Professor Stacey Conchie is a leading academic in the Department of Psychology at Lancaster University , specializing in socio-psychological factors influencing behavior in high-risk environments. As Director of the ESRC National Centre for Research and Evidence on Security Threats (CREST) and co-lead of the EPSRC SPRITE+ Network Plus , she bridges academic research with practical applications in security, privacy, and trust. Her work is funded by government agencies, private industry, and charities.
Maël Kubli is a Postdoctoral Research Fellow at the Department of Political Science at the University of Zurich (UZH), where he also earned his PhD in Political Science. He serves as Developer for the Digital Democracy Lab since summer 2019 and contributes to the PRODIGI project, focusing on digital technologies' impact on democratic processes. His interdisciplinary work bridges political science methodology with computational techniques. PhD in Political Science, University of Zurich Kubli's research centers on digital democracy and computational social science, investigating how AI and machine learning reshape political discourse and public engagement. He examines the framing of AI narratives in EU political and media contexts, content moderation practices on digital platforms, and social media's role in political agenda-setting. His methodological approach combines computational methods with political theory, leveraging programming skills in R, HTML, CSS, and Bash to develop research tools for analyzing large-scale political discourse. His publication portfolio reveals a clear evolution from traditional election campaign analysis toward sophisticated computational studies of digital governance. Recent work demonstrates increasing application of large language models to analyze political discourse at scale, particularly in platform governance, media influence on content moderation, and AI's role in political communication. His research consistently addresses the tension between technological innovation and democratic resilience. Kubli actively collaborates within the University of Zurich's Digital Society Initiative (DSI), particularly through the DSI Community Democracy. As a Developer for the Digital Democracy Lab, he applies technical expertise to build research infrastructure for studying digital political processes, contributing to projects that examine how digital technologies both challenge and strengthen democratic institutions.
David Cohen is a University Professor and Hospital Practitioner affiliated with the ACIDE team at AP-HP (Assistance Publique - Hôpitaux de Paris). His research spans child and adolescent psychiatry , autism spectrum disorders , and neurodevelopmental conditions , with a focus on integrating social robotics and affective computing into clinical interventions. His recent work explores AI-based analysis of child-therapist interactions , ICT applications for neurodevelopmental disorders , and robot-assisted therapies . Key publications include systematic reviews on depression assessment tools in autism, longitudinal studies of emotional interventions, and collaborative research on human-robot interaction in clinical contexts. David Cohen collaborates extensively with teams across clinical psychology , robotics , and public health , contributing to journals like Journal of Autism and Developmental Disorders , Research in Developmental Disabilities , and Frontiers in Psychiatry . No specific awards, grants, or educational credentials are detailed in the provided texts.
Brian E. Perron is a Professor at the University of Michigan School of Social Work, where he has established himself as a leading researcher at the intersection of data science and social work practice. His academic journey includes a PhD in Social Work from Washington University (2007), an MSW from the University of Wisconsin (1998), and a BA in Psychology from The College of St. Scholastica (1995). Currently teaching courses including Data Visualization Applications, Quantitative Methodologies for Socially Just Inquiry, and Project and Program Design through Spring/Summer 2025, he maintains an active presence in both classroom instruction and cutting-edge research. Dr. Perron's research interests focus on service research, data science, artificial intelligence applications in social work, and non-profit data consulting. He has developed expertise in helping community-based organizations implement data management systems and create interactive visualizations for non-technical users. His work with the Child & Adolescent Data Lab examines services for vulnerable youth and families in the child welfare system. Notably, he has become a pioneer in exploring the ethical application of AI tools like machine learning and natural language processing within social work contexts, publishing extensively on retrieval-augmented generation systems, word embeddings, and API integration for social work research. His publication record reveals a significant shift toward AI integration in social work, with 12 of his 15 most recent articles (2023-2025) focusing on artificial intelligence applications. These works demonstrate his leadership in developing practical AI tools for social workers while addressing critical ethical considerations. His research spans child welfare systems, substance abuse, mental health services, and educational curriculum development, with particular attention to racial disparities and data privacy concerns in vulnerable populations. Among his scientific achievements, Dr. Perron received an award from Casey Family Programs and has secured research funding from the National Institutes of Health, Department of Veterans Affairs, and the state of Michigan. His work on prenatal cannabis exposure and child maltreatment has generated significant policy implications, particularly regarding racial bias in newborn drug testing practices. As an educator, Dr. Perron specializes in making research and data analysis accessible to students without strong math backgrounds while also teaching diagnosis and treatment of mental health and substance use disorders. He maintains his expertise through continuous learning, including participation in MOOCs to stay current with technological developments. His work with the Child & Adolescent Data Lab represents a significant institutional contribution to improving service outcomes for vulnerable youth through data-driven approaches.
