Marzio Sorlini is an Associate Professor of Industrial Process Sustainability at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), affiliated with the Department of Innovative Technologies (DTI) and the Sustainable Production Systems Laboratory (LSPS). He has been active at SUPSI since 2009, combining teaching in Management Engineering with research in sustainability, manufacturing innovation, and circular economy. His work focuses on integrating sustainability into industrial processes through methodologies like Life Cycle Assessment (LCA) and Lean manufacturing. Education: Master of Science in Management Engineering (Politecnico di Milano, 2001) and PhD in Industrial Engineering (Università degli Studi di Parma, 2017). Professional roles include co-founding Technology Transfer System s.r.l. (2006) and leading SUPSI’s Sustainable Production Systems research initiatives. Research interests emphasize sustainable manufacturing systems, industrial symbiosis, and digital tools for environmental performance assessment. Key projects include SAM (sustainability in mould&die industry), CO-VERSATILE (pandemic response manufacturing), and Circular TwAIn (AI-driven circularity). His publications address topics like LCA methodologies, industrial agglomeration sustainability, and anthropocentric workplace design. Notable contributions include developing the Sustainability Platform (SP) for LCA applications and advising on mass customization and lean-green production paradigms. Current roles involve strategic leadership in EU projects targeting sustainable manufacturing resilience and circular economy innovation.
Fabio Candotti is a full professor at the University of Lausanne (UNIL) in the Faculty of Biology and Medicine and serves as head physician in the Department of Immunology and Allergology at the Lausanne University Hospital (CHUV). A world-renowned expert in immunodeficiency, pediatrics, and gene therapy, he has held academic and research leadership roles in both the United States and Europe, including a long tenure at the National Institutes of Health (NIH). Born: 1962, Italian nationality Education: MD, University of Brescia (1987); specialization in pediatrics (University of Pavia) and immunology/allergology (Brescia) Postdoctoral training: National Institutes of Health (NIH), USA (1992) Academic appointments: Associate Professor at UNIL (2014), promoted to Full Professor (2022) Prior roles: Senior Investigator and Head of Immune Diseases Section, NHGRI/NIH; Head, NIH Gene Therapy Interest Group His research and clinical work focus on primary immunodeficiencies, particularly adenosine deaminase deficiency (ADA) and Wiskott-Aldrich syndrome (WAS). He investigates the molecular and cellular mechanisms underlying immune dysregulation, autoimmunity, and metabolic defects in these disorders. His work has revealed how high adenosine levels and DNA repair deficiencies contribute to inflammatory, fibrogenic, and oncogenic processes in ADA-deficient patients. Using murine models, he has advanced understanding of autoimmune complications in WAS. A major focus of his work is the development and application of gene therapy strategies, including viral vector-based tools and transgenesis, for curative treatments. The trends in his recent publications highlight sustained leadership in translational immunology and gene therapy, with recurring themes in immune reconstitution, metabolic consequences of immunodeficiency, long-term patient outcomes, and innovative therapeutic design. His work bridges basic science and clinical application, emphasizing durable correction of immune function and quality of life. He has received multiple scientific awards, including: National Institutes of Health Award of Merit (1999, 2006) National Human Genome Research Institute Service Award (2002) US Government Service Award (2003, 2008) Best Docs in Northern Virginia (2009) Candotti is actively involved in medical education, having taught at Metropolitan Washington D.C. Medical Genetics since 2004 and contributed to numerous fellowship and residency training programs in immunology and hematology. He is an adjunct member of the Graduate Faculty at the University of Medicine and Dentistry of New Jersey. He also contributes to the scientific community through editorial roles in journals such as Clinical and Translational Science , The Open Gene Therapy Journal , Frontiers in Primary Immunodeficiencies , and Journal of Clinical Immunology . He is a member of the Italian Society of Pediatric Allergology and Immunology, the American Society for Gene Therapy, and the European Society for Immunodeficiencies. His work is supported by long-term patient cohorts and clinical studies aimed at developing new drugs and gene-based therapies. He leads a research team focused on advancing gene therapy for monogenic immune disorders, with a vision of translating laboratory discoveries into safe and effective clinical applications.
