Christian B. Teeter holds the rank of Adjunct Professor at the University of West Los Angeles, teaching part-time while maintaining private sector roles as a financial controller and HR director. His academic career includes prior positions at UC Berkeley Graduate School of Education, California State University, and Golden Gate University. Education: Earned B.A. in History (Phi Beta Kappa) from Colgate University, followed by M.B.A. and Ed.D. degrees from the University of Southern California. Research focuses on liberal arts education's relevance, workforce development strategies, and professional network dynamics in academia. Key themes include crisis responses in education systems, social capital formation among marginalized populations, and institutional branding in higher education. Publications span over a decade, with recent work analyzing pandemic impacts on food insecurity and longstanding contributions to incarcerated education access. His scholarship bridges educational theory with practical workforce development challenges.
Michael O'Dea is a Senior Lecturer in the Department of Computer Science at the University of York, United Kingdom. He has held previous academic positions at York St John University, Beijing University of Technology, University of Hull, and Waikato Institute of Technology, bringing extensive international experience in computer science education. He is actively engaged in pedagogical scholarship and leadership in higher education innovation. Senior Lecturer, Department of Computer Science, University of York Senior Lecturer in Computer Science, York St John University Lecturer in Software Engineering, Beijing University of Technology, China Lecturer in Computer Science, University of Hull Lecturer in Information Technology, Waikato Institute of Technology, NZ Dr. O'Dea earned his Ed.D. in Computer Based Learning from the University of Leeds. His research centers on the integration of artificial intelligence into educational practices, with a strong emphasis on AI literacy, the effectiveness of generative AI in teaching and learning, and the evolving landscape of technology acceptance in higher education. He investigates how AI tools can enhance student learning, faculty development, and institutional policy. His recent publications span topics such as AI literacy assessment, the future of online and blended learning, the application of machine learning in earthquake prediction, and international study abroad effectiveness. These works reflect a broad interdisciplinary approach, combining computer science, educational theory, and policy analysis. His scholarship is increasingly focused on the transformative potential of generative AI in academic settings, as evidenced by his leadership in special journal issues and funded research projects. Dr. O'Dea holds significant editorial responsibilities as Associate Editor and Lead Guest Editor for the Journal of University Teaching and Learning Practice and as Guest Editor for a special issue in Education Sciences on generative-AI-enhanced learning. He is also an Invited External Academic Affiliate at the King's Institute for Artificial Intelligence, King's College London. Associate Editor - Special Issues, Journal of University Teaching and Learning Practice Lead Guest Editor, Special Issue on Technology Acceptance Models, JUTLP (2024) Guest Editor, Special Issue on Generative-AI-Enhanced Learning, Education Sciences Principal Investigator, QAA Collaborative Enhancement Project on Graduate Attributes in the Era of GenAI (2025) He has delivered numerous invited talks and workshops at institutions such as the University of York, Queen Mary University of London, and international conferences including the Academy of Management and the International Conference on Artificial Intelligence in Education. His work bridges research, practice, and policy in higher education, with a strong commitment to inclusive and innovative teaching methodologies.
Charles C. Davis is a Professor of Organismic and Evolutionary Biology at Harvard University and Curator of Vascular Plants in the Harvard University Herbaria. He leads the Davis Lab, focusing on plant diversity through integrative research in systematics, paleobiology, ecology, and molecular biology. His work emphasizes phylogenetic theory, biogeography, and the application of herbarium collections to address global change challenges. Davis is particularly noted for leveraging herbarium specimens to study plant responses to climate change, phenology, and biodiversity patterns. Research interests include plant-insect interactions, genome architecture evolution in parasitic plants, and the ethical use of herbarium specimens. His lab has pioneered high-throughput phylogenomic pipelines (e.g., PhyloHerb) and explored the future of herbaria in the digital age. Collaborations span global institutions, emphasizing digitization, spectral imaging, and AI-driven analysis of biodiversity data. Recent work highlights the impact of anthropogenic change on plant communities, the role of phenology in species survival, and strategies to mitigate collecting biases in herbaria. Davis teaches courses on plant systematics and evolution, and his lab actively participates in public engagement through exhibits like the HMNH’s *In Search of Thoreau’s Flowers*. His research has been featured in *Trends in Ecology & Evolution*, *Molecular Phylogenetics and Evolution*, and *Current Biology*, with a focus on advancing methodologies for biodiversity science while addressing ethical challenges in specimen sampling.