Lei Chen is a Chair Professor and Director of HKUST Big Data Institute at the Hong Kong University of Science and Technology , where he has served since 2005. His research spans data-driven machine learning , crowdsourcing systems , and uncertain database processing , with notable contributions in privacy-preserving spatial queries and graph neural networks . Ph.D. in Computer Science, University of Waterloo (2004) MS in Computer Science, Asian Institute of Technology (1997) BS in Computer Science, Tianjin University (1994) His work focuses on: Spatial Crowdsourcing - Efficient task assignment and privacy frameworks Graph Processing - Novel indexing for heterogeneous networks Explainable AI - Human-centric model interpretation techniques Uncertain Data - Probabilistic query processing with crowdsourcing Recent publications cluster around secure data federation , distributed graph training , and privacy-preserving mobility systems , reflecting his leadership in ACM and IEEE communities. Students include 15 active Ph.D. candidates and 20+ graduated researchers now at institutions like BeiHang University and Huawei Noah's Ark Lab . Awards: ACM Fellow (2024), VLDB Best Paper (2022), SIGMOD Test-of-Time Award (2015).
Shukla Sikder is a Senior Lecturer in Early Childhood Education at Charles Sturt University's Faculty of Arts and Education. With nearly 14 years of multicultural teaching and research experience across Australia, Singapore, and Bangladesh, she specializes in early childhood science, technology, and play-based pedagogy. Her academic credentials include a BEd (Hons) and MEd from University of Dhaka, and a PhD in Early Childhood Science Education from Monash University. Dr. Sikder's research focuses on science learning through play and everyday practices across home and educational settings, digital technology as an innovative teaching method, children's learning and development (birth to eight years), and STEM/STEAM education. Her work employs cultural-historical theory to examine how children develop scientific concepts through family practices and play-based experiences. Current projects include the Children Draw Talking Project and research on culturally valued play-based practices for STEAM education. Analysis of her 49 research outputs (2015-2025) reveals a strong emphasis on cultural-historical perspectives in early childhood STEM education, with increasing output in recent years (17 publications in 2024 alone). Her work spans diverse formats including peer-reviewed articles, creative storybooks, conference presentations, and commissioned reports, demonstrating interdisciplinary approaches that bridge theory, practice, and children's voices. Monash Education Research Community Publication Award (A$500) Monash University Postgraduate Research Travel Grant (A$3,000) Monash University Faculty Research Fund (A$1,000) Australian Endeavour Postgraduate Award (A$234,500) Little Scientists Program Evaluation Grant (A$82,771) Dr. Sikder actively supervises research students and collaborates internationally through the Children's Voices Centre and STEM Education Research Group. Her current projects include evaluating how digital visual observations establish inclusive practices in early childhood research and developing frameworks for enhancing inclusive STEAM pedagogy. She serves as a key researcher in the Machine Vision and Digital Health (MaViDH) Research Group, focusing on innovative approaches to document children's learning processes.
Virginia Polytechnic Institute and State UniversityUnited States
Taylan G. Topcu is an Assistant Professor of Systems Engineering & Analysis at Virginia Tech's Grado Department of Industrial and Systems Engineering. He holds a Ph.D. from Virginia Tech (2020), an M.Sc. from the University of Alabama in Huntsville (2015), and a B.Sc. in Aerospace Engineering from Middle East Technical University (2009). His roles include Director of the Systems Engineering Master’s Degree and Coordinator of the Mission Engineering Certificate Program at Virginia Tech. His research focuses on systems engineering, microeconomics, and data-science integration to address socio-technical measurement challenges in complex systems design, emphasizing architecture theory and safety-critical systems management. Key research themes include modularization's impact on system complexity, digital twins for healthcare sustainability, and leveraging AI tools like Large Language Models for systems engineering tasks. He collaborates with NASA, INFRABEL, and MITRE to ground theoretical insights in real-world contexts. Professional affiliations include INCOSE, ASME, INFORMS, and the Design Society. Recent courses taught include ISE 5834 (Decision Analysis for Engineers) and ISE 5204 (Systems Engineering Capstone Project). His work bridges academia and industry, addressing both foundational theory and practical applications in complex system design and management.
