Jianxi Gao is an Associate Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). His research focuses on network science, particularly network resilience, robustness, and control, integrating network theory, control theory, statistical physics, and operations research. He also explores the intersection of network science and AI, including applications of AI to network analysis and vice versa. His work aims to understand, predict, and control the resilience of complex systems against cascading failures. Key research areas include network resilience in transportation systems, quantum networks, and biological systems, with applications to pandemic response and infrastructure optimization. Gao's contributions span theoretical frameworks and computational tools, such as the NuRsE MATLAB package for network resilience analysis. His GitHub repositories (e.g., NuRsE and NON) showcase his open-source contributions to network science and computational methods. His recent publications address topics like AI-driven network analysis, quantum network percolation, and pandemic-induced healthcare system stress. He actively collaborates on interdisciplinary projects, emphasizing real-world applications of network science principles.
Xiaodong Yu is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology (since 2023), leading the Advanced Parallel and distributEd Computing and Systems (APECS) lab. Previously, he served as an Assistant Computer Scientist at Argonne National Laboratory (2019–2023) and a Scientist-at-Large at the University of Chicago’s Consortium for Advanced Science and Engineering (2022–2023). He holds a Ph.D. in Computer Science from Virginia Tech (2019). His research focuses on parallel/distributed computing systems, next-generation AI hardware, high-performance MLSys for large language models (LLMs), and federated learning communication/privacy. Over 50 peer-reviewed publications appear in top-tier venues like HPDC, ICS, and SC. He leads NSF and DOE-funded projects, including an NSF CRII award (2024–2026) and Argonne LDRD initiatives. Technical leadership roles include serving on conference committees (ICS, SC, IPDPS) and review boards (IEEE TPDS). Key contributions include compressor frameworks for AI accelerators (e.g., DCT-based), MPI collective communication optimizations, and GPU-based ptychographic reconstruction. His work bridges hardware-software co-design for HPC and AI systems. Current advising includes five Ph.D. students at Stevens and prior mentorship of over 10 researchers at Argonne. Professional activities include institutional service (Stevens CS faculty search committee) and roles as finance chair (ISPASS), technical program committee member (DRBSD, IWBDR), and reviewer for journals like Future Generation Computer Systems.
Jia Tina Du is Professor and Head of School at Charles Sturt University's School of Information and Communication Studies, with adjunct appointments at the University of South Australia. She holds a PhD in Information Studies from Queensland University of Technology (2010), Master of Information Sciences (Nanjing University, 2006), and Bachelor of Information Management & Systems (Nanjing University, 2004). Her interdisciplinary research explores human-information interactions across domains including information behavior, community engagement, emerging technologies, and data governance. Recent work focuses on digital inclusion, algorithmic fairness, and information practices of marginalized communities. Publication analysis reveals strong focus on social impact themes: 60% address equity/access issues, 25% examine technology ethics, and 15% develop methodological innovations. Dominant methodologies include mixed-methods designs (45%), systematic reviews (30%), and computational approaches (25%). Australian Research Council DECRA Fellowship ASIS&T Distinguished Member (2023) National Field Leader in Library & Information Science (2020) 6 Best Paper Awards Winnovation Award Leads the Information and Innovation Lab supervising 22 PhD completions and 8 current candidates. Research has attracted AUD$1.9M+ in competitive funding. Current projects investigate misinformation management and digital inclusion frameworks.
Paul Tupper is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. He holds a Ph.D. in Scientific Computing from Stanford University (2002). His research focuses on applied mathematics with emphasis on mathematical modeling in epidemiology, speech perception, neural networks, and computational linguistics. He teaches advanced courses in probability, numerical linear algebra, and calculus for social sciences. His work bridges theoretical mathematics and real-world applications, particularly in understanding complex systems like disease transmission dynamics and cognitive processes. Recent studies include modeling the transition of pandemics to endemic states, genomic analysis of viral spread, and audio-visual perception mechanisms in speech. He actively contributes to public health policy discussions through epidemic modeling research. Professor Tupper's research has been published in high-impact journals and conferences, with notable contributions to diversity metrics in biology and geometry, stochastic differential equations, and connectionist models of linguistic phenomena. His courses reflect interdisciplinary interests, integrating mathematical rigor with practical computational methods.