McKenzie F. Johnson is an Assistant Professor at the University of Illinois Urbana-Champaign's Department of Natural Resources and Environmental Sciences (NRES), part of the College of Agricultural, Consumer and Environmental Sciences. She is also affiliated with the Institute for Sustainability, Energy, and Environment (ISEE). Her research focuses on global environmental politics, environmental justice, and human security, with a particular interest in how environmental governance initiatives impact marginalized communities. Education: Began with fisheries governance studies, earned a Master of Arts in Conservation Biology from Columbia University (2007), and a PhD in Environmental Policy from Duke University. Her Fulbright scholarship in West Africa (2010s) focused on extractive industries and social justice linkages. Research Interests: Explores intersections between environment, institutions, and justice through projects in South Asia, Africa, and South America. Current U.S.-focused work examines environmental impact assessments and pipeline development's effects on communities. The (in)Secure Landscapes Lab she leads investigates environmental governance's role in creating human security and justice. Key Themes in Publications: Energy infrastructure conflicts (e.g., Dakota Access Pipeline), environmental peacebuilding in post-conflict regions (Colombia, Afghanistan), carbon market equity, and invasive species governance. Her work critiques neoliberal environmental governance models and advocates for inclusive decision-making processes. Lab Activities: The IL Lab analyzes environmental governance's societal impacts, emphasizing participatory approaches. Current projects include studying soil carbon markets' equity implications and climate change adaptation strategies in conflict zones.
Dr. Hilary Cremin is the Head of the Faculty of Education at the University of Cambridge and holds the rank of Professor. Her work centers on peace education, conflict transformation, and innovative methodologies such as photo-voice, poetry, and autoethnography. She chairs the Cambridge Peace Education Research Group (CPERG), which promotes research and practice in peace education through seminars and online resources. Her research explores the intersection of education and peacebuilding, emphasizing ethical and embodied approaches to conflict resolution in schools and communities. Education : PhD in Education, University of Leicester PGCE, University of Oxford (Wolfson College) BA (Hons) in French and European Literature, University of Warwick Research Interests : Hilary’s work addresses global challenges in education and peacebuilding, including: Developing transrational approaches to peace education Reducing epistemic violence through reflexive methodologies Empowering youth through restorative justice and civic pedagogy Exploring embodiment and arts-based practices in educational research Awards : Fellow of the Royal Society of Arts Leadership and Service : In addition to her academic role, Hilary serves as Chair of the Degree Committee in the Faculty of Education and has extensive experience as a mediator and educational consultant. She has collaborated with schools, communities, and international organizations to advance conflict transformation and peacebuilding initiatives. Labs/Teams : Cambridge Peace Education Research Group (CPERG) — fostering global dialogue and practical resources for peace education researchers and practitioners.
Paulo Blikstein is an Associate Professor of Communication, Media, and Learning Technologies Design at Teachers College, Columbia University. He holds affiliations with the Mathematics, Science & Technology department and the Communication, Media, and Learning Technologies Design program. His expertise spans curriculum design, digital innovation, science education, and educational technology. Dr. Blikstein earned a Ph.D. in Learning Sciences from Northwestern University (2009), M.Sc. in Media Arts & Sciences from MIT Media Lab (2002), and degrees in Engineering from the University of São Paulo (Brazil). His research focuses on leveraging technology to enhance learning through computational modeling, maker education, and tangible interfaces. He leads the Transformative Learning Technologies Lab and the FabLearn Program, which develop innovative tools like MoDa and PlayData, integrating computational thinking with real-world science experiments. His work emphasizes equitable access to technology-driven education, particularly in the Global South. Recent projects include deploying cloud labs for biology education, analyzing disinformation dynamics via agent-based models, and exploring how social media influences political radicalization. He critiques commercial education technology discourse through a critical pedagogy lens, advocating for culturally responsive, hands-on learning. Blikstein’s research bridges theory and practice, addressing systemic challenges in science education through participatory design with teachers and communities. His labs create sustainable educational technologies, such as DIY liquid handling robots and haptic feedback systems, to democratize STEM access. He also investigates computational identity formation in K-12 students and the role of making in fostering gender equity in STEM.
