Marina Alberti is a Professor of Urban and Environmental Planning at the University of Washington, affiliated with the Department of Urban Design and Planning. She directs the Interdisciplinary Ph.D. Program in Urban Design and Planning and leads the Urban Ecology Research Laboratory. Her expertise lies in studying the interactions between urban development patterns and ecosystem dynamics, focusing on resilience, socio-ecological innovation, and biocomplexity. Her research emphasizes interdisciplinary approaches to urban ecological problems, with a strong focus on Geographic Information Systems (GIS), land cover change, and environmental performance metrics. She has developed simulation models integrating urban development and ecological processes, supported by NSF, NOAA, and Washington state grants. NSF Biocomplexity Program Grant : Developing simulation models for urban-ecological integration. Washington State Department of Transportation Grant : Remote sensing for impervious surface estimation. NOAA/Washington Department of Ecology Grant : GIS analysis of land use and shoreline habitats. Her work spans urban planning, ecological assessment, and policy-making, with a methodological emphasis on GIS, remote sensing, and land use dynamics. She has contributed extensively to journals like International Regional Science Review , Urban Ecosystems , and BioScience , addressing topics such as urban gradients, biodiversity, and environmental sustainability. Alberti's teaching includes Urban Ecology, Environmental Planning, GIS, and advanced research design, reflecting her commitment to training the next generation of urban ecologists.
Madelon Hulsebos is a Researcher at CWI in Amsterdam, where she leads the Table Representation Learning (TRL) Lab and contributes to the Database Architectures group. She is also a faculty member of the European Laboratory for Learning and Intelligent Systems (ELLIS) Amsterdam unit. Her career bridges academia and industry, including a postdoctoral fellowship at UC Berkeley and prior industry experience in automating data analysis pipelines with ML. Education : PhD in Computer Science (University of Amsterdam, 2023), with research at Sigma Computing and MIT; Postdoctoral Fellow (UC Berkeley, 2024). Her research focuses on establishing tabular data as a key AI modality through Table Representation Learning , generative models for relational data, and robust systems for data analysis. Key interests include: Relational Table Embeddings LLMs for QA/text2SQL and data wrangling Retrieval over Data Lakes and Databases Agentic Systems for Data Science Democratizing insights from structured data Recent work highlights trends in benchmarking table retrieval (TARGET), semantic column detection (AdaTyper, Sherlock), and large-scale tabular data curation (GitTables, SchemaPile). These projects address challenges in metadata utilization, data lake search, and end-to-end systems for structured data. She has secured significant funding, including the NWO AiNed Fellowship Grant ($1M) for her 5-year DataLibra project. Madelon organizes workshops at NeurIPS , SIGMOD , and ACL , and reviews for top venues like VLDB and NeurIPS. Scientific Awards : NWO AiNed Fellowship Grant ($1M) She actively mentors students and collaborates on European AI initiatives, including monthly TRL seminars and workshops. Her lab's tools (GitTables, TARGET) are widely adopted for training foundation models on tabular data.
Steven L. Manly is a Professor of Physics at the University of Rochester within the College of Arts, Sciences and Engineering. He has been affiliated with the University of Rochester since 1998, following a decade at Yale University as both a postdoc and faculty member. Professor Manly received his BA in chemistry, mathematics, and physics from Pfeiffer College in 1982 and his PhD in experimental high-energy physics from Columbia University in 1989 under Charles Baltay. His research spans high energy, nuclear, and gravitational physics, with a current focus on neutrino physics across multiple major experiments. His primary research interests include neutrino interactions and oscillations, with significant contributions to the T2K experiment (for which he shared the 2016 Breakthrough Prize in Fundamental Physics), the MINERvA experiment at Fermilab, and the Deep Underground Neutrino Experiment (DUNE). His work aims to understand neutrino properties, measure oscillation parameters, and investigate potential connections to matter-antimatter asymmetry in the universe. The recent publications reflect a strong focus on neutrino cross-section measurements, detector calibration techniques, and data analysis methods for the T2K and DUNE experiments. His research group contributes significantly to advancing our understanding of neutrino properties and interactions through precision measurements. NY State Professor of the Year (2003) Mercer Brugler Distinguished Teaching Professor (2002-2005) American Association of Physics Teachers (AAPT) Award for Excellence in Undergraduate Teaching (2007) Breakthrough Prize in Fundamental Physics (2016, shared as member of T2K) Professor Manly has authored or co-authored numerous publications in leading physics journals, with recent work focusing on neutrino interaction measurements, detector development, and data analysis techniques. His research has involved collaborations with major international facilities including Fermilab, J-PARC in Japan, and Brookhaven National Laboratory. While specific grant information isn't detailed in the provided text, his participation in large-scale international collaborations suggests significant research funding support.
