Dr. Marcel Dettling is a Group Lead in Data Analysis and Statistics at the ZHAW School of Engineering , focusing on predictive analytics, applied statistics, and complex data analysis. He also serves as a Lecturer at ETH Zurich , teaching advanced statistical methods. Education : PhD in Mathematics (2000-2004), ETH Zurich Postdoc in Applied Statistics (2004-2006), Johns Hopkins University His research spans predictive analytics (regression, classification, time series), data mining, and applications in health economics, transportation safety, social sciences , and business analytics . Recent work includes pharmaceutical cost group analysis for Swiss healthcare and predictive maintenance for marine vessels. Selected publications highlight his expertise in flight trajectory modeling , deep learning error mitigation , and statistical frameworks for rehabilitation finance . His projects address diverse fields like crowdworking in nursing, energy optimization for shipping, and customer behavior prediction.
Academic Profile: Damir Filipovic is a Full Professor and the Swissquote Chair in Quantitative Finance at the College of Management of Technology (CDM) of École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He previously held academic positions at the University of Vienna, University of Munich, and Princeton University, and served as Head of the Vienna Institute of Finance. Research Focus: Quantitative finance, risk management, stochastic processes, term structure modeling, volatility risk, and machine learning applications in financial markets. Industry Collaboration: Co-developed the Swiss Solvency Test for insurance capital requirements while consulting for the Swiss Federal Office of Private Insurance. Publications: Contributed extensively to journals like Journal of Financial Economics, Mathematical Finance, and Annals of Applied Probability, with a textbook on Term-Structure Models. Academic Service: Editorial board member of multiple journals and organizer of advanced workshops on systemic risk and financial technology. Recent Research: His work emphasizes machine learning for portfolio risk management, kernel-based yield curve estimation, and robust stochastic modeling. Keynote speaker at international conferences on finance and insurance mathematics, with over 15 recent publications in 2023-2025 addressing high-dimensional financial problems, neural control systems, and causal inference in market data. Education: Ph.D. in Mathematics from ETH Zurich (2000). Graduate of ETH Zurich and University of Vienna. Teaching & Mentorship: Supervises current and former EPFL Ph.D. students in quantitative finance, including Nicolas Camenzind, Joshua Hayes, Andrea Ruglioni, and ten others. Former students like Damien Ackerer and Lotfi Boudabsa now lead research in risk management. Labs & Programs: Directs EPFL's Finance and Technology Programme, leads the Computational Finance Group (CSF) at EPFL, and contributes to Swiss Finance Institute initiatives. Scientific Leadership: Served on EPFL Committee of Academic Evaluation and Doctoral Program Finance committee.
Natalia Díaz Rodríguez is an Assistant Professor of Artificial Intelligence at ENSTA ParisTech, where she works in the Computer Science and Systems Engineering department within the Autonomous Systems and Robotics Lab (U2IS). She is also affiliated with the INRIA Flowers team, focusing on developmental robotics. Her research spans deep learning, reinforcement learning, continual learning, and symbolic AI, with applications in explainable AI, computer vision, and robotics for social good. Her academic background includes a double PhD in Artificial Intelligence from Abo Akademi University and the University of Granada, alongside MSc degrees in Soft Computing and Computer Engineering from the University of Granada. She contributes to interdisciplinary AI, particularly in robotics, ethics, and healthcare applications, and co-organizes workshops on continual learning. Double PhD in Artificial Intelligence (2015), Abo Akademi University and University of Granada Doctoral diploma on Innovation and Entrepreneurship (2017), EIT Digital MSc in Soft Computing and Intelligent Systems (2012), University of Granada MSc in Computer Engineering (2010), University of Granada Her recent publications focus on trustworthy AI, including bias identification, counterfactual explanations, and continual learning strategies, reflecting her commitment to ethical and robust AI systems. She also explores AI applications in structural engineering, climate visualization, and financial risk assessment, emphasizing practical deployment and interpretability.
