Meet the Co-Authors of ARMDAT
Dr. Gordon H. Dash joined the faculty of the College of Business (COB) in 1974. As a Full Professor, he holds appointments in Finance & Decision Sciences and the Interdisciplinary Neuroscience Program. He completed his undergraduate degree in business administration at Coe College (1968). He earned a master’s and two-field Doctorate in Finance and Operational Research from the University of Colorado at Boulder (1978).
Dr. Dash has ongoing research projects that link traditional optimization algorithms to neural network algorithms for classification and prediction. His research emphasizes the development of algorithmic extensions in combinatorial optimization and radial basis function neural networks. Using newly developed algorithms, Dr. Dash has produced a current research strain that spans topics such as the neuroeconomics of SMART cities, ethical AI and the determinants of chronic disease states (neuroethics), and sustainability in multiobjective behavioral portfolio optimization. Dr. Dash’s research is published in journals that target portfolio optimization and management, multiobjective optimization, complex decision analytics, energy management, and more. Research service includes serving as an associate editor for the International Journal of Applied Optimization Studies and co-chair of the European Working Group for Operational Research on Development (EWG-ORD). Since 2018 the annual EWG-ORD international meeting format has promoted the dissemination of recent research findings on the integration of operational research and the United Nation’s seventeen sustainable development goals. Dr. Dash’s research has been presented globally in over thirty countries spanning the continents of Europe, Africa, and Asia.
Besides contributing several book chapters to books in financial engineering, Professor Dash is the co-author of two e-books. His book on derivatives market valuation is titled “Applied Risk Management: Valuation of Derivatives under AI and Data Science Technologies”. The twenty-two-chapter text features valuation applications that draw upon his state-of-the-art algorithmic tools. His investment and portfolio management textbook is titled “AI and Data Science in Applied Security and Investment Management.” The textbook includes new valuation analytics to incorporate the latest in portfolio optimization and the use of neural networks to model key prediction targets such as corporate earnings.
Dr. Dash is a member RiskGroupAuth (Greece), an interdisciplinary research group (think-tank) specializing in developing risk assessment and management tools for modern energy systems. He remains a member in good standing of several honorary societies, including Beta Gamma Sigma, Phi Kappa Phi, and Delta Sigma Pi.
Nina Kajiji is a Principal of The NKD Group, Inc.
She is also an adjunct associate professor in the Computer Science and
Statistics Department at the University of Rhode Island. She has been
conferred the title Accredited Professional Statistician™ from the American
Statistical Association.
Her principal research interests are in applied optimization, volatility
modeling, and artificial intelligence (AI). Application fields include:
socially responsible investing, modeling risk, neuroscience-based modeling for
the development of smart cities, ‘big data’ analysis of intra-day municipal
bond yield curves, and obtaining complex educational assessment elasticity
metrics. Her research continues to expand to include ‘big data’ analytics
featuring visualization, high-performance computing, and explainable AI (XAI)
to assist in complex data mining. Dr. Kajiji’s academic research has been
published in several operational research journals, finance journals, and, most
recently, in the journal Neuroscience. Besides contributing several book
chapters, Nina is currently co-authoring two e-Books titled “Applied
Risk Management: Valuation of Derivatives under AI and Data Science
Technologies” and “AI and Data Science in Applied Security and Investment Management.”
Nina is the co-architect of the cloud-based computing platform,
WinORSe-AI 2021. The computing platform is geared towards solving
problems using techniques commonly used in financial engineering, statistics,
operations research, and economics. Research models using WinORSe-AI have been
presented to capital market professionals in Italy, India, Thailand, South
Africa, Lithuania, Turkey, and the UK. Some specialized techniques incorporated
in WinORSe-AI 2021 include combinatorial non-linear goal programming,
Bayesian-enhanced regularized univariate and multivariate radial basis neural
networks, Explainable AI (XAI) using SHAP, and more.
Nina has taught courses in Statistics, Time Series Analysis, Operations
Research, and Finance at various Universities in the U.S. and internationally.
She is a member of the Greek-based RiskGroupAuth, an interdisciplinary
research group (think-tank) specializing in developing risk assessment and
management tools for modern energy systems.
When not actively working, her personal interests center on container
gardening and volunteering with non-profit organizations. She is the co-chair
of the European Working Group on Operational Research for Development. For her volunteer work,
Nina has been awarded the President’s Volunteer Service Award (Gold
Level) from the President’s Council on Service and Civic Participation.
Nina was also honored with the 2013-14 Woman of the Year Award from the National Association
of Professional Women.