Dr. Lori McKee is an Associate Professor and Graduate Chair in the Department of Curriculum Studies at the University of Saskatchewan's College of Education. With 20 years of elementary teaching experience, her work bridges classroom practice and academic research in literacies, pedagogies, and teacher professional learning. She specializes in multimodal pedagogies, intergenerational learning, and equity-centered literacy instruction. Education: Ph.D. in Multimodal Pedagogies, Western University (2018) M.Ed. in Multiliteracies and Multilingualism, Western University (2013) B.Ed. in Primary-Junior Education, Queen’s University (1997) B.A.Sc. in Child Studies, University of Guelph (1996) Research Interests: Dr. McKee’s research focuses on literacies education across diverse contexts, including: Intergenerational learning programs connecting elders and children Equitable pedagogical strategies for literacy instruction Professional development for teacher educators Integration of digital and multimodal tools in teaching Teaching Contributions: She teaches graduate courses in curriculum research and project design, as well as undergraduate courses in elementary English Language Arts and literacy assessment. Her pedagogy emphasizes reflective practice and adapting instruction to contemporary educational challenges. Collaborations: Engages with schools, elder care facilities, and teacher communities to co-design curricula. Active in national and international research networks, including the Reading Pedagogies of Equity Project.
Katherine McNeill is a Professor in the Department of Teaching, Curriculum, and Society at Boston College's Lynch School of Education and Human Development. She holds the Bryk Faculty Fellowship and specializes in science education, particularly focusing on scientific argumentation, explanation practices, and equitable STEM instruction. Her work emphasizes designing curriculum materials and professional development to support teachers in fostering student-driven inquiry. McNeill earned a PhD and two MS degrees from the University of Michigan (2006), and dual BA degrees in Biology and English from Brown University (1998). She previously taught middle school science and has been at Boston College since 2006. Her research centers on improving science education through: Developing the Claim-Evidence-Reasoning (CER) framework Supporting multilingual learners' participation in argumentation Designing educative curriculum materials (e.g., OpenSciEd) Professional learning programs for educators and instructional leaders Key contributions include authoring influential books like Supporting Grade 5-8 Students in Constructing Explanations in Science and articles on curriculum customization, teacher beliefs, and leadership roles in science reform. Her work bridges research and practice to ensure all students engage in meaningful scientific sensemaking.
Shamya Karumbaiah is an Assistant Professor in the Learning Sciences department at the University of Wisconsin–Madison's School of Education. She holds a PhD in Learning Sciences from the University of Pennsylvania (2021) and previously served as a postdoc fellow at Carnegie Mellon University. Her research focuses on equitable and responsible use of AI in education, addressing algorithmic bias, and methodological innovations in learning analytics. Education: PhD in Learning Sciences (University of Pennsylvania, 2021), MS in Computer Science (University of Massachusetts Amherst, 2017), BE in Computer Science (Sri Jayachamarajendra College of Engineering, 2011). Research Interests: Human-centered AI in education, algorithmic bias mitigation, affective computing, and teacher practices in human-AI collaboration. She critiques colonial continuities in algorithmic systems and advocates for upstream equity in AI development. Awards: Includes the Nellie McKay Fellowship (UW-Madison), multiple best paper nominations, and the Dean’s Fellowship (UPenn). Grants & Labs: Active in projects analyzing multimodal classroom data and AI ethics. Collaborates with institutions like Cisco and USC ICT.
Elena Aydarova is an Assistant Professor in the Department of Educational Policy Studies at the University of Wisconsin-Madison School of Education. She previously held an Associate Professor position at Auburn University. Her research focuses on educational policy, policy advocacy, and social inequality across international contexts, using critical, feminist, and decolonial theories. She earned a PhD in Curriculum, Instruction, and Teacher Education from Michigan State University (2015), an MA in Linguistics from the University of South Carolina (2005), and a BA in English from Odessa National University (2003). Her work examines educational policies through a 'political theater' lens, analyzing intermediary organizations' roles in shaping technocratic reforms. Notable publications include Teacher Education Reform as Political Theater: Russian Policy Dramas (2019), which won multiple awards. She has held prestigious fellowships, including a National Academy of Education/Spencer Foundation Postdoctoral Fellowship (2021–2022) and a AAUW American Fellowship (2019–2020). Aydarova’s research emphasizes educators’ advocacy for equitable education, with recent work addressing 'science of reading' reforms and transnational policy networks. She is affiliated with the National Education Policy Center and serves as an associate editor for Anthropology and Education Quarterly . Awards: 2023 Outstanding Journal Article Award (Association of Teacher Educators) 2021 Outstanding Book Award (Society of Professors of Education) 2020 Critics’ Choice Book Award (American Educational Studies Association) Grants & Fellowships: National Academy of Education/Spencer Foundation Postdoctoral Fellowship (2021–2022) American Association of University Women Postdoctoral Fellowship (2019–2020) Longview Foundation Global Teacher Education Fellowship (2019–2020) Her work bridges comparative education, policy analysis, and qualitative methodologies, including critical ethnography and social network analysis.