Anthony Holtmaat is a Professor and Group Leader in the Faculty of Medicine at the University of Geneva. He leads a research laboratory focused on synaptic plasticity and cortical network dynamics in the adult brain, utilizing in vivo imaging and molecular techniques in transgenic mouse models. Research Interests: His work centers on understanding how synaptic connectivity changes in response to experience and learning. Using longitudinal in vivo two-photon microscopy, his lab investigates dendritic spine and axonal bouton dynamics, synapse formation and elimination, and the molecular stability of synaptic proteins. His research integrates structural and functional analyses to explore how neural circuits adapt during sensory processing, memory formation, and aging. Recent Research Trends: His latest publications reveal a strong focus on thalamocortical feedback mechanisms, sensory-evoked cortical plasticity, and the regulation of neuronal excitability through metabotropic glutamate receptors. A landmark study generated a comprehensive atlas of synapse protein lifetimes across the mouse brain, linking synaptic stability to brain development, aging, and neurodevelopmental disorders such as autism and schizophrenia. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: He mentors a diverse team of postdoctoral researchers, PhD candidates, and technicians, indicating active supervision and training. His research is supported by competitive funding, as evidenced by publications in high-impact journals and collaborations with international teams. The mention of NIH funding (R01 MH060919) in a co-authored paper suggests involvement in externally funded research projects. Laboratory and Team: His lab at the CMU (Centre Médical Universitaire) in Geneva includes多名 researchers such as Tanika Bawa, I-Wen Chen, and Ronan Chereau, working on various aspects of synaptic and circuit plasticity. The team employs cutting-edge techniques including two-photon calcium imaging, optogenetics, viral tracing, and advanced data analysis to dissect neural circuit function.
Denis Jabaudon is a Professor and Group Leader in the Department of Basic Neurosciences at the Faculty of Medicine, University of Geneva, where he leads a research laboratory at Campus Biotech. His work focuses on the genetic and molecular mechanisms governing cortical neuron circuit assembly during development. His research interests include developmental neurobiology, neuronal circuit formation, gene expression dynamics in neural progenitors and postmitotic neurons, thalamocortical connectivity, and neural plasticity. He investigates how intrinsic genetic programs and sensory experience interact to determine neuronal identity and circuit function. His lab employs techniques such as in vivo electroporation, transgenic mouse models, single-cell RNA sequencing, and electrophysiology. The most recent publications highlight a strong trend in understanding the temporal patterning of neural progenitors and how this influences the sequential generation of diverse neuronal subtypes. His work demonstrates that molecular birthmarks in progenitors are transmitted to daughter neurons, which then undergo activity-dependent refinement. This research bridges developmental biology, epigenetics, and systems neuroscience, with implications for brain repair and reprogramming. Scientific Awards and Funding: Swiss National Science Foundation Grant (PP00P3-123447) Velux Foundation Funding 3R Foundation Funding Brain and Behavior Research Foundation Funding Joint Leenaards Foundation for Scientific Research Award Denis Jabaudon actively mentors a team of early-career researchers. His lab includes scientific collaborators such as Riccardo Bocchi and Sabine Fièvre, postdoctoral researchers like Awais Javed and Sergi Roig Puiggros, and graduate students including Eline Balavoine, Natalia Baumann, and Moein Sarhadi. His research has been supported by multiple competitive grants, indicating sustained funding and scientific impact. The lab’s work contributes significantly to understanding the fundamental principles of brain development and plasticity. Laboratory and Team: The Jabaudon lab is based at Campus Biotech in Geneva and comprises a multidisciplinary team of scientific collaborators, postdoctoral fellows, graduate students, and technical staff, working collaboratively on projects related to neocortical development and circuit assembly.