Prof. Karen Alim is a Professor of Biological Physics and Morphogenesis at the Department of Physics, Technische Universität München (TUM), affiliated with the TUM School of Natural Sciences. She holds a PhD from the Ludwig-Maximilians-Universität München (2010) and conducted postdoctoral research at Harvard University (2010–2015) before leading a Max Planck Research Group in Göttingen. Her research focuses on non-neuronal information processing in living systems, particularly using Physarum polycephalum to study physical principles of network adaptation, fluid dynamics, and morphogenesis. Education: PhD in Physics, Ludwig-Maximilians-Universität München (2010) Studies at Universität Karlsruhe, LMU München, and University of Manchester Research Interests: Prof. Alim explores how biological systems process information without neurons, emphasizing adaptive flow networks, mechanical signaling in plants, and collective behavior in active matter. Her work combines theoretical modeling with experimental systems like slime molds and plant tissues. Awards: ERC Starting Grant (2020) Elisabeth-Schiemann-Kolleg Fellowship (2013–2018) DAAD Stipendium (2011–2014) John Birks Award (2004) Advising & Grants: While specific grant details beyond the ERC award are not listed, her research has been supported by major funding bodies. No student advisees are explicitly listed in the provided materials. Labs/Teams: Leads the Biological Physics and Morphogenesis group at TUM, focusing on interdisciplinary studies of living systems' physical principles.
Satoshi Tomioka is a Professor in the Department of Linguistics and Cognitive Science at the University of Delaware, affiliated with the College of Arts & Sciences. He holds a B.A. from International Christian University (Tokyo) and a Ph.D. from the University of Massachusetts Amherst. His expertise spans semantics, pragmatics, syntax, and prosody, with a focus on Japanese and comparative East Asian linguistics. Education: B.A. in Liberal Arts (1987), International Christian University; Ph.D. in Linguistics (1997), University of Massachusetts Amherst. Research interests include ellipsis, anaphora, wh-interrogatives, distributivity, and contrastiveness. He investigates how prosody interacts with syntax and semantics in Japanese, exploring topics like focus marking, intervention effects, and scalar implicatures. Recent work examines associative plurals and pragmatic disambiguation in embedded questions. Publications analyze theoretical and empirical questions in linguistic interfaces, such as Bare quotatives as embedded speech acts (2024) and Focus without pitch boost (2022). His work bridges formal semantics and experimental approaches, addressing challenges in cross-linguistic typology and cognitive aspects of language processing.
Professor Jorge Eduardo Pinto Santos holds the position of Professor of Theoretical Physics at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge. His career includes roles as Reader (2018–2022), Lecturer (2013–2018), and a Junior Visiting Professorship at the Institute for Advanced Study in Princeton (2019–2020). He completed his Ph.D. at DAMTP between 2006 and 2010, followed by postdoctoral fellowships at Stanford University (2013–2014) and the University of California, Santa Barbara (2010–2013). His research focuses on general relativity, quantum gravity, and gravitational aspects of string theory. Key areas include numerical relativity and applications of general relativity to condensed matter physics via holography. Notable contributions involve studies on black hole binaries, extremal black holes, and the AdS/CFT correspondence. His recent work explores topics such as spinning black binaries in de Sitter space, gravitational instabilities in anti-de Sitter space, and entanglement islands in higher dimensions. He is affiliated with the High Energy Physics and Relativity and Gravitation research groups at DAMTP. His advising record includes seven doctoral students, with ongoing supervision of William Boyce (2024–present) and John Crump (2021–present). Publications highlight interdisciplinary advances, including investigations into black hole thermodynamics, cosmic censorship violations, and holographic models of condensed matter systems. His research bridges fundamental physics and numerical methods, addressing both theoretical and applied questions in gravitational physics.
