Nichole Denise Pinkard is the Alice Hamilton Professor of Learning Sciences and Faculty Director of the Office of Community Education Partnerships at Northwestern University's School of Education and Social Policy. She founded the Digital Youth Network (DYN) and developed L3, a social learning platform connecting youth's learning across multiple environments. Education: PhD, Learning Sciences, Northwestern University, 1998 MS, Computer Science, Northwestern University BS, Computer Science, Stanford University Research Focus: Designs socio-technical systems to support ecological models of learning, emphasizing community-level learning ecosystems and pedagogical-based social networks. Her work bridges technology and urban education contexts to reimagine learning documentation and opportunities. Awards: 2010 Common Sense Media Award 2004 Jan Hawkins Award 2000 NSF Early Career Fellowship Projects & Initiatives: Leads Chicago City of Learning and Digital Youth Divas programs, advancing digital equity and youth empowerment through technology. Collaborates with city agencies to create existence proofs for innovative urban learning models.
Professor Patricia H. Reiff is a Professor in the Department of Physics and Astronomy at Rice University and Associate Director for Outreach Programs at the Rice Space Institute. Her research focuses on space plasma physics, magnetospheric dynamics, auroras, and space weather. She has led missions like the Magnetospheric Multiscale (MMS) and contributed to the Dynamics Explorer, Polar, and Cluster missions. Reiff has pioneered public education initiatives, including the Discovery Dome portable planetarium system and the 'Totality!' planetarium show, reaching global audiences. She has trained fourteen PhD students and directs the Master of Science Teaching (MST) program, which has produced 36 teacher alumni as of 2024. Education: B.S. Physics (Oklahoma State University, 1971), M.S. Space Science (Rice University, 1974), and Ph.D. Space Physics and Astronomy (Rice University, 1975). Her research uses data from missions like MMS and citizen science projects like Citizen CATE to study magnetic reconnection and space weather effects. Key research interests include solar wind-magnetosphere-ionosphere interactions, magnetospheric reconnection, and the societal impacts of space weather. She has authored over 160 refereed publications and holds an H-index of 40. Reiff’s awards include AGU Fellow (1997), the Athelstan Spilhaus Award (2009), and NASA Group Achievement Awards. She is a vocal advocate for STEM education, frequently appearing in media to discuss eclipses and space science, including her role as a solar eclipse tour guide and science commentator.
Dr. Ryan Carney is an Associate Professor of Digital Science at the University of South Florida's Department of Integrative Biology (College of Arts and Sciences). His research bridges paleontology and epidemiology, using cutting-edge technologies like 3D imaging, AI, and VR/AR. He studies dinosaur biomechanics (e.g., Archaeopteryx flight mechanics) and mosquito-borne diseases (e.g., malaria, Zika) through NSF/NIH-funded collaborations with agencies like NASA and the CDC. His work integrates citizen science platforms (iNaturalist, Mosquito Alert) for disease surveillance and develops AI tools for mosquito classification. Education: PhD (Brown), MPH/MBA (Yale), B.A.'s in Biology & Art (UC Berkeley) Lab: SCA 107 (focuses on digital paleontology and disease modeling) Research interests span dinosaur functional morphology, epidemiological modeling using GIS/remote sensing, and translating science through immersive technologies. He received awards like the National Geographic Emerging Explorer and multiple USF teaching/research accolades. Awards: CAS Liberal Arts Teaching Award, Outstanding Research Achievement, etc. Grants include NSF funding for AI-driven mosquito surveillance and NIH support for disease control. His lab's innovations include the Global Mosquito Observations Dashboard (GMOD) and VR reconstructions of prehistoric species.
