Embedded Files
Research Associates
Research Associates
Research associates of the Center are generally members of one of the Universities instructional departments. The Center also recruits outside researchers as consultants and referees. In addition, research assistants are employed when funding allows.
Following is a list of Research Center Associates, their departments, and academic specialties:
Prof. Udi Makov, Head of Center (Dept. of Statistics)-
statistical methods in insurance; Bayesian statistics, models of mixture distributions
Prof. Nachum Biger (Dean, Graduate School of Business)-
financial management, economics, investments and capital markets
Prof. Yair Babad (University of Chicago, College of Business Administration)-
evaluation theory, welfare insurance, quality
Prof. Menachem Berg (Dept. of Statistics)-
risk analysis, inventory and maintenance control, Stochastic models in management science
Prof. Shaul Bar-Lev (Dept. of Statistics)-
statistical inference, asymptotic theory, characterizations, exponential families, marketing survey
Prof. David Perry (Dept. of Statistics)-
stochastic processes, stochastic models and applied probability
Prof. Zinoviy Landsman (Dept. Of Statistics)-
actuarial science, credibility theories, mathematical statistics, multi-variate analysis
Prof. Steve Haberman (Dean, Faculty of Mathematics, Actuarial Science, and Statistics, City University, London)-
mortality, welfare insurance, pensions, and risk management
Prof. Doron Kliger (Chair of Dept. of Economics)-
insurance and probability, asset pricing, bond rating, behavioral economics and finance, decision making, industrial organization, and insurance pricing
Prof. Esther Frostig (Chair of Dept. of Statistics)-
insurance probability models, applied probability, stochastic modelling
Prof. Abraham Zaks (Faculty of Mathematics, Technion-IIT)-
Mathematics of financing, life contingencies, pension funds.
Edward Furman (Assistant Professor, Dept. of Mathematics and Statistics, York University, Canada)-
Actuarial Science: Multivariate families of distributions applied to financial risk measurement, risk measures, risk decomposition techniques, premium principles; Multivariate Analysis
Prof. Fima Klebaner (Monash University, Melbourne) - Stochastic Processes in Finance and Actuary
Dr. Jing Yao (Department of Business; Vrije Universiteit Brussel)-
Financial and actuarial valuations, dependence modeling, optimal detivatives, risk management
Chiragiev Arthur (Clal Insurance Company, Risk analyst) - Risk measures, Multivariate Structures
Dr. Yaniv Zaks, (Department of Mathematics, Bar-Ilan University, Israel) -
Capital allocation, Premium principles, Optimization in life and non-life insurance
Dr. Jennifer Chan, (School of Mathematics and Statistics, The University of Sydney) -
Generalized linear mixed models, Geometric process models, Likelihood and Bayesian methods, Drop-out models, Heavy tail and asymmetric distributions, Scale mixtures distributions, Loss reserving insurance models, Medical, finance and insurance applications.
Dr. Boris Choy, (Discipline of Business Analytics, The University of Sydney) -
Bayesian computation, Bayesian inference, Bayesian robustness, Bayesian non-parametric modelling, Bayesian networks,Hierarchical models, Symmetric and asymmetric scale mixtures distributions, Heavy-tailed distributions, Copulas, Statistical association, Simulations, Financial time series models, Stochastic volatility models, Probit and Tobit models, Quality control, Sampling inspection plans, Social statistics, Applications in insurance, finance, health and medicine.
Dr. Tomer Shushi, (Department of Physics, Ben-Gurion University of the Negev, Israel) -
Risk measures, Skewed distributions, Optimization theory, Premium principles, Multivariate Analysis
Dr. Rom Aviv (Department of Statistics, University of Haifa & Argsup Capital) -
Risk Management and Modelling, Premium Calculations, Stochastic Optimization
Dr. Moshe Kelner (Head of the Forecasting and Data department at "Noga" the Israeli Independent System Operator, Israel) -
The objective of this unit is to forecast electricity demand using innovative models, advanced computing tools, and the latest statistical methods combined with human knowledge, Linear and nonlinear models, Dependence structures and copula functions, Credit Risk models, Forecasting models, Machine learning, AI, Deep Learning.
Report abuse