Ruihong Huang is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University, part of the College of Engineering. She holds a Ph.D. from the University of Utah (2014) and completed a postdoctoral fellowship at Stanford University. Her research focuses on Natural Language Processing (NLP) with emphasis on information extraction, event recognition, media bias analysis, and ethical AI applications. She teaches courses like Information Storage and Retrieval (CSCE 470), Natural Language Processing (CSCE 638), and has advised numerous PhD, Master’s, and undergraduate students. Key research contributions include work on event coreference resolution, discourse analysis, and detecting media bias through event relation graphs. Huang has developed benchmark datasets like UAL-Bench and EMONA, and her work spans applications in disaster management, fake news detection, and moral reasoning in LLMs. She is a recipient of the NSF CAREER Award and serves on program committees for top conferences like ACL and EMNLP. Her academic service includes roles as Area Chair for ACL 2024 and Senior Area Chair for EMNLP 2024. She maintains an active lab group focusing on NLP fundamentals and real-world applications, with projects involving propaganda identification, polarity calibration for opinion summarization, and multimodal dialog act classification. Huang has published over 80 papers in top-tier venues such as NAACL, EMNLP, ACL, and NeurIPS.
Allison Koenecke is an Assistant Professor of Information Science at Cornell Tech and a field faculty member in Computer Science at Cornell University. Previously, she was a postdoctoral researcher at Microsoft Research New England and completed her PhD at Stanford University’s Institute for Computational & Mathematical Engineering. Her research focuses on algorithmic fairness, computational social science, and causal inference in public health, addressing disparities in automated systems like speech recognition and policy decision-making. Education : PhD, Stanford Institute for Computational & Mathematical Engineering MA/MS, Stanford University BA/BS, Massachusetts Institute of Technology Research Interests : Dr. Koenecke’s work bridges economics and computer science, emphasizing equity in AI systems. Key areas include: Algorithmic bias in speech recognition (e.g., racial disparities in voice assistants) Fairness in policy tools like environmental justice data systems Causal analysis in public health interventions Ethical implications of large language models in education Media & Impact : Her research has been featured in outlets like New York Times , Science , and Scientific American . Notable studies include exposing racial gaps in speech-to-text systems and advocating for inclusive dataset development. She also explores societal impacts of AI in education and governance. Awards : Sloan Research Fellow in Computer Science Forbes 30 Under 30 in Science NSF Awards Cornell CIS Teaching Excellence Award (2024) Teaching & Outreach : She teaches courses like Designing Fair Algorithms and Data Science for Global Development , emphasizing interdisciplinary collaboration. Her PhD Professionalization course addresses hidden curricula in academia. She advises on AI ethics for nonprofits, tech companies, and government agencies.
Armin Falk is a Professor of Economics at the University of Bonn, leading the BonnEconLab since 2004 and co-founding the briq Institute on Behavior & Inequality. He holds affiliations with the IZA, DIW, CEPR, and Max-Planck-Institut. His research focuses on behavioral economics, exploring how social preferences like fairness and trust influence decision-making, and investigates early childhood development's role in inequality. Falk has received prestigious awards, including the Leibniz Prize and Yrjö Jahnsson Award. His work combines lab experiments, field studies, and representative data analysis. Education includes a PhD in Economics from the University of Zurich after studies in Economics, Philosophy, and History at the University of Cologne. Key research highlights include global climate action support studies (Nature Climate Change, 2024), socioeconomic disparities in child development (JPE, 2021), and global economic preferences surveys (QJE, 2018). He co-authored the book *Warum es so schwer ist, ein guter Mensch zu sein* to popularize behavioral economics. Awards include recognition for contributions to behavioral and development economics. Mentoring programs like *Balu und Du* aim to reduce educational inequality. His work bridges academic research with public discourse through media contributions and policy engagement.