Jay Henderson is an Assistant Professor in the Department of Computer Science at Memorial University of Newfoundland, leading the Human-Computer Interaction (HCI) Lab. His research focuses on quantitative HCI, measuring human performance with emerging technologies such as mixed reality and generative AI. He holds a PhD from the University of Waterloo and has held roles including Postdoctoral Fellow at Carleton University and Senior Research Scientist at Huawei Technologies Canada. Education: PhD in Computer Science, University of Waterloo (2021) BSc Hons in Computer Science (minors in Mathematics and Psychology), Mount Allison University (2016) Research Interests: Henderson’s work bridges computer science with psychology and mathematics, emphasizing objective measurement of user interactions. Key areas include mixed reality productivity, generative AI interfaces, gesture-based input, and multimodal feedback systems. His quantitative approach ensures practical insights for technology design. Key Grants & Awards: NSERC Discovery Grant ($155,000 over 5 years) NSERC Discovery Launch Supplement ($12,500) David R. Cheriton Graduate Scholarship ($20,000 over 2 years) Teaching & Supervision: Teaches courses like COMP 4303 (AI for Games) and supervises multiple graduate and undergraduate researchers. Notable supervisees include Daniel Jo (MSc) and Arunav Saha (BSc Honours). Service Contributions: Served on ACM committees including Graphics Interface and CHI Late Breaking Work. Acted as Virtual Chair for ACM ASSETS 2024 and contributed to ACM’s name change policy as a transgender advocate.
Professor Stephen Cave is the Academic Director of the Leverhulme Centre for the Future of Intelligence (CFI) and Co-Director of the Institute for Technology and Humanity at the University of Cambridge. With a PhD in Philosophy from Cambridge, he previously served as a policy advisor and diplomat in the British Foreign Office for nearly a decade before returning to academia. Education: PhD in Philosophy, University of Cambridge Current Roles: Academic Director (CFI), Co-Director (Institute for Technology and Humanity) Media Engagement: Regular contributor to Financial Times , Guardian , New York Times , and media appearances on BBC and NPR His research bridges philosophy and ethics of technology , with two primary strands: (1) the ethics of AI and robotics , focusing on responsible AI development and societal impact through works like AI Narratives and Feminist AI ; and (2) the ethics of life-extension and immortality , explored in Immortality and Should You Choose to Live Forever? . Recent publications analyze algorithmic fairness, gender representation in AI narratives, and digital death. The articles listed reflect his engagement with responsible AI frameworks , cultural portrayals of AI , and existential implications of life extension . His work often synthesizes historical, ethical, and cultural perspectives to address contemporary challenges in AI governance and mortality philosophy.
Dr. Steven H. H. Ding is an Assistant Professor at McGill University's School of Information Studies, specializing in cybersecurity, machine learning, and data mining. His research focuses on AI-driven solutions for malware detection, software vulnerability analysis, and reverse engineering. He holds a PhD from McGill University and has been supported by BlackBerry Cylance and DRDC. His work bridges theoretical advancements with practical applications in military systems and avionics cybersecurity. Dr. Ding earned his PhD in 2019 with notable awards including the FRQNT Doctoral Research Scholarship and McGill's Dean’s Graduate Award. His educational background includes degrees from McGill, Concordia University, and the University of Shanghai for Science and Technology. His research interests span cybersecurity domains such as zero-day malware identification, code obfuscation countermeasures, authorship verification for digital forensics, and AI applications in avionics anomaly detection. He actively contributes to open-source tools like the Kam1n0 MapReduce-based assembly clone search system. Recent work emphasizes adversarial machine learning for evasive malware generation, transformer-based anomaly detection in avionics, and automated SBOM generation for firmware analysis. His publications reflect a focus on real-world cybersecurity challenges in both civilian and defense sectors. Dr. Ding leads the L1NNA Lab and collaborates with industry partners on cutting-edge projects. His contributions include novel techniques for phishing detection leveraging large language models and innovative approaches to reverse engineering software composition in JavaScript applications.
