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Michael A. CohenView profile

Assistant Professor

• Perception
• Memory
• Cognition
• +3 more

Michael A. Cohen is an Assistant Professor in the Department of Psychology and Program in Neuroscience at Amherst College, and a Research Scientist at MIT’s McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences. His research explores the boundaries of perception, memory, and cognition using fMRI, EEG, psychophysics, and computational modeling. Cohen completed his PhD at Harvard University under Ken Nakayama and George Alvarez, following a postdoctoral fellowship at MIT with Nancy Kanwisher. His undergraduate work at Tufts University was under Daniel Dennett. Research interests focus on neural mechanisms of visual awareness, perceptual limits, and cognitive processes. Notable work challenges computational theories of consciousness and demonstrates early specialization in infant visual pathways. Methodologically, he combines empirical and theoretical approaches, including task-optimized neural networks to explain brain specialization. Recent studies highlight graded perceptual awareness and predictive simulations in the brain’s ‘physics engine’. His articles frequently address foundational questions in perception and consciousness, with contributions to debates over integrated information theory and the role of attention. Though no formal grants or awards are listed, his cross-institutional collaborations reflect active engagement in cognitive neuroscience research. Labs/Teams: Affiliated with Amherst College Psychology Department labs and MIT’s McGovern Institute, focusing on human brain imaging and developmental neuroscience studies.

• Resilience in 5G
• Distributed Artificial Intelligence (Edge AI)
• Information assessment
• +3 more

Tobias Meuser is a Researcher at the Multimedia Communications Lab of Technische Universität Darmstadt, leading the "Adaptive Communication Systems" group since 2020. He holds a PhD (2019) focused on vehicular network data management and has been a central figure in the third phase of the Collaborative Research Center (CRC) MAKI as a principal investigator in subproject B1. His work emphasizes resilient 5G networks, edge AI, and distributed systems. Education: B.Sc. Business Informatics (Fernuniversität Hagen) M.Sc. Informatics (TU Darmstadt) Research Interests: Resilience in 5G and beyond Edge AI and distributed machine learning Information assessment in vehicular networks Collaborative perception systems Hardware acceleration for network functions Key Projects: Principal Investigator in CRC MAKI's B1 (Monitoring and Analysis) Collaborations with Opel (cooperative maneuvering) and Deutsche Bahn (5G resilience) Labs/Teams: Head of Adaptive Communication Systems group at Multimedia Communications Lab Member of Distributed Sensing Systems group (2016–2020)

Evaggelia PitouraView profile

Professor

• Data Management
• Distributed Databases
• Database Systems
• +7 more

Evaggelia Pitoura is a Professor in the Department of Computer Engineering and Informatics at the University of Ioannina, Greece, and a Lead Researcher at the Archimedes Research Unit, ATHENA Research Center. Her work spans data management, distributed systems, and mobile computing, with a strong focus on database personalization and diversity. Her research interests include data diversity, contextual preferences, XML data management, peer-to-peer systems, and mobile computing . She has pioneered work in result diversification, preference-aware querying, and distributed data indexing, publishing extensively in top venues such as PVLDB, ICDE, SIGMOD, and IEEE TPDS. The recent publications highlight a consistent trend in personalized, context-aware database systems , with emphasis on improving user experience through intelligent query processing, recommendation, and efficient data organization in distributed environments. Best Paper Award, SIGMOD 1999 Best Paper Award, PVLDB 2013 She has advised several PhD students, including Georgia Koloniari, Kostas Stefanidis, Marina Drosou, and Konstantinos Semertzidis, who now hold positions in academia and industry. She teaches courses in Databases and Information Retrieval and actively contributes to the academic community through program committee roles in major conferences like ICDE, VLDB, SIGMOD, and WWW. She leads the Distributed Management of Data Group , fostering innovation in scalable and intelligent data systems.