John T. Evans IV is an Assistant Professor in the Department of Agricultural & Biological Engineering at Purdue University. He specializes in Machine Systems and Automation, focusing on agricultural machine design, precision agriculture technologies, and automated systems modeling. His research bridges robotics, digital agriculture, and sustainable farming practices, contributing to advancements in autonomous navigation and machine learning applications for crop management. Bachelor's in Biosystems Engineering, University of Kentucky Master's in Biosystems Engineering, University of Kentucky PhD in Biological Systems Engineering, University of Nebraska–Lincoln Evans' work centers on optimizing agricultural machinery through synthetic vision systems, digital twin environments, and deep learning algorithms. Key areas include autonomous roadside mowing , corn row identification , and transfer performance optimization for robotic systems. His publications reveal interdisciplinary trends combining robotics, precision agriculture, and computational modeling. He actively mentors Purdue's Quarter Scale Tractor Team and contributes to the American Society of Agricultural and Biological Engineers. His research involves collaboration with Purdue's ADM Agricultural Innovation Center and Discovery Park facilities, focusing on automation, data science, and machine learning in agricultural contexts.
Marianna Marra is an Associate Professor in International Business and Innovation at the University of Sussex Business School. She holds a PhD from Aston University and is a Fellow of the Royal Society of Arts and the Higher Education Academy. Her research focuses on multinational knowledge networks, institutional contexts impacting MNEs, and social network analysis. She leads roles such as Director of MSc Entrepreneurship & Innovation and chairs the British Academy of Management's International Business/International Management SIG. Key achievements include grants like the 2021 Research Excellence Award and editorial roles in journals including Journal of Management Studies and International Business Review . Marianna actively supervises doctoral students exploring international business, innovation, and quantitative methods. Education: PhD from Aston University (UK), PhD from University of Salerno (Italy), visiting scholar at Rutgers University (US). Research Interests: Multinational knowledge networks, institutional analysis, social network methods, and technology innovation management. Her work bridges international business theory and practice, analyzing how institutions and networks shape MNE strategies. Recent grants include seed funding from the University of Sussex and the Society for the Advancement of Management Studies. She has organized major events like the 2023 BAM Conference and the EIBA Summer School on Research Methods in International Business. Awards: Excellence Education Award (2019), Research Excellence Awards for Emerging Scholar (2021). Grants: Over 10 grants since 2018, including BA/Leverhulme Small Research Grant (2020). Marianna contributes to academic communities through editorial boards and conference leadership, emphasizing sustainability and disruptive innovation in business practices.
Sandra Waxman is a Professor of Cognitive Psychology and holds the Louis W. Menk Chair in Psychology at Northwestern University. She is an IPR Fellow, leading research at the intersection of developmental science, cognitive psychology, and cultural studies. Her work focuses on how infants and young children acquire language and conceptual knowledge across linguistic and cultural contexts. She directs the Infant and Child Development Center, exploring cross-linguistic development in languages like English, Mandarin, and Spanish, as well as cultural influences on understanding human-nature relationships. Her research bridges disciplines, emphasizing the interplay between language and cognition. Collaborations include projects with Native American communities and Argentina’s CONICET. Waxman’s studies reveal how early language shapes cognitive development and how cultural frameworks influence children’s reasoning about the natural world. She also investigates how infants integrate sensory and linguistic information to form concepts, with implications for education and policy. Waxman’s contributions span over 200 publications, addressing topics like infant categorization, bilingualism, and the role of context in learning. Her work underscores the universality and diversity of developmental processes, advocating for interdisciplinary approaches to understand childhood cognition. She actively engages with policymakers through IPR, translating research into strategies that support early childhood development.
Antal van den Bosch is a Professor of Language, Communication, and Computation at Utrecht University’s Faculty of Humanities. He also serves as Board Member and Domain Chair for Social Sciences and Humanities at the Dutch Research Council (NWO). His career includes roles as Director of the Meertens Institute (KNAW) and professorships at Radboud University and Tilburg University. His research focuses on machine learning and computational linguistics, particularly Generative AI and Large Language Models. He emphasizes interdisciplinary collaboration, exploring intersections between AI and societal challenges like governance and cultural heritage. Education: Ph.D. in Advanced Computing Sciences at Maastricht University. Key affiliations include guest professorships at the University of Antwerp’s CLiPS and fellowships with EurAI and the Royal Netherlands Academy of Arts and Sciences. Research Interests: Generative AI and LLMs Language Technology Cultural AI Social Implications of AI Historical Language Analysis Articles Trends: Recent work addresses AI governance, societal impacts of generative models, and computational methods in humanities research. Projects like Better-Mods and Cultural AI Lab highlight applied AI for societal benefit. Awards: Vici Grant (NWO), KULAK Francqui Chair, and membership in prestigious academic societies. Advising & Grants: Supervises over 20 Ph.D. students. Leads projects on AI moderation tools, cultural heritage digitization, and digital humanities infrastructure. Notable grants include NWO-funded Better-Mods and Horizon 2020 initiatives like HiTiME and TwiNL. Labs/Teams: Active in CLARIAH, Nederlab, and the Digital Humanities Lab (KNAW). Software contributions include Frog (Dutch NLP suite), T-Scan, and Colibri Core.
