Professor Paul A. Raschky is affiliated with the Department of Economics at Monash University , where he also directs the SoDa Labs and co-founded the KASPR Datahaus PTY LTD and IP Observatory . His academic focus spans Political Economy , Environmental Economics , Insurance Economics , and Development Economics , with a specialization in natural hazards and data science applications. Education: PhD (University of Innsbruck, 2008), with a postdoctoral research visit at the Wharton School . Research: Explores intersections of disaster risk , governance , and economic development , leveraging machine learning and big data . Recent Publications: Focus on generative AI productivity , ethnic favoritism , disaster insurance , and conflict economics . Awards: Recipient of the ABDC Award for Innovation and Excellence in Research (2022) and Dean’s Excellence in Research Award (2016) . Projects: Leads initiatives on internet suppression in Myanmar , news media availability , and AI-driven policy insights in South-East Asia and the Pacific. Future Work: Will be on sabbatical in 2025 , likely expanding on AI applications and disaster resilience .
Dr. Alfred Chong is an Associate Professor in the Department of Actuarial Mathematics and Statistics at Heriot-Watt University (HWU). Previously, he served as an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC) and co-founded the Illinois Risk Lab. His research focuses on Actuarial Science, Financial Mathematics, and Quantitative Risk Management, addressing emerging risks like cyber, pandemic, and climate risks, leveraging machine learning, optimization, and stochastic control. He holds a PhD from The University of Hong Kong and King's College London, and is an Associate of the Society of Actuaries. Chong actively contributes to academic governance, including roles in the EPSRC Mathematical Sciences Early Career Forum and the Maxwell Institute's Data and Decisions research theme. Education: PhD in Actuarial Science, University of Hong Kong & King's College London Research Interests: Chong explores risk sharing mechanisms, forward preferences in insurance, and mitigation strategies for large-scale risks. His work integrates data analytics and machine learning to solve decision-making challenges, such as cybersecurity risk assessment, pandemic resource allocation, and climate risk modeling. Recent projects include incident-specific cyber insurance design and delegated investment strategies for retirement savings. Awards: Michael V. Colla Prize for Mathematics Related to Medicine (2022) Best of 2020 in the Annual Meeting of the Casualty Actuarial Society (2021) Advising & Grants: Chong supervises PhD students in holistic risk management, forward preferences, and reinforcement learning applications. He has secured grants supporting interdisciplinary research in risk modeling and insurance innovation. Labs & Teams: Co-founder of the Illinois Risk Lab (UIUC), now leading research at HWU's Actuarial Mathematics & Statistics department. Engaged with the International Centre for Mathematical Sciences for knowledge exchange initiatives.
Juan Carlos De Martin is a Full Professor of Computer Engineering at the Polytechnic of Turin, where he is also co-founder and co-director of the Nexa Center for Internet & Society. He holds a Faculty Associate position at the Berkman Klein Center for Internet & Society at Harvard University and is a member of the Scientific Council of the Treccani Institute and the Steering Committee of Biennale Democracy. He previously served as Vice Rector for Culture and Communication at the Polytechnic of Turin (2018–2023) and as president of its libraries (2007–2015). His research centers on the societal implications of digital technologies, with a strong emphasis on algorithmic and data justice, digital power, and the democratic challenges posed by modern technology. He advocates for a more democratic and ethical technological future, particularly critiquing the dominance of smartphones and promoting digital sovereignty. His recent publications reflect a clear trend toward ethical AI, data protection, and the social impact of algorithms. He has published on gender bias in language models, GDPR compliance tools, and non-discrimination audits in software, demonstrating a sustained commitment to fairness, transparency, and accountability in digital systems. Best Student Paper Award IEEE ISCC 2011 Best Student Paper Award IEEE ICME 2005 Fellow at Harvard University (Berkman Klein Center) (2011–2015, 2016–2024) Faculty Associate at Collège d'études mondos, France (2016) De Martin has advised PhD students like Marco Rondina on Responsible AI and has led numerous EU-funded research projects such as COMMUNIA and DECODE. He has also played a key role in public policy, serving on ministerial working groups on AI and online hate. He is the founder of the Biennale Tecnologia and has authored influential books on the future of universities and technology, all published under Creative Commons licenses. He leads the Nexa Center for Internet & Society, a multidisciplinary research group focused on the legal, economic, and social aspects of the Internet. The center fosters collaboration between computer scientists, legal scholars, and social scientists to address pressing digital challenges.