Danai Koutra is an Associate Professor in Computer Science and Engineering at the University of Michigan, Ann Arbor, and an Amazon Scholar. Her research focuses on large-scale graph mining, graph neural networks, and interpretable machine learning methods for understanding complex networks. Key roles include leading the GEMS Lab and contributing to projects like DeltaCon (graph similarity) and VoG (graph summarization). She holds a PhD from Carnegie Mellon University and has authored over 80 publications in top venues like KDD, SDM, and NeurIPS. Educations: PhD in Computer Science, Carnegie Mellon University (2015) MS in Computer Science, Carnegie Mellon University (2015) Diploma in Electrical & Computer Engineering, National Technical University of Athens (2010) Research Interests: Her work spans graph mining, anomaly detection, knowledge graph completion, and applications in neuroscience, healthcare, and social networks. Recent projects include MAGNET (multi-agent graph networks) and GT2VEC (multimodal graph-text encoders). Grants & Awards: Recipient of the 2025 PECASE award, NSF CAREER Award (2019), and the 2016 ACM SIGKDD Dissertation Award. Active in organizing conferences like KDD and ECML/PKDD. Labs & Teams: Directs the GEMS Lab, collaborating on projects like FIDDLE (clinical data preprocessing) and SpecGreedy (dense subgraph detection). Engages in interdisciplinary efforts, including M-DICE (urban mobility analysis with Detroit).
Brendan O'Connor is an Associate Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. His research focuses on computational social science and natural language processing (NLP), particularly exploring how social factors influence language technologies and using text analysis to understand societal trends. His work includes studies on racial bias in NLP, political event analysis, and social media linguistics. He holds a PhD in Machine Learning from Carnegie Mellon University (2014) and dual MS/BS in Symbolic Systems from Stanford University (2006). Education: PhD in Machine Learning, Carnegie Mellon University (2014) MS in Symbolic Systems, Stanford University (2006) BS in Symbolic Systems, Stanford University (2006) Research interests span AI ethics, social media analysis, and computational methods for studying language and society. Notably, he investigates racial disparities in NLP systems, linguistic variation in African American English, and event detection in news and social media. His work has been recognized with NSF CAREER and Google Faculty awards, and his research has been cited thousands of times. His lab, the Statistical Social Language Analysis Lab, develops tools for analyzing large-scale text data. He is affiliated with the Center for Data Science, Center for Intelligent Information Retrieval, and Computational Social Science Institute. Recent projects include analyzing global news coverage of critical events and developing frameworks for zero-shot argument explication. Awards and Honors: NSF CAREER Award Google Faculty Research Award Best Paper Award Advising and Grants: O'Connor has advised projects on social media polling representativeness and demographic analysis. His grants include collaborative research on sociopolitical event extraction and bias mitigation in AI systems. He has also contributed to platforms like Rookie for news archive exploration and ezCoref for coreference resolution. Labs/Teams: Leads the Statistical Social Language Analysis Lab and collaborates with the UMass NLP Group and Harvard Institute for Quantitative Social Science. His work bridges NLP with social science methodologies, emphasizing transparency in algorithms and causal inference using text data.
Suchi Saria is the John C. Malone Associate Professor at Johns Hopkins University , with appointments in the Whiting School of Engineering (Computer Science), the Bloomberg School of Public Health (Health Policy & Management), and the Whiting School (Applied Math & Statistics). She directs the Machine Learning and Healthcare Lab and co-founded the Bayesian Health startup. Education: PhD in Computer Science from Stanford University (advisor: Daphne Koller), NSF Computing Innovation Fellowship at Harvard (2011), prior research at UMass (Barto, Madhavan), and industry experience at Aster Data Systems (acquired by Teradata). Research Focus: Saria develops statistical machine learning tools to extract insights from heterogeneous clinical data (structured/unstructured EHRs, sensor streams). Her work enables counterfactual reasoning for personalized treatment plans, dataset shift mitigation in healthcare AI, and weak supervision frameworks for mobile health apps. Key applications include sepsis prediction , Parkinson’s symptom tracking , and critical care optimization . Article Trends: Recent publications emphasize AI safety (2024-2025), addressing racial bias , transparency frameworks , and dynamic monitoring for clinical deployments. Her work spans conformal prediction , causal modeling , and policy guidelines for health AI. Scientific Awards: Sloan Research Fellowship (2018) DARPA Young Faculty Award (2016) MIT Technology Review TR35 Innovator (2017) Popular Science Brilliant 10 (2016) IEEE Intelligent Systems AI’s 10 to Watch (2015) NSF Computing Innovation Fellowship (2011) Rambus Fellowship (2004-2010) Best Paper Awards in ML, Informatics, and Medicine venues Advising & Grants: Saria mentors PhD students/postdocs in machine learning and health informatics , including funded projects like the NSF Smart and Connected Health Grant (2014) and Google Research Award (2014). Her lab’s TREWScore system (Science Translational Medicine 2015) is deployed in hospitals, while her Bayesian Health startup commercializes AI solutions for provider experience.