Frédéric Kaplan serves as Director of the College of Humanities at École Polytechnique Fédérale de Lausanne (EPFL), where he holds the Chair of Digital Humanities. He also serves as President of the Time Machine Organisation, a nonprofit entity comprising over 600 institutions. His academic appointments span multiple departments including the Digital Humanities Laboratory (DHLAB), School of Architecture (SAR), and School of Humanities (SODH), demonstrating his interdisciplinary leadership across EPFL's academic structure. Dr. Kaplan's research focuses on the intersection of computational methods and humanities, particularly in historical urban analysis, cultural heritage digitization, and the development of the Mirror World concept. His work bridges computational techniques with historical scholarship, creating new methodologies for analyzing historical documents, maps, and urban structures through advanced digital tools. His research has significant implications for how we understand and reconstruct historical urban environments and cultural heritage. His publication record reveals a strong emphasis on computational approaches to historical data, with recent work focusing on LLM applications for historical cadastre navigation, historical map analysis through deformation patterns, 4D city modeling, and language technology applications for historical document processing. This research trajectory demonstrates an evolving focus from basic digitization toward sophisticated analytical frameworks that extract deeper historical insights from digital representations. Kaplan has supervised numerous doctoral students whose work spans digital heritage applications, historical document analysis, computational cartography, and language technology. His research has been supported through multiple institutional frameworks at EPFL and has resulted in practical applications demonstrated through exhibitions at major institutions including the Venice Architecture Biennale, Grand Palais, Centre Pompidou in Paris, and the Museum of Modern Art in New York. He leads the Digital Humanities Laboratory (DHLAB) which serves as a nexus for computational approaches to humanities research. The lab focuses on developing methodologies for historical data analysis, creating digital tools for cultural heritage institutions, and exploring the theoretical implications of computational approaches to historical scholarship. The lab's work with the Time Machine Organisation represents one of the most ambitious efforts to create comprehensive digital reconstructions of historical urban environments.
Professor Peter Y. K. Cheung is a Professor of Digital Systems at Imperial College London, holding dual affiliations within the Department of Electrical and Electronic Engineering and the Dyson School of Design Engineering. His work focuses on reconfigurable systems, FPGA architectures, and high-level synthesis tools. He co-founded one of the UK's leading FPGA research groups with Professor Wayne Luk, addressing challenges in variability mitigation, reliability, and application-specific FPGA deployments. His research spans Field-Programmable Gate Arrays (FPGAs) Reconfigurable computing Neural network acceleration Cryptographic protocols Embedded systems He has pioneered techniques such as logic shrinkage for FPGA-based neural networks and developed frameworks like LUTNet for efficient inference. His contributions also include fault-tolerant FPGA designs and methodologies for distributed computation protocols. Key collaborations include work with the Department of Computing on FPGA-based AI acceleration and cybersecurity applications. His recent work explores edge computing, secure decentralized systems, and pandemic modeling using adaptive control strategies. Notable projects include the DSCS protocol for secure distributed computation, acceleration of gravitational wave detection algorithms, and energy-efficient CNN implementations. His research bridges hardware-software co-design with real-world applications in healthcare, finance, and aerospace.
Professor Jane Ohlmeyer is the Erasmus Smith’s Professor of Modern History at Trinity College Dublin and Chair of the Irish Research Council. She previously served as Trinity’s first Vice-President for Global Relations (2011-14) and Director of the Trinity Long Room Hub Arts and Humanities Research Institute (2015-20). Her research focuses on New British and Atlantic Histories, early modern Irish and British history, and the intersection of humanities with digital innovation. Key projects include the 1641 Depositions digitization (€22m in grants), SHAPE-ID (EU-funded interdisciplinary collaboration), and Human+ (placing humanity at the center of technology). She has published 13 books, including *The Cambridge History of Ireland* (2018) and edited volumes on Irish colonialism and empire. Her current project explores 'Ireland, Empire and the Early Modern World', slated for the 2021 Ford Lectures at Oxford. Teaching and supervision include courses on early modern war and nobility, with 11 PhD students, 8 postdocs, and 9 Marie Skłodowska-Curie fellows supervised. She has served as external examiner for Oxford, Cambridge, and other institutions. Public engagement includes the 'Behind the Headline' series and commentary on Brexit and democracy crises.
Jennifer K. Ryan is a Professor and Division Head for Numerical Analysis, Optimization & Systems Theory at the Department of Mathematics, KTH Royal Institute of Technology, Stockholm. She is affiliated with the Digital Futures Faculty, a cross-disciplinary research center jointly established by KTH, Stockholm University, and RISE Research Institutes of Sweden. Her research focuses on developing numerical schemes for extracting enhanced accuracy from simulations, with applications in imaging, data analysis, and fluid dynamics. Ryan’s work emphasizes improving computational efficiency through theoretical insights and practical algorithms. Her academic roles include teaching courses like Numerical Methods for Differential Equations II and supervising student projects in numerical analysis. She has contributed to the SIAC MAGIC toolbox, a software package for accuracy-enhancing filtering techniques. Ryan’s research group actively explores discontinuous Galerkin methods, SIAC filtering, and multi-resolution analysis, addressing challenges in computational physics and engineering. Her publications span high-order numerical methods, mesh adaptivity, and applications in plasma physics and wave equations. Projects include error estimation for boundary integral methods and developing filters for noisy data. Ryan collaborates internationally, contributing to both theoretical advancements and practical implementations in computational science.
