YieldWise provides contract research services for clients seeking top experts for cutting-edge analytical projects. Our research group keeps YieldWise analytical and software development technologies and services at the forefront of the industry. Below are just some of our research and development projects:

  • A systematic approach for discovering the source of faults that cause the degradation of yield in the manufacturing process of semiconductor IC (integrated circuits) devices. 
  • Model discrimination criterion for finding the active factors in screening experiments.
  • A systematic approach intended to reduce the purchase cost of Web proxy caches by making better use of available resources.
  • Development and deployment of complex statistically designed experiments for digital and TV advertising.
  • Development of models, algorithms and programs for digital content recommendation and personalization.
  • Behavior nano-clustering of customers using Relational Bayesian Networks methodology.
  • Prospects response modeling and direct mail attribution modeling.
  • Development and deployment of advanced statistical methods and algorithms for measurement of Search and Advertising processes such as web search, mobile search, shopping search, etc.
  • Development and implementation of advanced methods and algorithms for discovering and analysis of commercial intentions of web visitors.
  • Development of predictive models of customers purchasing behavior (time-to-event predictions).
  • Development of methods and algorithms for predicting the popularity of messages on Twitter. Implementation of the system intended to predict whether or not a message will be retweeted, and predict the volume of retweets.
  • Development of advanced metrics for identification of changes in data streams.
  • Development of a software solution for advanced survey analytics using polytomous Rasch measurement model, inter-raters agreement and Bayesian Relational Networks. This solution can be used for workforce planning, talent management, and market research (consumers and products profiles).
  • Development and deployment of a system that monitors borrowers’ financial behavior and creates optimal actions to prevent loan defaults (design of statistical experiments, machine-learning classification using support vector machine method and its probabilistic variations, time-to-event models).
  • Instantaneous risk estimation and prediction of losses (Bayesian Cox hazard model, asset depreciation based on Gompertz-like model).
  • Design of large-scale statistical experiments for identification of active factors that impact customer purchasing behavior (Plackett-Burman designs followed up with fractional factorial plans).
  • Price, supply, and demand elasticities estimation to optimize prices per store/product (Hidden Markov model, support vector machines, design of statistical experiments).
  • Development of large-scale product recommendation system using Bayesian Relational Networks.