Gias Uddin is an Associate Professor at York University's Lassonde School of Engineering and an Adjunct Professor at the University of Calgary . His research bridges Software Engineering (SE) and Artificial Intelligence (AI) , focusing on AI Trustworthiness Assessment (SE4AI) and AI-Driven Productivity Tools (AI4SE) . PhD in Software Engineering & AI, McGill University (2018) MSc in Software Engineering, Queen’s University (2008) BSc in Computer Science & Engineering, Bangladesh University of Engineering and Technology (2004) His research explores: Metamorphic Relations for LLM Hallucination Detection AI-Enhanced Software Documentation Foundational Models for Runtime System Modernization Developer-Centric AI Tooling Recent article trends show expertise in LLM Trustworthiness , Low-Code Platforms , and IoT Developer Communities . Awards include Distinguished Paper at FSE 2025 , multiple IBM Champion recognitions, and York Research Award . He leads the Data Intensive Software Analytics (DISA) Lab and mentors PhD students in SE-AI Intersections .
Susanne Bornelöv is a Professor in the Department of Biochemistry at the University of Cambridge. Her research focuses on computational genomics and gene regulation, particularly exploring posttranscriptional mechanisms such as codon optimality-mediated mRNA decay and transposon silencing. She uses computational methods, ribosome profiling, and Drosophila models to study how codon usage, tRNA availability, and RNA modifications influence gene regulation and genome evolution. Her work integrates artificial intelligence (AI) and comparative genomics to model gene regulatory processes and design novel regulatory elements. Key research areas include piRNA clusters' roles in suppressing retroviruses, codon usage bias in pluripotent stem cells, and the interplay between mRNA methylation and protein synthesis. The Bornelöv Group collaborates widely, including with institutions like Cold Spring Harbor Laboratory, to advance understanding of fundamental gene expression principles. Publications highlight contributions to topics like deep learning in genomics, evolutionary conserved piRNA mechanisms, and transcriptional regulation. She leads a group open to interns, students, and researchers, fostering interdisciplinary approaches to address complex biological questions.
Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.
Bo An is a President's Chair Professor and Head of the Division of Artificial Intelligence at the College of Computing and Data Science , Nanyang Technological University, Singapore . He also holds a courtesy appointment as Professor at the School of Physical & Mathematical Sciences and serves as Director of the Centre of AI-for-X. Previously, he was a Nanyang Assistant Professor (2014-2018), Associate Professor at the Chinese Academy of Sciences (2012-2013), and Postdoctoral Researcher at the University of Southern California (2010-2012). His academic journey began with B.Sc. and M.Sc. degrees from Chongqing University, followed by a Ph.D. in Computer Science from the University of Massachusetts, Amherst (advised by Victor Lesser). Research Interests : Artificial Intelligence Multiagent Systems Computational Game Theory Reinforcement Learning Automated Negotiation Optimization Research Impact : Applications in infrastructure security (deployed by US Coast Guard and Federal Air Marshals), e-commerce, sensor networks, and financial technology. Over 150 publications in top venues like AAMAS, IJCAI, AAAI, ICML, NeurIPS, KDD, and ACM/IEEE Transactions. Scientific Recognition : 2010 IFAAMAS Victor Lesser Distinguished Dissertation Award 2012 INFORMS Wagner Prize 2018 & 2022 Nanyang Research Awards 2017 Microsoft Collaborative AI Challenge IEEE Intelligent Systems 'AI's 10 to Watch' (2018) Leadership Roles : Editor-in-Chief of IEEE Intelligent Systems, Associate Editor for AIJ, JAAMAS, and ACM Transactions. Served as General Co-Chair for AAMAS'23 and Program Chair for IJCAI'27.
