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Thomas LengauerView profile

Professor

• Computational Biology
• Bioinformatics
• Applied Algorithmics
• +10 more

Prof. Dr. Thomas Lengauer is a leading figure in computational biology and applied algorithmics at the Max Planck Institute for Informatics, part of the Max Planck Society in Saarbrücken, Germany. He heads the Department of Computational Biology and Applied Algorithmics, where he drives research at the intersection of computer science, genomics, and medicine. His work integrates algorithm development with biological applications, particularly in viral genomics, epigenetics, and personalized treatment prediction. Research Interests: His research focuses on computational methods for analyzing complex biological data. Key areas include HIV and hepatitis virus evolution, antiretroviral therapy outcome prediction, DNA methylation and epigenomic analysis, machine learning applications in medicine, and the integration of big data in biological research. He has made significant contributions to understanding viral drug resistance and host-pathogen interactions through computational modeling. The recent publications (2019–2025) reflect a strong trend toward integrating temporal genomic data, machine learning, and public health surveillance. His work spans from fundamental algorithm development (e.g., RnBeads, MeDeCom) to applied clinical research (e.g., dolutegravir resistance, SARS-CoV-2 interventions). Key domains include epigenomics , virology , machine learning in healthcare , and biological database systems , increasingly incorporating AI-driven approaches. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: While no formal list of students is provided, Prof. Lengauer leads a large collaborative research group, evidenced by frequent co-authorship with researchers such as Walter, Bock, Müller, Kaiser, and Pirkl. He participates in major consortia (e.g., DEEP Consortium, Respiratory Virus Network), suggesting leadership in funded collaborative projects. His work is likely supported by Max Planck Society core funding and competitive third-party grants, though specific grants are not listed. Labs and Teams: He leads the Computational Biology and Applied Algorithmics group at the Max Planck Institute for Informatics. The team develops computational tools for epigenomic data analysis (e.g., RnBeads, DecompPipeline), viral resistance prediction, and public health modeling. The group collaborates extensively with clinical and biological researchers across Europe, functioning as a hub for interdisciplinary bioinformatics research.

Adam PererView profile

Associate Professor

• Human-Centered AI
• Data Visualization
• Healthcare Informatics
• +4 more

Adam Perer is an Associate Professor at Carnegie Mellon University, where he is a member of the Human-Computer Interaction Institute within the School of Computer Science. He serves as Co-Director of the Data Interaction Group and holds leadership positions as Area Papers Chair at IEEE VIS and Visualization Subcommittee Papers Chair at ACM CHI. Previously, he worked as a Research Scientist at IBM Research. Ph.D. in Computer Science from the University of Maryland, College Park Perer's research integrates data visualization and machine learning techniques to create visual interactive systems that help users make sense of big data. His work focuses on human-centered data science, extracting insights from clinical data to support data-driven medicine, and facilitating human-AI collaboration. He investigates how people engage with and make decisions using data, designing new interfaces to interact with complex information while assisting impactful domains drowning in data. His recent publications reveal a strong trend toward healthcare applications of AI and visualization, particularly in clinical decision support and overdose prevention. There's also a significant focus on explainable AI (XAI), with multiple papers examining how imperfect explanations affect human-AI collaboration and decision-making in critical contexts like healthcare. His work consistently bridges visualization theory with practical applications in high-stakes domains. Best Paper Honorable Mention for 'Dead or Alive: Continuous Data Profiling for Interactive Data Science' (VIS 2023) Best Paper for 'Neo: Generalizing Confusion Matrix Visualization' (CHI 2022) Most Reproducible Paper Award for 'SQLShare' (SIGMOD 2016) Perer actively mentors students across all levels, with PhD students focusing on human-AI collaboration in healthcare settings, visualization techniques, and clinical decision support systems. His lab receives funding for projects related to human-centered AI, data visualization in healthcare, and explainable machine learning systems. The Data Interaction Group, which he co-directs, focuses on empowering everyone to analyze and communicate data through interactive systems. His research has been supported by collaborations with medical institutions and appears in premier venues for visualization, human-computer interaction, and medical informatics. Current projects include Eye into AI (improving XAI interpretability), Predicting and Visualizing Overdose Risk, and AI applications in intensive care units.