Dr. Florian Foos is an Associate Professor in Political Behaviour at the LSE's Department of Government, affiliated with the LSE Data Science Institute and Electoral Psychology Observatory. He holds a D.Phil from Nuffield College, Oxford. His research focuses on partisan election campaigns using randomized field experiments to study electoral mobilization, opinion change, and political activism. Key interests include social influence in political networks, media effects, and campaign strategies. He has published in top journals like the American Political Science Review and American Journal of Political Science. Current projects include the Register to Vote '21 initiative testing voter registration methods. He teaches experimental methods and political behavior, and co-organizes the European Workshop in Empirical Political Science (EuroWEPS). Research highlights include analyzing tabloid media's impact on Euroscepticism, the role of partisan cues in mobilization, and subnational representation effects. His work combines experimental designs with quasi-experimental methods, emphasizing causal inference. Collaborations span institutions like King's College London and German parliamentarians. He advises on policy experiments and serves on academic advisory boards.
Gaetano Valenza is an Associate Professor of Bioengineering at the University of Pisa, Italy, where he leads the Neuro-Cardiovascular Intelligence Lab at the Enrico Piaggio Research Centre. He holds affiliations with the Neuroscience Statistics Research Laboratory at MIT and has served as a Research Fellow at Harvard Medical School and Massachusetts General Hospital. His academic work spans bioengineering, computational physiology, and affective computing. His research focuses on statistical and nonlinear biomedical signal and image processing , cardiovascular and neural modeling , and physiologically interpretable artificial intelligence . He develops wearable systems for physiological monitoring, with applications in autonomic nervous system assessment, brain-heart interactions, and mental health. His work has led to novel metrics such as the Sympathetic and Parasympathetic activity indices derived from ECG. The 15 most recent publications reflect a consistent trend in brain-heart interplay , complexity analysis of physiological signals , explainable AI in healthcare , and virtual reality applications in mental health . His work integrates advanced signal processing, nonlinear dynamics, and machine learning to decode emotional and cognitive states from physiological data. Dr. Valenza is a Senior Member of IEEE and serves on several technical committees. He is an active editorial leader, currently serving as Associate Editor for IEEE-EMBC , Plos One , Complexity , and Scientific Reports , and has guest-edited special issues in Philosophical Transactions of the Royal Society A and IEEE Journal of Biomedical and Health Informatics . He has led or participated in numerous international research projects, including FP7 and H2020 initiatives such as NEVERMIND and EXPERIENCE. He teaches courses in Biostatistics, Probability & Biostatistics, and Advanced Image Processing at the University of Pisa. As lab head and project coordinator, he leads a multidisciplinary team working on neuro-cardiovascular intelligence, wearable systems, and AI-driven mental health interventions.
Prof. Dr. Amelie Hagelauer holds a professorship in Micro- and Nanosystem Technology at the TUM School of Computation, Information and Technology, Technical University of Munich. Her work focuses on advanced electronics and systems integration across quantum computing hardware, resistive memory technologies, and high-frequency RF systems. She has contributed to innovations in superconducting qubit readout architectures, multi-level RRAM designs, and 3D-integrated CMOS-compatible quantum devices. Research interests span quantum hardware design, nanoelectronic devices, RF front-end systems, and emerging memory technologies. Her work emphasizes practical implementation challenges such as low-power operation, high-voltage handling in RF switches, and wafer-scale fabrication processes. Recent projects include D-band radar systems, energy-efficient 60 GHz transceivers, and antenna tuning solutions for 5G applications. Publications from 2023-2025 showcase advancements in resistive switching device characterization, mitigation of TLS losses in superconducting qubits, and reconfigurable AI accelerators using RRAM-based digital twins. Her work bridges theoretical device physics with practical integrated circuit design, addressing scalability and reliability in next-gen electronics. Awards and grants: None explicitly listed in provided texts. Active collaborations include EU-funded projects on quantum computing platforms and TUM's Electronic Photonic Integration initiatives. Leads research teams in microsystem technology with emphasis on cross-disciplinary approaches combining CMOS processes, MEMS, and quantum engineering.