Tianxi Cai, ScD, holds the John Rock Professorship in Population and Translational Data Sciences at the Harvard T.H. Chan School of Public Health and is a Professor of Biomedical Informatics at Harvard Medical School. She directs the Translational Data Science Center for a Learning Health System (CELEHS). Her work bridges clinical and basic science data to advance personalized medicine and disease understanding. Institution: Harvard University Departments: Biostatistics (T.H. Chan School) and Biomedical Informatics (HMS) Key Roles: Faculty member since 2002, NIH-funded researcher, and leader in EHR data analytics Research focuses on biomarker evaluation, predictive modeling, high-dimensional data analysis, and survival analysis. Collaborates with the I2B2 Center to integrate clinical and genomic data. Active in developing semi-supervised learning methods for noisy EHR data and real-world evidence generation. Funding : Recent grants include NIH projects on rheumatoid arthritis treatment response (R01AR080193, R21AR078339) and semi-supervised EHR denoising (R01LM013614). Co-leads initiatives on chronic disease endpoints using multi-source data (U01FD007929). Labs/Teams : Directs CELEHS and leads the Cai Lab, focusing on translational data science and machine learning applications in healthcare.
Henrik Boström is a Professor of Computer Science specializing in Data Science Systems at the Division of Software and Computer Systems, KTH Royal Institute of Technology. His research focuses on trustworthy machine learning , with emphasis on conformal prediction (for confidence-calibrated predictions) and explainable AI . He is the developer of Python packages crepes (conformal classifiers/regressors) and xrf (explainable random forests). His primary research domains include: Developing robust methods for uncertainty quantification in predictive models Creating interpretable machine learning frameworks Optimizing ensemble techniques for high-dimensional data Applying ML to healthcare informatics and industrial diagnostics Analysis of his recent publications reveals strong emphasis on: (1) advancing conformal prediction theory for trustworthy AI, (2) enhancing interpretability of complex models like random forests and GNNs, and (3) developing efficient algorithms for uncertainty-aware learning in domains including healthcare, graph data, and high-dimensional regression. He serves as examiner for multiple degree projects and teaches courses including Programming for Data Science (ID2214) and Research Methodology and Scientific Writing (II2202) . He leads development of open-source tools for conformal prediction and model interpretation.