Markus Christen is a researcher and Managing Director of the Digital Society Initiative at the University of Zurich, where he leads the Digital Ethics Lab within the Institute of Biomedical Ethics and History of Medicine. His work bridges empirical ethics, neuroethics, and ICT ethics with a focus on data analysis methodologies. Affiliation: University of Zurich (Faculty of Medicine) Role: Managing Director of the Digital Society Initiative Lab: Digital Ethics Lab Christen’s research explores ethical challenges in AI, cybersecurity, and digital health. He investigates value conflicts in technology design, moral sensitivity training through serious games, and human-AI accountability frameworks . His recent publications address digital twins in medicine , AI accessibility for disabled students , and cross-cultural responsibility gaps in AI systems. Key trends in his work include: Empirical ethics applied to AI and cybersecurity Neuroethical dimensions of technology Responsible AI integration in education and healthcare Data privacy and fairness in insurance Cross-cultural ethical assessments Christen’s lab develops frameworks for value-sensitive design and human-AI collaboration , with projects like "Responsible AI in practice" and "DSI AI-WEEK."
Tarun Ramadorai is Professor of Financial Economics at Imperial College London, with a distinguished career spanning household finance, financial economics, behavioral economics, real estate, and international finance. He serves as Executive Editor of the Review of Financial Studies and holds prestigious fellowships including Research Fellow of the Centre for Economic Policy Research (CEPR), Senior Academic Fellow of the Asian Bureau of Finance and Economics Research (ABFER), and Nonresident Senior Fellow at the National Council of Applied Economic Research (NCAER). Education BA in Mathematics and Economics from Williams College MPhil in Economics from the University of Cambridge PhD in Business Economics from Harvard University Research Interests Professor Ramadorai's research spans household finance, financial economics, behavioral economics, real estate, and international finance. His work examines how households make financial decisions across different markets and countries, with particular focus on housing markets, investment behavior, and financial inclusion. He has established himself as a leading expert in international comparative household finance, having previously served as Principal Investigator on a transformational initiative financed by the Sloan Foundation to establish this sub-field of finance and economics. His recent research explores the intersection of technology and personal finance, housing market dynamics, and optimal tax policy. He has demonstrated how housing costs impact fertility decisions, how machine learning affects credit markets, and how privacy policies influence consumer data extraction. His work combines rigorous theoretical frameworks with innovative empirical approaches using large-scale datasets from diverse markets. Publication Trends Professor Ramadorai's recent publications reveal a strong focus on household decision-making in financial markets, with increasing attention to the digital transformation of finance. His work bridges theoretical insights with practical policy implications, particularly in emerging economies. There is a clear trend toward interdisciplinary research that combines finance, economics, and data science to address pressing questions about financial inclusion, housing affordability, and the impact of technology on traditional financial services. Scientific Awards Brattle prize for best paper in the Journal of Finance Jensen prize for the best paper in the Journal of Financial Economics Wharton School-WRDS Best Paper Award in Empirical Finance James A Lebenthal Excellence in Municipal Finance Research Prize FMA Napa Conference Best Paper Prize INQUIRE Europe third prize Viz Risk Management Best paper prize Policy Engagement and Advisory Roles Professor Ramadorai has made significant contributions to policy discussions worldwide. He served as Chairman of the Inter-Regulatory Committee on Household Finance constituted by the Reserve Bank of India, which produced the influential "Indian Household Finance" report. He has advised numerous institutions including the Economic Advisory Council to the Prime Minister of India, the European Securities and Markets Authority, and the Norwegian Sovereign Wealth Fund. Currently, he co-chairs the Fintech workstream of the India-UK Financial Partnership, helping to shape the future of financial technology across borders. Research Initiatives Professor Ramadorai previously led the Initiative on International Comparative Household Finance, funded by the Sloan Foundation, which established household finance as a distinct sub-field of research. He is in the process of setting up a new initiative at Imperial College Business School to further advance this area of study. His work has influenced both academic research and practical policy interventions in financial markets around the world.
