Prof. Dr. Katharina Simbeck is a Lecturer at HTW Berlin University of Applied Sciences , affiliated with the College of Computer Science, Communication and Business and the Department of Business Informatics . Her teaching and research focus on the intersection of Artificial Intelligence and Digitalization in educational and human resource contexts. Teaching Areas : Business Informatics, Finance, AI in Education and Human Resources, Fair and Explainable AI. Research Focus : Digitalization, Learning Analytics, HR Analytics, and addressing bias in AI systems. Additional Roles : Examination Board Member for Business Informatics (B and M programs). She supervises 1 doctoral student and has contributed to 85 publications and 16 research projects. Office hours are held on Wednesdays from 12:00 PM to 1:00 PM, with registration via email during breaks. Course details are available on her website .
T. Clifton Green serves as the John W. McIntyre Professor of Finance at Goizueta Business School, Emory University, where he has been a faculty member since 1999. He teaches Corporate Finance in the BBA program, Behavioral Finance in the PhD program, and has previously instructed Security Analysis and Portfolio Management for MBA students. His educational background includes: PhD in Finance from New York University MA in Economics from the University of Virginia BS in Economics from Texas A&M University Green's research focuses on Investments, Behavioral Finance, and Market Microstructure, examining how investors process information and how trading mechanisms impact market efficiency. His work has been featured in The Wall Street Journal, The New York Times, Financial Times, and CNBC television, demonstrating real-world relevance of his academic contributions. Recent publications (2022-2025) reveal expanding research frontiers in retail trading behavior, AI applications in finance, and alternative data utilization. His studies leverage unique market events and large datasets to investigate anomalies in investor decision-making and market dynamics, maintaining continuity with his foundational work on bond markets and macroeconomic news impacts. Professor Green maintains active research collaborations across finance and interdisciplinary domains, with multiple 2025 working papers under review at top journals including the Review of Financial Studies and Journal of Financial and Quantitative Analysis.
Dr Elham Shafiei Gol is a Senior Lecturer in Information Systems at Brunel Business School, part of the College of Business, Arts and Social Sciences at Brunel University London. Her research focuses on digital transformation, the future of work, digital labour platforms, and responsible innovation. Education: PhD in Information Systems, Copenhagen Business School (CBS), Department of Digitalization MSc in Information Technology, Amirkabir University of Technology Her research explores how digital platforms govern creative crowdwork to enable organizational value creation through co-creation, open innovation, and absorptive capacity. She employs sociotechnical and practice-based perspectives to understand the interplay between organizations, individuals, technologies, and work activities in digital environments. Her work bridges theory and practice, informed by her prior experience as a business analyst and usability researcher in the banking industry. Her recent publications span high-impact journals such as Information Systems Research and The Journal of Strategic Information Systems , as well as top conferences including ICIS and ECIS. Collectively, her articles reflect a sustained focus on governance models in crowdwork platforms, the motivations of skilled gig workers, and the role of digital innovation in organizational transformation. Scientific Awards and Recognition: Fellow of the Higher Education Academy (FHEA) Dr Shafiei Gol serves as a reviewer for leading journals including Journal of Strategic Information Systems and Information & Management . She is actively involved in the academic community through memberships in the Association for Information Systems (AIS), AIS Women’s Network, and the Platform Economy Interest Group (PEIG) at the University of Oxford. She teaches courses in digital transformation, AI strategy, mobile and social media services, and research methods, contributing to both undergraduate and postgraduate programs. She also mentors students and supports research development within her division of Sustainability and Innovation.
Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).
Yafit Lev-Aretz is an Assistant Professor at the Department of Law within the Zicklin School of Business at Baruch College, City University of New York (CUNY) . She holds an SJD from the University of Pennsylvania Law School, an LLM from Columbia Law School, and an LLB from Bar-Ilan University School of Law. Her research focuses on Intellectual Property Law , Privacy Law , Artificial Intelligence , and Legal Implications of Big Data . Her work explores the intersection of Technology , Privacy , and Regulation , with recent publications addressing Decentralized Finance (DeFi) , Automated Privacy Decisions , and Cybersecurity in a Pandemic context. She has received the Award for Teaching Excellence from The Zicklin School of Business (2020). Lev-Aretz is actively involved in academic committees and serves as the Director of the Tech Ethics Program at the Zicklin Center for Corporate Integrity. She contributes to Consumer Privacy and Financial Regulation discussions, with empirical studies on Privacy Agents and Algorithmic Bias in credit scoring.