Carlos Toxtli is an Assistant Professor at Clemson University where he leads the Human-AI Empowerment Lab. His research applies Human-Centered AI to create fair workplace tools, focusing on NLP, computer vision, and crowdsourcing ethics. He holds a PhD in Computer Science from Northeastern University and previously worked at Google, Microsoft Research, and Snap Inc. Education: Ph.D. Computer Science, Northeastern University M.S. Innovation & Technological Entrepreneurship, Monterrey Institute of Technology MBA, IEDE Business School B.S. Computer Science, University of the Valley of Mexico B.S. Computer Engineering, National Autonomous University of Mexico Research Focus: Dr. Toxtli develops AI systems promoting workplace fairness through ethical frameworks. His work spans: 1) Human-AI collaboration for task management, 2) Bias mitigation in gig economies, 3) Multimodal interfaces for accessibility, and 4) Culturally adaptive systems. His lab explores how AI can augment human capabilities while ensuring transparency. Publication Trends: Recent works demonstrate strong focus on LLM reliability (7 papers), human-AI teaming (4 studies), and ethical AI frameworks (3 publications). Over 60% of 2024-2025 output addresses real-world deployment challenges. Awards: UNESCO IRCAI Global Top 100 project Google Apps Developer Challenge 1st Place Facebook Developers World Hack 1st Prize Intel Innovation Latin App 1st Prize NSF STIR Labs Research Grant Leadership: Founded the Human-AI Empowerment Lab securing $1.2M in NSF/industry grants. Supervises 8 PhD students in human-centered computing projects. Previously co-founded 5 tech startups including ComproPago (acquired by Coca-Cola FEMSA).
Dr. Song Jiang is a Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). He holds a PhD from the College of William and Mary (2004) and has held academic positions at institutions such as Wayne State University and Los Alamos National Laboratory. His research focuses on system infrastructure for large language models (LLMs) and big data processing, including GPU/CPU memory systems, file and storage systems, and high-performance computing (HPC) I/O systems. He has received significant funding from the National Science Foundation (NSF) and industry partners like VMware and Tencent. Education: B.S. and M.S. from University of Science and Technology of China (1993, 1996), Ph.D. in Computer Science from College of William and Mary (2004). Postdoctoral research at Los Alamos National Laboratory (2004–2006). Research interests include file and storage systems, data management, big data analytics, and optimizing computing architectures for AI/ML. Key contributions include the LIRS replacement algorithm (adopted in MySQL and NetBSD), CLOCK-Pro page replacement (used in Linux), and swap token algorithms (Linux kernel). Awards include the 2022 ACM SIGMETRICS Test of Time Award and 2009 NSF CAREER Award. His work has led to 15+ patents and impactful industry collaborations with Facebook, Baidu, and others. Advising: Supervised 14+ PhD/Master’s students, including current advisees Chen Zhong and Sujit Maharjan. Active roles in doctoral committees and thesis supervision. Grants: Over $2.5M in NSF funding for projects like 'Software Defined Cache for Index Search' and 'Taming Small Data Writes'. Industry grants include VMware’s $240K project on distributed key-value storage. Labs/Teams: Leads research on persistent memory systems, key-value stores, and LLM infrastructure through UTA’s CSE department and collaborations with industry partners.
Ebru Cankaya is a Senior Lecturer II in the Department of Computer Science at the University of Texas at Dallas (UTD), part of the Erik Jonsson School of Engineering and Computer Science. She holds a Ph.D. in Computer Science from Ege University (Turkey) and has extensive academic experience across multiple institutions, including adjunct roles at Southern Methodist University and visiting professorships at Izmir University of Economics and Earlham College. Her research focuses on cybersecurity, risk modeling in databases, lossless text compression, and cloud computing. She has received numerous teaching awards, including the 2019 Outstanding Faculty of the Year award at UTD. Educational Background: Ph.D. in Computer Science, Ege University (2004) M.S. in Computer Science and IT & Management, Ege University (2004/2009) MBA in Economics and Administrative Sciences, Ege University (2000) B.Sc. in Computer Engineering, Ege University (1994) Research Interests: Computer and Network Security: Including access control models (e.g., Bell-LaPadula, Chinese Wall) and cryptographic techniques. Risk Modeling in Databases: Focusing on privacy-preserving data storage and obfuscation strategies. Text Compression: Innovations in encoding methods like Star Encoding and hybrid techniques. Cloud Computing: Security and dependability in distributed systems. Awards and Recognition: 2022: Teaching Award, Jonsson School 2019: Outstanding Faculty of the Year 2013: Faculty of the Month (NACURH) Multiple nominations for University and System-Wide Teaching Awards TUBITAK/EBILTEM Research Awards (2003–2004) Her professional activities include organizing doctoral symposiums (e.g., COMPSAC 2012/2013), participating in faculty development programs (e.g., Working Connections IT Institute), and mentoring undergraduate researchers. She has held academic roles across Turkey and the U.S. since 1997, including research assistantships at Ege University and a decade-long tenure at Ege University as a lecturer and assistant professor.
