Scientific Consulting

for Professionals and Researchers

DESIGN BY STATISTICAL INFERENCE

The conceptual design phase is very crucial for every construction, shipbuilding, and industrial project.

Most managers base decisions on their own experience ignoring that posterior costs caused by initial design errors are much higher.

Data mining and predictive analytics can help managers selecting the best initial parameters design, minimizing future costs.

These are some of the solutions that I can provide:

Prediction of Ship Design parameters based on Data Mining techniques
Artificial Neural Networks for Hull Resistance Prediction
Ship speed profile prediction based on Machine Learning techniques
Optimization of Characteristic Value of Concrete Structure by Probabilistic Analysis
Industrial product design by Data Mining and Machine Learning

PROJECT RISK MANAGEMENT

Currently, construction and shipbuilding managers tend to allocate contingency intuitively rather than systematically. A common practice for allocating contingency is to set a predetermined percentage from the overall project cost. This decision is mainly due to intuition and past experience. Due to the general "flat" attitude of the probability S-curve from about 70% to 100% of contingency, we need a substantial investment to reach a higher level of certainty. This is the reason that actual cost in construction projects can tend to exceed the project budgets if at the base of the project we set wrong level of certainty.

There are several commercial software used by organizations to perform Project Risk Management analysis and simulations. They have the limit of using their own algorithms for total project cost estimation that can’t be modified or adjusted.

Data mining, predictive analytics, actuarial risk analysis and open-source computing can help managers selecting and maintain the best project cost contingency.

These are some of the solutions that I can provide:

Open source Monte Carlo simulations to estimate total project costs
Factor analysis, clustering and variable selection to predict variables affecting cost contingency
Multiple regression analysis and fitting distributions to all the aspects of Project Risk Management in which we need a "forecasting approach"
Definition of more accurate sub-additive Risk Measure
Copula-based Monte Carlo simulation to predict construction projects’ total costs with dependent (random variables) cost

QUANT CONSULTING

Custom built financial algorithms for trading, investing, and financial risk.

These are some of the open-source solutions that I can provide:

Data-driven approach to Asset and Risk Management
Financial risk models including operational, credit, and market risk models
Development of derivative pricing models
Monte Carlo risk models and dependency modelling tools such as copulas
Testing macro trading factors with panel data analysis.

MEDICAL STATISTICS

There has been a growing recognition of the importance of mathematical and statistical techniques in medicine, particularly in areas such as epidemiology and randomised clinical trials. Medical scientist and researchers appreciate and need statistical thinking.

I can provide a comprehensive statistical service, ranging from meta analysis, clinical trial design, causal inference, to machine learning.

Clinical study design
Statistical analysis from basic specific techniques (Anova, non-parametric tests, logistic regression, survival analysis, post hoc analysis) to more advanced analysis ( handling non convergence of models, Pattern Mixture Models, hierarchical testing, machine learning and AI)

CUSTOM SOFTWARE DEVELOPMENT

The omnipresence of vast amounts of data means data-driven algorithms are increasingly being used to inform business decision making and optimize industrial/contruction processes.

I can build open-source custom data-driven analysis that can help scientist, designers, project and risk managers to bridge the technology gap faced by many businesses.

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