Panagiotis Papapetrou is a Professor of Data Science and Deputy Head of Department at the Department of Computer and Systems Science , Stockholm University (since 2017). He also serves as Head of the Data Science Research Group and holds an Adjunct Professor position at Aalto University (Finland). As a Board Member of the Swedish Association for Artificial Intelligence (SAIS) , he contributes to shaping AI research directions in Sweden. Research Pillars: Algorithmic data mining, interpretable machine learning, time series classification, and health informatics Key Projects: AI for societal fairness, digital twins for smart buildings, EXTREMUM for explainable medical AI, and e-learning personalization Teaching Legacy: Developed courses in Data Mining (HT2013-2022), Machine Learning (VT2022-2024), and Health Informatics (VT2018-2021) His work focuses on interpretable AI for healthcare applications, particularly through counterfactual explanations for time series classification and forecasting. This includes developing methods like Glacier for constrained counterfactuals and Ijuice for k-justified explanations. His research also explores multimodal clustering of sepsis patient records and federated learning approaches for ICU mortality prediction. Recent scientific contributions include: CounterFair (2024): Group fairness analysis via counterfactual burden metrics M-ClustEHR (2024): Multimodal clustering for electronic health records COMET (2024): Constraint-based glucose forecasting explanations Temporal pattern mining (2024-2025): Enhanced forecasting models through decomposition Z-Time (2024): Interpretable multivariate time series classification His editorial leadership includes: Action Editor at Machine Learning Journal (since 2024) Action Editor at Data Mining and Knowledge Discovery (since 2018) Guest Editorial Board for ECML/PKDD Journal Track (2014-2019)
Dr. Karin van Es is an Associate Professor in the Department of Media and Culture Studies at the Faculty of Humanities, Utrecht University. She serves as project lead for the Humanities at Data School and is an affiliate and impact liaison at the Centre for Digital Humanities. Her work bridges academic research and practical applications in the digital society, with a focus on collaborating with external parties on interdisciplinary projects. She is also part of the GenAI in Education Humanities taskforce at Utrecht University. Her research is situated at the intersection of television studies, software studies, and critical data and algorithm studies, with a particular focus on streaming video culture and industries. Her notable publications include the book The Future of Live (Polity Press, 2016) and co-edited volumes such as The Datafied Society (AUP, 2017), Situating Data (AUP, 2023), Collaborative Research in a Datafied Society (AUP, 2024), and Governing the Digital Society (AUP, 2025). She has published extensively in journals including Television & New Media , Media, Culture and Society , Critical Studies in Television , Social Media + Society , Big Data and Society , and First Monday . Dr. van Es's recent publications reveal a strong focus on the governance of digital platforms, AI ethics, and the impact of streaming services on media consumption. Her work increasingly examines the societal implications of datafication, with particular attention to educational contexts, public values, and methodological innovations for studying digital phenomena. She has pioneered approaches like "data walking" and "data donations" as research methods for understanding how people interact with digital platforms in everyday contexts. Her scholarship demonstrates a consistent commitment to critical technical practice that bridges theoretical insights with practical interventions in the datafied society. Editor of special issue "Critical Technical Practice(s)" for Convergence Organizer of "Innovative Methods for Video-on-Demand Research" workshop (2024) Speaker at ECREA 2024 conference on "Netflix Uncovered: Insights from Data Donations" Member of editorial board for Convergence journal Her media contributions include appearances on WORT 89.9 FM discussing "The Meaning of Live" (2022), participation in the "De Maatschappelijk Betrokken Docent" program (2022), and earlier contributions on topics like "Wat is beeldradio?" (2016) and "Live liveness in realtime" (2015). She leads the Media and Performance Studies research group and is deeply involved with the Data School at Utrecht University. Her work with the GenAI in Education Humanities taskforce focuses on understanding and shaping the integration of generative AI in educational contexts. Through her role at the Centre for Digital Humanities, she helps bridge academic research with practical applications in society, particularly around issues of data governance, digital literacy, and the societal impact of emerging technologies.
Catherine D’Ignazio is an Associate Professor at the Department of Urban Studies and Planning , School of Architecture and Planning , Massachusetts Institute of Technology (MIT) . She serves as the director of the Data + Feminism Lab , which focuses on feminist technology, data literacy, and social justice. Her work spans AI bias , gender-based violence , and equitable urban planning , with a strong emphasis on intersectional approaches to data science. Education : She holds an MS from the MIT Media Lab , an MFA from the Maine College of Art , and a BA in International Relations from Tufts University (Summa Cum Laude, Phi Beta Kappa). D’Ignazio’s research explores the intersection of technology , design , and social justice , particularly through grassroots data activism and feminist AI. Her 2024 book , Counting Feminicide: Data Feminism in Action , analyzes how communities collect and use data to challenge gender-related violence. Earlier, her 2020 book Data Feminism (co-authored with Lauren Klein) outlines ethical data science practices. Scientific Awards include recognition from the Tanne Foundation , Turbulence.org , and the Knight Foundation . Her art projects have been exhibited at venues like the Venice Biennial and the ICA Boston . She teaches courses such as Interactive Data Visualization & Society and Urban Science for Public Good , emphasizing participatory design and critical data literacy . Her lab collaborates with global networks like the Design Justice Network and Data Against Feminicide .
Jacob N. Shapiro is a professor of politics and international affairs at Princeton University and a nonresident scholar in the Carnegie Technology and International Affairs Program. His work focuses on conflict, security, and the information environment. Shapiro co-founded and directs the Empirical Studies of Conflict Project, a multi-university consortium studying politically motivated violence. He also leads Princeton’s Accelerator initiative, building global research infrastructure to understand modern conflict and disinformation. His peer-reviewed articles and research papers explore influence operations, digital security, and the efficacy of countermeasures against disinformation. These works emphasize empirical methods and interdisciplinary collaboration. Scientific Awards: 2016 Karl Deutsch Award (International Studies Association) Shapiro has advised government agencies, NGOs, and technology companies on transparency, disinformation, and security. He earned a Ph.D. and M.A. from Stanford University and a B.A. from the University of Michigan, and served in the U.S. Navy.
