Researchers in the Atomic-Molecular-&-Optical-Sciences Group, within the Chemical Sciences Division at Berkeley Lab (LBNL), have developed a novel technique for the investigation of nonlinear physical processes with time-resolved measurements scaled at the molecular level.

In this project I built open-source visualization tools supporting research analytics both in and out of the lab. A bottleneck identified in group workflow was related to a reliance on proprietary software to visualize and analyze their data. To solve the problem I developed a web app written entirely in Python. Now everyone can analyze the data on their individual workstations, at the same time, as well as customize any component should a need for change arise. This dashboard is used to track key objectives, validate data quality, drive research decisions, and accelerate impact.

An open-source driving simulator was used to build a self-driving car model, while a few select technical scenarios were analyzed in detail. For example, the use of an artificial neural network to train a deep learning model to steer a car around a virtual track was explored through variations in convolution architecture.

The collaboration's mission was to incentivize stakeholders in the auto-industry to invest in the self-driving R&D efforts of smaller, mid-sized companies. In the end, it was found that less than 30 minutes of training data was sufficient for the model to navigate around a virtual track, so long as image augmentation was conducted on ~60% of data during pre-processing, and vehicle speed was reduced to half the maximum. Additionally, GPU accelerated processing of image data was at least six times faster than by CPU processing alone. It was demonstrated that cloud services are an affordable option for collaborative training environments and that the technicality of cloud computing is not a major roadblock for even the smallest data teams.

When it comes to appraisal of individual real estate, the quality and quantity of many physical property-attributes must be taken into consideration. Examples of key characteristics include location, size, and the ratio of bedrooms-to-bathrooms. In this project, supervised machine learning is applied to a dataset consisting of over 800 properties sold in Ames, Iowa from 2006-2010. Correlation analysis, with feature engineering, and several algorithms, such as multiple regression, are applied to optimize a model in predicting the final sale price of homes.

It is demonstrated that the baseline null model can be improved upon by over 36% with the inclusion of carefully chosen explanatory features. Furthermore, these extracted insights are capable of informing decisions that optimize ROI as it relates to the sale of, or investment in, individual properties.

Competing in the 2021 Data Science competition Predicting Impact of News Sentiment on the Stock Market, hosted by the Global Association for Research Methods and Data Science (RMDS), was a super fun way to collaborate with former Bootcamp teammates while stepping outside my comfort zone.

It was a rare opportunity to do a time-series analysis and forecast with real-world financial data. An interactive overview of our analysis, as well as a summary of our team submission, can now be explored with a web app hosted on Streamlit.io!

NOTE: This project section is currently under development, please check back for updates.

A county-level analysis of California’s Class of 2019 SAT/ACT performance, as it relates to a so-called education “equitability score”, with data visualizations in Matplotlib and Seaborn. The scoring methodology is explicitly defined and is dependent on only two metrics, average household income per county and countywide expenditure per pupil.

NOTE: This project section is currently under development, please check back for updates.

API calls were made in batches to gather over 200,000 submissions from two similarly themed subreddits, r/suicide_watch and r/depression. Machine learning algorithms such as Multinomial Naive Bayes were applied to predict the origin of each submission with greater than 70% accuracy. The baseline model was improved upon by at least 20% after an analysis revealed 80 words, 40 unique and topically related to each subreddit, were exploited in building the model.