Fatemeh VafaeeView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Systems Biology
• +5 more

Associate Professor Fatemeh Vafaee is a leading researcher at the University of New South Wales (UNSW) , holding appointments as Associate Professor in the School of Biotechnology and Biomolecular Sciences (BABS) and Deputy Director (Science) of the UNSW AI Institute . She previously served as Deputy Director of the UNSW Data Science Hub (uDASH) and has held academic positions at the University of Toronto and the University of Sydney. PhD in Artificial Intelligence from University of Illinois at Chicago Postdoctoral Fellowships at University of Toronto and University of Sydney Founded the AI-Enhanced Biomedicine Laboratory in 2017 Her research focuses on deploying advanced AI techniques to address biomedical challenges through: Biomarker Discovery for cancer and neurodegenerative diseases Single-Cell Multi-Omics data integration and analysis Computational Drug Repositioning and network pharmacology Multi-Omics Data Fusion and temporal network modeling Recent publications demonstrate expertise in liquid biopsy development , single-cell imaging , and AI-driven cancer diagnostics . Her methodological contributions include novel deep learning architectures for omics data analysis and graph neural networks for drug synergy prediction. Scientific accolades include: Winner, Women in AI Asia-Pacific Health Award (2023) Runner-Up, WAI-APAC Innovator of the Year (2023) Top 10 Women in AI in Asia-Pacific (2023) Australian Bioinformatics and Computational Biology Society Research Excellence Award (2023) She supervises PhD candidates across computational biomedicine and AI in healthcare , with significant grant achievements exceeding $17M in competitive funding, including schemes from ARC Discovery , NHMRC , and Medical Research Future Fund .

• Architectural History
• Digital Design Theory
• Cultural Technologies
• +4 more

Mario Carpo is the Reyner Banham Professor of Architectural History and Theory at The Bartlett School of Architecture, University College London. His work bridges early modern architectural history and contemporary digital design theory, with a focus on cultural technologies and the Vitruvian tradition. Education: Dr.arch (University of Florence, 1984), PhD (European University Institute, 1990), HDR (Art History, France, 2009) His research examines the intersection of digital design theory , computational discretism , and historical architectural representation . Recent publications explore the implications of AI, big data, and robotic construction on architectural authorship and practice. Key scientific awards include: Guggenheim Fellow (2022-2023) Senior Fellow, National Gallery of Art (2014) Grantee, The Graham Foundation (2013) Resident, American Academy in Rome (2004-05) His recent articles (2023-2025) analyze post-digital architecture , biological computing , and algorithmic curation , revealing trends in computational design's socio-technical context and the dissolution of traditional authorship models.

Christopher Andrew BailView profile

Professor

• Artificial Intelligence
• Political Polarization
• Social Media
• +5 more

Christopher Andrew Bail is a Professor of Sociology, Computer Science, Political Science, and Public Policy at Duke University's Trinity College of Arts & Sciences. He directs the Society-Centered AI Initiative and co-founded the Polarization Lab. His research focuses on AI's impact on human behavior, particularly in social media contexts, aiming to mitigate political polarization and design prosocial digital platforms. Bail holds a Ph.D. from Harvard University (2011). His expertise spans computational social science, online political discourse, and AI ethics. Notable contributions include groundbreaking work on social media's role in polarization, interventions to reduce partisan animosity, and the development of AI-driven dialogue platforms. He has authored influential books like Breaking the Social Media Prism , which led to policy changes at Twitter and inspired NextDoor's conflict mediation tools. Recent publications emphasize AI's societal implications, experimental methods to improve online interactions, and consensus-based reforms in machine learning research. His work appears in Science , Nature , and the New York Times , among others. Awards include the Guggenheim Fellowship (2018) and Carnegie Fellowship (2017). Bail's grants include funding from NSF, Templeton Foundation, and others for projects on ideological distance in social media, computational social science training, and AI for intellectual humility. He teaches advanced courses on AI in sociology and interdisciplinary data science. His media engagements span major outlets like CNN, BBC, and NPR, where he discusses digital polarization and tech policy. He co-founded the Summer Institutes in Computational Social Science and advises the NSF's Social, Behavioral, and Economic Sciences Directorate. Current initiatives include the Trinity Social AI Initiative and Duke’s Interdisciplinary Data Science Program.

