics, and Visualisation and has authored major textbooks including Modern Multidimensional Scaling and Applied Multidimensional Scaling and Unfolding . His work appears in top journals like Nature Genetics , Psychometrika , and Journal of Machine Learning Research . Recent publications highlight advancements in convex clustering , genomic prediction , and robust statistical methods . He has developed influential R packages such as GenSVM , SVMMaj , and smacof for multiclass classification and MDS software. His supervisory record includes 14 PhD students across economic psychology, genetics, and data science. Academic activities include editorial roles at Advances in Data Analysis and Classification and Psychometrika , alongside organizing international conferences like CARME 2007 on correspondence analysis. His contributions to nonlinear biplots and response style modeling have advanced visual data interpretation in social sciences.

• Digital History
• Criminology
• Historical Social Data Science
• +۳ مورد دیگر

Zoë Alker is a Lecturer in Historical Social Data Science at Lancaster University's History department. She joined in 2022, specializing in the digital history of crime, justice, and punishment in 18th-20th century Britain. Her work integrates history, osteoarchaeology, criminology, and digital humanities to advance interdisciplinary research. Key projects include the British Academy-funded 'Skin and Bone' (2022), analyzing Londoners' bodily experiences through data mining, and 'Convict Tattoos' (2019), exploring cultural significance of tattoos in convict populations. Research focuses: Digital humanities, historical data science, crime history, gender studies, and Victorian society. Editorial board member for Routledge's SOLON series since 2022. Recent activities include organizing a 2024 workshop on data science and bordering technologies at The Storey, Lancaster. Her work has been featured in BBC, The Times, and other media outlets. She collaborates with policy groups like the Howard League for Penal Reform.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Ioannis Maniadis Metaxas is a lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. He teaches undergraduate and postgraduate courses including Artificial Intelligence, Machine Learning, and Machine Learning for Visual Data Analysis. His research focuses on: Unsupervised representation learning Computer vision systems Large vision-language models Deep clustering techniques Generative models for data augmentation E-commerce personalization systems Recent publications demonstrate consistent focus on efficient learning methods for visual data, particularly investigating unsupervised pretraining, cluster optimization, and model fine-tuning approaches. His work shows progression from foundational 3D data augmentation to cutting-edge vision-language model optimization.

Yudi Pawitanمشاهده پروفایل

استاد

• Bioinformatics
• Statistical Genetics
• Genomics
• +۲ مورد دیگر

Yudi Pawitan is Professor at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet, where he leads the research group on Statistical and Bioinformatics Analyses of High-Throughput Molecular Data. His work focuses on developing statistical methods for genomic studies including SNP/RNA arrays and next-generation sequencing. Education includes BSc in Statistics (Bogor Agriculture Institute, 1982), MSc in Statistics (UC Davis, 1984), and PhD in Statistics (UC Davis, 1987). Research interests span bioinformatics, cancer genomics, and statistical genetics with emphasis on high-dimensional data analysis, genetic correlations, and neural cell biology. His group addresses fundamental questions in genomic data interpretation and neurodegenerative processes. Publications demonstrate strong focus on genetic epidemiology, single-cell analytics, and statistical methodologies. Recent works explore machine learning applications in longitudinal data visualization, neural cell characterization, and cancer biomarker discovery. Awards: Not documented in provided texts. Supervises doctoral candidates including Linda Lindström and Ralf Kuja-Halkola. Manages the Live Imaging Facility at St. Vincent's Centre for Applied Medical Research. Research funded by Swedish Research Council and Swedish Cancer Society grants. Leads interdisciplinary collaborations through the Statistical and Bioinformatics research group, integrating computational biology with experimental neuroscience.

Diana Galvan Sosaمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Knowledge Acquisition
• Applications of NLP in Education
• +۳ مورد دیگر

Diana Galvan Sosa is a Researcher at the University of Cambridge, affiliated with the ALTA Institute (Automated Language Teaching and Assessment) and the NLIP group. She holds a doctoral degree in Information Science from Tohoku University (Japan). Her research focuses on Natural Language Processing (NLP), particularly knowledge acquisition and applications of NLP in education, including feedback generation systems for second language (L2) learners. She also contributes to clinical text analysis and hate speech detection research. Education: PhD in Information Science from Tohoku University, Japan. Research Interests: Grammatical Error Correction Educational Technology Clinical NLP Explainability in AI Temporal Relation Extraction Multimodal Reasoning Recent Work Trends: Her most recent publications (2023-2025) emphasize advanced NLP applications like GPT-4 analysis for error correction, hate speech detection leveraging platform guidelines, and evaluation frameworks for AI explanations. Earlier work includes foundational studies on clinical text analysis and addiction treatment populations. Labs/Groups: Active member of the ALTA Institute and NLIP group at Cambridge. Also contributes to open-source projects like the GECToR framework for grammatical error correction, reflected in her GitHub repositories.