Minsu ParkView profile

Assistant Professor

• Computational Social Science
• Human-centered Data Science
• Cultural Consumption
• +9 more

Minsu Park is an Assistant Professor of Social Research and Public Policy at New York University Abu Dhabi (NYUAD), with affiliate status at the Center for Data Science at New York University (NYU). He holds a PhD in Information Science from Cornell University, where he was advised by Michael W. Macy and Mor Naaman. His academic work bridges computational techniques and social science theory, focusing on cultural consumption, social networks, and human-centered data science. PhD in Information Science, Cornell University His research centers on understanding how individuals form cultural preferences through social and psychological mechanisms, particularly in domains like music, food, fashion, and science. He leverages large-scale digital trace data—including social media, streaming platforms, and physiological signals from smart devices—to model behavior. His interdisciplinary approach draws from sociology, social computing, and data science, aiming to uncover both individual and global patterns in cultural dynamics. Key themes include variety-seeking behaviors, affective preferences, and the interplay between social position and taste. The 15 most recent publications reflect a strong trend toward using computational methods to explore sociocultural phenomena. Topics span affective preference rhythms in music, testing sociological theories like cultural omnivory, imputing user attributes from digital behavior, and enhancing model interpretability. These works are published in high-impact venues such as Nature Human Behaviour and ICWSM, indicating recognition in both computer and social sciences. Minsu Park is deeply committed to responsible data science, addressing issues like algorithmic bias, transparency, fairness, data privacy, and research ethics. He emphasizes mixed-methods approaches and critical reflection on data generation and usage. Published in Nature Human Behaviour Presented at ICWSM Focus on ethical and interpretable AI Advocate for reproducibility and data curation He mentors students through capstone projects in computer science and teaches courses such as Human-Centered Data Science and Textual Analysis for the Social Sciences. While specific grants are not detailed in the text, his research program suggests involvement in interdisciplinary, data-intensive projects likely supported by academic or scientific funding bodies. His technical expertise includes Python, R, machine learning frameworks (TensorFlow, Keras), and tools like Gephi and AWS. He leads or contributes to research initiatives involving smartwatch-based data collection, surname-based ethnicity matching, and global music consumption analysis, as evidenced by his public GitHub repositories. These projects highlight his commitment to open science and collaborative research.

Michael CochezView profile

Assistant Professor

• Machine Learning
• Knowledge Graph Embedding
• Semantic Web
• +5 more

Michael Cochez is an Assistant Professor in the Learning and Reasoning group at the Faculty of Science, Vrije Universiteit Amsterdam . His research focuses on Machine Learning , Knowledge Graph Embedding , and Prototype-Based Ontologies , with applications in Scalable Hierarchical Clustering , Ontology Matching , and Knowledge Evolution . He teaches Intelligent Systems , Data Mining , and Machine Learning courses. His research explores integrating Knowledge Graphs into end-to-end ML models , addressing challenges in Approximate Query Answering and Knowledge Representation . He has also contributed to Multi-Agent Systems and Cloud Communication through projects like Graphino.nl (his consultancy business). His students include Jiawen Chen (supervised thesis on Smart Semantic Multi-channel Communication ).

• Distributed Computing
• Federated Learning
• Cloud Computing
• +12 more

Salman Zubair Toor is a researcher at Uppsala University, Sweden, specializing in distributed computing, federated learning, and cloud/edge infrastructure optimization. His work spans resource scheduling, data streaming, and secure anomaly detection.

Roman DibiaseView profile

Associate Professor

• Geomorphology
• Erosion Processes
• Tectonics
• +6 more

Roman Dibiase is an Associate Professor in the Department of Geosciences at Pennsylvania State University within the College of Earth and Mineral Sciences . His research focuses on Geomorphology , Erosion Processes , and Landscape Evolution , particularly examining how water, tectonics, and climate change shape mountainous and bedrock-dominated terrains. His recent work explores Debris Flow Dynamics , Bedrock Fractures , and Critical Zone Studies , with fieldwork in Pennsylvania, Taiwan, and permafrost regions. He utilizes cosmogenic nuclides like 10 Be to quantify erosion rates and investigates post-wildfire sediment transport, hillslope-channel coupling, and lithologic controls on landscape form. Scientific awards and student advising details are not explicitly mentioned in the provided texts. However, his publications highlight collaborations with researchers across institutions, focusing on Sediment Transport , Hydrological Modeling , and Climate-Landform Interactions .