Teruko Mitamura is a prominent researcher at Carnegie Mellon University with over three decades of contributions to natural language processing, computational linguistics, and artificial intelligence. Her work spans from foundational research in event representation to advanced applications in multimodal systems and question answering. Her research interests focus on event detection and understanding, question answering systems, information retrieval, and multimodal processing. She has made significant contributions to event coreference resolution, timeline construction, and cross-document event analysis, developing methodologies that have become standard in the field. Her work often bridges theoretical advances with practical applications, particularly in complex information environments requiring deep semantic understanding. Natural Language Processing : Specializing in event extraction, coreference resolution, and narrative understanding with over 179 publications Question Answering Systems : Developing advanced techniques for complex question answering, particularly through NTCIR QA Lab and PoliInfo tasks Multimodal Processing : Integrating textual, visual, and temporal information for richer understanding in systems like ProMQA Evaluation Methodologies : Creating robust frameworks for assessing NLP systems through TAC KBP Event Tracks Her recent publication trends show a strong focus on leveraging large language models for event understanding, multimodal question answering, and timeline construction. She has expanded her research into specialized domains including patent analysis and novelty examination, demonstrating the breadth of her research impact across academic and practical applications. Active participant in major NLP conferences including ACL, EMNLP, NAACL, and AAAI with consistent publications Long-standing collaborator with researchers at CMU's Language Technologies Institute including Eduard H. Hovy and Eric Nyberg Contributor to shared tasks that have shaped research directions in event processing and question answering Organizer of multiple NTCIR QA Lab tasks focused on political information question answering Dr. Mitamura has mentored numerous researchers who have gone on to make their own contributions to the field, as evidenced by her extensive co-authorship network and the progression of her former students and collaborators into faculty and research positions. Her work continues to evolve with the field while maintaining her focus on deep semantic understanding of events and narratives.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
Dr. Konstantin (Kostia) M. Zuev serves as Teaching Professor in the Computing + Mathematical Sciences Department at California Institute of Technology , where he has made significant contributions to network science and computational statistics since 2016. His dual PhDs in Mathematics (Moscow State University, 2008) and Civil Engineering (HKUST, 2009) underpin his interdisciplinary research spanning differential geometry, stochastic simulation, and network dynamics. Education PhD in Mathematics, Lomonosov Moscow State University (2008) PhD in Civil Engineering, Hong Kong University of Science & Technology (2009) His research focuses on network science , particularly course-prerequisite networks and complex financial systems , with recent work extending to network navigability in cosmological models and rare event simulation. Over his career, he has developed innovative Bayesian inference methods and geometric preferential attachment theories while maintaining active collaborations across mathematics, physics, and biomedical domains. Recent publications highlight network analysis in education ( 2023 ), hyperbolic graph theory ( 2024 ), and pandemic-informed cancer mortality studies ( 2023 ). His 15 most recent articles demonstrate methodological innovations across disciplines including statistics, physics, finance, and cosmology. Scientific recognition includes Humboldt Research Fellowship (2021) Carver Mead Seed Fund Grant (2023) ASCIT Teaching Award (2018, 2023) Northrop Grumman Teaching Excellence Prize (2019) As Graduate Option Representative for Information and Data Sciences at Caltech and faculty advisor for multiple student organizations including the Caltech Karate Club and Caltech Chess Club , he actively bridges academic rigor with community engagement through outreach initiatives like the virtual math education channel and university math circles for K-12 students.
Rita Cucchiara is a Full Professor at the Department of Engineering 'Enzo Ferrari' of the University of Modena and Reggio Emilia. She leads the AImageLab research laboratory, part of the Artificial Intelligence Research and Innovation Center (AIRI) in Modena. Her research focuses on Computer Vision, Pattern Recognition, Machine Learning, and Multimedia, with applications in video surveillance, medical imaging, human-centered AI, and generative models. She is actively involved in interdisciplinary projects like ELIAS (European Lighthouse for AI Sustainability) and ELSA (European Lighthouse on Secure AI). Her recent roles include being elected Rector of the University of Modena and Reggio Emilia in 2025. She has organized and participated in major AI events, including workshops at NeurIPS, CVPR, and ECCV, and has contributed to advancements in multimodal models, deepfake detection, and trustworthy AI. Education details are not explicitly provided, but her extensive academic and research experience at the University of Modena underscores her expertise. She collaborates with institutions like NVIDIA, CINECA, and industry partners such as Digital Design and NVIDIA's AI Technology Center. AImageLab's projects include developing systems for medical imaging, ethical AI, and generative adversarial networks (GANs) for design surfaces. She co-organizes initiatives like the ELLIS Summer School on Large-Scale AI and contributes to policy discussions on AI ethics and societal impact. Her work spans from foundational research (e.g., vision transformers, continual learning) to applied projects (e.g., DDGan system for surface printing). Key grants and collaborations include the FAIR project and PNRR-M4C2 initiatives. She advises students and researchers in AI, with 7 PhD positions funded under national programs. Her leadership roles in AIRI and AImageLab highlight her commitment to bridging academia and industry, fostering innovation in AI-driven solutions for sustainability and healthcare.
