Nusret Cakici holds the Felix E. Larkin Distinguished Professorship in Finance at Fordham University's Gabelli School of Business, where he has taught since 2008. His research employs machine learning and econometric techniques to analyze asset pricing anomalies, factor returns, and market efficiency across global equity and cryptocurrency markets. Recent work explores textual analysis of financial reports, methodological challenges in international finance, and predictive modeling of stock returns. His publications demonstrate consistent focus on empirical approaches to market behavior and investment strategy innovation.
Samir Trabelsi is a Professor of Accounting at the Goodman School of Business, Brock University. His research focuses on corporate governance, financial accounting policies, and the intersection of environmental and social factors with financial decision-making. Key areas of study include executive compensation dynamics, CSR performance impacts on investment behavior, and the role of disclosure practices in shaping creditor and investor perceptions. His work spans topics such as real earnings management in IPO contexts, the influence of religiosity on leadership appointments, and greenwashing effects on debt costs. Dr. Trabelsi has contributed to understanding bankruptcy prediction models, mutual fund governance in Canada, and cross-cultural financial attitudes across six nations. His research has been published in high-impact journals and addresses contemporary issues such as ESG integration in financial reporting, regulatory frameworks for XBRL adoption, and the implications of Shariah-compliant banking practices.
Dr. Dimitriou Loukas is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Cyprus, leading the Laboratory of Transportation Engineering. He holds a PhD from the National Technical University of Athens (NTUA) and has held academic positions at King Saud University and the University of Cyprus. His research focuses on sustainable transportation systems, leveraging AI, big data, and econometrics to optimize transport performance and policy design. He teaches 2 mandatory undergraduate and 5 postgraduate courses in Transport Infrastructure Management. His work spans smart city mobility, micromobility systems, freight transport optimization, and emerging transport technologies. He has secured significant national and EU research funding, reviews for top journals, and participates in international conference committees. Key research areas include: Equity in transit budget allocation Spatiotemporal analysis of shared mobility Electric vehicle policy optimization Data-driven infrastructure maintenance Airport network pandemic control strategies His laboratory develops decision-support frameworks for transport systems, integrating machine learning with traditional engineering methods to address urban mobility challenges.
Michael Loewy is an Associate Professor and MA Director in the Department of Economics at the University of South Florida. His research focuses on dynamic macroeconomics, monetary economics, and growth theory. He has published in prestigious journals such as the Journal of Monetary Economics and the Journal of Economic Growth. Prior to his current position, he held roles at The George Washington University and the University of Houston. Professor Loewy teaches courses in macroeconomic theory, monetary economics, American economic history, and economic growth. He holds a Ph.D. in Economics from the University of Minnesota. His work emphasizes theoretical frameworks addressing policy design, trade impacts, and long-term economic development. Notable contributions include analyses of bank runs, tariff policies, and regional income convergence. Loewy’s research integrates empirical and theoretical methods to explore how policy interventions affect economic stability and growth. His publications span topics from endogenous growth models to the macroeconomic effects of financial crises. His teaching philosophy, inspired by Mark Perlman, emphasizes rigorous analytical tools over descriptive methods. While no specific grants or awards are listed, his extensive publication record reflects sustained scholarly engagement with critical issues in macroeconomics and development. His work contributes to both academic discourse and practical policy debates in global economic systems.
Michael O'Doherty is a Professor of Finance and Charles Jones Russell Distinguished Professor at the University of Missouri's Robert J. Trulaske, Sr. College of Business. He serves as the PhD Program Coordinator for the Department of Finance, teaching at undergraduate, MBA, and PhD levels. His research focuses on asset pricing, financial econometrics, mutual funds, and hedge funds, with publications in top journals like the Journal of Finance and Journal of Financial Economics . Key contributions include work on stock return predictability, volatility-managed portfolios, and hedge fund replication. Education: BS in Chemical Engineering and Finance from Iowa State University (2004), PhD in Finance from the University of Iowa (2011). Dr. O’Doherty has received the 2018 TIAA Paul A. Samuelson Award for scholarship on lifelong financial security. His research bridges theoretical finance and practical investment strategies, emphasizing econometric methods and market dynamics. Recent work explores long-term investment risks, mutual fund performance evaluation, and the impact of tax policies on retirement savings. He actively contributes to academic discourse, with over 15 peer-reviewed articles since 2010. Current research interests include lifecycle investment strategies and cross-market equity analysis.