• Work and Employment Relations
• Employee Voice and Industrial Democracy
• Trade Unions and Labour Relations
• +13 more

Tony Dobbins is Professor of Work and Employment Relations and Department Director of Research in the Department of Management at Birmingham Business School, University of Birmingham. He joined the university in May 2018 from Bangor University, where he was Professor of Employment Studies. He is a member of the Organisation, Work and Employment (OWE) research group and leads the Inclusive Labour Governance and Employee Voice stream in the Work Inclusivity Research Centre (WIRC). PhD in Industrial Relations, University of Limerick MA in Industrial Relations, University of Warwick BA in HRM & Business Administration, Keele University Postgraduate Certificate in Higher Education, Bangor University Tony Dobbins’ research centers on work and employment relations, with core interests in employee voice, industrial democracy, trade unions, the living wage, decent work, and the future of work. His work explores how employment relationships are regulated, the sociology of work, and the impact of economic restructuring on labour markets. He investigates industrial relations frameworks, employee silence, and the role of unions in workplace cooperation. His research also focuses on the ethical and policy dimensions of the real living wage, particularly in UK football clubs, and the implications of remote and hybrid working post-pandemic. His recent publications reflect a strong trend toward policy-relevant research on decent work, flexible working, and pandemic impacts on employment. These works span journals in industrial relations, human resource management, and public policy, indicating a multidisciplinary approach. Key themes include employee autonomy, workplace governance, ethical pay, and institutional resilience in crises. Parliamentary Academic Fellow, UK Parliament (2021–2022) President, British Universities Industrial Relations Association (BUIRA) (2019–2022) Fellow of the Higher Education Academy Visiting Professor, University of Limerick and University of Malta Tony Dobbins has secured significant research funding as Principal Investigator and Co-Investigator from bodies including the ESRC, British Academy, Leverhulme Trust, Research England, and the European Commission. He collaborates with leading scholars globally on projects related to employee voice, living wage, and the future of work. He has advised organizations such as the UK Parliament, CIPD, Eurofound, and Siemens. He supervises PhD students and has mentored early-career researchers, including former PhD student Emma Hughes, now a lecturer at the University of Liverpool. Tony is actively involved in research centres and networks, including the Work Inclusivity Research Centre (WIRC) and international collaborations with scholars from Australia, Ireland, the USA, and New Zealand. He co-edits special journal issues and leads policy-facing projects such as the Midlands Good Work Charter in partnership with Acas.

Dan SteinbergView profile

Academic

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +7 more

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

Mohamed AllaliView profile

Associate Professor

• Data Engineering
• Machine Learning
• Climate Data Analysis
• +3 more

Mohamed Allali is an Associate Professor at Chapman University, affiliated with both the Fowler School of Engineering (Department of Electrical Engineering and Computer Science) and Schmid College of Science and Technology (Department of Mathematics). His research spans data engineering, machine learning, climate informatics, and mathematical education. He has contributed to constraint-based intelligent tutoring systems, data drift detection using KL divergence, and geospatial analysis for environmental monitoring. Education: University of Oklahoma (BS, MA, PhD). His recent work focuses on data distribution divergence, climate modeling for sea turtle habitats, and neural network applications in medical imaging. He has collaborated extensively on drought indices, satellite data validation, and educational technologies. His publications highlight expertise in statistical learning, computational methods, and interdisciplinary environmental applications.

Paul BradshawView profile

Associate Professor

• Data Journalism
• Investigative Journalism
• Online Journalism
• +6 more

Paul Bradshaw is an Associate Professor at Birmingham City University, leading the MA in Data Journalism program while serving as a consultant data journalist for the BBC England data unit. He founded the pioneering crowdsourcing platform Help Me Investigate, which pioneered collaborative investigative techniques in journalism. His research focuses on data journalism , digital news production , and investigative innovation , with emphasis on data visualization, source protection, and mobile-first storytelling. Bradshaw's work bridges academic theory and industry practice, developing frameworks like the 'Model for the 21st Century Newsroom' adopted globally for newsroom operations and journalism education. Analysis of his recent publications reveals critical trends in ethical data communication during crises (e.g., pandemic uncertainty reporting), evolving security challenges for journalists post-Snowden, and the integration of social media into core news production workflows. His scholarship consistently addresses the practical implementation of data-driven techniques across diverse media environments. Awards and Recognition Shortlisted for Multimedia Publisher of the Year (2010) Talk About Local Investigation of the Year Award (2010) CNN MultiChoice African Journalist Award for Nigerian football agents investigation (2016) BBC England Data Journalism Award for housing discrimination reporting (2017) Bradshaw mentors students through the MA in Data Journalism program while providing consultancy to major media organizations including Sky, Trinity Mirror, The Telegraph, and the Balkan Investigative Reporters Network. His training programs focus on practical implementation of data scraping, social media analysis, and investigative techniques for newsrooms worldwide. He leads the Help Me Investigate platform as a living laboratory for crowdsourced journalism and collaborates with the BBC England data unit on high-impact investigations. His current work explores the intersection of AI tools, ethical data practices, and evolving business models in digital journalism.

