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 #### Technical Skills: Python, SQL
 
 ## Education
-- Ph.D., Physics | The University of Texas at Dallas (_May 2022_)								       		
-- M.S., Physics	| The University of Texas at Dallas (_December 2019_)	 			        		
-- B.S., Physics | The University of Texas at Dallas (_May 2017_)
+- BSc., Computing Visualisation | Sheffield Hallam (_July 2009_)								       		
 
 ## Work Experience
 **Data Scientist @ Toyota Financial Services (_June 2022 - Present_)**
 - Uncovered and corrected missing step in production data pipeline which impacted over 70% of active accounts
 - Redeveloped loan originations model which resulted in 50% improvement in model performance and saving 1 million dollars in potential losses
 
-**Data Science Consultant @ Shawhin Talebi Ventures LLC (_December 2020 - Present_)**
-- Conducted data collection, processing, and analysis for novel study evaluating the impact of over 300 biometrics variables on human performance in hyper-realistic, live-fire training scenarios
-- Applied unsupervised deep learning approaches to longitudinal ICU data to discover novel sepsis sub-phenotypes
-
 ## Projects
 ### Data-Driven EEG Band Discovery with Decision Trees
 [Publication](https://www.mdpi.com/1424-8220/22/8/3048)
 
 Developed objective strategy for discovering optimal EEG bands based on signal power spectra using **Python**. This data-driven approach led to better characterization of the underlying power spectrum by identifying bands that outperformed the more commonly used band boundaries by a factor of two. The proposed method provides a fully automated and flexible approach to capturing key signal components and possibly discovering new indices of brain activity.
 
-![EEG Band Discovery](/assets/eeg_band_discovery.jpeg)
-
-### Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales
-[Publication](https://www.mdpi.com/1424-8220/22/11/4240)
-
-Used **Matlab** to train over 100 machine learning models which estimated particulate matter concentrations based on a suite of over 300 biometric variables. We found biometric variables can be used to accurately estimate particulate matter concentrations at ultra-fine spatial scales with high fidelity (r2 = 0.91) and that smaller particles are better estimated than larger ones. Inferring environmental conditions solely from biometric measurements allows us to disentangle key interactions between the environment and the body.
-
-![Bike Study](/assets/bike_study.jpeg)
 
 ## Talks & Lectures
 - Causality: The new science of an old question - GSP Seminar, Fall 2021
-- Guest Lecture: Dimensionality Reduction - Big Data and Machine Learning for Scientific Discovery (PHYS 5336), Spring 2021
-- Guest Lecture: Fourier and Wavelet Transforms - Scientific Computing (PHYS 5315), Fall 2020
-- A Brief Introduction to Optimization - GSP Seminar, Fall 2019
-- Weeks of Welcome Poster Competition - UTD, Fall 2019
-- A Brief Introduction to Networks - GSP Seminar, Spring 2019
 
 - [Data Science YouTube](https://www.youtube.com/channel/UCa9gErQ9AE5jT2DZLjXBIdA)
 
 ## Publications
 
-
 - [Data Science Blog](https://medium.com/@shawhin)