Matthias Bannert is a Lecturer at the Department of Management, Technology, and Economics at ETH Zürich, where he works at the KOF Swiss Economic Institute (Konjunkturforschungsstelle). His work focuses on the intersection of economics, software development, and data management, with particular expertise in time series analysis and official statistics. Bannert designs solutions for state-of-the-art data processing, management, and publishing of economic data and research. Bannert completed his doctoral thesis titled "Survey Based Research in Economics - Essays on Methodology, Economic Applications and Long Term Processing of Economic Survey Data" at ETH Zürich in 2016. His academic journey began when he joined KOF in late 2008, initially working as a researcher for the Business Tendency Survey group before transitioning to the institute's IT department. Dr. Bannert's research interests span several interconnected domains at the nexus of economics and data science. He specializes in developing software environments for official statistics, with particular focus on processing and managing economic time series data through open-source driven data pipelines. His technical expertise includes R programming and PostgreSQL database systems, which he applies to create robust solutions for economic data analysis. Bannert is particularly interested in survey methodology, nowcasting techniques, and the development of reproducible research workflows. His work bridges the gap between theoretical economics and practical software implementation, ensuring that economic research can leverage state-of-the-art data processing techniques. Analysis of Bannert's publication record reveals a consistent focus on the application of data science techniques to economic research problems, particularly in the domain of official statistics and survey-based economics. His work demonstrates a progression from theoretical survey methodology to practical software implementation, with increasing emphasis on real-time economic forecasting and data management systems. A distinctive feature of his research is the development of open-source R packages that make advanced economic data analysis more accessible to researchers and practitioners. As an active contributor to the R language for Statistical computing and the open source community, Bannert has developed several notable software packages including timeseriesdb, tstools, and kofdata, which are available on CRAN. These tools reflect his commitment to creating reproducible, transparent, and efficient workflows for economic data analysis. Bannert serves as a data science supervisor for multiple KOF research projects and is a co-Principal Investigator in an SNF-funded Digital Lives project in collaboration with KOF's labor market expert group. His teaching activities include "Hacking for Sciences - An Applied Guide to Programming with Data" and involvement in the Nowcasting Lab, which provides live out-of-sample forecasting and model testing capabilities for economic researchers. Dr. Bannert is affiliated with the KOF Swiss Economic Institute, where he contributes to several research groups including the KOF Macroeconomic Forecasting group and the KOF Data Science and Macroeconomic Methods group. His work at KOF bridges the institute's traditional economic research with modern data science approaches, helping to position the institute at the forefront of data-driven economic analysis.
Thorsten Hens is a Swiss Finance Institute Professor of Financial Economics at the University of Zurich and Adjunct Professor at the University of Lucerne and Norwegian School of Economics (NHH). He studied at Bonn and Paris, and previously held academic positions at Bielefeld and Stanford University. His research focuses on Behavioral Finance , Evolutionary Finance , Fintech , and Sustainable Finance . Co-founder of Behavioral Finance Solutions (a UZH-HSG spin-off) and the Swiss Fintech Innovations Association Founder of the UZH Blockchain Center Holds mandates in pension funds, insurance companies, and banks Regular speaker at practitioner conferences His recent publications explore topics such as ESG-CAPM, personality-driven investment styles, evolutionary portfolio dynamics, and experimental retirement decision-making. He has co-authored over ten books and published more than eighty peer-reviewed articles. Thorsten Hens' work bridges academic rigor with practical applications in wealth management , asset management , and financial technology .
Kai Gehring is a Professor for Political Economy and Sustainable Development at the University of Bern , with affiliation to the Wyss Academy for Nature and the ifo Institute in Munich. He holds a PhD in Economics from the University of Göttingen (advisor: Axel Dreher) and a Diplom (M.Sc equivalent) from the University of Mannheim. His academic career includes research stays at Harvard, Cambridge, Stanford, and MIT, along with a 4-year Ambizione Grant from the Swiss National Science Foundation. Member, CESifo Member, European Development Network (EUDN) Member, Development Economics Committee of the German Economic Association Member, Globalization and Development Group (GlaD) His research integrates economics with political science, sociology, and history to examine institutional dynamics in developed and developing countries. Key areas include political economy of international organizations (IMF, World Bank, EU), conflict resolution , sustainable development , and narrative economics using NLP on media/social data. Recent methodological projects employ satellite imagery and machine learning for mining activity tracking. Major article trends span three decades of African government formation challenges ( Minister Project ), climate change policy narratives, artisanal mining impacts, and terrorist propaganda analysis. He has published 17+ peer-reviewed articles across journals like European Economic Review , World Development , and Journal of Development Economics . Scientific Awards Ambizione Grant (Swiss National Science Foundation) As an educator, Gehring has taught at the University of Mannheim, Heidelberg University, Kaiserslautern University of Applied Sciences, and the University of Zurich. His Minister Project actively engages citizen scientists to build comprehensive datasets on African government members' linguistic and regional origins.