Prof. Manfred Reichert is a full professor at the University of Ulm, serving as Director of the Institute of Databases and Information Systems and Dean of Studies at the Faculty of Engineering and Computer Science. He holds dual expertise in Computer Science and Mathematics, with academic leadership roles including chairing examination boards and strategic research initiatives. His interdisciplinary affiliations include co-opted membership in the Faculty of Mathematics and Economic Sciences. Reichert's research focuses on Business Process Management (BPM), IoT-driven processes, service-oriented architectures, and e-health applications. Notable contributions include co-developing the ADEPT process management system and pioneering object-centric process modeling. He has led over 20 international research projects (EU FP7, DFG, Industry) and authored >200 peer-reviewed papers, earning an h-index of 60+ and prestigious awards like the Merckle Forschungspreis. Education: PhD in Computer Science & Mathematics Diploma Leadership: Former associate professor at University of Twente, former director of CTIT research center Service: Conference chair for BPM, CoopIS, EDOC; steering committee member for GI SIG Databases Recent work explores IoT integration in BPM, generative AI for process models, and neuroscience-informed usability studies. His research bridges technical innovation with human-centric design, addressing challenges in process lifecycle management, federated learning, and mobile health interventions. Awards: IFIP TC2 Manfred Paul Award, doIT Software Award Key Projects: BPMN extensions for IoT, data-driven healthcare solutions
Dr. Ingrid Arocho is an Associate Professor in the School of Civil and Construction Engineering at Oregon State University. She holds a Ph.D. in Civil Engineering from North Carolina State University and specializes in construction equipment emissions, mass timber constructability, and resilience in construction. Her teaching portfolio includes undergraduate and graduate courses in construction cost estimation, project management, and controls. Research Interests: Her work integrates environmental sustainability with construction practices, focusing on quantifying pollution from equipment fleets and developing frameworks for sustainable building materials like cross-laminated timber. Publications: Dr. Arocho's recent articles emphasize empirical analysis of construction emissions and safety, utilizing advanced modeling to balance economic and environmental factors in infrastructure projects. Awards: ELECTRI International Early Career Awards Finalist (2019) ASCE ExCEED Workshop Participant (2016) Multiple NSF Travel Grants Advising & Grants: Advised 8+ graduate students on theses covering emissions analysis and safety protocols Secured $206K in grants for research on accident prevention and timber construction links Laboratory: Part of the Construction Research Group investigating sustainable infrastructure solutions.
Matthew Janssen is a Research Assistant Professor at the Department of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology. His research focuses on coastal hazards, littoral processes, and developing computationally efficient models to assess risks to coastal infrastructure using field observations, numerical modeling, and data-driven techniques. He holds a PhD (2022), MS (2016), and BS (2011) in Ocean Engineering from Stevens Institute of Technology and the University of Rhode Island, respectively. His work emphasizes understanding storm erosion potential, dune performance under climate change scenarios, and the impact of coastal structures. Notable contributions include methodologies for quantifying storm erosion considering sea level rise and probabilistic forecasting of coastal storm impacts. He currently serves as Assistant Director of the NJ Coastal Protection Technical Assistance Service and has prior industry experience with firms like Rising Tide Waterfront Solutions and McLaren Engineering Group. Key Research Areas: Coastal resilience, numerical modeling, climate adaptation, dune dynamics, sediment transport. Recent Focus: Long-term dune performance under extreme and nuisance erosion events; integration of machine learning (CART models) for erosion prediction. Publications highlight his work on hurricane impacts, breakwater effectiveness, and navigation channel management. He received the John P. Breslin Award (2022) and is active in professional societies like ASBPA and COPRI. His technical reports include analyses of New Jersey beach sediment characteristics and shoreline impacts at North Wildwood. He collaborates on projects balancing engineering solutions with ecological and economic considerations.