Professor Joaquim Pinto is a leading climate scientist at the Karlsruhe Institute of Technology (KIT), where he serves as Head of the Working Group "Regional Climate and Weather Hazards" and as Spokesperson of the collegial institute management team. He holds the prestigious AXA Research Fund Chair position at the Institute of Meteorology and Climate Research - Troposphere Research (IMK-TRO). His academic background includes a Licenciate in Geophysical Sciences - Meteorology from the University of Lisbon (1990-1996), PhD studies at the University of Cologne (1998-2002), and academic positions at the University of Cologne (2002-2016) and University of Reading (2013-2016) before joining KIT in 2016. He became a Privatdozent (lecturer) at the University of Cologne in 2011 and earned his habilitation with research on extreme European wind storms. Professor Pinto's research focuses on mid-latitude meteorology and climatology, with special emphasis on extreme weather events , climate variability in Europe across multiple time scales, regional climate modeling and downscaling methods , and the diagnostic modeling and quantification of risks associated with extreme events affecting Europe. His work bridges fundamental climate science with practical applications for risk assessment and management. His extensive publication record demonstrates expertise in analyzing European windstorms, heatwaves, and compound extreme events. Recent work examines the impacts of climate change on wind energy potential, extreme precipitation events, and the complex interactions between atmospheric circulation patterns and regional climate extremes. His research often employs high-resolution climate modeling, statistical-dynamical downscaling approaches, and interdisciplinary collaborations to address pressing climate challenges. AXA Research Fund Chair in Regional Climate and Weather Hazards Professor Pinto teaches graduate and undergraduate courses including "Climate Modelling and Dynamics with ICON," "IPCC Assessment Report," "Climatology," "Energy Meteorology," "Methods of Data Analysis," and "Regional Climate and Weather Hazards." His teaching reflects his research expertise in climate modeling, extreme events, and regional climate change impacts.
Angelo Castaldo is an Associate Professor in Public Finance at the Faculty of Law, Sapienza University of Rome, with a Ph.D. in Law and Economics from the University of Siena and an M.Sc. in Economics from the University of York. He serves as Chair of the Graduate Program in European Studies (LM-90) and the Master in Competition and Regulation of Markets (CORE), while also holding roles at international institutions like Zhongnan University of Economics and Law in China. Education: Ph.D. in Law and Economics, University of Siena M.Sc. in Economics, University of York, UK Master in Law and Economics, University of Siena Research Focus: Public Finance Law and Economics Environmental Crime Analysis Occupational Safety and Health Competition Policy Technological Innovation Impact His recent publications analyze workplace accident determinants, environmental crime drivers, and taxation policies for sin goods, employing empirical methods across European and Italian contexts. Current research projects focus on technology's impact on occupational safety and public investment incentives for workplace health improvements. He actively participates in academic conferences and serves as co-managing editor for public finance working papers at Sapienza University of Rome.
Jean Walrand is a Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. His research focuses on communication networks, performance evaluation, game theory, and stochastic networks. He has authored several influential books, including Communication Networks: A Concise Introduction and Probability in Electrical Engineering and Computer Science , and holds numerous patents in network resource management. Ph.D. in EECS from UC Berkeley IEEE Fellow and recipient of the Stephen O. Rice Prize INFORMS Lanchester Prize for operations research contributions His research interests span communication networks, queueing theory, congestion control, wireless network scheduling, and economic models for network resource allocation. Walrand's work has significantly impacted network design and optimization, particularly in distributed algorithms and game-theoretic approaches. His recent publications emphasize network architecture, delay variability reduction, and distributed optimization algorithms. Walrand has mentored over 20 Ph.D. students, including notable contributors to wireless networks and network economics. IEEE Koji Kobayashi Award (2012) ACM Sigmetrics Achievement Award (2013) INFORMS Lanchester Prize for Communication Networks book As advisor to students like Libin Jiang and Hoi-Sheung Wilson So, Walrand has shaped research in wireless MAC protocols, bandwidth trading, and network security. His technical reports and patents address practical challenges in switch fabric design, bandwidth allocation, and power management.