Prof. Dr. Johannes Sauer is a Professor at the Technical University of Munich (TUM), holding dual affiliations with the TUM School of Management and the TUM School of Life Sciences . His academic focus is on production and resource economics, with expertise in agricultural sustainability, policy evaluation, and circular economy strategies in Asia-Pacific and Latin America. Education: Bachelor’s/Master’s in Agricultural Economics from Universities of Tübingen, Hamburg, and Imperial College London. Doctorate (summa cum laude) in Agricultural Economics from the University of Bonn (2004). Research Interests: Prof. Sauer’s work spans productivity and resilience in agricultural systems, adoption of sustainable technologies, water resource valuation, and bioeconomic trends. He emphasizes smart technologies and circular economy applications in developing regions. His research employs econometric methods to analyze policy impacts and environmental-economic trade-offs. Key Trends in Publications: Recent articles highlight climate-smart agriculture, ICT adoption in rural China, and agroforestry’s role in reducing ecological-economic trade-offs. He explores behavioral drivers of policy participation and innovative tools like the IoFarm decision support system. Awards: 2021 Honorary Professorship at ANU OECD Fellow Awards (2010, 2016) Best Paper Awards (2008–2015) Consulting & Roles: OECD and Asian Development Bank consultant, ESRC Peer Review College member, and liaison lecturer for the German National Academic Foundation. He advises on agri-environmental policies and international development. Labs/Teams: Active in collaborative projects such as PONDERFUL Framework and IPM Popilia , focusing on nexus research and pest management strategies.
Charles A. Bouman is the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering at Purdue University, with a courtesy appointment in Mathematics. He is a leading researcher in computational imaging, integrating statistical signal processing, physics, and computation for applications in healthcare, scientific, and industrial imaging. Education: B.S.E.E., University of Pennsylvania, 1981 M.S., University of California at Berkeley, 1982 Ph.D. in Electrical Engineering, Princeton University, 1989 His research focuses on computational imaging , including statistical image models, multiscale techniques, tomographic reconstruction, and fast algorithms. Key areas include Model-Based Iterative Reconstruction (MBIR), Plug-and-Play priors, document processing, and multiscale segmentation. His work has led to foundational contributions in total variation regularization and sparse-view reconstruction. The recent publications highlight a strong trend in integrating machine learning with physical models for image reconstruction, particularly through Plug-and-Play methods. His work spans optical tomography, halftoning, image scaling, and document compression, demonstrating consistent innovation in both theory and practical software implementation. Scientific Awards and Honors: Member, National Academy of Inventors Life Fellow, IEEE Fellow, IS&T; Honorary Member (2022); Service Award (2023) Fellow, SPIE and AIMBE IEEE Signal Processing Society Claude Shannon-Harry Nyquist Award (2021) Electronic Imaging Scientist of the Year (2014) SIAM Imaging Science Best Paper Prize (2020) Founder, IS&T Computational Imaging Conference (2003) Co-Founder, IEEE Transactions on Computational Imaging Vice President, IS&T; Former VP of Publications (2000–2004) Bouman has advised numerous graduate students and leads a vibrant research group developing open-source tools like MBIRJAX , SVMBIR , and OpenMBIR . His research has been supported by the National Science Foundation, General Electric, Intel, Xerox, Hewlett-Packard, and the State of Indiana 21st Century Fund. He maintains an active presence through tutorials, conference leadership, and educational resources including video lectures and a textbook on Foundations of Computational Imaging. Labs and Research Teams: His group develops cutting-edge software for tomographic reconstruction, clustering, segmentation, and dynamic sampling. Projects include Gaussian Mixture modeling (GMCluster), Plug-and-Play implementations, Sparse Matrix Transforms, and UAV sensing datasets. The research is highly interdisciplinary, bridging engineering, mathematics, and biomedical applications.