Christina Schuler is a Lecturer and Researcher at the Institute of Nursing , School of Health Sciences , Zurich University of Applied Sciences (ZHAW). She holds a PhD in Biomedical Sciences (University of Geneva, 2022-2025) and a Master in Science/Nursing Science (ZHAW, 2019-2021). Her research focuses on Maternal, Neonatal, and Child Health (MNCH) , Global Health , Rare Diseases , Qualitative Research , and Implementation Science . PhD: Biomedical Sciences, University of Geneva (2022-2025) Master in Science/Nursing Science, Zurich University of Applied Sciences (2019-2021) Master of Advanced Studies (MAS) International Health (2019-2021) Advanced Federal Diploma in Nursing (2014-2017) Her research interests span maternal and neonatal healthcare in low-resource settings, implementation science for family-centered care, and qualitative studies on patient experiences. She leads projects like " Facing future challenges in pediatric primary health care " and contributes to " Caring for Patients with Neuromuscular Diseases in Switzerland " as a team member. Her scientific awards include the Winning Essay - Small and Sick Newborn Care Essay Contest (2024) Helmut-Wolf Award for junior scientists in global child health (2024) . She has published extensively on neonatal care in Ghana , health system drivers , and implementation challenges using participatory action research. Her work emphasizes interprofessional education , care coordination , and socio-cultural influences on healthcare adherence.
Masashi Sugiyama is a Professor at the Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, where he has been serving since 2014. He concurrently serves as the Director of the RIKEN Center for Advanced Intelligence Project (AIP) since 2016, leading research groups focused on fundamental AI technologies, AI applications, and social issues of AI. His academic journey began at Tokyo Institute of Technology, where he earned his Bachelor, Master, and Doctor of Engineering degrees in Computer Science in 1997, 1999, and 2001 respectively, before becoming an Assistant Professor and later Associate Professor at the same institution. Sugiyama's research primarily focuses on statistical machine learning, with particular expertise in weakly supervised learning, learning from noisy supervision, and learning under distribution shift. His work has established important theoretical frameworks for density ratio estimation, covariate shift adaptation, and non-stationary environments. He has developed numerous practical algorithms including KLIEP (Kullback-Leibler importance estimation procedure), uLSIF (unconstrained least-squares importance fitting), and LSDD (least-squares density difference), which have become standard tools in the machine learning community. Analysis of his recent publications reveals a strong emphasis on reliable and robust machine learning techniques that can handle imperfect supervision and data distribution changes. His work spans theoretical foundations, algorithm development, and practical applications across various domains including bioinformatics, anomaly detection, and dimensionality reduction. The recurring themes in his research include direct density ratio estimation methods, importance weighting techniques, and mutual information-based approaches for various machine learning tasks. Award for Science and Technology from the Japanese Minister of Education, Culture, Sports, Science and Technology (2022) Japan Academy Medal (2017) Japan Society for the Promotion of Science Award (2017) Faculty Award from IBM (2007) Nagao Special Researcher Award from the Information Processing Society of Japan (2011) Young Scientists' Prize for the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology Japan (2014) Sugiyama has served as program co-chair for major machine learning conferences including NeurIPS 2015, AISTATS 2019, and ACML 2010 and 2020. He has received prestigious fellowships including the Alexander von Humboldt Foundation Research Fellowship (2003-2004) and the European Commission Program Erasmus Mundus Scholarship (2006). He is the (co-)author of influential machine learning monographs including Machine Learning in Non-Stationary Environments (MIT Press, 2012), Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), Statistical Reinforcement Learning (Chapman & Hall, 2015), and Machine Learning from Weak Supervision (MIT Press, 2022). At RIKEN AIP, Sugiyama leads research initiatives focused on developing robust AI systems that can operate reliably in real-world conditions with imperfect data. His laboratory maintains an active software development effort, providing open-source implementations of many of his research contributions, which has significantly influenced both academic research and practical applications of machine learning.