Miguel Sales DiasView profile

Researcher

• Machine Learning
• Smart Cities
• Immersive Technologies
• +6 more

Miguel Sales Dias is affiliated with ISCTE - Instituto Universitário de Lisboa, specifically the Iscte Business School. His research spans multiple domains, including Machine Learning , Immersive Technologies , and Urban Mobility . His work focuses on: Developing AI-driven systems for energy consumption optimization in public buildings Creating co-design interfaces for housing customization Exploring mHealth applications for cardiac diagnostics Advancing virtual reality for education and visitor experiences Recent publications demonstrate expertise in smart city solutions , bioinformatics , and human-computer interaction . While specific awards or educational background weren't explicitly mentioned, his extensive publication record indicates significant contributions to interdisciplinary research.

• Disaster Risk Reduction
• Community Resilience
• Environmental Studies
• +4 more

Erin O'Connell is an Associate Professor (Teaching Stream) at the University of Waterloo , where she also serves as Associate Chair, Undergraduate . Her research focuses on post-disaster vulnerability reduction and building sustainable resilience in affected communities, with fieldwork concentrated in Asia. Key research areas include Disaster risk reduction in global contexts Post-disaster livelihood recovery Housing reconstruction processes Disaster-religion intersections (faith impact, community networks) Climate change adaptation frameworks The 15 most recent publications show a thematic progression from community-based disaster recovery in Southeast Asia (2015-2017) toward broader climate adaptation challenges and disaster-policy linkages. Her methodological approach combines fieldwork with system dynamics modeling and comparative case studies, emphasizing both technical resilience metrics and socio-cultural factors. Her teaching portfolio includes ENVS 195: Introduction to Environmental Studies , reflecting her broader interest in human-nature interactions. Current projects investigate the interplay between disasters, faith systems, and environmental perceptions.

• Graph Theory
• Data Mining
• Network Analysis
• +2 more

Aristides Gionis is a Research Professor Fellow at Aalto University specializing in advanced network analysis and data mining. His work bridges theoretical graph algorithms with practical applications in dynamic and signed networks. His research focuses on graph theory and temporal network analysis , particularly in discovering dense subgraphs, community structures, and event patterns in evolving networks. Key methodologies include contrastive subgraph mining for explainable AI in brain networks and polarization detection in social systems. His publication trends reveal consistent contributions to top-tier venues like KDD, PAKDD, and ECML PKDD, with emphasis on algorithmic solutions for network segmentation, correlation mining, and interpretable classification. Primary application domains include social networks, neuroscience, and temporal data streams. As a Fellow at Aalto University, he leads research in computational network science without explicit departmental affiliation stated in available sources.

Pedro Trapero FernandezView profile

Researcher

• Ancient History
• Landscape Archaeology
• Geographic Information Systems (GIS) in Archaeology
• +3 more

Dr. Pedro Trapero Fernandez is a Postdoctoral Researcher at the University of Cádiz, affiliated with the Department of History, Geography and Philosophy and the Institute of Viticulture and Agri-Food Research (IVAGRO). He leads the HUM240 Historical Heritage of Andalusia in Antiquity research group and specializes in Ancient History, with particular focus on Roman viticulture, landscape archaeology, and geospatial analysis. PhD in Ancient History and Archaeology (2020) with thesis on Roman Viticulture in the Hasta Regia and the Guadalquivir Estuary Research expertise in non-invasive techniques: GIS, drones, ground-penetrating radar, magnetometry Active academic collaborations with international institutions including FORTH (Greece) His research integrates classical agronomic texts like Columella with modern geospatial technologies to reconstruct ancient agricultural practices and mobility networks. Key projects include Roman road network analysis, viticulture modeling, and archaeological applications of GIS in Spain and Portugal. He has contributed to coastal and inland connectivity studies in Roman and Medieval contexts. Scientific publications demonstrate technical proficiency in geophysical prospecting and historical modeling. Notable works include: GIS applications for Roman property surveying, multidisciplinary analysis of wine production, and innovative methodologies for studying ancient transportation networks. As an educator, he has taught at undergraduate and master's levels through predoctoral contracts and developed flipped classroom methodologies for humanities teaching. Current affiliations include the Agustín de Horozco Seminar on Economic Studies and multiple international research partnerships.