Keren Li is an Assistant Professor in the Department of Mathematics at the University of Alabama at Birmingham (UAB), affiliated with the College of Arts and Sciences. She holds a PhD in Statistics from the University of Illinois at Chicago (2018), an MS in Mathematics from Louisiana State University (2004), and a BA in Mathematics from Nankai University (2001). Before joining UAB in 2022, she served as an Associate Scientist at UAB's Informatics Institute (2023–2024) and a Postdoctoral Fellow at Northwestern University’s NSF-Simons Center for Quantitative Biology and Department of Statistics and Data Science (2018–2022). Her research focuses on distributed learning, federated learning, deep learning, bioinformatics, and financial mathematics. Key projects include developing tools like DNAcycP (predicting DNA cyclizability) and RiboDiPA (analyzing ribosome profiling data). She also explores statistical methods for optimal design and generalized linear models. Her work bridges computational methods and real-world applications, with contributions to genomics, energy networks, and financial modeling. Her lab ( klilab ) emphasizes interdisciplinary collaboration and innovative algorithm development.
Florian Huber is a Professor of Economics and Vice-Head of the Department of Economics at the University of Salzburg. His research focuses on Bayesian macroeconometrics, particularly large-scale non-linear multivariate time series modeling. He has published in top journals such as the Journal of Econometrics and the Journal of Applied Econometrics. His work integrates machine learning and nonlinear methods to analyze macroeconomic dynamics and uncertainty. Huber serves as a Scientific Consultant to the Oesterreichische Nationalbank (OeNB), European Central Bank (ECB), and European Commission. He holds roles as Associate Editor of Macroeconomic Dynamics, Senior Scientist at the International Institute for Applied Systems Analysis (IIASA), and Research Fellow of Bocconi University’s Baffi Center. His accolades include the 2024 Kurt-Zopf-Förderpreis and Fellow status in the Society for Economic Measurement. Research interests span Bayesian econometrics, state space modeling, forecasting, and financial spillovers. His recent work addresses topics like growth-at-risk, nonlinear VAR models, and real-time inflation forecasting. Huber’s contributions bridge theoretical econometrics with policy-relevant analysis, emphasizing the integration of big data and structural models.
Professor Xiaohui Tao is a distinguished academic at the University of Southern Queensland's School of Mathematics, Physics and Computing, leading the Computing Discipline Team and chairing the ICT Programs Governance Committee. He holds a PhD in Information Technology from Queensland University of Technology (2009). His research focuses on artificial intelligence, machine learning, and health informatics, with over 200 publications in top journals like IEEE TKDE and conferences such as AAAI and IJCAI. He has mentored 10 doctoral students and leads a research group developing real-world AI applications. Education: PhD in Information Technology, Queensland University of Technology, 2009 Research Interests: Dr. Tao's work spans AI-driven health informatics, machine learning algorithms, natural language processing, and privacy-preserving technologies. His projects address critical challenges like mental health monitoring, smart healthcare systems, and privacy in 5G/IoT environments. Recent advancements include federated learning for unlearning mechanisms and multimodal fusion for medical decision support. Grants & Awards: Awarded Australia Research Council grants (DP220101360), Australian Endeavour Fellowships, and multiple best paper awards at conferences like BESC’22 and WI-IAT’20. Recognized for contributions to remote patient monitoring and AI in depression treatment. Labs & Teams: Leads a research group focused on AI applications in healthcare and data science, collaborating on projects like computational social science for mental health and privacy-preserving IoT systems.