• Human Evaluation
• Data-to-text Generation
• Natural Language Generation
• +3 more

Chris van der Lee Dr. Chris van der Lee is an Assistant Professor at Tilburg University's Tilburg School of Humanities and Digital Sciences (TSHD),隶属 Department of Communication and Cognition. His work focuses on human evaluation of natural language generation systems, data-to-text generation, and reproducibility challenges in computational linguistics. He actively contributes to advancing methodologies for assessing AI-generated content and improving transparency in research practices. Research Interests Key areas include: Human evaluation frameworks for automated text generation Data-to-text generation using small datasets Reproducibility in NLP research Conversational AI and chatbot interactions Awards Best Paper Award (2019) Best Thematic Paper Award (2021) Outstanding Reviewer Recognition (2020) Collaborations Dr. van der Lee collaborates with international researchers on projects like the ReproHum initiative, investigating reproducibility barriers in human evaluations. His work also involves interdisciplinary projects, such as developing digital health assistants for medication reconciliation.

Michael BartonView profile

Professor

• Complex Systems Science
• Human Ecology
• Landscape Dynamics
• +3 more

Michael Barton is a Professor and Director of Education in the School of Complex Adaptive Systems at Arizona State University (ASU), with affiliations in the School of Human Evolution & Social Change. His research focuses on long-term socio-ecological dynamics, integrating computational modeling, geospatial technologies, and archaeological data to understand human-environment interactions. He leads interdisciplinary projects like the Mediterranean Landscape Dynamics (MedLanD) initiative, exploring resilience in socio-ecological systems through fieldwork and simulation. He is a core developer of GRASS GIS, an open-source geospatial tool, and directs the Network for Computational Modeling in Social and Ecological Sciences (CoMSES). Barton’s work emphasizes open science, FAIR standards, and model reproducibility, supported by NSF and Sloan Foundation grants. He teaches courses on complex systems, geoarchaeology, and spatial technologies, and has contributed to over 150 peer-reviewed publications. Barton’s research bridges archaeology, earth science, and computational science, addressing topics like climate intervention strategies, Neolithic transitions, and agent-based modeling. He collaborates globally, advancing tools like the Open Modeling Foundation to foster interdisciplinary scientific collaboration.

Sasha TalaveraView profile

Professor

• Financial Economics
• Banking
• Fintech
• +8 more

Sasha Talavera is a Professor in Financial Economics at the Department of Economics, Birmingham Business School, University of Birmingham, where he joined in 2019. He previously held professorial and academic positions at Swansea University, University of Sheffield, Durham University, University of East Anglia, and DIW-Berlin. PhD in Economics, European University Viadrina MA in Economics, Boston College MA in Economics, Kyiv School of Economics BA in Economics, Ostroh Academy His research focuses on financial economics, with specific interests in online prices, big data analytics, banking, fintech, corporate finance, behavioral finance, and emerging markets. His work integrates empirical methods with real-world financial and economic data to address policy-relevant questions. The recent publications reflect a strong trend in analyzing financial behavior under crisis (e.g., war in Ukraine), monetary policy communication, and the application of big data and text analysis in economics. His research spans disciplines including econometrics, financial stability, labor economics, and public choice, often using innovative data sources such as online job ads and social media. Sasha Talavera is actively involved in the academic community, serving on the editorial boards of Bank i Kredyt , Visnyk NBU , and as founder of VoxUkraine.org . He is also an expert for the International Monetary Fund (IMF). He supervises several PhD students, including Zixi Guo, Mohammad Aldoub, and Fei Ge, and has successfully placed past advisees in institutions such as the Capital Market Authority of Kuwait, Aston University, and NHS England. He has not received any explicitly mentioned scientific awards in the provided text. He leads a dynamic research agenda involving big data, crisis economics, and financial decision-making, contributing significantly to both academic and policy-oriented discourse.

• Natural Language Generation
• Vision and Language Models
• Data Pruning
• +8 more

Albert Gatt is a researcher at the University of Malta , with extensive contributions to Natural Language Generation (NLG) , Vision-and-Language (V&L) models , and evaluation practices in NLP . His work spans multimodal reasoning, data pruning efficiency, and reproducibility challenges in human evaluations. Key collaborations include studies on temporal grounding in image sequences (TempVS benchmark) and automated legal violation detection in cookie banners. Research highlights include bridging linguistic theory with computational models (e.g., VALSE benchmark for multimodal grounding) and improving generation quality through contrastive learning frameworks. Scientific awards are not explicitly mentioned in the provided texts. His work emphasizes rigor in automatic metric validation and cross-modal interpretability , particularly in multimodal model attention mechanisms and logical formula minimization for text generation.