Mihai Ion is an Assistant Professor of Finance at the University of Oklahoma's Price College of Business. His research lies at the intersection of corporate finance and asset pricing, focusing on how uncertainty influences firm decision-making and market outcomes. Education: Ph.D. in Finance, Purdue University (2013) MBA in Finance, Purdue University (2008) B.S. in Mathematics, Jacobs University Bremen (2007) Research Areas: Policy uncertainty and corporate investments International trade and cross-sectional return predictability Factor models and behavioral asset pricing Machine learning applications in finance Teaching Awards: Eller Dean’s Award for Undergraduate Teaching Excellence (University of Arizona) Scrivner Teaching Award (University of Arizona) Award for Distinguished Teaching (Purdue University) Award for Outstanding Teaching (Purdue University)
Meryem Schalck is an Assistant Professor of Data Science at IPAG Business School in Nice, France. She earned her PhD in Economic Sciences from the University of Orléans (2022) and has over 15 years of professional experience in the insurance sector as a Data Scientist. Her research focuses on applying AI and machine learning techniques to financial fraud detection, survival analysis, and econometric models in insurance. Research and Publications: Her recent work includes developing fraud scores for automobile insurance using cross-data analysis and predicting SME failures in France with machine learning methods, published in journals like Research in International Business and Finance and Applied Economics . Teaching Expertise: She teaches courses on AI, digital technologies, and statistical analysis for business management across IPAG's PGE, BBA, and MSc programs. Her pedagogical interests include NO CODE tools and business games. Professional Background: Prior roles include Senior Manager at Addactis (pricing software expertise), consultant at Fraeris, and actuarial roles at GI Analytics (Aviva) and Tricast, specializing in predictive modeling and SAS-based statistical tools.
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.
Dr. Roy E. Welsch is the Eastman Kodak Leaders for Global Operations Professor of Management and a Professor of Statistics and Data Science at the MIT Sloan School of Management and the MIT Center for Statistics and Data Science . He currently serves as Director of the MIT Center for Computational Research in Economics and Management Science . Education : AB in Mathematics from Princeton University (1969), MS and PhD in Mathematics from Stanford University Dr. Welsch's research spans advanced statistical methodologies and their interdisciplinary applications. His work focuses on: Robust statistical methods for regression and covariance modeling Applications in financial markets, biomedical imaging, and drug repurposing Machine learning algorithms for high-dimensional data analysis Statistical computing and computational finance Uncertainty quantification in experimental design His recent publications emphasize cross-domain applications of machine learning, particularly in financial forecasting, biomedical diagnostics, and network behavior modeling. Notable trends include: Development of robust statistical frameworks Integration of NLP techniques in financial analysis Biomedical image processing algorithms for disease quantification Time-series and high-content analysis Portfolio optimization under uncertainty Scientific Recognition : Fellow of the Institute of Mathematical Statistics Fellow of the American Statistical Association Fellow of the American Association for the Advancement of Science Eastman Kodak Leaders for Global Operations Professorship As a dedicated educator, Dr. Welsch teaches Data Analysis and Applied Statistics courses focusing on regression modeling, experimental design, and quality control with applications in finance and marketing.
Dr. Harald Lohre is a Quantitative Finance researcher affiliated with Lancaster University Management School as an Honorary Researcher and with the Hamburg Financial Research Center as a Research Fellow. His career spans leadership roles in quantitative equity research and portfolio management at Robeco, Invesco, and Deka Investment GmbH. Doctorate in Finance from University of Zurich Diploma in Mathematical Finance from University of Konstanz Former Fellow at Cambridge Judge Business School His research focuses on factor investing , portfolio optimization , and causal inference in finance , with publications in journals like Journal of Empirical Finance and Quantitative Finance. Recent work explores covariance matrix estimation and causal network modeling for systematic investing strategies. Scientific achievements include: Sir Clive Granger Memorial Best Paper Prize Bernstein Fabozzi/Jacobs Levy Award EFM 2020 Top Download Award Multiple CFA Society Germany Investment Research Awards He has supervised five PhD students and contributes to academic governance as an Associate Editor for the Journal of Systematic Investing and committee member of Inquire Europe. His work bridges academic rigor with industry applications in risk-based portfolio construction.