Sadaf Salehkalaibar is an Assistant Professor in the Department of Computer Science at the University of Manitoba, Winnipeg, Canada. She holds an office in the EITC building (E2-416) and has previously held academic positions at the University of Tehran, University of Toronto as a research associate, and visiting roles at McMaster University, Telecom Paristech, and National University of Singapore. Her research focuses on explainable artificial intelligence, generative models, and information theory with an emphasis on rate-distortion-perception tradeoffs in video and image processing. Her educational background includes teaching courses such as Signals and Systems, Digital Signal Processing, and Network Security at the University of Tehran. She currently teaches COMP4190 (Artificial Intelligence) at the University of Manitoba. Research interests revolve around developing efficient algorithms for AI systems, with key contributions in learned video compression, federated learning, and privacy-preserving techniques. Notable work includes the M22 algorithm for communication-efficient federated learning and the NSERC Discovery Grant-funded project on data-driven learning efficiency. Recent publications highlight advancements in perception loss functions, Gaussian vector source analysis, and secure distributed hypothesis testing. She actively serves on editorial boards (e.g., IEEE Transactions on Communications) and conferences (ISIT, ITW). Awards include the prestigious NSERC Discovery Grant (2025). Supervision highlights 13 MSc students at the University of Tehran, focusing on topics like privacy-preserving systems and distributed learning. Labs/teams: Leads research group at University of Manitoba focusing on AI and information theory applications in multimedia systems.
Besiki Stvilia is a Professor in the School of Information at Florida State University's College of Communication and Information. He holds a Ph.D. and M.S. in Library and Information Science from the University of Illinois at Urbana-Champaign, along with an M.S. in Applied Mathematics from Tbilisi State University. His research focuses on data quality assurance, digital curation, social informatics, and knowledge organization. He has led projects on research data management, metadata frameworks, and collaborative data practices in scientific communities. Stvilia has taught courses including LIS 6205 (Information Behavior), LIS 5263 (Information Retrieval Theory), and LIS 5787 (Metadata Practices). His funded research includes grants on data quality infrastructure for repositories, researcher identity curation, and mobile wellness application behavior studies. His work emphasizes bridging theoretical models with practical applications in digital libraries and academic systems. His research spans topics like GenAI credibility assessment, ontology development for data repositories, and user engagement with social Q&A platforms. He has advised multiple collaborative projects involving interdisciplinary teams and has published extensively in journals like Journal of Documentation and Information Processing & Management .
Christina Niklaus is an Assistant Professor in the Department of Computer Science at the University of St. Gallen (HSG). Her research focuses on Natural Language Processing (NLP), Artificial Intelligence (AI), and Computational Argumentation, with an emphasis on text simplification, knowledge representation, and responsible AI. She leads the project 'Conversational AI: Dialogue-based Adaptive Argumentative Writing Support' funded by SNSF Basic Research (2022–2026). Education : Ph.D. in Computer Science, University of Passau, Germany (2022) M.Sc. in Computer Science, University of Passau, Germany (2016) B.Sc. in Applied Computer Science, University of Bamberg, Germany (2011) Research Interests : Christina Niklaus develops AI systems to bridge complex information with human understanding, emphasizing accessibility and ethical integrity. Her work includes: Context-aware NLP systems for simplifying technical texts Computational tools for enhancing argumentation skills in academic writing Open Information Extraction (OIE) for structured knowledge representation Responsible AI frameworks ensuring fairness and transparency Recent Article Trends : Her recent work explores large language models (LLMs) in education, discourse-aware text simplification, and argument quality assessment. Key contributions include applying FinBERT for financial text analysis and developing adaptive writing support systems. Awards : Best Paper Award (ECEI 2024) Best Paper Nomination (CSEDU 2023) Nominated for GI-Dissertationspreis (2022) delina Innovation Award (2021) Teaching & Grants : Teaches Database Systems and Advanced Databases at HSG Principal investigator of multiple grants including SNSF Basic Research Labs/Teams : Her research group focuses on interdisciplinary AI applications in education and argumentation, collaborating with institutions like LEARNTEC and CHI.