Patrick Gagliardini is a Full Professor of Econometrics at the University of Lugano (USI) within the Faculty of Economics and the Institute of Finance. He also serves as Pro-Rector at USI. His academic journey includes a PhD in Econometrics from USI (2003) and studies in Physics at ETH Zurich (1998). He has held roles such as Visiting Fellow at CREST Paris (2003) and Assistant Professor at the University of St. Gallen (2004–2006). His research focuses on econometric methods (nonparametric techniques, GMM, latent factor models) and financial applications such as credit risk, asset pricing, and risk management. Competence areas include Big Data, investment decisions, and systematic risk analysis. He teaches courses in econometrics, financial econometrics, and time series at the undergraduate, graduate, and PhD levels. Recent publications explore latent factor models, econometric testing (e.g., eigenvalue tests for factor detection), and financial decision-making in small data regimes. His work bridges theoretical econometrics with practical applications in finance and risk modeling. Notably, his research addresses challenges in dynamic latent factor models, hedge fund performance evaluation, and granularity theory in financial systems. He maintains an active academic profile with contributions to both theoretical and applied econometrics.
Prof. Dr. Tobias Benjamin Müller serves as a Professor at the Bern University of Applied Sciences (BFH) within the School of Health Professions and the Institute of Health Economics and Health Policy. His academic profile demonstrates significant leadership in health economics research with direct policy implications for the Swiss healthcare system. His educational foundation includes a PhD in Health Economics from the University of Lucerne (2013-2017), participation in the International Doctoral Program in Health Economics and Policy at the Swiss School of Public Health, and a Master of Science in Economics from the University of Bern (2011-2013). Müller's research program focuses on critical healthcare efficiency questions, particularly examining hospital quality variation, low-value care prevalence, and physician decision-making processes. His methodology combines rigorous econometric analysis with emerging machine learning techniques to extract meaningful insights from complex healthcare datasets. Current projects investigate whether Swiss hospitals provide consistent quality to patients, the extent of unnecessary care in outpatient settings, and how personalized information affects medical decisions. His publication record shows a clear trajectory toward increasingly sophisticated analytical approaches, with recent work (2022-2025) emphasizing machine learning applications for risk adjustment in hospital comparisons and behavioral economics frameworks for understanding health plan choices. The consistent funding from organizations like SNSF underscores the policy relevance of his research. As project leader for multiple significant initiatives including Hospital Comparison in the Swiss Inpatient Sector and Low-Value Care in Outpatient Care, Müller directs research teams investigating fundamental questions about healthcare value and efficiency. His work directly contributes to the United Nations Sustainable Development Goals related to good health and well-being, with practical applications for healthcare policymakers seeking to optimize resource allocation.
Jan von der Assen is a doctoral student at the Communication Systems Group (CSG) within the Department of Informatics at the University of Zurich . His research focuses on Holistic Cybersecurity , spanning Threat and Asset Management , Security Economics , and AI-powered Malware Mitigation . Education : MSc in Software Systems from University of Zurich (2021) Research Affiliations : Involved in 12+ national/EU projects (2021–2025) including CheeseChain , CONCORDIA , and DSI Cybersecurity . His work emphasizes Moving Target Defense (MTD) for IoT security, Decentralized Federated Learning with blockchain-based reputation systems, and Ransomware Detection using hypervisor-level system call monitoring. Recent publications include: GuardFS (JISA 2025) for Linux ransomware mitigation HyperDtct (IEEE CSR 2025) on hypervisor-based detection ThreatFinderAI (CNSM 2024) for LLM threat modeling
Angelo Ciaramella serves as a lecturer and Competence Center Manager for the CAS FH in Strategic Talent Acquisition, while operating as Managing Director of ciaramella & partner GmbH and founder of the People Attraction Institute. His professional affiliations include board membership at the HR Association ZGP and advisory roles for HR Today, positioning him at the intersection of academic instruction and industry transformation in talent management. Ciaramella's research focuses on the strategic evolution of People Attraction beyond traditional HR frameworks, emphasizing data-driven competence centers, sales-oriented service divisions, and value-added consulting units. His work examines how changing market demands necessitate repositioning talent functions as strategic sparring partners and change agents, with particular attention to AI integration and organizational development in recruitment processes. This research bridges academic theory with practical implementation across insurance, banking, media, manufacturing, real estate, transportation, and IT sectors. His recent publications demonstrate a clear trajectory toward redefining talent acquisition as a revenue-generating strategic function rather than a transactional HR activity. The articles reveal consistent themes of operational transformation, technological integration (particularly AI), and the critical need for change agent skills among executives leading talent functions. Ciaramella consistently advocates for holistic people attraction models that function as value-adding strategic units rather than support services. Through ciaramella & partner GmbH, he operationalizes these concepts by running Switzerland's first CAS FH in Strategic Talent Acquisition, developing client recruitment processes to achieve state-of-the-art Holistic People Attraction Excellence. The consultancy's approach emphasizes agile solutions that transform people attraction into a strategic sparring partner, data-driven competence center, sales-oriented service division, value-added consulting unit, and change agent for executives – embodying the theoretical frameworks explored in his publications.