Manuel Hess, a German national, serves as an Associate Professor in the Department of Accounting, Law & Finance. His academic work focuses on Venture Capital, New Venture Growth, Gender Equality, Entrepreneurship, Corporate Governance, New Venture Boards, and Social Networks. Hess's research provides critical insights into startup funding dynamics, board governance structures, and gender-based disparities in entrepreneurial success. Hess investigates venture capital decision-making processes with emphasis on corporate strategic search, technology sourcing ambidexterity, and gender bias in investor evaluations. His work reveals how physical appearance affects women entrepreneurs' funding outcomes and how emotional dynamics undermine gender equality in boardrooms. He also develops practical frameworks like the Startup Navigator to guide entrepreneurs through scaling challenges and governance complexities. Analysis of Hess's 2020-2025 publications shows a cohesive research trajectory centered on venture capital-performance linkages, gender dynamics in entrepreneurship, and startup governance evolution. Key contributions include identifying performance spillovers from core-focused corporate venture capital, documenting attractiveness biases in funding decisions, and exposing emotional barriers to boardroom equality. His recent work increasingly integrates practical tool development with theoretical insights for entrepreneurial ecosystems.
Hyunjoo Kim Karlsson is a researcher at the Department of Economics and Statistics, School of Business and Economics, Linnaeus University. Her work focuses on statistics and finance, particularly in high-dimensional data analysis, wavelet decomposition, and machine learning applications. Doctoral thesis: Dynamics of macroeconomic and financial variables in different time horizons (2012), Jönköping International Business School. Her research spans shrinkage estimators, outlier detection, time series modeling, and multivariate analysis under multicollinearity. Recently, she has expanded into statistical learning and mixed data sampling (MIDAS) for economic nowcasting. Key publication trends include oil price impacts on economies, exchange rate dynamics, and nonlinear financial modeling using wavelet methods and machine learning. She collaborates with researchers like Krister Månsson and R. Scott Hacker. Hyunjoo is part of the Deterministic and Stochastic Modelling group within Linnaeus University's Data Intensive Sciences and Applications (DISA) center, contributing to interdisciplinary sustainable co-creation projects.
Soulaymane Kachani is a Professor and Senior Vice Provost at Columbia University, where he oversees teaching, learning, and innovation strategies. He is affiliated with the Columbia Data Science Institute (Financial and Business Analytics Center, Foundations of Data Science Center), Columbia Center for Financial Engineering, and Computational Optimization Research Center. Education: PhD and MSc in Operations Research from MIT, Diplôme d'Ingénieur in Applied Mathematics from École Centrale Paris. Research: Focuses on financial engineering, dynamic pricing, logistics, algorithmic trading, and transportation analysis, integrating machine learning and data science methodologies. Awards: Recipient of multiple teaching and service awards, including the Egleston Distinguished Service Award (2012) and Diversity Award (2010). Leadership: Spearheaded the establishment of the Center for Teaching and Learning (2015), driving online education initiatives and HyFlex teaching during the pandemic. His work bridges academic innovation with corporate consulting, particularly in supply chain management and quantitative finance.
Paul Peter Hager serves as an Assistant Professor in the Department of Statistics and Operations Research at the University of Vienna, where he teaches courses including Linear Algebra and Applied Optimization. Previously, he held a junior research group leader position at Technische Universität Berlin. His research centers on: Mathematical Finance Machine Learning Stochastic Control Mean-Field Games Fractional Processes Gaussian Multiplicative Chaos Volatility Modeling Hager pioneers applications of rough path signatures in financial mathematics, developing novel frameworks for stochastic control and calibration problems. His work bridges theoretical probability with practical machine learning implementations, particularly in volatility modeling using fractional processes and log-correlated fields. Recent publications reveal a dominant trend in signature-based methods for optimal stopping and mean-field games, with significant contributions to fractional Brownian motion theory. His collaborative work with leading researchers like Peter Friz and Christian Bayer consistently targets high-impact journals in applied probability and financial mathematics. Dr. Hager maintains active research collaborations and has delivered invited talks at institutions including KAUST, focusing on computational implementations of signature methods in finance.
Stephan Leible is a Researcher at the Department of Computer Science , University of Hamburg (MIN Faculty). Holding both M.Eng. in Business Engineering and an MBA, he focuses on employee-driven digital innovation, intrapreneurship, and leveraging generative AI for organizational transformation. Research Interests : Employee-driven Innovation, Intrapreneurship, Generative AI Governance, Design & Data Thinking, IT Innovation Management Contact : stephan.leible@uni-hamburg.de | Room 117C, Vogt-Kölln-Straße 30, Hamburg His work bridges citizen development with public sector innovation, emphasizing value co-creation, real-time analytics, and ethical AI implementation. Current studies explore: Generative AI adoption patterns and limitations Interpretable machine learning for urban mobility Participatory frameworks for smart city futures Methodologies for continuous improvement in conversational agents