Professor Spiridon Ivanov Penev is a leading academic in the School of Mathematics and Statistics at the University of New South Wales. He holds a PhD in Mathematical Statistics from Humboldt University (Berlin, Germany) and has been affiliated with UNSW since 1992, progressing from Lecturer to Professor in 2019. His research spans wavelet methods, saddlepoint approximations, structural equation models, and stochastic risk analysis. Education: PhD in Mathematical Statistics, Humboldt University Current Affiliation: Department of Statistics, School of Mathematics and Statistics, UNSW His work focuses on advanced nonparametric techniques, including wavelet-based signal recovery with adaptive sampling rates, and robust inference in structural equation models. He has developed bias-corrected reliability measures for psychometric applications and contributed to stochastic optimization problems in finance and engineering. Recent publications highlight his expertise in semiparametric regression, robust portfolio optimization, and marine engineering applications using machine learning. Key trends include the use of Bregman divergence for shape-preserving estimation and Markov chain methods for climate model weighting. Scientific Awards: DAAD award Elected member of the International Statistical Institute (ISI) He has supervised numerous grants as Chief Investigator, including Australian Research Council projects and industry collaborations. Administrative roles include membership in the School of Mathematics and Statistics Executive Committee. Teaching duties span advanced statistical inference, multivariate analysis, and data science applications.
Jonathan Beller is a Professor in the Humanities and Media Studies department at the School of Liberal Arts & Sciences , Pratt Institute. His work sits at the intersection of visual culture, media theory, and political economy. Educated at Columbia University (B.A.) and Duke University (Ph.D.) Editorial Collective member of Social Text His research examines the cinematic mode of production, attention economy, racial capitalism, and the sociopolitical implications of digital media. Key themes include visuality as labor exploitation, algorithmic violence, and media's role in shaping economic systems. Recent publications analyze racialization in digital systems , computational capital , and decentralized financial media , reflecting his long-standing interest in media's material conditions. Contact: jbeller@pratt.edu
Marylyn D Ritchie, PhD, is the Edward Rose, M.D. and Elizabeth Kirk Rose, M.D. Professor at the Perelman School of Medicine, University of Pennsylvania. She concurrently serves as Director of the Institute for Biomedical Informatics, Vice President for Research Informatics for the University of Pennsylvania Health System, Director of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics, and Vice Dean of Artificial Intelligence and Computing. Education: BS in Biology, University of Pittsburgh at Johnstown, 1999 MS in Applied Statistics, Vanderbilt University, 2002 PhD in Statistical Genetics, Vanderbilt University, 2004 Research Interests Dr Ritchie’s work integrates computational genomics , bioinformatics , pharmacogenomics , and systems genomics to advance precision medicine. She develops statistical and machine-learning approaches to dissect epistasis , genetic epidemiology , and evolutionary computation in large-scale biobanks, with a special focus on cardiovascular disease and Alzheimer’s disease . Her group is also pioneering translational informatics methods that incorporate social determinants of health and fairness metrics into AI-driven clinical decision support. Publication Trends In 2025 alone, Dr Ritchie co-authored more than fifteen high-impact studies spanning vision-language models for 3D CT , multi-omics Alzheimer’s risk prediction , fairness in neuroimaging AI , ancestry-specific pharmacogenomics , and cloud-based polygenic risk score platforms . The collective work highlights a shift from single-omics discovery to integrative, equitable, and clinically actionable models across diverse ancestries. Awards & Honors While specific named awards were not detailed in the text, Dr Ritchie’s endowed professorship and multi-institutional leadership roles signify sustained recognition. Grants & Advising Dr Ritchie leads large NIH, foundation, and industry-funded initiatives that support interdisciplinary teams of postdocs, graduate students, and data scientists. Her lab actively mentors trainees from UPenn’s Cell and Molecular Biology and Genomics and Computational Biology graduate groups. Laboratories & Teams She directs the Ritchie Lab (ritchielab.org), which develops open-source visualization tools such as PhenoGram , PheWAS-View , and Synthesis-View for genome-wide and phenome-wide data exploration. The lab operates within the Institute for Biomedical Informatics and collaborates closely with the Penn Medicine BioBank and multiple clinical departments to translate big-data discoveries into precision medicine workflows.