Thomas Maillart is a Senior Lecturer and researcher at the Research Institute for Statistics and Information Science at the University of Geneva. His work bridges complex systems, collective intelligence, and cybersecurity, with a focus on modeling human dynamics and digital risks. Education: PhD in Science, ETH Zurich (2011) Master’s degree, EPFL (2005) His research centers on understanding how incentives, structures, and social interactions shape collective behavior in online and physical environments. He investigates topics such as cyber risks, privacy, resilience, and technological innovation. His work often applies statistical physics and data science to socio-technical systems, including open-source software development, cybersecurity policy, and human behavior modeling. His recent publications reflect a strong trend toward interdisciplinary research, combining insights from computer science, economics, psychology, and public policy. He explores how machine learning can forecast digitization labor needs, how collective action enhances cybersecurity, and how bio-sensors can aid in medical diagnosis. His work frequently appears in high-impact journals such as Science , PLOS ONE , and Physical Review . Scientific Awards: Zurich Dissertation Prize (2012) for pioneering work on cyber risks Thomas Maillart has advised and collaborated with numerous researchers and students, though specific names are not listed in the provided text. He has been involved in significant research grants and initiatives, including cybersecurity consulting for governmental and private organizations and co-founding a cybersecurity startup in 2005. His academic service includes organizing and presenting at international conferences such as WEIS and ACM conferences. He is associated with several research platforms, including ResearchGate, Google Scholar, LinkedIn, and Twitter, and has contributed to both peer-reviewed and practitioner-oriented publications. His recent editorial work includes co-editing a book on critical information infrastructure security.
Michael Natusch is a Lecturer at The Open University and Head of Data Science Services for EMEA at Pivotal. With a PhD in theoretical physics from the University of Cambridge and an MBA, his expertise spans predictive analytics applications across telecommunications, financial services, retail, automotive, and aviation industries. Current roles: Pivotal (EMEA Data Science Services), Open University (Lecturer) Research interests: Machine learning, customer churn reduction, pricing optimization, logistics, and telecoms network deployment Academic background: PhD in Theoretical Physics (University of Cambridge), MBA His work focuses on transforming business data into actionable insights using the Pivotal platform. He has contributed to practical implementations in HR analytics, health insurance claims optimization, and machine-to-machine data processing. Scientific Awards Fellow of the Royal Statistical Society
Christine Butti is an Assistant Professor of Statistics at the Department of Business Economics, Health and Social Care (DEASS) of the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). She has been a lecturer and researcher at SUPSI since 2001, focusing on statistical methodologies and multidimensional deprivation analysis. Education: PhD in Methodological Statistics (University of Trento, 1995) Master's in Advanced Biostatistics for Clinical Research (University of Padua, 2020) Degree in Statistical and Economic Sciences (University of Padua, 1990) Her research centers on developing innovative clustering techniques (e.g., Self-Organizing Maps) to analyze multidimensional well-being and deprivation patterns using Swiss Household Panel data. Key projects include mapping deprivation dynamics in Switzerland, studying gambling behaviors in Ticino, and assessing labor market integration for AI-insured individuals. Recent publications highlight her expertise in applying machine learning to social sciences, with a focus on non-monetary indicators of vulnerability. She has collaborated extensively with institutions like the Swiss National Science Foundation and the University of Bergamo. Awards: Appointed Adjunct Professor at SUPSI (2009) Christine leads the Research Methodology Competence Center at SUPSI and has held leadership roles in DEASS (2014-2018). Her work bridges statistical theory with social policy applications, emphasizing biographication of risks and structured inequality.