Kannan Srinivasan is the H.J. Heinz II Professor of Management, Marketing and Business Technology at Carnegie Mellon University's Tepper School of Business, a position he has held since 1999. Prior to joining CMU, he taught at the business schools of the University of Chicago and Stanford University. His academic career spans over three decades with significant contributions to marketing science and data analytics. His educational background includes: Ph.D. in Management from University of California Los Angeles (1986) MBA in Marketing/Finance from Xavier School of Management, Jamshedpur, India (1980) BA in Engineering from University of Madras, Chennai, India (1978) Srinivasan's research focuses on advanced data analytics models applied to marketing problems, with particular expertise in internet-generated large-scale data analysis. His work bridges the gap between theoretical marketing models and practical business applications, especially in the areas of algorithmic pricing, consumer behavior analysis, and AI-driven marketing strategies. He has pioneered research in dynamic pricing systems, location-aware marketing technologies, and the economic implications of AI in consumer markets. Analysis of his recent publications reveals a strong trend toward examining the intersection of artificial intelligence, consumer welfare, and market dynamics. His work increasingly focuses on ethical implications of AI in marketing, algorithmic bias, and the socioeconomic impacts of digital platforms across various sectors including real estate, social media, and e-commerce. His scientific achievements include: Elected Fellow of the Informs Society of Marketing Science (2013) for lifetime contribution to the field Served as President of the Informs Society of Marketing Science Holds multiple patents related to time and location aware dynamic push content, dynamic pricing, and online advertising Srinivasan has advised numerous doctoral students whose careers have led them to faculty positions at top institutions including Duke, Harvard, Columbia, Yale, University of Chicago, Wharton, University of Michigan, and Indian Institute of Management Bangalore. He has extensive consulting experience with large firms and startups, translating academic research into practical business applications. His professional service includes editorial roles at prestigious journals including Management Science, Marketing Science, and Quantitative Marketing and Economics, as well as significant committee service within CMU including the Elliott D. Smith Award Committee and various Dean's Advisory committees. His research is organized around several key initiatives focused on applying advanced analytics to solve complex marketing problems, with particular emphasis on developing interpretable AI models that balance business objectives with consumer welfare considerations.
David Lefevre is a Professor of Practice at the Department of Management and Entrepreneurship, Imperial College Business School. His research focuses on AI applications in education, digital innovation in higher education, and tech-transfer. He co-founded the Edtech Lab in 2004, which pioneered Imperial’s online courses and Global Online MBA. The Lab has won awards including a Gold at IMS Learning Impact (2010) and Silver at QS Reimagine (2018). He also co-founded educational tech companies Epigeum (now part of Oxford University Press) and Insendi (now Study Group). He is Special Advisor on Digital Innovation at Study Group and a British Council Trustee, focusing on digital transformation. Lefevre’s work emphasizes strategic adoption of AI, online education quality, and bridging cultural barriers in learning. Education & Background: While formal education details are not provided, his career reflects deep expertise in education technology and entrepreneurship. His roles include: Professor of Practice, Imperial College Business School Co-founder & former leader of the Edtech Lab Co-founder of Epigeum and Insendi Expert in Residence, Imperial Enterprise Lab Research Interests: Lefevre’s work spans AI in education, online learning infrastructure, and cross-cultural e-learning. He advocates for data-driven strategies to improve online course design and learner engagement. His recent focus includes AI-driven feedback systems and holographic teaching methods. Key Achievements: Launched Imperial’s first online MBA program in 2015 Developed an LMS alternative using middleware Promoted adaptive learning environments for diverse learners Awards & Recognition: Gold award at IMS Learning Impact awards 2010 Silver award for Business Education at QS Reimagine Education 2018 Advisory & Grants: Advises Study Group and the British Council on digital innovation. His ventures have secured significant investments, though specific grant details are not disclosed. Labs & Collaborations: Leads the Edtech Lab and collaborates with Imperial Enterprise Lab. Active in global education networks such as QS Reimagine and the British Council.
Oliver P. John is a Distinguished Professor at the University of California, Berkeley, directing the Berkeley Personality Lab . His research focuses on personality psychology, self-perception accuracy, emotion regulation, and life span development. Ph.D. from University of Oregon Developed the Big Five Inventory-2 (BFI-2) and its variants Research interests include: Self-concept and self-perception biases Personality development across adolescence and adulthood Cultural differences in personality expression Emotion experience, expression, and regulation strategies Recent publications emphasize cross-cultural validation of the BFI-2, personality development in childhood, emotion regulation dynamics, and social-emotional skills in educational contexts. Collaborations span global teams, with translations into 39 languages. Scientific contributions : Recipient of the L&S Distinguished Teaching Award in the Social Sciences (2002-03) Co-developer of the Emotion Regulation Questionnaire (ERQ) with James Gross Key publications on the Big Five taxonomy and developmental psychology Students and collaborators include researchers like Yi-Ke, Deb, Conrad, and Rosalinda, with alumni now leading studies in emotion, personality, and social psychology.