Miyuki Hino is an Assistant Professor in the Department of City and Regional Planning and an Adjunct Assistant Professor in the Environment, Ecology, and Energy Program at the University of North Carolina at Chapel Hill. She holds a Ph.D. in Environment and Resources from Stanford University and a B.S. in Chemical Engineering from Yale University. Her research focuses on climate hazards, governance, and public policy , with emphasis on equitable adaptation to climate change. Key areas include sea level rise impacts, flood risk on property markets, and managed retreat strategies. She has conducted extensive work on floodplain development policies, household relocation programs, and community resilience frameworks. Dr. Hino's interdisciplinary approach integrates environmental science, urban planning, and social equity . She collaborates with academic and municipal partners, such as the Center for Urban and Regional Studies and Annapolis, MD local governments, to develop actionable solutions for climate adaptation. Her work bridges technical analyses (e.g., sensor networks, machine learning) with policy design to ensure both effectiveness and justice in climate responses. Recent projects emphasize preventing future 'trapped households' by analyzing zoning policies and market dynamics that drive risky development. She advocates for climate-smart growth strategies to balance economic needs with environmental safety, while addressing disparities in vulnerability across communities. Her research has been featured in Science Advances , Nature Climate Change , and interdisciplinary journals. She actively engages with policymakers to translate findings into practical measures, such as equitable buyout programs and floodplain management reforms.
Dr A. I. Shihab is a Senior Lecturer at Kingston University's Faculty of Engineering, Computing and the Environment, Department of Networks and Digital Media. He teaches programming languages (C++/Java), data structures, web development, and AI/machine learning. His research focuses on affective computing and machine learning applications including: Acoustic event detection in sports environments Audio signal analysis for tennis match modeling Multi-camera visual surveillance systems Medical imaging analysis using fuzzy clustering techniques Publications demonstrate expertise in combining audio/video modalities for sports analytics (tennis rallies, court-shots) and developing Markov models for sound event sequence analysis. Contact: a.shihab@kingston.ac.uk
Jim Hall is a Professor of Climate and Environmental Risk at the University of Oxford's School of Geography and the Environment, and serves as Director of Research there. He is also a Visiting Fellow at Linacre College and holds leadership roles including Chair of the Science Advisory Committee at IIASA, and Expert Advisor to the UK's National Infrastructure Commission. His work focuses on systemic risk analysis, infrastructure resilience, and policy implications of climate change adaptation. Prof Hall has pioneered methodologies like the National Infrastructure Systems Model (NISMOD) and chairs the Data and Analytics Facility for National Infrastructure (DAFNI). His research spans flood risk management, energy systems decarbonization, and transboundary water resource conflicts in regions such as the Eastern Nile Basin and the Caribbean. Key research areas include robust decision making under uncertainty, info-gap theory applications, and integrated assessments of human-environmental systems. He has contributed to major international assessments, including the IPCC's Fourth Assessment Report, and developed frameworks for multi-hazard stress testing of infrastructure networks. Scientific Awards: George Stephenson Medal (2001), Prince Sultan Prize for Water (2018), Royal Academy of Engineering Fellowship (2010) His advising and grants work includes mentoring a DPhil student Erin Canning and leading projects like MARIUS and ENHANCE. He has also developed innovative modeling tools for coastal erosion prediction and probabilistic assessments of global shipping fuel transitions. Prof Hall’s research groups actively engage in interdisciplinary projects, including the Oxford Martin Programme on Resource Stewardship and the UK Infrastructure Transitions Research Consortium. His work emphasizes bridging scientific analysis with actionable policy solutions for climate adaptation.