Thomas Pape is a Professor at The Natural History Museum of the University of Copenhagen, specializing in the systematics, taxonomy, phylogeny, biogeography and evolution of Diptera (two-winged insects), with special emphasis on Calyptratae families including Sarcophagidae, Oestridae, and Calliphoridae. He has been a professor since 2025 after serving as an Associate Professor from 2004-2024. PhD from Copenhagen University (1990) Docent at Stockholm University (1998) Associate Professor at Danish Bilharziasis Laboratory (1990-1992) Research Entomologist at Naturhistoriska Riksmuseet, Stockholm (1994-2004) His research spans taxonomy, systematics, phylogeny, biogeography and evolution of Diptera with special emphasis on calyptrate families. He collaborates on Systema Dipterorum, conducts comparative larval morphology studies, and investigates the phylogeny of Calyptrata. His work includes Diptera nomenclature and large-scale biodiversity inventories. Analysis of his recent publications reveals a strong trend toward molecular phylogenetics (using RAD-seq and anchored phylogenomics), forensic entomology applications, and biodiversity informatics. His research spans entomology, systematics, evolutionary biology, and increasingly intersects with bioinformatics and conservation science, with significant contributions to understanding flesh flies, bot flies, and blow flies. President of the International Council of Zoological Nomenclature (since 2016) Chair of the Council for the International Congresses of Dipterology (2010-2018) Member of editorial boards for Entomotropica, Studia dipterologica, Stuttgarter Beiträge zur Naturkunde, and Tijdschrift voor Entomologie Dr. Pape teaches courses in Entomology (structure, phylogeny and biology of terrestrial arthropods), Field Biology II - Zoology, and Ecology and Evolution of East Africa (a 2-week field course in Tanzania with Nikolaj Scharff). He has served on scientific panels for SYNTHESYS and as chair of the scientific advisory committee for Museum Koenig in Bonn (2006-2009). His research group employs both morphological and molecular approaches to address evolutionary questions in Diptera, with particular expertise in calyptrate families. The laboratory maintains active international collaborations across multiple continents, as evidenced by his extensive publication record and research network.
Elaine J. Francis is a Professor of English and Linguistics at Purdue University, where she also serves as the Associate Head of the Department of English. She holds affiliate appointments in the Department of Linguistics and the Department of Speech, Language, and Hearing Sciences. At Purdue, she directs the Experimental Linguistics Lab and teaches linguistics courses at both graduate and undergraduate levels. Francis completed her B.A. in Linguistics at the College of William and Mary in 1993, followed by her M.A. (1995) and Ph.D. (1999) in Linguistics at the University of Chicago under the direction of Salikoko Mufwene. Her dissertation examined variation among members of the same lexical category in English using Sadock's Autolexical Grammar framework. Prior to joining Purdue in 2003, she served as an Assistant Professor at the University of Hong Kong from 1999 to 2002, where she collaborated with Stephen Matthews on research concerning syntactic categories and relative clauses in Cantonese. Her research focuses on syntax and its interfaces with semantics, discourse information structure, and language processing in production and comprehension. Francis employs experimental methods to investigate syntactic, semantic, discourse-pragmatic, and cognitive factors underlying the grammar and usage of complex sentence structures. Her specific interests include word order alternations, filler-gap dependencies, resumptive pronouns, relative clauses, grammatical categories, and syntactic alternations. She has published extensively in top linguistics journals including Language and Cognition, Glossa Psycholinguistics, Linguistics, Lingua, Cognitive Linguistics, and the Journal of Psycholinguistic Research. Analyzing her recent publications reveals a consistent focus on experimental approaches to syntactic phenomena. Her work demonstrates a strong interest in gradient acceptability, syntactic priming, cross-linguistic comparison (particularly involving English and Cantonese), and the relationship between grammatical theory and language processing. Her 2022 book Gradient Acceptability and Linguistic Theory represents a major contribution that synthesizes experimental findings with theoretical linguistics frameworks. Francis plays an active role in the broader linguistics community. She regularly teaches short courses at the Linguistic Society of America Linguistic Institutes, serves on the LSA Ethics Committee, and is on the editorial board of Glossa Psycholinguistics. She has also edited several books and special journal issues, including Mismatch: Form-Function Incongruity and the Architecture of Grammar (2003) and Polymorphous Linguistics: Jim McCawley's Legacy (2005). As an educator and administrator, Francis has supervised numerous graduate students and currently serves as Associate Head of the Department of English at Purdue. Her Experimental Linguistics Lab provides research opportunities for students interested in the intersection of theoretical syntax and experimental methodology. While she recently announced she will not be accepting new graduate students for the 2025-2026 cycle, her established mentorship record demonstrates her commitment to training the next generation of linguists. The Experimental Linguistics Lab, which she directs, serves as a hub for research combining theoretical linguistics with experimental methods. Her collaborative work extends across disciplines, including collaborations with researchers in speech-language pathology, cognitive science, and computational linguistics, reflecting the interdisciplinary nature of modern linguistic research.