Uwe Roehmمشاهده پروفایل

دانشیار

• Distributed Data Management on Modern Hardware
• Integrated Machine Learning with Databases
• Interactive Big Data Analytics
• +۵ مورد دیگر

Associate Professor Uwe Roehm is a faculty member at the University of Sydney's School of Computer Science, specializing in database systems and big data analytics. He holds a PhD from ETH Zurich and joined the University of Sydney in 2004. His research focuses on distributed data management, integrating machine learning with databases, and scalable data processing for bioinformatics. Education: PhD in Computer Science, ETH Zurich (2002) Diplom-Informatics (MSc equivalent), University of Passau Research Interests: Database Systems and Transaction Processing Human-Centred Data Management Freshness-Aware Scheduling (FAS) Big Data Analytics and Cloud Computing Key Projects and Contributions: Developed Serialisable Snapshot Isolation (SSI), implemented in PostgreSQL Created the Master of Data Science program at the University of Sydney Recipient of the 2018 ACM SIGMOD Test of Time Award Awards: 2018 ACM SIGMOD Test of Time Award 2013 Dean's Award for Outstanding Teaching 2008 ACM SIGMOD Best Paper Award Teaching and Grants: Teaches courses on database systems, data science platforms, and cloud computing Principal Investigator on ARC-funded projects, including Linkage and Discovery grants Labs and Teams: Leads the Database Research Group and contributes to the Human-Centred Technology Research Cluster at the University of Sydney.

• Data Visualization
• Machine Learning
• Neural Networks
• +۵ مورد دیگر

Professor Masa Takatsuka is a faculty member in the School of Computer Science at The University of Sydney, holding the rank of Professor. He serves as Associate Head of School (Postgraduate). His research focuses on data visualization, machine learning, and human-computer interaction, with notable contributions in spectral clustering, neural networks, and collaborative systems. He teaches courses such as COMP5048 Visual Analytics, COMP5116 Design of Networks & Distributed Systems, and others. Research interests include: Data visualization techniques (e.g., SOM-based methods) Graph neural networks and spectral clustering algorithms Interactive systems for large displays and collaborative environments Geospatial analysis and visualization tools like GeoVISTA Studio Recent publications span topics like random projection forests in graph convolutional networks (2023), topology-aware clustering (2018), and crowd-powered evaluation visualization (2017). His work emphasizes practical applications in domains like medical data analysis and social media sentiment exploration. He has secured grants including ARC Linkage Projects (2011, 2010) and industry partnerships. Current research students include Michael ASHSHIDDIQ PODBURY working on topological clustering methods. Labs/teams: Collaborations involve UnderworldGUI (geodynamic modeling tools) and projects leveraging self-organizing maps (SOM) for multidimensional data analysis.

Franziska Sofia Hafnerمشاهده پروفایل

پژوهشگر

• Algorithmic Fairness
• Machine Learning
• Interactive Data Visualization
• +۳ مورد دیگر

Franziska Sofia Hafner is a Researcher at the Oxford Internet Institute (OII), University of Oxford, focusing on algorithmic fairness, machine learning, and interactive data visualization. She holds an MSc in Social Data Science from the OII and a Bachelor’s degree in Computer Science and Public Policy from the University of Glasgow. Her work bridges technical AI research with societal impacts, particularly addressing bias in language models and healthcare algorithms. Education: MSc in Social Data Science, Oxford Internet Institute (2023–2024) Bachelor of Science in Computer Science and Public Policy, University of Glasgow Her research interests include mitigating gender and ethnicity biases in AI systems, with notable contributions to gender performativity theory in language models and ethnicity-aware algorithm design. She presented her work on gender bias at the 2024 NeurIPS conference and has published in journals like AI & Society and Social Network Analysis and Mining . Research Contributions: Franziska’s articles explore cultural differences in sentiment analysis, bias in healthcare algorithms, and equitable algorithmic design. Her work highlights how AI systems encode societal inequities, such as gender binaries and ethnic disparities in medical diagnostics. She collaborates with the OII’s Equitable Access to Quality Information Lab and Digital Ethics and Defence Technologies group to advance ethical AI practices. Public Engagement: Her research has been featured in press releases discussing AI’s impact on health equity and gender representation, emphasizing the need for inclusive algorithmic frameworks.

Christoph Kinkeldeyمشاهده پروفایل

مدرس

• Data Visualization
• Visual Analytics
• Uncertainty Visualization
• +۳ مورد دیگر

