Isaac Wright, Phd Email & Phone Number

Boston, Ma, Us

• Collaborate with actuarial teams to develop and enhance reserving models using advanced statistical and machine learning techniques.• Analyze and interpret large datasets to identify patterns, trends, and insights that inform the reserving process.• Build predictive models to estimate and forecast claims and loss reserves, incorporating various factors such as policy information, historical data, and external variables.• Apply data science methodologies to optimize reserving processes, including data preprocessing, feature engineering, model selection, and validation.• Conduct thorough research and stay updated on emerging trends, technologies, and best practices in data science and actuarial science fields.• Present findings and recommendations to stakeholders, including actuaries, underwriters, and senior management, in a clear and concise manner.• Collaborate with cross-functional teams to integrate data science solutions into actuarial software and systems, ensuring seamless implementation and usability.• Contribute to the development of data infrastructure and automation tools to streamline data collection, processing, and analysis for reserving purposes.• Work closely with IT teams to ensure data integrity, data security, and compliance with regulatory requirements.• Assist in the development and maintenance of actuarial dashboards and reports for monitoring reserving results and communicating insights to stakeholders.

University Park, Pa, Us

• Collaborate with lead faculty on a $250,000 grant to develop a model for predicting messages that traders send to exchanges during periods where there is a shock to liquidity. This model will help regulators develop tools to respond to crashes in the market as well as identify where the market failure is occurring.• Leverage computing skills in R to apply machine learning (ML) techniques to classify different trader types in financial markets based on limit order books, including high frequency traders, liquidity suppliers, market makers, liquidity demanders, illegal market manipulators, etc.• Implement a Bayesian mixture model setup to identify clusters of similar limit order book.

University Park, Pa, Us

This MS Thesis focuses on the application of Machine Learning techniques to financial data. The abstract is below.• Build a classifier to predict the direction of stock market returns using past prices and volume as predictors.• Implemented techniques such as Random Forest, Boosting, and Quadratic Discriminant Analysis to evaluate the first level of the EMH called the Weak EMH.• Wrote R code for parallel computing to increase computational speed and significantly reduced run time.• Previous research in the area focused heavily on linear models and used their failed models as evidence of the EMH. This thesis takes an alternative approach an instead uses non-linear models since there is no reason to believe returns are linear functions of past prices and volume.AbstractIn classic financial theory, financial markets are thought to be efficient. Therefore, stocks are assured to have any information already about them already used in the price discovery process which means models cannot forecast the future price of stocks. Empirical test of this hypothesis frequently use linear models to assess whether the hypothesis is true. However, there is no reason to believe that stock prices are linearly related to the past prices or other information like volume. In this thesis, we conduct an empirical test of market efficiency using models which do not necessarily assume that the price is linearly related to past prices. Specifically, our goal is to classify a future stock price as either a positive return or a negative return. We find that even after allowing for a nonlinear structure, the returns are indeed still unforecastable and the efficient market hypothesis holds.

University Park, Pa, Us

•Carry out statistical analysis and work with the client to interpret the results and how they impact their research.•Report findings back to a client, whether it is using a formal report, a client presentation or simply a conversation over the phone.•Establish ongoing client relationships to encourage longer-term working partnerships.

Boston, Ma, Us

• Improved overall accuracy of predictive models by 12%.• Designed and implemented novel Machine Learning methodology to boost predictive performance for Catastrophe severity models. • Gathered, organized, and analyzed large data sets to create highly predictive features for machine learning models.• Revised and revamped previous severity models to take advantage of newer data and previously unused data structures. • Communicated business impact of new methodology to leadership team through presentation deck.

Boston, Ma, Us

• Met with various business units to scope a project that would provide the business with significant cost savings.• Built a modeling data set by implementing data wrangling techniques in R to combine multiple data sets into one.• Leveraged my statistical background to train and evaluate several machine learning (ML) models' usefulness in providing data-driven guidance to underwriters.• Produced a cost-benefit analysis (CBA) comparing the ML models to current guidance to show the models would provide an estimated $1.1 Million in annual savings (~49% cost reduction).• Created a presentation deck to share with leadership to communicate the significant cost-savings a data-driven approach would provide to the business.

Savannah, Georgia, Us

Savannah, Georgia, Us

Project Title: The Fascinating Gamma FunctionThis paper will discuss properties of the Gamma Function and provide proofs for each property.

Ann Arbor, Michigan, Us

•Derived and coded a likelihood function for bivariate censored data using optimization tools in R software. •Coded simulations to assess the accuracy of the likelihood function for censored data.•Presented research on censored data solutions at the SROP 2016 Symposium.

Columbus, Ohio, Us

• Assisted research professors in Mathematics and Statistics with statistical analysis of data.• Developed computer programs in R for sorting and interpreting numerical data files.• Presented research findings at the SROP Symposium.

Savannah, Georgia, Us

• Advice peers academically and socially.• Tutor for Mathematics and Computer Science classes.• Coordinated and hosted final exam workshops for students in mathematics and statistics courses

University Park, Pa, Us

• Eight-week intensive summer research project working in Penn State’s Fluid Mechanics lab.• Conducted multiple experiments and analyzed data using Mathematica programming languages• Presented research on decay rates of shallow-water waves at the SROP Symposium.

Nashville, Tn, Us

• Reinforce learning of mathematics/statistics material or skills initially introduced by the teacher in small groups and with individual students.• Assist the teacher in developing strategies for reinforcing mathematics/statistics material or skills based on student interests and abilities. • Coordinates outreach activities for students to gain exposure to careers in the STEM fields.

Doctor Of Philosophy - Phd, Statistics

Master Of Science - Ms, Statistics

Bachelor Of Science (Bs), Mathematics