Areas of Expertise 

My background and current work are in performing and automating organizational administrative tasks like hiring people. As a product manager, I work with a lot of people in diverse job functions and administrate the execution of various product lines. The products I work on are in automatting and improving the user experience of high volume hourly and professional hiring. Check out Paradox here ->

One of my passions is sustainability, and transforming our economy to be more environmentally friendly. I am figuring out the entrepreneurship tasks for starting a solar or algae green-tech company. My experience now in sustainability is as a Treasurer at the Social Justice and Engineering Initiative, and my previous role was as a Research Coordinator at Ka Moamoa Labs, which exists now at Georgia Tech.

My master's thesis is on modeling financial decision-making based on government policy around health insurance in NetLogo a multi-agent modeling language. I have significant experience in financial modeling with Excel and programmatic visualization libraries in R and Python. My work at Credit Suisse (now UBS) involved creating machine learning visualization libraries for financial tasks done by traders in the bank. 

Machine learning is the area where I have the most breadth and depth in projects accomplished. My work involved, active learning, deep learning, optimization problems, regression, personalization, content recommendations, and anomaly detection. My active learning, deep learning, bayesian optimization, predictive regressions, supervised learning, and anomaly detection.

One of the most concrete value propositions of AI is its ability to automate both menial and advanced cognitive tasks. During my time working at Argonne National Laboratory, I had a significant project in automating experimental decision-making. My work at Paradox involves automating the "smart switching" of qualified candidates to new jobs instead of dispositioning or ignoring them. 

During my time at Credit-Suisse/UBS, I worked on building tools for interpreting results and decisions from machine learning systems using the interpretability libraries LIME and SHAP. Explainability is an integral part of establishing a path to develop and use AI ethically. However, it must only be seen as a part of a more holistic process stemming all the way from comprehensive risk analyses performed directly after the ideation. Read more here.