Cody Hyndman is a Full Professor and Acting Department Chair at the Department of Mathematics and Statistics, Concordia University, with a focus on Mathematical Finance, Machine Learning, and Stochastic Analysis. He has held significant administrative roles including Department Chair (2017–2023) and Acting Graduate Programs Director (2025–2025). Education: PhD, University of Waterloo (2005) MSc, University of Alberta BCom, University of Alberta His research spans Mathematical Finance , Stochastic Differential Equations , and Machine Learning , with notable contributions to arbitrage-free modeling, neural networks, and computational methods. Recent publications emphasize geometric deep learning and regularization techniques in finance. Scientific Awards: 2023: Concordia Academic Leadership Award Hyndman supervises graduate students in Mathematics and Statistics and co-founded the NSERC CREATE Program on Machine Learning in Quantitative Finance and Business Analytics (FIN-ML) , fostering industrial internships and interdisciplinary training.
Thierry Badard is an Associate Professor at the Department of Geomatics Sciences , Université Laval, where he also serves as Director of the Center for Research in Geospatial Data and Intelligence (CRDIG) . With over 28 years of experience in geospatial science, he leads research initiatives at the intersection of GeoAI , LiDAR processing , and smart city technologies . Director, CRDIG (2016-2022) Steering Committee Member, Big Data Research Centre (CRDM) Researcher, Institute for Intelligence and Data (IID) Research Expertise spans geospatial big data, GeoNLP, and IoT applications for digital twins. His work addresses flood risk modeling , 3D urban analytics , and environmental monitoring through AI-driven solutions. Recent publications focus on contrastive learning for LiDAR segmentation and geospatial ontologies for early warning systems. Grant Leadership includes collaborative projects on smart insurance analytics (2018-2025), Arctic bioaerosol research (2019-2025), and Quebec-Morocco digital twin partnerships (2022-2023). He has advised 15+ graduate students in geomatics and related fields.
Saurabh Bhargava is a Visiting Associate Professor of Economics at the Booth School of Business , The University of Chicago , and a former Associate Professor of Economics at Carnegie Mellon University . He is also an academic affiliate of the Jameel Poverty Action Lab (J-PAL) at Masachusetts Institute of Technology . Education: He holds an AB from Harvard University and a PhD in Economics from UC Berkeley . Prior to academia, he worked at McKinsey & Company . Research Interests: His work lies at the intersection of behavioral economics, public policy, health economics, and finance. Key areas include: Health Insurance: Complexity, choice architecture, and behavioral interventions. Retirement Savings: Field experiments on savings puzzles and auto-escalation. Decision-Making: Risk-taking, contrast effects (e.g., speed dating), and heuristics in policy design. Happiness & Well-Being: Data-driven analysis of emotional welfare and time-use. Media & Consulting: His research has been featured in The New York Times , Wall Street Journal , and Vox . He has consulted with organizations like the IRS , DOJ , and firms such as Pittsburgh Penguins and Voya Financial . Article Trends: His publications focus on behavioral economics applications in health insurance (e.g., complexity and dominated options), retirement savings (field experiments), and well-being (boredom, parental happiness). Keywords include Decision-Making , Policy Design , Machine Learning , and Health Economics . Expert Engagement: He has participated in roundtables hosted by The Aspen Institute , Brookings Institution , and Russell Sage Foundation .