Ron Fedkiw is the Canon Professor of Computer Science at Stanford University's School of Engineering. He holds a PhD in Applied Mathematics from UCLA. His research focuses on computational algorithms for applications in computational fluid dynamics, computer graphics, biomechanics, and machine learning. Fedkiw has pioneered techniques for simulating natural phenomena in film and video games, earning two Academy Awards for his contributions to visual effects. He leads the PhysBAM lab and collaborates with industry through consulting roles at Epic Games and former work with Industrial Light & Magic. Education: PhD in Applied Mathematics, UCLA (1996). Notable awards include the National Academy of Science Award, Packard Fellowship, and multiple teaching honors. His lab has graduated 40 PhD students, many of whom have made significant impacts in academia and industry. Research interests span fluid dynamics, cloth simulation, facial animation, and integrating machine learning with physical models. Key contributions include algorithms for two-way fluid-solid coupling, muscle-based facial modeling, and neural network approaches for cloth and deformable bodies. Current projects explore physics-informed machine learning and real-time interactive simulations. Scientific Awards include two Oscars, PECASE, and Okawa Foundation grants. His work bridges computational physics and visual effects, with over 140 research papers and a textbook on level set methods. Advising and grants: Supervised 40 PhD students, securing funding through NSF, ONR, and industrial partnerships. Lab collaborations include SAIL (Stanford AI Lab) and Epic Games. Future work focuses on AI-driven physical simulations and biomedical applications.
Dr. Lourdes Pena-Castillo is a Professor jointly appointed in the Departments of Computer Science and Biology at Memorial University of Newfoundland's Faculty of Science. Her research focuses on applying machine learning and bioinformatics to study bacterial gene regulation, with emphasis on transcriptomics, gene expression pathways, and microbiology. She leads the Bioinformatics Lab at MUN, developing computational tools like Promotech for promoter prediction and sRNARFTarget for sRNA target identification. Education: BSc in Information Systems Engineering, ITESM-Mexico MSc in Computer Science, University of Alberta PhD in Computer Science (Doktoringenieurin), Otto-von-Guericke Universität Magdeburg Postdoc in Bioinformatics, University of Toronto Research Interests: Bioinformatics, Genomics, Machine Learning, Artificial Intelligence, Transcriptomics, Gene Regulation, Microbiology Her work integrates computational methods with biological data to address challenges in molecular biology, including analyzing bacterial sRNA functions, promoter recognition, and disease diagnostics using machine learning. She has advised numerous graduate students, including PhD candidates Purvikalyan Pallegar and Bonita McCuaig, and MSc students like Ruben Chevez-Guardado and Kratika Naskulwar. Her lab focuses on translational research with applications in both basic science and clinical contexts. Publications span computational methods for bacterial gene regulation, bioinformatics tool development, and interdisciplinary projects in VR and healthcare informatics. Her research has contributed to understanding symbiotic relationships in marine organisms, inflammatory bowel disease diagnostics, and clavulanic acid production in Streptomyces. Grants & Collaborations: Works with interdisciplinary teams across computer science and biology, supported by grants enabling projects in bacterial genomics and computational tool development. Labs & Teams: Leads the Bioinformatics Lab at MUN, fostering collaborations with researchers in microbiology, computer science, and healthcare.
