Bradley Hatch Email & Phone Number

Provo, UT, US

Bridging the gap between industry and academia by giving local businesses access to AI/deep learning expertise. Each semester we center the course content around a client's business problem, and equip students with the skills and knowledge to meet the challenge.

Developing complex multi-agent LLM systems to address difficult challenges in wealth and asset management.LLM projects:- Prompt learning for better retrieval - Fine tune and bootstrap examples to learn the best prompt for a taskFinancial advisor chatbot - Aided in portfolio creation given a customer's risk tolerance and investment goalsInconsistency.

Creating deep learning algorithms for client software.- Overseeing all quantitative research including discrete choice models, customer segmentations, and key driver analysis.- Developing interactive simulators to make the output of discrete choice models useful to executives.

Designing and building equity trading models for systematic trading. This includes:- Guiding a small team in the development of deep learning models for time series forecasting, tabular data modeling, self-supervised pretraining, hyperparameter optimization, reinforcement learning for portfolio optimization, and back testing methods.- Implementing.

Developing text augmentation and semi-supervised deep learning models for data labeling using LLMs and consistency learning techniques.

Provo, Utah Area

Developed novel multi-modal attention model that combines customer behavior and characteristics to predict future purchases. Augmented NLP classification models with external datasets and attention mechanisms.

Cupertino, CA

Currently, working on image segmentation, facial recognition, and neural machine translation.

Menlo Park, California

I develop deep learning solutions and code to a variety of problems:- Project Poseidon: Applying software-defined networking and deep learning to security. Uses hierarchical recurrent networks to encode sessions. With a trick of manually creating anomalous traffic, 84% of attacks were caught with a 0.5% false positive rate on the ISCX IDS dataset. The.

Helped create and teach a new course on quantitative business research.- Solicited local companies as research partners each semester.- Students collected and analyzed real world data related to the customers of the sponsoring companies. At the end of the semester the findings were presented to the executives.- Quid pro quo with sponsoring companies.

Master'S Degree, Computer Science And Machine Learning

Combining concepts from physics and information theory to machine learning.

Master'S Degree, Statistics