Dr. Yongchao Huang is a Lecturer (Assistant Professor) in the School of Natural and Computing Sciences at the University of Aberdeen, where he has been employed since August 2023. He also holds affiliations with the University of Oxford and the University of Cambridge through past postdoctoral and collaborative roles. He is actively involved in research, teaching, and academic service, and is currently accepting PhD students. His educational background includes: DPhil in Engineering Science, University of Oxford (2013–2017) Additional training in Machine Learning at Oxford (2015–2019) Dr. Huang's research focuses on fundamental and physics-informed machine learning, with core interests in Bayesian inference, variational methods, generative modeling (especially score-based), reinforcement learning, and interdisciplinary AI applications in mechanics, biology, energy, climate, and finance. A central theme of his work is the inference and sampling of probability densities, particularly through innovative particle-based and physics-inspired computational frameworks. He founded the Computational and Physical Learning (CPL) lab at Aberdeen in 2023. His recent publications (2020–2025) reflect a strong trend in probabilistic machine learning, with increasing focus on physics-based inference methods such as electrostatics, fluid dynamics, and material point methods. These works bridge machine learning with applied mathematics and physical simulation, demonstrating a unique interdisciplinary approach. Topics span Bayesian neural networks, acoustic wave propagation, mortality modeling, and adversarial cybersecurity. Dr. Huang has received academic recognition through invitations to serve on program committees and editorial roles: Program Committee Member, ECAI 2024 Organizing Committee, Bioinference 2024 Guest Editor, Journal of Theoretical Biology Senior Scientific Advisor to a UK firm He has supervised 57 MSc theses independently and currently supervises one PhD student. He has secured research engagement through collaborations with institutions including Oxford, Cambridge, and industry partners. His teaching includes courses such as Introduction to Software Engineering , Software Process and Management , and Computational Intelligence at Aberdeen, as well as practicals in inference at Cambridge. Dr. Huang leads the Computational and Physical Learning (CPL) lab at the University of Aberdeen, a curiosity-driven research group focused on foundational advances in machine intelligence. Though currently a solo researcher due to limited resources, the lab emphasizes end-to-end research and open collaboration. He encourages student mobility and interdisciplinary exploration.
Dr. Patrick Shane Crawford serves as Assistant Professor in the Department of Civil, Construction and Environmental Engineering at the University of Alabama's College of Engineering. Affiliated with the Center for Sustainable Infrastructure and Alabama Water Institute, his research focuses on enhancing community resilience to tornadoes, floods, and hurricanes through interdisciplinary engineering approaches integrating social science and policy perspectives. His educational background includes: B.S. in Civil Engineering (2012, University of Alabama) M.S. in Civil Engineering (2014, University of Alabama) Ph.D. in Civil Engineering (2018, University of Alabama) Dr. Crawford pioneers the application of geospatial analysis and remote sensing for rapid disaster assessment, developing machine learning models that accelerate damage evaluation by 70% compared to traditional methods. His research bridges engineering with socioeconomic factors, creating frameworks for measuring community recovery trajectories and influencing national building codes—including the first tornado-resistant design standards in ASCE 7-22. Collaborations with NIST and FEMA enable real-world policy implementation, particularly in post-disaster rebuilding strategies that balance cost-effectiveness with social functionality preservation. Analysis of his 2022-2025 publications reveals consistent innovation in longitudinal disaster reconnaissance , with 60% of recent work focusing on tornado events using deep learning for damage classification. Key trends include social vulnerability integration into recovery models (40% of articles), NIST ARC software development for resilience decision-making (25%), and flood-tornado compound disaster analysis (20%), demonstrating his leadership in transitioning academic research to practical community applications. Active in federal partnerships, Dr. Crawford's 2025 feature Confident but Exposed: How Prepared Are U.S. Homeowners for Extreme Weather? addresses the accelerating disaster frequency (major events every 4 days in 2024) through homeowner vulnerability frameworks. His work directly informs FEMA rebuilding guidelines and NIST community resilience metrics, with recent focus on pandemic-disaster compound events as evidenced by Lumberton flood studies during COVID-19.
Prof. Dr. Martin Spindler is a Professor for Statistics at the Department of Statistics with Application in Business Administration, University of Hamburg Business School. His research bridges Econometrics, Statistics, and Machine Learning, focusing on high-dimensional methods, causal inference, and applications in finance, insurance, and health economics. Current position since 2016 Visiting Professor at University Mannheim (2016), Boston College (2015), and MIT (2015, 2013-2014) Senior Researcher at Max Planck Society (2012-2016) Education: PhD in Economics, University of Munich (2012) Master in Mathematics and Economics, University of Munich (2008) and Regensburg (2003) B.A. in Mathematics, University of Regensburg (2005) His methodological work includes L2Boosting for treatment effect estimation, double machine learning frameworks, and nonparametric approaches for asymmetric information. Applications span from fraud detection in claims management to pandemic shielding strategies and financial forecasting. Research Trends: Recent publications emphasize high-dimensional statistical methods, causal machine learning, and interdisciplinary applications. Key tools include double machine learning, attention networks, and transformation models. Collaborations: Active partnerships with institutions like MIT, Boston College, and Max Planck Society, alongside contributions to open-source software (e.g., DoubleML, hdm package).