Edoardo Charbon is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Engineering, where he leads the Advanced Quantum Architecture Lab (AQUA). He also serves on the School Council STI and is Co-Director of STI-SSIQ Administration. Previously, he was a full professor and chair at Delft University of Technology from 2008 to 2016. Charbon received his Elektrotechnik Diploma from ETH Zurich, M.S. from UC San Diego, and Ph.D. from UC Berkeley, all in electrical engineering. His career spans industry experience at Cadence Design Systems and Canesta Inc. before joining EPFL in 2002. His research focuses on ultra high-speed and 3D optical sensors, with applications in LiDAR, FLIM (Fluorescence Lifetime Imaging Microscopy), PET (Positron Emission Tomography), FCS (Fluorescence Correlation Spectroscopy), and NIROT (Near-Infrared Optical Tomography). He has pioneered deep-submicron CMOS SPAD technology, which is now mass-produced and used in smartphones, telemeters, and medical diagnostics. His recent work bridges cryo-CMOS circuits for quantum computing with advanced optical sensing techniques. Analysis of his recent publications reveals a strong trend toward integrating quantum technologies with practical imaging applications. His work spans from fundamental device development (SPAD sensors, cryo-CMOS circuits) to applied systems (LiDAR engines, medical imaging devices), with increasing integration of machine learning techniques for real-time processing. 2023 IISS Pioneering Achievement Award Fellow of the IEEE Distinguished visiting scholar, W. M. Keck Institute for Space at Caltech Fellow, Kavli Institute of Nanoscience Delft Distinguished lecturer, IEEE Photonics Society Professor Charbon has authored or co-authored over 500 papers and two books, and holds 27 patents. His research has been supported by collaborations with organizations including Bosch, X-Fab, Texas Instruments, Maxim, Sony, Agilent, and the Carlyle Group. He has driven significant innovation in CMOS SPAD technology, which is now commercially deployed in various applications. He leads the Advanced Quantum Architecture Lab (AQUA) at EPFL, which focuses on the development of advanced sensor systems combining quantum technologies with conventional electronics. The lab has been instrumental in creating SPAD-based imaging systems that push the boundaries of time-resolved optical detection.
Judith Driscoll is Professor of Materials Science at the University of Cambridge in the Department of Materials Science & Metallurgy. She holds the prestigious Royal Academy of Engineering Chair in Emerging Technologies and serves as a Visiting Staff Member at Los Alamos National Laboratory. As the founding Editor-in-Chief of APL Materials, she has significantly contributed to the materials science community. Dr. Driscoll's research focuses on Energy Efficient Oxide Materials for Information and Communications Technologies and energy devices. Her work spans the development of non-volatile memory, resistive switching devices, and ferroelectric materials for neuromorphic computing applications. She investigates oxide thin films for applications ranging from data storage to energy generation and conversion, with particular emphasis on creating more energy-efficient device technologies to handle the exponential growth of data-centric applications. Her recent publications demonstrate strong trends in developing novel oxide-based memory devices with improved energy efficiency, particularly for AI applications. The work shows significant progress in hafnium-zirconium oxide ferroelectrics, resistive switching mechanisms, and vertically aligned nanocomposite structures for enhanced device performance. These innovations address critical challenges in reducing the unsustainable energy demands of modern computing, particularly for artificial intelligence systems. Fellow of the Royal Academy of Engineering Fellow of the Materials Research Society Fellow of the American Physical Society Fellow of IOM3, IOP, and Women Engineers Society Fellow of the American Academy of Arts and Sciences Recipient of ERC Advanced Grant Editor-in-Chief of APL Materials Dr. Driscoll leads a vibrant research group that has secured significant funding including her Royal Academy of Engineering Research Chair, an ERC Advanced Grant, and an ECCS-EPSRC grant in collaboration with researchers from the USA. She has founded the Cambridge Centre for Neuromorphic Computing (Neucam) in 2023. Her group operates world-leading growth equipment including pulsed laser deposition with RHEED control, high temperature oxide sputtering, and spatial ALD systems. She collaborates extensively across the University of Cambridge and with international partners to solve complex materials challenges, with her group's role often being to identify optimal materials for functional goals, predict fabrication methods, and then create and characterize these materials.
