Thomas Wood

I established that the signup form in a job board was causing the company to lose customers. I was able to establish that users were confused by some fields.

Since users also uploaded their CV which contains explicitly lots of personal information, as well as implicit information such as the job type or salary that someone was looking for, I was able to train a deep neural network on past signup data over several years, to analyze the CV and fill out some of the fields in the signup form automatically. This allowed a field to be removed, which boosted the conversion rate of the form by 7%, measured by A/B testing.

A client in the retail industry had a fleet of vehicles delivering produce at different times of the day. They used third-party logistics software to plan the delivery schedules, however, an element of the delivery schedules that was hard to plan was the unloading time of the vehicle when it arrived at the store.

Fortunately, there was a system in place for recording vehicle ignition events, GPS location, and geofencing to identify the arrival and departure times of delivery vehicles, and past schedules were available to identify the quantity and type of product delivered on each drop, which driver was in charge, and the time of day and type of vehicle used.

Using this trove of logged data I was able to train a simple regression model that would predict the unloading time of any future delivery at the time that the schedule is being generated.

This allowed the client to save money on driver overtime, disruption caused by late deliveries, and fines due to drivers working longer than their legally permitted hours.

When a pharmaceutical company develops a drug, it needs to pass through several phases of trials before regulators can approve it.

Before the trial is run, the drug developer writes a document called a protocol. This contains vital information about how long the trial will run for, what is the risk to participants, what kind of treatment is being investigated, and so on.

The problem is that each protocol is up to 200 pages long, and the structure can vary.

For one pharmaceutical company, I developed and trained a deep learning tool to predict more than 50 output variables from a clinical trial protocol. This allows pharma companies and regulators to analyze and quantify large numbers of protocols, allowing more accurate cost estimation.

The technique can be extended to other industries where large unstructured or semi-structured documents are the norm.

I have worked on several different projects where a client needed to parse scientific literature and identify occurrences of molecules or proteins.

As an example, the molecule on the right is Aspirin. This is still a trademark of Bayer in some countries. But in a paper, it could appear under acetylsalicylic acid, 2-acetoxybenzenecarboxylic acid,

C9H8O4, or a number of identifiers such as DB00945. There could also be identifiers that refer to other molecules or identifiers that refer to only one version of a molecule.

I have developed several tried and tested techniques to disambiguate these terms. Usually, I need several annotated examples to start with, and we will train a machine learning model to learn from these examples and annotate new publications as they come in.