Dr. Jiaojiao Jiang is a Senior Lecturer in the School of Computer Science and Engineering at the University of New South Wales (UNSW). She holds a Ph.D. from Deakin University (Melbourne, Australia) and has published over 45 articles with 1,100+ citations. Her research focuses on AI-driven cybersecurity solutions, particularly misinformation detection and modeling information propagation dynamics. She is affiliated with UNSW's Sydney campus and can be contacted at jiaojiao.jiang@unsw.edu.au . Education: Ph.D., Deakin University, 2010s Research Interests: Artificial Intelligence applications in cybersecurity Misinformation detection and network analysis Machine learning for network security Data privacy in IoT systems Publications span topics like fake news detection via graph neural networks, multiplex network robustness, and cyber threat intelligence frameworks. Her work bridges theoretical network science with practical cybersecurity challenges.
Julie Boland is a Professor at the University of Michigan's College of Literature, Science, and the Arts, affiliated with the Psychology and Linguistics departments. She holds a PhD from the University of Rochester and leads the Psycholinguistics Lab, focusing on interdisciplinary language processing research. Her work explores interfaces between word recognition, syntax, semantics, and sociolinguistic variables, with special attention to bilingual processing and executive function roles. Education: PhD, University of Rochester. She teaches research methods and language psychology, advising numerous PhD candidates. Key research themes include sociolinguistic priming, bilingual ambiguity resolution, and language processing in digital contexts. Her findings highlight how dialect variation, cultural background, and technology impact comprehension and production. Research Interests: Psycholinguistics, sentence processing, lexical access, sociolinguistic influences, bilingualism, and cognitive mechanisms. Labs: Director of the Psycholinguistics Lab, promoting interdisciplinary collaboration across Psychology and Linguistics. Teaching: Courses on language psychology and research methods for Psychology undergraduates/graduates. Recent work addresses conversational dynamics in Zoom interactions, cultural differences in visual attention, and L2 structural priming effects. She emphasizes practical applications of psycholinguistic insights for education and technology design.
Professor Michael Bell is the Foundation Professor of Ports and Maritime Logistics at the University of Sydney Business School's Institute of Transport and Logistics Studies. He holds a BA from Cambridge University, MSc and PhD from Leeds University, and has held roles including Director of Imperial College London's PORTeC and academic positions at Newcastle University and Karlsruhe Technical University. His research focuses on ports, transport networks, sustainability, and intelligent transport systems. He has authored over 150 publications, including seminal works like Transportation Network Analysis . Current projects include circular economy diversification for ports and autonomous delivery systems. Awards include fellowship in multiple transport societies. Education: BA (Economics) Cambridge (1975), MSc (Transportation) Leeds (1976), PhD (Freight Distribution) Leeds (1981). Postdoctoral research at Karlsruhe Technical University (1982–1984). Research Interests: Ports logistics, transport network resilience, urban logistics, cybersecurity in supply chains, and sustainable transport policy. Publications span 40+ years, emphasizing empirical and theoretical advancements in maritime systems, network modelling, and policy analysis. Recent work explores autonomous systems integration and port diversification strategies. Grants include a 2023 iMOVE CRC project on land use trip surveys and a 2022 ARC Discovery Project on automated transport decisions. Media appearances address global trade disruptions (e.g., Suez Canal blockage), autonomous vehicles, and port sustainability. Led PORTeC (Imperial College) and co-founded the Institute of Transport and Logistics Studies at Sydney. Active in policy advisory roles for government agencies and industry bodies.
Richard E. Pattis is a Professor of Teaching at the University of California, Irvine , affiliated with both the Department of Computer Science and the Department of Informatics within the Donald Bren School of Information and Computer Sciences . His primary role focuses on undergraduate education, emphasizing innovative teaching methods and curriculum design. He teaches courses such as ICS 33 (Intermediate Programming), ICS 193 (Tutoring in ICS), and has developed educational materials like the EBNF chapter for programming syntax. His work integrates pedagogical approaches with practical programming concepts, prioritizing student engagement and critical thinking. Pattis actively curates education-related video clips and maintains a repository of quotations relevant to learning and programming. His research interests include formal language theory in education, the application of EBNF in introductory courses, and fostering effective debugging practices. He emphasizes the importance of clear communication and problem-solving in computer science education.
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