Prof. Christoph Kinkeldey is a Lecturer at Hamburg University of Applied Sciences, affiliated with the Department of Information, Media and Communication within the Faculty of Design, Media and Information. He holds a doctorate in Geoinformatics from HafenCity University Hamburg (2015) and has conducted research globally, including at PennState University, University of Melbourne, and Inria. His work focuses on data visualization, visual analytics, and uncertainty visualization, emphasizing how visual tools aid decision-making in complex data environments. Education: PhD in Geoinformatics, HafenCity University Hamburg (2015) Research Interests: Interactive visual data analysis Uncertainty communication in visualizations Blockchain data exploration (e.g., Bitcoin network analysis) Evaluation of visualization techniques His research bridges cartography, computer science, and human-centered design to empower diverse stakeholders in understanding complex information. Publications: Recent work emphasizes uncertainty visualization for data analysts, machine learning interpretability, and blockchain analytics. Key contributions include the BitConduite tool for Bitcoin network analysis and participatory design methods for non-technical users. Awards: 2016 VAST Mini Challenge 2: Honorable Mention for Clear Analysis Strategy Advising & Collaboration: Currently on parental leave until August 2024, he collaborates with the gicentre (City, University of London) and Monash University’s Department of Human-Centered Computing. His research teams focus on interdisciplinary projects merging visualization theory with practical applications. Labs/Teams: Active in the gicentre’s visualization initiatives and Monash’s Human-Centered Computing group, contributing to open-source tools and international research networks.

Jessica Olivaresمشاهده پروفایل

استادیار

• Supply Chain Management
• Supply Chain Disruptions
• Industry 5.0
• +۳ مورد دیگر

Jessica Olivares is an Assistant Professor of Supply Chain Management at the Shannon School of Business, Cape Breton University. Her expertise spans supply chain resilience, digital twins, and Industry 5.0, with a focus on mitigating disruptions in global networks. Dr. Olivares contributes to both academic research and practical solutions for sustainable supply chain management. Her academic credentials include: B.S. in Industrial Engineering, University of the Americas Puebla (UDLAP), Mexico M.S. in Industrial Engineering, University of the Americas Puebla (UDLAP), Mexico Ph.D. in Industrial and Manufacturing Systems Engineering, University of Windsor, Canada Dr. Olivares' research centers on supply chain management, with specific interests in disruption recovery, digital twin applications, and sustainable design. She explores how Industry 5.0 principles can humanize smart manufacturing while enhancing resilience. Her work addresses critical gaps in perishable food supply chains and resource distribution during crises, integrating risk assessment with technological innovation to build robust systems. Her recent publications (2021-2025) show a strong emphasis on digital twins for supply chain resilience, with increasing attention to sustainability and multi-objective optimization. She has pioneered frameworks for recovery from major disruptions, including pandemic impacts, and investigates energy-aware scheduling in manufacturing. Her scholarship bridges theoretical models with real-world applications in food systems and global networks. No scientific awards were documented in the available sources. Information on graduate student supervision and research funding was not provided, though her active publication record suggests engagement in scholarly mentorship and potential grant-supported projects. No details about laboratories or research teams were mentioned.

Federica Mucciمشاهده پروفایل

دانشیار

• International Law
• Cultural Heritage Protection
• European Union Law
• +۴ مورد دیگر

Federica Mucci is an Associate Professor of International Law at the University of Rome Tor Vergata, affiliated with the Department of History, Humanities and Society. She specializes in international protection of cultural heritage, European Union law, and treaty law. Her teaching includes courses on cultural heritage protection and EU law for programs in Education and Tourism. Legal Expert: Italian Ministry of Foreign Affairs UNESCO Delegation Member: Contributed to the 2005 UNESCO Convention on cultural diversity and its implementation. Her research focuses on international law frameworks for cultural heritage, maritime law, treaty interpretation, and environmental protection. Key publications include monographs on cultural heritage law (2012) and a PhD thesis on EU copyright law (1998). Publications span interdisciplinary topics such as topological data analysis, though the majority of her work aligns with legal and humanities disciplines. Awards: No specific prizes are mentioned, but her scholarly output includes influential books and articles on international law. Advising/Grants: No formal student advisees or grant details provided in text. Labs/Teams: No specific lab affiliations mentioned, though her role at UNESCO implies collaboration with international bodies.

Ansaf Salleb-Aouissiمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Data Science
• Medical Informatics
• +۴ مورد دیگر

Ansaf Salleb-Aouissi is a Senior Lecturer in the Department of Computer Science at Columbia University’s Fu Foundation School of Engineering and Applied Science. She holds affiliations with the Foundations of Data Science and Health Analytics centers. With a PhD from the University of Orleans, France (2003), she pursued postdoctoral training at INRIA Rennes before joining Columbia as an Associate Research Scientist in 2006. She transitioned to her current role in 2015 after serving as an adjunct professor in Computer Science and Data Science from 2014–2015. Her research focuses on machine learning applications in healthcare, education, and infrastructure systems. Key areas include medical informatics (e.g., preeclampsia prediction, genetic associations in pregnancy), educational data mining (intelligent tutoring systems, bootcamp design), and power grid reliability. Notable achievements include winning the NIH Maternal Morbidity Data Challenge and developing tools like LogicLearner for logic education. She has contributed to projects such as analyzing CDC pregnancy data and optimizing the New York City power grid. Her work bridges theoretical machine learning with real-world applications, emphasizing interpretability, bias mitigation, and collaboration across disciplines. She has published extensively in venues like JMLR, TPAMI, and ECML, addressing topics from counterfactual explanations to ensemble learning with missing data.