Dr. Matteo Fasiolo is a Senior Lecturer in the School of Mathematics at the University of Bristol, specializing in Statistical Science. His research focuses on advanced statistical modeling with significant applications in electricity demand forecasting and medical statistics, leveraging Generalized Additive Models (GAMs) as a core methodology. His primary research interests span: Generalized Additive Models and their extensions for complex data structures Covariance matrix modeling for high-dimensional energy forecasting Probabilistic forecasting techniques for uncertainty quantification Statistical machine learning including variational inference and contrastive learning Applications in electricity grid management and medical diagnostics Recent publications (2023-2025) reveal a dual focus: developing scalable statistical methods for electricity net-demand prediction in Great Britain using additive covariance models, and applying distributional regression to medical challenges like kidney function decline and cardiovascular risk prediction. His work on SoftCVI demonstrates innovation in variational inference, while extensions to GAMs address both mean modeling and full distributional forecasting. Dr. Fasiolo has supervised at least one student as indicated by university records. His research outputs include 16 publications and 2 publicly available datasets, reflecting active contributions to methodological statistics and domain-specific applications in energy systems.
Tyler Simko is an Assistant Professor of Political Science at the University of Michigan, specializing in US state and local politics, political geography, and computational social science. His research focuses on understanding and addressing inequality in American public policy through innovative methodological approaches. Education: Ph.D. in Government, Harvard University (2024) A.B. in Politics, Princeton University Simko's research examines state and local politics in the United States with particular focus on political geography and subnational policymaking. His active research agendas include legislative redistricting ("gerrymandering"), local public meetings, school segregation, affordable housing, and data privacy. Methodologically, he develops new techniques in computational social science and machine learning to evaluate subnational inequality and how it can be reduced. His work regularly involves partnerships with federal, state, and local officials to improve the design of public policy. His recent publications demonstrate a strong focus on applying computational methods to address real-world policy challenges, particularly in school desegregation, redistricting, and local government transparency. His research often leverages large-scale data collection efforts, such as LocalView (the largest database of local government meetings in the US), to analyze patterns of political behavior and policy outcomes across different jurisdictions. Awards and Recognition: APSA 2024-25 Best Paper in Education Politics and Policy Award APSA 2024-25 Best Paper in Urban and Local Politics, Honorable Mention MPSA 2024 Robert H. Durr Award for "the best paper applying quantitative methods to a substantive problem" Derek C. Bok Award for Excellence in Graduate Student Teaching of Undergraduates (2023) Simko teaches graduate and undergraduate courses in American Politics and Political Methodology at the University of Michigan. His teaching experience spans multiple institutions, including Harvard University and Princeton University. He has designed innovative courses on US Local Policymaking, data science, and computational social science. As a Data Scientist at the Office of Evaluation Sciences, he partners with federal, state, and local officials to improve program design and reduce administrative burdens. He is a co-PI of the Algorithm-Assisted Redistricting Methodology (ALARM) Project and co-creator of LocalView, the largest audio, video, and text database of local government meetings in the United States. These projects represent significant contributions to the field of computational social science and provide valuable resources for researchers studying local governance and policy-making.
Elisabeth Wetzer is an Associate Professor in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway. Her research bridges artificial intelligence with healthcare applications, focusing on multimodal image registration, bias mitigation in AI, and physics-informed learning models. Current Role: Associate Professor, Machine Learning Group Research Themes: AI ethics, medical imaging, cross-modal representations, algorithmic fairness Her recent work explores technical challenges in PET imaging analysis and societal implications of AI bias. Collaborative projects span medicine, mathematics, and computer science disciplines. Key scientific contributions include: Physics-informed deep learning for PET image data Studies on multi-task learning efficacy in medical classification Research on gender bias in algorithmic systems She actively participates in diversity initiatives and public outreach, including presentations at Nobel laureate conferences and media engagements on AI ethics.
Aleksandra Krstić is a full professor at the Faculty of Political Science, University of Belgrade, where she teaches courses including Political Journalism , Visual Representation of Politics , and Media and Journalism in a Global Context . She serves as Vice-Dean for Research Activities and leads the Center for Media and Media Research. PhD in Culture and Media (2015) from University of Belgrade Master's and BA in Journalism from Faculty of Political Science Her research examines media representation of EU institutions , authoritarian communication , digital journalism ethics , and migrant portrayal in Serbian media . Recent work analyzes visual security narratives , presidential communication strategies , and opinion dynamics in social media . Scientific awards include the Đoko Vlajković Foundation Award (2018) and multiple European integration media awards. She leads international projects like DepolarisingEU (COST Action 22165) and Strengthening Local Media in Serbia (US Alumni Grant). Krstić collaborates with institutions including International Organization for Migration (IOM) and European Commission, and serves on editorial boards for Journal of Regional Security and Political Perspectives .