Dr. Rene Ferdinands is a Lecturer at the University of Sydney's School of Health Sciences, specializing in sports biomechanics, particularly in cricket and golf. He leads research programs focused on optimizing techniques and minimizing injury risks, such as lumbar load analysis in fast bowlers and spin bowling biomechanics. His work includes developing 3D models for analyzing bowling actions and spin delivery mechanics. Education: MSc and PhD from the University of Waikato. Research Themes: Cricket biomechanics, golf swing dynamics, equine biomechanics, and injury prevention. Professional Roles: Editor of the Cricket Coaching Information service for the International Society of Biomechanics in Sports, Honours Committee Member. His research interests span biomechanical analyses of sports techniques, including the development of smart cricket balls for performance assessment and injury mitigation. Key contributions include refining bowling action legality criteria and advancing understanding of lumbar kinetics in elite athletes. He actively supervises Honours and PhD students in biomechanics research across cricket, golf, and other sports. Notable grants include studies on hydration status in cricket performance (2008), lumbar injury prevention (2013), and biomechanical modeling of fast bowling (2009). His work bridges applied research with practical applications in sports technology and athlete development. Dr. Ferdinands collaborates with the Biomechanics Research Team at the University of Sydney, focusing on innovative tools like smart balls and advanced motion analysis techniques to improve sports performance and safety.
Karsten Borgwardt is a Professor and Director of the Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry. He holds a PhD in Computer Science (2007) from LMU Munich and has held academic positions at ETH Zürich, Universität Tübingen, and the Max Planck Institutes in Tübingen. His research focuses on machine learning applications in biology and medicine, including biomarker discovery, personalized medicine, and systems biology. Education: PhD in Computer Science (2007), LMU Munich M.Sc. in Biology (2003), University of Oxford Diplom (M.Sc. equivalent) in Computer Science (2004), LMU Munich Research Interests: Development of machine learning algorithms for large biomedical datasets Pattern recognition in genomic and clinical data Applications in sepsis biomarkers, antimicrobial resistance prediction, and personalized medicine Grants & Projects: Scientific Coordinator of Marie Curie Networks (2013-2022) Swiss National Science Foundation Starting Grant (2014) Personalized Swiss Sepsis Study (CHF 5.3M, 2018) Awards: Krupp Award (2013), Golden Owl Teaching Award (2017), multiple 'Top 40 under 40' recognitions (2014-2016). Labs: Leads the Machine Learning and Systems Biology Department at MPI, collaborating with 22+ labs in sepsis research and international networks.
Margaret E. Roberts is a Professor in the Department of Political Science at the University of California, San Diego. She co-directs the China Data Lab at the 21st Century China Center and serves as an affiliate at the UC Institute on Global Conflict and Cooperation. Her academic appointments reflect her interdisciplinary approach combining political science, statistics, and computational methods. University of California, San Diego, Department of Political Science (Current) Co-director, China Data Lab at the 21st Century China Center Affiliate, UC Institute on Global Conflict and Cooperation Roberts earned her PhD in Government from Harvard University (2014), MS in Statistics from Stanford University (2009), and BA in International Relations and Economics from Stanford University (2009). Her educational background bridges political science, statistics, and computational methods, forming the foundation for her interdisciplinary research approach. Professor Roberts' research focuses on the intersection of political methodology and the politics of information, with specific expertise in automated content analysis and the politics of censorship and propaganda in China. Her work employs innovative methods including social media analysis, online experiments, and large-scale text analysis to understand how censorship and propaganda influence information access and political beliefs. She has made significant contributions to text-as-data methodologies, developing tools like the Structural Topic Model (stm) R package that have become widely used in social science research. Roberts' research portfolio demonstrates consistent focus on authoritarian information control, particularly in China, while expanding into broader applications of text analysis in political science. Her publications span top journals in political science, computer science, and interdisciplinary fields, reflecting the cross-disciplinary nature of her work. Goldsmith Book Award Best Book Award in the Human Rights Section Best Book Award in Information Technology and Politics Section Best Book Award of the last decade in the Political Communication Section of the American Political Science Association Chancellor's Associates Endowed Chair at UCSD Foreign Affairs Best Books of 2018 Professor Roberts has secured significant research funding supporting her work on Chinese censorship, propaganda, and text analysis methodologies. Her research has practical applications for understanding digital authoritarianism, content moderation, and the development of computational tools for social science research. She has mentored numerous students and collaborators, contributing to the next generation of scholars working at the intersection of political science and computational methods. Roberts also leads the China Data Lab, which serves as a hub for research on Chinese politics and society using digital methods.