Markus Kondziella is an Assistant Professor of Quantitative Economics at the University of St. Gallen (HSG), affiliated with the School of Economics and Political Science (SEW). His research focuses on macroeconomic dynamics, firm behavior, and distributional economic issues. He holds a PhD and has published extensively on topics such as firm dynamics, wealth distribution effects, and inflation analysis. Research Interests: Quantitative Macroeconomics Economic Growth Mechanisms Portfolio Choice Behavior Structural Economic Shifts Monetary Policy Impacts Publications highlight his work on firm-level data analysis and macroeconomic forecasting, with recent contributions exploring how microeconomic heterogeneity influences aggregate outcomes. His work bridges theoretical models with empirical validation using advanced econometric techniques.
Wang Fengjiao is an Assistant Professor and Lecturer at the Kahlert School of Computing, University of Utah. Her research focuses on machine learning, data mining, and social network analysis, with notable contributions to semi-supervised learning, generative models, and social media analysis. She holds a position in one of the founding institutions of the internet (ARPANET), leveraging computational advancements for interdisciplinary challenges. Her work spans theoretical innovations and applied systems, including algorithms for tabular data, image generation via Fréchet distance minimization, and probabilistic text recommendation models. Recent trends in her publications emphasize scalable learning frameworks, spatial-temporal event modeling, and privacy-preserving techniques in multi-platform social networks. Wang's research has addressed challenges such as user geolocation inference, collaborative co-clustering in heterogeneous data, and steering information diffusion under attention constraints. Her contributions to content-aware POI recommendations and distance-based social discovery further highlight her expertise in integrating machine learning with real-world social systems. No scientific awards or grants are explicitly mentioned in the provided materials. Contact information is available at fengjiao@cs.utah.edu, and her office is located in MEB 3102.
Marina Astitha is an Associate Professor at the University of Connecticut's College of Engineering , specifically within the School of Civil and Environmental Engineering . Her research bridges atmospheric science with practical applications in energy systems and environmental management. Ph.D. in Physics from the University of Athens (2007) Specializes in high-resolution weather modeling and machine learning integration Research Interests include: Atmospheric Physics, Dynamics, and Chemistry Extreme Weather Event Prediction Multi-Media Modeling Systems Uncertainty Quantification in Atmospheric Models Climate Change Impacts on Wind Energy Resources Real-Time Weather and Air Quality Forecasting Scientific Awards : No awards explicitly mentioned in the provided data, but her publications and research activities indicate significant contributions to meteorology and environmental engineering fields. Advising and Grants : Specific details about grants and students are not provided in the available information, though her research focus suggests involvement in funded projects related to climate change, renewable energy, and environmental modeling. Labs and Teams : Leads the Atmospheric Modeling Group at UConn, integrating numerical weather prediction with machine learning techniques for environmental and energy applications.
Spyridon Vrontos is a Professor and Head of the School of Mathematics, Statistics and Actuarial Science at the University of Essex. He holds a PhD in Statistics from Athens University of Economics and Business (2005), alongside MSc and BSc degrees in Statistics from the same institution. His expertise lies in Actuarial and Financial Data Science, with a focus on predictive modeling, risk management, and asset-liability management. He has received the Charles A. Hachemeister Prize from the Casualty Actuarial Society and has secured funding from organizations like the Society of Actuaries and Innovate UK. His research spans actuarial science, financial mathematics, and quantitative finance, addressing topics such as bonus-malus systems, volatility modeling, and pension fund performance evaluation. Professor Vrontos has extensive consulting experience in areas like pension fund valuation, insurance ratemaking, and financial planning tools. His work combines theoretical advancements with practical applications, bridging academic research and industry needs. He has supervised numerous projects funded by entities including the Hellenic Foundation for Research and Innovation and various insurance companies. His contributions to the field are evident through publications in top journals such as the International Journal of Forecasting and Quantitative Finance. His research portfolio reflects interdisciplinary collaboration, integrating statistical methodologies with financial and actuarial challenges. Recent work includes modeling the economic impacts of the pandemic using dynamic panel models and exploring machine learning approaches for volatility forecasting. He actively engages in teaching and curriculum development, having led the BSc and MSc Actuarial Science programs at Essex.