• Geospatial Big Data Analytics
• GeoHealth
• GeoAI (Geospatial Artificial Intelligence)
• +7 more

Tao Hu is an Assistant Professor in the Department of Geography at Oklahoma State University (OSU), serving since 2021. He holds a PhD (2015) and BS (2009) in Geography from Wuhan University, China. Prior to OSU, he worked as a Postdoctoral Research Fellow at Harvard University's Center for Geographic Analysis (2019–2021) and Kent State University (2016–2019). Research Focus: Leverages geospatial big data (remote sensing, social media, smartphone mobility) and GeoAI models to address health disparities, environmental health, and urban sustainability challenges. Key Contributions: Over 60 peer-reviewed articles, including 5 ESI Highly Cited Papers (top 1%). Research supported by NSF, USDA, and ESIP. His work spans FAIR workflow systems, disaster public perception analysis (e.g., Ohio train derailment), heat vulnerability modeling, and healthcare accessibility via mobility data. He emphasizes reproducible GIScience and societal impact through actionable insights. Teaching includes courses on GIS in public health, health geography, and programming for geospatial applications. His lab integrates geospatial AI with health equity, environmental justice, and smart city initiatives.

Fangzheng LyuView profile

Assistant Professor

• GIS Computational and Data Science
• Urban Informatics
• CyberGIS & Geospatial Computing
• +7 more

Fangzheng Lyu is an Assistant Professor in the Department of Geography at Virginia Polytechnic Institute and State University (Virginia Tech), affiliated with the College of Natural Resources and Environment. His research focuses on advancing GIScience, geospatial data science, and computational methods to address complex urban and environmental challenges using big data. He holds a Ph.D. from the University of Illinois Urbana-Champaign (2024), an M.S. from the same institution (2021), and a B.E. from the University of Hong Kong (2018). Dr. Lyu's expertise includes geospatial data science, cyberGIS, urban informatics, and geospatial AI. His work spans three core areas: (1) urban dynamics modeling using machine learning and cyberGIS, (2) scalable spatial algorithms for remote sensing and big data analysis, and (3) democratizing access to advanced geospatial cyberinfrastructure. He teaches courses such as Principles of GIS, Geospatial Data Science, and Spatial Data Science at Virginia Tech. His research has been published in leading journals like International Journal of Geographical Information Science , IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , and Sustainability . Notable contributions include frameworks for urban heat island prediction, spatiotemporal image fusion models (e.g., Multi-stream STGAN), and methods for analyzing spatial accessibility during public health crises. Dr. Lyu actively contributes to interdisciplinary initiatives, including the Center for Geospatial Information Technology at Virginia Tech. His work emphasizes reproducibility and collaboration, with a focus on bridging geospatial research and education through cyberGIS platforms like CyberGIS-Jupyter. He has received funding for proposals integrating geospatial technologies with urban sensing and epidemiological modeling.

Alec SmithView profile

Associate Professor

• Behavioral Economics
• Experimental Economics
• Neuroeconomics
• +7 more

Alec Smith is an Associate Professor in the Department of Economics at Virginia Tech, with interdisciplinary affiliations as a Faculty Affiliate in the School of Neuroscience and the Kellogg Center for Philosophy, Politics, and Economics. He serves as Co-Director of the Virginia Tech Economics Laboratory, where he leads experimental research on human decision-making. His research lies at the intersection of behavioral economics, experimental economics, and neuroeconomics, focusing on how cognitive and emotional processes shape economic decisions. Key interests include risk perception, social emotions (e.g., guilt, frustration), cognitive load, neural correlates of decision-making, and market dynamics such as bubbles. His work often integrates neuroimaging, transcranial stimulation, and eye-tracking with economic experiments. The most recent publications reflect a strong trend toward methodological rigor, reproducibility, and the application of machine learning to behavioral data. Themes include replication studies, variability in neuroimaging analysis, and modeling of bargaining and social preferences. His research has been published in high-impact journals such as Nature , Proceedings of the National Academy of Sciences , Management Science , and Games and Economic Behavior . Smith has collaborated with leading scholars including Colin Camerer, P. Read Montague, and Antonio Rangel, underscoring his integration into the forefront of neuroeconomic research. While no formal awards are listed, his publication record and leadership roles suggest significant recognition in the field. He advises students and researchers through his lab and departmental roles, contributing to training in experimental and neuroeconomic methods. His work is supported by institutional infrastructure such as the Virginia Tech Economics Laboratory, which facilitates controlled behavioral experiments. Future research directions likely include expanding the use of AI in behavioral prediction, studying pandemic-related behavioral shifts, and further exploring the neural underpinnings of social preferences.

