A bit about me: I'm a 22-year-old recent college graduate from Chicago. I have an undergraduate degree in Statistics from the University of Illinois in Urbana-Champaign, conducted research at a cognitive neuroscience & DNA biotechnology lab for two years, and was a technical consultant intern at a high-growth start-up this past summer.
Leveraging both my scientific knowledge and previous experiences, I am able to quickly research and explain a variety of new complex technological concepts, understand user needs and behaviors, and build intuitive interfaces that convey complex data to clients.
On the weekends, you can find me hiking with my golden retriever Beni or reading a book about Jeff Bezos.
Azure, AWS.
Chat-GPT all the way.
C#, .NET, HTML, CSS, TS, Angular MVC-based framework
I enjoy working with my team to create winning strategies.
I'm passionate about building clean, efficient code that produces tangible and *measurable* results to buisnesses.
Developing and executing data processing patterns(ETL) for steaming and batch data via Hadoop, Spark, and other distributed processing frameworks for Big Data applications.
Check out my projects:
▪ Analyzed data from a dataset containing 9568 data points over a period of 6 years and
used outputs to guide the design
of predictive models to determine the efficiency and economic operation of a Combined Cycle
Power Plant (CCPP) while
simultaneously considering real-life constraints.
▪ Evaluated the performance of various predictive models using quantitative performance
metrics, such as comparing AIC
and ROC curve values, as well as utilizing Generalized Cross-Validation procedures.
Team project for STAT429 Final. Our end product aimed to predict the property price based on
these features and explore
the relationship between variables in different areas. We leveraged the 10-Year Breakeven
Inflation Rate Data provided
by the Federal Reserve Bank of St. Louis to observe how inflation may affect our predictions.
▪ Deployed predictive analytics models to forecast future housing trends using ARIMA models.
▪ Utilized data visualization techniques to present and explain complex data sets.
▪ Built a recommendation system using Bash, Python, and AWS to provide users with select
plant-based snack
recommendations based on user input and historical data for top-selling vegan snacks on
Amazon.
▪ Currently working on developing an iOS application using Swift and novel Apple ML
libraries to integrate custom
recommendation system.