Mutian He is a PhD candidate and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, affiliated with the Idiap Research Institute and the School of Engineering. He is pursuing his doctoral studies in Electrical Engineering under the supervision of Phil Garner. He holds a B.E. from Beihang University (BUAA) and an MPhil from the Hong Kong University of Science and Technology (HKUST). B.E., Beihang University (BUAA), 2019 MPhil, Hong Kong University of Science and Technology, 2022 PhD Candidate, École Polytechnique Fédérale de Lausanne (EPFL), ongoing His research focuses on spoken language understanding, speech synthesis, and the intersection of speech and language processing with machine learning. He explores efficient model architectures, pretraining strategies, multilingual and low-resource modeling, and the use of large language models in speech tasks. His work spans both theoretical and applied aspects, including distillation to linear-complexity models, robust TTS, and commonsense reasoning via conceptualization. His recent publications at top venues such as ICLR, EMNLP, Interspeech, and KDD demonstrate a strong trend towards efficient and scalable models for speech and language, with increasing emphasis on multilingualism, knowledge transfer, and real-world deployment in low-resource settings. He has also contributed to open-source implementations and community tools like Speech Rankings. Joint Fine-tuning and Conversion of Pretrained Speech and Language Models towards Linear Complexity (ICLR 2025) Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization (AIJ 2024) The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation (Findings of EMNLP 2023) Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding (Interspeech 2023) Multilingual Byte2Speech Models for Scalable Low-resource Speech Synthesis (2022) Mutian He has served as a teaching assistant for courses including Introduction to Natural Language Processing at HKUST and Introduction to Speech Processing at Idiap. He has also worked on speech synthesis at Microsoft, focusing on robustness and multilingual conditions. He is actively involved in research advising under Phil Garner and has collaborated with multiple researchers across institutions. He is affiliated with the LIDIAP (Laboratory of Intelligent Data Analysis and Pattern Recognition) at EPFL, where he contributes to research in deep learning for speech and language. His lab work involves developing novel neural architectures, conducting experiments on multilingual datasets, and open-sourcing code to promote reproducibility.
Philippe Cudré-Mauroux is a Full Professor at the University of Fribourg, Switzerland , where he leads the eXascale Infolab . He has held visiting researcher positions at MIT and Microsoft CISL , and serves on the Research Council of the Swiss National Science Foundation and the Scientific Advisory Board of the CHIST-ERA EU Research Programme . Research Interests: His work spans exascale information management , big data , AI , knowledge graphs , linked data , time series data repair , and emergent semantics . He focuses on building scalable, intelligent data systems that integrate storage, computation, and semantics. Publication Trends: His recent work emphasizes schema-aware knowledge graph completion , time series imputation and benchmarking , hardware-accelerated data systems , and large language models for data cleaning . His research bridges database systems, AI, and systems architecture, often targeting high-performance, real-world applications. Scientific Awards: ERC Consolidator Grant (2016) Google Faculty Research Award (2013) Verisign Internet Infrastructures Award (2012) Best Paper Awards at VLDB (2020), AAMAS (2019), and Swiss Data Science Conference (2020) EPFL Doctorate Award and Press Mention (2007) Best Mentor Award at ISWC (2010) Advising and Grants: He mentors a large group of researchers and students, many of whom are co-authors on his publications. He has secured significant funding, including a €2M ERC Grant and multiple Google and Amazon grants, supporting a vibrant research lab focused on next-generation data infrastructure. Labs and Teams: He leads the eXascale Infolab at the University of Fribourg, a dynamic research group actively publishing in top-tier venues and developing innovative tools for data management and AI integration.
Elena Tonin is a Doctoral Assistant and PhD student in the Doctoral Program in Biotechnology and Bioengineering at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences and the Institute of Bioengineering. She works under the supervision of Prof. Naef in the UPNAE research unit, focusing on systems and computational biology. Education: Currently pursuing a Doctoral Degree in Biotechnology and Bioengineering at EPFL. Her research interests lie at the intersection of biology and quantitative modeling, with a focus on understanding complex biological systems through computational approaches. The work in the Prof. Naef Group emphasizes the dynamics of gene expression, circadian rhythms, and neural coding using mathematical and statistical models. The absence of published articles in the provided text prevents a detailed trend analysis; however, the research environment suggests strong emphasis on interdisciplinary approaches combining experimental data with theoretical modeling in life sciences. Scientific Awards: No awards mentioned in available text. Advising and Grants: No formal advisees listed; as a doctoral student, she is likely mentored rather than supervising others. No specific grants mentioned, though doctoral assistantships at EPFL are typically funded through institutional or research council grants. Labs and Research Teams: Elena is a member of the Prof. Naef Group (UPNAE), a research team dedicated to understanding information processing in biological systems, particularly through the lens of dynamical systems theory and stochastic modeling. The lab is part of the broader Institute of Bioengineering at EPFL, which fosters interdisciplinary research in life sciences.