Junpei KomiyamaView profile

Assistant Professor

• Machine Learning Methodology
• Decision-making Models
• Algorithmic Bias
• +7 more

Junpei Komiyama is an Assistant Professor in the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University, where he has been a faculty member since September 2019. His research bridges machine learning methodology and business applications, focusing on decision-making, fairness, and reproducibility in data-driven systems. His educational background includes a PhD in Information Science, an MS in Applied Physics, and a BTech, all from the University of Tokyo. Professor Komiyama's research interests center on machine learning with a strong emphasis on ethical and robust methodologies. He investigates decision-making models such as multi-armed bandits, algorithmic fairness , experimental design , and the reproducibility of data science findings. His work aims to redefine how we validate and trust results generated by machine learning systems. The recent articles reflect a strong trend in fairness-aware machine learning , online decision-making , and reproducible research practices . They span applications in business analytics, recommender systems, pricing, and cloud computing, demonstrating both theoretical depth and practical relevance. Presented work at top venues including NeurIPS and KDD Focus on ethical AI and robust scientific methodology He has advised students in machine learning and data science, though specific names are not listed. His research has been supported through academic grants related to AI ethics and data science innovation, though specific grant details are not provided. Professor Komiyama teaches courses in programming and machine learning, contributing to the technological education of business students at Stern. He is actively involved in advancing the integration of rigorous machine learning methods into business decision-making frameworks.

Kayvan KoushaView profile

Research Fellow

• Scientometrics
• Research Evaluation
• Altmetrics
• +4 more

Kayvan Kousha is a Senior Researcher at the University of Wolverhampton, leading the Statistical Cybermetrics and Research Evaluation Group within the Faculty of Arts, Business, and Social Sciences. With a PhD in Information Science and Scientometrics from the University of Tehran, his career spans academic appointments in both Iran and the UK. His research focuses on innovative methods for assessing research impact through digital sources, including: Developing AI-driven tools for evaluating REF impact case studies Analyzing grey literature and social media citations Studying peer review acceleration during global crises Creating semi-automatic URL classification systems for non-academic impact Key contributions include: 15+ recent publications on research evaluation Editorial roles at Scientometrics and Springer's AI for Scientometrics Being ranked among the top 2% of global scientists in information management Pioneering methods for assessing educational impact through online syllabi His work has significantly influenced how institutions measure research impact beyond traditional citations, incorporating digital footprints like Wikipedia, Goodreads, and YouTube.

Scott L. AlthausView profile

Professor

• Political Communication
• Public Opinion
• Data Science Methods
• +7 more

Scott L. Althaus is Merriam Professor of Political Science, Professor of Communication, and Director of the Cline Center for Advanced Social Research at the University of Illinois Urbana-Champaign, with additional faculty appointments in the School of Information Sciences and National Center for Supercomputing Applications. He joined the university in 1996 and has established himself as a leading scholar in political communication and public opinion research. Professor Althaus's research explores communication processes that support political accountability in democratic societies and empower political discontent in non-democratic systems. His work examines how journalists construct news in hybridized communication ecosystems, how leaders shape news coverage for political advantage, how citizens interpret news about public affairs, and how citizens convey preferences through collective behaviors like voting and civil unrest. His specific interests include popular support for war, data science methods for extreme-scale news analysis, political psychology, and communication concepts in democratic theory. His recent publications reveal a strong focus on computational approaches to political communication, with significant contributions in automated content analysis of global news coverage, event data validation, and the development of theoretical frameworks connecting media, politics, and public opinion. His work increasingly applies text analytics and machine learning to analyze millions of news stories for societal insights. 2023 International Journal of Press/Politics Hazel Gaudet-Erskine Best Book Award for Building Theory in Political Communication 2023 Campus Excellence in Public Engagement Faculty & Staff Team Award 2006 Political Communication Article of the Year Award 2004 David Easton Prize and Goldsmith Book Prize Dean's Award for Excellence in Undergraduate Teaching (2013) Professor Althaus serves on editorial boards of Political Communication and Public Opinion Quarterly, with research appearing in top journals across political science, communication, and interdisciplinary fields. His work with the Cline Center has produced significant public engagement through projects like SPOTLITE (documenting police uses of lethal force) and the Coup d'État Project. He regularly appears on the university's List of Teachers Ranked as Excellent by Their Students and has advised numerous research initiatives applying data science to political communication challenges.