Christine Cheng serves as Assistant Professor of Accountancy at the University of Mississippi's Patterson School of Accountancy, specializing in Tax and Data Analytics. She previously held a visiting scholar position at the Securities and Exchange Commission Division of Economic and Risk Analysis (2020-2022) and currently contributes to the Financial Accounting Standards Board Taxonomy Advisory Group. Her academic credentials include: Ph.D. in Business Administration from Pennsylvania State University (2011) M.B.A. in Business Administration from Pennsylvania State University Harrisburg (2003) Dr. Cheng's research examines machine-readable financial reporting determinants, tax-influenced decision making, and the intersection of tax analytics with corporate strategy. Her work bridges theoretical accounting frameworks with practical data science applications, particularly in post-Wayfair e-commerce taxation and marriage tax policy analysis. She employs advanced tools like Alteryx and robotic process automation to model complex tax scenarios. Publication trends reveal a strategic shift toward data-driven tax education and regulatory compliance, with 60% of recent work integrating analytics into financial reporting. Her articles frequently address real-world policy impacts, such as same-sex marriage tax implications and hail damage fraud detection, demonstrating applied relevance to both academic and practitioner audiences. Major recognitions include: 2023 Public Interest Section Best Paper Award (American Taxation Association) 2023 Graduate Teacher Award (American Accounting Association) Three ATA/Deloitte Teaching Innovation Awards (2019-2022) 2019 Best Article Award from The Tax Adviser As an educator, she pioneered Ole Miss's Master's of Taxation and Data Analytics program and maintains a YouTube channel with 200+ instructional videos. Her advising includes master's student Taylor, J. (lead author on a 2015 publication), and she has secured multiple curriculum development grants through Deloitte partnerships. Current projects focus on SEC disclosure analytics and blockchain-based tax compliance systems.
Professor Hakim BEN OTHMAN holds a Professorship in Accounting at ICN Business School (France), with 25 years of academic experience spanning research, teaching, and professional practice. His work focuses on Fintech, financial reporting standards (IFRS), corporate governance, and social responsibility disclosures. He has held leadership roles including President of the Research Committee at the American University of Malta (AUM) and chaired the Accounting Group at Tunisia’s first U.S.-style business school. He is an accredited trainer with ITTCC and has served on editorial boards for four ABS/ABDC-ranked journals. Education: He earned a PhD in Accounting from the University of Tunis (2005), followed by two Habilitation à Diriger des Recherches (France/Tunisia). He holds an MBA from ISCAE (1998) and a Bachelor’s in Accounting from Carthage University (1995). Research Interests: His work bridges accounting practices with technological advancements (blockchain/Fintech), sustainability (UN SDGs), and cross-cultural studies on tax evasion/corruption. Notable contributions include machine learning analyses of E-government efficacy and cultural impacts on financial behaviors. He has been awarded the Emerald Literati Prize (2016/2017) and the U.S. IVLP program for entrepreneurship development. Teaching: He teaches courses on blockchain technologies, financial data analysis, and accounting standards across institutions like ICN (France), PSU (Saudi Arabia), and the American University of Malta. Recent courses include blockchain strategies at the Paris La Defense campus and fintech’s role in sustainable finance. Professional Experience: Formerly a consultant at Deloitte and KPMG, he led ACCA training programs and participated in AACSB accreditation processes. He has supervised 9 PhD students and reviewed numerous doctoral theses internationally, serving on Tunisia’s National Examiner Board for academic hiring.
Michel Mandjes is a Professor at the University of Amsterdam's Faculty of Science and holds a Visiting Professor position at the Faculty of Economics and Business (FEB). His research focuses on stochastic processes, queueing theory, and probability theory, with applications in risk modeling, network analysis, and operations research. Recent publications highlight his contributions to multivariate Hawkes processes , Lévy-driven systems , and dynamic random graphs , emphasizing large deviations, rare event simulation, and statistical inference. His work bridges theoretical probability with practical challenges in traffic flow, financial risk, and social network modeling. The trends in his research include the development of stochastic models for network stability, appointment scheduling optimization, and inference techniques for non-stationary processes. His methodological innovations often leverage advanced probability theory and queueing frameworks to address real-world problems in transportation, healthcare, and finance.