Victor Kristof is a researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the INDY2 laboratory. His work spans interdisciplinary domains, combining Natural Language Processing (NLP) , Machine Learning , and Social Process Modeling to analyze legislative dynamics, vote prediction, and environmental perception. Research Focus: Kristof develops interpretable models for democratic transparency, including aligning interest group positions with parliamentary speeches. He pioneered methods for predicting legislative edit acceptance using matrix factorization and NLP. His work on Swiss referendum prediction integrates historical data with real-time analysis via the Predikon platform . Methodological Contributions: He applies Bayesian statistics , time-dynamic pairwise comparison models , and active learning algorithms to diverse problems, from carbon footprint perception to sports analytics. His War of Words framework reveals ideological patterns in EU law-making, while his Player Kernel model improves football match prediction. Labs & Collaborations: Based at EPFL's Laboratory of Dynamic Information and Networks (INDY2) , he collaborates with researchers like Matthias Grossglauser and Patrick Thiran. His datasets on legislative edits and carbon perception have advanced transparency studies.
Dr. Norman M. Kearney is a Senior Research Scientist at the Centre for Development and Environment (CDE) at the University of Bern. His work focuses on SDG interactions, food systems, sustainability transformations, and knowledge integration, utilizing methodologies like system dynamics modeling and scenario analysis. University of Bern Centre for Development and Environment (CDE) Research interests span sustainability science, social psychology, political economy, and international relations theory. He explores systemic approaches to sustainable development, emphasizing knowledge integration and policy alignment. His publications highlight applications of system dynamics modeling, natural language processing, and scenario analysis to sustainability challenges. Key themes include SDG acceleration, climate resilience, and participatory governance mechanisms. Scientific recognition includes fellowship in the Earth System Governance network. He contributes to global initiatives like the G20 Land Initiative and 2030PLUS, collaborating across institutions such as the Wyss Academy and Stockholm Environment Institute. Projects include nature-based solutions for food systems, SDG Task Force participation, and studies on forest fire risk perception. He is proficient in English, Spanish, German, and French.
Christian Matt is a Full Professor and Co-Director of the Institute of Information Systems at the University of Bern. His academic journey includes positions as Associate Professor (2020-2024) and Assistant Professor (2013-2020) at the same institution. Previously, he served as an Academic Councillor and Research Assistant at LMU Munich. His work focuses on how organizations can strategically and responsibly leverage digital technologies, particularly artificial intelligence, to create sustainable value. Dr. Matt received his doctorate from Ludwig Maximilian University of Munich, where he also completed his habilitation in 2017. His educational background includes a Master of Business Research from LMU Munich, an M.Sc. in Management/European Master in Management from LMU Munich/EM Lyon/Aston Business School, and an M.Sc. in Computer Science from the University of Colorado at Boulder. He also studied business informatics at TU Darmstadt. Prof. Matt's research centers on digital transformation and value creation, with a special focus on the responsible design and use of AI. He investigates how digital innovations can make concrete value contributions to organizations, working closely with practical partners including companies and public organizations. His interdisciplinary approach examines the strategic implementation of new technologies while considering ethical and societal implications. His recent publications reveal a strong focus on AI ethics, digital fairness, and the practical implementation of digital technologies. Key themes include moral decision-making with AI systems, user perceptions of voice agents and diagnostic devices, ephemeral content on social media, and the impact of digital tracing technologies. His work bridges theoretical research with practical applications across business, legal, and healthcare domains. Distinguished Member of the Association for Information Systems (AIS) (2022) Digital Transformation and Society: Outstanding Editorial Board Member Award (2024) Internet Research: Outstanding Associate Editor Award (2018) Electronic Markets: Outstanding Reviewer Award (2016) Dissertations-Förderpreis des Forum Münchner Betriebswirte e.V. (2013) Prof. Matt actively supervises students and leads multiple interdisciplinary research projects funded by the Swiss National Science Foundation. His current projects include "ELCI – Epl(AI)ning Legal Communication to Individuals," "A Multi-perspective Assessment of Channel-related Unfairness in Voice Assistants," and "Algorithmic Management – Establishing Fair and Participative Shift Planning in Healthcare." He collaborates extensively with industry partners to translate research into practical applications. As Co-Director of the Institute of Business Information Systems at the University of Bern, Prof. Matt leads a research team investigating digital transformation challenges. He is also involved with the Research Center "Law and Digitalization" at the University of Bern's Faculty of Law, and co-organizes the international "Workshop on the Digitalization of the Individual" (DOTI) held in cities worldwide. His research center focuses on practical applications of business information systems.