Xinlian Zhang is an Assistant Professor in Residence at the Herbert Wertheim School of Public Health & Human Longevity Science at UC San Diego. Their research focuses on microbiome dynamics, liver diseases (including NAFLD and alcoholic hepatitis), and statistical modeling in biomedical contexts. Collaborations include work with institutions like Altman Clinical and Translational Research Institute. Key research areas include the role of mycobiome and virome in liver pathology, host-microbiota interactions, and the application of advanced statistical methods to microbiome data. Zhang has contributed to studies linking microbiota composition to disease severity, drug responses, and psychological factors like loneliness. Publications emphasize interdisciplinary approaches, integrating virology, microbiology, and computational biology. Notable work includes analyzing fecal mycobiome in NAFLD patients and identifying virome signatures in alcoholic hepatitis. Co-authors include prominent researchers like B. Schnabl and R. Loomba. No specific awards or grants are listed, but their work has been cited widely (e.g., 87+ mentions for a 2020 Gastroenterology paper). Collaborations span institutions within the UC system and beyond, reflecting a network engaged in translational and computational biomedical research.
Brit Davidson is a Senior Lecturer at the University of Bath in the Department of Management Information, Decisions & Operations. She is affiliated with multiple research centers including the Applied Digital Behaviour Lab, Centre for Qualitative Research, and Institute for Digital Security and Behaviour. Her work focuses on using digital traces to understand human behavior in security and health contexts, employing methods like machine learning, computational linguistics, and qualitative techniques. She teaches MSc courses on Data Mining and Machine Learning. Davidson also serves on the Social Sciences Research Ethics Council (SSREC) at Bath and the Security Research Ethics Council (SREC) at Lancaster University. Her research explores how individuals adapt behavior across online and offline contexts, with a focus on measurement challenges and ethical considerations. Notable projects include studies on sentiment analysis, computational reproducibility, and inclusive digital mental health tools. She has held honorary roles at the University of Bristol in Digital Health and served as a Policy Advisor in the UK Cabinet Office. Davidson has been recognized as a Global Ambassador for the UK under the Global Council for Responsible AI (2025). Her work contributes to UN Sustainable Development Goals related to health and innovation. She leads projects funded by government agencies, focusing on areas like social data bias and AI ethics.
Professor Jochen Leidner is a Visiting Professor in the Department of Computer Science at the University of Sheffield and a Research Professor for Explainable and Responsible AI at Coburg University of Applied Sciences, Germany. He has held leadership roles including Director of Research at Thomson Reuters and Refinitiv, and has founded companies like Polygon Analytics and KnowledgeSpaces. His academic background includes degrees from the University of Erlangen-Nuremberg, University of Cambridge, and a PhD in Informatics from the University of Edinburgh. Research focuses on AI ethics, natural language processing, information extraction, and geoinformatics. Notable contributions include work on question-answering systems (QED/ALYSSA), spatial toponym resolution algorithms, and risk-mining frameworks. Awards include the ACM SIGIR Doctoral Consortium Award and twice winning Thomson Reuters Inventor of the Year for patents. He has taught at institutions across Europe and advises EU funding bodies (FP7/Horizon). Holds multiple patents in information retrieval and mobile computing. Active in industry collaborations, blending academic research with real-world applications in finance, legal tech, and supply chain analytics.
Feixiong Liao is a tenured Assistant Professor in Urban Planning and Transportation at Eindhoven University of Technology. He serves as Education Coordinator for the USRE Unit and holds affiliations with EAISI Mobility. His research focuses on developing large-scale urban transportation planning models. Education includes a doctorate degree (cum laude) from TU/e, where he developed multi-state supernetwork approaches for metropolitan accessibility. Post-doctoral research involved international collaborations on travel behavior analysis. Research interests center on network modeling for mobility demand, accessibility measurement in urban contexts, behavior analysis with bounded rationality, service innovation for mobility issues, and sustainability in mobility-energy-social nexuses. His work integrates land-use transport modeling, AI applications, and climate-neutral mobility solutions. Publications show strong focus on transportation modeling innovations, particularly in multimodal systems, activity-travel behavior, and sustainable mobility solutions. Recent works increasingly incorporate machine learning and big data approaches to urban mobility challenges. Best Paper Award (in CTS) 2019 Best Paper Award (POMS in China) 2019 Education Innovation Fund 2020 HKSTS Best Student Paper Award 2011 Supervises 5-7 PhD students and post-docs annually. Leads the international joint research program JPI Urban Europe (2019-2024) on accessibility valuation. Secured multiple grants including a five-year education innovation fund for challenge-based learning in smart mobility. Leads research groups including EAISI Mobility and Urban Planning and Transportation Group. Coordinates the UBeX Urban Behavior eXtended reality lab project until 2026.