Dr. Haiyan Liu is an Associate Professor of Quantitative Methods, Measurement, and Statistics in the Department of Psychological Sciences at the University of California, Merced, within the School of Social Sciences, Humanities, and Arts. She earned her Ph.D. in Quantitative Psychology from the University of Notre Dame (2018). Her research focuses on advanced statistical modeling of psychological and educational data, including high-dimensional, longitudinal, and social network data. She develops Bayesian methodologies and machine learning techniques to enhance understanding of human behavior, with recent emphasis on structural equation modeling, network dynamics, and nonparametric growth curves. Her work addresses challenges in survey methodology and behavioral data analysis. Dr. Liu’s educational background includes a Ph.D. in Quantitative Psychology from the University of Notre Dame (2018), complementing her current academic role. Her lab, accessible at https://sites.google.com/view/ucmhaiyanliu , supports her research activities. Her research interests span Bayesian SEM, social network analysis, and applications of machine learning to behavioral data, aiming to bridge methodological innovation with practical psychological inquiry. Her recent articles highlight advancements in Bayesian model selection, longitudinal sentiment analysis, and social network mediation. She emphasizes prior specification rigor in Bayesian frameworks and explores nonlinear relationships in social dynamics. Though no awards are explicitly listed, her contributions to statistical methodologies in psychological research reflect significant scholarly impact. Dr. Liu advises students in quantitative methods and has developed software tools like logistic4p for misclassification correction in logistic regression. Her work integrates computational methods with theoretical advancements, positioning her as a key contributor to modern quantitative psychology.
Marco Janssen is a Professor in the School of Sustainability and Director of the Center for Behavior, Institutions, and the Environment (CBIE) at Arizona State University (ASU). He holds affiliated roles across multiple institutions including the Biosocial Complexity Initiative, Center for Social Dynamics and Complexity (CSDC), and the ASU-SFI Center for Biosocial Complex Systems. His work focuses on governance of shared resources (water, energy, critical minerals) through behavioral experiments, agent-based modeling, and case studies. Education: PhD in Mathematics (Maastricht University, 1996) and MA in Econometrics (Erasmus University Rotterdam, 1992). Research interests include social-ecological systems, collective action challenges, and institutional design. Active in open science initiatives to enhance accessibility of research and educational materials. Key affiliations include the Julie Ann Wrigley Global Futures Laboratory, School of Human Evolution and Social Change, and the Complex Adaptive Systems School. Grants funded by NSF and Sloan Foundation support projects on commons governance, cyberinfrastructure for modeling, and urban sustainability (e.g., ASU Project Cities).
Sean Reardon is the endowed Professor of Poverty and Inequality in Education at Stanford University's Graduate School of Education and Professor (by courtesy) of Sociology. He serves as Director of the Stanford Interdisciplinary Doctoral Training Program in Quantitative Education Policy Analysis and is a Senior Fellow at the Stanford Institute for Economic Policy Research. Reardon is also a member of the National Academy of Education and the American Academy of Arts and Sciences. His educational background includes an Ed.D. in Educational Administration, Planning, and Social Policy from Harvard Graduate School of Education (1997), M.Ed. from Harvard Graduate School of Education (1992), M.A. in International Peace Studies from University of Notre Dame (1991), and B.A. in Program of Liberal Studies with a minor in Honors Mathematics from University of Notre Dame (1986). Reardon's research investigates the causes, patterns, trends, and consequences of social and educational inequality, with particular focus on residential and school segregation and racial/ethnic and socioeconomic disparities in academic achievement. He develops methods for measuring social and educational inequality, including segregation and achievement gaps, and advances causal inference methods in educational research. His work has significantly shaped understanding of how poverty and school segregation impact educational outcomes across America. Analysis of Reardon's 15 most recent publications reveals a consistent focus on educational inequality, with particular emphasis on measuring achievement gaps, school segregation patterns, and the relationship between socioeconomic status and educational outcomes. His research increasingly utilizes large-scale administrative datasets, most notably the Stanford Education Data Archive (SEDA), which he developed based on 300 million standardized test scores to provide measures of educational opportunity across all U.S. public school districts. William T. Grant Foundation Scholar Award National Academy of Education Postdoctoral Fellowship Andrew Carnegie Fellow Member of the National Academy of Education Member of the American Academy of Arts and Sciences Reardon directs the Stanford Interdisciplinary Doctoral Training Program in Quantitative Education Policy Analysis, mentoring the next generation of education researchers. His work has been supported by major research grants that have enabled the development of the Stanford Education Data Archive, a groundbreaking resource that provides detailed metrics on educational opportunity across all U.S. public school districts. This archive has become a critical tool for policymakers and researchers seeking to understand and address educational inequality. As developer of the Stanford Education Data Archive, Reardon leads a significant research initiative that has transformed how educational opportunity is measured and understood across the United States. His work has established new methodologies for analyzing large-scale educational data and has provided policymakers with concrete evidence about the relationship between poverty, school segregation, and academic achievement.