Arnaldo Camuffo is a Full Professor of Management at Bocconi University and Co-Director of the ION Management Science Lab at SDA Bocconi School of Management. He teaches Strategic Decision Making, Entrepreneurship, Innovation, and Lean Management in graduate and executive programs. Camuffo has held visiting positions at MIT's Industrial Performance Center, the University of Michigan's School of Management, and Universidad Deusto in San Sebastian. His research spans strategic decision-making, innovation, modularity, lean production, lean startup, strategic human capital, human resource management, and executive compensation. His work has been published in top-tier journals such as Management Science, Academy of Management Journal, Strategic Management Journal, and Organization Science. Camuffo earned a degree and PhD in Business Administration from Università Ca’ Foscari in Venice, and an MBA from MIT Sloan School of Management. His awards include the 2023 Excellence in Research Award from Bocconi University, multiple conference finalists for research contributions, and best paper recognitions dating back to 2002. His recent publications focus on theory-driven management decisions, lean methodologies in entrepreneurship, and digital transformation. These works often bridge academic research with practical applications in organizational design and competitive strategy. Excellence in Research Award - Università Commerciale Luigi Bocconi, 2023 Finalist - SMS Research Methods Conference Paper Prize, 2019 AoM Annual Meeting – OMT Division Finalist: Best Symposium Award, 2017 Distinguished Track Paper - Decision Science Institute, 2012 Chris Voss Best Honourable Mention Paper Award - EUROMA, 2004 Best Paper Award - British Academy of Management, 2002 Camuffo co-directs the ION Management Science Lab, which integrates data science and machine learning into business decision-making frameworks. His work emphasizes empirical validation and scalability of management theories through rigorous experimental designs.
Prof. Dr. Markus Ammann is a Lecturer at the Department of Environmental Systems Science at ETH Zurich. His work focuses on interdisciplinary research connecting environmental processes with atmospheric chemistry. Research Interests : Atmospheric Chemistry Environmental Systems Modeling Climate Change Impacts Air Quality Analysis Contact : Email: markus.ammann@usys.ethz.ch Work: +41 56 310 40 49
Giona Casiraghi is a Senior Researcher at ETH Zurich specializing in network science and complex systems, with a primary focus on resilience modeling in social organizations and data-driven network analysis. His work bridges theoretical advances in statistical network models with practical applications in supply chain management, open-source software ecosystems, and online social dynamics. His research interests center on developing quantitative methods for analyzing complex systems, particularly through the generalized hypergeometric ensemble of random graphs (gHypEG). Casiraghi's work spans multiple disciplines including network science, statistical physics, data science, and resilience theory, with particular expertise in temporal network analysis, multi-edge networks, and zero-inflation models for sparse networks. His research group develops the ghypernet R package , providing open-source tools for network regression and inference. Analysis of his recent publications reveals a strong trend toward applying network science to real-world resilience problems, particularly in pharmaceutical supply chains and social organizations. His 2025 Science paper on US tariffs threatening medicine supply chains exemplifies this practical turn, while his methodological work on zero-inflated network models (PNAS Nexus 2025) demonstrates continued theoretical innovation. Casiraghi frequently collaborates with Frank Schweitzer and others in the Systems Group at ETH Zurich, producing interdisciplinary work that bridges computer science, economics, and social science. Casiraghi's research has significant implications for understanding how social organizations withstand shocks and how supply chains can be made more resilient to disruptions. His work on the gHypEG framework provides foundational tools for network scientists across multiple disciplines, while his applied research offers concrete insights for policymakers and industry practitioners dealing with complex system failures. He has contributed to numerous projects examining online migration after community bans, developer productivity in open-source projects, and reconstruction of social relations from interaction data. His research methodology typically combines large-scale data analysis with advanced statistical modeling, often developing new network analysis techniques to address specific research questions.
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